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Filtered Dragnets and the Anti-Authoritarian Fourth Amendment

July 2024July 2024Jane R. Bambauer*

Filtered dragnets are digital searches that identify a suspect based on the details of a crime. They can be designed to withhold information from law enforcement unless and until there is a very high probability that the individual has committed the offense. Examples today include DNA matching, facial recognition from photographs or video of a crime, automated child sexual abuse material detection, and reverse geolocation (geofence) searches. More are sure to come, and their wide-scale use will be irresistible to improve the low rates of criminal detection that currently afflict many communities.

However, filtered dragnets imperil society precisely because they detect crime too well. Sudden increases in the detection of criminal conduct will intensify the pathologies of American criminal justice: namely, that too many marginally harmful acts are criminalized, crimes are punished too harshly, and police and prosecutors have too much discretion. If nearly everybody commits some technical violation of criminal law that can be easily detected and harshly punished, all Americans will be at the mercy of the constable’s pity.

These threats are not well constrained by current Fourth Amendment jurisprudence, based on privacy rights, because filtered dragnets detect crime without revealing irrelevant details. Thus, Fourth Amendment theory and doctrine must strengthen the anti-authoritarian objectives endowed in its roots. A search conducted with a filtered dragnet should be considered reasonable only if it is administered in an evenhanded manner, and a subsequent seizure of a person is reasonable only when the misconduct is abhorrent enough to justify arrest and imprisonment.

INTRODUCTION

Nearly forty years ago, Justice Brennan asked his colleagues, who had just given a constitutional stamp of approval to the drug-sniffing dog, to imagine a device “that, when aimed at a person, would detect instantaneously whether the person is carrying cocaine.”¹ If the device could detect the presence of cocaine inside a building, “there would be no constitutional obstacle to the police cruising through a residential neighborhood and using the device to identify all homes in which the drug is present.”² He believed the prospect of police having a tool of near-perfect detection presented a catastrophic threat that the courts have a duty to stop.

We are not too far off from this scenario anymore,³ and some strategies already in use by law enforcement and intelligence agencies are similar to Brennan’s machine. Examples include DNA matching, facial recognition from photographs or video of a crime when it was in progress, automated child sexual abuse material detection, and reverse digital searches (where police use information known about the crime, such as location, timing, or special instrumentalities, to cross-check against service provider data in order to identify a suspect). Many more of these investigative techniques are sure to come, especially if or when the Internet of Things reaches its potential by placing increasingly powerful sensors on nearly every machine.

Twenty-first century policing will increasingly use data collected from tracking and sensing technologies to conduct investigations that work backwards. Law enforcement will use the particulars of a crime as a “fingerprint,” so to speak, to determine who should belong in the pool of suspects. Unlike the standard dragnet, which permits law enforcement to observe large amounts of data and to choose their targets, filtered dragnets force investigations to focus on the evidence of a crime. Computers will automatically scan through data without exposing it and will make a disclosure only when there is probable cause to believe that a person’s data matches the signature of the crime. Moreover, even when data is disclosed, filtered dragnet programs can be designed so that the only data revealed is potentially relevant data; extraneous details can be withheld.

When surveillance technologies meet all these benchmarks—that is, when (1) they are used to find an individual related to a crime (rather than to find a crime related to an individual), (2) when they report details from an otherwise private database only after meeting a high threshold of confidence (e.g., probable cause or higher), and (3) when they withhold details that are ex ante unlikely to be relevant to the current criminal investigation, the nature of that surveillance is different from other types of police work. Filtered dragnets, as I will call them, are structured to avoid many problems traditionally associated with mass surveillance.

Fourth Amendment theory and reasoning is just starting to find its legs in digital search cases,⁴ but filtered dragnets will destabilize criminal procedure law again. They will whittle down most of the privacy rationales for Fourth Amendment protection. Mounting a Fourth Amendment defense will require a litigant to convincingly argue that even though the defendant very likely committed a crime, and even though the police did not see or have discretionary access to data for any other persons and did not even have irrelevant data about the defendant for that matter, the search was nevertheless unreasonable. That sort of privacy über alles argument might work for crimes of questionable legitimacy—drug possession, for example—but it won’t work in the context of universally reviled conduct like murder.

What is more, filtered dragnets may reduce privacy intrusions on net, as compared with current investigation techniques, because they can remove many people from the scope of suspicion who would otherwise become targets of investigation. In other words, filtered dragnets break the privacy-security trade-off because they simultaneously increase criminal detection and privacy. As Bennet Capers has explained, they may be a useful tool to simultaneously tackle under-protection and over-policing problems.⁵ Outright bans of these technologies, as have been advocated in many corners,⁶ would be irresponsible.⁷

Nevertheless, even if filtered dragnets detect crime and nothing else, they pose serious social risks that Fourth Amendment law and scholarship are ill equipped to handle: What happens to Fourth Amendment theory and the practice of criminal justice if nearly every crime could be detected?

In the late 1990s, Larry Lessig asked this very question.⁸ He anticipated that digital technologies may create a wedge between the privacy and anti-authoritarian rationales for criminal procedure. But most Fourth Amendment scholars do not even recognize a schism between privacy and anti-authoritarian goals. Instead, they continue to focus on privacy as the key constraint on any police activity that leverages large amounts of personal data. The scholars who have recognized liberty and anti-authoritarianism as a Fourth Amendment lodestar have insisted that all technology-assisted surveillance is a tool of abusive state power per se.⁹ As a result, Fourth Amendment scholars lump filtered dragnets with all other surveillance and advocate for the strictest access controls, guaranteeing the continuation of a low rate of criminal detection.

This is the wrong course. The threat from filtered dragnets is tyranny, and the Fourth Amendment will be more effective and coherent if we recognize that. Filtered dragnets will dramatically increase the detection of crime, and this will intensify existing pathologies in American criminal justice that have little to do with privacy. Namely, we have too many crimes, too much punishment, and too much police and prosecutorial discretion. These problems jointly produce the risk of authoritarian power. An overly expansive criminal code paired with harsh penalties ensures that nearly everybody could be subjected to incarceration.¹⁰ When the state also has unchecked power to choose where and when to investigate within the ocean of criminal-but-typically-ignored conduct, the populace is at the mercy of the state’s will.¹¹

Today, the criminal justice equilibrium rests on an unspoken compromise. The state has broad substantive law, harsh punishment, and unchecked discretion, it is true, but the populace has privacy rights that nearly guarantee low detection, even when police are highly motivated. When filtered dragnets give police near-perfect detection, the bargain has to be renegotiated.

This Article proposes a new grand bargain for Fourth Amendment law: the Supreme Court should recognize filtered dragnets as a legitimate and even desirable tool for criminal investigations. But constitutional rules should guarantee that the substance of American criminal law will be limited to conduct that is commonly recognized as heinous, that the severity of the punishment fits the reprehensibility of the crime, and that the enforcement of criminal laws is equitable and nonarbitrary.¹² Without these civil rights, if the substance of criminal law is left as broad and vague as it is today,¹³ and if penalties and the impact of prison are as debilitating as they are now, filtered dragnets would give the government the means of exercising tyrannical control through the omnipresent threat of criminal enforcement and the power of discretionary clemency.

This Article proceeds as follows: Part I describes some filtered dragnets that are already in use and lays out the essential features that distinguish them from other investigation tools.

Part II describes the potential social benefits that can be gained from the responsible use of filtered dragnets.

Part III describes the scholarship and caselaw challenging the constitutionality of filtered dragnets on privacy grounds and disagrees with it. By most common-sense meanings of privacy, filtered dragnets are in fact much more private than the sorts of investigations that routinely occur.

Part IV shows that the threat of filtered dragnets comes not in the form of privacy but in the form of tyranny. Perfect detection of crime in a system where criminal statutes are sprawling and criminal penalties are harsh will either create a country of convicts or will give government too much power to engage in selective leniency.

Part V reinterprets the Fourth Amendment prohibition of unreasonable searches and seizures to fit the criminal justice problems that emerging surveillance technologies will cause. The reasonableness of a seizure should depend on whether the defendant’s conduct truly warrants criminal liability and penalties. The reasonableness of a search should depend on both expectations of privacy and on evenhanded investigation practices.

Part VI explains why the Constitution, and the Fourth Amendment in particular, are well suited to carry out this shift even though it would mark a departure from twentieth century precedent.

The agenda laid out in this Article is ambitious—almost embarrassingly so. What I propose here would require a seismic shift in Fourth Amendment principles that would cross the procedural/substantive divide.¹⁴ Given that, I take comfort in the fact that I am not painting on blank canvas. This project is a remix of themes developed by Bill Stuntz,¹⁵ Bennett Capers,¹⁶ Elizabeth Joh,¹⁷ Bernard Harcourt and Tracey Meares,¹⁸ Chris Slobogin,¹⁹ Mark Kleiman,²⁰ and many others. Even so, it is awfully presumptuous to suggest courts might start invalidating criminal laws or sentencing rules using a new-fangled conception of the Fourth Amendment. But I will suggest it anyway because it is the only desirable and realistic option. The criminal justice system needs to be transformed in a manner that accepts much greater levels of detection in exchange for many fewer criminal prohibitions and punishments. It is a trade that has to be executed simultaneously in order to avoid disastrous consequences.²¹ No legislative or local government process could pull off a massive rights horse trade of the sort that is required. It can only be accomplished through the style of landmark constitutional cases that, every generation or so, help realign Fourth Amendment operational rules with the ultimate purpose of Fourth Amendment protection.²²

I. WHAT ARE FILTERED DRAGNETS?

The progenitors of filtered dragnets have been around for a while. Fingerprinting analysis is a well-known and time-honored method of backwards investigation where the facts from the scene of a crime (the fingerprint markings) are cross-checked against a large stockpile of information in order to make a fairly confident match to a particular suspect.²³ Police dogs are another example.²⁴ We know that the mind-boggling sensitivity of a dog’s nose is such that, if it could talk, it could reveal vast amounts of information about a person—what is inside their bag, how their health is, whether they’ve been in recent contact with other people—that are unobservable to we mere humans. In some sense, the mind of a police dog is a treasure trove of personal information that remains inaccessible to police most of the time. But when they are trained to alert to contraband or to specific scents sampled from a crime scene, the dog and the training combine to create a “binary search”—a mechanism that tells the police nothing unless there is probable cause that a crime is being committed.²⁵

These crime-driven, quasi-filtered investigations are the outliers in a system of police investigation that relies much more heavily on witnesses, confessions, and physical searches.²⁶ But we can expect the practice to rapidly expand because of the greater amounts and variability of data available for cross-checking the facts of a crime against data from the population of potential suspects.

This Part lays out the two required features of filtered dragnets that will cause an unprecedented shock to Fourth Amendment theory. We will then visit examples of techniques that are already in use that either already satisfy the definition of filtered dragnets or soon will.

A. Required Elements to Qualify as a Filtered Dragnet

Filtered dragnets provide a suspect’s data to police only if (a) their data matches uniquely criminal details such that there is a high probability they have engaged in criminal conduct; and (b) their data has been pared down to provide only relevant details about the suspected crime to the police. When combined, these features make filtered dragnets a qualitatively different style of police investigation.²⁷

1. Automated Matching of Uniquely Criminal Details

Filtered dragnet investigations will trawl through and process large amounts of data. There is no doubt that they are a dragnet. But to qualify as a filtered dragnet, the filter of the dragnet must constrain the system’s ability to leak information. A filtered dragnet must be programmed to alert police only if an individual’s data matches a unique fingerprint of a crime.²⁸ In other words, the system blinds the police until at least probable cause (and hopefully more suspicion) is established.

Filtered dragnets are a subset of the category of investigations that Christopher Slobogin calls “suspectless searches.”²⁹ But they are a narrow subset. Very few of the suspectless searches that Slobogin analyzes (many of which I describe below) have the potential to become filtered dragnets. As they are practiced today, they will not meet the heightened standards for filtered dragnets because they do not use unique signatures of criminal behavior. For example, geofencing and familial DNA-matching procedures often allow police today to access data about a handful of individuals, all but one of whom are necessarily innocent, in order to help the police create leads for traditional follow-up investigation. To find the Golden State Killer, the FBI found a genetic match to a family member, and then used traditional genealogy to trace from that family member to the suspect.³⁰ The revelation of that family member’s identity would not qualify as matching to “uniquely criminal detail.”

Slobogin argues that even when a small number of people, some of whom are guaranteed not to be the perpetrator (such as somebody whose DNA only partially matches that of the sample from a crime scene), are identified to the police, the intrusion into privacy is fairly minimal and should be handled through Fourth Amendment doctrines that allow for warrantless searches and seizures, like checkpoints.³¹ I agree with nearly all of Slobogin’s proposals about how courts should interpret the Fourth Amendment with respect to these examples. But they still do not meet the criteria I am setting—criteria that, when met, challenge the most basic conceptions of Fourth Amendment privacy. To meet the definition of a filtered dragnet for my purposes, police will remain ignorant to details and identities until there is a high probability that the information identifies and pertains to the perpetrators and no one else.

2. Nondisclosure of Irrelevant Details

The first requirement on its own ensures that filtered dragnets are analogous to “binary searches” like drug-sniffing dogs—the sort that alert only if there is probable cause of a crime. But there is an additional affordance that should be exploited: filtered dragnets must refine the information that is ultimately disclosed to police by filtering out personal, irrelevant details even about a suspect. This is equivalent to a drug-sniffing dog that could magically produce a suspect’s drugs without any of the rifling through cars and pockets that are necessary today. Thus, the suspect will retain privacy over details that are not relevant to the criminal investigation at hand.

To be clear, neither of these requirements are meant to be absolute guarantees. All systems have error, and even if police are able to set very demanding thresholds for false positives, police will occasionally access licit, irrelevant details when a filtered dragnet falsely identifies a suspect who is then subjected to an arrest or probable cause–based search. But the requirements for disclosure in a filtered dragnet system can be calibrated to fit societal needs and expectations: the chance of false accusation error can be driven down to practically zero if we would like, if we are willing to tolerate the consequences that there will be more false negatives (more crimes that are not detected) or that police departments will need to access more data in order to maintain the same level of detection.

A. Examples

Next, we will visit a set of backwards investigation techniques that are in use today. These use the particularities of a crime to lead police to a suspect. While most cannot meet the demanding definition of “filtered dragnet” formalized above, with time and additional data resources, they will surely get there.

1. DNA Matching

DNA-matching investigations use parts (non-revelatory portions) of a DNA sequence produced from a sample collected at a crime scene or from a crime victim in order to identify a suspect using DNA databases. They are an obvious extension of fingerprinting analyses with some souped-up features. First, DNA matching can set a very high threshold of statistical probability of true match (or, in other words, a very low probability of a false match) because each DNA sequence has a large amount of data.³² Second, they can make use of popular commercial and ancestry databases for cross-checking and are therefore not limited to identifying individuals who have a history with the criminal justice system.

Third, familial or partial DNA matches are very useful for police investigations in a way that partial fingerprint matching is not. In familial DNA-matching investigations, such as the one that eventually led to the arrest of the Golden State Killer, police departments recover the identity not of the suspect but of one or more of the suspect’s genetic relatives.³³ This raises privacy concerns for the relatives whose identities are revealed to law enforcement in the course of finding the perpetrator.³⁴ So, as practiced today, familial DNA searches do not fit the definition of a filtered dragnet. They fail the second element (filtering out innocent and irrelevant details) by revealing identities and information about family members who are definitely not the perpetrator of the crime.³⁵ However, it is conceivable that in the future, if multiple databases are able to be accessed and triangulated, familial DNA matching can be part of a filtered dragnet system that automatically finds a familial match, trawls other data sources in order to identify the correct relative of familial match (based on, e.g., age, location, or personal history of the relatives), and discloses the identity of the suspect and the relevant details only when and if there is sufficient confidence that the correct suspect has been identified.³⁶ All of this can be automated.

DNA evidence holds an esteemed place in criminal justice and public perception. DNA evidence is durable (as long as it is handled properly) and judges and juries can justifiably place a high degree of confidence in the reliability of DNA-matching investigations.³⁷ Other types of data beyond DNA can have these qualities, too, but they provoke much more suspicion and dissent. Distinguishing them from DNA matching will become increasingly untenable.

2. Facial Recognition

Facial recognition uses large databases of identified photographs (often scraped from the public Internet) to discover the identity of a person who would otherwise be anonymous.³⁸ The technology can be used as a filtered dragnet when police departments deploy facial recognition on photographic evidence from the scene of the crime.³⁹ For example, law enforcement has used facial recognition to pin identities to individuals who appeared in surveillance footage from the Capitol on January 6, 2021, as well as to robberies and street crimes.⁴⁰ Although facial recognition algorithms are less accurate for female and non-white faces,⁴¹ industry members claim this is not the case for top-performing algorithms in active use.⁴²

3. Automated CSAM Detection

Last year, Apple unveiled a program that would automatically scan iPhoto images and cross-check them against a library of known child pornography when the images were uploaded to the iCloud. Apple had planned to use a hashing technique to check all files sent from Apple devices to be stored on iCloud servers. Essentially, every image received by an Apple phone is converted to a code that corresponds to the visual image.⁴³ When a person’s iPhoto images produce ten matches, Apple employees would automatically be alerted and would share the information with authorities. Thus, while every image would be hashed and cross-checked against child pornography, only the images that matched could lead to a disclosure to law enforcement. Apple has since abandoned its plans in response to criticism,⁴⁴ but the technological capability still exists.

4. Geofences and Other Reverse Searches

In 2019, a spate of arsons involving vehicles parked in commercial lots was committed in short succession.⁴⁵ Based on the locations, surveillance footage, and similar modi operandi, police had reason to believe that a single set of co-conspirators was involved in all six arsons. When federal investigators requested that the court issue a warrant requiring Google to search its time-logged geolocation records for cellphones that were at or near the scenes of the arsons during the times that they were committed, a U.S. magistrate judge complied.⁴⁶ This type of process—where police start with the location, approximate time, and other details of a crime and ask service-providers to find a matching account—is known as a “geofence warrant,” and magistrate judges have issued orders authorizing their use under certain conditions. Judges have refused to issue warrants (without deciding whether warrants are actually necessary) when the request cast too wide a net—that is, if too many devices are likely to be identified as matching the search criteria.⁴⁷ For example, if police are investigating a crime that took place during a Beyoncé concert, even a geofence with a small radius, during a fairly precise window of time, will draw in too many false matches—too many phones of innocent bystanders. But this concern falls away if police can use multiple details or the intersection of several geofences in order to create a search criteria that will be unique to the perpetrator.⁴⁸ For example, in one recent case, a perpetrator who was suspected to have cased the location of a murder on the day before he committed it was identified using overlapping geofences from the day before and the day of the murder.⁴⁹ License plate readers, drone footage, Internet of Things data, and satellite surveillance imaging could also be sources of geolocation information in the likely circumstance that criminals begin to leave their devices at home.⁵⁰

Geolocation data can be combined with other types of information, too, to form a signature of crime that is more likely to be unique. As an illustration, US intelligence agencies located Osama bin Laden in part by looking for locations where they would expect to find Internet and cell service but in fact found none.⁵¹ There are data sources outside of location data that can create a signature for reverse searching. For example, while investigating an arson case, the Denver police department sought and received a “keyword warrant”—a court order requiring Google to reveal the account information of users who had recently searched for the address of the arson during a fifteen-day period leading up to the crime.⁵² Cyberstalking, child pornography, and many other online crimes have used forms of reverse searches in order to identify the accounts associated with IP addresses that were used to engage in those crimes.⁵³

5. Scanners, Sensors, Cameras, and Microphones

Red light cameras were one of the first ventures into automated policing and were also much despised.⁵⁴ These systems used sensors to detect if a car entered an intersection after the light had turned red, took a photograph of the car, and later used the image of the car (and its license plate) to track down the owner and mail a ticket. These systems are not dragnets per se (they do not make use of pre-existing collections of data), but they set the stage for Automatic License Plate Readers that do capture an abundant amount of data in case some particular parts of it are useful later, as when police are searching for a stolen vehicle.⁵⁵

Patterns that are highly suggestive of crime can also be automatically detected using recording devices with cameras, microphones, or sensors that operate in “always on” mode.⁵⁶ One example in use today is ShotSpotter microphones that are constantly “listening” in a public setting but alert the police and save data long term only when the noises captured by the shot-spotter match the sounds of gunshots.⁵⁷ In theory, Alexa, which also constantly records to respond to watchwords like “Hey Alexa,”⁵⁸ could be designed to detect sounds that are particular to domestic violence or home invasion and automatically alert the authorities.

Other sensitive devices like terahertz scanners can detect when naturally occurring radiation is blocked by metal objects. When the blocking metal objects are gun shaped, the scanners can be programmed to alert.⁵⁹ But this is nothing compared to what quantum magnetometry will be able to do in the near future.⁶⁰ Quantum sensing is so sensitive to minute differences in magnetic fields that the sensors will be able to detect trace amounts of chemicals, even when they are concealed behind walls. So, Justice Brennan’s nightmare scenario is here: we will soon have contraband detection devices.

This survey of suspicionless searches and backwards investigations demonstrates that there is increasing viability and interest in using these types of techniques. The practices currently in use do not usually meet the two formal requirements for “filtered dragnets,” but it is useful to assume they eventually will. By assuming investigations will eventually meet the demanding definition of filtered dragnets, we will be able to state with more rigor precisely why it is we are nervous about these law enforcement technologies, and what the policy or constitutional response should be.

II. THE ADVANTAGES OF FILTERED DRAGNETS

This Article will eventually explain why filtered dragnets impose serious risks on society that are not adequately (or even nominally) addressed in Fourth Amendment theory. But first, we will explore reasons to embrace, rather than resist, the integration of filtered dragnets into policing.

Filtered dragnets offer several advantages over the investigation practices in common use.⁶¹ These include decreased exposure of innocent details, increased accuracy and efficacy of criminal investigations, increased detection and deterrence of crime, decreased discretion for suspect selection, and decreased risk to witnesses and victims. In combination, these advantages contribute such compelling benefits to society that courts and attorneys should feel a moral obligation to harness their powers as much as possible.

A. Decreased Exposure of Innocent and Irrelevant Details

Filtered dragnets protect the privacy of innocent individuals, as well as the innocent-and-irrelevant details of a suspect. They protect innocent individuals whose data is scanned in the process by allowing police and courts to set a high standard for false match error. That is, filtered dragnets can be programmed to alert and reveal personal information only when the statistical probability that the person has engaged in crime is greater than 50%, or 80%, or 99%. This would ensure that the number of innocent individuals who are initially approached and investigated will be only a fraction of the number of criminals who are found.⁶²

Moreover, filtered dragnets limit the type of information that is revealed even about the proper subjects of investigation who have committed a crime. This is a game-changer. If police could have searched a house or a car in a manner that blinded them to everything except contraband or criminal evidence, the text and interpretation of the Constitution would probably differ from what we have today. The closest analogy we have to filtered dragnets, as I have mentioned before, are drug-sniffing dogs. Police dogs are allowed to sniff and alert based on the (mostly defensible) assumption that they will be trained well enough to have a low error rate.⁶³ The dog sniff and subsequent alert are, controversially, treated as a non-search in Fourth Amendment law unless the dog has trespassed into the home or curtilage of a resident.⁶⁴ But once the dog alerts, the police have probable cause to perform an entire human-conducted unfiltered search of a person’s vehicle, home, or effects, thereby revealing intimate and innocent details while they look for contraband. Filtered surveillance is more privacy-protective than drug-sniffing dogs because it can restrict the sort of data that is revealed even as police are verifying that the alert is accurate.

I do not mean to suggest that filtered dragnets avoid all revelations about innocent people or activities. Relevant data disclosed to police as a result of a high probability match will frequently, maybe even usually, reveal information that is not directly tied to wrongdoing. For example, if in the future the police used a system that combines familial DNA matching with other records to identify a sexual assault offender, police may see and use the identity of the family member in order to confirm that the identification is sound and to show how the case was solved to a jury. This could reveal the identity of estranged parents or children of the suspect or could uncover paternity that was not previously known.⁶⁵ But this is a consequence of the fact that all successful investigations impose some irreducible privacy costs on the innocent. Even using traditional strategies, police will occasionally and appropriately question a spouse in a manner that reveals the suspect is having an affair or may make other similar sensitive revelations. If the revelations are in service of pursuing a probable cause–backed investigation, these will be innocent-but-relevant details.⁶⁶ The advantage I describe here pertains to the shielding of innocent-and-irrelevant information.

B. Increased Accuracy

By definition, filtered dragnets identify suspects and reveal information only when there is a high probability of crime. This is a form of increased accuracy—a reduction in false positive error. (In the next subsection, I will discuss the other form of increased accuracy—the reduction in false negative error—which would allow filtered dragnets, if deployed consistently, to solve more crimes and increase clearance rates.)

If filtered dragnets are held to higher probability standards than standard investigation techniques, they will cause proportionally fewer false starts and erroneous arrests and searches along the way.⁶⁷ In time, a shift toward filtered dragnets should decrease the dangers and anxiety that come from false suspicion and conviction at every stage of criminal investigation. Indeed, facial recognition systems that identify a suspect based on photographs or surveillance footage from a crime already outperform the accuracy rates of average eyewitnesses and PC-based warranted searches by a large margin.⁶⁸

Skeptics will have at least two critiques of my optimistic prediction: all systems have some error, and the sort of error that comes from a highly technical and data-driven system might be particularly worrisome since a falsely accused defendant will have to go up against a trusted and more accurate system.⁶⁹

It is true that no investigation tool is free from error, and it is also possible that police, prosecutors, and juries could be at risk of reflexively trusting the results of a filtered dragnet system because they are so reliable. But the premise of the critique might be plain wrong. When a filtered dragnet produces a spurious result, the error could very well be easier to catch than when an informant or witness makes a spurious identification. For example, when a man named Michael Usry was the target of an investigation based on his father’s partial genetic match to crime scene DNA, Usry was cleared as soon as his own DNA sample was collected and analyzed because it did not match the sample collected at the scene of the crime.⁷⁰ This should generalize: the more independent sources of data there are, the more protection there should be for innocent.⁷¹ A person wrongly identified by facial recognition is more likely to have a credible digital alibi (e.g., geolocation data that puts them in an entirely different state at the time of a crime) than a wrongly identified person who was accused by a confidential informant.

The facts of United States v. Chatrie⁷² illustrate the propensity for the erroneous targets of filtered dragnets to be cleared earlier and easier than erroneous targets in traditional investigations. In that case, police used a geofence warrant to access the deidentified location data of individuals who were near the scene of a bank robbery during the hour that the crime took place.⁷³ The geofence produced the deidentified location records of nineteen individuals, only one of whom was the perpetrator.⁷⁴ These facts do not fit the requirements of a filtered dragnet because law enforcement accessed and manually examined information related to the eighteen individuals who were not the perpetrator, but we can think of these eighteen as stand-ins for those who are wrongly targeted by filtered dragnet. One hour of anonymous geolocation data conclusively ruled out sixteen of them, and an additional hour ruled out the other two. None of the eighteen were identified (by name or other direct identifier) to the police, and none were questioned.⁷⁵ By contrast, consider the experiences of two individuals who were briefly implicated in the investigation before the FBI used geofence technologies. Using traditional policing methods, the FBI first investigated the ex-boyfriend of a woman who saw news reports about the bank robbery and called the police to offer a false tip. They also investigated somebody who owned the same kind of car that was used as the getaway vehicle when a bank employee reported the possible tip, but that, too, was a dead end.⁷⁶ It is not clear from the opinion what sorts of encounters and information-gathering the police used to rule out these two, but I suspect the anxiety and privacy burden absorbed by them was greater, by almost any measure, than the burden to the eighteen individuals whose approximate movements in public during one to two hours were disclosed in deidentified form. If this case is representative, the geofence warrant process should be a method of first resort, rather than last resort, because it is likely to lead more quickly to both the identification of the right suspect and the elimination of wrong ones.

A second skeptical critique is that I am describing the positive qualities of filtered dragnets under the assumption that the systems will be deployed as intended and will not be manipulated or tampered with. This is a legitimate concern to which the long history of flaws in forensic labs can attest.⁷⁷ But as a comparative matter, data-driven techniques of this sort might be more accountable and auditable than old-school forms of criminal investigation. When the same level of scrutiny and doubt is applied to traditional investigations that would have to continue in the absence of new technologies—the risks of error and manipulation present in eyewitness testimonies, suspect interrogation, or warrant affidavits⁷⁸—the prediction that filtered dragnets will be more corrupt and error-prone is hard to believe.⁷⁹

C. Increased Detection and Deterrence

The accuracy and efficiency of filtered dragnets can help tackle longstanding social problems of chronically unsolved crime, assuming filtered dragnets are used regularly.⁸⁰ About twenty-five million Americans—8% of the population—suffer from a violent felony or a felony-level theft each year.⁸¹ These events are of course disproportionately likely to beset low-income households. While violent crime rates today are still down compared to the high-water marks in the 1980s and early 1990s,⁸² the statistics are still grim, particularly for communities of color. In the U.S., about five people in every 100,000 are murdered each year.⁸³ For African-Americans, the rate is above six per 100,000.⁸⁴ (By comparison, the rates in France and Italy are 1.28 and 0.52 per 100,000, respectively.)⁸⁵ In addition to the trauma and losses to crime victims, society also absorbs a range of economic costs and psychological distress in the course of guarding against crime.⁸⁶ It is all too easy for scholars, lawmakers, and others who live in safe neighborhoods to forget: serious crime is just awful.

Crime clearance rates (that is, the proportion of crimes actually reported to the police that have led to an arrest or otherwise been considered solved) for violent crime is 42%, and the rate is under 15% for property crimes.⁸⁷ Only about half of violent crimes and one-third of property crimes are ever reported to the police, and many arrests and convictions are erroneous. The low likelihood of reporting a crime, the low clearance rates, and the somewhat sizable chance of false arrest altogether mean that the probability a criminal will be prosecuted for any particular violent crime is probably under 20%.⁸⁸

Clearance rates in black neighborhoods are even worse. The events over the last decade validate Bill Stuntz’s observation that “poor black neighborhoods see too little of the kinds of policing and criminal punishment that do the most good, and too much of the kinds that do the most harm.”⁸⁹ Dampening crime in lower income black communities is a civil rights goal of longstanding stature.⁹⁰ Bennett Capers described underenforcement as the criminal justice problem that gets short shrift,⁹¹ and that was before George Floyd’s murder made police violence and over-policing problems an issue of pressing global salience. There is some squeamishness today in discussing crime in black neighborhoods (and certainly in referring to that crime as “black on black”), but it is foolish to expect criminal justice reform to be lasting and meaningful if it does not tackle both of the scourges of inner-city policing: harsh policing and civilian violence.

The most obvious and natural way to curb future violent crime is to increase the detection of very serious crimes today.⁹² Some scholars, Tom Tyler chief among them, have made the case that in the long run, law-abiding behavior has less to do with criminal law enforcement tactics than with cultural, economic, community, and norms-based factors.⁹³ Occasionally, this insight has been oversimplified and distorted to leave the impression that law enforcement detection rates have nothing to do with crime rates.⁹⁴ This is a mischaracterization of the evidence.⁹⁵ While there are multiple “root causes” of crime,⁹⁶ data and common sense confirm that holding other factors steady, criminal behavior is sensitive to the probability of law enforcement detection. The relevant criminology studies consistently find evidence that detection reduces the incidence of future crime.⁹⁷ There is also some evidence that the swiftness of enforcement—the “celerity”—makes a difference.⁹⁸

Increased detection of crime not only reduces crime rates, but also improves other measures of social mobility and security as well. Greater crime detection increases the likelihood that offenders will seek and find employment, enroll in education, and live in a stable family environment, and it reduces school absenteeism in the community.⁹⁹ Indeed, given how dramatic the impact of detection is on increasing pro-social behavior, it is not at all clear that law enforcement should even be distinguished from the so-called “root causes” of crime. Fear that crime will not be well controlled is a root of many of the root causes of crime.¹⁰⁰

So, an enduring and well-documented fact is that an increased likelihood of detection and enforcement drives crime rates down. This is much less true, and possibly not true at all, for the severity of punishment, where increasing the length of prison sentences is found to have no impact or even criminogenic effects.¹⁰¹ Thus, the state’s essential duty to protect its constituents from the violence and exploitation of others is well served by good detection. Unfortunately, crime rates are currently under the management of the American criminal justice system’s haphazard style of enforcement: occasional, error-prone, and harsh.¹⁰²

D. Decreased Discretion for Suspect Selection

Filtered dragnets are crime-driven rather than suspect-driven. In suspect-driven investigations, police have developed suspicion—or a hunch—around a particular individual and focus their observations in an attempt to develop a case.¹⁰³ Suspect-driven investigations are propelled by the theories of police officers and proceed within their discretionary control. Police also have some control over filtered dragnet investigations (e.g., over where and when to deploy them), but once they are put into service, police lose control over the results. If facial recognition or reverse searches identify a wealthy or politically connected individual as the suspect of a crime, it will be much more difficult for police and prosecutors to avoid pursuing investigation and prosecution, as compared to cases where police use informants or witnesses as the main source of identification.

In later Parts, this Article describes the ways in which police can still exercise too much discretion by, for instance, using a filtered dragnet tool preferentially to solve some crimes and not using it on others that are substantially similar. But we should not lose sight of the ways filtered dragnets do constrain discretion. One of the greatest risks from mass surveillance (that is, dragnets) is its potential to create a resource for selecting the suspect first and then finding a crime, or for using legal but sensitive information to discredit political enemies and personal foes.¹⁰⁴ Police cannot exert this type of control over filtered dragnets.¹⁰⁵

The Supreme Court caselaw that has found fault with Big Data policing has involved digital searches in which the police first selected their target and then accessed long histories of their target’s whereabouts without a warrant.¹⁰⁶ The Court is right to constrain investigations that permit police to access sensitive and detailed information without any justification or checking mechanism. Even when police have developed suspicion against a target, the low-tech factors that go into building up suspicion about a particular individual (e.g., testimony from an informant or presence in a “high crime neighborhood”) can impose an indirect racial tax on innocent minorities that could mostly be avoided with filtered surveillance programs that have very low error.¹⁰⁷

Not all agree with this assessment. Kiel Brennan-Marquez has argued that “nothing about the logic or practice of data-driven law enforcement makes [] redistributive impulses necessary. On the contrary, they will be hard fought—and particularly in our current political climate, unlikely.”¹⁰⁸ I share a certain degree of Brennan-Marquez’s cynicism (I have wondered, for example, if law enforcement’s sloth-like speed in adopting crime-driven investigation practices rather than suspect-based practices are related to the loss of control over defining the pool of suspects),¹⁰⁹ but he goes too far. There already is some evidence that data-driven policing has redistributed the costs of law enforcement and will continue to do so. DNA-based exonerations, for example, have proven the innocence of disproportionately more minority convicts than whites.¹¹⁰ This suggests that, going forward, DNA-based investigations will shift police focus not only toward the guilty, but also away from wrongfully accused Black and minority suspects.

E. Decreased Risk to Victims, Witnesses, and Suspects

Police investigations cause a range of problems that are not captured in the variables I have discussed so far—privacy intrusions, erroneous arrest, et cetera. When police have to rely on old school methods of case investigation, the system necessarily puts victims, witnesses, and suspects at risk of physical or economic harm.

Let us start with crime victims and witnesses. Cooperating with the government is a perilous activity for these individuals, as captured by the saying “snitches get stitches.”¹¹¹ By one theory, clearance rates for serious crimes are low in the U.S. because proving homicide or robbery cases requires victims and witnesses to testify and put themselves at risk.¹¹² Bill Stuntz hypothesized that police forces increased their focus on drug and gun possession charges because these crimes were “self-proving” once contraband was discovered, and therefore did not necessitate the cooperation of a victim or witness.¹¹³ As a result, more serious crimes were harder to clear than low-level crimes. But, of course, those are the crimes that are more damaging to the community. If reverse searches, facial recognition, and other filtered dragnets could allow police to prove cases independently, without exposing victims and witnesses to the risk of social stigma and retaliation, they would contribute benefits to society that are not accounted for in the usual privacy-versus-security debates.

As for the suspects, the manner in which traditional policing builds up cases leave much to be desired. Police stops and searches are often vectors for bias and disrespect where swearing, insults, unwarranted accusations and suspicion, and unjustified physical contact lead to demoralization and distrust.¹¹⁴ Traditional investigations are costly in terms of time, fear, property damage, and general unpleasantness. A person who is pulled over for a secondary inspection when a police dog alerts to her car may very well have no recourse when the police slash open the seats of her car to try to find drugs. Home searches and interrogations cause additional physical, emotional, and economic strain to suspects, irrespective of what sorts of private information is revealed. These costs will become more obvious and more salient when technology obviates the need for a government agent to tear open the upholstery of a suspect’s car, dishevel a dresser, and “grope[] and grab[] our children” at the airport.¹¹⁵

***

In combination, these factors show that filtered dragnets should be part of any responsible law enforcement program. They extend the “pareto frontier” by allowing privacy and crime detection to increase at the same time.¹¹⁶ It would be counterproductive for law to prohibit their use based on a formalistic or expansive notion of Fourth Amendment protection. And yet, as the next Part shows, there is some risk that courts and lawmakers may do just that.

III. FILTERED DRAGNETS AND PRIVACY

Most of the courts, scholars, and civil society organizations that have considered the societal impact of filtered dragnets such as geofencing and reverse keyword searches have concluded that they pose serious threats to privacy.¹¹⁷ Putting aside for a moment whether filtered dragnets are consistent with the full set of Fourth Amendment principles, this Part argues that filtered dragnets pose almost no threat to Fourth Amendment privacy. What I mean is, among all of the meanings and purposes that the right to privacy is meant to capture, the only ones that are meaningfully violated by filtered dragnets are related to abuses of power. The privacy expectations of the non-offender, which are the ones that predominate Fourth Amendment analysis, suffer at most a technical violation. If we separate out the anti-authoritarian goals of privacy, nothing is left of the privacy critique of filtered dragnets.

This does not mean that filtered dragnets are harmless—to the contrary, as Part V will argue, they pose significant dangers to civil liberties. But by ruling out privacy as the vector of abuse, courts can harvest the benefits of analytical precision and adjust Fourth Amendment law to better match the problems. This Part describes how courts and scholars have responded to filtered dragnets so far and then explains why Fourth Amendment principles are so poorly suited to address the negative reactions.

A. Judicial Reactions to Filtered Dragnets

Courts are not prepared for the challenges that filtered surveillance pose to Fourth Amendment jurisprudence. Indeed, they are struggling as it is to find principled limits in more common and straightforward digital dragnet cases.¹¹⁸

So far, lower court opinions are surprisingly unfriendly to technologies and practices that will be the predicates to filtered dragnets. For example, Baltimore tried to set up a program called Aerial Investigation Research (“AIR”) in which its police department collected and retained 45 days’ worth of aerial surveillance footage, but would not be allowed to access the footage unless a violent crime occurred and was likely to be caught on camera.¹¹⁹ Civil liberties organizations successfully challenged the program, arguing that the Fourth Amendment should constrain the government from amassing data that can be used for longitudinal location tracking no matter how constrained the Baltimore Police Department’s access and use of the data might be.¹²⁰ The Fourth Circuit used the theoretical possibility of government access to information as a sufficient reason to find that a Fourth Amendment search on all Baltimore residents took place, regardless of the design, practice, and risk of abuse for the program.¹²¹ If this reasoning is adopted throughout the judiciary, law enforcement will not be able to collect their own information for filtered dragnets and will have to rely on data that is collected and held by private industry.

Many courts have expressed similar reservations when the government asks a private company like Google to trawl through its data to conduct reverse searches, too.¹²² But these opinions suggest that a warrant process that is sufficiently narrow and “particularized” so as to avoid disclosing data of innocent bystanders to the police would satisfy Fourth Amendment requirements.¹²³ This leaves an opening for filtered surveillance. It suggests that the automated scan that Google or another third party would perform of all its data in the process of identifying responsive records would not be a search in and of itself. In other words, the focus of the courts that have analyzed geofence warrants is not on the data that is scanned at all, but on the data that is ultimately revealed to police.

Courts might begin to clamp down on third-party scanning for law enforcement purposes following the logic of the Fourth Circuit’s decision in the Baltimore AIR case. Many scholars are advocating for this, as I describe next. But it is still not clear that filtered dragnets will be understood to be a search at all given that they are designed to alert only when probable cause of a crime has been established. Even if police use computing technologies to automatically scan through large amounts of personal data, the constitutionally relevant event is the revelation and use of information to the government agents who are making decisions.¹²⁴

This is best captured by the binary search doctrine—the rule establishing that, for example, a drug dog’s alert is not a search under the Fourth Amendment because it reveals only the presence of contraband and criminal wrong-doing. There is little reason to believe the Supreme Court will backpedal. The Court has found that a universal fingerprinting database, possibly even one that requires involuntary contributions of fingerprints by individuals who are not yet in the database, could be justified, given that fingerprinting is an “inherently more reliable and effective crime-solving tool than eyewitness identification or confessions.”¹²⁵ More recently, in Maryland v. King, the Supreme Court found that police can forcibly swab an arrestee and cross-check his DNA against the database of DNA samples from unsolved crimes.¹²⁶ The opinion focused almost entirely on the physical act of swabbing and took for granted that the cross-checking of a DNA sample to a crime database will not be a search because it reveals either nothing at all or reveals only a high-confidence match to a crime.¹²⁷

That said, some of the Supreme Court decisions in the last ten years written by Justice Scalia incorporated a strong property-based formalism. In United States v. Jones, the use of a GPS device was a search not because of the sensitivity of the information gathered, but because of the touching of the suspect’s car.¹²⁸ And in Florida v. Jardines, use of a drug-sniffing dog on a front porch was a violation of the Fourth Amendment because the practice involved a trespass with information gathering.¹²⁹ The fact that the information gathering was in the form of a binary search did not alleviate the flaw, according to the majority.¹³⁰ If Scalia’s formalism for real and tangible property is extended to personal data, filtered dragnets could be considered a search of all individuals whose data is mechanically scanned in the process, irrespective of how trivial the invasion to them may be.

Even if courts come to agree that mechanically processing data is a Fourth Amendment search, this would still not guarantee the death of the filtered dragnet. They might be reasonable searches under the special needs or checkpoints doctrines.¹³¹ In the context of checkpoints, bulk searches, and other dragnets, the Supreme Court has articulated the factors that it would use to determine whether the searches are “reasonable” despite a lack of individualized suspicion. These factors include the intrusiveness of the search, the public and government interest that is served by the dragnet, and the degree of oversight or limitations on discretion that are involved.¹³²

Thus, judicial reasoning seems to be on a collision course between (a) cases that are eager to expand the recognition of privacy rights to cover all data subjects in large databases whose information is theoretically accessible to police and (b) cases that find highly probative “binary searches” are outside the ambit of Fourth Amendment prohibition.

B. Scholarly Reactions to Filtered Dragnets

Lawrence Lessig saw this train wreck coming. In Code, he pointed out that the Internet and digital information technologies will allow police to identify a perpetrator with high confidence while remaining blind, by design, to the intimate details of the innocent. He explained that this will cause the privacy rationale for Fourth Amendment protection to lose relevance, at least when filtered dragnet investigations are possible. He expected these technologies would force a wedge between privacy and anti-authoritarian justifications for criminal procedure, when in the past, the two types of arguments traveled together.

Fourth Amendment scholars have doubled down on privacy.¹³³ They have lumped filtered dragnets together with all other digital surveillance in order to hinder police access. Dragnets of every sort, including the filtered sort, still suffer from analytical chaos because of value judgments and predictions that too often stay latent in the scholarship.¹³⁴ As a result, scholars are all over the map in terms of the proper treatment of digital dragnets, and none have focused on the right factors.

A few examples. Daphna Renan has argued that the collection, retention, and theoretical capability for law enforcement to access data is alone sufficient to constitute a privacy harm. Consent or a warrant should be required before the government collects any privately held data, and even before they access or request machine scanning of that data by third parties, irrespective of how limited and careful the readout is.¹³⁵ Natalie Ram has approvingly held up Maryland’s law prohibiting law enforcement from using genomic databases to solve crimes unless they have received consent from all individuals whose data is in the genomic dataset.¹³⁶ More generally, this brand of scholars use access to data, rather than how it is used, as the sine qua non for Fourth Amendment analysis and ask why anybody should be under “lifetime surveillance.”¹³⁷

Scott Sundby and Nadine Strossen take the more moderate position that dragnets (of any sort) should be used only as a last resort,¹³⁸ though it is not clear they would apply their conclusions to filtered dragnets in particular. Eldar Haber, in considering how the Internet of Things can become a rich source of police investigatory data for reverse searches, advocates for a warrant requirement that goes beyond the “super-warrant” requirements of the current Wiretap Act to create an “ultra-warrant” requirement.¹³⁹ Since the super warrant requires police to exhaust all other means of investigating before securing a wiretap warrant, the effect and objective of Haber’s recommendation is similar to Sundby’s and Strossen’s—to ensure that the criminal justice system strongly disfavors use of Internet of Things data in investigation.¹⁴⁰

Continuing down the spectrum, some scholars appreciate the potential benefits of filtered dragnets and have advocated for a style of restraint that differs from prohibition or PC-based warrant requirements. Stephen Henderson and Kiel Brennan-Marquez argue that police departments should have a budget for searches and seizures (including digital investigations that, at least right now, operate outside the formal definition of a Fourth Amendment search) so that they are incentivized to use the most efficacious practices rather than the most expedient ones.¹⁴¹ Christopher Slobogin has explicitly called for a more nuanced understanding of dragnets and suspicionless surveillance. He would allow dragnets that meet a standard of “generalized reasonable suspicion” where their efficacy outweigh the privacy intrusion enough to merit their use in criminal investigations.¹⁴² Jeffrey Bellin recommends locating the Fourth Amendment interest in databases with the owner or holder of data, rather than the subject of the data searches, which would give a company the right to either consent to a search or to demand a warrant.¹⁴³ Andrew Ferguson would allow the use of dragnets as long as the legislative branch explicitly authorizes their use.¹⁴⁴

Reaching the other end of the spectrum, some scholars (myself included), see the use of filtered dragnets as a move toward justice rather than away from it.¹⁴⁵ The prohibition of a highly reliable investigation tool is unethical when the prohibition would push police toward more invasive and less accurate investigation techniques and when serious crime would too often go undeterred. David Kaye and Michael Smith have made this argument with respect to DNA matching.¹⁴⁶

Where does this leave us? Hopefully with an open mind and a hunger for reasoning from first principles.

C. The Pointlessness of Fourth Amendment Privacy

Filtered dragnets will disrupt the equilibrium between the government, criminals, victims, and bystanders. That is obvious enough. Orin Kerr has made the descriptive and normative claim that courts intuitively adjust Fourth Amendment rules to strike a new balance between privacy and security whenever the government gains a significant new surveillance capability.¹⁴⁷ Filtered dragnets implicate only a few Fourth Amendment interests, and those few are not well served by the reasonable expectations of privacy test, by the warrant requirement, or even by intuitive adjustments. We are in new terrain in which a technology increases both privacy and crime control.

1. Theoretical Dimensions of Fourth Amendment Privacy

Borrowing from a rich literature that catalogues and elucidates the concept of privacy,¹⁴⁸ the following arise most frequently in the context of government intrusions and surveillance:

i. Freedom from Embarrassing Revelations, Social Dislocation, and Harassment

Perhaps the most common and robust form of privacy is the recognition that everybody has some legitimate, pro-social reason to want to keep licit details about their lives away from at least a subset of people.¹⁴⁹ They want the freedom that comes from relative obscurity,¹⁵⁰ where their decisions and behavior are not under the scrutiny and judgment of others.¹⁵¹ Everybody deserves to be shielded, at least to some degree, from embarrassment over the things they have said or done that did not cause any lasting harm to others and that can be misunderstood.¹⁵²

The scope of this interest ranges from trivial embarrassments (the regrettable hairstyle, the piece of toilet paper stuck to a shoe) to the truly life-changing (the ostracism of an HIV diagnosis, the physical attack carried out with the help of location information).¹⁵³ Much of the time, the sensitivity of a piece of information will depend greatly on context,¹⁵⁴ but the point is that “everyone has facts about themselves that they don’t want shared, disclosed, or broadcast indiscriminately.”¹⁵⁵ When information is permitted to leap from one context to another and to be used in unexpected ways, it will cause harm.¹⁵⁶

Filtered dragnets relieve, rather than exacerbate, these concerns. By shielding data from police (and everyone else) unless and until they match the fingerprint of a crime, filtered dragnets keep as much information private as practically possible.¹⁵⁷ Indeed, if more police investigations were conducted through filtered dragnets, members of the community would be much more obscure and unknown vis-à-vis the state as compared with programs that involve heavy use of interviews, street patrols, traffic stops, and home searches.

ii. Freedom from Manipulation

An actor can exploit access to another person’s data by discovering their vulnerabilities or gaps in rationality and then using those to persuade, cajole, or threaten the data subject into doing something.¹⁵⁸ Again, as with freedom from embarrassment, filtered dragnets present a lower, rather than higher, risk of this sort because law enforcement and other government actors are blinded from nonrelevant information. The only use to which the dragnet data are put involves solving a crime.

iii. Freedom from Indignity

The privacy literature prizes at least two forms of dignity that are not captured in other concepts on this list. First, privacy intrusions sometimes bring about an indignity from being singled out for suspicion.¹⁵⁹ Dragnets, whatever their faults, do not have this intrusion. Nearly everybody suffers the same indignity when bulk data is scanned, just as they do at TSA checkpoints and DUI roadblocks.¹⁶⁰ Another form of dignity concerns being treated as a human rather than being processed as a faceless line of data. This has some overlap with the concept of “individualized suspicion,” which I will discuss below, and which (in my opinion) filtered dragnets more than adequately should meet. Nonetheless, it is undeniable that filtered dragnets are entirely mechanical up until the point when a limited set of information is disclosed to police. Whether this should make a difference in the moral and legal status of filtered dragnets, though, is debatable.¹⁶¹

iv. Freedom from Anxiety

A common theme throughout the discourse revolves around the idea of loss of control and the uncertainty and anxiety that arises from it.¹⁶² When the government has personal information about a subject, the subject is uncertain how the information could be used and fears that it may be used against them. This fear is, in and of itself, a social cost. Kiel Brennan-Marquez has argued that new data-gathering technologies create, and to some extent have already created, an omnipresent low-level form of anxiety similar to the feeling one gets when seeing a patrol car in the rear-view mirror and “feeling your pulse quicken; awareness heightened and senses alert, as you try not to break any traffic rules.”¹⁶³

A natural follow-up question is: What havoc can the government cause with data?¹⁶⁴ The greatest risk posed by filtered dragnets is to offenders, and it is the risk that their offense (and nothing more) will be detected. Thus, for filtered dragnets, freedom from anxiety calls for a freedom from law enforcement itself. It vindicates the rights of the supposedly “guilty” rather than the innocent. Fourth Amendment privacy recognizes no such interest.

2. Routine Compliance with Reasonable Expectations of Privacy

Data-driven policing has inspired a series of gloomy articles that predict the Fourth Amendment’s reasonable expectations of privacy test has become irrelevant.¹⁶⁵ As long as the third-party doctrine stands, permitting police to access data held by third-party companies without justification or oversight, privacy will be insufficiently protected. I agree with these scholars.¹⁶⁶ But courts are already addressing this problem. Cases like Carpenter v. United States—in which the Supreme Court found that police access to several days’ worth of geolocation data constitutes a search that would require a warrant or appropriate warrant exception—have proven that for suspect-driven searches, Fourth Amendment privacy is not yet irrelevant and is becoming more powerful by the day.¹⁶⁷

Nevertheless, the reasonable expectations of privacy test is very unlikely to impede the adoption of filtered dragnets. That test has repeatedly been interpreted to deny privacy interests of the guilty. “[A]ny interest in possessing contraband cannot be deemed ‘legitimate,’ and thus government conduct that only reveals the possession of contraband ‘compromises no legitimate privacy interest.’ ”¹⁶⁸ Jed Rubenfeld’s synthesis of Fourth Amendment caselaw seems to get it right: the Fourth Amendment aspires to support “a justified belief that if we do not break the law, our personal lives will remain our own.”¹⁶⁹ Filtered dragnets pass this test.¹⁷⁰

To be clear, there are reasons, independent of privacy, to protect law-violators-as-violators. These arguments, which I describe in depth in the next Part, are critical for understanding the threat from filtered dragnets. But they are only loosely related to “privacy” as the term is typically used, and they will not be incorporated into the reasonable expectations of privacy unless that test is changed beyond all recognition.

3. The Irrelevance of the Warrant Requirement

In U.S. v. Chatrie, the geofence case described earlier, the court suggested it would approve a geofence warrant process if a magistrate or court got to make a probable cause determination before the geolocation data of a target were de-anonymized.¹⁷¹ Generalizing to other filtered dragnets, law enforcement would seek a warrant after the filtered dragnet system alerts, but before any identifying data is revealed.

This process might be a good component for accountability and oversight, and to ensure that filtered dragnets are performing at or above the expected “hit rate,” but it is hard to imagine why a warrant could ever be denied. A warrant is valid as long as it is issued by a neutral judge or magistrate, is based on probable cause, and states with sufficient particularity what is to be searched or seized.¹⁷² The standards for both probable cause and particularization will be met—more than met—given that the definition of filtered dragnets I am using requires them to withhold information until the probability that the target has engaged in the investigated crime meets a high standard. As for particularization, because the filtered dragnet procedure begins with the signatures of a crime and works backwards to find the perpetrator, the profile for matching (what I have been calling the “fingerprint” of the crime) is as particularized to a crime as it can be.¹⁷³

Privacy advocacy groups have argued that warrants issued for reverse searches are tantamount to general warrants because they do not identify (or even anticipate) a particular suspect before they are issued.¹⁷⁴ But the only similarity that geofence warrants have to general warrants from the Colonial Era is the lack of a named suspect. In every other way, geofence warrants restrict the information that is revealed to that which is closely linked to a particular crime. By comparison, general warrants authorized agents of the colonial government to look for stolen or untaxed goods anywhere the agent “[should] think convenient to search.”¹⁷⁵ The only manner in which the geofence warrant is unconstrained—by allowing police to discover who the suspect is rather than requiring police to come with a suspect in mind—is a feature of geofence warrants that should be praised, as it limits the discretion of the police to select their targets in advance. This is the critical distinction between filtered dragnets like geofence warrants or DNA searches and suspect-driven searches—one that scholars and commentators too frequently gloss over.¹⁷⁶

Thus, a warrant requirement is irrelevant to the adoption of filtered dragnets, apart from the time, resources, and general system friction involved, because they should routinely be granted.

***

Privacy scholars are courting disaster by lumping filtered dragnet techniques in with other types of dragnets and digital searches. Even if there are court victories in the short term, they will be pyrrhic. The very concept of “privacy” will become increasingly vulnerable to the “I have nothing to hide” argument that is loathed by the field (and rightly so).¹⁷⁷ Courts might fail to sufficiently constrain unfiltered dragnets and suspect-driven investigations because of the utility and low harm of filtered dragnet techniques that happen to share the same Fourth Amendment bucket.

Arguments against mass surveillance often start with the observation that surveillance fundamentally shifts power from the surveilled to the surveillor.¹⁷⁸ This is true as far as it goes, but if the surveillor is constrained and can only see evidence of a crime, that power shift will often be a desirable one. In fact, assuming that the law is legitimate, the enforcement of a law is one of the most legitimate acts the government can do. The burden is therefore on surveillance scholars to explain why those who have violated the law may have justified interests in being protected from state detention and prosecution, even when their law-abiding conduct remains private. There are answers to this challenge, but they sound in tyranny rather than invasions of privacy. There is a virtue to being precise about the problems of filtered dragnets without reliance on capacious notions of privacy that would implicate nearly every law enforcement function.

IV. FILTERED DRAGNETS AND TYRANNY

Filtered dragnets will provide a highly concentrated dose of criminal detection. Even though, in theory, the whole point of having law enforcement departments is to detect and prosecute crime, a drastic increase in criminal detection can have toxic effects on society. The dynamics and interaction of other criminal justice factors have come of age in a time of low detection and only make sense if detection continues to be difficult.

This Part begins by revisiting the interests that privacy scholars have identified that would be affected by filtered dragnets. Each of them is really an anti-tyranny concern garbed in the language of privacy. If we are more explicit about the goals and analyze the risks of authoritarianism that filtered dragnets may drag along with them, the problems (and, therefore, the remedies) become much more obvious.

The true threats from filtered dragnets are that: (1) many Americans will confront a real risk of criminal liability based on our overbroad criminal codes; (2) prosecutions of those crimes could lead to life-altering detentions in our inhumane prison systems; and (3) without the shield of abysmally low detection rates, the only protection is lenity, which is no protection at all from a government that attempts to exert authoritarian power.

A. Privacy as a Stalking Horse for Anti-Authoritarianism

Neil Richards claims that privacy is a necessary bulwark “if we want political freedom against the power of the state.”¹⁷⁹ But privacy is inadequate on its own to protect the broad range of liberty and equality interests that arise with abuse of power. Filtered dragnets prove it. They can be used to trample liberties and to serve the public unequally even though the government will not know any irrelevant details about licit activities.

Instead of trying to expand the meaning of “privacy” to tackle every possible state abuse, courts and criminal justice scholars alike should seize the moment and force constitutional theory to shift its focus from privacy to anti-authoritarian constraint. To be sure, courts should continue to refine the conception of Fourth Amendment privacy interests to address unfiltered digital dragnets. But if we have any hope of harnessing the great potential of filtered dragnets without creating a despot’s playground, the Supreme Court will need to simultaneously cultivate an anti-authoritarian strand of Fourth Amendment rules.

When surveillance scholars use the concept of privacy to curb abuses of power, they are concerned about unnecessary social control and abuses of discretion.¹⁸⁰

1. Unnecessary Social Control

Law enforcement serves the obvious and highly valued function of social control. As Kiel Brennan-Marquez explains, “we want people to worry about breaking the rules”¹⁸¹—at least, when the rules are good rules, and when the consequences for breaking rules are proportional and fair. However, Brennan-Marquez is concerned that data-driven policing tools will leave the police “awash in probable cause,” allowing them to stop, search, or arrest nearly anybody.¹⁸² This concern gets to the heart of the matter. But it is ultimately a critique of the substance of criminal law and the discretion of criminal justice decisionmakers. These are the same themes that Bill Stuntz repeatedly raised when he critiqued Fourth Amendment cases and scholars for allowing privacy to be a distraction from more pressing threats.¹⁸³

Let us return for a minute to Brennan-Marquez’s metaphorical driver who has just discovered a patrol car in the rearview mirror. If the government had done a massive purge of its penal codes and the only crimes left on the books were murder, rape, arson, armed robbery, and aggravated assault, and if false positive police error was vanishingly small, would the driver feel anxiety? For a time after the change, yes of course. There will be a short-term period of distrust and adjustment when technologies or rules change suddenly and dramatically.¹⁸⁴ But in the long run, anxiety will ebb under the pressure of persistent feedback of non-events and the absence of harm.

Public opinion surveys find that attitudes about privacy are mediated through attitudes about the substantive criminal law that is being enforced: a dog that is sniffing for bombs is perceived as less privacy-invasive than a dog that sniffs for drugs even though the experience is identical for the investigation target (at least, up until the moment that the dog alerts, that is).¹⁸⁵ If assessments of privacy change not because of the revelations or techniques that are used but because of the crimes that are prosecuted, the concept of privacy is standing in for objections to the substance of the law.

The concern about unnecessary social control is better addressed by defining, as best we can, which types of antisocial conduct rise to the level of being worthy of criminal punishment and which do not. And the concern raises important questions about whether criminal violators are treated too harshly. Privacy is a blunt instrument for these purposes. It draws lines that have only a vague relationship to the distinctions we mean to draw.

2. Selective Attention

Another serious concern is that police might make use of a system of surveillance to rifle around for something to use against a specific person or group.¹⁸⁶ Motivations could range from political persecution to racism to personal vengeance to simply wanting to make a quota or appear well in performance metrics within a bureaucratized police department.

As with unjustified social control, the problem of discretion and selective attention is only indirectly related to privacy. Indeed, it is not even clear that privacy has any positive influence on police discretion. Privacy steers police toward information sources that disproportionately expose low-income and minority groups: if police cannot bring a drug-sniffing dog to a house, they will bring it to apartments and cars.¹⁸⁷ If police cannot search the full set of government and commercial DNA databases for a match to a crime scene sample, they will just use the government’s database of arrestee DNA data.¹⁸⁸ At the same time, police can also engage in selective inattention by avoiding leads that could cause problems for friends or powerful people and by failing to give crimes perpetrated against low-status victims the same attention as the ones inflicted on high-status victims. When communities are under-protected, it is a form of too much privacy vis-à-vis the government.

The policy antidote to government discretion and bias is to directly limit discretion and bias. Filtered dragnets already do this, to some extent, because once they are employed, police lose control over who will ultimately be identified as a suspect. But law enforcement can still deploy filtered dragnets unfairly when selecting the neighborhoods or cases in which filtered dragnets will be deployed.¹⁸⁹

Thus, in the context of filtered dragnets, “privacy” concerns are attempting to capture and curb something bigger: too much social control at the discretion of the government.

B. Filtered Dragnets and the Risks of Tyranny

An authoritarian regime thrives when it has unlimited discretion to issue stiff punishment based on criminal behavior that has negligible negative consequences (and possibly even positive consequences) to society. This threat is blunted if the state lacks the means to acquire evidence of criminal behavior, but with reliable surveillance mechanisms, law enforcement officials will be able to exert as much social control as they please, because nearly every person can be charged with a crime.¹⁹⁰

Thus, filtered dragnets present risks that run along three vectors: (1) overbreadth of criminal law; (2) overly harsh punishment of criminals; and (3) overly discretionary investigations and enforcement. If these three forces remain unchecked, filtered dragnets could cause more harm than good. In the wrong hands, filtered dragnets could cause catastrophic risks of the sort that the Constitution is meant to prevent.

1. Overbreadth of Criminal Law

A government that has the capacity to detect criminal behavior at very high rates must come under heightened standards of care when it promulgates or maintains its criminal laws. If we wince at the thought that everybody who commits a minor offense will get caught and will be prosecuted if they do not seem to qualify for a privilege or defense, this is a sign that the conduct is a poor fit for criminal law, and legislators must consider alternatives (e.g., warnings, civil fines, or positive incentives for pro-social conduct) instead.¹⁹¹

Right now, constitutional case law does very little to constrain the creation of criminal laws. Outside criminal statutes that would intrude upon specific individual liberties recognized in the Bill of Rights, the courts hold legislatures to very low standards of care (the rational basis test).¹⁹² This latitude on substance has a curious relationship with the procedural restrictions imposed by the Fourth Amendment: as long as police have probable cause to believe that a person is violating or has violated a criminal law, police can make an arrest or initiate a search, no matter how trivial the offense. Thus, in Atwater v. Largo Vista, the Supreme Court found that the government acted within the bounds of the constitution when a police officer arrested a woman who was driving with two small children for the violation of a seatbelt law.¹⁹³

Even if the Court is reluctant to interfere with legislators’ management of criminal codes, common sense dictates that some crimes are much worse than others. The state’s attention should focus on conduct that causes serious harm to others. There is a reason, for example, that the states that have regulated familial DNA-matching programs have allowed their use only for serious offenses like murder and rape,¹⁹⁴ and Baltimore’s Aerial Investigation Research (“AIR”) system, before it was dismantled, was restricted to use in investigating a limited set of very serious crimes.¹⁹⁵ It is the same reason that the federal Wiretap Act permits courts to issue wiretap orders only when there is probable cause to investigate one of the explicitly listed serious criminal offenses.¹⁹⁶ The same impulse explains why there is scholarly criticism and public outrage when a surveillance system adopted for the purpose of detecting one set of serious criminal violations (like smuggling or terrorism) is simultaneously used to detect violations of drug laws.¹⁹⁷ The unstated assumption is that some crimes should be detected as well as possible (terrorism, for instance) and some should not.¹⁹⁸

The fact that state and federal criminal law has dramatically expanded in quantity and complexity is not in dispute.¹⁹⁹ And yet, curiously, responses to the problem tend to focus on procedural rather than substantive limits.²⁰⁰ The unchecked growth of substantive criminal law ironically creates a problem for public safety because the fear of prosecution prompts a demand for privacy and law enforcement obstruction.²⁰¹

The first and most obvious reason to place limits on criminal liability is to reduce the opportunity for unnecessary social control. The relationship between the government and the governed changes profoundly when a crime has been committed. The defendant in Atwater should have put a seatbelt on her children, and the government has an interest in encouraging, even requiring, that behavior. But not through criminal law.²⁰² A second reason to constrain the substance of criminal law is to increase compliance with the rules we care about most.²⁰³ Overstuffed criminal codes also bleed into the problems of law enforcement discretion (discussed at greater length below) because the government has too much power to decide which members in the nation of criminals to send to prison.

Consider two examples that illuminate the problem through opposite ideological lenses. First, abortion will be criminalized in many states in light of Dobbs v. Jackson Women’s Health Organization.²⁰⁴ Some states are considering criminal liability for women who seek out an abortion.²⁰⁵ For liberals and progressives, criminal liability for abortion-seekers represents an intolerable overreach of the state. To combat the substance of these laws, organizations such as the ACLU have already issued warnings about the risk that geofence searches could facilitate arrests and prosecutions of a law that a sizable portion of the state’s constituents believe is unjust.²⁰⁶

By contrast, conservatives might be concerned about overzealous enforcement of gun restrictions.²⁰⁷ Geolocation and credit card transaction data could be used to create a filtered dragnet that finds individuals without a gun license who cross state lines, attend a gun show, make a sizable purchase, and immediately return to their state.

In both cases, perceived flaws in the substance of the law would not be so troubling if the laws carried only modest punishments—warnings or fines, for example, rather than the incarceration and downstream labor and housing problems that inevitably follow conviction.²⁰⁸ But given the breadth and severity of criminal law, plus the mostly unchecked discretion that police departments have when deciding which among an ocean of technical criminal violations to investigate, the prospect of near-perfect detection takes on a more sinister character. Thus, when people have reservations about, for example, Alexa devices being used to detect the sounds of domestic violence, the reservations stem not from the specific use case but the general capabilities. They wonder, for good reason, what mischief can be made from such a technology when the set of conduct that is forbidden and harshly punished is sprawling and unevenly enforced.²⁰⁹

Criminal codes are often expanded when the state has not gotten a handle on crimes of violence and property theft. The criminalization of vice (alcohol and drugs) was supported by the community not necessarily out of concerns that the drugs themselves cause to users but because of the “unconscionable violence” that came along with trafficking and addiction.²¹⁰ In other words, substantive criminal law is expanded to compensate for deficiencies in the detection and prosecution of crimes that were already on the books so that police could arrest for lower level crimes and (stochastically) reduce the incidence of more serious crimes.²¹¹ If detection of the serious crimes were more functional, this should relieve the need for sprawling criminal codes.

Hence the dilemma: better crime detection could help stop the pattern of an upward ratchet, but as long as the criminal codes are already sprawling, there will be resistance to increasing detection.

2. Overly Harsh Punishment

On severity of punishment, the United States stands out among developed nations. We use incarceration intensively. In France and the U.K., a criminal who punches a person in the nose would be sentenced to less than six months in jail.²¹² The same conduct in the U.S. would result in a sentence of about three years.²¹³ Moreover, no outsider would mistake our prisons for institutions of rehabilitation: the entire sentence is usually carried out in a facility that is punishing, with drab quarters, humiliating toilet and bathroom facilities, and rancid food.²¹⁴ Once released, the negative consequences continue as the housing and labor markets penalize criminal convicts.²¹⁵ Long sentences also create risks of abuse by giving police officers and other state agents leverage to extract bribes, pleas, and false confessions.²¹⁶

The harshness of our sentences is the byproduct of a low detection rate. Communities that at various times have been disfigured from crime waves tend to demand more and harsher criminal penalties.²¹⁷ The intuitive appeal of using long prison sentences to make up for low detection rates became the explicit policy of federal and local governments following the landmark work of Gary Becker. Becker modeled crime with a simple formula determined by the probability of conviction and the severity of punishment.²¹⁸ Because it is much easier and cheaper for the state to ratchet up punishment than to catch more perpetrators, his work persuaded many politicians to manage crime through tough sentencing.²¹⁹

The sparseness of Becker’s model for crime rates leaves much to be desired for anybody looking for a comprehensive explanation for crime—crime, of course, has a range of social and economic causes²²⁰—but as Part II explained, there is little doubt that detection has a significant influence over the amount of crime in a given community.²²¹ Punishment, by contrast, seems to have a U-shaped relationship to recidivism, where no punishment and long, harsh punishment both tend to increase the odds that a perpetrator will recidivate.²²²

I do not want to overstate the case for reducing prison time. Roughly half of the inmates in prison are individuals with such consistent sociopathic and antisocial behaviors that for those inmates, long-term incapacitation has positive externalities. Not only does incapacitation prevent these particular individuals from committing additional crimes (specific deterrence), but their families and particularly children may benefit from having less, rather than more, exposure to them.²²³ Nevertheless, the social costs of harsh punishment do not seem to serve deterrence or otherwise be justified outside the context of heinous or repeated criminal activity.

Over-punishment and criminal detection are inextricably connected. We cannot expect to find a political will to reduce punishment unless the police have—and use—new means to detect and root out crime. Filtered dragnets can jolt and resettle the criminal justice system in a new equilibrium where detection, rather than harsh punishment, is the key mechanism for crime control.

3. Discretionary Application

Once the police have committed to investigating a particular crime, filtered dragnets take discretion away from the police to drive the investigation. But there are other points in time before and after a filtered dragnet may be used when government agents can exert control over the process:

i. Selective Protection

When it comes to serious crimes of violence and theft, American police forces have a troubling history of systematically ignoring the suffering of minority communities. Police once actively conspired to deprive former slaves of their right to protection by joining the murderous mobs.²²⁴ Over the subsequent century, police started to exhibit a more passive form of selection by simply not investigating and pursuing crimes committed against African-Americans as zealously as crimes committed against whites.²²⁵ This is a form of inequality that is not adequately addressed in constitutional caselaw.²²⁶ Thus, courts must prevent police from using filtered dragnets to solve crimes committed against one set of privileged crime victims while failing to use the same tools to solve comparable (and comparably detectable) crimes committed against others.

ii. Selective Crackdowns

Police also decide which crimes to target,²²⁷ and when and where to focus their resources.²²⁸ For example, police will decide which crime scene images should be subjected to facial recognition. There is no guarantee that they will pursue arrest and prosecution of violent or destructive participants at Black Lives Matter protests or at a pro-Trump rallies with the same vigor.

iii. Controlling the Data

Whether police use government-held data or data held by private companies to operate a filtered dragnet, they can exert some influence over the process if they are allowed to use a subset of available information to run through the filtered dragnet.²²⁹ For example, if the government were able to limit DNA-matching to the data collected from ex-convicts only, or if a geofence warrant could direct a service provider to look for matching records only among customers who live in a certain precinct, the police could do an end run around the discretion-reducing function of filtered dragnets.

iv. Downstream Decisions

After a suspect is identified by a filtered dragnet, police and prosecutors still have unchecked power to use leniency and to simply not pursue the leads that they do not like.²³⁰

The unifying theme across these decision-making practices is that the Supreme Court has avoided interfering with law enforcement discretion any time it has a plausible connection to judgment about the best use of resources. In Whren v. United States, the Supreme Court rejected a constitutional challenge by a criminal defendant who was pulled over for making an illegal U-turn. The defendant argued that the police would not have pulled over a white person, or any person about whom the police did not have a pre-existing “hunch,” under similar circumstances.²³¹ The court believed that the defendant’s theory of unequal enforcement of minor traffic infractions was irrelevant and unworkable.²³² At the time it probably was.²³³ But it is not anymore and will be even less so in the future. Today, a defendant bringing a case like Whren might have the data, thanks to GPS tracking of police and civilian cars, to demonstrate that police pull over only a small fraction of the illegal U-turns and other traffic infractions that they observe, and that the enforcement disproportionately targets minority drivers (if this is so).²³⁴

If police are able to use filtered surveillance to solve crimes at minimal expense, there will be even less need for discretion. So, if police have a filtered dragnet, courts must make sure they have an acceptable response to the question: “Why did you enforce the criminal law here and not there?”²³⁵

In summary, a government that has the capacity to detect criminal behavior at very high rates must come under heightened standards of care with respect to the promulgation of criminal laws, the use of incarceration and punishment, and the application of detection tools.

V. THE ANTI-AUTHORITARIAN FOURTH AMENDMENT

Anti-authoritarianism, rather than privacy, should be the benchmark for the Fourth Amendment when police develop cases using filtered dragnets. What makes facial recognition or a geofence or some other form of filtered dragnet “reasonable” is not that the privacy of the innocent is protected—they will all do that. Rather, an “unreasonable” use of these technologies means the state is misusing its power to punish and control.

The current trajectory of Fourth Amendment caselaw suggests that we are headed for one of two suboptimal endpoints: either the state will be able to use filtered dragnets with little to protect its citizens from the perils of broad criminal laws, harsh criminal sentences, and selective enforcement, or the state will effectively be prohibited from using filtered dragnets, leaving a criminal justice status quo that nobody would devise and few would defend.²³⁶ But if the courts start to take seriously the fundamental differences between filtered dragnets and other investigation techniques—if they recognize that technology can explode longstanding assumptions about the nature of risk when police increase the detection of crime—courts can harness the disruptive technology and help society land in a better equilibrium.

Thus, the Fourth Amendment must evolve to demand “reasonableness” when detection is easy. The thrust of my proposal is that the phrase “reasonable searches and seizures” should be understood as a more expansive and robust guarantee of reasonableness.²³⁷ Specifically, the requirement of “reasonable” seizures should guarantee that the consequences of a seizure (e.g., carceral arrest and a possible prison sentence) are fitting and proportionate to the gravity of the suspected crime. The requirement of “reasonable” searches should guarantee not only that the search is conducted based on probable cause and in line with established warrant requirements, but also that the decision to search or not search is reasonable and non-arbitrary. The former ensures that the criminal law being enforced is serious enough to justify the loss of rights that comes along with an arrest or a long sentence. The latter ensures that criminal detection tools are used in an even-handed manner.

A. Reasonable Seizing—Restricting the Substantive Criminal Law

The prospect of near-perfect detection requires more care in defining a reasonable seizure. In order for a carceral seizure of a person to be reasonable, state uses of force and coercion involved must be justified by the harm that the arrestee has imposed on society. “Freedom from unreasonable . . . seizures” should be interpreted to protect the interests of individuals who have engaged in conduct that is technically illegal but not morally reprehensible.²³⁸ Thomas Jefferson’s unfinished vision laid out in the Declaration of the Rights of Man and of the Citizen provides the blueprint. Article 4 states, “Liberty consists in the power to do anything that does not injure others”; Article 5 states, “The law has the right to forbid only such actions as are injurious to society”; and Article 8 states, “The law ought to establish only penalties that are strictly and obviously necessary.”²³⁹

A seizure should only be reasonable if the underlying criminal conduct and the resulting punishment are also reasonable. While substantive due process rights and the Eighth Amendment provide some absolute constitutional limits against unreasonable criminal codes or punishments, these rights must be bolstered in the face of near-perfect detection. An analysis of reasonable seizures in light of filtered dragnets has two aspects to it: (1) whether the behavior is sufficiently blameworthy to belong in the criminal code at all, and (2) if so, whether the punishment fits the risks and harms of the crime.

Is the conduct crime-worthy? The first inquiry asks whether the suspect’s conduct is bad enough to justify arrest and incarceration at all.²⁴⁰ This is a threshold issue. Criminal conviction needs to be blameworthy and stigmatizing. Defining what sort of conduct is “blameworthy” raises deep philosophical questions, but there is an aspect of the question that is empirical: it needs to be rare. If the conduct captured by the scope of the criminal codes is commonplace, the actor’s community evidently has not incorporated restraint deeply into its moral fabric.²⁴¹ In those cases, government intervention short of criminal liability (including expressive law, civil fines, or positive reinforcement for its opposite) should be used.²⁴²

This is at odds with cases like Atwater, where the court refused to second-guess a local government’s decision to criminalize a minor driving infraction,²⁴³ but Fourth Amendment case law does occasionally break rank with Atwater and peeks at the substance of the criminal violation in order to gauge the reasonableness of a procedure. For example, when analyzing whether a warrantless traffic checkpoint is constitutional as a reasonable warrantless seizure, the Supreme Court explicitly considers “the gravity of the public concerns served by the seizure” as one of the factors.²⁴⁴ And the Court has refused to allow exigent circumstances to excuse the failure to secure a warrant for a home search and arrest when the underlying crime is a minor offense.²⁴⁵ And Atwater is ahistorical: a quick tour of the notorious cases the Crown directed against colonists that inspired the Bill of Rights are offensive, in large part, because of the substance of the crimes. These included crimes such as writing or publishing “gross and scandalous reflections and invectives upon his majesty’s government” or the crimes of illegal trade and inadequate record-keeping.²⁴⁶

Is the punishment too harsh? If the suspect’s conduct is reprehensible enough to pass the initial threshold test, a post-conviction seizure could still be unreasonable if the quality and length of detention is disproportionately harsh.²⁴⁷ The sentences of many crimes, even violent crimes, could probably be reduced to weeks or days, or even converted to non-carceral forms of punishment (like public service or surveillance-enabled supervised release) without increasing crime rates if detection rates were much higher than they currently are. Long-term prison sentences can be reserved for murder, treason, severe sexual assault, severe child abuse, and for the incapacitation of repeat criminals.²⁴⁸ For other crimes, detection through filtered dragnets, rather than a small chance of very harsh punishment, can be the door jamb that stops the metaphorical revolving door of recidivism.

B. Reasonable Searching—Minimizing Discretion

A police department’s use of filtered dragnets will be fair if it avoids gaps in the protection from crime as well as gaps in leniency from enforcement.

1. Duty to Search

All cases of reported or otherwise known crimes that are equally suitable for filtered dragnets should be investigated.²⁴⁹ For example, if a police department can use filtered dragnets to detect gun violence or robberies, and it fails to investigate daytime violence and robberies taking place near low-income schools even though it investigates every daytime robbery or assault that takes place near high-income schools,²⁵⁰ the uneven use of filtered dragnets would render it an unreasonable search. As a practical matter, while it would make more sense for a constitutional challenge to come in the form of a § 1983 claim brought by a resident who is harmed by a detectable or deterrable crime, the challenge is more likely to emerge when a criminal defendant brings a claim similar to the claim brought in Whren (arguing that although they committed an offense, the crime is unequally enforced).²⁵¹ Courts should be open to a claim and evidentiary proof of this sort.

2. Duty to Cast a Large Dragnet

Law enforcement should not have undue control defining the search pool that will be used by a filtered dragnet. The database that will be used to cross-check against the facts of a crime should include everyone possible whose data is accessible and whose participation in the crime would not be an impossibility. This reduces the risk of arbitrariness or bias that could result if police search for potential leads and matches in one population while ignoring another.

By this standard, facial recognition systems like Clearview AI are more legitimate (in the sense of being less susceptible to bias or discretion, at least) when they match surveillance footage at a crime scene against the largest possible set of publicly available portraits on the open web. Contrast this with DNA filtered dragnets: it is increasingly common and popular to restrict local law enforcement who are running DNA searches to CODIS, the federally maintained database of arrestee or convict DNA samples.²⁵² Whatever rationale might justify subjecting convicts to greater likelihood of being caught in their own future crimes, the logic does not follow to arrestees or to individuals whose crimes are detected through familial DNA.²⁵³

The principle of evenhanded enforcement is consonant with what Bennett Capers meant when he argued that equitable policing may require “redistributing privacy.”²⁵⁴ But it may require courts to enforce subpoenas or issue warrants in order to pierce through corporate policies that resist law enforcement access.²⁵⁵ These policies are already in place at some companies.²⁵⁶ Of course, there may be times when law enforcement resources really are constrained so that investigating every trackable crime or casting the widest possible dragnet will not be possible, but the police should be able to offer some reasonable explanation. And an explanation that would not be reasonable is that too many individuals would be caught: if the availability of filtered dragnets forces law enforcement to confront the problem that there are too many criminal acts, the proper government response is to revisit and narrow or purge some of the substantive criminal laws.

C. Police Culture: The Era of the Nerdy Police Force

The adoption of filtered dragnets will require law enforcement agencies to become more technocratic. Much of the initial investigation work is likely to be centralized, in upper management working at desks, and their compliance with Fourth Amendment restrictions will require competence, if not expertise, in statistical methods and data auditing procedures. To some extent, this change in operations is already happening with the gradual introduction of DNA forensic labs, facial recognition, and now, reverse searches. With clear Fourth Amendment guidance for filtered dragnets, police forces could rapidly adopt filtered dragnets and divest somewhat from traditional techniques. Police operations would shift away from self-initiated patrols and field-based investigation toward data-driven initiation and investigation. This will change who is qualified for and attracted to a policing job. Police investigators who are used to solving cases through interrogations and informants will begin to feel like the baseball scouts who still visit high school and college teams looking for “good legs” while their younger, nerdier, and (eventually) better paid colleagues use Bill James-style statistics to prioritize the team’s recruiting efforts.²⁵⁷

This may prove to be a feature—a way to achieve the reform of police culture by working backwards from shared ends that are appealing to both suburban families and Black Lives Matter activists (lowering crime, reducing false convictions, and achieving even-handed enforcement). The cultural shift can provide counterpressure to a problem that currently plagues police recruitment—that the people most interested in working for law enforcement have stronger-than-average preferences for meting out punishment.²⁵⁸ All the more reason civil liberties organizations should reconsider their instinctive negative reactions to filtered dragnets.

The criminal defense bar may get transformed, too. Andrew Ferguson has made the case that law enforcement data-collection and data-mining practices can be inverted to discover negligent or abusive practices within police departments.²⁵⁹ Defendants can make use of “blue data” to prove their cases that, for example, law enforcement had used an unreasonably narrow dragnet.²⁶⁰ This may offend a police department’s sense of agency and self-determination, but this is a reasonable price to pay for the power and efficiency of filtered dragnets.²⁶¹

VI. ADDRESSING FRIENDLY OBJECTIONS

Some readers will no doubt disagree with my description of the looming opportunities and problems that will arise with filtered dragnets, and as a result will reject the policy solutions offered in Part V. I addressed doubts about the upsides of filtered surveillance or the downsides of near-perfect detection as best I can in those earlier Parts. Whatever disagreements about the policy implications remain will have to be aired in other fora. Here, I address some objections that will be raised even by readers who agree that the policies advanced in this Article are sound.

“Friendly” critics will wonder why it is necessary to constitutionalize these policies rather than advocating for a legislative response. The answer, in brief, is that constitutional protections are the only viable tools when several criminal justice rules must be changed at the same time.

Friendly critics may also wonder why the Fourth Amendment is the right vehicle for course correction even if all agree that constitutional law must be pressed into service. On this question, I am more neutral. If the Eighth Amendment and Due Process clauses can be interpreted to reach the same anti-authoritarian objectives, there is little reason to insist on the Fourth Amendment as the primary source of these rights. But since filtered dragnets will inevitably cause seismic activity in Fourth Amendment law, and since highly efficient searches are the reason that the threat of government tyranny will become more pronounced, it is at least fair to say that the Fourth Amendment could be the right constitutional source for the anti-authoritarian rights described in Part V.

A. Why the Courts? (Or, Why Not the Legislature?)

Not every problem in law enforcement needs to be solved through the constitution, but this one does. The political process is exceedingly unlikely to get us out of our criminal justice rut, where low detection rates are messily compensated through criminal liability for minor infractions. Political winds bob from too much lenity to authoritarian severity,²⁶² and as a result, surveillance restrictions and decriminalization usually rise and fall together depending on whether the mood is pro-rights or anti-crime. Political institutions do not have the tools to break surveillance and substantive criminal law apart and to work out a criminal justice horse trade. But a horse trade is what we need: we simultaneously need the police to detect more violent crime while also ensuring that no person who is caught with a $10 baggie of drugs could ever be in a position to go to prison for the rest of their life.²⁶³

This trade—reduced criminal liability in exchange for greater detection—can only be accomplished through constitutional adjustment. If criminal liability and punishment are reduced without a simultaneous increase in detection, crime rates will rise and the ballot box consequences for political actors will be harsh. If detection capacity is increased without any change to the criminal codes, the political actors’ constituents will be justifiably nervous about how the newfound power of detection will be used. But if the two reforms happen at the same time—if the state is constrained by constitutional interpretation from detaining or imprisoning individuals based on minor infractions, or from levying long sentences for anything other than the most serious and violent offenses—surveillance is defanged because the threat of unjust prosecution is reduced.²⁶⁴

Put another way, the political pressure to limit or ban surveillance tools might make sense as a second-best solution if decriminalization and reduced sentencing is politically infeasible, but the risk is that the strategy can lock out the first best solution—the low penalty/high detection solution. Indeed, in the wake of rising murder rates, the decriminalization and police reform movements are already more politically controversial than they were just a couple years ago. If crime rates continue to rise while detection is capped or suppressed through new legal constraints on technology, politically accountable decisionmakers will continue to use mass incarceration to manage crime.

To be fair, many luminaries in the field of criminal justice have seen roughly the same patterns of dysfunction and technological disruption that I have recounted and have recommended solutions in the form of legislation, administrative regulation, and restoring the role of local government. Bill Stuntz, for example, argued that many of the abuses of power in the criminal justice system would be avoided if local governments (rather than states) were the primary promulgators of criminal law and if juries (rather than prosecutors) were the decisionmakers who most often determined whether a defendant should be convicted or serve time.²⁶⁵ Chris Slobogin, Barry Friedman, Maria Ponomarenko, Catherine Crump, and Andrew Ferguson have argued that legislatures and regulatory agencies should be more active in structuring how (non-filtered) dragnet and surveillance technologies should and should not be used in the field.²⁶⁶ But they also acknowledge that politically accountable bodies always run the risk that their decisions will disproportionately benefit the politically powerful and will be relatively indifferent to problems of under-protection and prejudiced enforcement.²⁶⁷

Daphna Renan has argued, convincingly in my opinion, that political processes alone cannot be expected to produce the sort of basic rights and counter-majoritarian protections that the Constitution should guarantee.²⁶⁸ Our agreement ends there, though, because Renan advocates for a Fourth Amendment superstructure, or set of principles, that would set requirements and boundaries on administrative agencies (such as the Privacy and Civil Liberties Oversight Board) tasked with creating law enforcement surveillance programs.²⁶⁹ But no board, no matter how independent, could actually make the grand maneuver that I’m asking readers to consider here—where filtered dragnets are permitted, but in exchange for protection from bad laws, harsh punishment, and discretionary application. Renan’s proposal may be a good second-best solution, but a dramatic reorientation of constitutional priorities can only be done by the Supreme Court. It is time for constitutional renewal in search of a better equilibrium.²⁷⁰

B. Why the Fourth Amendment?

The harder question, and I confess this is where I am on shakier ground, is why the anti-authoritarian principles that I claim are so important during this inflection point are the responsibility of the Fourth Amendment to solve rather than other parts of the Bill of Rights or notions of substantive due process.²⁷¹ The case is somewhat easier for the principle that reasonable searching requires evenhandedness. At the founding, the Fourth and Fifth Amendments were meant to prevent the government from being able to rummage through a disfavored target’s things looking for evidence of a crime, so equal and non-arbitrary treatment was always a goal.²⁷²

The case for using the Fourth Amendment to put constraints on substantive criminal law and sentencing is a bit harder. After all, the Supreme Court has repeatedly authorized law enforcement agencies to execute stops, searches, and arrests, no matter how trivial the law-violating behavior may be to overall public safety.²⁷³ As early as Boyd v. United States, decided in 1886, the Court found that Fourth Amendment protections do not apply to those who have committed a public offense, and courts have declined to second-guess whether the public offense was valid in the course of a Fourth Amendment analysis.²⁷⁴ And one may reasonably think that if courts are going to invalidate an overly harsh prison sentence on constitutional grounds, as I argue they should under the guise of protecting against unreasonable seizures, they would have already imposed these limits under the Eighth Amendment’s cruel and unusual punishment clause.²⁷⁵

Perhaps it would make as much sense to make Eighth Amendment or Due Process protections more robust to ensure that criminal liability is not overbroad and sentences aren’t overlong.²⁷⁶ But a long view of the Fourth Amendment can support a shift from the protection of the property, privacy, and autonomy of non-offenders to the protection of those same interests of those who are innocent in the more platonic sense.

In many ways, the history of Fourth Amendment caselaw shows a faltering and incoherent attempt to get to the main point: to make sure the state does not have too much power to enforce silly crimes and scare its constituents into submission.²⁷⁷ Silly crimes have been at the center of the original construction of the Fourth Amendment and each of its major reforms. Shortly after the American Revolution, sedition laws motivated creative lawyers like Alexander Hamilton to use procedure in order to correct flaws in the substantive criminal law that were not, at that time, adequately constrained by the First Amendment.²⁷⁸ In the context of that time, when states had nearly full rein to search for physical evidence and when prosecutions were proved primarily using witnesses, the thought that constitutional protections could get in the way of convicting rapists and murderers would have been preposterous.²⁷⁹ After all, the founders did not expect the Fourth Amendment to constrain how local law enforcement investigated crimes, and group searches executed without particularized warrants were tolerated.²⁸⁰ Thus, at that time, the buildup of procedure to help protect against crimes of belief and thought had little cost to the control of more conventional crimes.

Courts again increased Fourth Amendment procedural protections during two subsequent periods when the substance of criminal law was directed at questionable, arguably victimless vice crimes like gambling, alcohol (during prohibition), obscenity, and recreational drugs.²⁸¹ In the twentieth century, new information technologies changed the nature of police investigation by enabling wiretapping and forms of long-term tracking of suspects without reliance on trespass or witness cooperation. The standard story is that these technologies unsettled the balance between conflicting societal goals related to police investigations, which is true enough. But another important factor is that the test cases involved the detection and enforcement of gambling, bootlegging, and drug distribution crimes. Katz v. United States, the Fourth Amendment case that developed the reasonable expectations of privacy test, involved bugging a phone a bookmaker was using.²⁸² And it followed the logic of Justice Brandeis’s dissent in an earlier case, Olmstead v. United States,²⁸³ which involved the wiretapping of a bootlegger.²⁸⁴ Katz marked the end of a primarily property-based conception of Fourth Amendment rights and ushered in the privacy phase. When test facts making their way to the Supreme Court involved more serious crimes, like stalking, the Supreme Court avoided finding a privacy violation.²⁸⁵

To be clear, there are other reasons, separate from the substance of the criminal law being enforced, that justify a focus on privacy. Twentieth century surveillance capabilities certainly left Americans—criminals and the innocent alike—at greater risk of unwanted observation of licit activities. But there is also a clear pattern: courts have used criminal procedure to frustrate the enforcement of controversial criminal statutes that cover activities in which a sizable proportion of Americans willingly participate.²⁸⁶ Once privacy posed a significant obstacle to police investigations, procedural rights became the default defense against a tyrannical state. There was less pressing need to press the Constitution into service to challenge whether conduct should even be considered criminal in the first place or whether the police are protecting communities fairly. For better or worse, the Fourth Amendment privacy rule created a tractor beam for public defenders and civil liberties organizations to concentrate their anti-authoritarian efforts.

Scholars have occasionally attempted to refocus the Fourth Amendment on a more general purpose to create a constraint on power.²⁸⁷ Bill Stuntz faulted Fourth Amendment’s turn to privacy because it “tend[ed] to obscure more serious harms that attend police misconduct.”²⁸⁸ More recently, Thomas Crocker has argued that the Fourth Amendment should be understood as a substantive right, not just a procedural one, that follows in the vision of the First, Second, and Ninth Amendments.²⁸⁹ But ultimately, Crocker advocates for the use of this substantive right to argue for a more thorough protection against surveillance.²⁹⁰ Naturally, I think this misses the point. A citizen whose government makes nearly all conduct and action illegal will never feel secure no matter how many restrictions on surveillance are in place. And conversely, a government that is rigidly constrained from expanding its criminal laws beyond the conduct that is nearly universally reviled will be limited in its ability to threaten a citizen’s sense of liberty no matter how much surveillance is in place.

The happenstance of technology provides another reason to prefer the Fourth Amendment over other constitutional sources to redress the problems of overcriminalization and uneven protection. The privacy of the innocent was mediating the clash between American values in freedom and security. Increasing use of filtered dragnets will make this arrangement untenable. If we expect the role of the Fourth Amendment to be meaningful—to be something other than a brief paperwork requirement in the process of securing warrants for filtered dragnets—it is both necessary and appropriate that Fourth Amendment caselaw starts to look for its root function and embrace its substantive as well as procedural dimensions.

CONCLUSION

In 1967, Alan Westin, a leading light among privacy scholars, said that “the modern totalitarian state relies on secrecy for the regime, but high surveillance and disclosure for all other groups.”²⁹¹ This is probably a true statement, but highly incomplete. Surveillance is a necessary condition for authoritarian control, but not sufficient on its own. Indeed, all modern states need surveillance. Modern systems of taxation, public benefits distribution, medical services, and public health could not function without copious amounts of personal data. Thus, surveillance is necessary for all states, not just despotic ones. Moreover, surveillance is no more unique to totalitarianism than are weapons, prisons, and other tools the state must use to carry out the most basic obligations to support social order and security.

The tools that live exclusively in the toolbox of despots are repressive substantive criminal laws, harsh punishment, and discretion to choose when to enforce the law. Even in George Orwell’s dark depiction Nineteen Eighty-Four, Big Brother was oppressive partly because of the substance of the law: the wrong thought could land a person in jail.²⁹²

Against this threat of uncontrolled surveillance, many privacy scholars recommend the dismantling of the surveillance apparatus. This Article focused instead on the “uncontrolled” quality of uncontrolled surveillance. Filtered dragnets are a highly controlled dragnet that reveal only criminal violations. Thus, they are only as threatening to society as the criminal statutes that they enforce and the discretion of the government agents who use them. With the right alignment of Fourth Amendment rules to authoritarian threats, the state can be made to heel—to detect crimes fairly without burdening any communities with under-protection or over-punishment. This will require some intrusion of the traditionally procedural domain of the Fourth Amendment into the substantive realm of criminal law and punishment. If the state can suddenly detect every violation, prison must be reserved for truly awful behavior, and law enforcement should have less latitude to seek out or avoid the investigations of members of certain groups.

These are radical proposals. They go well beyond the privacy framework that has dominated Fourth Amendment theory for over half a century. But they respond to a radical tool that will shock a criminal justice system that is already in crisis and deserves rescue.

97 S. Cal. L. Rev. 571

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Jane R. Bambauer*

* University of Arizona James E. Rogers College of Law. The author is grateful for the advice and invaluable feedback from Jordan Blair Woods, Tracey Maclin, Farhang Heydari, Toni Massaro, Tammi Walker, John Villasenor, Andrew Woods, Lilla Montagnani, Kiel Brennan-Marquez, Jeffrey Fagan, Christopher Slobogin, Derek Bambauer, Mark Verstraete, Xiaoqian Hu, Andrew Coan, Niva Elkin-Koren, Uri Hcohen, and Tal Zarsky.

Secondary Trading Crypto Fraud and the Propriety of Securities Class Actions

April 2024April 2024Menesh S. Patel*

Traders participating in secondary crypto asset markets risk significant loss. Some trading loss will arise simply because of market dynamics, including inherently volatile crypto asset prices. But secondary crypto asset traders also risk considerable monetary injury resulting from fraudulent statements or acts by crypto asset sponsors or others occurring in connection with their secondary transactions. If subjected to such fraud, the affected crypto asset traders may turn to a Rule 10b-5 class action for redress.

Crypto asset traders’ reliance on Rule 10b-5 class actions implicates important doctrinal and public policy questions. This Article analyzes two of these questions—one doctrinal and another in the domain of public policy. In its doctrinal analysis, the Article evaluates issues pertinent to the threshold definitional question of when an exchange-traded crypto asset will constitute an investment contract and therefore fall within the definitional perimeter of a security. The Article proposes a slight generalization of the horizontal commonality test that renders the test suitable for use in both primary transaction and secondary transaction cases, and also addresses aspects of Howey’s efforts of others prong that are relevant to Howey’s application in the crypto asset context.

With respect to the public policy question, the Article evaluates whether the public policy justification for crypto asset-based Rule 10b-5 class actions is significantly weaker than for stock-based Rule 10b-5 class actions. The Article’s public policy determinations break in different directions and in some respects are to be considered preliminary, but the analysis does not justify limiting the availability of crypto asset-based Rule 10b-5 class actions any more than stock-based Rule 10b-5 class actions.

INTRODUCTION

Asset digitization through distributed ledger technology has transformed trading markets. Traders in the United States now routinely trade hundreds of crypto assets on various crypto exchanges, and the pool of tradable assets is growing.¹ Through these secondary transactions, crypto asset traders have seen both financial gain and financial loss, which at times have been substantial.

Recent events have amplified the prospect of secondary crypto asset traders incurring significant monetary loss through incidents of fraud. Misconduct was commonplace in the 2017 to 2019 time period, when a high frequency of crypto asset initial offerings were riddled with fraud, causing investors to lose substantial amounts.² Now, traders transacting in secondary crypto asset markets risk being subject to fraud by crypto asset sponsors or others occurring in connection with their secondary transactions, which the Article refers to as secondary trading crypto asset fraud.³ The injurious effects of secondary trading crypto asset fraud extend beyond the defrauded traders. Such fraud, in combination with other types of misconduct, has the potential to fully undermine the legitimacy of the entire crypto asset ecosphere, including causing collateral damage to the reputation of economically scrupulous actors, and to strengthen the calls by some that the sector be subject to intense regulatory scrutiny.

To take an example that mirrors allegations from a recent fraud suit, suppose that a crypto asset sponsor develops a novel blockchain protocol and an accompanying crypto asset that serves as the blockchain’s native token.⁴ Suppose that the crypto asset goes on to trade on one or more crypto exchanges after its initial offering. At some later point, the crypto asset sponsor falsely represents that a payment provider has adopted the developed blockchain to process payments. Because the fraudulent statement is understood to evidence a new and potentially monetizable use value for both the blockchain and the associated crypto asset, secondary traders update their valuation of the crypto asset, which causes additional trading activity resulting in the crypto asset’s price appreciating on the secondary markets in which it trades. Traders who purchase the crypto asset at the resulting higher price will suffer financial harm once the market becomes aware of the falsity of the sponsor’s fraudulent statement and the crypto asset’s price falls in response. Depending on the magnitude and nature of the fraud, traders’ losses may be substantial.⁵

Additional regulation of the crypto asset space may diminish the prospect of fraud ex ante, but defrauded crypto asset traders may seek ex post relief in the form of private litigation. Traders sustaining losses in connection with secondary transactions of stock and other more conventional assets routinely seek class-wide relief under Rule 10b-5,⁶ which serves as the workhorse of federal securities laws’ antifraud prohibitions. Given the prominence of Rule 10b-5 class actions in modern securities litigation, defrauded crypto asset traders likewise may turn to Rule 10b-5 class relief to recover their secondary trading losses.

These observations raise an important question: Should defrauded crypto asset traders be able to rely on Rule 10b-5 class actions to recover their secondary trading losses, both as a doctrinal matter and as a matter of public policy? A host of considerations bear on this question, and this Article focuses on two leading considerations, one doctrinal and one public policy related.

A primary consideration pertinent to the doctrinal propriety of secondary crypto asset traders relying on Rule 10b-5 class actions is the fundamental question: under what conditions will an exchange-traded crypto asset be within the definitional scope of a security because it is an investment contract under the multipronged test enunciated by the Supreme Court in Howey?⁷ Scholars have dedicated considerable attention to this definitional inquiry but not with a specific focus on exchange-traded crypto assets.⁸

The Article evaluates the investment contract issue as it relates to exchange-traded crypto assets with an emphasis on Howey’s “common enterprise” and “efforts of others” prongs. The contours of these and Howey’s other prongs have been shaped by courts in primary transaction cases, that is, cases in which investors directly or indirectly transacted with the enterprise’s promoter. In a secondary transaction case—such as a case involving an exchange-traded crypto asset—investors will have transacted with their trading counterparties, perhaps with the involvement of one or more intermediaries, and those counterparties ordinarily will not have been the enterprise’s promoter. Unlike crypto assets, earlier occurring investment contract cases arising in connection with primary transactions did not involve instruments that readily lent themselves to secondary trading, so courts have not had much occasion to consider the operation of Howey in the secondary transaction context.

In many instances, the investment contract rules that courts have developed in primary transaction cases have been articulated in a manner that allows them to be sensibly applied to secondary transaction cases. That is not the case for the horizontal commonality test, one of the three tests that courts use to assess Howey’s common enterprise prong. As the Article explains, because of its pooling requirement, that test is ill-suited for use in secondary transaction cases and thus requires reorientation.

The Article proposes a slight generalization of the horizontal commonality test that renders the test suitable for use in both secondary transaction and primary transaction cases. The generalized test recognizes that pooling is but one method by which investors’ financial interests in the underlying enterprise can become intertwined in the manner that horizontal commonality requires. Under the generalized test, horizontal commonality will be present if there is some mechanism, pooling or otherwise, that ties investors’ fortunes to one another and dependent on the enterprise in which they are invested.

The generalized test reasonably broadens the scope of instruments for which horizontal commonality would be found. As relevant to the Article’s question of interest, even if there were no pooling of a secondary crypto asset investors’ purchase amounts, the generalized horizontal commonality test may still be satisfied because the asset’s trading price can serve as a non-pooling mechanism that causes the pecuniary interests of the crypto asset’s traders to be linked and dependent on the success of the crypto asset and any of its associated applications. For the crypto asset’s price to actually have served that non-pooling role for purposes of the generalized horizontal commonality test, the crypto asset’s price must generally respond to material, public information in a directionally appropriate way. As part of its analysis, the Article also explains why certain facts that are present in the investment contract cases that courts have analyzed to date—such as the presence of a contract among the investment contract’s promoter and the investors—simply represent common factual features shared by the decided cases, rather than elements of the pertinent legal rule.

The Article also addresses two aspects of Howey’s efforts of others prong relevant to application of Howey to exchange-traded crypto assets. First, the Article explains that Howey’s efforts of others prong should not be understood as requiring the presence of a centralized body that exerts the requisite entrepreneurial or managerial efforts. Instead, Howey’s efforts of others prong is better understood as requiring investors to have reasonably believed that their profits were significantly determined by the entrepreneurial or managerial efforts of those other than the investors themselves, whether or not those “others” constituted a centralized group.

Second, as the Article explains, investors’ expectations concerning the use of their sales proceeds is doctrinally irrelevant to Howey’s efforts of others analysis, which instead focuses on investors’ expectations concerning whose entrepreneurial or managerial efforts significantly determined their expected profits. Thus, the fact that investors’ sales proceeds in a secondary crypto asset transaction case may not have flowed to the crypto asset’s sponsors would not itself prevent Howey’s efforts of others prong from being met. This and the Article’s other Howey-related conclusions are not limited to the specific context of a Rule 10b-5 class action and instead also are applicable to other securities claims involving secondary crypto asset transactions.

The Article’s public policy analysis is prompted by the observation that stock-based Rule 10b-5 class actions have been the subject of academic criticism, intense at times. Supported by two longstanding primary critiques known as the circularity critique and the diversification critique, prominent voices have argued that stock-based Rule 10b-5 class actions fail to properly advance their intended public policy objectives of deterrence and compensation. Other scholars have disputed the relevance of the circularity and the diversification critiques and also have identified theories that provide alternate public policy justifications for stock-based Rule 10b-5 class actions, with the leading example being a corporate governance justification for stock-based Rule 10b-5 class actions.

A normative inquiry into whether defrauded crypto asset traders should be able to rely on Rule 10b-5 class actions implicates a range of subsidiary questions. One constituent question is whether the public policy justification for crypto asset-based Rule 10b-5 class actions is significantly weaker than for stock-based Rule 10b-5 class actions. If so, then that would support legal change that limits the availability of crypto asset-based Rule 10b-5 class actions, relative to stock-based Rule 10b-5 class actions, such as the adoption of prophylactic steps in the form of legislative action or doctrinal modification that would curb crypto asset-based Rule 10b-5 class actions before they become commonplace as stock-based Rule 10b-5 class actions have become. The Article evaluates that specific public policy question in terms of the circularity and diversification critiques and the corporate governance justification.

While its public policy determinations are mixed and in part preliminary, the Article’s analysis does not lend support to the notion that the public policy justification for crypto asset-based Rule 10b-5 class actions is significantly weaker than the public policy justification for stock-based Rule 10b-5 class actions. As reflected in the discussion below, the circularity critique has significantly less relevance in the crypto asset context than in the stock context. While the diversification critique may be more or less relevant in the crypto asset context than the stock context, nothing in the analysis indicates that it is significantly more relevant in the crypto asset context than the stock context. An offsetting consideration is that the corporate governance justification loses its relevancy in the crypto asset context.

The Article is organized as follows. Part I provides a high-level summary of three key features of crypto assets that are pertinent to the Article’s substantive analysis. Part II addresses the investment contract question, while Part III provides the public policy analysis.

I. FEATURES OF EXCHANGE-TRADED CRYPTO ASSETS

While exchange-traded crypto assets vary in their characteristics and features, they share key points of commonality relevant to an inquiry into the propriety of defrauded secondary crypto asset traders relying on Rule 10b-5 class actions as a means of redress. Three points of commonality are discussed below: operational decentralization, the absence of cash flow, and significant price volatility.

A. Operational Decentralization

As a rough approximation, a crypto asset’s lifecycle will have three stages. The first stage is the period preceding the asset’s initial offering, during which the crypto asset’s sponsors develop the asset and any associated applications.⁹ The second stage of a crypto asset’s lifecycle is the asset’s offering period. During this stage, the asset’s sponsors first offer and sell the crypto asset, or rights to the future delivery of the crypto asset, to the public and others.¹⁰ Historically, crypto asset offerings have been unregistered offerings, with very limited exceptions.¹¹

com/articles/1186166 [https://perma.cc/QSQ8-ZDJJ] (describing an offering pursuant to Regulation A). There appears to be just one instance of a registered crypto asset offering. See INX Ltd., Registration Statement Under the Securities Act of 1933 (Form F-1) (Aug. 19, 2019), https://
http://www.sec.gov/Archives/edgar/data/1725882/000121390019016285/ff12019_inxlimited.htm [http://web.
archive.org/web/20230324012325/https://www.sec.gov/Archives/edgar/data/1725882/000121390019016285/ff12019_inxlimited.htm] (showing INX Ltd.’s registered offering of its INX crypto asset). The third and final stage is the period following the crypto asset’s offering, or the period after the crypto asset is delivered to those who previously purchased rights to its delivery, during which the asset trades on one or more crypto exchanges.¹² In some instances, a crypto asset may have a fourth stage when it is delisted from the crypto exchanges on which it trades and then ceases all secondary trading.¹³

At some point in this lifecycle, the development, operation, management, and promotion of a crypto asset and any associated applications may move from a small group of sponsors to a significantly larger group of stakeholders. This latter process can be referred to as operational decentralization, and the resulting set of designated decision‑makers ordinarily will include the crypto asset’s holders. The modifier “operational” reflects the fact that other aspects of a crypto asset or its application may be decentralized, but in ways not directly relevant to the securities law definitional question discussed in Part II below.¹⁴

As an example of these observations, consider the application Filecoin, which is an innovative blockchain-based data storage network that enables those needing computing storage to remotely use others’ idle computing storage.¹⁵ So, for instance, a large data center or an individual maintaining unused computing storage space can have that dormant storage incorporated in Filecoin’s storage network, thereby allowing other Filecoin users to access its idle storage in exchange for payment.¹⁶ Filecoin generates economic benefit by facilitating mutually beneficial transactions, allowing unused computing storage space to be put to productive use.

The crypto asset “FIL” is associated with and facilitates Filecoin’s storage network. Transactions on the Filecoin network are conducted in FIL, in that users of Filecoin’s storage network pay storage providers in FIL rather than fiat currency.¹⁷ Filecoin’s users who want to acquire or sell their FIL holdings can do so on various crypto exchanges.¹⁸ As reflected in publicly available information, FIL’s holders do not buy and sell the crypto asset purely for its use value on the Filecoin network, but also, or perhaps primarily, trade the asset for investment purposes, seeking financial gain from appreciations in the crypto asset’s price.¹⁹ FIL presently has a market capitalization near $2.9 billion and its 24-hour transaction volume ordinarily exceeds $200 million.²⁰

Protocol Labs, an innovative research and development company founded in 2014, developed both Filecoin and FIL.²¹ In 2017, Protocol Labs conducted two Reg D offerings through which it sold accredited investors the rights to the future delivery of FIL²² and raised over $200 million.²³ In 2020, Filecoin became fully operational, and Protocol Labs distributed FIL to the accredited investors who had purchased the future delivery rights to FIL in the two 2017 Reg D offerings.²⁴ The crypto asset thereafter began trading on a number of crypto exchanges.²⁵

FIL and its associated application Filecoin exhibit features of the operational decentralization discussed above. In the years following FIL’s initial offering in 2017, Protocol Labs continued to develop Filecoin and FIL but continuously expanded the ability of other stakeholders, including the general public, to contribute to Filecoin and FIL’s development. In the immediate period following FIL’s initial offering, the public’s role in facilitating Filecoin and FIL’s development was limited to referring potential employees and early users to Protocol Labs and suggesting improvements to the underlying protocol.²⁶ Subsequently, but before Filecoin became fully operational and FIL started trading in secondary markets, Protocol Labs made several key aspects of Filecoin and FIL’s software code available to the public for review and comment.²⁷ This important milestone provided the public with an indirect way to guide Filecoin and FIL’s development but ultimate authority remained vested in Protocol Labs.

Protocol Labs’ current decision-making authority over Filecoin and FIL is much more attenuated than before. Now, while Protocol Labs remains actively involved in Filecoin and FIL’s development²⁸ and potentially may still maintain significant holdings of FIL,²⁹ Protocol Labs does not have sole decision-making authority over the crypto asset or its associated application. First, another centralized body, Filecoin Foundation, facilitates governance of the Filecoin network.³⁰ Moreover, any person can influence Filecoin’s governance by submitting a Filecoin Improvement Proposal.³¹ Filecoin’s many stakeholders, including FIL holders and Filecoin’s developers, determine whether to adopt the proposal.³² Modifications and improvements to Filecoin’s technical features are undertaken through a similarly decentralized process, with any individual able to propose a technical change and then Filecoin’s many stakeholders deciding whether to adopt the technical modification.³³

B. Absence of Cash Flow

A specific crypto asset may provide its holders with a range of benefits. In addition to investment gain, some crypto assets also may be used as methods of payment for conventional goods and services, while others may enable their holders to use an associated application or exercise governance rights with respect to the crypto asset or an associated application.³⁴

Despite these benefits, a crypto asset ordinarily will not provide its holders with dividends or cash flow in any form, realized or expected. Even if there exists a centralized body with some involvement in the crypto asset’s development and operation, the crypto asset’s holders usually will not be entitled to any income from the profits of that centralized body. In contrast, a public company’s common shareholders will receive cash flow at the board’s discretion in the form of dividends paid from the company’s net income.

More generally, a crypto asset’s holders usually will not be entitled to income from any entity or individual involved in the development and operation of the crypto asset and any associated applications. Holders of some crypto assets may earn income through staking, which is the process through which a crypto asset holder agrees to lock up their assets to facilitate the validation of transactions on a blockchain that uses a proof-of-stake consensus mechanism.³⁵ But staking is an optional process that requires the holder to forgo transacting the staked assets.³⁶ While it is theoretically possible for a crypto asset to entitle its holders to cash flow, very few crypto assets with this feature have actually been implemented to date.³⁷

C. Significant Price Volatility

Crypto assets exhibit significant price volatility. Crypto asset prices can change markedly, even in relatively short periods of time. Take for instance, “SOL,” the crypto asset associated with the Solana blockchain. On July 1, 2022, SOL traded at $32.80, according to CoinMarketCap’s calculated average price on a group of crypto exchanges.³⁸

solana/historical-data [http://web.archive.org/web/20230627040245/https://coinmarketcap.com/
currencies/solana/historical-data]. On August 1, 2022, and September 1, 2022, SOL traded at $41.79 and $31.59, respectively, according to CoinMarketCap’s calculated average price.³⁹ So, within one month, the price of SOL appreciated by more than 27%, but then dropped by more than 24% the next month. Crypto asset prices can swing dramatically even over shorter durations, such as weeks or days.

Statistical analysis shows that crypto asset prices can be much more volatile than stock prices. For instance, Liu and Tsyvinski examined the returns of over 1,700 crypto assets between January 1, 2011 and December 31, 2018.⁴⁰ The authors created an index of the crypto assets in their sample and found that over the sample period, the standard deviation of daily returns of the index was 5.46%, which was five times higher than the standard deviation of daily stock returns over the sample period.⁴¹ The authors also found that crypto asset returns over the sample period yielded extreme losses and gains with high probability.⁴² According to their findings, a trader who held the constructed index over the sample period would have experienced an extreme 20% negative return to daily returns with a probability of 0.48% and an extreme gain of 20% positive return to daily returns with a probability of 0.89%.⁴³

Though crypto asset prices may be more volatile than stocks, some crypto assets may exhibit significantly less price volatility than others.⁴⁴ The volatility of some crypto assets may be closer to that of stock. Additionally, there is some empirical evidence showing that crypto asset volatility decreases over time. For instance, returning to the study discussed above, the authors found that the standard deviation of the index’s returns diminished over the sample period.⁴⁵

II. THE DOCTRINAL PROPRIETY OF CRYPTO ASSET-BASED RULE 10B-5 CLASS ACTIONS

The propriety of crypto asset traders using Rule 10b-5 class actions as a means of recovering losses caused by secondary crypto asset fraud implicates a set of important doctrinal and public policy considerations. In the discussion below, the Article focuses on the leading doctrinal question of when secondary trading crypto asset fraud constitutes securities fraud and so is properly within the scope of Rule 10b-5. The pertinent issue is whether the exchange-traded crypto asset on which the Rule 10b-5 claim is predicated is definitionally a security because it is an investment contract.⁴⁶ To better frame the issue, it is helpful to first provide some observations on the nature of secondary trading crypto asset fraud and Rule 10b-5 relief.

A. The Nature of Secondary Trading Crypto Asset Fraud and Rule 10b-5 Relief

Secondary trading crypto asset fraud can inflict trader harm by altering the prices at which traders transact. The motivating hypothetical from the Article’s Introduction involved the sponsor of an exchange-traded crypto asset making misrepresentations about a new and potentially monetizable use value for the crypto asset. Defrauded crypto asset traders who purchased at the resulting inflated prices may seek relief though a Rule 10b-5 class action. Their ability to viably do so requires, among other things, that (1) the at-issue crypto asset satisfies Howey’s four-part test for an investment contract—the focus of the discussion in the next Section; (2) the substantive elements of Rule 10b-5 are met; and (3) the pertinent elements of Rule 23 are met.

Different variants of the Introduction’s hypothetical may cause the case to turn more heavily on one of the necessary legal determinations. For instance, suppose that the false or misleading statement instead was made by a person of notoriety that the crypto asset sponsor had monetarily incentivized to provide promotional services, but all other facts of the hypothetical were unchanged. In this case, if the plaintiffs asserted their Rule 10b-5 claim against the influencer, greater focus may be on the materiality of the statement than if it were made directly by the crypto asset sponsor as in the baseline hypothetical. Depending on the circumstances, such as the identity of the influencer and other background considerations, a reasonable person may not consider the misrepresentation important to their trading decision, in which case it would not be material,⁴⁷ while they may consider it important to their trading decision if it had instead been made by the crypto asset’s sponsor.⁴⁸ Or consider a statement by an influencer opining about a crypto asset’s expected future price. In addition to the statement potentially being immaterial, it may be a nonactionable opinion statement under the rule in Omnicare.⁴⁹

Some crypto assets may be more amenable to secondary crypto asset fraud than others. In the hypothetical from the Introduction, the associated crypto asset had potential use value, in that its associated blockchain could be used to facilitate economically meaningful activity. That is not the case for all crypto assets. Consider meme coins, which are crypto assets that are based on an Internet meme or joke. These assets often have no use value, though they vigorously trade on crypto exchanges and can have significant market capitalization. The body of statements that investors may consider important to their trading decisions may be circumscribed. For instance, if a meme coin has no intended use value, and traders understand that fact, then they may not consider a statement about a potential use value for the crypto asset to be relevant to their trading decision.⁵⁰

The alleged fraud in each of these examples is an instance of statement-based fraud. Secondary trading crypto asset fraud can also be in the form of deceptive schemes. In the hypothetical in the Introduction, suppose that the crypto asset’s sponsor and the payment provider instead had devised a clandestine scheme that caused the crypto asset’s traders to believe that the payment provider would begin using the crypto asset’s blockchain to process payments. Traders who purchased the crypto asset at the resulting higher prices would suffer financial injury, just as in the baseline hypothetical in which the fraud was in the form of a false statement by the crypto asset’s sponsor.

Finally, crypto asset traders’ ability to rely on Rule 10b-5 class actions to recover losses sustained in connection with secondary crypto asset transactions raises doctrinal issues beyond the definitional one addressed below. For instance, putting Affiliated Ute to the side,⁵¹ secondary market crypto asset traders will only be able to litigate their Rule 10b-5 claims as a class if they are able to avail themselves of fraud on the market.⁵² The question thus arises whether fraud on the market properly extends to the crypto asset context. Or, to take another example, a private plaintiff Rule 10b-5 claim only reaches transactions that are within the extraterritorial reach of the securities laws as defined by Morrison.⁵³ But suppose the crypto exchange on which the at-issue transactions occurred is not a registered exchange and maintains no trading operations in the United States.⁵⁴ This scenario raises the doctrinal question of whether those secondary crypto asset transactions cannot be the subject of a private Rule 10b-5 suit because they do not satisfy Morrison’s requirements.⁵⁵ While some academic focus has been directed at these non-definitional doctrinal questions, additional research is necessary.⁵⁶

B. Is Secondary Trading Crypto Asset Fraud Securities Fraud?

If secondary crypto asset traders incur trading loss because of fraud, they will be able to pursue Rule 10b-5 relief based on those secondary transactions only if the exchange-traded crypto asset at issue is an investment contract under Howey’s multipronged test.⁵⁷ Many issues pertinent to that definitional inquiry will be the same as those relevant to an assessment of whether a crypto asset at its offering stage satisfies Howey’s definition of an investment contract.⁵⁸ For instance, if an exchange-traded crypto asset is promoted for its use value because it enables its holders to use an associated application, and if the asset’s holders in fact hold the asset primarily for that purpose rather than its investment value, then Howey’s “expectation of profit” prong would not be met under Forman’s investment/consumption distinction.⁵⁹ This very issue has been litigated in cases in which a crypto asset was alleged to have been an investment contract at its offering stage.⁶⁰

But there are issues pertinent to the application of Howey in the context of exchange-traded crypto assets that are not present, or are much less salient, in the context of crypto assets at their offering stage. This Section explores a set of such issues relating to Howey’s common enterprise and efforts of others prongs.

1. Exchange-Traded Crypto Assets and Common Enterprise

Doctrinal development of Howey’s common enterprise prong, as with all other parts of Howey’s test, has occurred through investment contract cases involving a primary transaction, that is, a transaction in which investors purchased the instrument when it was first offered for sale directly or indirectly from the enterprise’s promoter.⁶¹ That was the case in Howey, for instance. The other investment contract cases to date have similarly involved primary transactions and include such varied examples as sale-and-leasebacks,⁶² annuities,⁶³ and crypto assets.⁶⁴ There are virtually no investment contract cases concerning secondary transactions, in which investors purchased the putative investment contract from other investors.⁶⁵

The factual orientation of the body of investment contract cases naturally has resulted in courts shaping investment contract doctrine around primary transactions. But a Rule 10b-5 case involving an exchange-traded crypto asset will involve secondary transactions, rather than primary transactions, and the two transactions differ in important ways. As noted, in a primary transaction, investors transact directly or indirectly with the promoter. In a secondary transaction, investors transact with their trading counterparties, perhaps with the involvement of one or more intermediaries, and those counterparties ordinarily will not be the promoter.⁶⁶ Also, depending on the circumstances, it may also be that when a secondary transaction occurs, the promoter who facilitated the instrument’s initial offering no longer has any meaningful involvement in the underlying enterprise, though there may be other non-investors who facilitate the enterprise.

In many instances, the legal rules that courts have developed in primary transaction cases concerning the investment contract question are equally sensible in secondary transaction cases. Take, for instance, the rule that Howey’s “investment of money” prong does not require a cash payment and instead is satisfied when any form of consideration is provided.⁶⁷ That rule is as sensible in the secondary transaction context as the primary transaction context, as consideration in either context may involve cash or noncash payment. That is not the case for the horizontal commonality test, one of the three commonality tests that courts have developed in primary transaction cases to evaluate the presence of common enterprise.⁶⁸ As discussed below, the horizontal commonality test, as it is presently articulated, is analytically ill-suited for use in secondary transaction cases because of the test’s requirement that investors’ assets be pooled.

i. Secondary Transactions, Horizontal Commonality, and the Pooling Requirement

The horizontal commonality test evaluates relationships among the investment contract’s investors⁶⁹ and inquires whether the investors’ fortunes are intertwined and collectively dependent on the success of the enterprise in which they are invested.⁷⁰ The test usually is defined in relation to a pooling requirement, which requires investors’ assets be combined and comingled in a manner that causes investors’ fortunes associated with the enterprise to be codetermined. Specifically, in the primary market transaction cases in which the test was developed, courts usually find horizontal commonality only when there is “the tying of each individual investor’s fortunes to the fortunes of the other investors by the pooling of assets.”⁷¹

A good description of the pooling requirement comes from the court in Savino v. E.F. Hutton:⁷²

“Pooling” has been interpreted to refer to an arrangement whereby the account constitutes a single unit of a larger investment enterprise in which units are sold to different investors and the profitability of each unit depends on the profitability of the investment enterprise as a whole. Thus, an example of horizontal commonality involving brokerage accounts would be a “commodity pool,” in which investors’ funds are placed in a single account and transactions are executed on behalf of the entire account rather than being attributed to any particular subsidiary account. The profit or loss shown by the account as a whole is ultimately allocated to each investor according to the relative size of his or her contribution to the fund. Each investor’s rate of return is thus entirely a function of the rate of return shown by the entire account.⁷³

In other words, pooling can be understood as the usual mechanism in a primary transaction case that causes investors’ fortunes in the enterprise to be interconnected and dependent on the enterprise’s success. Consider, for instance, the Seventh Circuit’s decision in Milnarik v. M-S Commodities.⁷⁴ There, the plaintiff opened a discretionary trading account in commodities futures with a broker.⁷⁵ Many other investors also had opened their own discretionary trading accounts with the same broker.⁷⁶ The plaintiff’s account sustained losses, and the plaintiff sued for violation of Section 5’s registration requirement, on the theory that the discretionary trading account contract was an investment contract.⁷⁷ The Seventh Circuit rejected that claim because it found no pooling and thus no investment contract under Howey.⁷⁸

The absence of the pooling of investors’ funds unsurprisingly led to the court’s conclusion in Milnarik that the investors’ fortunes were not intertwined and mutually dependent on the success of their collective trading accounts.⁷⁹ Because investors’ accounts were separately maintained and their funds not combined, the value of any given investor’s trading account was independent of the value of any other investor’s trading account.⁸⁰ This would not have been the case had the arrangement instead involved the defendant combining the various investors’ funds in a single account, executing trades with respect to that single account, and then distributing any profits to the investors. If this had been the case, then every investor would have been made financially better off as the account became more profitable and financially worse off as its value dropped. In other words, the aggregation of investors’ funds would have caused the investors’ individual financial interests in the combined account to be tethered together and dependent on the underlying enterprise.

But pooling is not an analytically meaningful way of evaluating the presence of horizontal commonality in an investment contract case involving secondary transactions. In primary market transactions, like the ones in Howey and Milnarik, investors will have transacted directly or indirectly with the promoter. In such cases, the promoter may have pooled investors’ assets in a manner that caused investors’ fortunes in the enterprise to rise or fall together, as horizontal commonality requires.

On the other hand, secondary market investors will have transacted with trading counterparties. If those trading counterparties were separate persons or economic entities, then those counterparties would have no reason to aggregate the amounts they received from their sales, except in rare and idiosyncratic circumstances. If, alternatively, the trading counterparties included one or more persons or entities who sold to multiple traders, then it is possible that the counterparty aggregated the amounts it received for its sales, because it may have some business or other reason for doing so. Nonetheless, the counterparty’s aggregation of secondary investors’ assets, unlike the promoter’s aggregation of primary market investors’ assets, will usually not create a linkage between the secondary investors’ financial interests in the enterprise because the success of the underlying enterprise will not turn on whether the counterparty aggregated the sales proceeds it received or how it used any aggregated amounts. Simply put, there is no analytical justification for the horizontal commonality question in a secondary transaction case to turn on the pooling requirement.

An evaluation of horizontal commonality in a secondary transaction case using the lens of pooling can be both underinclusive and overinclusive. First, in a secondary transaction case, investors’ financial interests in the underlying endeavor may still be interdependent even if the investors’ sales proceeds were not aggregated. To see this, suppose that in Howey, each of the primary market investors had sold their interests to another, later stage investor. Those secondary investors’ purchase amounts presumably will not have been pooled. The secondary investors purchased from the primary market investors, rather than the promoters, and those primary market investors would ordinarily have no reason to aggregate their individual sales proceeds. Nonetheless, horizontal commonality would be present with respect to the secondary investors because those investors’ profits would have been intertwined and dependent on the success of the enterprise. If, for instance, there was a poor harvest because of the promoters’ neglect or malfeasance, each of the secondary investors would have seen their profits fall.

Second, just as the absence of an aggregation of investors’ assets does not demonstrate a lack of horizontal commonality, the presence of asset aggregation, by itself, may not necessarily establish horizontal commonality in a secondary transaction case. In the example in the previous paragraph, suppose that the primary market investors in fact had aggregated the proceeds from their resales because, for instance, they wanted to collectively invest in a new venture. That pooling of the secondary investors’ assets by the primary market investors itself has no bearing on whether the secondary purchasers’ profits associated with the orange orchard enterprise would have moved in tandem as required by horizontal commonality.

Imposing a pooling requirement in secondary transaction cases not only would be analytically infirm but also would prevent nearly all investment contracts that arise in connection with secondary transactions from satisfying the horizontal commonality test.⁸¹ That would effectively cause those transactions to be categorically excluded from the investment contract category in those jurisdictions in which horizontal commonality is the only recognized test for common enterprise.⁸² Such limitation finds no basis in logic or public policy and also runs roughshod over the Supreme Court’s directive that the term security be interpreted in fidelity to economic reality and not hindered by rigid formalities.⁸³

ii. Generalization of the Horizontal Commonality Test

Because it is logically inapt in secondary transaction cases, the pooling requirement renders the horizontal commonality test ill-suited for use in those cases. Hence, the test must be appropriately generalized so that it is articulated in a manner that renders it sensible both in secondary transaction cases and the primary transaction cases in which it and Howey’s other rules have been developed. As discussed below, the necessary reformulation of the horizontal commonality test requires only a slight generalization of the test from its present form.

As an initial observation, recall that pooling is neither necessary nor sufficient for investors’ profits to be intertwined and mutually dependent on the success of the underlying enterprise as doctrinally required. Instead, as discussed above, pooling is the usual way that the requisite financial linkages arise in a primary transaction case. In other words, pooling is the usual path to interrelated investor profits in a subset of investment contract cases. An appropriately generalized articulation of the horizontal commonality test must recognize pooling as just one possible mechanism that ties investors’ financial interests in the enterprise together.

So that it has a sensible analytical meaning in both primary transaction cases and secondary transaction cases, the horizontal commonality test must be framed so that the test is met whenever the pooling of investors’ assets or some other non-pooling mechanism causes investors’ fortunes to be tied to one another and dependent on the success of the enterprise in which they are invested. In other words, the horizontal commonality rule must be articulated so that it accurately reflects that pooling is but one mechanism that results in investors’ profits being intertwined, not the only mechanism. Note that the generalized test does not merely require that pooling or some other mechanism caused investors’ fortunes to be tied together but, consistent with the underlying analytical underpinning of the test, also requires their fortunes to be dependent on the underlying enterprise.⁸⁴

The generalized test is consistent with Howey, in that there is nothing in the opinion indicating that the Court sought to impose a pooling requirement, even in primary market cases. In fact, it is difficult to support a conclusion that there was a pooling of investors’ assets in Howey, and for that reason the presence of horizontal commonality under the test’s present formulation. In Howey, the promoters sold each investor their own tract of land and an individual service contract.⁸⁵ The promoter did not aggregate investors’ purchase amounts and then use that aggregated amount to sell investors’ a single tract of land serviced by the promoter in which each investor maintained a fractional interest, as the usual definition of pooling would require.⁸⁶

The proposed generalization is superior to the present articulation that implicitly assumes that pooling is the only path to investor wellbeing interdependence. First, a primary transaction case in which a court would find horizontal commonality under the present test would continue to satisfy the horizontal commonality test under the generalized test outlined above. The presence of pooling necessary for a finding of horizontal commonality under the current test would also cause the generalized test to be met.

Second, the generalized test does not excessively broaden the scope of horizontal commonality in primary transaction cases. If a primary transaction case would not satisfy the horizontal commonality test as it is presently articulated because of a lack of pooling, the generalized test would admit a finding of horizontal commonality only if there was some other mechanism that caused investors’ profits to be intertwined and dependent on the success of the underlying enterprise. For instance, returning to Milnarik, there are no facts in the opinion suggesting that there was some non-pooling mechanism that caused investors’ profits to be intertwined.⁸⁷

The generalized formulation would admit a broader array of investment contracts in primary transaction cases than under the current formulation, but these would be sensible additions. For instance, suppose in Milnarik, the broker’s policy and practice was to execute identical transactions for each of the accounts over which it had discretionary authority. In this case, while there would be no pooling of the investors’ assets,⁸⁸ there would be horizontal commonality under the generalized test, as the value of investors’ portfolios would move in unison because of the broker’s trading policy and practice. The investors in this example can be understood to be in a common enterprise with one another because the value of each of their accounts is dictated by the same trading practice, even though their funds were not pooled.

Unlike the present restrictive formulation, the generalized formulation would result in investment contracts that arise in connection with secondary transactions satisfying the horizontal commonality test even in the absence of pooling, so long as there was some non-pooling mechanism that met the doctrinal requirement that investors’ profits were interrelated and dependent on the success of the underlying enterprise. The generalized test is sufficiently circumscribed and not all investment contracts arising in connection with secondary transactions will meet it. For instance, suppose that the investors in Milnarik had sold their interests in their accounts to other investors, with all other facts the same. In addition to an absence of pooling, there would be no other mechanism connecting the profits of those later investors to one another and thus no finding of horizontal commonality as to those secondary transactions under the generalized test.

a. Application to Exchange-Traded Crypto Assets

Investors in a crypto asset offering ordinarily will have the proceeds from their purchases pooled by the crypto asset’s sponsors to facilitate the asset and any associated applications.⁸⁹ That may not be the case for secondary crypto asset traders who transact on crypto exchanges, as those transactions would have occurred with trading counterparties and those trading counterparties, in turn, may have had no reason to pool the amounts they received. Despite any lack of pooling of the secondary investors’ purchase amounts, the crypto asset may still meet the generalized horizontal commonality test through its price, which can serve as a potential non-pooling mechanism that causes the pecuniary interests of the crypto asset’s traders to be linked and dependent on the success of the underlying enterprise, that is, the crypto asset and any associated applications.

Start first with the requirement that secondary traders’ fortunes in the crypto asset are linked. A given exchange-traded crypto asset can trade on multiple exchanges,⁹⁰ which may either be centralized or decentralized. A centralized crypto exchange will involve an intermediary to facilitate transactions, while a decentralized crypto exchange will not. The two types of exchanges also may differ in their pricing mechanism. A centralized crypto exchange will use a limit order book to match buyers and sellers, and therefore the exchange’s prices will be set directly by traders’ submitted orders.⁹¹ Rather than relying on a limit order book, a decentralized exchange may facilitate transactions using an automated market maker, in which prices are set through a pricing algorithm.⁹²

Whether a crypto exchange uses a limit order book or an automated market maker, the exchange’s pricing mechanism will generate, for a given crypto asset, a single price at which any trader can transact, holding fixed other traders’ transactions. That single trading price links together the financial wellbeing of all the crypto asset’s secondary investors. Every investor holding the crypto asset is made financially better off as the crypto asset’s price on the exchange rises and each is made worse off as the price drops. The fact that a crypto asset trades on multiple exchanges does not break the linkages between the financial wellbeing of traders on different exchanges since arbitrage causes crypto asset prices across different exchanges to closely align.⁹³

A crypto asset’s trading price thus provides a mechanism that links together its secondary investors’ financial interests. It is the case that a crypto asset’s trading price will be influenced by market fluctuations, but the doctrinal relevance of that observation is better understood as concerning Howey’s efforts of others prong, which is discussed below, rather than the common enterprise prong.⁹⁴

A crypto asset’s price also may provide the doctrinally necessary linkage between the financial interests of the crypto asset’s secondary traders and success of the underlying enterprise. Empirical studies show that the prices of exchange-traded crypto assets generally respond in the directionally appropriate way to material, public information.⁹⁵ For this reason, as a general matter, the financial interests of a crypto asset’s holders will be dependent on the success of the crypto asset and any associated applications. If, for instance, the crypto asset undergoes some value-enhancing change, then once that change is publicly known, the crypto asset’s price would be expected to increase, because of the directionally appropriate responsiveness of crypto asset prices to material, public information as a general matter.

Nonetheless, it is possible that while the prices of crypto assets—as an asset class—generally respond in a directionally appropriate way to material, public information, that is not the case for any given exchange-traded crypto asset. If the specific crypto asset being evaluated as a potential investment contract lacks that requisite informational responsiveness, then the crypto asset’s price would not connect the financial interests of the crypto asset’s secondary traders with success of the underlying enterprise. For instance, if the crypto asset underwent some value-reducing change, but the asset’s price was either impervious to material, public information or moved in the directionally inappropriate way to material, public information, then the value reducing change would either have generated no change to the crypto asset’s price (and thus would have made the crypto asset’s holders no better or worse off) or increased the crypto asset’s price (and thus would have made the crypto asset’s holders better, not worse, off).

Accordingly, a crypto asset’s price can serve the role of a non-pooling mechanism that satisfies the requirements of the generalized horizontal commonality test only if the crypto asset’s price generally responds to material, public information in a directionally appropriate way. If the plaintiffs in a crypto asset case implicating the Howey question rely on the asset’s price to serve that non-pooling role, then the generalized horizontal commonality test demands that there be a showing of the necessary price responsiveness. The plaintiffs can make that showing using an event study that demonstrates that the crypto asset’s price generally responds to material, public information in a directionally appropriate way.

If the plaintiffs cannot establish the necessary price responsiveness of the crypto asset, then the asset’s price cannot serve the role of a non-pooling mechanism that satisfies the requirements of the generalized horizontal commonality test, because in that circumstance, the plaintiffs will not have established that the asset’s price connects the secondary investors’ pecuniary interests to the success of the enterprise in which they are invested. In this case, the generalized horizontal commonality test will be met with respect to the at-issue crypto asset only if there was pooling of the secondary traders’ purchase amounts or there was some non-pooling mechanism other than the crypto asset’s price that caused the pecuniary interests of the crypto asset’s traders to be linked and dependent on success of the crypto asset and any associated applications.

b. Other Reformulations of the Horizontal Commonality Test

In addition to generalizing Howey’s horizontal commonality test in the manner discussed above, there are other sensible ways to reformulate the test so that it is suitable for use in both secondary transaction and primary transaction cases. One possibility is to broaden the test so that it is also met in secondary transaction cases if (1) there was pooling of the primary market investors’ assets, and (2) the primary market investors purchased the instrument only because they reasonably expected the ability to resell their interests to secondary investors. If these two conditions are met, then the secondary investors can be understood to have effectively pooled their assets, in the sense that the reasonable expectation of eventual resales to secondary investors was a necessary condition to the primary market investors engaging in the transactions that resulted in their assets being pooled. This type of pooling by the secondary market investors can be referred to as effective pooling.

Finally, unlike the horizontal commonality test, the two vertical commonality tests do not require reformulation to be analytically workable notions in secondary transaction cases. Strict vertical commonality is met when “the fortunes of investors [are] tied to the fortunes of the promoter” and broad vertical commonality is met when the “the fortunes of the investors [are] linked . . . to the efforts of the promoter.”⁹⁶ It is worth observing that the role of the promoter in secondary transaction cases will be different than in primary transactions cases. In a primary transaction case, the promoter ordinarily will have facilitated the enterprise in part by soliciting investors. In a secondary transaction case, the promoter likely will not have engaged in any such solicitation because it usually will not have been an active participant in the secondary markets, though the promoter may have directed other efforts to facilitate the enterprise.

c. The Irrelevance of a Contractual Relationship

Finally, while a primary transaction case ordinarily will involve contracts between the promoter and the investors, that usually will not be the case in secondary transaction case, because secondary market traders will not have transacted with the promoter, except in rare circumstances.⁹⁷ Nonetheless, the absence of a contractual relationship between the promoter and investors—whether those investors were secondary market traders or purchasers in a primary market transaction—does not provide a proper basis for defeating a finding of an investment contract. In Howey, the Supreme Court did not limit the investment contract category to just formal contractual arrangements between the promoter and the investors. Instead, the Court articulated the definitional category more expansively so that, in addition to contractual arrangements, the investment contract category also encompasses “transactions” and “schemes.”⁹⁸ Though the Court did not define the term “scheme,” had it meant for scheme to simply mean a series of contractual arrangements, then it would have just used the term “contracts” rather than scheme.

Howey’s lack of a contract requirement is sensible. As a matter of public policy, the investor protection objectives of the securities laws are not weakened simply because the relevant transactions were not undertaken pursuant to a formal contract.⁹⁹ And while Howey and the other Supreme Court’s investment contract cases to date have involved contractual arrangements between the promoter and the investors, this common factual feature has not become a part of the Court’s enunciated rule.¹⁰⁰ Instead, the Supreme Court’s post-Howey investment contract cases have consistently invoked Howey’s articulation of the investment contract category as encompassing schemes.¹⁰¹

Stated differently, simply because a set of cases share a common factual predicate does not mean that the factual predicate necessarily becomes a component of the pertinent rule of law. As another example of this somewhat unremarkable observation, note that the profits that investors received in the Supreme Court’s investment contract cases arose through income generated by a business enterprise that was organized and facilitated by the promoter. But the fact that these cases share this common factual predicate does not mean that the factual predicate is part of the operative rule. As the cases recognize, investors’ “profits” for purposes of the Howey determination are not limited to proceeds from an investment in a business enterprise and instead include capital appreciation more generally.¹⁰² This observation is especially relevant to the crypto asset context because, as noted in Section I.B above, a crypto asset’s holders ordinarily do not receive and are not entitled to any income arising from development and operation of the crypto asset or any associated applications.

2. Exchange-Traded Crypto Assets and Efforts of Others

For a given instrument to be an investment contract, it must also satisfy Howey’s efforts of others prong. In the context of an exchange-traded crypto asset, that requirement will be met if investors reasonably expected the crypto asset’s value to be significantly determined by the entrepreneurial or managerial efforts of others.¹⁰³ Whether this requirement is met will depend on the at-issue crypto asset’s specific features, including the extent of its operational decentralization. This subpart explores issues pertinent to application of Howey’s efforts of others prong in the secondary trading crypto asset context.

The discussion below makes two points regarding Howey’s efforts of others prong. First, the discussion explains why operational decentralization, by itself, is not a per se bar to Howey’s efforts of others prong being met, though there may be specific factual features that result in a particular exchange-traded crypto asset not satisfying that Howey element. Second, the discussion below also explains the doctrinal irrelevancy of investors’ expectations concerning the use of their sales proceeds.

i. Why Operational Decentralization Is Not a Per Se Bar

The first issue to consider is whether a crypto asset’s operational decentralization should preclude satisfaction of Howey’s efforts of others prong. To structure the analysis, consider two possibilities. The first possibility is that the exchange-traded crypto asset has achieved some operational decentralization but a centralized third party continues to direct some entrepreneurial or managerial efforts toward the crypto asset’s success. The second possibility is that the crypto asset has achieved complete operational decentralization, in the sense that no centralized third party directs entrepreneurial or managerial efforts toward the success of the crypto asset; instead, those efforts are undertaken by a decentralized group of unaffiliated persons.¹⁰⁴

a. Continued Involvement by Sponsors or Other Centralized Third Party

If the crypto asset’s sponsors or some other centralized third party continue to exert entrepreneurial or managerial efforts such that investors reasonably expect those efforts to significantly determine the crypto asset’s value, as usually embodied by its trading price, then Howey’s efforts of others prong will be met.¹⁰⁵ This observation is reflected in courts’ determinations of the Howey question as it pertains to crypto assets at their offering stage,¹⁰⁶ which have found the efforts of others prong to have been satisfied because the crypto asset’s investors reasonably expected their profits to arise from the sponsor’s entrepreneurial or managerial efforts.¹⁰⁷

Presently, nearly all crypto assets appear to be associated with one or more centralized bodies that have at least some involvement facilitating their success, including through developing, operating, managing, and promoting the crypto assets and any associated applications.¹⁰⁸ While the importance of the efforts of such centralized bodies on a given crypto asset’s success may ebb as the crypto asset matures and becomes the subject of additional secondary trading, those efforts may remain instrumental to the crypto asset’s success. Even crypto assets like ether that have experienced significant operational decentralization have at times benefited from the focused efforts of a collective group of developers.¹⁰⁹

Whether the presence and activities of these centralized groups is sufficient to satisfy Howey’s efforts of others prong will hinge on the nature of the centralized third party’s involvement. A series of issues await judicial determination. For instance, a crypto asset or its associated applications, if any, ordinarily will have a presence on software code repositories and messaging platforms, where the crypto asset’s developers, investors, and others come together and communicate to improve the asset or its associated applications.¹¹⁰ Some of these activities may be managed by the crypto asset’s sponsors rather than investors.¹¹¹ If those managerial efforts are important to the viability of the crypto asset or any associated applications, then that would militate in favor of a finding that Howey’s efforts of others prong was met.¹¹²

The availability of pricing data opens the possibility of using empirical techniques to assess Howey’s efforts of others prong in investment contract cases involving an exchange-traded crypto asset. An assessment of whether a crypto asset’s trading price was influenced by the activities of a centralized body is relevant to the efforts of others question, which demands a determination whether reasonable investors would expect the asset’s value, as ordinarily measured by its price, to be significantly determined by the entrepreneurial or managerial efforts of the centralized body. If a crypto asset’s price was influenced by the efforts of a centralized body, then the crypto asset’s price would be expected to move in a directionally appropriate way once value-relevant activity by the centralized body became known to the market. For instance, an announced improvement in a crypto asset’s associated application by the centralized body would be expected to cause the crypto asset’s price to increase, assuming that Howey’s efforts of others prong was met.

An event study therefore could be used to assess the extent to which the at-issue crypto asset does or does not respond to potentially value-relevant activities of a centralized body.¹¹³ However, the use of event studies in that context should be undertaken with care. First, there are important methodological considerations, such as the issue of low power, which are amplified in the crypto asset context because of high crypto asset price volatility.¹¹⁴ Second, the event study may be underinclusive in that it would not capture the effects of a centralized body’s ongoing influence on a crypto asset’s price and instead would be limited to analysis of how episodic events associated with the centralized body affected the asset’s price. Finally, even if the event study showed that the crypto asset’s price responds to value-relevant activities of a centralized body, that finding would not fully resolve the pertinent question of whether investors reasonably expected the crypto asset’s price to be significantly determined by the centralized body’s entrepreneurial or managerial efforts, though it would be one important determinant in that inquiry.

b. Absence of Any Centralized Third Party

Now, suppose instead that the crypto asset is fully decentralized in that there is no centralized third party that directs entrepreneurial or managerial efforts toward the crypto asset’s success; instead, those efforts are undertaken by a decentralized group of unaffiliated persons. The prospect of full decentralization raises the question of whether Howey’s efforts of others prong requires the existence of one or more centralized third parties whose entrepreneurial or managerial efforts significantly affect the investment contract’s success. If such centralized third parties in fact are necessary, then sufficient decentralization would by itself preclude satisfaction of Howey’s efforts of other prong.

SEC staff guidance concerning the application of Howey in the crypto asset context can be reasonably interpreted to envision the presence of one or more such centralized third parties for purposes of evaluating Howey’s efforts of others prong.¹¹⁵ That guidance defines an “Active Participant” as “a promoter, sponsor, or other third party (or affiliated group of third parties)” and then goes on to explain that Howey’s efforts of others prong in the crypto asset context requires an inquiry into whether “the purchaser reasonably expect[s] to rely on the efforts of an [Active Participant]” and the nature of those efforts.¹¹⁶ In other words, the SEC staff’s definition of an Active Participant could be read to exclude the efforts of a decentralized group of unaffiliated third parties from meeting Howey’s efforts of others prong. Scholars also have proposed tests for assessing Howey’s efforts of others prong in the crypto asset context that similarly appear to hinge on the presence of one or more centralized third parties, such as the crypto asset’s sponsors.¹¹⁷

The well-publicized 2018 speech by the SEC’s then-Director of Corporate Finance, Bill Hinman, can also be interpreted as implicitly adopting the notion that Howey’s efforts of others prong requires the presence of a centralized third party. In that speech, then-Director Hinman observed that increasing operational decentralization during a crypto asset’s lifecycle could cause a crypto asset that previously satisfied Howey’s test of an investment contract to no longer satisfy that test because no centralized group is tasked with the crypto asset’s entrepreneurial or managerial functions.¹¹⁸ That proposition has been featured prominently in crypto asset litigation that implicate the Howey question¹¹⁹ and has been the subject of academic inquiry.¹²⁰

Howey should not be read as requiring the presence of one or more centralized third parties for purposes of its efforts of others prong. There is nothing in the language or reasoning of Howey suggesting that the requisite entrepreneurial or managerial efforts must be undertaken by a centralized third party.¹²¹ Howey’s efforts of others prong instead is better understood as requiring investors to have reasonably expected their profits to have been significantly determined by the entrepreneurial or managerial efforts of those other than the investors themselves, whether or not those “others” constituted a centralized group.¹²²

Compared with a formulation of Howey’s efforts of others prong that requires the presence of a value-enhancing centralized party, an advantage of a formulation that permits the prong to be satisfied even in the absence of a centralized party is that it better focuses the analysis on an essential feature of an investment: delegation of entrepreneurial or managerial efforts to those outside of the investor class. So long as investors are sufficiently passive, in the sense they ceded sufficient entrepreneurial and managerial efforts to others, the putative investment contract will bear this indicium, independent of the degree of centralization of the group to whom those efforts were delegated. The investment contract cases addressing whether investors’ managerial involvement in the enterprise defeats Howey’s efforts of others prong embody this observation. Those cases evaluate the efforts of others prong by focusing on the extent of investors’ passivity.¹²³

Because Howey’s efforts of others prong should not be understood as mandating the presence of a value-generating centralized body, the prong may be met even if a crypto asset has undergone substantial operational decentralization such that there is no centralized third party that exerts entrepreneurial or managerial efforts influencing the crypto asset’s value. The relevant inquiry is whether the crypto asset’s investors reasonably believed the asset’s value was significantly determined by the entrepreneurial or managerial efforts of individuals or entities other than the investors themselves. If the asset’s investors had those reasonable expectations, then Howey’s efforts of others prong would be met even if the pertinent efforts were undertaken by a dispersed and large number of unaffiliated individuals or entities.

Not all exchange-traded crypto assets will satisfy Howey’s efforts of others prong. First, if the putative investment contract is such that it requires no ongoing entrepreneurial or managerial efforts to succeed, then Howey’s efforts of others prong would not be met. Mining, the energy-intensive process of validating transactions on proof-of-work blockchains,¹²⁴ should be considered ministerial rather than entrepreneurial or managerial.¹²⁵ Second, if the investors were the ones who significantly directed the entrepreneurial or managerial efforts pertinent to the investment contract’s success, then Howey’s efforts of others prong also will not be met.¹²⁶ This may be the case if the crypto asset provided investors with extensive governance rights that they can readily exercise.

Additionally, Howey does not admit as investment contracts instruments whose value is driven almost entirely by market forces. In such a circumstance, it would not be reasonable for the putative investment contract’s investors to believe that its value is significantly determined by any person’s entrepreneurial or managerial efforts.¹²⁷ That is the case, for instance, for such varied tradeable items such as gold, baseball cards, and bitcoin, which are all understood to have their value driven almost entirely by market forces rather than by any person or persons’ entrepreneurial or managerial efforts. At the same time, even if the crypto asset’s price is determined in part by market forces—for instance, if its price moves in part because of price changes of another crypto asset such as bitcoin—investors may still reasonably expect the asset’s price to be significantly determined by the entrepreneurial or managerial efforts of others, in which case Howey’s efforts of others prong will be met.¹²⁸

ii. The Irrelevance of Investors’ Expectations Concerning the Use of Their Sales Proceeds

In a primary transaction case, investors’ sales proceeds ultimately will flow to the promoter, who then is expected to use the proceeds to facilitate the enterprise in which the purchasers are invested. That will not be the case in a secondary transaction case. In this circumstance, investors’ sales proceeds instead will flow to the trading counterparties, who ordinarily will not be the enterprise’s promoter and also will not direct the sales proceeds to the promoter. For instance, in a secondary crypto asset transaction, the purchasers’ proceeds usually will not flow to the crypto asset’s sponsors and instead will be retained by the trading counterparties. For this reason, while investors in a primary transaction case may have a reasonable expectation that their sales proceeds will be used by the promoter to facilitate the enterprise in which they are invested, investors in a secondary transaction case generally will not reasonably have those expectations, as their sales proceeds will directly flow to trading counterparties, who will usually not be the promoter, though investors may reasonably have those expectations in certain circumstances.¹²⁹

The fact that investors in a secondary transaction case may not reasonably believe that their sales proceeds will be used by the promoter to facilitate the enterprise is doctrinally irrelevant to Howey’s efforts of others prong. Howey’s efforts of others prong requires that investors reasonably expected their profits to have been significantly determined by others’ entrepreneurial or managerial efforts, and the operative rule makes no mention of investors’ expectations concerning the use of their sales proceeds.¹³⁰ So, for example, while investors’ sales proceeds in a secondary crypto asset transaction case may not have flowed to the crypto asset’s sponsors, Howey’s efforts of others prong will still have been met so long as traders reasonably expected the crypto asset’s value to have been significantly determined by the entrepreneurial or managerial efforts of others, such as the sponsor.¹³¹ In other words, the appropriate focus of Howey’s efforts of others prong is on investors’ beliefs about whose entrepreneurial or managerial efforts significantly determined their expected profits, not investors’ beliefs about how their sales proceeds specifically would be put to use.¹³²

There is no public policy justification for limiting the investment contract category to only those circumstances in which investors reasonably expected the promoter to use their funds to facilitate the enterprise in which they are invested. First, the adoption of that limiting rule would permit instruments that otherwise would be investment contracts to permissibly be the subject of an unregistered public offering if the offering were structured in a manner that investors could not readily discern whether their proceeds would flow to the sponsor.¹³³ For example, if a promoter simultaneously undertook multiple investment projects, the promoter could pool all investors’ funds, which may result in investors of any given project not knowing whether the promoter specifically used their funds to finance their project, even though there was no question that the investors’ profits would be significantly determined by the promoter’s entrepreneurial or managerial efforts.

Second, limiting the investment contract category so that it only encompasses circumstances in which investors reasonably expected the promoter to use their funds to facilitate the enterprise would exclude an expansive swath of secondary transaction investment contract cases from the scope of federal securities law. This near wholesale carveout of an entire transaction class from the reach of the securities laws would serve no public policy goal and instead would undermine the investor protection objectives that the securities laws seek to promote.

C. The Value of Additional Definitional Clarity

Crypto asset sponsors and crypto exchanges sometimes criticize Howey’s investment contract analysis when applied to the crypto asset context as unreasonably uncertain.¹³⁴

20231119200747/https://www.sec.gov/files/rules/petitions/2022/petn4-789.pdf] (“Applying the Howey test[] piecemeal to an entire market sector has proven itself to be an unworkable solution.”). Any offering of securities, unless exempted, must be registered, and any exchange that facilitates securities transactions must register, unless exempted. It is thus important to crypto asset sponsors and exchanges that they have clear guidance on which of the crypto assets they may offer or list are securities under federal securities law. Crypto asset sponsors and crypto exchanges contend that Howey fails to clearly inform them which crypto assets may be securities, and thus subject them to federal securities law, including its robust registration requirements.¹³⁵ The effect of Howey’s uncertainty on crypto asset sponsors and exchanges is heightened because the pertinent transactions are not one-off or episodic transactions but instead are the foundations of those market participants’ business models.

The discussion in the previous Section shows that the effects of any uncertainty in Howey’s application in the crypto asset context extends beyond crypto asset sponsors and exchanges and also encompasses crypto asset traders. Crypto asset traders who are subject to secondary crypto asset trading fraud, or other forms of misconduct prohibited by the federal securities laws such as market manipulation, may seek to recover through claims asserted under the securities laws but only to find their claims dismissed on grounds that the pertinent transactions did not involve securities.¹³⁶

As the case law grows and matures, crypto asset market participants’ uncertainty about Howey’s analysis in the crypto asset context should abate.¹³⁷ The opinions courts have authored to date in crypto asset cases concerning the investment contract question have been detailed and reasoned (even if one disagrees with their reasoning or conclusions).¹³⁸ Future opinions at that level of care should provide market participants with a clearer understanding of when crypto asset transactions are within the scope of securities law. SEC staff may also offer additional guidance on crypto assets and the definitional question.¹³⁹

The pace of such doctrinal development may be slower than market participants prefer, especially crypto asset sponsors and exchanges.¹⁴⁰ Further clarity may come in the form of legislation that seeks to articulate with more specificity the circumstances when a given crypto asset will be within the scope of securities law. Some of the introduced or contemplated bills would define a large class of crypto assets as commodities rather than securities.¹⁴¹ To the extent a crypto asset is deemed to be a commodity rather than a security, traders sustaining losses from secondary trading crypto asset fraud could seek recovery through a CFTC Rule 180.1 class action rather than a Rule 10b-5 class action.¹⁴² If the substantive claim underlying secondary trading crypto asset fraud class actions were to shift to Rule 180.1, the public policy discussion in Part III below would also apply in that context.

Finally, it is worth observing that certain aspects of the securities laws’ registration and post-offering disclosure requirements are not especially well-suited for the crypto asset context. With respect to the registration process, scholars have observed that because the disclosures required by registration were developed with an eye to offerings of more conventional securities like stocks and bonds, they do not always align well with crypto asset offerings.¹⁴³ This point about incongruity also applies to the regulatorily mandated post-offering disclosures. For instance, suppose that a crypto asset sponsor conducts a registered offering of the crypto asset. Through section 15(d) of the Securities Exchange Act,¹⁴⁴ the sponsor becomes subject to the ongoing reporting requirements of section 13(a) of the Exchange Act, such as the requirement to prepare and file an annual report.¹⁴⁵ Suppose that, at some point, the crypto asset undergoes complete operational decentralization such that the crypto asset sponsor ceases to be involved in any aspect of the crypto asset and instead the development, operation, management, and promotion of the crypto asset and any associated applications are undertaken by a decentralized group of other stakeholders.

In this case, should the sponsor, as the crypto asset’s issuer, still be obligated to make the required ongoing disclosures, on the ground that section 13(a) obligates the “issuer” to make those disclosures?¹⁴⁶ Alternatively, if the ongoing reporting obligations instead were to somehow apply to the decentralized non-issuer group, then how, as a practical matter, could such a diffused group be able to prepare the necessary periodic and current reports? There is also the question of whether the information called for by the required post-offering disclosures is meaningful and appropriate for the crypto asset context. These questions demonstrate that some regulatory effort should be directed at reformulating the post-offering disclosure requirements so that they are better suited for the crypto asset context.¹⁴⁷

III. PUBLIC POLICY CONSIDERATIONS PERTINENT TO CRYPTO ASSET-BASED RULE 10B-5 CLASS ACTIONS

In addition to the doctrinal propriety of defrauded crypto asset traders relying on Rule 10b-5 class actions, there is the normative question of whether defrauded traders should be able to rely on Rule 10b-5 class relief as a matter of public policy. That issue arises in part because of the considerable skepticism that some legal scholars have expressed about the use of Rule 10b-5 class actions in stock-based cases as effective compensation and deterrence mechanisms.

The assault on stock-based Rule 10b-5 class actions has primarily been through two longstanding critiques—the circularity and diversification critiques.¹⁴⁸ More recently, some scholars have challenged the relevancy of those critiques,¹⁴⁹ while others have articulated theories that provide alternate public policy justifications for stock-based Rule 10b-5 class actions, with the leading example being a corporate governance justification for stock-based Rule 10b-5 class actions.¹⁵⁰ Despite the lingering skepticism by some academics that stock-based Rule 10b-5 class actions fail to achieve their public policy objectives, they remain a core fixture of securities practice.

If the public policy justification for crypto asset-based Rule 10b-5 class actions is significantly weaker than stock-based Rule 10b-5 class actions, then we may want a preemptive curtailment of those litigations through legislative action or doctrinal reorientation before they become commonplace as stock-based Rule 10b-5 class actions have become. More generally, if the public policy justifications are significantly weaker for crypto asset-based Rule 10b-5 class actions than stock-based ones, that would justify different legal treatment of the two types of class actions. This Part of the Article evaluates that particular public policy question viewed through the lens of the circularity and diversification critiques and the corporate governance justification.

The public policy determinations below are mixed and preliminary in part, but do not lend support to the notion that the public policy justification for crypto asset-based Rule 10b-5 class actions is significantly weaker than the public policy justification for stock-based Rule 10b-5 class actions. First, the circularity critique—the leading critique in the stock-based Rule 10b-5 context—is significantly attenuated in the crypto asset context because the principal factors supporting the circularity critique in the stock context are substantially absent in the crypto asset context. There are countervailing reasons why the diversification critique may be more or less relevant in the crypto asset context than in the stock context, but no reason to expect that the diversification critique has significantly more force in the crypto asset context than in the stock context. On the other hand, the corporate governance justification loses relevance in the crypto asset context.

Sections A, B, and C below address the circularity critique, the diversification critique, and the corporate governance justification, respectively. Section D provides a few comments concerning the issue of frivolous litigation.

A. The Circularity Critique

The key critique against Rule 10b-5 stock-based class actions is circularity, which is the idea that when class actions settle, as nearly all do, the settlement is ultimately paid for by the company’s shareholders.¹⁵¹ This serves to undermine both the deterrence and compensatory features of the class action process. Because of its centrality to public policy analysis of securities class actions, it is valuable to work through some of the details of the circularity critique before turning to its applicability in the crypto asset context.¹⁵²

1. Circularity in the Stock Context

Circularity arises in the stock context for two reasons. The first driver of the circularity critique is that individually named directors and officers usually will not directly pay any of the settlement amount because of D&O insurance and indemnification. A study by Klausner, Hegland, and Goforth, for instance, evaluated a sample of over two hundred and fifty securities class actions that had settled and found that directors and officers did not make any payments in 98% of those cases.¹⁵³ That number is not surprising given that nearly all public companies purchase D&O insurance.¹⁵⁴ Empirical studies also indicate that directors and officers may not pay any reputational penalty when they are accused of fraud.¹⁵⁵ The lack of director and officer liability thus mitigates the deterrence effect of securities class actions on director and officer conduct.

The second driver of the circularity critique is the relationship between shareholders and the company’s net income. Because individually-named defendants ordinarily do not contribute to stock-based securities class action settlements, settlements instead are paid for by the company, either directly or through the company’s D&O insurance, or some combination of the two.¹⁵⁶ Accordingly, settlement of a Rule 10b-5 class action against an issuer and its directors and officers usually will be funded by the issuer directly or indirectly through the cost of the D&O insurance that the issuer has purchased. Because shareholders are the company’s residual claimants, these corporate expenditures associated with settlement payments are ultimately borne by shareholders in the form of diminished cash flow.

One group of shareholders bearing the cost of settlement will be the same ones who were injured by the fraud (assuming they did not sell their shares). Because these shareholders will be partially footing their own recovery, full compensation will not be achieved. The other of the firm’s current shareholders responsible for the settlement will be ones who were not class plaintiffs. These shareholders have no direct responsibility for the fraud but will be paying for the injured shareholders’ recovery, which implicates fairness considerations.

The circularity critique can be more formally illustrated through a simple model that embodies these observations. Consider a stock-based Rule 10b-5 class action in which the subject company has N shares outstanding that were trading at a pre-fraud price of P₀ per share. Assume there was a fraudulent material misrepresentation attributed to the issuer and its directors and officers that increased the stock’s price to P₁, which eventually returned to the pre-fraud level of P₀ once the market became aware of the fraudulent statement.

Suppose that the class of the company’s shareholders who purchased shares at the inflated price bring a Rule 10b-5 class action against the company and its directors and officers. For simplicity, assume these injured shareholders do not sell their shares. Of the company’s N shares outstanding, suppose that n shares are represented by the litigating class. So, if π is the fraction of the company’s outstanding shares represented by the litigating class, then π = n/N. The case settles and then pays s dollars per share to each of the n shares purchased during the class period, for a total settlement payment of s*n. Given the discussion above regarding corporate obligations for class action settlements, the company will pay a fraction α of the settlement, where α is between 0 and 1, which ultimately will be borne by the firm’s shareholders holding the N shares. In discussions of the circularity critique it is ordinarily assumed, either expressly or implicitly, that the company directly or indirectly pays the entirety of the settlement, which corresponds to the circumstance in which α = 1.

Given this setup, first consider the post-settlement welfare of the shareholders who were injured by the fraud because they paid the inflated price for the company’s stock. For expositional simplicity, consider a shareholder who is a member of the class and who purchased just a single share of the company’s stock. The value of the share that the shareholder maintains is P₀, but they purchased the share for P₁, which means that the net value of their portfolio is P₀– P₁. The shareholder receives a settlement payment of s but because shareholders ultimately bear the company’s settlement expenditure of α(s*n), each of the firm’s shareholders bears a per share settlement expense equal to α(s*n)/N, or α(s*π). Thus, a class plaintiff receives a per-share net settlement amount of s – α(s*π). Collecting terms, the per-share post-settlement welfare of a class plaintiff is:

P₀ – P₁ + s(1 – α*π) (1)

Even in the hypothetical but unrealistic world in which there are no litigation costs and no plaintiffs’ attorney fee awards,¹⁵⁷ and even if the settlement were to compensate defrauded shareholders for the full amount of their overcharge, a settlement would not make the injured shareholders whole so long as the corporation pays at least some portion of the settlement. That is evident in the model above. To see this, suppose there are no litigation costs or plaintiffs’ attorney fee awards and the settlement fully pays the overcharge—that is, s = P₁ – P₀. In this case, the post-settlement welfare of the injured shareholder discussed above who holds one share of the stock is – α*π(P₁ – P₀),¹⁵⁸ which is negative whenever the corporation pays at least some portion of the settlement, that is, whenever α is greater than 0.

In other words, while the settlement makes class shareholders whole in the first instance, they ultimately are not fully compensated because they each pay a portion of the settlement amount equal to α(s*π) per share. Each of the other firm’s shareholders also pay a per-share amount equal to α(s*π) to finance the settlement. As this example shows, the circularity critique supports the position of those who argue that stock-based Rule 10b-5 class actions fail to meet compensation and deterrence objectives and implicate fairness concerns.¹⁵⁹

2. Circularity in the Crypto Asset Context

Circularity is a significantly attenuated consideration for Rule 10b-5 crypto asset class actions because the drivers of the critique discussed above are substantially absent in the crypto asset context. To start, individual defendants in crypto asset Rule 10b-5 class actions are much less likely to be able to rely on insurance or indemnification as a shield from personal liability, relative to the stock-based context. First, because of the operational decentralization discussed in Section I.A above, an individual wrongdoer may not be associated with any entity such as a corporate body that provides indemnification rights or insurance coverage. Second, while publicly available data is lacking, D&O coverage appears very limited in the crypto asset context because of an avoidance by D&O carriers of the crypto space, as well as high premiums and unfavorable terms.¹⁶⁰

news/international/2022/12/19/699978.htm [https://perma.cc/VME7-JJG3] (“Insurers were already reluctant [prior to the collapse of the crypto exchange FTX] to underwrite asset and directors and officers (D&O) protection policies for crypto companies because of scant market regulation and the volatile prices of Bitcoin and other cryptocurrencies. Now, the collapse of FTX . . . has amplified concerns.”); Josh Liberatore, Crypto Winter Raises Host of D&O Coverage Issues, Law360 (Feb. 10, 2023, 9:38 PM), https://www.law360.com/articles/1575237 [https://perma.cc/FLC9-2XG9] (quoting a D&O lawyer for the observation that “[m]ost D&O underwriters view crypto firms as toxic in today’s environment, so the availability of D&O insurance for those firms is quite limited . . . . Even when available, the insurance is expensive and somewhat limited in scope of coverage”). So, even if an individual wrongdoer is affiliated with a centralized entity, the individual may not have the protection of D&O coverage, or only very limited protection, relative to an individual defendant in a stock-based Rule 10b-5 action. Furthermore, the apparent rarity of D&O coverage presumably would make indemnification a rarity as well, as a crypto asset entity would not be readily able to purchase Side B coverage to cover its indemnification expenses.¹⁶¹

The absence of crypto asset holders’ cash flow rights further diminishes the relevance of the circularity critique in the crypto asset context. As discussed in Section I.B above, except in very rare circumstances, a crypto asset’s holders will not be the recipients of any profit distributions resulting from their crypto asset holdings. So, if a Rule 10b-5 crypto asset class action settles, then the crypto asset’s holders may not bear any of the cost of the settlement, as would be the case in the stock context.

For instance, suppose the defendant set in a Rule 10b-5 crypto class action includes an entity involved in developing the crypto asset and the entities’ directors or officers. Suppose that the class action settles for s dollars per asset purchased during the class period. None of the settlement amount will be borne by the crypto asset’s holders (other than any defendant who may be a holder). Even if only some of the settlement is paid by the individual defendants, leaving some of the settlement to be paid by the named entity, that expenditure will not be passed down to the class plaintiffs or any other of the crypto asset’s traders because none have cash flow rights in the named entity.

With respect to the stylized model above, the named entity defendant may pay a fraction α of the settlement but because that amount is not borne by the crypto asset’s traders, the class plaintiffs’ welfare after the settlement is P₀ – P₁ + s for each share purchased during the class period. Putting aside any litigation costs or attorney fee awards, this then supports the feasibility of complete compensation if the settlement amount is set equal to the overcharge.¹⁶² One countervailing consideration is that, to the extent the defendant is actively involved in developing or supporting the crypto asset or any associated applications, a settlement payment by the defendant may impede its ability to effectively engage in those facilitating efforts. By decreasing the perceived value of the crypto asset or any associated applications, the settlement may lower the crypto asset’s price, which would adversely affect the crypto asset’s holders, including class plaintiffs.

In addition to the possibility of full compensation, because crypto asset traders outside of the class are not paying for the settlement of the class plaintiffs, the fairness concerns noted above are ameliorated in the crypto asset context. A related implication of the circularity critique in the stock-based context is that putting litigation costs to the side, litigation is zero-sum, in that shareholders’ aggregate wealth is unchanged after a settlement or judgment.¹⁶³ That is not the case in the crypto asset context. Because the cost of a settlement is not borne by the crypto asset’s traders, their aggregate welfare will increase after a settlement, putting aside the point above about a settlement potentially having adverse effects on development of the crypto asset or any associated applications. Finally, deterrence is heightened relative to the stock context because of the significantly greater likelihood that the individual defendants responsible for the fraud will incur monetary liability and thus be better incentivized to avoid that conduct in the first instance.

B. The Diversification Critique

Diversification is another leading critique lodged in the literature against stock-based Rule 10b-5 class actions. While circularity focuses on compensation and deterrence considerations in a single securities class action, the diversification critique peers with a broader lens. It inquires how a shareholder’s entire portfolio is affected by fraud and concludes that the cost of fraud can be diversified away, thereby nullifying the role of Rule 10b‑5 class actions as a remedial mechanism.¹⁶⁴

The key features of the diversification critique can be seen through a simplified model. Suppose that there are N publicly traded firms and a single investor. There are two time periods, period one and period two. In period one, the investor decides, for each one of the N firms, whether or not to purchase a single share of the firm’s common stock. So, in the first period, the investor can purchase up to N shares—one share of each of the N firms—but may invest in just a subset of the N firms. In the second period, the investor sells all of the shares that they purchased in the first period.

Suppose further that each of the N firms will be the target of fraud, the effect of which will be to artificially and temporarily inflate the firm’s stock price. Assume, for further simplicity, that all firms have the same fundamental, that is, non-fraud, share price and that the fraud will have the same price-inflating effect on each firm’s stock. For any given firm, there are two possibilities of the timing of the fraud. One possibility (which can be referred to as scenario one) is that the fraud occurred immediately before period one and is revealed to the market between period one and period two. The second possibility (which can be referred to as scenario two) is that the fraud occurred immediately after period one and is revealed to the market after period two. Firms are randomly assigned to the two scenarios with equal probability and the firms’ assignments are uncorrelated.

This setup illuminates the two key tenants of the diversification critique. First, the diversification critique postulates that, for any given issuer, every shareholder of the firm ex ante is as likely to be a victim of fraud as a beneficiary. This can be seen in the model above. For any firm in which the investor became a shareholder in period one, the investor’s likelihood of being in scenario one (in which case the investor will have purchased at the fraud-inflated price and sold at the lower, fundamental price) is the same as the likelihood of being in scenario two (in which case the investor will have purchased at the fundamental price and sold at the higher, fraud-inflated price). This means that even without a compensatory scheme in place, the expected cost of fraud to the investor for any given stock in their portfolio is zero: the likelihood that a shareholder will incur the cost of fraud is the same as the likelihood that they benefit, and the cost and gains are the same. But note that while the expected cost to the shareholder from fraud directed at any given firm in which the shareholder is invested is zero, fraud still affects the variability of the shareholder’s portfolio, since half the time the trader will be a victim of fraud and the other half the time, a beneficiary.

The second key tenant of the diversification critique is that investors can diversify away the risk that fraud injects into their portfolio. In the stylized model above, that diversification occurs through the investor taking positions in a greater number of firms. In the context of that model, while fraud will have the same, non-zero effect on the expected value of a portfolio comprised of the shares of a single firm and a portfolio comprised of the shares of many firms, fraud will result in the latter portfolio being less risky than the former portfolio. If stock traders are sufficiently diversified, then fraud will not only have zero expected cost on their portfolios but also will cause traders’ portfolios to be exposed to only limited additional risk.¹⁶⁵

As this discussion indicates, the strength of the diversification critique as a basis for concluding that fraud has no ex ante adverse effect on shareholder welfare turns primarily on two things. First, the theory’s strength depends on the extent to which shareholders are diversified. If shareholders are not well-diversified, then even though fraud will not affect the expected value of shareholders’ portfolios, it will increase their portfolios’ riskiness, which will undermine the welfare of risk averse shareholders. Second, the critique’s strength turns on the extent of shareholder risk aversion. If shareholders are strongly risk averse, then the effects of fraud on shareholder welfare through increased portfolio volatility will be more pronounced than if they were less risk averse, all else equal. The reason is that a more risk averse shareholder experiences greater disutility from an increase in portfolio risk than a less risk averse shareholder, all else equal.¹⁶⁶

These observations show that an assessment of whether the diversification critique is more or less pronounced in the crypto asset context than the stock context should focus, at least in the first instance, on comparing the extent of stock traders’ diversification and risk aversion with the extent of crypto asset traders’ diversification and risk aversion.¹⁶⁷ Empirical work is needed in order to be able to competently assess how the extent of crypto asset investors’ diversification and degree of risk aversion compares to that of stock traders.

Though strong conclusions are not possible in the absence of this empirical analysis, it is reasonable to expect that the implications of the diversification and risk aversion considerations break in different directions, but nothing suggests that those considerations are such that the diversification critique has significantly greater relevance in the crypto asset context than in the stock context. Turning first to trader diversification, it is likely that stock traders are better diversified than crypto asset traders. Through the widespread availability of index funds, index-based exchange-traded funds (“ETFs”), and managed funds, equity traders can readily and cheaply diversify their stock portfolios. The prominence of those instruments suggests that many equity traders do maintain diversified stock portfolios. That likely is not the case for crypto asset investors given that the means for crypto asset investors to easily diversify their crypto asset holdings, such as through tokenized index funds that track a broad basket of crypto assets, are not commonplace, and crypto asset investors appear to prefer purchasing and selling individual crypto assets rather than funds.

To the extent crypto asset traders are less diversified than stock traders, this would translate into the diversification critique having less relevance in the crypto asset context than in the stock context. On the other hand, it is reasonable to expect the risk aversion consideration to work in the other direction, because crypto asset traders may be less risk averse than stock traders. As discussed in Section I.C above, crypto asset prices are very volatile as a general matter and more volatile than stock prices as a relative matter. That crypto asset traders are willing to trade in the face of such volatile prices may be reflective of those traders being more willing to accommodate risk than stock traders. To the extent that is correct, then this would provide a mechanism for the diversification critique to have more, not less, relevance in the crypto asset context than in the stock context.

C. The Corporate Governance Justification

The circularity and diversification critiques have been the primary arguments asserted against stock-based Rule 10b-5 class actions. One rejoinder to those critiques is a corporate governance justification that posits that stock-based Rule 10b-5 class actions advance public policy through improvements in corporate governance.¹⁶⁸

The corporate governance justification focuses on securities law’s disclosure regime. The justification is based on the notion that more accurate disclosures by companies subject to the disclosure regime translate into improvements to legal and nonlegal channels of corporate governance. These improved corporate governance mechanisms, in turn, incentivize managers to be better focused on share value maximization, which results in economic gain. For example, the corporate governance justification posits that more accurate corporate disclosures increase the disciplinary power of a hostile takeover. The underlying reasoning is that more accurate company disclosures enable potential acquirers to more readily identify managerial deviations from share value maximization, where the threat of such takeover better incentivizes managers to maximize share value in the first instance.¹⁶⁹ The corporate governance justification concludes that securities class actions work alongside public enforcement to improve the accuracy of company disclosures, which serves to facilitate these and other forms of economic gain.¹⁷⁰

The corporate governance justification loses relevance in the crypto asset context. The primary reason is that crypto asset sponsors are not reporting companies, and thus subject to the securities law’s ongoing disclosure obligations, at least under current law and practice.¹⁷¹ Crypto asset sponsors also do not voluntarily furnish the market with information that is substantively similar to the disclosures provided by public companies.¹⁷² So, it is not meaningful to ask whether crypto asset-based Rule 10b-5 class actions generate disclosure improvements.

Second, the various channels of corporate governance that the corporate governance justification posits to be improved by stock-based Rule 10b-5 class action have no or little applicability in the crypto asset context. For example, crypto asset sponsors are not publicly traded companies and so cannot be the subject of a takeover effort. Even if a party were to acquire significant amounts of a crypto asset, that would not allow the acquirer to exercise control over the crypto asset’s sponsor or to replace its management, as may be the case with the acquisition of sufficient voting shares of a publicly traded company.

D. Price Volatility and Frivolous Litigation

The analysis above, when aggregated, does not provide a basis for concluding that the public policy justification for crypto asset-based Rule 10b-5 class actions is substantially weaker than the public policy justification for stock-based Rule 10b-5 class actions. The circularity critique is significantly less relevant in the crypto asset context than in the stock context, and the diversification critique may be more or less relevant in the crypto asset context than the stock context, but nothing indicates that it is significantly more relevant. An offsetting consideration is that the corporate governance justification ceases relevancy in the crypto asset context.

Absent from the discussion above is the issue of frivolous litigation, which can impose social cost by causing the defendants to divert resources away from value-enhancing activity to paying legal expenses and incurring settlement payments. One question pertinent to the Article’s public policy question is whether unmeritorious Rule 10b-5 class actions are more likely to be expected in the crypto asset context than in the stock context.

The prospect of frivolous lawsuits is heightened in the crypto asset context because of the significant price volatility discussed in Section I.C above. A crypto asset’s traders may lose significant amounts simply because of inherent price changes. In the face of a significant volatility-induced price drop, financially impaired crypto asset traders may seek to use Rule 10b-5 to recover their non-fraud losses, understanding that such cases often result in at least some recovery through settlement. Instead of crypto asset investors leading the charge to the courtroom in such circumstances, lawyers may be the first movers.¹⁷³ In either case, frivolous suits may deplete or deteriorate the budgets of crypto asset sponsors and others who are involved in the development of crypto assets and their applications, which would serve to diminish incentives to innovate. That prospect of dampened innovative activity is amplified given the apparent current rarity of D&O insurance.¹⁷⁴

This is an important consideration, but the same price volatility that may incentivize non-meritorious suits may also work to disincentivize them. At various points of their Rule 10b-5 class action, crypto asset traders will need to establish aspects of their case through statistical methods. For instance, the plaintiff traders will need to establish loss causation, which will necessitate use of an event study to show that the crypto asset’s price responded in a statistically significant manner to one or more corrective disclosures.¹⁷⁵ As has been documented elsewhere, event studies in Rule 10b-5 class actions may not be able to identify statistically significant price effects because of low power.¹⁷⁶ The issue of low power is heightened when there is high price volatility, as in the crypto asset context.¹⁷⁷ For this reason, whether or not a crypto asset Rule 10b-5 case is meritorious or not, the issue of low power will make it difficult for crypto asset traders to establish elements of their claim. That inability combined with an awareness that other aspects of their claim may have poor factual support may dissuade crypto asset traders from bringing frivolous Rule 10b-5 cases.¹⁷⁸ As this discussion shows, the same relatively high price volatility that could cause more frivolous crypto asset Rule 10b-5 class actions to be litigated than stock-based Rule 10b-5 class actions simultaneously provides a reason why there may be fewer frivolous suits of the former type than the latter.

CONCLUSION

Traders who participate in secondary crypto asset trading markets understand that any trading gains are accompanied by the risk of trading losses. Most traders presumably also understand that their losses at times can be significant because of the high volatility of crypto asset prices. But accompanying these market-determined losses are potentially significant trading losses caused by fraud occurring in connection with traders’ secondary transactions. In response to incidents of secondary trading crypto asset fraud, crypto asset traders may seek recovery for their trading losses through Rule 10b-5 class actions. The propriety of crypto asset traders relying on that form of relief implicates a host of doctrinal and public policy questions. This Article sought to analyze two such questions, one doctrinal and one public policy related.

In its doctrinal analysis, the Article evaluated issues pertinent to the threshold definitional question of when an exchange-traded crypto asset will constitute an investment contract and therefore fall within the definitional perimeter of a security. That analysis identified a slight generalization of the horizontal commonality test so that the test is suitable for use in both primary transaction and secondary transaction cases. The analysis also explained why Howey’s efforts of others prong should not be understood to require the presence of a centralized third party and also explained why the prong does not concern itself with investors’ expectations concerning the use of their sales proceeds. These findings, though, are legal propositions. Whether or not a particular exchange-traded crypto asset is or is not an investment contract will depend on the pertinent facts and the totality of the circumstances.

In its public policy analysis, the Article evaluated whether the public policy justification for crypto asset-based Rule 10b-5 class actions is significantly weaker than stock-based Rule 10b-5 class actions. It structured its analysis around the primary theories advanced in the literature to assess whether stock-based Rule 10b-5 class actions advance their public policy objectives. The Article’s public policy determinations break in different directions and in some respects are to be considered preliminary, but the analysis does not justify limiting the availability of crypto asset-based Rule 10b-5 class actions any more than stock-based Rule 10b-5 class actions.

96 S. Cal. L. Rev. 1571

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Menesh S. Patel*

* Professor of Law, UC Davis School of Law. This Article benefited from helpful comments by Jordan Barry and Jill Fisch, as well as participants at the University of Southern California’s Digital Transformation in Business and Law Symposium. Parts of this Article build on and draw from points in a prior work. See Menesh S. Patel, Fraud on the Crypto Market, 36 Harv. J.L. & Tech. 171 (2022). I thank Merritt Fox for his comments on that earlier work, which motivated me to address points in Part III of this Article. I also thank Madeline Goossen, Jessica Langdon, Remy Merritt, and the other journal editors for their helpful suggestions and editing assistance. Maximilian Engel, Katherine Gan, and Ada (Xia) Wu provided excellent research assistance.

Data Valuation and Law

April 2024April 2024Jordan Barry* & D. Daniel Sokol†

Data has become an increasingly valuable asset. Numerous areas of law—including contracts, corporate law, intellectual property (“IP”), antitrust, tax, privacy, and bankruptcy—require parties and courts to determine the value of assets, including data. Unfortunately, data valuation has been hindered by a lack of clarity over what data is and why it is valuable. This lack of clarity also increases the chances of legal decisionmakers valuing data in inconsistent ways, which would create further confusion, inefficiencies, and opportunities for regulatory arbitrage.

This Article proposes a unified framework for valuing data that will promote consistent valuations across fields of law. It begins by conceptualizing data as building blocks: It is of little value on its own. But when placed in skillful and creative hands, it can unlock choices for its holders—choices they would not otherwise have—that can generate tremendous profits. Thus, data constitutes what is known as a “real option.” This Article shows how using real options to value data can significantly improve upon existing data valuation practices.

INTRODUCTION

The rise of data analytics has been staggering. In 2021, 1.134 trillion megabytes were created every day, totaling 74 zettabytes for the year.¹ As large as this is, projections for 2022 are over 25% higher.² Big data and new information technology are changing the tools, business models, operations, and mindset that firms, nonprofits, and governments use every day, quietly transforming business and society.³

These changes come with challenges. A variety of legal regimes govern economic activity; in many instances, those legal regimes must determine the value of owning or using particular assets, including data.

For example, one area in which data valuation plays an important role is in contracting. Firms contract with each other daily with regard to the sale of data. This includes first-party data sales, such as when Target sells data that it has collected to Proctor & Gamble, as well as third-party data sales, in which data aggregators or brokers sell data that others have collected. If one party breaches the contract, what remedies are available to their counterparty?⁴ In corporate law, target boards have fiduciary duties to make sure their shareholders are being appropriately compensated during mergers and acquisitions. This requires having a handle on the value of the target firm’s assets, including its data.⁵ In tax, the taxation of intangible assets and specifically of data is a growing issue of concern.⁶

These questions can potentially be even thornier when specific aspects of data must be valued, rather than full ownership. To take another example, suppose that one firm’s negligence results in another firm’s proprietary data leaking to the public. To award damages, a court must determine how much the damaged firm lost from having the data become public—but how much is that?⁷ Similarly, in antitrust, when control of data plays an important role in anticompetitive behavior, is it ownership of the data itself that creates the problem, or the use of the data?⁸ Does sharing the data with competitors make matters better or worse?⁹ The rise of generative artificial intelligence (“AI”), which requires data for its machine learning models, may create additional concerns as to the value of various data usage rights.

Unfortunately, the difficulties of conceptualizing data have hampered law’s attempts to incorporate the data revolution into multiple legal doctrines. This has opened the door to confusion, inconsistency, and inefficiency. Decisionmakers have confused data with algorithms, and struggled with how to apply certain doctrines to the legal rights that data owners and data users possess. This increases the risks that regulators in different substantive areas of law, as well as in different jurisdictions, will take inconsistent approaches. This creates inefficiencies as parties subject to multiple regimes work to navigate them. Different legal regimes also creates opportunities for regulatory arbitrage, in which regulated parties take advantage of divergent regulatory rules to achieve the regulatory treatment they want while making only minor changes to their economic activities.

To address these concerns, this Article offers a general framework for valuing data based on real options valuation. The financial economics literature pioneered the use of real options to better assess business decision-making under uncertainty.¹⁰ This approach has since been extended beyond finance to address other areas of uncertainty.¹¹ Real option analysis provides a better path forward than the current patchwork of doctrinal and analytical approaches. A real options approach is conceptually correct and thus has the potential to ameliorate the confusion, inconsistency, and inefficiency of existing approaches. To our knowledge, this is the first article to utilize real options as a method to value data, in law or otherwise.

Along with its potential benefits as a method of data valuation, real options analysis does have its drawbacks. Real options theory is complicated, which creates implementation challenges that must be overcome, or at least managed, to achieve the benefits described above. That said, real options analysis is an improvement over existing approaches. Applying a more unified theory also allows for a more standardized approach that can then be tailored to specific doctrines and areas of law.

This Article proceeds as follows. Part I provides context regarding the big data revolution and the growing importance of data. In doing so, it reviews the extant theoretical and empirical literatures on data valuation. Part II identifies the implications of data valuation for law by providing some case studies across fields. It includes vignettes demonstrating the types of issues that emerge and some current legal approaches. Next, in Part III, the Article explores how real options analysis offers a viable potential solution to the current patchwork of legal approaches. The Article concludes on how agencies and courts would benefit from such an approach, notes limitations on the use of real options, and offers avenues of future research.

I. THE DATA REVOLUTION AND THE VALUE OF DATA

To understand the importance of data valuation methods to the law, one must understand two other, related points. First, one must have a grounding in why and how data is used in the modern economy. Second, one must consider how to think about how those use cases translate into value estimates.

A. Digital Transformation

To understand the role of data in the modern economy, one must consider three related points: (1) The increase in AI techniques that can generate value from data; (2) The increase in data to which such AI techniques can be applied; and (3) The amount of value that these techniques are creating. Understanding these dynamics allows us to explore specific case studies that apply these insights across a number of areas of law.

1. Generating Value from Data with AI

As a starting point, companies across the economy have moved to increasingly digitized, AI-enabled business strategies, producing profound effects on value creation and innovation.¹² We use the term AI broadly here, as a way to encompass algorithms that improve prediction and decision-making.¹³ There are different approaches to AI, such as neural networks and machine learning, among others.¹⁴

When thinking about data and AI, it can be helpful to consider a simple, three-tier vertical model of how companies and other actors use data and AI to further their goals.

Figure 1.

At the first stage is data. If AI is the product or output, data serve as the input. Data feed the needs of AI-enabled technologies. Data underlie machine learning and prediction models, and it is data that has fueled digital transformation.¹⁵ Without sufficient quantity and quality of data, many current AI techniques simply cannot produce very good results.

Data often is the input to the next stage—powering an algorithm. The algorithm itself is not the end of the production. Rather, the algorithm simply enables better prediction. It is at the stage of prediction where there are outputs to AI—outputs that can generate tremendous value.

For example, when a user types terms into a search engine, that engine might consider data about what sites other users who typed in similar terms ultimately clicked on (among other data) when deciding what results should appear. Diagnostic software might compare a patient’s MRI to millions of MRI images that have already been analyzed by doctors to estimate the likelihood that the patient has breast cancer. Data drives the AI, the AI makes predictions, and those predictions enable better decision-making, which creates economic value.

2. Increase in Data

While many facets of AI are themselves not new, the speed of data collection and processing have significantly improved these tools’ impact.¹⁶ Data is vast and the various ways to use it have grown significantly, such that there are distinct data-related strategies that firms may adopt.

The data ecosystem is worth exploring briefly. Data can be bought and sold like many other inputs.¹⁷ It can be acquired from public sources. It can be collected from what can be termed data suppliers. For example, first-party companies such as Netflix or Spotify can sell their data and databases to other companies—firms regularly sell large quantities of this type of data through basic business transactions.¹⁸ Third-party data brokers, apps and internet service providers (“ISPs”) that can provide locational or other data, and data aggregators also play significant roles in the data ecosystem.¹⁹ Data brokers buy and sell data, thereby allowing firms to acquire new data to make better predictions.²⁰ This increase in data sources is an important change, as it makes data more widely available. This both enables more actors to put it to use and to experiment and innovate with it.²¹

Indeed, data has become both a make and buy decision.²² That is, firms have significant opportunities to generate their own data—such as Target keeping track of what consumers buy at Target—and to acquire third-party data from other actors. This is especially true with respect to end-consumer data.²³

3. Amount of Value

What is this power of data? Typically, data is defined across four “V’s”: velocity, veracity, volume and variety.²⁴

http://www.americanbar.org/content/dam/aba/administrative/antitrust_law/comments/october-2019/clean-antitrust-ai-report-pt1-093019.pdf [https://perma.cc/F9S2-8P5Q]. Combined, these four Vs create data value. Velocity is the speed at which data is collected and used. Volume is the sheer amount of data that is generated, which (at least at present) overwhelms our ability to process it; there is more data than ever before and every day we create 328.77 million terabytes of new data.²⁵ Veracity goes to the increasingly important issues of data accuracy and trustworthiness. Finally, variety reflects the diversity of data types that can be collected and used, such as e-mails, PDFs, and videos.

Data may come from many sources. The general rule of data is that the more the data, the greater the ability to feed AI and the better the ability to improve prediction,²⁶ although there are limits to what data alone can do.²⁷ Data must be processed, via AI or otherwise, to reap benefits.²⁸ When properly processed, big data allows firms to improve their products and services and to develop new such products and services.²⁹

The academic and practitioner literature on data valuation is complex. First, there is the literature on data brokers. In some senses, the costs of data are lower now than ever before.³⁰ The reduced cost of data allows for the creation of a wide variety of sophisticated algorithms that can produce insights that would elude unassisted humans.³¹ The ability to utilize data to feed AI allows for opportunities to better create, appropriate, and deliver economic value not merely for AI-driven firms but for the different users of digital platforms such as advertisers, complementors, and customers.³²

This transformation creates significant economic value, but the drivers of that value are not well understood by courts and regulatory bodies. In some cases, regulation might stymie the use of data and chill innovation and investment.³³ In other cases, the potential portability of certain types of data has motivated increased legislative and regulatory action.³⁴ In other situations, courts have held that owners of certain types of data have certain rights, such as the right to exclude others from such data. The exact value—either of the underlying data itself or of the rights to exclude others—may not always be clear.³⁵ There are yet other areas in which data-related transactions occur on a regular basis, but which have not produced judicial decisions to date.³⁶

It is these sorts of complexities as to law and data to which we next turn.

B. Disagreements on How to Think About Data Creating Value

Valuing data presents conceptual challenges because data is unlike other assets, including other intangible assets. The first problem is to understand how even though data is a building block for constructing a final product, data is not like traditional tangible assets such as bricks and steel used to make a factory. Data can be collected and mixed in a number of different, complex ways. Further, unlike bricks, data is non-rivalrous; more than one firm can use the same data.³⁷ For instance, someone’s driving history can be used at the same time by multiple firms, in the same or different industries (for example, advertisers, insurance companies, credit card companies). As Jones and Tonetti explain:

An analogy may be helpful. Because capital is rival, each firm must have its own building, each worker needs her own desk and computer, and each warehouse needs its own collection of forklifts. But if capital were nonrival, it would be as if every auto worker in the economy could use the entire industry’s stock of capital at the same time. Clearly this would produce tremendous economic gains. This is what is possible with data.³⁸

Thus, non-rivalry means that valuation may be harder across a number of the traditional measurements.

Further complicating data is that it is (mostly) non-exclusive.³⁹ For example, if someone collects public records about home purchases into a comprehensive database, that does not prevent others from collecting that same information in the same way. This is a stark contrast from some other intangible assets, including traditional forms of IP such as patents and copyrights, which create value by conferring exclusive rights on their holders.⁴⁰

Both of these indicia suggest that the underlying value of the data, rather than that of the algorithm, may be small. When the input (data) is easily available to all, it is the actor’s ability to make use of the input—that is, the algorithm—that creates the value, not the input itself. For example, a classic crème brûlée recipe has only four ingredients—cream, sugar, egg yolks, and vanilla. All of these items are widely available. The ability to charge a premium for the final product is a function of the baking skill of the pastry chef.

Beyond non-rivalry and non-excludability, some regulation, such as the European Digital Markets Act⁴¹ requires fair, reasonable, and non-discriminatory (“FRAND”) licensing. Even in IP and antitrust, FRAND terms are not always clearly understood.⁴² It stands to reason that in data, with fewer cases to provide guidance across different areas of law, the nature of FRAND obligations is even less clear. Further, certain types of data have sharing requirements in practice that may change the valuation of data, such as requirements for data portability.

Data is also unlike some other intangible assets because of the speed at which data can become obsolete.⁴³ Much data gets stale over time.⁴⁴ This suggests that much data is a diminishing asset, something which IP such as patents or copyrights do not face nearly as quickly because those rights last for longer periods.

II. THE IMPLICATIONS OF DATA VALUATION FOR LAW

There are many areas of law for which valuation of various assets is important. Data is an increasingly valuable asset. Unfortunately, there is currently relatively little law on how to value data. Courts and regulators have generally avoided the question whenever possible, perhaps out of concern for the difficulty of the problem, or uncertainty on how to proceed, and often such cases get decided upon other grounds. This raises the chances that different legal areas will use different valuation methods. Such inconsistency creates dilemmas as to how to allocate legal rights and responsibilities. Perhaps the clearest way of understanding this tension across areas of law is to consider the purpose of damages. Damages exist to compensate a potential victim for violations of law and/or to deter the violator from doing so again.⁴⁵ Methods across areas of law might include: (1) a cost-based approach based on the replacement cost; (2) a market-based approach based on similar acquisitions of data (or companies with data); and (3) an income-based approach, to the extent that the data is producing income via sales or even licensing. To this, we add the importance of a fourth possibility, an options-based approach. Often, outcomes seem to be highly contextual rather than based on valuation methodology.⁴⁶ A lack of consistency is significant because of the growing stake of data as an important part of economic activity.

Which approach ultimately to take across areas of law such as IP, antitrust, mergers and acquisitions (M&A), bankruptcy, torts, and other areas of law varies. One important driver is what information courts and parties can easily measure. When contracts (and comparable transactions) are not easy to find, private negotiations between contracting parties in the shadow of the law are another important driver. These questions become more salient as we try to understand how issues involving big data reverberate across a number of areas of law and in terms of the value of data overall. The biggest question is how much value do we think is in big data?⁴⁷

A. Valuation Is Important to Many Areas of Law

Below we offer some examples of how data valuation plays a role across various areas of law. We highlight these examples as a way to understand some of the complexity that requires a more generalized rethink as to valuation method of data. Understanding these complexities helps clarify the value of data as well as some of the struggles that different areas of law are currently experiencing as they seek to value data.

1. Antitrust

Antitrust has tried to address the questions of competition and the exercise of market power in two contexts—mergers and conduct cases. These produce two types of antitrust cases—those where data is an input and those in which data is a product. However, there is little caselaw in each area. Consequentially, the problem with both sets of circumstances is that we tend not to see litigated cases that get to the valuation issue of the data.

Antitrust primarily addresses behavior one of two ways. The first is through ex ante enforcement through merger control. Essentially, regulators can block mergers that are expected to produce antitrust problems. On the mergers side, most cases do not go to court, which means that litigated cases may not be representative. Even in those cases for which there is a judicial opinion, not all issues may get addressed. Scholars have expressed general frustration with what gets decided under the shadow of merger law.⁴⁸ Thus, the basis for decisions on many issues, including data valuation, is limited or incomplete. As Professors Katz and Shelanski lament, “The overall picture of current merger enforcement practice is, therefore, murky.”⁴⁹

Cases provide some guidance on how antitrust courts and agencies think about data, which gives some insight on how to think about data’s value. Yet much uncertainty remains. As of this writing, no mergers have been blocked on data theory grounds in the United States. Nor have there been any decided cases that explain the valuation method used for such transactions that weigh the data rather than its use to a specific platform.

In the case of data, let us begin with mergers and the possibility that data is itself the market. One such deal that included data as the market is the 2014 CoreLogic-DataQuick merger.⁵⁰ In that transaction, the Federal Trade Commission cleared the transaction with a database divestiture but did not explain the valuation technique employed. Alas, this has been typical with regard to antitrust analysis of mergers that include data as a market. Similarly, people generally have not discussed mergers that include valuable data as an input (for example, Microsoft/LinkedIn, Apple/Shazam) as matters of valuation. At best, there are transactions that have received some sort of conditional approval such as Nielsen/Arbitron but without an explicit discussion of data valuation.⁵¹

Antitrust, through public and private enforcement, polices against anticompetitive conduct by one or more firms that harms competition. Conduct cases in antitrust involving data issues have not resolved the data valuation question, either. Complicating antitrust further is that duties to deal with competitors are limited, which means that such data sharing cases do not get to the data valuation stage of the case. Rather, these cases are decided based on the premise that data is not required to be shared in the first place. Yet, understanding such cases helps to explore the value of data because the discussion helps to inform the value of data use and ownership.

For example, Section 2 of the Sherman Act generally imposes no requirements to deal with one’s competitors.⁵² In Aspen Skiing Co. v. Aspen Highlands Skiing Corp., the Supreme Court held that there are some limited circumstances under which Section 2 requires monopolistic firms to deal with their rivals.⁵³ Courts have further narrowed Aspen Skiing’s holding since. Most recently, the DC Circuit dismissed a monopolization case that forty-six states brought against Meta based on the court’s narrow reading of Aspen Skiing.⁵⁴

Cases brought under other provisions of the Sherman Act have also implicated the value of data. However, much like the Section 2 monopolization cases, courts examining Section 1 of the Sherman Act have offered little guidance on how to value data. For example, in Authenticom, Inc. v. CDK Global, LLC, Authenticom brought a claim against CDK for closing its system for data and thereby barring data scrapers from access. The Seventh Circuit ruled in favor of CDK on the basis that forced data sharing was inconsistent with precedent.⁵⁵ Because of this ruling, which dismissed the case on essential facilities grounds, the data valuation issue was never addressed. Of course, that does not mean that the data does not have value, merely that the court was able to dispose of the case without determining what the data’s value was.

Similar to antitrust enforcement, competition regulation increasingly plays an important role in big data valuation. This comes up specifically in the case of the Digital Markets Act (“DMA”), the European approach to ex-ante regulation of data.⁵⁶ Regarding “gatekeeper” firms, the DMA states:

The gatekeeper shall provide to any third-party undertaking providing online search engines, at its request, with access on fair, reasonable and non-discriminatory terms to ranking, query, click and view data in relation to free and paid search generated by end users on its online search engines. Any such query, click and view data that constitutes personal data shall be anonymised.⁵⁷

Of course, data from a gatekeeper will not generate profits on its own; gatekeeper data must still be combined with some effort by recipients. But this reality makes it harder to assess the incremental profits the recipient earns as a result of having access to the data.⁵⁸

2. Business Law

Business law increasingly confronts data valuation. Unfortunately, it does so in ways that do not always show the precision that we believe is necessary to unlock a more accurate value of data assets. For example, data valuation questions arise within the context of both mergers and acquisitions (“M&A”) and bankruptcy. A number of factors arise in each context that make data valuation more difficult. Within the merger context, the purpose of valuation is to best help the acquiring and target boards to fulfill their fiduciary duties to ensure that the price paid for the acquisition is an appropriate one.

Overall, corporate law has grappled with how to account for intangibles. Many assets, including branding and intangibles such as IP, are lumped together under the heading of “goodwill.” However, the goodwill from reputation and branding is different than goodwill that is the basis of a regenerative asset such as data. Further, how data is stored and how easily it can be processed and integrated make such a valuation more challenging.⁵⁹

Different data sets may have different levels of privacy requirements, such as data that is protected under the Health Insurance Portability and Accountability Act (“HIPAA”) versus commercial health data, which has less stringent requirements. Identifying what sort of data companies may keep, for how long, how stale such data get, and the potential liabilities of such data are complex.⁶⁰ Yet, there are very few cases that offer direct guidance on how to value data in the corporate and M&A setting. Thus, data valuation ends up a financial black box with potentially large implications if and when such cases go to litigation. This sort of uncertainty creates potential risk for deals, particularly those deals for which the underlying data may be a significant asset.⁶¹

Finally, unresolved issues include requirements of how to store data⁶² as well as how to destroy data.⁶³ The lack of uniform federal privacy legislation makes such analysis more difficult. Federal agencies, especially the FTC, enforce privacy protections,⁶⁴ but private actions also play a role.⁶⁵ Moreover, states can impose additional rules on top of the federal ones. For example, California took inspiration from the General Data Protection Regulation (“GDPR”) and adopted the California Consumer Privacy Act (“CCPA”) and the California Privacy Rights Act (“CPRA”).⁶⁶

This issue of data valuation similarly plays itself out in the bankruptcy setting. In some settings, the data itself, such as customers’ spending behavior,⁶⁷ may be the asset. Take the example of the bankruptcy proceeding for Caesar’s Entertainment Operating Corp casinos.⁶⁸ Creditors viewed the company’s data (customer-specific data on spending habits) as one of the company’s most important assets. Yet, as is often the case in bankruptcy proceedings, this issue was resolved through negotiations in the shadow of the law, leaving behind no case law to help shape future data valuation inquiries. On one side, there was a note by the bankruptcy court examiner that properties of Caesar’s that were sold off were worse off because they could not leverage the data of the rewards program—but at the same time, the examiner recognized that it would be difficult to sell the rewards program to other buyers.⁶⁹ Thus, the court never ultimately decided how to value the data in light of these complexities. This is common in bankruptcy, where few decisions come in the form of a bankruptcy court ruling.⁷⁰

3. Synthesis

These case studies lead to a number of conclusions. First, courts do not always get to valuation questions. This may be because cases are decided on other grounds for legitimate reasons or because judges feel uncomfortable getting to the actual valuation and so they rule on different grounds to avoid the exercise. Second, there is uncertainty of valuation methodologies across areas of law, as well as potential for some such issues to simultaneously emerge in multiple contexts (for example, M&A and antitrust, M&A and bankruptcy, antitrust and data privacy) that may employ different methodologies. Accordingly, we believe that a more consistent approach may better facilitate business certainty with regard to valuation models.

III. REAL OPTIONS AS A SOLUTION

Real options analysis provides a framework that can be used to value data across different contexts, including different areas of law. We provide a basic introduction to real options before discussing the advantages and disadvantages of using them to value data. We then discuss how this approach might be employed in the real world.

A. Real Options

An option is the right, but not the obligation, to do something. For example, if Maria has the right to paint her house green, to travel to Paris, or to order pizza for lunch, those are all options.

In finance, the most well-known options give their holders the right to buy or sell a specific quantity of a particular asset at a specified time for a specified price. These options are known as financial options.⁷¹ For instance, Jacinta might have the right to buy 1,000 shares of Apple stock in three months’ time at a price of $150 per share. That right would be quite valuable if, three months from now, Apple stock is trading at $200 per share: Jacinta could buy 1,000 Apple shares for $150,000,⁷² then immediately sell them to other investors for $200,000,⁷³ netting her $50,000 of profit.⁷⁴

Real options, like financial options, reflect the value of being able to react to changing conditions. However, rather than representing merely the right to buy or sell, they can encompass one’s ability to change one’s behavior in all manner of ways.⁷⁵ This ability to change course can be extremely valuable. A pair of simple, stylized examples help illustrate this point.

Example 1. Suppose that you are an executive at a company, and you are considering whether the company should launch a new product. It is unclear how consumers will react to the product; they may love it (iPods) or they may not (Zunes). Suppose that there is a 50% chance that the product will be a success, in which case it will generate $10 million of profits per year for the next ten years.⁷⁶ On the other hand, there is a 50% chance that the product will be a commercial failure, in which case it will cost the company $20 million per year for the next ten years.

Under the facts of Example 1, the company should not launch the product.⁷⁷ Half of the time, the product will produce $100 million of profit;⁷⁸ the other half of the time it will produce losses of $200 million.⁷⁹ On average, then, launching the new product will cost the company $50 million.⁸⁰

Example 2. The facts are the same as in Example 1, except that now the company has the ability to stop making the new product after its first year on the market.

Under the facts of Example 2, the company should absolutely launch the product. When the product is a success, it will keep the product on the market. Everything will remain the same in that circumstance, and the company will earn $100 million of profit. But when the product is a commercial failure, the company can now cut its losses after one year. By doing so, the company will reduce its total losses when the product fails from $200 million to only $20 million.⁸¹ On average, the new product will now generate $40 million of profit.⁸²

Taken together, Examples 1 and 2 show how valuable the ability to change course can be. Simply having the ability to give up on the product when it is not profitable transforms a project that loses $50 million into one that earns $40 million—a $90 million swing.⁸³ Since the only difference between these two Examples was the real option to give up on the product after a year, that option is worth $90 million.

Real options come in a variety of common forms. Companies can expand or contract their businesses, such as by opening new locations or closing existing facilities. They can accelerate or delay projects, such as by hiring more workers to build a factory or pausing construction. They can switch production processes, trade-off between workers and automated processes, or shift production between in-house divisions and outside contractors. Taken together, real options encompass a wide range of actions spread across an expansive set of possible circumstances.

B. Real Options as a Model for Data Valuation

As a framework for valuing data, real option analysis has many virtues. First, the value of data is that it enables a person to take new actions that were not available previously.⁸⁴ Real option analysis is how finance values the ability to take new courses of action. Thus, as a conceptual matter, real option analysis is a natural fit for valuing data. Further, real option analysis is a flexible and expansive tool that can be used to model an extraordinarily wide range of scenarios and circumstances. This makes it capable of handling the range of new possible outcomes that data, paired with modern statistical analysis, can produce.

Moreover, as noted previously, current approaches to data valuation offer little guidance. This increases the potential for confusion, inconsistency, and regulatory arbitrage. In some instances, they assign data no value at all.⁸⁵ Applying real options analysis to data valuation would help ameliorate all of these problems. Real options analysis gives a clear theoretical framework, providing guidance and structure for those trying to determine data’s value. This would help align and unify the disparate valuation approaches that have been employed to date. Improved alignment would also reduce the opportunities for regulatory arbitrage that can result when different regulatory regimes adopt inconsistent valuation methodologies.⁸⁶

While real option valuation offers a number of benefits, it also entails a significant drawback: correctly valuing real options is quite difficult. To do so precisely, one must anticipate, and then think through, all of the possible future states of the world, their respective likelihoods of occurring, how one would respond to them all, and how much one would ultimately reap as a result. From there, one can work backwards from these endpoints to determine the right course of action at each decision point and the scenario’s expected value overall. This is a tall order—especially when valuing data, an asset whose value depends in part on future developments in statistical analysis.

To put a somewhat finer point on it, consider financial options once more. Valuing financial options is a difficult mathematical problem. Fischer Black, Myron Scholes, and Robert Merton’s options pricing model was a watershed advance for the field, ultimately garnering a Nobel Prize in 1997.⁸⁷ Even with the solution in hand, the mathematics remain challenging. As important as options are to modern finance, many undergraduate finance courses do not cover the application of their formula, let alone its derivation.⁸⁸

Valuing real options is even harder than valuing financial ones. There are more possibilities to consider, more actions available, and more variables of interest.⁸⁹ It would be extremely difficult to write and apply a regulation with a precise formula that generalized across different types of data from diffuse contexts and industries. The complexity of real options also poses challenges for parties, for judges, and for juries.

This is a serious problem. A valuation method that has attractive theoretical properties, but that is impossible to apply in practice, would seem to be of extremely limited value.

C. A Way Forward

Despite its complexities, we nonetheless believe that real options analysis holds great promise as a framework for valuing data. If one wants to value data accurately, one must have the right model. In our view, real options analysis captures what makes data useful, and thus offers the best framework to think about data’s value. If data’s value is complicated and depends on many factors, then this is not a fault of the model; the model can only help a user identify and focus on the things that matter, even if that’s a long list.⁹⁰ Put another way, to get the right answer, one must ask the right question. The right question may be a hard one—but answering a different, easier question means avoiding the problem, not solving it.

Moreover, it is worth stating what may be obvious: the real options approach need not be perfect to be an improvement over existing practices.⁹¹ Getting all interested parties asking the right question—or even the same question—would be valuable. It would reduce conceptual confusion, inconsistencies, and opportunities for regulatory arbitrage. Moreover, real options always have positive value.⁹² Whenever taking an available course of action is profitable, one can do so; if that course of action is not profitable, one can simply decline to take that action.⁹³ Real options analysis would underscore the point that data has value and thus should not be ignored.⁹⁴ These combined benefits may be considerable.

Furthermore, if decisionmakers use real options analysis to value data, they may find ways to ameliorate the complexity problems over time. Trial and error can produce insights. As agencies and courts experiment with the framework, approximations may arise that are easier to calculate. Even if these approximations are not precisely accurate, they may be close enough to be useful. In particular, they may be significant improvements over existing data valuation methods.

That dynamic—of finding heuristics that are simpler but informative—has been borne out in other settings. For example, basic corporate finance theory teaches that profit-maximizing firms should use net present value analysis to allocate their resources.⁹⁵ Yet many firms, including large, sophisticated ones, analyze other metrics as well.⁹⁶ These metrics include the profitability index, which measures how much profit a project generates per dollar invested, and the payback rule, which considers how long it takes for a project to repay its startup costs.⁹⁷ Both of these simple rules have well-known flaws that can cause them to produce absurd results.⁹⁸ Why, then, do they remain common?

One possible answer is that these simple rules produce information about projects’ real option value. For example, recouping one’s initial investment means that those recovered dollars can be redeployed toward other purposes, increasing the range of decisions available to the firm.⁹⁹ Researchers have found that, under a variety of circumstances, such simple rules can allow firms to make nearly optimal decisions.¹⁰⁰ The relative accuracy of these rules, combined with their simplicity, may explain why firms use them more frequently than real options analysis.¹⁰¹ These types of heuristics, and others, may prove useful to valuing data.

Alternatively, real option analysis can inform other modes of valuation. One response to complicated valuation problems is the method of comparables: To determine an item’s value, identify similar items whose values are known (that is, comparables), then make appropriate adjustments. This method is frequently employed to value items such as real estate, art, and active businesses.¹⁰² Under the right circumstances, this method can produce accurate valuations.

The method of comparables can be tricky to apply to data for several reasons. First, it may be difficult to identify similar data sets with known values. Sale prices are often used as the measure of value for comparable items, and sale prices for data may not be public. But even when sale prices are available, data sets can differ from each other along a variety of dimensions. Which of those differences are important, and how much should value estimates be adjusted to account for these differences? For example, which is more valuable—a data set that is twice as large, or one that includes data drawn from twice as much time? Is data more valuable when the future is more uncertain or less? These are but a few of the dimensions one might wish to consider.

Real option theory sheds insight into some of these questions. It identifies a number of factors that directly affect real option value, and thus the value of data. These factors can then be considered and adjusted for when using comparables to value data.

One factor that informs a data set’s value is its informational uniqueness. To what extent does that data tell its user something that they otherwise would not know? Having insights that no one else has can be extremely valuable. On the other hand, when competitors have access to comparably informative data, profitably exploiting the data gets harder, as competition among firms puts the firm’s counterparties in a comparatively stronger position.

Two other factors stem from the payoffs available from exploiting data. Unsurprisingly, the higher the potential future profits that the data can unlock, the more valuable the data is. What is less obvious is that the value of data increases as the future becomes less certain. This is somewhat abnormal; in finance, safer cash flows are usually considered more valuable than riskier ones.¹⁰³ Options are an important exception to this general rule, however. Because options allow one to change behavior in response to different circumstances, they actually become more valuable when a project has a wider range of possible future payouts.¹⁰⁴

Another important factor in real option valuation is the length of time over which one can continue to change one’s behavior.¹⁰⁵ The longer that one can change direction, the more actions that one has available, and the more valuable the option. In the data context, this corresponds to the useful life of the data. As noted earlier, some data remains useful and informative for years or even decades; other data grows stale quickly.¹⁰⁶ All else equal, the former is more useful than the latter.¹⁰⁷

Relatedly, interest rates affect the value of real options, and thus of data.¹⁰⁸ Profits earned in the future are more valuable when interest rates are low than when rates are high.¹⁰⁹ Interest rates have more of an effect on data with a longer useful life, and less of an effect on shorter-lived data.

How quickly and cheaply one can change one’s behavior also affects a real option’s value. The quicker one can act, the more nimble one is, the more ways in which one can profitably change one’s behavior. Similarly, options that can be exercised at little cost are more valuable than those which are expensive to utilize.¹¹⁰

These factors are more amenable to forming legal standards than a strict formula for valuing real options would be. Accordingly, they may provide a path forward for data valuation.

Finally, real options theory could inform attempts to value data in a different way. Experience may convince policymakers that valuing data is simply too hard, and that they should act accordingly. Such actions could take multiple forms.

One response to a difficult valuation problem is to simply exit the field as much as possible. Section 83 of the Internal Revenue Code provides a good example of this approach.¹¹¹ It addresses the questions of how much income a taxpayer has when they receive property in exchange for performing services, and when the taxpayer is taxed on that income. Section 83’s general rule is that employees are taxed on property based on its fair market value, and they are taxed at the time it becomes clear that they will get to keep the property.

For example, startup companies frequently include some form of equity interest in the company as part of their employees’ compensation packages.¹¹² These interests can come in various forms, including stock, restricted stock units, or stock options.¹¹³ If employees leave their employer before a certain date—if they quit to take a new job or are fired—then they forfeit some or all of their equity interests. The date after which an employee gets to keep an equity interest, even if the employee leaves the firm, is known as that interest’s vesting date. If an employee leaves the employer before the vesting date, they lose their unvested equity.

Under the general rule of Section 83, an employee is typically taxed on the value of their equity interest at the time those interests vest.¹¹⁴ However, as noted previously, valuing stock options is difficult. Accordingly, Section 83 exempts stock options from its general rule—unless they have a visible market price (in which case they are easy to value).¹¹⁵ Instead, employees who receive stock options generally are not taxed until they exercise those options, at which point they receive stock in their employer, which is easier to value.¹¹⁶ This limits taxpayers’ ability to take aggressive valuations of hard-to-value stock options.¹¹⁷ Regulators can adopt similar tactics in the context of data valuation.

A potentially complementary approach would be to foster a market for data, with standardized features, in order to make private transaction prices more visible and data sets more easily comparable. In a number of instances, legislative and regulatory interventions have helped shift markets characterized by bespoke arrangements toward more commoditized features and greater transparency.¹¹⁸ Such standardized markets can make the job of valuation much easier, and can also protect unsophisticated parties operating in those markets.¹¹⁹

CONCLUSION

While data has become increasingly valuable and important, the law’s attempts to value data have lagged, remaining confused and underdeveloped. Situating data valuation law within an economic framework built on real options analysis would resolve conceptual confusion among courts, agencies, and legislatures. It would also create greater predictability among private actors, which in turn would reduce the risk of regulatory uncertainty and facilitate investment. A clearer legal approach that cuts across different areas of law and jurisdictions would limit opportunities for regulatory arbitrage across fields of law addressing data valuation. Furthermore, a consistent approach reduces politicization of results, preventing favored groups from shifting unclear legal rules in their favor when there is no economic basis for such a shift. A consistent approach also makes decision-making less opaque, thereby increasing the legitimacy of outcomes.

While the real options approach is not without potential problems, we believe that it is the least bad alternative available. Moreover, increased use of real options analysis over time may generate heuristics that simplify data valuation by courts and agencies. These heuristics may prove so effective that private parties incorporate them into arm’s length transactions. Further research is needed to identify what heuristics work best in the data valuation context, as well as how to encourage more transparent and comparable pricing in burgeoning data markets worldwide.

96 S. Cal. L. Rev. 1545

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Jordan Barry*

* John B. Milliken Professor of Law and Taxation, USC Gould School of Law.

D. Daniel Sokol†

† Carolyn Craig Franklin Chair in Law, Professor of Law and Business, USC Gould School of Law and USC Marshall School of Business, and Senior Advisor, White & Case LLP.

The Bankruptcy Court as Crypto Market Regulator

April 2024April 2024Yesha Yadav* & Robert J. Stark†

In the second half of 2022, several large and systemically important cryptocurrency firms, such as BlockFi, Celsius, FTX, and Voyager, collapsed into bankruptcy. Their sudden implosion can be attributed, at least in part, to a scant pre-existing framework for oversight, allowing firms to engage in runaway risk-taking, exuberant opportunism, and, in some cases, outright fraud. Bankruptcy courts adjudicating these cases found themselves in a strange role: serving as a sort of proxy overseer for a maturing cryptocurrency industry, and forced into doing some of the work historically entrusted to regulatory agencies like the SEC, CFTC, and Fed. This Article explores the implications of bankruptcy courts being drafted into this kind of quasi-regulatory service. We observe that bankruptcy’s intervention comes with numerous payoffs, given that Chapter 11’s end-goals often align with traditional regulatory objectives. Indeed, by case necessity, bankruptcy courts have overseen broad and detailed reporting from some of the industry’s darkest corners, rendered decisions that likely will have lasting impact on customer protection, directed regulatory attention to particular points of public vulnerability, and afforded opportunity for regulatory agencies to advance their policy initiatives. Nevertheless, we also observe that bankruptcy courts are inadequate proxies for administrative, technocratic oversight. Focused mainly on the debtor’s fate, the Bankruptcy Code is ill-equipped to address, in a prophylactic way, system-wide risks in crypto markets. Even disclosure––a foundational regulatory tool––works idiosyncratically when delivered in the bankruptcy context, intended to inform the debtor’s stakeholders in furtherance of bankruptcy-specific imperatives, rather than to facilitate knowledgeable investing by the general public. Bankruptcy courts are, moreover, statutorily constrained in ways that lack the mission, modalities, and mechanisms to protect an industry and its participants. As we show here, even as bankruptcy courts have stepped up to do their work, their role in overseeing crypto bankruptcies firmly establishes a paramount need for comprehensive regulation tailored for the digital asset space.

INTRODUCTION

The collapse of the FTX cryptocurrency exchange in November 2022 was a pivotal moment for the digital asset industry. The company’s sudden implosion triggered billions in economic damage across the sector, as well as immeasurable personal pain for millions of everyday customers.¹

Prior to its failure, FTX had been one of crypto’s brightest stars, serving as a leading trading hub for digital assets, offering a panoply of sophisticated financial products, and boasting a (supposedly) enviable balance sheet.² Just one year after its founding in 2019, the company was hosting $385 billion in annual trading volume.³ The following year, it reported five million customers worldwide, more than $1 billion in revenue, and almost $275 million in earnings.⁴ By January 2022, FTX was valued at $32 billion.⁵ The company had also been absorbed into popular culture, helping to demystify digital assets for everyday Americans: the FTX brand was emblazoned across the Miami Heat’s basketball stadium; it was endorsed by celebrities like Tom Brady and Larry David, including a memorable advertisement aired during the 2022 Superbowl; and, Sam Bankman-Fried, FTX’s once-wunderkind CEO, became known for contributing lavishly to political campaigns and marketing himself as the legitimizing, ethical face of crypto.⁶ In fewer than four years, FTX had become big, powerful, and ubiquitous––bridging Wall Street, Main Street, and the nation’s capital to a brand new crypto marketplace––which too had become far too big, powerful, and ubiquitous to ignore.⁷

But, in November 2022, FTX was outed as a possible fraud, suspected of grossly misrepresenting its enterprise value and misusing customer deposits.⁸ Within weeks, Bankman-Fried was in handcuffs,⁹ other FTX executives were cutting plea deals,¹⁰ and the company was in bankruptcy.¹¹ The resulting Chapter 11 case is sweeping, both in scale and complexity, spanning over 130 entities worldwide, with total value estimates ranging anywhere from $10 to $50 billion.¹² The administrative fee burn has been commensurately immense, with the debtor’s bankruptcy professionals seeking over $200 million in fees for the initial six months of work.¹³

Intriguingly, the FTX story is not unique.¹⁴ The company’s meteoric rise and sudden descent tracks that of other crypto behemoths. Firms like BlockFi, Celsius Network, Core Scientific, Genesis Global, Three Arrows Capital, and Voyager Digital each found themselves intermediating billions in crypto assets only a few years after launch and, like FTX, imploding in the wake of a sharp market downturn. Several major crypto bankruptcies have also generated substantial allegations of executive wrongdoing, and those allegations overlap, reflecting somewhat repeating patterns of alleged customer deception and sloppy safeguarding of customer assets.¹⁵

None of this should be terribly surprising. The crypto market has, through its evolution, lacked a systematic regulatory framework to constrain excessive risk-taking, interconnection, and propensities for predation against customers.¹⁶ This has meant, for example, a lack of vetted, mandatory public disclosure about the business dealings of some of its most significant enterprises, as well as their corporate governance and risk management practices.¹⁷ Nor has regulation imposed comprehensive standards for protecting customer assets.¹⁸ It has thus failed to speak on how the market should ensure the overall safety and soundness of crypto firms––and, importantly, what procedures crypto businesses need to follow in order to legally insulate the value of customer assets against instances of theft, hacks, and firm bankruptcy.¹⁹ This relatively threadbare regulatory environment has afforded considerable space for firms to take excessive financial risks or institutionalize problematic practices (e.g., opaque governance), with predictably costly consequences. This has left bankruptcy courts to become, oddly, the frontline responders–– tasked with cleaning up the fallout by imposing their jurisprudence onto an otherwise lightly governed crypto marketplace.

This Article shows that, by dint of historical happenstance, bankruptcy law has been required to partially fill an administrative void and to function in an almost quasi-regulatory capacity. Several bankruptcy courts in New York, Delaware, and New Jersey have come to simultaneously oversee what is, collectively, a sort of grand public inquest into crypto market infrastructure and operations, surveying a wide spectrum of industry-specific transactions, practices, and methods of corporate decision-making. These courts have also decided issues of first impression that will likely leave a lasting impact on the maturing crypto industry (e.g., modified terms of service).²⁰ The courts have been doing their work in advance of a mainstay framework for regulating cryptocurrency markets, driven by case imperatives to perform certain functions commonly entrusted to financial supervisors like the Securities and Exchange Commission, the Commodity Futures Trading Commission, and the Federal Reserve.²¹

In forwarding this argument, this Article moves to examine the implications of bankruptcy law and its courts being drafted into quasi-regulatory service. It makes three points. First, we observe that bankruptcy has stepped into an arena where financial regulators have struggled to craft a system of rules and standards, applying its own principles and processes to the messy task of preserving and allocating economic value. In many respects, crypto represents an inherently complicated challenge for U.S. financial regulation, given the industry’s extraterritorial nature, fast-moving technology, and originating anti-government spirit.²² But, even as the likes of FTX are far from the first crypto players to fail,²³ the scale of alleged wrongdoing and magnitude of damage caused by 2022’s “crypto winter”²⁴ have laid bare the significance of sparse regulation and deepened the strains experienced by the New Deal administrative apparatus in policing the digital asset space.²⁵ This has left the bankruptcy system charged with, among other things, calculating the economic costs of regulatory failure and, where possible, developing mechanisms to safeguard and redistribute enterprise value within otherwise under-protected crypto markets.

Second, we show that bankruptcy law offers a number of advantages when its courts are, by default, performing traditional regulatory functions. By its very design, bankruptcy involves a system of rules that advance certain core regulatory objectives. For example, Chapter 11 is demanding when it comes to disclosure, a phenomenon highlighted by the production of startling revelations across various crypto Chapter 11 proceedings (e.g., FTX, Celsius, Voyager, and BlockFi).²⁶ Chapter 11’s adversary process typically divulges more as the case unfolds. And, in bankruptcies involving particularly troubling facts, the court may compel the appointment of an examiner to deliver a “tell-all” report, as it did in two crypto cases (Cred and Celsius)²⁷ and is poised to do in FTX.²⁸ This emphasis on disclosure can meaningfully promote management accountability and, in turn, help ward away bad C-Suite behavior. In the Celsius case, for instance, the 689-page examiner’s report presented a damning account of the company’s historical business practices.²⁹ The report presaged, and likely contributed to, the Celsius CEO’s eventual indictment and arrest, which occurred only a few months after the report’s publication.³⁰

Bankruptcy disputes also deliver poignant teaching moments for government overseers and the wider public. For instance, a value allocation contest in the Celsius bankruptcy––pitting depositors in interest-bearing accounts against depositors in “wallet” accounts––revealed just how fragile customer ownership rights can be when deposited crypto-value exists in digital and legally ambiguous form.³¹ Customers came to learn that, contrary to marketing promises,³² the cryptocurrency ceased being legally “theirs” upon deposit in interest-bearing accounts. That is, customers were deemed to be merely unsecured creditors in the bankruptcy case, left to fight for scraps near the bottom of the priority ladder.³³ The bankruptcy court, in so ruling, not only resolved a critical case issue, it also delivered a hard truth to crypto customers: entrusting savings to an unregulated crypto exchange or “bank” comes with serious risks, given that these companies are not well policed for fraud and that customer savings lack conventional protective mechanisms, like federal deposit insurance.³⁴ Such lessons can be unsparing, yet also clarifying about the economic and legal vulnerabilities faced by crypto customers––who, en masse, were tempted by tantalizing marketing promises but ultimately found themselves exposed to inherently complex, opaque legal and economic risks.³⁵ By highlighting the traps, bankruptcy courts direct agency attention to acute public vulnerabilities, hopefully motivating regulators to develop the kind of customer protections that have long existed in more traditional marketplaces (e.g., securities or commodities markets).³⁶

As a concluding observation on this point, we show how bankruptcy represents a forum where regulatory agencies can press specific policy objectives in advance of a new statutory framework and without facing the usual set of political/rulemaking constraints and ramifications. Regulators have some leeway to inject themselves into bankruptcy proceedings, promoting an agency’s policy priorities.³⁷ The SEC and the federal government, for example, intervened in Voyager’s Chapter 11 case to object to its proposed sale to Binance.US, the American affiliate of Binance––the world’s largest crypto exchange, by volume.³⁸ The government contended that the proposed Chapter 11 sale came with serious regulatory problems, suggesting that Binance.US may not be a fully law abiding corporate citizen and that distributions to Voyager creditors (via Binance.US) might violate securities laws.³⁹ The government’s arguments floundered in court,⁴⁰ but its highly public attack effectively terminated the transaction.⁴¹ This case study illustrates how agencies can, with efficiency, produce regulatory impact when the target of their action falls under the bankruptcy court’s stewardship.

Nevertheless, in our third contribution, we observe that reliance on bankruptcy courts to perform regulatory functions comes with serious shortcomings. Bankruptcy courts are tribunals of limited jurisdiction, and their powers are localized to the specific debtor and its stakeholders, not the public welfare more generally.⁴² They are, in turn, intended to work in tandem with functioning regulatory arms of government; they are not supposed to assume their oversight responsibilities.⁴³ These courts are particularly ill-equipped to address risks arising from an interconnected and multifaceted financial market, especially in a prophylactic way.⁴⁴ Stated differently, corporate bankruptcy is not structured to expressly entertain regulatory imperatives, like stopping financial calamity before it happens or ensuring that a firm’s distress does not trigger systemic contagion within the wider market.⁴⁵

Further, Chapter 11’s legal and normative rules––focused on maximizing each debtor’s distributable value, allocating that value among stakeholders, and where possible rehabilitating the broken business––are not friendly to outsiders, even government outsiders seeking to advance public policy aims.⁴⁶ Competition between economic and regulatory agendas can, in fact, lead to value-deteriorating outcomes, such as dooming Voyager’s sale to Binance.US, contrary to bankruptcy’s primary mission. Even concerning matters of disclosure, the objective is case-specific (e.g., maximizing and allocating estate value) and often strategic in nature (e.g., the debtor’s desire to remain in possession of estate assets), not to obviate risk in the industry generally.⁴⁷ In some cases, the court may not favor augmented public disclosure if doing so may be prohibitively costly or where greater public disclosure threatens an orderly Chapter 11 process.⁴⁸ This may explain why examiner reports were commissioned in the Cred and Celsius cases, but not in the FTX case (that is, until compelled by the Third Circuit Court of Appeals).⁴⁹ Stated simply, even as bankruptcy is (by case necessity) doing important regulatory work, it is far from its natural functionality and is an inherently inadequate substitute for administrative agencies whose mandates include establishing a set of robust, lasting, and standardized rules that protect marketplaces both in peacetime and in crisis.

This Article proceeds as follows. Part I describes the cryptocurrency ecosystem and the challenges of establishing regulatory perimeters for this emerging asset class. Even though regulators have struggled to develop rules-of-the-road for the digital asset industry, this Part highlights some key risks (e.g., systemic risk, information deficits, and user vulnerability) that are commonly cited to justify the application of traditional financial regulation. Part II explains how Chapter 11 has been drafted into quasi-regulatory service to help clean up the mess enabled by crypto’s sparse regulatory environment. This Part illustrates how bankruptcy court oversight has generated a slew of benefits, with the potential to promote insight, expertise, clarity, and good governance. Part III explores the fuller implications of bankruptcy serving quasi-regulatory functions. It shows that, despite all their good and hard work, bankruptcy judges are imperfect overseers for the crypto marketplace. Not only do they lack the statutory directive and powers to address market risks, their decision-making is further limited by the estate-specific focus of bankruptcy’s adversary process, the case-specific nature of bankruptcy disclosures, as well as general inexperience in addressing complex, esoteric, and systemic financial risks––especially risks arising outside prevailing regulatory frameworks. Relying on bankruptcy courts for quasi-regulatory assistance, instead of technocratic rulemaking, is thus profoundly problematic, as Part IV concludes.

I. CRYPTO’S MISSING REGULATORS

Despite acquiring popular appeal and developing a sophisticated array of financial services and products, the market for cryptocurrencies has come of age largely outside of a comprehensive system of regulation.⁵⁰

papers.cfm?abstract_id=4405036 [https://perma.cc/S8C4-TN4B] (discussing the legality of “regulation by enforcement” and exploring why agencies rely on this approach, alongside the trade-offs of doing so, especially in the context of using litigation to test novel/ambitious applications of law to innovation). There are many reasons to explain this historical gap in oversight. For one, the asset class is legally complex, with agencies, most notably the SEC and CFTC, publicly at odds over which of them has authority.⁵¹ In other words, jurisdictional wrangling is underway over whether some or all crypto-assets ought to be legally defined as securities (the purview of the SEC) or commodities (the purview of the CFTC)––this determination being critical to situating crypto within existing bodies of securities and commodities regulation. Additionally, digital assets are far from monolithic in their design, with different types of tokens implicating different kinds of risks and entitlements: more decentralized and volatile cryptocurrencies like Bitcoin, for example, operate distinctively from so-called stablecoins, digital assets typically attached to an identifiable issuer and designed to maintain a steady one-token-to-one-dollar correspondence.⁵² Even while navigating such definitional challenges, digital assets raise intriguing considerations for policymakers looking to calibrate their supervisory toolkit, such as: how should domestic national authorities oversee risks arising across decentralized, globally dispersed blockchains; and, do existing administrative processes suffice, or might regulators benefit from crafting tailored solutions to match novel attributes of the asset class (e.g., decentralization)?⁵³

This Part has two objectives. First, it summarizes key features of crypto markets to highlight some of its distinguishing features and risks. Second, it describes fundamental theories of financial regulation that generally explain and justify its application (e.g., to protect financial stability and enhance consumer welfare). This Part shows that crypto markets exhibit the kinds of risks that fall under usual rationales justifying the application of financial regulation. We observe, however, that the crypto market has evolved largely outside of a dedicated system of financial regulation, leaving it intrinsically vulnerable to costly externalities and failure.

A. Some Key Features of Crypto Market Structure

Broadly, the cryptocurrency market is made up of three major parts: (1) at its most fundamental, it originates within globally dispersed computer networks that work to produce a “distributed ledger” (or blockchain) recording the transactions submitted to and verified by each network; these automated networks often mint digital tokens/coins as a means of rewarding users that work to maintain the system’s integrity;⁵⁴

click&module=RelatedLinks&pgtype=Article [https://perma.cc/P7DL-YD3C]; Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System, Bitcoin.org 2–4, https://bitcoin.org/bitcoin.pdf [https://perma.cc/HFX5-DAWH]. (2) various types of more centralized firms like cryptocurrency exchanges and quasi-banks that intermediate access to cryptocurrency assets (e.g., coins) and offer related financial services and products;⁵⁵ and (3) a slate of digital applications aiming to offer financial products in a more decentralized manner, harnessing the verification capacity of blockchain networks. These applications derive their utility by running automated programs (colloquially, “smart” contracts), rather than relying on centralized firms like exchanges or banks to provide an intermediary service.⁵⁶ A detailed discussion of each of these component parts is outside the scope of this Article. However, the summary below outlines some of their defining characteristics (and risks).

1. The Building Blocks: Chains, Coins, and Ledgers

The origin story of modern-day cryptocurrencies emerges from the Bitcoin white paper, written by Satoshi Nakamoto, that sets out a vision for an entirely digital payments network capable of operating globally on a person-to-person basis.⁵⁷ Its radicalism lies in envisioning the creation of a payments system that does not look to centralized intermediaries like banks to validate flows of money, nor does it presuppose the power of the state to enforce bargains or maintain the integrity of the system. Instead, it conceptualizes an infrastructure for making payments that depends on a network of computers, running a common protocol, to verify and record transactions. In place of a bank checking key details (e.g., whether the sender has enough money in his account) or regulators monitoring transactions, these tasks are approximated by the application of computerized code. By running the Bitcoin protocol, participating networks of computers (“nodes”) apply verification rules that examine incoming transactions to check whether they conform to the protocol’s standards of accuracy and integrity. Once nodes agree, by consensus, that a transaction is valid, it can be accepted, processed, and written into the protocol’s “ledger.” Transactions are batched into blocks and presented for validation, a practice that has given rise to the nomenclature of the “blockchain.” Unlike a bank payment, which remains confidential between the parties and the bank, the ledger is public and verifiable. This transparency is supposed to provide a mechanism whereby external scrutiny constitutes a means of interrogating whether the system is running in a safe and trusted way (e.g., that the same coins are not being sent twice or double spent).⁵⁸ Once accepted and validated, transactions are generally irreversible. This aspiration for immutability provides a proxy for certainty and reliability within the system, where it is not subject to idiosyncratic changes by one or another player.⁵⁹

2021), https://medium.com/coinmonks/finality-in-bitcoin-f82890bf39b7 [https://perma.cc/ZHD7-NJLB] (noting that finality on the Bitcoin blockchain is probabilistic).

The “coins” underlying the Bitcoin blockchain speak to digital rewards given to those that work to safeguard the network. Within Bitcoin, the dispersed network of nodes is vulnerable to the risk that a node (or a group) turns malicious––seeking to disrupt its function or to use it for its own benefit (e.g., by only proposing transactions that are sent to accounts connected to operators of a malicious node).⁶⁰ To secure the network’s integrity, the blockchain looks to a system of “protectors” tasked with looking into the pool of transactions entering the system and picking those for approval that should meet the protocol’s standards.⁶¹

The network creates incentives for participants to become “protectors” by awarding “coins” to those that succeed.⁶² In the Bitcoin network, “protectors” can also collect any discretionary fees that users might attach to a transaction.⁶³ Bitcoin looks to a “proof of work” validation mechanism, where network protectors––or “miners”–– competitively deploy extensive computing power to solve a mathematical challenge. A winning miner then builds a block of transactions for the network to approve and receives new Bitcoin (and fees) for their effort.⁶⁴ The “proof of stake” validation mechanism is also common across major blockchains (e.g., Ethereum). Broadly, in a proof-of-stake blockchain, those that already have a number of coins in the system can win the chance to build the block and collect more coins (and fees) as rewards.⁶⁵

While this description is highly simplified, it serves to highlight some legal puzzles confronting regulators. Major blockchain networks, like Bitcoin or Ethereum, are global and open to anyone, anywhere, willing to download and run the relevant protocol on their computer.⁶⁶ Additionally, users do not give their real-world names in order to join, as they would when using a bank. Instead, users are known and accounted for on a blockchain by their “public keys,” a form of pseudonymous public handle, that links to a private password known to the user.⁶⁷ If a user loses her password, she cannot access her account or make and receive payments, meaning that value on the network is lost.

This globally distributed system, designed to operate outside of traditional private and public intermediation, presents unusual regulatory conundrums. How should U.S. regulators construct a system of rules capable of applying to an automated cross-border network that aims to avoid centralized governance and control altogether? What tools can regulation deploy to overcome information gaps, address potential misconduct, or costly fragilities existing within a blockchain’s operation?⁶⁸ And, what legal classification ought to apply to coins minted on blockchains: do they constitute securities or commodities under conventional stipulations of federal law, extending existing regimes to crypto assets; or, do they fall under an entirely different, more tailored legal category?

As such, while market regulation is usually equipped to accommodate innovation, crypto assets have come to pose a significant challenge.⁶⁹ For example, the definition of innovative kinds of security––as covered by the concept of an “investment contract” in the Securities Act of 1933––was elaborated by the 1946 case of SEC vs. Howey. Per Howey, a security is a claim that represents: (1) an investment of money; (2) in a common enterprise; (3) for profit; and (4) through the effort of others, where those that promote an investment exercise managerial control over any scheme.⁷⁰ A discussion of the jurisprudence born out of Howey is outside the scope of this Article. But concepts like “common enterprise” or “through the efforts of others” signal the difficulties confronting policymakers when seeking to apply conventional precepts to cryptocurrencies and their blockchains. Emphasis on miners/stakers extracting higher returns relative to other network participants, for example, sits uneasily with long-rooted notions of a horizontal common enterprise. The task of identifying promoters with managerial powers strains in the context of public blockchains that seek to structure themselves in ways that look to be deliberately diffuse from a governance standpoint and where self-help constitutes a basic rule-of-thumb.⁷¹ With these thorny definitional questions key to establishing how regulators legally assert authority in the first place, it is not surprising that debates on the issue have become contentious as between regulators themselves, each seeking to jostle for their agency to have primary jurisdiction.⁷² This administrative squabbling has arguably played an important part in delaying the production of a comprehensive system of rulemaking for digital asset markets, leaving them to evolve largely outside of everyday administrative oversight.

2. Centralized Finance in Crypto Markets

As much as decentralization is popularly perceived as the distinguishing feature of cryptocurrencies, the everyday experience of digital asset markets for many is often intermediated through “centralized finance.” Engaging with sophisticated blockchains, setting up public keys, protecting their private passwords, or learning technical specifics of the computing involved can act as a barrier to entry for the average person looking to enter the crypto market. Finding a party through which to buy and sell crypto on a blockchain might similarly be impractical for those unfamiliar or uncomfortable with searching online for brokers.

So-called “centralized finance” firms have emerged as essential conduits for mitigating these difficulties and increasing crypto’s appeal for the mainstream. Exchanges, in particular, have established themselves as organizing architecture for the crypto markets, bringing together volumes of institutional and retail users, developing a variety of financial products, and helping to popularize the asset class for everyday people.⁷³ By connecting to users through smartphone apps, advertising on prime time television slots (e.g., the Superbowl), and using top-flight celebrity endorsements, crypto exchanges like Coinbase, Binance, Kraken, and infamously, FTX have established a prominent position both within crypto as well as financial markets more broadly.⁷⁴

Exchanges deploy established market structure tools to connect cryptocurrency buyers and sellers. By creating an organized marketplace, users no longer have to worry about seeking out a counterparty privately within an ecosystem of pseudonymous users who could be located anywhere in the world. The need for self-help is also reduced. Centralized firms provide a known point of contact, capable of correcting problems (e.g., hacked accounts), as well as offering users compensation and recourse if they suffer damage.⁷⁵ Unlike public blockchains that demand that their users be capable of looking after their own interests or dealing with the consequences (e.g., irreversible transactions), exchanges offer services to facilitate uptake of cryptocurrency trading (e.g., by offering loans for trading, custody services, or educational resources). By reducing the transaction costs and building avenues for accessible participation, exchanges have introduced everyday users to cryptocurrency markets. Tellingly, leading exchanges were drawing in eye-catching trading volumes during most of 2021––the cryptocurrency market’s boom year. Binance, for example, intermediated around $7.7 trillion in trading over 2021, reportedly generating $20 billion in revenue.⁷⁶ FTX, founded in 2019, saw its valuation grow over 1000% in the course of 2021 to around $1.1 billion, soaring to $32 billion by 2022––before collapsing into insolvency in November 2022 and liqudidation in January 2024.⁷⁷ Even as trading volumes fell sharply with the onset of “crypto winter” and FTX’s failure, crypto exchanges remained financially significant for the digital asset ecosystem. In its first quarter earnings report for 2023, Coinbase reported revenues of $773 million, up 23% from the final quarter of the previous year.⁷⁸ In April 2023, Binance saw sharply reduced activity, losing almost 50% in trading volume, while still recording approximately $287 billion in trading activity for the month.⁷⁹

In addition to exchanges, centralized finance includes firms performing a variety of financial services (e.g., lenders, hedge funds, broker-dealers, and specialist traders). Cryptocurrency deposit/lending and investment firms, in particular, have assumed considerable importance. Crypto quasi-banks, for instance, took in vast sums of customer capital/crypto––offering lucrative interest rates on these deposits––and for a shot time profited handsomely by relending those deposits. Predictably, as the crypto markets suffered a sharp downturn in 2022, these entities were hit especially hard with loan defaults and collapsing collateral prices, pushing several of the more prominent quasi-banks into bankruptcy.⁸⁰

Take Celsius. Founded in 2017, Celsius billed itself as a way for everyday people to “unbank” themselves––meaning, exiting the traditional banking system and putting money into a vehicle that promised depositors tantalizing returns. At its height, Celsius marketed investments that would pay as much as 18% interest on customers’ crypto deposits. Given such dazzling promises, the firm ended up controlling assets of around $20 billion, reaching 1 million or so customers.⁸¹ Its business model relied on putting customer assets into high-yield, high-risk investments. The value of these investments eventually plummeted with the onset of “crypto winter” in May 2022. Owing approximately $4.7 billion to its customers and unable to make good, Celsius filed for Chapter 11 protection.⁸²

Genesis Global, alongside two of its lending subsidiaries, also found itself in Chapter 11 in January 2023. Genesis, too, functioned like a quasi-bank; it took in customer deposits, offering high interest rates, and redeployed those deposits as loans extended to other industry players, like hedge funds.⁸³ With a loan book totaling around $12 billion in 2021, Genesis found itself in a vulnerable position with the onset of “crypto winter”: first, it lent $2.4 billion (partially collateralized) to the defunct crypto hedge fund, Three Arrows Capital, that collapsed in Spring 2022; and, second, it lent hundreds of millions of dollars to FTX’s affiliated hedge fund, Alameda Research, which imploded a few months later.⁸⁴ The mounting losses, alongside larger struggles in the crypto market, contributed to Genesis entering into Chapter 11.⁸⁵

Centralized firms have come to exercise enormous economic influence within the cryptocurrency marketplace.⁸⁶ As exemplified by the likes of FTX, Celsius, and Genesis, centralized firms routinely hold deep pools of crypto capital and convene a crowded and diverse range of stakeholders within their institution.⁸⁷ This capacity to build scale and complexity within a purportedly decentralized marketplace is hardly accidental. As noted above, centralized firms often offer a range of services and conveniences that bypass many of the novel and technically quirky facets of crypto market structure.⁸⁸

The far-reaching pull of centralized platforms within crypto has given rise to sources of vulnerability, creating risk for everyday users and market integrity. For example, platforms routinely require customers to transmit the password to their crypto “wallets” to the venue.⁸⁹ Practically speaking, by taking custody of user passwords (or “keys”), the venue is able to move the user’s crypto into accounts (i.e., the “wallets”) that it (the platform) controls, meaning that assets can be pooled and placed by the venue into various onward investments. With the platform holding the customer’s passwords, users confront the risk that they lose control of––and, indeed, potentially even legal title to––their own assets.⁹⁰ Because crypto’s foundational design assumes that those that hold the password to an account constitute its owners, a platform’s custodianship can leave customers suddenly bereft should the platform fail or end up losing the passwords for whatever reason (e.g., a theft or fraud).⁹¹

From a broader structural standpoint, the ability of centralized firms to pool and deploy capital has resulted in the creation of fragile interconnections between various types of market participants. Described above, exchanges and firms like Celsius and Genesis have emerged as prolific investors, putting customer capital into various crypto ventures. Such investments have taken the form of loans––where funds have made their way into crypto-lending arrangements promising (sometimes) double-digit interest rates (e.g., Celsius). BlockFi, for example, found itself in Chapter 11 after making bad loans to failed hedge funds, Three Arrows and Alameda.⁹² But, they can also comprise equity investments. That is, platforms put capital into the riskiest slice of the corporate balance sheet in a bid to secure potentially unlimited upside should the venture succeed. Exchanges, for example, have emerged as active investors in start-ups. FTX, notably, collapsed holding an eclectic balance sheet comprising crypto as well as more mainstream equity investments, reportedly worth around five billion dollars at the time of its failure.⁹³ Seen as a whole, centralized finance firms have shown themselves to be economic lynchpins of the crypto ecosystem, creating close financial linkages between themselves, their customers, as well as any number of stakeholders through often opaque, complex investments. Such relationships have resulted in regulators confronting a broad tangle of interconnected exposures, where risks from one entity can be transmitted to other firms, and ultimately to everyday customers, resulting in potentially heavy economic fallout whose permutations are not understood ex ante and cannot be easily remedied ex post.

B. Rationales for Regulation in Crypto and Finance

Though crypto markets have evolved mostly outside of the regulatory perimeter, they showcase a number of features that have traditionally proven persuasive in anchoring oversight for financial markets: (1) vulnerability to systemic risks; (2) information asymmetries; and (3) customer and investor protection. While a full discussion examining theoretical grounds justifying financial regulation is outside the scope of this Article, the observations below demonstrate that the relative absence of oversight in crypto markets represents a costly gap out-of-step with established paradigms in financial market design.

1. Mitigating Systemic Risks

Traditional financial regulation is often justified by reference to the importance of reducing “systemic” risk.⁹⁴ The task of defining systemic risk, in practice, has proven to be notoriously slippery.⁹⁵

153927406281.pdf [https://perma.cc/MH65-GS8B] (noting the role of interconnectedness in the definition of systemic risks); Morgan Ricks, The Money Problem: Rethinking Financial Regulation, 52–77 (2016) (highlighting short-term run-risks within the unregulated money market sector as a key indicator of systemic risks, justifying financial regulation). Particularly in the shadow of the 2008 financial crisis, the capacious intervention of the federal government to backstop the safety of financial markets pointed to a concept whose parameters might only become clear ex post, when failure illuminates sources of previously unknown but intolerably high risks within the marketplace. Even as banking regulators invoked an emergency “systemic risk” exception to fully protect deposits at two fairly large but relatively niche banks in March 2023 (Silicon Valley Bank and Signature Bank), the ensuing debate surrounding the need and propriety of such interventions has only served to underscore the tricky boundaries of conceptualizing systemic risk and what regulators ought to do about controlling it.⁹⁶

Notwithstanding these definitional difficulties, containing systemic fallout has long been a critical objective of financial regulation. Broadly seen, it references two core scenarios. The first scenario is one in which a firm’s behavior leads it to take risks that result in it creating dangers that can spread far beyond its own four walls. In other words, a risky, failing firm lacks the resources to pay for its own behavior, forcing others to bear the losses, risking collapse themselves. The second scenario is where a shock to the market (e.g., a pandemic) causes similarly situated firms to face potential distress, resulting in crisis impacting multiple firms simultaneously.⁹⁷ Simplifying things, certain kinds of firms have traditionally been viewed as being especially susceptible to failure, with the potential to trigger a larger crisis. Specifically, firms vulnerable to sudden runs––for example, they owe money short-term and may have invested it in longer-term ventures––can face catastrophe if creditors seek to take out their money all at once. This can force a firm to sell its longer-term investments at distressed prices, plunging its balance sheet into the red, as assets end up fetching less than the money it owes. Conventionally, banks represent the quintessential purveyors of such run-risk. Their depositors constitute short-term (on-demand) creditors, while their assets typically take the form of longer-term loans. But, exemplified by the wide-ranging rescue of institutions like money market mutual funds in 2008, other types of firms and markets can become vulnerable to sudden crises, setting-off the possible specter of systemic collapse.⁹⁸

Regulation normally wields a range of tools to prevent such crises from occurring, as well as to respond to them when they do. Ex ante levers can include, for example, mandatory requirements on vulnerable firms to maintain buffers of high-quality assets that make a firm safer and less likely to end up without money.⁹⁹ Firms might be subject to regular “stress tests,” designed to interrogate how well they might withstand a sudden shock.¹⁰⁰ Federal insurance might prevent customers from panicking and rushing for the exits, where the state stands behind the promises made by a financial firm. U.S. bank accounts, notably, are protected by insurance that promises to cover up to $250,000 worth of deposits.¹⁰¹ Expert monitoring by regulators can help spot and punish the kinds of risky behaviors that might lead to a crisis and loss of customer confidence.¹⁰² In turn, ex post tools can also mitigate harm as and when they arise. Regulators might step in with emergency loans. The Federal Reserve, for instance, offers banks a “lender of last resort” facility, providing bridge lending during difficult times.¹⁰³ In extreme cases, liquidity support can take the form of federal facilities set up with the specific purpose of prioritizing systemic stability, even if such rescues protect firms that otherwise deserve to fail.¹⁰⁴ Or, if there is no prospect of a rescue, a specialist insolvency regime can step in to wind down a failing institution before its collapse can contaminate the rest of the market. In the context of banking, the Federal Deposit Insurance Corporation¹⁰⁵ operates a resolution regime for failed banks, designed to ensure that their loans and deposits can be transferred to viable firms without lengthy bankruptcy regimes that might leave depositors in limbo.¹⁰⁶

Crypto markets have shown themselves capable of inhabiting an ecosystem where systemic risks can manifest in a number of ways. First, as highlighted above, it is home to a number of centralized firms that constitute singularly important points of failure. Crucially, these firms have tended to become interconnected to a web of stakeholders, creating transmission pathways for losses to flow from one institution to another. FTX offers perhaps the most compelling example of such entanglement, where its sudden failure caused firms like BlockFi and Genesis also to seek bankruptcy protection.¹⁰⁷ Several traders failed too, as they were unable to retrieve their deposits from the FTX the platform.¹⁰⁸

Second, major centralized firms have shown themselves exposed to the costs of sudden runs, where customers seek to retrieve their funds en masse resulting in the platform suffering a cash crunch. FTX is again case in point, experiencing a wave of redemption requests from fleeing customers, eventually causing the firm to pause withdrawals.¹⁰⁹ Celsius, too, is instructive. According to a study by the Federal Reserve Bank of Chicago, 35% of all withdrawals in June 2022 (just before Celsius filed for bankruptcy protection) came from relatively wealthier depositors–– customers each with crypto worth more than $1 million in their accounts.¹¹⁰ Those holding $500,000 ended up being the fastest to retrieve their money. Put differently, larger institutional customers, likely possessing financial sophistication and reasonably roomy balance sheets, were among the most liable to trigger a panic. And, by dint of their size and resources, their private instincts to run resulted in a cost on those that could not adjust their behavior as quickly (i.e., less wealthy customers).¹¹¹

Unlike traditional markets, however, exposure to run-risk has come without the usual ex ante and ex post levers that might mitigate panic and control the costs of fallout. Even as a swath of crypto market participants––retail as well as institutional actors––faced the prospect of devastating losses, they lacked recourse to protections taken for granted in traditional financial markets (e.g., federal deposit insurance).

2. Addressing Information Gaps

A second key objective of financial regulation lies in addressing information gaps and the costs that they pose.¹¹² This involves ensuring that regulatory supervisors as well as market participants can acquire insight about the riskiness of claims and assets alongside an understanding of the institutions that operate within the perimeters of financial and capital markets. In seeking to intermediate the informational environment, policy can also seek to create ways in which thorough due diligence becomes less important, for example, where the claims being issued are presumed to be so safe that detailed investigation would be a waste of time and money.¹¹³ Broadly seen, regulation can work to provide tools and create incentives for reducing information costs, improving the accuracy by which risk is priced. It can help firms and investors protect themselves by equipping them with insight as well as offer spaces for creating informationally-insensitive claims, contracts that do not need a great deal of due diligence owing to their perceived safety, connecting parties in situations that might otherwise showcase complexity, and unknowable risks.¹¹⁴

perma.cc/2ZHA-GLDT] (highlighting varying interpretations of the notion of information insensitivity). A full discussion of this interplay between information deficits in markets and regulation is outside the scope of this Article. A few examples, however, serve to underscore how foundational this relationship is for shaping key aspects of market design.

First, regulation can help ensure that the marketplace enjoys a baseline level of insight about key claims and assets. When a company issues equity or debt in public markets, the worth of the promised cash flows emerges through an understanding of the capacity of the firm to deliver on its promises. At a very general level, whether and how it can do so constitutes a function of many aspects of its enterprise, such as its organization, governance, business model, and industry. This multiplicity of factors helps shape the kinds of results that a firm can achieve and, ultimately, what kinds of future cash flows investors and other stakeholders might expect to receive.¹¹⁵

Regulation has stepped in to overcome some of the frictions that might cause actors to withhold information about their firm. As modeled by Sanford Grossman and Oliver Hart, disclosure can be excessively costly for a firm, creating a disincentive for revelation. It also might expose a firm to outside scrutiny, give away competitive secrets, or highlight managerial failures.¹¹⁶ At the same time, where the firm constitutes the most knowledgeable repository of its own activities, the chances that single investors (or even regulators) might be able to obtain information efficiently about and from it are slim, if not outright impossible. Everyday investors will not be able to muster the resources, or obtain the access needed, to acquire key details of the risks governing their claim. Even deep-pocketed institutional investors may be loath to share the fruits of their labor, forcing others to replicate the same research and analysis that might still be incomplete.¹¹⁷

Where firms have few incentives to distribute information freely, regulation can mandate full and honest disclosure. In seeking to punish those that fail to disclose or lie, regulation modifies the incentives against putting information into the marketplace. Such broad and freely available distribution of prized information affords all investors access to this knowledge, reducing the pressure on their own pocketbooks and minimizing the risks of duplicative investigation. Rather, investors might focus on honing the quality of their analysis, making money, or deriving some other gain by bringing new interpretations of the disclosures to the fore.¹¹⁸ In this way, investors can learn about the kinds of risks that they are carrying in a relatively systematic and thorough manner. They can protect themselves by charging more for their capital, taking other precautions (e.g., putting only so much at risk as they are willing to lose), and ensuring that their prior biases and expectations are better kept in check.¹¹⁹

In addition to ensuring information about claims, regulation provides ways to increase understanding about entities within the marketplace. Regulators benefit from knowing whether entities that are active within financial markets can do so safely and have the resources to fulfill their obligations to stakeholders (including customers). This also entails supervisors knowing that firms can look after themselves, with sufficient and accessible resources to pay creditors and to reduce the systemic risks they create for others.¹²⁰ In place of enabling a free-for-all, allowing anyone to set-up shop, regulation imposes stipulations designed to procure detailed information from a firm. For example, eligibility criteria demand that those seeking to do business satisfy entry conditions concerning internal corporate governance, balance sheet capacity, and customer protection.¹²¹ Supervisors can conduct examinations on a regular basis to assure themselves that the firm conforms to expected rules and standards. Enforcement actions offer regulators and others a mechanism to learn more about an entity generating suspicion (e.g., via discovery).

Finally, regulation can control information gathering and dissemination to account for some of the costs and effects of disclosure. In particular, regulation can determine who gets data, how fully, at what speeds, and at what time intervals. Even where transparency constitutes a valuable policy goal, full openness to the inner workings of complex institutions can, in some situations, constitute a risk in itself. For example, regulators are typically careful about how much information is publicly disclosed about banks (e.g., through stress tests or supervisions).¹²² Revelations about a bank’s balance sheet might foster panic where information ends up interpreted by the public as presaging a collapse, triggering a needless run on the firm.¹²³ Relatedly, developing disclosure regimes can also look to policies in which the goal lies in ensuring that relationships do not have to require detailed disclosure between parties. For example, where money is lent on a very short-term basis and fully collateralized, lenders have less need to invest in uncovering information on a borrower. Instead, this debt becomes more informationally-insensitive, allowing for credit to flow more quickly, with fewer formalities, and still providing for risk mitigation by the terms of the debt agreement.¹²⁴

Limited comprehensive regulation for cryptocurrency markets has thus resulted in a relative paucity of tools for addressing the need to create information about the quality of claims being traded and market participants.¹²⁵ Interestingly, crypto represents a unique mix between the transparent and opaque. On the one hand, it is defined by its reliance on blockchains, which intend to provide the ultimate in transparency––by ensuring that each transaction is readily inspectable¹²⁶––as described above.

On the other hand, crypto’s larger ecosystem is opaque, with critical aspects of its workings taking place without adequate standardization and verifiability. For a start, digital assets themselves can exhibit unknown risks for which even the traditional regulatory system can be a poor match. Crypto inhabits an informationally complex environment from the point of view of its technology. As Chris Brummer, Trevor Kiviat, and Jai Massari observe, crypto combines legacy informational deficits (e.g., about a token issuer’s internal governance) with novel considerations about technological riskiness that conventional regulatory paradigms are ill-suited to match.¹²⁷ Without an applicable and properly tailored regulatory framework, token holders must take on the costs of diligence privately. Even where they can get some help (e.g., through “white papers” that typically launch new crypto ventures), a lack of regulatory vetting for these disclosures can result in limited accountability for those producing them.¹²⁸ Exchanges too might demand information from token issuers seeking to list the asset on their exchange. But, even here, the approach is ad hoc and varies by venue, creating a hodge-podge of regimes for customers to try to follow.¹²⁹

Crypto market regulation also lacks tools to acquire information about key market participants. As noted earlier, exchanges are key pillars within the crypto ecosystem. Notwithstanding this significance, considerable uncertainty exists about their inner governance, the quality of their balance sheets, or their readiness to respond in a crisis. According to a May 2023 Financial Times survey of 21 of the most prominent crypto firms, many refused to supply critical information about their governance, measures for customer protection, and balance sheets––underscoring concerns raised in the wake of “crypto winter” failures about opaque and complex governance structures that pose a risk for stakeholders.¹³⁰ In the absence of express disclosure regimes to stipulate eligibility criteria or supervisory regimes to ensure compliance, certain crypto firms appear to lean heavily on opacity as a part of their business model.¹³¹

3. Protecting Customers and Stakeholders

Perhaps the most straightforward rationale for financial regulation lies in protecting customers and stakeholders.¹³² Investors and financial consumers routinely fall prey to scams, display biases and impulsivity, and open themselves up to losses that can result in enormous personal suffering.¹³³

perma.cc/A9GZ-CNJV] (noting the especial prevalence of investment fraud); Sec. & Exch. Comm’n, Social Media and Investment Fraud––Investor Alert (Aug. 29, 2022), https://www.sec.gov/oiea/investor-alerts-and-bulletins/social-media-and-investment-fraud-investor-alert [https://perma.cc/PK5W-ZKDG] (noting the ways in which social media might lure investors in scams). Beyond safeguarding customers against predation, regulation can also step in to secure financial assets and their integrity. Predictably, where vast pools of customer money are entrusted to an agent (e.g., a fund or bank), there is the risk of misuse, misappropriation, and mismanagement. To counter such “agency costs,” regulation provides a slew of measures to safeguard customer interests and counter the negative incentives of those holding money for others.¹³⁴

Arguably the most consequential for a customer’s everyday peace-of-mind are rules designed to ensure that their assets are safely custodied and accounted for, and, where custody arrangements work, to prevent such assets from being mingled with those of the agent (e.g., a broker) in the event of an agent’s insolvency. Customer protection rules in securities and commodities regulation, for example, set out detailed procedures for ensuring that customer assets are diligently protected.¹³⁵ A variety of measures enable such assurance to be offered through regulation. For example, rules governing brokers of traditional securities and commodities provide that customer assets must be fully segregated, so that there can be no mixing between a broker’s funds and those of the customer.¹³⁶ Additionally, the broker must rigorously track how customer assets are being handled and can only entrust them to reputable custodians. To ensure compliance, firms face examination by regulators and must maintain an appropriate paper-trail.¹³⁷ Firms that fall short risk economic penalties and may suffer reputational damage.¹³⁸ Those risks can extend to supervisors, incentivizing more rigorous policing. When the failed brokerage firm, MF Global, was found to have breached applicable rules for protecting and safekeeping customer assets, its frontline regulator (the Chicago Mercantile Exchange) came under heavy scrutiny¹³⁹ and ultimately paid $130 million to the broker’s customers.¹⁴⁰

Crypto customers are subject to similar risks (e.g., being scammed and seeing their funds stolen or misappropriated) but they do not today enjoy specific protections as part of a regulatory scheme. This leaves crypto customers exposed to a slew of dangers that they have little power to mitigate, while being afforded few practical levers under law to safeguard their interests privately. The costs of this regulatory gap have come into sharp focus, as millions of everyday crypto customers fell victim to a series of high-profile firm failures during 2022’s “crypto winter,” leaving them caught in uncertain and costly bankruptcy proceedings, rather than protecting them from these processes in the first place.

II. BANKRUPTCY IN CRYPTO WINTER

Part I charted the limited federal regulatory landscape for the cryptocurrency industry. Post-pandemic, the crypto-market experienced sharp growth and, as a result, there was a period of time during which the digital asset marketplace was flush with customer money and able to operate freely in the relative shadows outside of a dedicated system of oversight. This created, predictably, room for mischievous C-Suite behavior, where billions in customer deposits could be lured with promises of outsized returns (typically adorned with marketing puffery about corporate integrity, transparency, and investment safety) but without providing customers any real capacity (e.g., through mandated disclosures) to know what was truly happening. A series of catalytic events would bring down large segments of the industry in mid-2022, starting the so-called “crypto winter.” Major Chapter 11 filings followed. But, while bankruptcy is used to cleaning up individual corporate messes, it is not the arm of government usually charged with taming unruly facets of a financial system. But, by necessity, that has become an inadvertent aspect of the work performed by bankruptcy courts in seminal crypto cases, as described in this Part below.

A. A Brief History of Crypto Winter

In May 2022, the Terra/Luna stablecoin ecosystem suffered a surprise crash, wiping out approximately $60 billion in value from digital asset markets.¹⁴¹ The hedge fund Three Arrows Capital held significant investments in Luna and, consequently, was immediately forced into liquidation in the British Virgin Islands.¹⁴² This resulted in the default of around $657 million in unsecured debt Three Arrows owed to Voyager, the crypto quasi-bank and brokerage firm.¹⁴³ As word spread, Voyager became inundated with customer withdrawal requests, prompting it to suspend trading and redemptions¹⁴⁴ A week later, Voyager filed for Chapter 11 protection.¹⁴⁵ Contagion also hit Celsius, another crypto quasi-bank. Celsius too was required to pause customer redemptions and withdrawals, ending up in bankruptcy come mid-July.¹⁴⁶ BlockFi, yet a third large quasi-bank, avoided bankruptcy by tethering itself to FTX, securing emergency financing from the then-powerful exchange.¹⁴⁷

On November 2, 2022, a leading news service dedicated to cryptocurrency, CoinDesk, reported (based on a leaked internal document) that the wealth of FTX’s hedge fund affiliate, Alameda Research, was largely comprised of FTX’s native token, called FTT.¹⁴⁸ This crypto asset was issued by the exchange itself and offered to customers, promising holders a variety of rewards like reduced trading fees, loyalty benefits, and miscellaneous customer services.¹⁴⁹ As the exchange’s popularity had grown, so too had the market value of FTT, even though the token’s intrinsic worth was controlled in key ways by FTX management (e.g., by calibrating the available float).¹⁵⁰ Thus, for purposes of determining FTX’s enterprise value, FTT may be better likened to FTX treasury stock than value independent of the corporate entity itself. ¹⁵¹

Prior to this publication, the public did not know the skewed composition of Alameda’s balance sheet. Once disclosed, the market reacted with fury. Binance, for example, promptly announced it would sell all of its FTT holdings.¹⁵² Watching its enterprise value plummet, FTX immediately offered to sell itself to Binance––which alone seemed financially positioned to catch the company in free-fall.¹⁵³ After some cursory due diligence, Binance passed on the offer,¹⁵⁴ thickening the cloud of suspicion hovering over FTX. Nine days after CoinDesk’s publication, FTX collapsed into bankruptcy.¹⁵⁵ Restructuring specialist John J. Ray III was appointed to succeed Bankman-Fried as CEO, and Ray promptly declared that, in his “40 years of legal and restructuring experience,” he had never seen “such a complete failure of corporate controls and such a complete absence of trustworthy financial information as occurred here.”¹⁵⁶ Bankman-Fried was soon arrested.¹⁵⁷

FTX’s sensational collapse deepened 2022’s “crypto winter.” The token native to Crypto.com, another large exchange, lost $1 billion in market value virtually overnight.¹⁵⁸ BlockFi, facing another round of withdrawal demands, liquidated all of its domestic crypto portfolio and filed for Chapter 11 protection.¹⁵⁹ Core Scientific, one the largest crypto mining firms, also filed for bankruptcy.¹⁶⁰ Genesis, the brokerage firm, lasted outside of bankruptcy only until mid-January 2023,¹⁶¹ as discussed above. Smaller and ancillary crypto companies succumbed as well.¹⁶²

On January 31, 2023, the court-appointed examiner in the Celsius Chapter 11 case filed her final report.¹⁶³ Purportedly, Celsius too operated in a deceitful manner: “In every key respect—from how Celsius described its contract with its customers to the risks it took with their crypto assets—how Celsius ran it [sic] business differed significantly from what Celsius told its customers.”¹⁶⁴ On July 13, 2023, the company’s founder and CEO, Alex Mashinsky, was arrested and charged with seven criminal counts, including securities and wire fraud.¹⁶⁵

The rash of bankruptcies and revelations of customer deception––following patterns that overlap across companies––began infusing popular culture. Late night television hosts turned crypto headlines into crypto punchlines.¹⁶⁶ The FTX logo was removed from the Miami Heat’s stadium.¹⁶⁷ Consumer fraud claims were filed against not only crypto executives but also celebrities that had provided paid endorsements.¹⁶⁸ Charlie Munger, Berkshire Hathaway’s venerable chairman, declared the cryptocurrency market to be “stupid and evil” and that digital assets are only useful to “kidnappers.”¹⁶⁹ Both chambers of Congress began a series of hearings focused on, among other things, what the government should do to rein in the perceived lawlessness.¹⁷⁰ But, neither Congress nor traditional regulatory arms of government (e.g., SEC and CFTC) seized the moment, essentially deferring to bankruptcy courts to assume immediate responsibility.

Chapter 11 thus became the default legal framework, overseeing not only the affairs of each individual debtor but also, seemingly, the trajectory of the industry more generally. Millions of individual customers had entrusted tens of billions to debtors that, collectively, controlled a substantial share of the ecosystem. How could all of this have happened? What kinds of value-maximizing strategies would be available to resolve these cases and deliver real value to customers as quickly and efficiently as possible? And how could bankruptcy’s recuperative powers help an industry in tumult, with government agencies still competing for jurisdiction, and a regulatory void still in existence? This simultaneously became the charge of several bankruptcy courts, primarily in New York, Delaware, and New Jersey. But, to better understand their particular case work, it first must be contextualized through the lens of Chapter 11’s general missions and mechanisms.

B. A Primer on Chapter 11’s Missions and Mechanisms

Chapter 11’s baseline theory is that business reorganization is preferable to liquidation.¹⁷¹ Rehabilitating productive, albeit insolvent, firms can generate more distributable value.¹⁷² It insulates contagion by preserving and continuing customer/vendor relations, jobs, retiree benefits, and future tax payments.¹⁷³ Reorganization also helps solve the so-called “common pool” problem––that is, the tendency of competing creditors to destroy value by racing to take before all others––by channeling stakeholders toward a durable system that prioritizes distributable value (e.g., equity in a reorganized entity) over distributable cash.¹⁷⁴ And, it provides legal rules that are not only flexible but also sophisticated about emerging economic and market theories,¹⁷⁵ as exemplified by developments in distressed debt financing and investment techniques.¹⁷⁶

The Bankruptcy Code, for all its size and complexity, boils down to five essentials: (1) the creation of the bankruptcy estate;¹⁷⁷ (2) the statutory pause and protective blanket of the automatic stay;¹⁷⁸ (3) interim steps a debtor may take to maintain and hopefully augment enterprise value, such as entering into a new financing arrangement (“debtor-in-possession” or “DIP” financing)¹⁷⁹ and the rejection of burdensome contracts and leases;¹⁸⁰ (4) rules governing value distribution to stakeholders, typically via a confirmed plan of reorganization;¹⁸¹ and (5) the debtor’s entitlement to lead the bankruptcy,¹⁸² subject to an effective adversary process.¹⁸³ The outcome is, in theory, supposed to distribute reorganization value largely consistent with stakeholder expectations established pre-petition under contract and other non-bankruptcy law.¹⁸⁴

The Bankruptcy Code does not look much further than the interests of the debtor and its stakeholders.¹⁸⁵ It provides a list of options available for the debtor to try to solve its financial woes; and, it offers rights and empowerments enabling stakeholders to counter or even undermine the debtor’s intended reorganization strategy.¹⁸⁶ The debtor is required to continue post-petition as a law-abiding corporate citizen¹⁸⁷ and the government’s police powers are excepted from the automatic stay.¹⁸⁸ But, the “general public interest” finds little quarter in the statutory regime.¹⁸⁹ The adversary process, rather, pits the debtor on one side of the bargaining table (and courtroom) against its stakeholders––typically, bank lenders and the official committee of unsecured creditors––on the other side.

Bankruptcy court jurisdiction hews close to this scheme. Bankruptcy courts are not Article III tribunals with full judicial power over life, liberty, and property; bankruptcy courts are, rather, Article I tribunals of limited authority.¹⁹⁰ Bankruptcy judges may only decide issues that are “core” to the bankruptcy, meaning those “arising in” or “arising under” the Bankruptcy Code.¹⁹¹ That includes matters such as DIP financing, asset sales, contract assumption or rejection, and plan confirmation¹⁹² Bankruptcy courts also may adjudicate matters “related to” the bankruptcy, but only if the litigants consent;¹⁹³ otherwise, the court may only issue proposed findings of fact and conclusions of law for the overseeing district court to consider.¹⁹⁴ Bankruptcy courts cannot conduct jury trials without litigant consent;¹⁹⁵ they cannot send anyone to prison for criminal contempt;¹⁹⁶ and, they cannot render judgments on personal injury claims.¹⁹⁷ Matters beyond what directly concerns the debtor and its stakeholders are for other courts to decide.¹⁹⁸

Separately, bankruptcy’s adjudicatory process is peculiar. In most commercial litigation, the plaintiff seeks redress for a past event. An alleged wrong happens, and the trial can be scheduled any time after the complaint is filed and pre-trial procedure has run its course. Chapter 11, by contrast, litigates to a future event, again most often confirmation of a plan of reorganization. The debtor’s business rehabilitation is, in other words, a sort of “becoming” in which much of the nucleus of operative fact develops post-petition, as the reorganization takes shape.¹⁹⁹ The process is, nevertheless, often pressured and time constrained. The debtor’s exclusivity periods to file and then solicit acceptances for a plan are not limitless.²⁰⁰ And, in cases where DIP financing is required (that is, most business cases), it is customary for such loans to include “milestone” covenants or a near-term maturity––essentially a ticking timebomb for the case.²⁰¹ The debtor must move the case along quickly, all the while meeting performance and other covenants, or the DIP lender may cut off liquidity.²⁰² The adjudicatory process thus invariably melds legal principle with pragmatism and business necessity.²⁰³ The Bankruptcy Code allows for this by establishing rules that, among other things, lean heavily on judicial discretion.²⁰⁴ But, in practice, that means bankruptcy courts are often required to make interim case decisions on relatively thin evidentiary records, always trying to preserve and advance the process to some form of successful outcome. ²⁰⁵

Further, getting to a confirmable plan can be brutal work.²⁰⁶ Section 1129 of the Bankruptcy Code imposes extensive structural, voting, and evidentiary requirements for plan confirmation, especially for so-called “cram down” on non-consenting classes.²⁰⁷ Stakeholders use those rules for their benefit, threatening and jockeying for larger helpings.²⁰⁸ They may accumulate “blocking” positions in critical debt classes.²⁰⁹ They may contest ambiguities and assumptions undergirding the debtor’s business plan and proposed reorganization value.²¹⁰ They may strategize to exclude others from plan treatments²¹¹ or exploit the debtor’s desperation for DIP or exit financing.²¹² Stakeholders exploit ingenious structures to fleece others in the capital structure, sometimes even above or within the same class.²¹³

These cases can, in sum, burn hot in their own self-contained crucible until extinguished by winnowing fuel or other paramount need for resolution. The announcement of a plan, any plan, can bring about hope and a sense of relief. The costs can be astounding, both in terms of administrative expense and consumption of judicial resources.²¹⁴ This is especially true in complex, multilayered cases.

To avoid this, bankruptcy tends to nudge stakeholders toward settlement. It does this in two primary ways. First, the Bankruptcy Code compels disclosure of substantial private information. Mandatory public disclosures include the debtor’s schedules of assets and liabilities,²¹⁵ statement of financial affairs,²¹⁶ monthly operating reports,²¹⁷ and a disclosure statement to inform voting on any plan of reorganization.²¹⁸ A debtor will invariably supplement the record with additional disclosures as it seeks interim relief from the bankruptcy court over the course of its Chapter 11 case.²¹⁹ Stakeholders may demand discovery in connection with any case dispute.²²⁰ They also may seek extraordinary discovery from the debtor and third-parties under Bankruptcy Rule 2004, so long as such discovery may serve a useful bankruptcy purpose.²²¹ In cases involving disconcerting facts, the bankruptcy court may order the appointment of an examiner to conduct an investigation and publish a “tell-all” report of their findings.²²² In these ways, bankruptcy embraces the unremarkable proposition that knowledgeable negotiations are ultimately more efficient and efficacious. Bankruptcy courts enforce this expectation.

Second, bankruptcy courts render decisions over the course of the Chapter 11 process that narrow points of disagreement. “Contested matters,” i.e., general bankruptcy motion practice, are resolved with procedural expediency;²²³ “adversary proceedings,” i.e., mini-lawsuits within the bankruptcy, follow more traditional federal civil procedure.²²⁴ But, either way, the bankruptcy court will often bring the matter to a quick evidentiary presentation, followed by a clear ruling that guides the case towards larger resolution. A bankruptcy court might, for example, determine, well in advance of a plan, whether a creditor does or does not have a perceived value entitlement; by resolving the dispute (one way or the other), the court clears a path to more effective plan negotiations.²²⁵ Same is true for corporate decision-making: if the case generates substantial allegations of corporate wrongdoing and such allegations start to inhibit negotiations, the court may prompt management changes.²²⁶

Respecting financial firms (e.g., a bank holding company or brokerage firm), bankruptcy relies on and works in tandem with regulatory authorities.²²⁷ By the time of filing, a financial debtor typically has been policed by government regulators (e.g., the SEC, CFTC, or the Fed) for quite some time. The company’s books, records, public disclosures, and manner of business have long been based on rules and expectations established by those administrative supervisors.²²⁸ The regulatory interplay is supposed to continue post-petition, with bankruptcy focusing primarily on a reworked balance sheet and regulatory authorities keeping an eye on operational developments.²²⁹ This affords regulatory agencies some leeway to intervene in the bankruptcy, asserting non-economic imperatives. As Jared Ellias, George Triantis, and Robert Rasmussen have observed, the interplay between bankruptcy and regulatory regimes can generate considerable case frictions.²³⁰ But, if all goes well, the company leaves bankruptcy in a stronger financial position, without objections voiced by regulatory supervisors.²³¹

This is the context in which bankruptcy courts have been engaged to oversee the factual development and consider the legal implications of 2022’s “crypto winter.” The crypto bankruptcies have, to date, shed disinfecting light on some of the industry’s darkest corners, revealing what may have occurred there and who may bear responsibility for the staggering losses. Bankruptcy courts have also rendered rulings that not only propel their cases forward, but also instruct the crypto community––and market regulators––more generally. Bankruptcy has, furthermore, provided a unique forum for regulatory involvement and, it seems, an occasional clash of economic and agency agendas. Below, we set out two case studies that exemplify the ways in which the bankruptcy court has emerged as a sort of default regulatory forum for crypto markets.

C. Crypto in Chapter 11: The Celsius and Voyager Cases

1. Celsius

Celsius, founded in 2017 and led by Alex Mashinsky, grew over a few years to be one the largest crypto finance platforms in the world. It presented itself as a sort of virtual bank. Individual customers could electronically, via computer or cellphone, deposit their crypto assets in a Celsius “Earn” account (akin to a traditional savings account) and accrue a relatively high rate of interest, payable in kind or in the Celsius native token, called the “CEL.”²³² Customers could borrow fiat money from Celsius (e.g., to pay household expenses with fewer tax consequences)²³³ collateralized by their deposited crypto in the Earn account.²³⁴ Celsius would, in turn, lend deposited crypto to third-parties, pocketing what it made in interest/fee income over what it owed to the account holders.²³⁵

Earn accounts, though functioning economically like general savings accounts, were not insured by the FDIC.²³⁶ Not to worry, said Celsius. The company’s management emphasized “safety,” touting that “our top priority is keeping your assets secure.”²³⁷ Celsius would not lend capital to third-parties without first conducting extensive diligence, and would use deposited capital only in “a very conservative” way, “such as only allowing very small or overcollateralized positions.”²³⁸ Even though Celsius was not a public reporting company, customers were promised even better disclosure: Celsius committed to “publish to a blockchain all our transactions which will provide users transparency as to how many coins we have and what they are used for.”²³⁹ Any Earn account holder that did not like how the business was operating had the ability to pull his money out at a moment’s notice.²⁴⁰

The company’s marketing strategy also sought to play into crypto’s anti-establishment ethos. As discussed above, Celsius was a home for those wanting to “unbank” themselves and thereby enjoy a newfound “financial freedom.”²⁴¹ Here, an everyday customer could “dream big” and help pursue “economic opportunity and income equality to everyone in the world,”²⁴² just as the people were freed from quarantine and the so-called “Great Resignation” became a mass phenomenon.²⁴³ Mashinsky presented himself as the leader of this “financial freedom” movement.²⁴⁴

The marketing strategy worked. By December 2020, Celsius had more than $3.3 billion under management²⁴⁵ and, by January 2021, that figure had grown to $4.5 billion.²⁴⁶ In October 2021, the business was valued at $3 billion.²⁴⁷ Management expedited plans to grow internationally, including the acquisition of an Israeli cybersecurity firm in October 2021.²⁴⁸ Come May 2022, Celsius had almost $12 billion under management and more than $8 billion in loans outstanding to third- parties.²⁴⁹ It boasted 1.7 million registered users by July 2022.²⁵⁰ Then it all came to an abrupt end: Luna’s collapse segued into a run-on-the-bank scenario for Celsius, leading to a brief suspension of withdrawals, and the company’s emergency Chapter 11 filing on July 13, 2022.²⁵¹

The bankruptcy was, from its inception, surrounded by controversy. In his “first day” declaration, Mashinsky asserted that Celsius was a sound, well-run company victimized by extraneous forces and rumor mongering.²⁵² He attributed the company’s financial troubles to the “macroeconomic” crypto environment and world economy, with only passing reference to certain “poor asset deployment decisions.”²⁵³ Purportedly, the bank-run was due to “unsupported and misleading” news reports.²⁵⁴

For many, the narrative did not add up. How could Celsius find itself in this position if it deployed capital in only “very conservative” ways? Indeed, Mashinsky’s own declaration admitted a “shortfall” in its balance sheet of at least $1.2 billion and about one-third of its loan book was comprised of “bad” debt.²⁵⁵ Moreover, news outlets started reporting that, while Celsius was touting CEL, Mashinsky was liquidating tens of millions of the native token from his personal account.²⁵⁶ Former employees began leaking stories of excessive risk-taking, disorganization, and perhaps even market manipulation.²⁵⁷

On September 14, 2022, the bankruptcy court entered an order directing the appointment of an examiner to conduct a broad-ranging investigation into the facts undergirding the case.²⁵⁸ Two weeks later, Mashinsky resigned as CEO.²⁵⁹ On September 29, 2022, the bankruptcy court approved the appointment of former federal prosecutor, Shoba Pillay, as examiner.²⁶⁰

On January 30, 2023, Pillay published her “tell-all” final report, a scathing 689-page description of the company and its historical practices. The report explained: (1) how the cryptocurrency ecosystem operates;²⁶¹ (2) Celsius’ important role in that ecosystem as a sort of virtual thrift bank for millions of individual customers;²⁶² (3) how the business operated day-to-day, including granular investment choices;²⁶³ and (4) how those operations and business decisions differed materially from what was represented to customers.²⁶⁴ Despite customer promises of disclosure and transparency, Celsius “frequently” made statements “that were inaccurate and misleading.”²⁶⁵ According to the report, Celsius ultimately could not generate earnings over what it owed customers, driving it into ever riskier investments that ultimately caused its undoing.²⁶⁶ The report includes an internal email describing certain corporate strategies as “very ponzi like.”²⁶⁷ It also revealed that, despite mounting corporate losses, Mashinsky pocketed nearly $70 million by selling his personal holdings in CEL, while the company was hawking CEL’s (supposed) intrinsic value to the market.²⁶⁸ The final report is a detailed account that, again, likely contributed to Mashinsky’s indictment and arrest seven months later.

Disclosure aside, Celsius came to bankruptcy with billions in assets, including fiat cash, crypto assets, a loan book, mining interests, and other hard and inchoate assets,²⁶⁹ which needed allocation among and distribution to the company’s creditors (predominantly customers). Prior to bankruptcy, management repeatedly communicated to the customer-base that crypto deposits remain “your” crypto,²⁷⁰ giving the customers the clear impression that Earn accounts liken better to safe deposit boxes than traditional savings accounts. With Celsius in bankruptcy, 600,000 Earn account holders, who had collectively deposited $4.2 billion, wanted “their” crypto traced, excepted from the automatic stay, and immediately released to their rightful owners.²⁷¹ This was, after all, what Mashinsky had promised all along.²⁷²

Celsius’ advertising puffery did not, however, match up with what was written in the customer agreements. Earn customers may not have realized, when they signed their Celsius contracts, that deep within the legalese was a transfer of ownership of all digital assets deposited into an Earn account.²⁷³ Earn depositors could redeem such assets at will, requiring Celsius to go into the market to cover any demanded crypto it did not then have in treasury. But, after deposit and prior to redemption, the crypto belonged to Celsius and could be exploited as management saw fit for the company’s own profit-making purposes.²⁷⁴ The contract relationship was, contrary to Mashinsky’s “unbank” representations, very much like that of traditional depository institutions.²⁷⁵

This entitlement issue was, as described by the bankruptcy court, “a gating issue at the center of many disputes in this case.”²⁷⁶ On January 4, 2023, following an evidentiary hearing, the bankruptcy court issued its opinion resolving the matter. The court concluded that, despite the marketing representations and client expectations, the language of the customer agreements control.²⁷⁷ Earn customers were merely unsecured creditors in the Celsius Chapter 11 cases, entitled to recover the remainderman’s interest after payment of ever-ballooning administrative expenses.²⁷⁸ Deposits were not, in sum, “your” crypto after all²⁷⁹ and, making matters worse, the deposits were not FDIC insured. The ruling delivered a painful lesson not only to the 600,000 Celsius Earn customers, but also hundreds of thousands of BlockFi customers who deposited their crypto in comparable accounts and came to learn that the Celsius ruling would be followed in BlockFi’s bankruptcy as well.²⁸⁰

2. Voyager

Voyager was founded a year after Celsius (in 2018) and, like Celsius, also focused its marketing strategy on individual crypto enthusiasts. But, Voyager was a hybrid brokerage and quasi-banking firm. Customers could trade, after depositing digital assets, using an interface accessible via the Voyager app.²⁸¹ They just needed to sign a customer agreement, download the app, and then select which of over one hundred asset types they wanted to buy or sell.²⁸² Voyager made money by pocketing the spread between the buy and sell prices of traded crypto assets and by relending customer deposits, akin to Celsius and BlockFi.²⁸³

Like Celsius, Voyager too experienced explosive growth.²⁸⁴ In 2020, Voyager counted only 120,000 users on its platform.²⁸⁵ A year later, Voyager’s app was among the top 10 in the world.²⁸⁶ At year-end 2021, Voyager had nearly $5.9 billion in assets under management.²⁸⁷ By springtime 2022, it counted over 3.5 million users.²⁸⁸ Then came the Luna collapse and Three Arrows defaulting on its $657 million Voyager loan. Mass customer redemptions followed.²⁸⁹ Voyager filed for bankruptcy protection on July 5, 2022.²⁹⁰

Given Voyager’s abrupt failure, the board of directors created a special committee to investigate underlying facts.²⁹¹ The special committee retained independent counsel to conduct this investigation.²⁹² The investigative report was made public (in redacted form) on February 14, 2023.²⁹³ The report focused on the decision-making process driving the Three Arrows loan, which was put in place only a few months before Luna’s collapse.²⁹⁴ As detailed, management conducted negligible diligence before agreeing to lend Three Arrows up to $1 billion. Prior to committing capital, Voyager: (i) received merely a single-line statement in lieu of detailed financials, to wit, “We confirm the following for Three Arrows Capital Ltd as at 1-January-2022 in millions of USD. NAV 3,729”;²⁹⁵ and (ii) conducted a single due diligence call with two executives from Three Arrows, where no mention was made of the fund’s Luna exposure.²⁹⁶ None of the loans were collateralized.²⁹⁷ At the time of Voyager’s bankruptcy filing, the Three Arrows debt represented nearly 58% of its loan book.²⁹⁸

Blame aside, Voyager’s bankruptcy––like all bankruptcies–– required an exit strategy. At case inception, Voyager proposed a plan of reorganization.²⁹⁹ This was, however, merely an aspirational statement, given the tumultuous state of the industry in July 2022.³⁰⁰ The plan, nevertheless, functioned as a kind of “stalking-horse” for alternative exit strategies, particularly a sale transaction.³⁰¹ On August 5, 2022, the bankruptcy court approved bid procedures, initiating an M&A process designed to find a buyer for Voyager.³⁰² That process concluded in September, with FTX advancing a $1.422 billion offer to buy the company.³⁰³ That transaction had not yet closed when, in November, CoinDesk published its article outing FTX as a possible fraud, and the company imploded.³⁰⁴

This was devastating news for Voyager and its stakeholders.³⁰⁵ By then, Voyager had incurred millions in professional fees chasing the FTX deal.³⁰⁶ Fortunately, Voyager found another potential suiter: Binance.US,³⁰⁷ the American affiliate of Binance, the behemoth cryptocurrency exchange.³⁰⁸ In December, Binance.US agreed to acquire Voyager for approximately $1.022 billion, and the transaction would be consummated as part of Voyager’s pre-existing plan of reorganization.³⁰⁹ Under the plan, Voyager customers would transition to the Binance.US platform, subject to various vetting procedures.³¹⁰ Ineligible customers would have their crypto liquidated and receive the cash proceeds.³¹¹ Same for customers located in jurisdictions where Binance.US was not licensed to provide digital currency services.³¹²

But, there was a problem. The federal government, as well as the SEC, United States Trustee, and several state regulatory agencies expressed concerns over Binance.US as purchaser.³¹³ Binance.US, it seems, was an entity of concern for federal and state regulators, evoking government suspicion that it was not a suitable buyer for Voyager’s expansive role in the U.S. market.³¹⁴ The SEC, in particular, contended that the Binance.US transaction and its distribution of digital assets to creditors might end up violating federal securities law,³¹⁵ with the federal government furthering that, as a matter of principle, the plan should not have any preclusive effect on regulatory authorities (federal or state) if the transaction or such distributions are subsequently found to be wrongful.³¹⁶ That meant, among other things, that Voyager and Binance.US executives, as well as bankruptcy professionals advising the debtors and the official committee of unsecured creditors, could face post-consummation regulatory scrutiny––perhaps even liability––for supporting and helping consummate the plan.³¹⁷

The bankruptcy court was unmoved by these arguments. The court accepted Voyager’s contention that the proposed transaction was the most value-maximizing path forward, with approximately $100 million in value over liquidation.³¹⁸ The court disagreed, as a matter of fundamental bankruptcy principle, that parties should remain liable under securities laws for helping the plan close and, in turn, fulfilling their statutory mandates under the Bankruptcy Code, especially as the government equivocated on whether the Binance.US transaction would or would not actually violate securities laws.³¹⁹ The court further chastised the government objectors for interposing objections rooted in speculation, not evidence.³²⁰ The court ultimately overruled the objections, and the plan was confirmed.³²¹ Voyager was thus authorized to move forward with the sale to Binance.US.³²²

The government appealed, focusing its argument on the plan’s exculpation provision, contending that it infringed on its regulatory authority to prosecute enforcement actions against, among others, those working to close the deal.³²³ A motion for stay pending appeal followed shortly thereafter.³²⁴ The appeal did not go far, however. In the face of these developments, Binance.US exercised its right to terminate the transaction, decrying the “hostile and uncertain regulatory climate in the United States.”³²⁵ On April 25, 2023, Voyager announced that it had pivoted to liquidation.³²⁶

The Voyager case story is, from the perspective of bankruptcy law, rather strange: the most value-accretive case solution was scuttled based on unproven contentions. But, considering the government’s larger regulatory ambitions, it is instructive. Management remained in possession throughout the case. The case background factswere not buried. The public ultimately received exacting, candid, and stark disclosures of how the C-Suite took excessive risks with customer deposits (i.e., the Three Arrows loan). These disclosures, when married with comparable revelations from the BlockFi, Celsius, Cred, and FTX cases, reflect patterns of governance failures that can be targeted by administrative agencies as well as the consuming public. Moreover, with respect to the failed Binance.US transaction, the case illustrates––in about as clear and impactful way as possible––how bankruptcy can provide an oddly effective forum for public regulators to advance their administrative agendas, prior to comprehensive regulatory reform, with relative ease and crisp effectiveness. That is so, even if the bankruptcy court is left almost entirely in the dark about what is motivating aggressive agency response.

These observations point to a number of gains for overseers arising out of the bankruptcy court’s role as accidental quasi-regulator. Just as traditional financial regulation seeks out ways to protect the marketplace, produce information on its risks, and safeguard user interests, the court’s unique legal toolkit can achieve outcomes aligned with these regulatory objectives. Indeed, there is an argument that the court’s intervention comes with specific advantages. The capacity of bankruptcy judges to exercise wide discretion in applying statutory measures, combined with powers to compel delivery of detailed disclosures, can allow for a flexible, solutions-orientated approach that may be especially well-suited to address the novel, evolving nature of the crypto industry. An objective examiner’s report (e.g., Cred and Celsius),³²⁷ for example, can reveal insights about a firm and its industry that may not be easily discernible through regular, standardized disclosures, where a company might present its affairs in an overly curated, sanitized light. Approaches to address thorny problems like valuation of crypto assets (e.g., Voyager) can reflect efforts on the part of any number of experts enlisted by the court, including regulatory agencies. This can better equip judges to develop resolution strategies that stand the best chance of success in addressing risks and distress within a novel, understudied asset class like crypto. Further, the public nature of the bankruptcy process means that the court’s efforts are afforded general scrutiny (including on social media). There is signaling of regulatory priorities (e.g., customer protection). And, the court’s judgments and analysis create opportunities for wider learning about the legal complexities (e.g., custody) and industry characteristics of crypto markets.

Yet, even as bankruptcy courts have risen to meet the legal and economic challenges posed by “crypto winter,” the consequences of their engagement reveal the high costs of relying on these courts to function as proxy financial regulators. As we discuss in Part III, bankruptcy courts are highly specialized actors that are poorly suited to act as general overseers and rule-makers for any financial industry.

III. THE BANKRUPTCY COURT AS (IMPERFECT) CRYPTO MARKET REGULATOR

Part II showed how bankruptcy is, functionally, administering the clean-up of large segments of the crypto ecosystem. It observes that some of bankruptcy’s work is serving non-bankruptcy regulatory objectives, including broad and exacting public disclosures, management accountability, loss allocation in ways that are instructive to regulators and crypto investors, even opportunity for traditional government supervisors to advance policy objectives before enactment of corrective regulation. This contention might, however, be troubling, perhaps even to the bankruptcy judges overseeing the crypto cases. As explained in Section II.B, bankruptcy’s purpose and intentions look no further than the estate and its stakeholders. Any larger-scale administrative objectives served by bankruptcy are, therefore, more or less incidental to––rather than and by virtue of––the Bankruptcy Code’s underlying design.

There lies the trouble with relying on bankruptcy courts to serve as default quasi-regulators. This Part surveys the implications. We observe that there are difficult tensions between core bankruptcy policies and those of more traditional financial regulation. Particularly on matters of systemic risk or customer protection, bankruptcy’s usual focus––looking to safeguard, augment, and ultimately distribute estate value––can result in destabilizing and costly externalities for actors like customers or creditors. Though knock-on hardships are commonplace and expected in insolvencies, bankruptcy courts cannot deploy the kind of tools available to financial regulators (e.g., to backstop customer money claims or provide emergency bridge financing for struggling counterparties) to shore up a hurting market or ensure its go-forward integrity.

Even disclosure, a foundational regulatory device, furthers a different imperative in bankruptcy. The timing, extent, and even reliability of bankruptcy disclosure encapsulates the point-counterpoint nature of bankruptcy’s adversary process. It is sharply focused on its intended audience––Chapter 11 stakeholders––not the markets more generally. Such disclosures can only instruct and, hopefully, positively affect crypto market development by presenting cautionary tales. Bankruptcy courts can do little more for the wider audience.

Finally, the frictions exemplified by BlockFi, Celsius, FTX, Genesis, Three Arrows, and Voyager illustrate the costs to financial market design where policy looks to the bankruptcy court as a frontline regulator––rather than as but one critical part of an otherwise larger, dedicated architecture for oversight and resolution. Requiring bankruptcy courts to step into a leadership role, rather than to adjudicate within an existing framework for oversight (where oversight is largely entrusted to other facets of government), imposes on these courts a responsibility far outside of their usual functions and capabilities, creating enormous inefficiency and, in the end, grave concerns over effectiveness.

A. Systemic Risk and The Bankruptcy Code

The interventions of bankruptcy courts in the context of crypto have exemplified the tensions between the Bankruptcy Code and financial regulatory approaches designed to address systemic risks. As noted in Part I, crypto markets showcase the potential for externalities––where institutions like exchanges (e.g., FTX, Genesis, and Voyager), quasi-banks (e.g., BlockFi and Celsius), and hedge funds (Alameda and Three Arrows) pose dangers to others, resulting in the creation of pathways for risk to move from one firm to others rapidly and unpredictably.

But, despite these risks, fundamental aspects of the Bankruptcy Code stand in tension with regulation’s emphasis on preserving market stability and assuring the safety and soundness of large, deeply networked financial firms. For one, the typical mission of bankruptcy courts looks to address the debtor’s insolvency, protecting and enhancing the value of the estate, and overseeing the development of a plan to distribute value to creditors. How bankruptcy courts achieve this has long elicited debate and prompted recourse to competing judicial philosophies to guide how the pie is best divided among stakeholders. Scholars have tussled, for example, over the workability of divergent economic approaches when deciding how much leeway to afford managers struggling to return a distressed business to profitability: whether only creditors’ rights ought to be recognized; or, if community interests should also be afforded some voice in a bankruptcy process; or even whether certain creditors (e.g., DIP lenders) ought to be permitted especially close control over the firm’s workings and managerial discretion.³²⁸ While not underplaying their importance, nor diminishing the attention bankruptcy courts often pay to non-economic stakeholders (like local communities and public policy imperatives), these variations generally operate with an overarching focus on the debtor and the financial distress that it is experiencing.³²⁹ Indeed, bankruptcy law expects third-parties to absorb loss, uncertainty, and distress of their own in order to afford the debtor an opportunity to reorganize.³³⁰ In other words, the focus of the Bankruptcy Code is almost exclusively on the debtor––rather than preventing the spread of distress to third-parties and the industry sector more generally.

Perhaps the most visible tension between the Bankruptcy Code and its effect on systemic risks can be seen in the broad application of the automatic stay. Designed to freeze attempts to collect debts against the debtor’s estate, it precludes any number of creditors from accessing and retrieving their funds.³³¹ In the context of crypto insolvencies, such as Celsius and BlockFi, this has meant precluding the firms’ customers from accessing assets and withdrawing them from the debtor platform, leaving billions of dollars trapped without clarity as to when they might be returned––if they might be returned at all.³³² Importantly, limited financial regulation has meant that the automatic stay is applied bluntly to crypto assets, without any calibration to reflect the common sense (but not, in the end, legal) notion that these assets constitute customer property.³³³ By contrast, in regulated securities and commodities markets, rulemaking mandates that assets be protected to clearly recognize investor ownership rights, with custody arrangements eliminating the risk of these assets becoming scooped up in a custodian’s bankruptcy.³³⁴

This tension has played out repeatedly across the major crypto insolvencies. Bankruptcy courts do not have discretion and must strictly enforce the automatic stay, without regard for potentially systemic consequences within the crypto-ecosystem and the economic damage inflicted on otherwise blameless retail creditors. For one, platform clients not been able to withdraw their assets, causing damaging knock-on effects, if they lack the cash to pay out on their own obligations.³³⁵ In the case of FTX, for instance, this included institutional creditors, such as BlockFi, that ended up pushed into their own insolvency.³³⁶ It also compromised millions of vulnerable retail interests, everyday savers with limited or negligible economic slack to absorb the shock.³³⁷ Indeed, in seeking to navigate the damage, retail creditors have been forced to reckon with sophisticated parties in crowded and confusing legal proceedings. This has required administrative investment in filing claims as well as in carefully following the trajectory of their legal entitlements.³³⁸ With these cases (and the automatic stay) stretching on for many months, the complex nature of crypto bankruptcies invariably threaten all customers, retail and institutional, with lengthy and legally burdensome separation from whatever value is ultimately left for them – no matter the resulting knock-on shocks.³³⁹

As an added source of risk, crypto holders confront reckoning with the shifting valuation of a highly volatile asset. As Anthony Casey, Brook Gotberg, and Joshua Macey write, the changing valuation of crypto assets can create incentives for a debtor to use these assets to fund itself at low cost.³⁴⁰ With crypto assets likely to have a depressed valuation on the filing date of a large bankruptcy, an exchange can gain by holding onto a base of assets with appreciating price, and to eventually reap winnings from the difference between a low-dollar customer claim and a higher valuation further into the insolvency process.³⁴¹

These risks are not new for insolvencies where the debtor’s failure might result in costly externalities for financial markets. Crucially, however, regulated markets have developed sophisticated conventions to recognize and privilege systemic risk considerations over the interests of the debtor. As noted above, custody arrangements in securities and commodities markets look to keep customer assets outside of the bankruptcy.³⁴² But, other provisions, too, are worth highlighting. For example, under the Bankruptcy Code, certain kinds of risky and short-term financial contracts are expressly exempted from the stay.³⁴³ For certain kinds of derivatives and short-term credit arrangements, a debtor’s counterparty is permitted to close-out the contract and set-off liabilities to secure what is owed to them.³⁴⁴ This process is designed to happen automatically, preventing these specific financial creditors from becoming locked in lengthy proceedings and facing the prospect of cash-shortages themselves.³⁴⁵ Of further note is the fact that certain kinds of financially systemic firms are saved from becoming subject to long and uncertain corporate bankruptcies. This is most clearly exemplified by the regime for addressing bank failures, where the process is managed by a particular government agency––the FDIC––rather than the courts. This design is supposed to offer a highly technocratic, fast, and minimally disruptive process, where customer deposits and outstanding bank loans are transferred (ideally) seamlessly to another bank, preventing worries about the larger solvency of the banking system and helping to prevent a run by frightened depositors.³⁴⁶

In other words, regulatory policy recognizes the tension between the Bankruptcy Code and the costs of system-wide fragility. Whereas rulemaking in securities markets, commodities, and banking regulation has looked to navigate this tension through well-established, Congressionally-approved, crafted tools, crypto markets have been left exposed to the vulnerability of systemic risks but with only the discretion and generalized case oversight of bankruptcy court for recourse. With courts equipped only with traditional tools (e.g., the automatic stay), bankruptcy law is ill-equipped to protect short-term creditors and vulnerable customers in crypto markets.

B. Bankruptcy Disclosure vs. Market Disclosure

The close nexus between financial regulation and disclosure finds its originating, and perhaps best, articulation in Justice Brandeis’ famous statement: “Sunshine is said to be the best of disinfectants; electric light the most efficient policeman.”³⁴⁷ And, so, while scholars have long debated the efficacy of disclosure as a regulatory tool, and contested even further how best it should be implemented to achieve its intended purpose, compelling businesses to periodically divulge core performance and governance data remains a vital component in the administration of financial systems.³⁴⁸ The general idea is that, if the law mandates regular and sufficient disclosure, the consuming public and markets more generally will do much of the policing on their own.³⁴⁹ The SEC and other regulatory agencies have, in turn, issued extensive guidelines and disclosure standards have evolved to aspire for clarity, consistency, and comparability in public communications.³⁵⁰

voxeu/columns/bank-transparency-regulation-and-stress-tests-what-works-and-what-does-not [https://
perma.cc/54KC-NXH8]. Broadly viewed, capital markets, as well as the general consuming public, have come to expect high-quality, reliable disclosures (as compelled by law and enforced by federal and state administrative agencies), assuring greater confidence in the efficient and safe workings of regulated markets.³⁵¹

That is not the nature of bankruptcy disclosure, however. Debtors do not have to broadly divulge information in their bankruptcy cases to accommodate a regulatory scheme intended to properly inform a market.³⁵² Come Chapter 11, the typical debtor’s securities are already delisted, ³⁵³ and disclosure imperatives arising under non-bankruptcy law shift to what is expected in bankruptcy. Thereafter, and as a normative attribute of Chapter 11, debtors tend to publicly disclose only what is necessary and only when they desire particular relief from the bankruptcy court.³⁵⁴ As explained in Section II.B, a debtor’s reorganization is a sort of “becoming” that often takes shape after the bankruptcy has started.³⁵⁵ Bankruptcy law does not compel the debtor to issue much in the nature of progress reports along the way. And, at least during the formative stages of the bankruptcy, a shroud of secrecy is generally acceptable, allowing key constituents, such as the official creditors committee, to do their work.³⁵⁶ Unlike the public more generally, key constituents receive sensitive information early on because they are the counter-balance in bankruptcy’s adversary process and they are the ones the debtor needs to eventually negotiate a plan.³⁵⁷ It is true, as mentioned above, that the Bankruptcy Code and Bankruptcy Rules compel granular public disclosures about the assets comprising and the debts burdening the estate, as well as public release of monthly operating reports.³⁵⁸ But, these disclosures are far from fulsome, they are not completely standardized, and they are neither designed nor intended to offer everyday market participants confidence, clarity, and comparability about firms and their workings.³⁵⁹ For example, monthly operating reports, untethered to a disclosed bankruptcy strategy or turnaround business plan, do little to elucidate where the case is going at any particular moment.³⁶⁰ It is not until the publication of a detailed disclosure statement that the “case story” comes together for the public more generally. But, by then, the story may be almost over.

Debtors do make interim disclosures in the bankruptcy––including, especially, the debtor’s so-called “first day” declaration (an explanatory, often lengthy, statement filed with the Chapter 11 petition)––and those disclosures often present a detailed case narrative: why and how the debtor finds itself in need in bankruptcy relief; what it hopes to achieve while in bankruptcy; how and when it expects to exit bankruptcy.³⁶¹ But, unlike disclosure requirements under non-bankruptcy law,³⁶² there are few repercussions for a debtor whose interim disclosures are ultimately found to be insufficient, incomplete, or even inaccurate.³⁶³ Bankruptcy anticipates that the debtor’s case narrative, including the “first day” declaration, may be inculcated with advocacy; it relies on the debtor’s case adversaries (e.g., the official creditors committee) to exploit discovery and other tools of bankruptcy to ferret out and eventually present the counter-narrative.³⁶⁴ Celsius, for example, initially presented its case narrative in the “first day” declaration of its CEO, Alex Mashinsky. This narrative was largely debunked in the examiner’s final report,³⁶⁵ and Mashinsky was arrested a short time later. But, tellingly, that did not lead to the appointment of a Chapter 11 trustee, conversion to a Chapter 7 liquidation, dismissal of the case, or even curtailment of the debtor’s exclusivity to file its own bankruptcy plan.³⁶⁶ Bankruptcy wants the parties to negotiate and, so, bankruptcy courts are loath to impose interim process changes over factual disputes, even where the debtor’s factual narrative is so blatantly wrong.³⁶⁷ Stated differently, bankruptcy rarely prioritizes factual accuracy in interim (prior to dissemination of a disclosure statement) public disclosure over an orderly Chapter 11 process.³⁶⁸

It is perhaps for this reason that examiner appointments have been rare occurrences in Chapter 11, historically reserved for only the most extreme cases.³⁶⁹ Examiners seize part of the adversary role occupied by creditor representatives, who are otherwise entrusted not only to learn the case facts but also to exploit them at bargaining table.³⁷⁰ Examiner appointments can, in other words, enervate the official creditors committee (among others) and that may not help the parties reach consensus on a plan.³⁷¹ Examiner reports also can be costly, eating into eventual recoveries, and they take time to prepare, resulting in case delay.³⁷² Moreover, examiners are required to make their investigative findings public––even the findings that may be best reserved for quiet negotiation––and this can further chill dealmaking.³⁷³ These dynamics may help explain why even in a case as extreme as FTX the bankruptcy the bankruptcy court was reluctant to order the appointment.³⁷⁴

Finally, and most specific to crypto, bankruptcy disclosure does not have permanence. Data delivered in cases such as BlockFi, FTX, and Voyager explain the root causes of failure, and thus can offer cautionary tales for regulatory authorities and the industry more generally to observe and consider.³⁷⁵ But, it can do little more. A “bad” Chapter 11 debtor will change its ways through the reorganization process; a liquidating debtor has no future; and, other industry participants have no obligation to study or heed any cautionary tale. Bankruptcy disclosure, therefore, offers little protection unless the lessons learned are formalized into some kind of mandatory rulemaking.³⁷⁶

C. An Imperfect Policymaker

Facing information deficits and without a mandate to address systemic risks and market stability, bankruptcy courts are a sort of “make-do” but ultimately highly imperfect proxy-regulator for the crypto-market. Yet, their decision-making is likely to have lasting effects that shape future rulemaking and constrain the room to maneuver available to policymakers looking to craft a framework for crypto oversight.

Perhaps the clearest illustration of the courts’ impact as imperfect policymaker is reflected in the ownership determinations respecting customer crypto assets deposited with bankrupt custodians. As detailed in Part I, cryptocurrencies reflect a relatively novel kind of asset class, where ownership rests with those holding the private keys (the passwords) to a crypto accounts. This design speaks to the fundamental self-help orientation of underlying blockchains that have emerged from a philosophical rejection of third parties like banks, brokers, or state regulators.³⁷⁷ However, as centralized actors have come to assume a critical role, attracting waves of customers, they have also become vast repositories of user assets, holding onto passwords and able to access accounts, the value of which they carry.³⁷⁸ As Adam Levitin notes, this leaves customer assets vulnerable, caught up in a legal gray zone where the fact of a custodian having de facto control and the capacity to access assets at will can leave customers holding a simple contractual––rather than a property-based––claim.³⁷⁹ It has also left the courts facing a complex policy conundrum, whether to (1) recognize customer property rights in crypto assets and, in turn, to permit those assets to remain outside of the custodian’s estate or (2) deem the assets property of the estate, repositioning customers as general unsecured creditors.³⁸⁰ Arguably, financial regulatory policy would favor recognizing and protecting customer’s property rights–and by extension their savings. As evidenced by the safeguards afforded to customer assets in securities and commodities markets, the emphasis placed by traditional financial regulation on investor protection is well-established and uncontroversial. Even where comingling of assets or failure to secure them has meant that customers have not been able to fully enforce their property rights, regulation has stepped in (e.g., MF Global) to ensure compensation and redress for those whose entitlements were abridged.³⁸¹

By contrast, the absence of a focused regulatory policy and a relative lack of prior rulemaking in crypto markets, has led the bankruptcy courts––the Celsius court in particular––to assert bankruptcy norms, thus reducing customer claims to a contractual (rather than proprietary) nature. As such, around $4.2 billion in customer assets deposited with Celsius were found to belong to the bankruptcy estate, and a broad swath of depositors entitled only to the remainderman’s interest after a long and torturous bankruptcy case.³⁸²

As detailed above, while this ruling might reflect bankruptcy’s interpretative norms, it nevertheless raises broader policy concerns surrounding fairness and market integrity. For one, the impact of this ruling can result in some customers faring better than others during a crisis. Specifically, the effect of the ruling means that those that leave assets with an intermediary face the risk that these assets can end up subsumed within a custodian’s estate. It follows that those able to hold their assets off-platform, hosted on their own private wallets face far better odds in maintaining their property rights. While straightforward, this scenario creates the risk of a two-tier market, where those possessing the technical savvy to protect themselves out-maneuver the risk, but those that are perhaps less knowledgeable or otherwise unable to take such steps lose their entitlements. Such a state of affairs appears especially problematic given that those most likely to see their assets tapped on a platform are likely to include the most vulnerable, with less knowledge and sophistication about using crypto technologies. In other words, rather than protect all customers equally, the decision leaves crypto investors to fend for themselves. Those that cannot––in other words, customers that are in the most precarious situation––end up unprotected and liable to be harmed.

The Celsius court’s ruling ended up being especially powerful in the absence of wider regulatory action to protect customers and support market integrity. This has meant that decisions of the bankruptcy court – formed within a particular system of constraints – have given rise to structural effects on the marketplace (e.g., interpretations of terms of service, review of custodianship norms). Unlike administrative rulemaking, however, this impact has taken effect without the benefit of precision market understanding, cost-benefit analysis, stakeholder consultation, or deliberation. While bankruptcy courts have done what they can within their mandate, bringing some order to the prevailing chaos, their intervention can hardly be considered as optimally engineered to provide a lasting and reliable set of guardrails for the crypto-marketplace, designed to operate both in peacetime and in crisis.

CONCLUSION

This Article has sought to offer a new account of cryptocurrency regulation to highlight bankruptcy’s role, by default, as a force in financial markets oversight. With the industry lacking a real framework to govern its integrity, customer protection, and relationship with regulators, bankruptcy courts have been required to step in, addressing gray areas and thorny problems surrounding cryptocurrency’s legal and economic underpinnings. In applying its expertise and authority, these courts have shown themselves to be deft and creative, bringing clarity to important questions impacting customer entitlements and the risk management practices adopted by crypto firms (e.g., in relation to crypto custody). But the courts’ role remains an imperfect and incomplete one. The focus of bankruptcy remains on the debtor. Bankruptcy courts cannot perform policy to address larger concerns––such as the immediate welfare of customers or the overall health of the market. Even as bankruptcy’s influence in this space has grown, its deficits have also become apparent, underscoring the larger costs of regulatory inertia and inaction for establishing standards of governance and safety within innovating industries. Ultimately, the bankruptcy court’s emergence as an accidental financial regulator raises deeper questions about how best to push administrative mobilization to rise to the challenge of complex innovation. As financial regulators endeavor to create new standards for crypto oversight, they face an even more complex task ahead, forced to maneuver in the shadow of the bankruptcy’s authority as a first mover in this arena.

96 S. Cal. L. Rev. 1479

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Yesha Yadav*

* Yesha Yadav is the Milton R. Underwood Chair, Associate Dean, and Professor of Law at Vanderbilt Law School.

Robert J. Stark†

† Robert J. Stark is Chair of the Bankruptcy and Restructuring Practice Group at Brown Rudnick LLP. We are deeply grateful to Kenneth Aulet, Jordan Barry, Preston Byrne, Cathrine Castaldi, Jill Fisch, Pamela Foohey, Andrew Hayashi, Elizabeth Pollman, Danny Sokol, Robert Rasmussen, Andrew Rizkalla, and Samuel Weinstein, as well as participants at the USC Digital Transformation in Business and Law Symposium, the Cardozo Law School Corporate Governance Symposium, and the BYU Winter Deals Conference for all of their valuable insights, comments, and perspectives. We also thank Matthew Fisher, Samuel Khan, and Roshni Parikh for excellent research assistance. In the interest of full disclosure, Robert J. Stark and/or his firm were involved in several of the cases discussed in this Article: he served as the examiner in the Cred Chapter 11 case; he represented the official creditors committees in the BlockFi and Prime Trust Chapter 11 cases, as well as the winning bidder (Fahrenheit LLC) in the Celsius Chapter 11 case; his firm represented an ad hoc claimants committee in the Genesis Global Chapter 11 case, the Bahamas Government in connection with the FTX collapse, and parties in other restructurings in the digital asset and mining spaces. This Article represents the views of the authors only. Errors are our own.

AI-Generated Inventions: Implications for the Patent System

April 2024April 2024Gaétan de Rassenfosse,* Adam B. Jaffe† & Melissa Wasserman‡

This symposium Article discusses issues raised for patent processes and policy created by inventions generated by artificial intelligence (“AI”). The Article begins by examining the normative desirability of allowing patents on AI-generated inventions. While it is unclear whether patent protection is needed to incentivize the creation of AI-generated inventions, a stronger case can be made that AI-generated inventions should be patent eligible to encourage the commercialization and technology transfer of AI-generated inventions. Next, the Article examines how the emergence of AI inventions will alter patentability standards, and whether a differentiated patent system that treats AI-generated inventions differently from human-generated inventions is normatively desirable. This Article concludes by considering the larger implications of allowing patents on AI-generated inventions, including changes to the patent examination process, a possible increase in the concentration of patent ownership and patent thickets, and potentially unlimited inventions.

INTRODUCTION

AI-generated inventions—inventions autonomously created by AI software—are around the corner.¹ They have already surfaced in some applications, including genomic.² “Invention machines,” as we will generically call them, will, in all likelihood, become more prevalent in the future with more and better data, methods, and computers. They will also fundamentally alter the innovation process, with inventions becoming cheaper and faster to produce—at least in some technological fields or for some types of inventions.

If the innovation process changes, so, perhaps, should the support schemes put in place to encourage it. Scholars have traditionally seen innovation activities as needing policy support with tools such as the patent system, grants, research and development (“R&D”) tax subsidies, and prizes, among others.³ It is not clear that the current policy toolbox is well adapted to this changing landscape.

One concrete question that has received a great deal of scholarly attention is whether AI-generated inventions can be protected by patents under existing intellectual property (“IP”) laws.⁴ The issue also received coverage from mainstream media when Professor Ryan Abbott’s team from the University of Surrey filed patent applications, as part of the Artificial Inventor Project, designating an AI system as the inventor at several patent offices worldwide.⁵ The applications were (so far) rejected by some patent offices (including in the United States, the European Patent Office, and the United Kingdom), but accepted by others (including in South Africa and Australia).⁶ The issues posed in that case were whether an AI-generated invention can be patented and whether an AI system can be named as an inventor in a patent application. The patentability of AI-generated inventions is also high on the policy agenda, with the main patent offices actively discussing the issue.⁷

However, the question of the patentability of AI-generated inventions under current patent laws is too narrow a framing of the issue. The important question is whether and how the emergence of this new invention technology changes our judgment as to how the patent system can best operate to achieve its objectives. The fundamental aspects of patent laws have barely changed since the 1474 Venetian Patent Statute. Having resisted two industrial revolutions, it is not immediately apparent that the patent system must adapt to the digital revolution. However, whereas the previous industrial revolutions essentially concerned invention-driven changes in the organization of production, AI affects the invention process itself and, consequently, the incentives for innovation that are the focus of the patent system.

This Article takes a normative approach to how the patent system should handle AI-generated inventions. It also discusses implications for the patent system of invention machines. It draws on arguments from economic theory and evidence from empirical analyses of analogous situations. The focus is on technical inventions that would clearly and unambiguously meet the novelty, non-obviousness, and utility criteria if invented by a human. We are concerned with inventions that AI has fully and autonomously invented; we are not considering the use of AI as a mere tool in the invention process. However, we note that many of the points we raise apply to this broader issue as well. The fact that AI speeds up and lowers the cost of inventing does change the innovation incentives—and, perhaps, the way we should conceive the patent system.

This Article proceeds in four parts. Part I considers whether patent protection for AI-generated inventions is normatively desirable. Part II examines how invention machines could affect the patentability standards, especially the non-obviousness requirement. Part III argues against a differentiated patent system for AI-generated inventions versus human-made inventions. Part IV discusses some systemic consequences of invention machines for patent systems and proposes potential solutions. The last Part offers concluding remarks.

I. SHOULD AI-GENERATED INVENTIONS BE PATENTABLE?

Artificial Intelligence is notoriously difficult to define but is commonly associated with the ability of a computer to learn. We utilize the term AI to refer to computer systems that can perform tasks that normally require human intelligence. AI is used in hundreds of ways all around us. Apple uses AI technology in its voice recognition software, Tesla in its self-driving technology, and Spotify and Amazon use AI to learn customer preferences. AI is used to identify the shape of proteins, which could lead to breakthroughs in drug discovery and development. AI chatbots like ChatGPT are poised to change the way students learn and study.⁸

AI, however, can also invent. Perhaps the most infamous AI-generated inventions include those associated with DABUS. DABUS is an AI system developed by Stephen Thaler. According to Thaler, DABUS created inventions that Thaler did not conceive.⁹ However, DABUS is far from the only AI system that has created inventions without human intervention, which rise to the level of inventor under current patent law.¹⁰ Among other examples, AI-generated inventions currently include an AI-designed airplane cabin and an AI-designed race car chassis.¹¹

In this Part, we address the fundamental economic question of whether society would be better off granting patent protection for AI-generated inventions instead of keeping them unprotected in the public domain. We do so by examining three canonical reasons for granting patent protection, the incentives to innovate, the incentive to commercialize inventions, and the ability of patents to encourage technology transfer. During our analysis, we assume that the invention machine autonomously creates patentable inventions at zero cost.

A. Do We Need Patents to Encourage AI-Generated Inventions?

The primary justification for the patent system is to provide incentives to innovate.¹² Patents enable inventors to recoup their research and development expenses by granting inventors the time-limited ability to exclude others from making, selling, or importing their inventions. By doing so, patents provide dynamic incentives for investments in new technologies.

Despite its primacy in theoretical discussions of the patent system, it is not immediately apparent that patents are needed to incentivize the act of inventing. Curiosity is a fundamental human trait, and exploration for its own sake is a widespread human activity. Inventions would undoubtedly occur in the absence of patents. It is possible that the incentive created by patents increases the rate of invention over its natural rate. This proposition is difficult to determine because we do not have good “natural experiments” comparing societies with and without patent systems.¹³

The issue of incentives to bring inventions to market plays out similarly for AI as for human-made inventions. With AI, the act of creating inventions moves away from a costly, time-consuming trial-and-error process towards an automated data-crunching task. This approach drastically reduces the cost and time of inventions, such that it costs nothing for the AI machine to produce an invention—bar the computing costs.¹⁴ Producing inventions is cheap, and machines do not need to be incentivized.

However, producing the invention machines is presumably costly. Thus, the relevant question is whether these machines would be developed in a world in which their output cannot be patented. In other words, would a patent on the invention machine itself provide enough of an incentive to create the machine, or would the machine’s outputs also need to be patent eligible?¹⁵ This question is difficult to answer, as the answer depends upon a number of factors, including the costs to produce an invention machine and the ability to monetize any invention the machine creates without patent protection. At the most, if innovators cannot secure the property of their AI inventions, there is limited financial incentive to produce invention machines in the first place. On the other hand, allowing every invention produced by an invention machine to be patentable seems like a windfall to the inventor. At some point, the reward will substantially outweigh the original incentive to innovate. As a result, it is unclear whether AI-generated inventions should be patentable based on the incentive to innovate alone.

B. Do We Need Patents to Encourage the Commercialization of AI-Generated Inventions?

Although it is uncertain whether we need patents on AI-generated inventions to maintain invention incentives, patents also play a critical role in invention commercialization. To be clear, we differentiate between “invention costs,” which are assumed close to zero with the invention machine, and “commercialization costs,” which are necessary to bring the invention to market—covering activities such as development, optimization of design, market research, scale-up of production, distribution, and the like.¹⁶ In the particular but important case of pharmaceuticals and medical devices, human safety and efficacy testing also form part of commercialization costs.

Recent history provides part of the answer to that question. Let us go back in time, to 1980, and call the invention machine a “public research organization” (“PRO”). The U.S. government used to retain title to inventions and license them only non-exclusively. As we now know, this situation led to many valuable inventions being left unused. According to a governmental report, at the time, “fewer than 5 percent of the 28,000 patents being held by federal agencies had been licensed,” compared with 25–30 percent of the federal patents for which the government allowed companies to retain title to the invention.¹⁷ Thus, many valuable inventions fell into oblivion.

The context changed with the Government Patent Policy Act of 1980, also known as the Bayh-Dole Act, which allowed PROs and universities to patent and exclusively license federally-funded inventions. Research on the effects of the Bayh-Dole Act shows that university patenting and licensing revenues increased after 1980, suggesting greater use of inventions.¹⁸ Several countries in Europe adopted similar legislation, including Germany and Italy.¹⁹

This situation is known in economics as the free-good problem.²⁰ A free good has zero opportunity cost, and the textbook example is air, which everyone can freely consume. By its very nature, nobody can possibly sell a free good. The picture changes when one introduces scarcity. Consider Swissbreeze, a startup that sells “the best, most pristine and freshest Swiss canned air, gathered in the most beautiful and remote lake and mountain regions.”²¹ Swissbreeze’s business model only works because not everyone has access to fresh air, let alone from the Swiss mountains. It is easy to imagine that wealthy consumers in Delhi, India, or Anyang, China—two of the world’s most polluted cities—may want to pay a high price for a shot of fresh air. Fresh air in these cities is scarce, and breathing it has a high opportunity cost.

Only scarcity makes the business model of bottling and selling fresh air viable. By the same reasoning, only scarcity makes viable the business model of bringing an invention to the market. Put differently, the inability to secure exclusive rights to an invention limits firms’ appetite for that invention. This fate was that of many PRO and university inventions before the Bayh-Dole Act. The need for investment to bring the product to market means that at least some level of scarcity (achieved with patent protection) is warranted.²² Thus, patent protection may be necessary to ensure commercial opportunities for the output of invention machines and, consequently, for creating invention machines themselves.²³

C. Do We Need Patents to Encourage Technology Transfer?

The third rationale for granting patents is to enhance technology transfer. If an invention is not patented, inventors may keep the invention secret.²⁴ Secrecy hampers transactions in markets for technology, as it hurts the search for a licensing partner. Secrecy reduces the search to a one-sided process, in which only the owner has the ability to reach out to interested parties.²⁵ Furthermore, even if the owner of the invention identifies an interested party, contracting over the information is notoriously difficult. Once the owner discloses the information, the interested party may be able to take it without paying.

Patents help increase technology transfer in two ways. First, a patent helps enable a two-sided search process where licensees and licensors search for each other. Hegde and Luo provide evidence that the publication of U.S. patent applications 18 months after their filing date rather than at the time of the patent grant has sped up licensing transactions.²⁶ They attribute this effect to the patent system being a “credible, standardized, and centralized repository [that] mitigates information costs for buyers and sellers.”²⁷ Second, patents may help solve the information disclosure paradox. Patent rights are legal title that protects buyers against the expropriation of the traded idea, including when searching for a licensing partner, which also facilitates technology transactions.²⁸

Implicit in this argument is that a transfer must occur between invention producers and implementers. Such transfers are necessary in the case of PROs and universities, which produce non-market-ready inventions and do not commercialize products. However, owners of invention machines may very well implement the inventions themselves. In practice, many patented inventions are traded and licensed on markets for technology.²⁹ Using patent reassignment data, Serrano found that about 12–16 percent of U.S. patents are traded over their lifecycle,³⁰ while Ciaramella et al. found 12 percent of European patents in medical technologies are traded.³¹ Furthermore, we may speculate that invention machines will exacerbate the division of innovative labor. Creating invention machines is costly, but producing inventions is cheap and fast, which may lead to more specialization (in other words, inventors versus implementers). In addition, the skills and capabilities required for creating invention machines differ drastically from those required to commercialize the inventions. If invention machines lead to a greater division of labor (where producers of inventions do not implement them), the issue of technology transfer will become particularly salient.

*******

In summary, under the traditional theory of incentives to innovate, it is uncertain whether AI-generated inventions should be patent eligible. AI makes inventing cheap, and AI machines do not need to be incentivized to invent. However, producing the AI invention machine is presumably costly. It is unclear whether these machines would be developed if their outputs cannot be patented. A stronger case for patenting AI-generated inventions is made under commercialization and technology transfer rationales for patents. Without protection, the output of the invention machine becomes more challenging to transfer and commercialize, which reduces the incentives to invent and develop such machines in the first place. Moreover, AI-generated inventions may result in the further stratification of labor markets, where producers of inventions do not commercialize them. This division of labor would make it more critical for AI-generated inventions to be patentable, as patents facilitate both technology transfer and commercialization. Of course, patents also impose costs on society, such as limiting competition and access to the invention. Thus, the benefits of allowing patents on AI-generated inventions should outweigh the costs. While we believe these arguments taken together make the uneasy case for allowing AI-generated inventions to be patented, we acknowledge that it is difficult to say so definitively. Before considering whether the patent system should treat AI-generated inventions differently, we discuss a potentially significant implication of AI systems for the patentability standards.

II. THE EXISTENCE OF AI-GENERATED INVENTIONS AND IMPLICATIONS FOR THE NON-OBVIOUSNESS STANDARD

The emergence of inventions generated by AI systems also has implications for how we interpret patent validity. At any given time, there is an unknown but presumably large set of inventions that are makeable in the sense that humanity’s underlying knowledge and technology base has advanced to the point where they are a feasible step beyond what has come before—an argument known as the “inevitability of inventions” at least since Ogburn and Thomas,³² and Ihde.³³ Historically, the flow of patent applications from this unknown feasible pool has been determined by some combination of the contemporary socio-economic context, the breadth of human ingenuity, and the resources devoted to finding them. The addition of AI systems to the technology for fishing in this pool of potential inventions will likely significantly relax the latter two constraints. Human ingenuity will quite literally no longer be necessary, and the cost of exploration may be so dramatically reduced that resources available for inventing will be much less binding (perhaps almost irrelevant) as a constraint.

To begin, countries are not uniform in allowing a machine to be an inventor of a patent. Appeals courts in both the United States³⁴ and England³⁵ have held that machines cannot be inventors of patents. In contrast, Australia and South Africa allow machines to be inventors of patents.³⁶ Thus, we acknowledge that the patent acts of some countries, such as the United States, may need to be amended in order for machines to be inventors of patents. Assuming such reform efforts will occur, the rest of this Part examines how AI-generated inventions may affect the non-obviousness standard of patentability.

An invention is deemed obvious (and, therefore, not patentable) if the differences between what is claimed and what has been done before are such that it is obvious to a person having ordinary skill in the art (“PHOSITA”) how to adapt existing technology to make the proposed invention.³⁷ The level of skill associated with the PHOSITA is critical in the non-obviousness inquiry. The PHOSITA is defined as an average person in a given field with “ordinary creativity, not an automaton,”³⁸ who has access to the same tools, skills, and knowledge base. The more skilled the PHOSITA, the more likely a new invention is obvious. Another key determinant of the obviousness inquiry is establishing what constitutes prior art, which references such as scientific articles may be used to determine whether an invention is obvious. The more prior art that can be considered, the more likely an invention is obvious. The emergence of AI systems for invention will likely have at least two ramifications for the obviousness inquiry.

First, we must confront the question of whether the PHOSITA includes AI systems. Said differently, if a proposed invention could have been adapted from existing technologies by a normally-skilled AI system, does that make the invention obvious and, hence, invalid? Currently, because most fields do not use AI, inventors do not have to disclose the use of AI to the Patent Office. Consider a scientist who decides to use neural networks to help come up with a new microchip design. The AI might help her calculate the ways that different materials can impact the microchip’s operations. The new microchip may represent an improvement in the technology, but if an ordinary microchip inventor could have arrived at the same invention, then the new microchip would not qualify for a patent. However, suppose the AI assists in developing a novel microchip design that is beyond the skill of the ordinary microchip inventor to design. In that case, the invention may qualify for a patent. As more companies and inventors use AI to create new inventions, the legal standard will have to adapt. At some point, patent examiners will have to start assuming that a PHOSITA, which is a legal fiction that is presumed to know the relevant prior art, has access to AI, which will raise the bar for obviousness in the patent process.

Second, AI machines may alter the analogous art doctrine, which limits the prior art considered in an obvious inquiry to only prior art in the same field of the invention³⁹ or reasonably pertinent to the problem faced by the inventor.⁴⁰ Because an obviousness inquiry often involves combining multiple prior art references to render the invention unpatentable, the analogous art doctrine was adopted by courts to reflect the practical conditions facing an invention. An inventor likely would focus on this type of prior art when inventing. Adopting a “normally skilled” AI system as the PHOSITA could lead to a reconsideration of the analogous art doctrine. A normally skilled AI system may easily search the entire world of prior art (including patents and printed publications, but also technical blogs, standard documents, and other resources), and thus removing the analogous art limitation on the obviousness inquiry may reflect the practical realities of shifting the skilled artisan to a skilled AI system. Such removal would also result in raising the bar to patentability.

There are, however, some difficulties associated with trying to define a “normally skilled” AI system. Making the determination as to what represents an inventive enough leap for a person of ordinary skill is challenging enough; doing so for an AI machine may be even more challenging. To begin, it seems difficult to distinguish the AI system that did find the invention from the fictional one that could have. This problem does not arise with human inventors because we accept as a matter of course that each human is unique, and a given invention can come from one human’s spark of genius without suggesting that any skilled human could have done it. Making this distinction for AI systems seems much harder.

A number of commentators argue that a PHOSITA AI system will place the bar for non-obviousness implausibly high, as a PHOSITA using AI can potentially create every invention—rendering “everything obvious.”⁴¹ However, as several commentators also note, this conception of AI currently is more science fiction than science,⁴² in that AI only works within circumscribed attributes that humans input. Importantly, our piece is explicitly assuming AI-generated inventions. In such a scenario, it is important to keep in mind that AI systems likely would raise the non-obviousness bar, making patents harder to obtain in the future.

III. A DIFFERENTIATED PATENT SYSTEM?

The previous Part considers how AI inventions may affect the non-obviousness standard. Assuming for the sake of the argument that AI-generated inventions are patentable, we turn now to considering whether we should treat AI-generated patents differently from patents on inventions generated by humans.

While the first Part of this Article makes the case for patent protection of AI-generated inventions, we have not yet addressed how strong such patent protection should be. At first, this problem seems a special case of sequential innovation with just one chain—that is, the invention machine and its inventions. Unfortunately, the vast literature on IP rights and sequential innovations is of little help. It usually assumes (1) that firms compete in the generation of follow-on inventions and (2) that follow-on inventions improve or complement, in some ways, the original invention.⁴³ In the present case, the same firm controls both the invention machine and the downstream inventions. Furthermore, downstream inventions are quite distinct from the invention machine itself.

It might be helpful to think of the invention machine and its offspring as one “mega invention.” This mega invention is characterized by high fixed costs (the cost of producing the machine) and low marginal costs (the cost of producing one more invention using the machine). Taking such a perspective leads to an intuitive parallel with the existing literature on optimal patent strength. If we allow downstream inventions to be patented, the fractional nature of the mega invention implies that more valuable (or fruitful) mega inventions will receive stronger protection. Put differently, mega inventions associated with a larger offspring will receive a larger number of patents—and thus, broader patent protection. In that simple setup, the breadth of patent protection is proportional to the inventive potential of the mega invention. A priori, such a naturally differentiated breadth of protection may seem desirable.

However, simply allowing more patents to more fruitful mega inventions may not be the first best. This discussion naturally takes us back to the literature on optimal patent breadth.⁴⁴ From a theoretical perspective, optimal patent incentives will always depend on the incentive structure of the invention and investment processes, which clearly differ across technologies and markets. Thus, the first-best patent policy has to be a highly differentiated one, in which many aspects of the patent process and characteristics of patent protection differ for different kinds of inventions.⁴⁵ This route is sometimes encouraged in the policy literature, which argues in favor of “a more differentiated approach to patent protection that depends on specific characteristics of the inventions.”⁴⁶

In the present context, the first best might be a differentiated system for AI-generated and man-made inventions, reflecting the fact that the invention processes are intrinsically different. A differentiated system requires a sui generis IP right, as already pointed out by some scholars.⁴⁷ In practice, we do not and cannot implement first-best policies; political and institutional realities and myriad information and transaction frictions constrain actual policies.⁴⁸ At the most fundamental level, the theoretical argument for differentiated patent treatment assumes that it is costless to separate different types of inventions from each other. A patent policy that awards AI longer/shorter or stronger/weaker patents than other inventions would require an articulated set of criteria that determine whether an invention is “AI” or “not AI.” If being “AI” resulted in less desirable treatment, we can be sure that applicants will figure out ways to characterize their inventions to meet the “not AI” criteria—and even more so if AI-generated inventions are deemed not patentable. We cannot know what fraction of truly-AI inventions would manage to escape the screen, but this positioning battle would inevitably waste resources and confuse the examination process. Recent history confirms this fear. In 2000, the United States Patent and Trademark Office (“USPTO”) held that business method patent applications would be subject to a “second pair of eyes” review (“SPER”), unlike other patent applications.⁴⁹ Allison and Hunter show that the introduction of SPER led applicants of business method patent applications to write their applications so that they would not be subject to the extra review.⁵⁰

A second problem with a differentiated patent system is that any differences in treatment would have to be introduced by statute, at least in the United States. Patent policy already is a highly political process. If legislation treating AI inventions differently were to be passed, it does not require a high degree of cynicism to expect that the differentiation eventually ending up in the legislation might bear little relation to what was suggested by the first-best theoretical analysis of incentives.

Further, there is a danger that such discussion would open a bigger door: if AI patents are to be treated differently, other interests would be sure to jump in and argue that their patents should be treated differently. And in each case, the interests most affected by such differentiation would be those who expect to apply for the new special category. They have much more at stake in seeking favorable treatment than anyone has at stake in protecting the broader public interest. Opening the door to special treatment might well result in a series of differentiations in which particularly active groups get favorable treatment. Again, believing or hoping that theoretical results from welfare optimization would drive the differentiation seems naïve.

Moreover, the creation of a sui generis right could distort the innovation ecosystem in unintended ways. Consider the case of a company that has a choice between allocating human pharmacologists and investing in an AI system to develop a new vaccine. It is not clear that we want to create a system whereby the firm decides to pursue one option over another depending on the type of right it will get at the end. The new vaccine should be produced in the most efficient manner, and IP rights should be neutral to this choice.

Finally, the creation of a differentiated patent system might run afoul of international treaty obligations under the Trade-Related Aspects of Intellectual Property Rights Agreement (“TRIPS”). TRIPS requires signatories to provide a minimum set of standards for all patents, such as the stipulation that the term of a patent must last at least twenty years from the filing date.⁵¹ However, it may be possible to create new sui generis intellectual property rights for AI-generated inventions that do not violate TRIPS obligations if such rights are not conceived as patents.⁵² A complete examination of this issue is beyond the scope of this Article.

Overall, although a differentiated system might be the first best solution, the realpolitik of the patent system suggests that developing a patent policy specifically for AI inventions is not likely to improve public policy and may violate international obligations.

IV. TAKING INVENTION MACHINES SERIOUSLY

This Part examines the bigger-picture implications of allowing patents on AI-generated inventions. In particular, this Part argues that patents on AI-generated inventions may overwhelm the examination capacity of national patent offices, increase the concentration of patent ownership, increase patent thickets, and lead to unlimited inventions. This Part also begins to examine changes to patent practice that might be desirable in light of these potential implications.

A. The Examination Process

It is easy to see why invention machines pose significant challenges to the functioning of the patent system. The first challenge is a potential backlog at patent offices that would come with a patent application explosion. Examining patent applications is (currently) a labor-intensive, time-consuming task. If inventing becomes cheap and fast, patent offices may not keep up with the increasing demand for examination.⁵³ The “global patent warming” of the mid-2000s,⁵⁴ which put the U.S. and European patent systems under strain, might look pale in comparison. Pendency could reach excessively long delays, which is detrimental to welfare.⁵⁵

The obvious policy response is that patent offices must also use AI to speed up the examination process. Currently, a third-party contractor with the USPTO utilizes AI to classify new patent applications so that they route to patent examiners with the right technological expertise.⁵⁶ The USPTO has also considered incorporating AI to improve prior art searching of patent examiners.⁵⁷ AI holds great potential for improving the search process associated with patent examination as well as locating relevant passages in the prior art, mapping them to elements of the current application’s claims, and hence suggesting potential rejections. Admittedly, AI may not be as helpful in reviewing patent applications on newer subject matters where inventors are just developing new patentable technologies.

Moreover, it seems unlikely that legislators will authorize a fully autonomous examination, that is, the automatic granting of traditional patent rights without a human in the loop. Some human intervention in the patent examination process may be necessary to satisfy a patent applicant’s due process rights or administrative law’s reason-giving requirements under current law.⁵⁸ Moreover, effectively keeping up with the increase in patent numbers requires patent offices to adopt AI tools as sophisticated as those of the most advanced applicants, which does not seem likely.⁵⁹ Because the need for human intervention puts a hard constraint on examination time, it is safe to assume that, on balance, pendency most likely will increase.⁶⁰

The USPTO has some experience with an increased onslaught in patent applications in the past. In the 1990s, the agency experienced a torrential rise in the number of patent applications filed on express sequence tags (“EST”) or small fragments of DNA.⁶¹ The USPTO estimated that it would take a single examiner over 90 years and cost the Agency upwards of 20 million dollars to review the EST patent applications in its queue. As a result, then USPTO Commissioner Bruce Lehman considered several possible changes to combat the growing backlog of DNA patent applications, including requiring patent applicants to do more work themselves or contract out the research for searching the prior art.⁶² Patent offices can consider these same approaches with respect to AI-generated patent applications.

Contracting out the research, however, would have the same problems as addressed above. That is, any contractor likely would need access to AI tools as sophisticated as those of the most advanced applicants. An alternative may be to require patent applicants on AI-generated applications to conduct their own patentability search and identify the most relevant prior art when they submit their applications to patent offices. Shifting the prior art search on the applicant would ease the burden on the patent offices as well as harness the most up-to-date AI search tools.⁶³ Moreover, the common refrain against requiring more search efforts of patent applicants—that such efforts would increase the cost of patenting and hence reduce patenting efforts for cost-conscious applicants—has less force for AI-generated inventions.⁶⁴ Given that invention machines presumably have processed and screened the prior art for coming up with the invention, it would be reasonably straightforward to identify the closest prior art. Nonetheless, shifting the patentability search to the applicant has its own set of drawbacks. Applicants, whose incentives may arguably cut against doing an exhaustive search, may find ways to game the search process.⁶⁵

Other work-sharing options may also ease the administrative burden associated with a rapid influx of AI-generated patent applications. The USPTO has patent work-sharing arrangements with foreign intellectual property offices to improve patent examination efficiency. Patent work-sharing permits patent offices to collaborate in the examination of commonly filed patent applications, reducing inefficiencies that patent offices experience when doing largely duplicative research into questions relating to patentability.⁶⁶ The most famous of these work sharing efforts occurs through the Patent Prosecution Highway (“PPH”) programs, in which the partial examination of an application in one office can result in the expedited review of that application in another office.⁶⁷ Various reports suggest that PPH results in faster and cheaper reviews of patent applications.⁶⁸ Nevertheless, in the fiscal year of 2021, the 6,000 patent applications filed under PPH are minuscule in comparison to the 650,000 patent applications filed at the USPTO.⁶⁹ As a result, work-sharing efforts seem unlikely to do much to combat the increase in filings associated with AI-generated patent applications.

A more radical approach might be to, in effect, aggregate examinations of patents produced by the same AI invention machine. Applicants could apply to have a specific AI algorithm certified as reliably generating novel and non-obvious inventions. Subsequent applications that could be shown to be the output of certified machines would be presumed valid and granted patent protection.⁷⁰ This two-track system does not necessarily imply a differentiated patent system since the nature of the patent right granted is the same across both tracks. Further, it does not seem to introduce the problem of people gaming the system to qualify for or avoid special treatment. An applicant could submit an “invention machine” for approval. Examiners would not need to determine whether the submitted “machine” meets some definition of AI; they would need only to determine whether or not it reliably produces inventions that meet the standards for patentability.

B. Market Impacts

The second challenge of cheap and fast inventions is the potential effects on the markets for innovation. This Section identifies two potential market impacts of allowing patents on AI-generated inventions. First, AI-generated inventions could result in an increase in the concentration of patent ownership. Owners of invention machines would have the opportunity to amass vast patent portfolios, possibly conferring on them strategic advantages over their rivals.⁷¹ Along this line, Professors Choi and Gerlach have shown that an increase in one firm’s patent portfolio unambiguously reduces the rival firm’s incentives to develop a new product. One could also think of more severe chilling and blocking effects.⁷²

Second, a market-related issue of a burst of inventions is an exacerbation of the problem of patent thickets, namely overlapping and fragmented patent rights.⁷³ Intertwined patent rights increase litigation risks for innovators, and the transaction costs associated with clearing these rights may become prohibitively expensive. This is especially true in industries in which many patent-protected technologies are necessary to manufacture a single product, such as a smartphone.

Relatedly, increased market concentration of patenting and patent thickets could also lead to the emergence of a new genre of patent assertion entities (“PAEs”), taking hold-ups and nuisance settlements to new heights. The leading critique of PAEs is that they assert weak or invalid patents against product manufacturers to extract nuisance settlements, which in turn stunt innovation.⁷⁴ While there is no reason to think that AI-generated inventions are inherently of lower quality than human-generated inventions, the rise of patenting fueled by AI-generated inventions could lead to more overlapping patent rights and could decrease the costs of amassing vast patent portfolios. Product manufacturers who face patent thickets often settle through cross-licensing agreements. This process is not possible for PAEs as they do not produce any products or services that could potentially infringe anyone else’s patents. Thus, an increase in patent thickets and a decrease in barriers to amassing vast patent portfolios may create tantalizing opportunities for PAEs.

The adverse welfare effects of vast patent portfolios and patent thickets suggest that rewarding machine-made inventions with as many patents as inventions produced may offer too large a reward. Considering that invention machines have high fixed costs and low marginal costs, there must be a point at which the machines are generating large numbers of very low value inventions. Past this point, additional patents have value to their owners only through the market power generated by a larger portfolio.⁷⁵ This optimal threshold is private information and varies across invention machines.

One could imagine several mechanisms to limit patent portfolios’ strength. The suggestion above of creating the applicant option to have an invention machine certified as producing patentable inventions likely would exacerbate the portfolio market power and patent thickets problem, but it also offers potentially incentive-compatible ways to limit such market power. Patents granted through this route could bear limitations such as a shorter validity period or forced availability under Fair, Reasonable, and Non-Discriminatory (“FRAND”) clauses—although FRAND clauses come with their own set of challenges.⁷⁶ However, as noted in Part III these limitations would need to be carefully crafted so as not to violate international treaty obligations under the TRIPS Agreement. Other options exist, such as increasing application fees with the size of the assignee’s patent portfolio or for each new invention produced by the same machine.

Putting conditions on patents from invention machines that potentially reduce the value of the patents would, again, introduce greater differentiation into the system. But this could perhaps be incentive-compatible rather than wasteful. It will be up to the applicants to decide whether to seek approval of an invention machine, and if they have an approved machine, whether to submit each new invention as a product of the machine or as a standard application. The machine route will yield faster but less valuable patents, while the standard route will yield slower but more valuable patents. In principle, these tradeoffs could be calibrated to limit the market power of vast portfolios while still affording appropriate incentives to patent the best inventions. Nevertheless, a differentiated system would still suffer from the political economy concerns set forth in Part III.

While it seems a priori desirable to limit the strength of AI-generated patent portfolios, the best mechanism to achieve this aim is unclear and deserves a careful theoretical investigation.

C. Unlimited Inventions?

Finally, even if the flood of inventions from AI is not all patented, the democratization of invention machines could still have systemic consequences for the patent system. Owners of such machines might not patent their inventions but generate a vast amount of prior art. This prior art would naturally form part of the literature used to assess the non-obviousness of inventions, implicitly raising the bar to obtain patents in these areas—perhaps to a point where it would be extremely challenging to obtain patents in a given area.⁷⁷ Firms may want to flood a technological area with prior art to ensure freedom to operate.⁷⁸ This practice could essentially impose patent-free technological zones with unknown consequences on product development and commercialization. Such situation would have similar consequences to allowing an AI-augmented PHOSITA. The issue would not be that the AI-augmented PHOSITA could have produced the invention, but an acknowledgement of the fact that a large pool of prior art exists that renders the invention obvious.

Taking this argument a step further (and maybe too far), suppose AI got so skilled at invention that invention itself became essentially irrelevant. Imagine a world where in some sense every invention that could possibly be made at a point in time was known to everyone, or knowable to anyone who cared at very low cost. At this point, there would be no need to provide any incentive for people to invent; indeed it would become somewhat unclear what it even meant to invent something. But there may still be a social need to provide incentives for people to invest in commercializing inventions, as argued above.⁷⁹

To make this consideration concrete, consider the (admittedly artificial) hypothetical case in which every chemical compound that might have therapeutic benefits to humans was known or knowable, so no one could meaningfully “invent” a new drug. But it still costs millions to test the drug in humans. We would want companies to pay to run those tests, but they would not do so if anyone could then sell the drug because it was proved safe and effective. In that world, we might want to give companies some kind of exclusive right to test and then market new drugs. But we couldn’t use first to file as the criterion to determine who got that right. One could imagine a different kind of examination system, where companies made proposals for developing products out of the pool of available inventions, and were somehow evaluated on how much they proposed to invest and/or how good their development plan was. But that sounds hard. To economists, an obvious solution would be to auction the rights. The development of a particular invention out of a publicly-known pool is somewhat like a slice of electromagnetic spectrum in a given geographic area. We want someone to use it, but we don’t want more than one entity to use it, so we auction it off.

We raise these possibilities neither to say that we know that AI will get that good, nor to suggest that we have done any careful analysis of the merits of public auctions for invention development rights. Rather, we only want to suggest that if AI becomes extremely successful at invention, we will need to think about potentially radical changes to innovation policy.

CONCLUSION

Patent law has traditionally adapted slowly to the changing environment. In 2004, the U.S. National Research Council issued a report entitled “A Patent System for the 21st Century.”⁸⁰ The report addressed issues that had plagued the U.S. patent system for decades or more, including questionable patent quality, impediments to disseminating information through patents, and international inconsistencies.⁸¹ Some inconsistencies, such as the United States’ first-to-invent principle compared to the rest of the world’s first-to-file principle, existed since the Patent Act of 1790. Many of the issues discussed in the report have not yet come to the fore. While they could materialize sooner than expected, the legislator is unlikely to act faster than expected. We hope that the patent system will be ready for the 22nd century by discussing these issues now.

In our view, some form of IP protection for AI-generated inventions is likely desirable. However, the nature of the IP regime is unclear and deserves in-depth theoretical and empirical examination. Regardless of whether AI-generated inventions are patentable, if AI radically reduces the cost and increases the production rate for inventions, it will have implications for the patentability standards that will have to be addressed. In addition, AI-generated inventions will have significant implications for the patent ecosystem more generally. A large increase in the rate of generation of patentable ideas will potentially overwhelm the examination process (if AI-generated inventions are patentable), make patents unavailable in wide swaths of technology (if AI-generated inventions are not patentable but saturate the prior art), and increase the concentration of patent ownership and the likelihood of patent thickets.

We have proposed a series of potential solutions to these problems. We do not claim that any of our proposed solutions are the best. We note also that AI-generated inventions have the potential to exacerbate the problem of increasing market power from highly concentrated patent portfolios, and that certifying invention machines might make this problem worse. Our hope is that this Article illustrates a need to seriously consider the protection of AI-generated inventions and that creative solutions do exist, but those solutions may have complex ramifications that should be thought through. In addition, these solutions also require global cooperation to harmonize legislations. Meanwhile, some concrete steps may already be implemented, such as a change in disclosure requirements. By forcing patent applicants to disclose the extent of the involvement of AI in the invention process, it becomes possible to track AI-generated inventions. This step is necessary to quantify the phenomenon and empirically study its effects.

The pressure for changes in the system that AI-generated inventions may create is also an opportunity. The structure of our current system is essentially the result of historical accident. As noted, it is difficult to measure the consequences of the system, or of specific aspects of the system, because we do not have natural experiments that allow us to test one practice against another. If changes are to be made in response to these new pressures, they should be structured initially to provide explicitly for quantified evaluation of the effects of new policies and procedures, potentially including structures such as randomized control trials that isolate the causal effect of specific changes.⁸²

There is little doubt that confronting the implications of AI playing a role in the invention process is now on the agenda, and is likely to become more and more important. This paper’s focus on one set of issues should not be taken to mean that these issues are the main challenges facing tomorrow’s patent system. Nor does it mean that there are no other ways of modernizing the patent system.⁸³ But AI is a rapidly evolving set of technologies, and the longer we delay determining how the innovation system should respond, the more likely we are to see socially undesirable consequences.

96 S. Cal. L. Rev. 1453

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Gaétan de Rassenfosse*

* Associate Professor, College of Management of Technology, Ecole polytechnique fédérale de Lausanne, Switzerland.

Adam B. Jaffe†

† Professor Emeritus of Economics, Brandeis University; Senior Research Associate, Motu Research, Wellington, New Zealand.

Melissa Wasserman‡

‡ Charles Tilford McCormick Professor of Law, Associate Dean for Research, University of Texas School of Law.

The Meme Stock Frenzy: Origins and Implications

April 2024April 2024Dhruv Aggarwal,* Albert H. Choi† & Yoon-Ho Alex Lee‡

In 2021, several publicly traded companies, such as GameStop, Bed Bath & Beyond, and AMC, became “meme stocks,” experiencing a sharp rise in their stock prices through a dramatic influx of retail investors into their shareholder base. Analyses of the meme stock surge and its implications for corporate governance have focused on the idiosyncratic creation of online communities around particular stocks during the COVID-19 pandemic. In this Article, we argue that the emergence of meme stocks is part of longer-running and more structural digital transformations in trading, investing, and governance. On the trading front, the abolition of commissions by major online brokerages in 2019 reduced entry (and exit) costs for retail investors. Zero-commission trading represents a modification of the payment for order flow (“PFOF”) system, which is itself a product of technological disruptions in the financial markets in the 1980s. With respect to investing, the emergence of social media communication amplified retail investors’ pre-existing dependence on social networks to make decisions regarding stock investing and portfolio construction. It also allowed them to coordinate their investing activities and affect the market price while expressing their non-financial interests. These structural changes imply that meme trading is here to stay. While some startups have attempted to bring the shareholder experience into the digital age and help retail investors participate in governance, these developments have been relatively modest. After tracing the meme stock phenomenon, we sketch a research agenda for law and finance scholars to explore the concrete effects of meme investing on corporate governance. First, we ask whether retail traders can transform into retail shareholders actively engaged in corporate governance. Second, we propose a broader metric for “meme-ness”: future scholarship can use modern advances in data science to better identify which companies are vulnerable to meme surges and social media-driven investing unrelated to their financial fundamentals.

INTRODUCTION

In the midst of the COVID-19 pandemic, the U.S. stock market experienced a rather unusual phenomenon. Several publicly traded companies, such as GameStop, Bed Bath & Beyond, and AMC, became “meme stocks” and experienced a dramatic influx of retail investors into their shareholder base.¹ A large number of retail investors responded to and engaged in a coordinated buying campaign, and over a short period of time, the stock prices surged to stratospheric levels.² Some of those companies, notably AMC and GameStop, took advantage of the surge and were able to raise a large amount of capital at elevated stock prices, thereby substantially improving their liquidity and solvency positions.³ While the stocks are no longer trading at such historic highs, prices are still (much) higher than the pre-surge levels, and many retail shareholders are staying “loyal” to the companies.⁴

The “meme surge” phenomenon, particularly the dramatic shift in shareholder base away from institutional ownership, presents a unique opportunity for analysts and scholars to (re)evaluate the current understanding of corporate finance and governance. To date, the observers of the meme stock surge and its implications for corporate governance have mostly focused on the idiosyncratic creation of online communities around individual stocks during the COVID-19 pandemic.⁵ The goal of this Article is to take a broader and longer-term view of the technological developments undergirding the meme surge. In so doing, we also sketch out a research agenda for scholars studying this topic.

We argue, in particular, that the emergence of meme stocks is part of longer-running and more systemic digital transformations in trading, investing, and governance.⁶ On the trading front, major online brokerages suddenly abolished commissions in 2019. This change echoed the business model of the popular retail trading app Robinhood, which had been growing its market share by not charging trading commissions. The abolition of commissions reduced (or eliminated) entry (and exit) costs and thereby encouraged greater retail investor participation in the stock market.⁷ Incidentally, zero-commission trading represents a modification of the payment for order flow (“PFOF”) system, which is itself a product of technological disruptions in the financial markets from the 1980s.⁸ With respect to investing, the growth of social media communication amplified retail investors’ pre-existing dependence on social networks to make decisions regarding stock investing and portfolio construction.⁹ These structural changes imply that the stock market is likely to experience meme trading and meme surges on an ongoing basis. Finally, while some startups have attempted to bring the shareholder experience into the digital age and help retail investors participate in governance, so far, these developments have been relatively modest.¹⁰

After examining the background technological developments—that we believe meaningfully contributed to the meme surge phenomenon—we sketch a research agenda for law and finance scholars to explore the concrete effects of meme investing on corporate governance outcomes. First, we ask whether retail traders can transform into retail shareholders actively engaged in corporate governance. Was the meme surge experience a social phenomenon limited to trading markets, or could it translate into a broader signal of engagement by retail shareholders? Some legal scholars have predicted that we will see more active retail shareholder engagement in governance issues, in terms of either traditional (bringing, or voting on, proposals) or contemporary (environmental, social, and governance (“ESG”) performance) dimensions.¹¹ At least in theory, one could argue that those retail investors who remain as shareholders after the surge would care about firm governance and performance and more actively exercise their rights as shareholders. While the jury is still out on the longer-term effect of meme-driven market entrants, to the extent that the meme surge event was driven mostly by coordinated trading rather than coordinated voting, it remains uncertain whether such an explosion of “retail governance” would, in fact, occur. Second, another puzzle presented by the meme surge was why some companies experienced the retail investor influx while other (similarly situated) companies did not. To address this puzzle, we explore a broader metric for “meme-ness,” and suggest that future scholarship should use modern advances in data science to better identify which companies are vulnerable to meme surges and social media-driven investing unrelated to their financial fundamentals.¹²

The Article is organized as follows. In Part I, we take a historical approach to sketch out the emergence and popularization of zero-commission trading by tying it back to the adoption of the PFOF protocol in the 1980s, under which broker-dealers get “rebates” from wholesalers (or “internalizers”) for delivering orders from their clients. In many ways, the elimination of trading commission for the retail shareholders, leaving broker-dealers to rely solely on PFOFs, was a logical evolutionary step from the PFOF system of the 1980s. In Part II, we take a closer look at the meme surge phenomenon, tying together several different factors: zero-commission trading, coordination through social media, and predatory trading. We also briefly discuss the implications of meme trading for securities regulation and consider the recent arguments about the shift towards retail shareholder base and possible democratization of corporate governance. Part III lays out a future research agenda, both with respect to coordinated voting and governance engagement and identification of meme stocks.

I. DIGITAL TRANSFORMATIONS IN TRADING, INVESTING, AND GOVERNANCE

A. Digital Transformation in Trading—Payment for Order Flow, 1980s and 2010s

In important ways, the meme stock revolution can be traced back to an unlikely digital transformation: Bernie Madoff’s promotion of the PFOF system in the 1980s. In 1983, following a congressional mandate, the Securities and Exchange Commission (“SEC”) required stock exchanges, like the New York Stock Exchange (“NYSE”), to publicly broadcast trading data in real time. This development marked a step toward bona fide “democratization” of investing: the market-making process of matching buy and sell orders on the NYSE was no longer restricted to its own specialists. Using the NYSE’s broadcasted quotes, market-makers in other venues, such as Madoff’s firm in the National Association of Securities Dealers Automated Quotations (“Nasdaq”), could execute trades on the NYSE at the best prices.¹³

PFOF is a conceptually straightforward system. A brokerage agrees to send its clients’ orders to another firm, often an internalizer or a wholesaler—such as Citadel and KCG Americas—which is a trading venue that matches buy orders with sell orders, in return for a small fee per transaction. After executing the order, the trading venue returns the payoff to the broker, which in turn transmits it to the client.¹⁴ Note that the broker is making money in two ways: from the transaction fee it collects from its client and from the trading venue. While the per-transaction fees paid by the trading venues under the PFOF system is a fraction of a dollar, the aggregate revenue accrued by brokers across thousands or millions of daily transactions can be economically significant. The trading venue, on the other hand, profits off the bid-ask spread and is guaranteed a higher volume of transactions because of its contractual arrangements with brokers.¹⁵ Madoff’s investment firm pioneered PFOF and acted as a trading venue in the 1980s, paying brokers one cent per share transmitted.¹⁶ This was a significant departure from the pre-PFOF system, in which the NYSE charged brokers between one and three cents to execute orders.¹⁷

From its beginnings in the 1980s, the PFOF ecosystem has revolved around the retail investor. Notably, Madoff would only perform market-making activities for orders of 5,000 or fewer shares¹⁸—on the understanding that these were uninformed retail investors who needed liquidity rather than informed professional traders who had superior information about the “true” value of the stock. Moreover, Madoff would avoid brokerages where a high share of traders was informed in order to avoid the economic phenomenon of “adverse selection.”¹⁹ Adverse selection is a widely-studied phenomenon wherein actors participate in economic activity because they possess “hidden knowledge.”²⁰ Applied to the PFOF context, a trading venue’s (such as Citadel) expected returns decrease if the investors on the other side are informed about the true value of the stock.²¹ Therefore, PFOF’s origins are inextricably linked to the notion that retail investors are relatively uninformed or unsophisticated, and primarily driven by liquidity concerns.

At a basic level, meme trading is a consequence of the classic PFOF model on steroids. In the mid-2010s, Robinhood pioneered the zero-commission model, charging users no commissions for placing trade orders.²² This zero-commission model was the driving force behind Robinhood’s emergence as the app of choice for young retail investors, who could now access the markets costlessly.²³ While the broker in the classic PFOF model was making money from two channels (first, the commission from the client, and second, the payment from the market-maker), Robinhood’s disruptive business model now focused exclusively on raising revenue through the market-maker’s payments for order flow. Robinhood’s hope was that the abolition of commissions would raise volumes from retail investors enough to compensate for revenues now solely depending on payments from its market-maker, Citadel.²⁴

Robinhood was a maverick—the new entrant whose unique business model allowed it to steal market share from more established online brokerages. Due in part to its innovation, Robinhood was able to grow relatively quickly. Older and established brokerage firms eventually responded to Robinhood’s challenge. On October 1, 2019, the major online brokerages Charles Schwab and TD Ameritrade eliminated commissions for all their customers.²⁵ These platforms were quickly followed by another major online brokerage, E-Trade. Collectively, these entities had dominated the online brokerage business before the emergence of Robinhood.²⁶

The significance of this event cannot be overstated. The advent of zero-commission trading has been widely cited as a root factor in the explosion in retail investing activity.²⁷ Indeed, one of the leading financial economics explanations for individual non-participation in the stock market is that there is a cost of investing (including the brokerage commissions) that deters the less wealthy from participating in the market.²⁸ By reducing the entry cost of trading (for example, brokerage commissions), the sudden 2019 decision by the major brokerages increased retail investor entry into the stock market.²⁹

Figure 1, which is replicated from our companion paper,³⁰ validates the importance of the abolition of commissions—specifically, for turnover in companies that experiences meme surges. The bar graphs show the average daily turnovers, that is, the percentage of outstanding shares that are traded, separately for companies that experienced meme surges (later) and other firms. The companies include AMC, Bed Bath & Beyond, Blackberry, Express, Inc., GameStop, Koss, Robinhood, and Vinco Ventures. They are identified based on Factiva and internet searches, as well as a survey of the nascent literature on meme stocks.³¹ The data for share turnover comes from the Center for Research in Stock Prices (“CRSP”). As Figure 1 indicates, while these firms had always seen a larger proportion of their outstanding shares traded, they saw a massive increase in turnover both after the abolition of commissions in October 2019 and the surge in 2021.

Figure 1. Average Turnover for Meme Stocks and Other Firms

Notes: This figure shows a graph of the mean share turnover (shares traded each day as a percentage of total outstanding common stock) according to CRSP data. The data is presented separately for meme and non-meme stocks. Meme stocks include AMC, Bed Bath & Beyond, Blackberry, Express, Inc., GameStop, Koss, Robinhood, and Vinco Ventures. “Pre-Zero Commission” refers to the period from January 2015 to September 2019, “Post-Zero Commission” refers to the period from September 2019 to December 2020, and “Post-Meme Surge” refers to the period from January 2021 to December 2022.

Put differently, viewed from the perspective of the longer institutional history of PFOF, the retail investor surge in companies like AMC and GameStop was less like a revolutionary break from history and more akin to the episodic technology-driven upheavals in financial markets. Like the live transmission of NYSE quotes and evolution of the classic PFOF model in the 1980s, the emergence of Robinhood and zero-commission trading in recent years allowed retail investors to participate in financial markets. While retail investor coordination through social media websites is clearly a novel contributing feature of the meme phenomenon, the longstanding role of digital disruptions and the PFOF model cannot be ignored.

B. Digital Transformation in Investing—Reddit and r/Wallstreetbets

Meanwhile, the online retail investing world was going through its own set of transformations. Social networks are central to the behavior and impact of retail investors. Inexperienced retail investors frequently turn to friends and family members for investing advice.³² Financial economists, for instance, have found that retail investors’ decisions on investing in the stock market and constructing their portfolios are highly correlated with those of their neighbors.³³ Interestingly, this research is consistent with braggartry being a key determinant of retail investors’ social behavior. It has been documented that when retail brokerages partnered with social networking platforms, investors became twice as likely to sell profitable assets and hold on to lossmaking stocks.³⁴ This is likely because of the “disposition effect”—retail investors wanted their peers to admire their stock-picking prowess, and not admit their mistakes.³⁵

Such bravado continues to characterize retail investors’ participation in online communities dedicated to meme stocks. The explosion in retail investor interest in meme stocks was propelled by posts on the Reddit group “r/Wallstreetbets.”³⁶ Posters engaged in bombastic exchanges, claiming to have made spectacular returns making bets on stocks that seems unmoored from realistic assessments of the companies’ business models or their fundamentals.³⁷ The Reddit board attracted thousands of new followers drawn to the prospect of sharing in the benefits from pushing up the prices of stocks like AMC and GameStop.³⁸

Another important aspect of the digital transformation in the investing community is that it allowed retail investors to coordinate their expressive participation in the financial markets. Beyond boasting about eye-popping returns, users of the r/Wallstreetbets board were able to express their idiosyncratic likes and dislikes about the business model or customer services of the video game or movie theater companies.³⁹ The design of investing apps such as Robinhood catered to this expressive function of investing, with flashy graphics and leaderboards allowing meme traders to derive non-pecuniary benefits from investing.⁴⁰ Scholars in other areas of the law have long recognized that individual actions are infused with social meaning, defined with reference to social norms.⁴¹ Social media platforms like Reddit thus represent a digital disruption that has allowed retail investors to exchange notes not just about their trading exploits, but also their expressive preferences about firms in a group setting.

A distinction ought to be made between digital transformations in trading versus those in investing. In the former, digital transformations gradually brought about changes in the business models of brokerage firms, thus providing the general public with greater access to capital markets. In the latter, digital transformations changed the social meaning of investing for individual investors. Investing is no longer just a form of rationally deferred consumption; it has become a social activity through which to bond with others and to express one’s preference and identity.

C. Digital Transformation in Governance—Corporate Forum Technology

Digital transformations have also shaped how management and shareholders engage in governance matters. To begin with, the onset of the COVID-19 pandemic has accelerated the trend toward allowing virtual shareholder meetings.⁴² A recent study found that many companies held their meetings exclusively online in 2020–21 due to the stay-at-home orders.⁴³ Forty-four states and the District of Columbia already permitted companies to hold their annual meetings virtually as of 2020,⁴⁴ but individual firms had been reluctant to allow online participation before the pandemic. Shareholder voting and engagement increased notably for firms that switched to online meetings.⁴⁵

Historically, retail shareholders’ propensity to cast their ballots in annual meetings has been low. According to one study, while retail domestic investors own approximately 26% (on average) of the outstanding shares of public companies,⁴⁶ they only account for 11% of voted shares because of their low turnout. In the aggregate, retail shareholders tend to vote, on average, only 32% of their own shares.⁴⁷ The contrast between retail investors and institutional investors in terms of corporate voting is stark: according to a proxy report, retail investors voted only 29% of their shares in 2014, while institutional investors voted 83%.⁴⁸

Multiple factors drive the low participation rate among retail investors. First, many retail shareholders may not even be aware that they have the right to vote in annual meetings. Often, they may not even receive notice of the meetings in a timely manner. Second, retail shareholders, many of whom do not have a significant stake, are busy with their daily lives and do not have incentives to spend the time or resources to understand the issues being voted on in corporate meetings. Voting can be particularly onerous when retail shareholders have a diversified portfolio and own shares in many (hundreds or even thousands of) companies. Third, because retail shareholders on average own only a tiny fraction of the outstanding shares, they will likely feel that their votes will not have an impact on the outcome.⁴⁹ Fourth, even for those interested in voting, the proposals being voted on can be complex, and retail shareholders may fear that they cannot make informed decisions in their best interest. In a similar vein, some shareholders may trust the management of the company and believe that they will act in the best interests of the shareholders, regardless of the outcome of the vote. All of these factors render retail shareholder apathy rational.

To address these concerns, a few startups have emerged, promising to harness technology to bring the shareholder experience into the twenty-first century. To this extent, there has been a noticeable increase in the development of shareholder voting apps.⁵⁰ This is due in part to the increasing popularity of mobile devices and the growing demand for convenience from investors. Shareholder voting apps are designed to make it easy for investors to vote their shares from their smartphones or tablets, without having to mail in a paper ballot, call a toll-free number, or log onto a website. Their features include: the ability to view and research company proposals; the ability to vote on company proposals; the ability to ask questions of company management; and the ability to receive timely updates on corporate news.

For example, Say Technologies is a platform recently acquired by Robinhood that allows shareholders to communicate directly with management, vote on polls, and submit questions for meetings and earnings calls, all through a smartphone app.⁵¹ Say Technologies is currently used by a variety of companies, including Tesla and Chevron.⁵² Other startups specifically focus on helping retail investors cast votes. Enhanced Broker Internet Platforms (“EBIPs”) serve retail investors by allowing them to access proxy materials and vote on their brokers’ websites.⁵³ Similar services are provided by Broadridge ProxyVote and eBallot—the latter being used by such companies as Apple, Amazon, and Facebook. Some apps provide more than just a platform for casting votes. For example, ProxyDemocracy goes further to inform retail investors how institutional investors plan to vote on different proposals.⁵⁴ Each of these apps is designed to reduce the cost of meaningfully participating in annual meetings for retail shareholders. From this perspective, these digital transformations can be compared to the abolition of trading commissions discussed in Section I.A.

Potentially more impactful than the development of these apps, sporadic movements have taken place among shareholders of various companies to coordinate their votes. For example, on March 20, 2021, a Wall Street Bets (“WSB”) “megathread” was formed “for the purpose of discussing how to vote at the 2021 AMC Entertainment shareholders’ meetings.”⁵⁵ If such threads were to become more commonplace and retail shareholders were to exhibit a herding behavior in their voting patterns or coordinate in voting, corporate governance could be democratized in ways akin to trading.

II. THE RISE OF MEME TRADING: CONSEQUENCES AND IMPLICATIONS

The previous Part examined the technological developments and new business models that facilitated greater retail investing and eventually opened an era of meme trading. GameStop’s meme surge from January of 2021 was just one prominent example of meme stock surges that have been taking place episodically in recent years. The New York Times noted that meme surges were initially attributed to “hobbyists stuck at home spending stimulus checks, crusading to topple Wall Street trading houses they felt had rigged the financial system against them,”⁵⁶ but conceded that these firms continued to see elevated stock prices into 2022 and concluded that this could be a longer-lasting market phenomenon.⁵⁷ Drawing on previous literature, this Part considers the consequences and implications of the rise of meme trading.

A. Meme Surges and Predatory Trading

The sudden influx of retail investors—coupled with a platform that facilitates costless transactions and an internet forum that enables communication—implies trading markets that look very different today. Previously, retail trading was thought to have little effect on stock price movements. Retail investors could not easily coordinate their trades, and as a result, their idiosyncratic trades would tend to cancel each other out.⁵⁸ Furthermore, in the presence of large institutional shareholders, including BlackRock, State Street, and Vanguard,⁵⁹ the volume of trade that originates from retail investors tends to be relatively modest, particularly for companies with a large market capitalization. With coordinated trading and meme stock surges, however, this is no longer true, at least for small- to medium-sized companies. Retail trades today can have significant price impacts for certain companies’ stocks.⁶⁰ This change comes at a cost, however. Retail trades—especially expressive trades—can be emotionally driven based on the underlying companies’ cultural relevance.⁶¹ There is no indication that meme stocks prices reflect information about the companies’ underlying fundamentals.

Recall how the events played out in the GameStop meme surge.⁶² GameStop had been losing money and was facing a liquidity crisis.⁶³ The market had been predicting (as evidenced by the low stock price) that the company would likely file for bankruptcy and possibly be liquidated in the near future.⁶⁴ A number of hedge funds—most prominently Melvin Capital—had taken a large short position against its stock, betting that the price would drop even further.⁶⁵ In January 2021, retail investors engaged in an active “buy” campaign to dramatically push up the GameStop stock price to the stratospheric level of over $483 per share from less than $4 per share.⁶⁶ Retail investors’ influx seems to have been driven in part to create a “short squeeze” against the hedge funds.⁶⁷ The end result was a large loss—and ultimate retreat—by the hedge funds.⁶⁸ Market analysts observed that meme traders used Reddit to decide on target firms that typically had a smaller number of outstanding shares, and delighted in punishing market participants that had taken short positions in the selected companies.⁶⁹

The short squeeze experienced by the hedge funds is an example of a more general class of trading, called “predatory trading”—trading that exploits known needs of other investors who must change their positions.⁷⁰ In an influential paper, Brunnermeier and Pedersen document historical examples of trades that exhibited these patterns and develop a formal model to analyze this scheme in the context where certain large investors have a known need to liquidate their portfolios.⁷¹ According to their analysis, where a large trader has a need to sell certain stocks, which is predicted by another large trader, this other trader can “front-run” and sell the stocks ahead, and subsequently buy them back at a lower price—after the original trader sells his stocks and further brings down the price. Under this pattern, “a trader profits from triggering another trader’s crisis, and the crisis can spill over across traders and across markets.”⁷² Importantly, the model assumes that the size of each strategic trade must be sufficiently large enough to have a price impact.⁷³

GameStop’s short squeeze was essentially the mirror image of the trading pattern analyzed by these authors: where a hedge fund’s need to buy stocks—to cover its short position—is known, other investors, as a group, can strategically buy a significant share of the same stock to front-run the fund first and later sell those shares at a higher price after the fund eventually engages in the buy. What is notable was that the GameStop surge is the first case of predatory trading attributable to retail investors.⁷⁴ The digital transformations we have witnessed in trading and investing have facilitated coordinated trades among retail investors to potentially participate in predatory trading for the first time and take a collective stance against hedge funds.⁷⁵ What also seems different about the meme surge is that, unlike traditional investing and predatory trading models, the retail investors (at least a large fraction of them) who participated in the surge seem to be driven not solely by the financial returns but seem to have been motivated by non-financial considerations, such as taking a stance against Wall Street or saving a company (possibly with some sentimental attachment) from bankruptcy. At least in theory, when a sufficiently large number of investors are willing to pay more than what a firm’s financials dictate, this could create a divergence between the stock price and the firm’s “fundamental” value.⁷⁶

An important question is whether the risk of short squeezes would discourage hedge funds from taking short positions on meme companies in the future despite their failing conditions. If hedge funds routinely stay away from short-selling meme stocks to avoid falling victim to meme surges, there will be a loss of price efficiency among those stocks. Of relevance, the SEC recently adopted a rule intended to increase transparency in short positions held by institutional investors.⁷⁷ The new rule would require certain institutional investment managers to report their short position data and short activity data for equity securities on a monthly basis.⁷⁸ In theory, this rule could potentially worsen the risk of short squeezes—a concern that the agency’s own economic analysis has acknowledged.⁷⁹ To address this concern, the SEC has decided to collect manger-specific data but release only aggregated and anonymized data to the public. The SEC believes that this arrangement “should reduce the likelihood of short squeezes” whole facilitating “improved detection of manipulative and potentially destabilizing activity.”⁸⁰ It is too soon to tell how this new rule may affect the future of meme trading.

B. At-the-Market Offering Opportunities

Meme surges do not affect investors alone. They have implications for meme stock companies as well. During its meme surge, GameStop took advantage of the elevated stock price and engaged in a large capital raising through a couple of stock sales—specifically, through at-the-market (“ATM”) offerings.⁸¹

d192873d424b5.htm [https://perma.cc/FUA9-PP4U] (“We have previously sold an aggregate of 3,500,000 shares of our common stock for aggregate gross proceeds of approximately $556,691,221 pursuant to the Sales Agreement and the prospectus supplement filed by us on April 5, 2021.”). An ATM offering allows an issuer to sell its stock at the prevailing market price. As a result, GameStop was able to address its dire need for liquidity. Once on the verge of running out of cash and filing for bankruptcy, GameStop was suddenly able to continue its business—thanks to its fan base that was purchasing its stock for reasons unrelated to its underlying business condition.⁸² Importantly, at the time GameStop engaged in stock sales, it openly acknowledged in its prospectus that its stock price was not correlated with any fundamental changes in its business.⁸³

Does the era of meme trading then imply an era of aggressive ATM offerings? While it is reasonable to expect most meme stock companies to raise capital during moments of meme surges, our search of the SEC’s public company filings system shows that only two companies—GameStop and AMC⁸⁴—took advantage of meme surges and made offerings.

It is unclear why other meme stock companies did not similarly choose to take advantage of meme surges. One theory, advanced by the columnist Matt Levine, is that these companies were more cautious and wanted to avoid being blamed for knowingly selling shares at an inflated price.⁸⁵ However, the extent to which any of these companies would be held liable for making an opportunistic ATM offering is unclear. Securities regulation is based on the principle of full disclosure.⁸⁶ Even if stock prices are over-inflated, there is no obvious theory of liability when these companies fully acknowledge the mismatch between stock price movements and the company’s underlying financial conditions. Nevertheless, the SEC may still find ways to prevent or delay certain offerings.⁸⁷

The possibility of ATM offerings amid meme surges points to an unusual consequence of expressive investing. In the olden days, the common wisdom was that if you want to support a company, you should buy its products or services, not its stock (in the secondary market). The company does not get to enjoy any of the proceeds from the secondary market transactions of its stock. However, the combination of ATM offering mechanisms and meme surges suggest this wisdom may be obsolete: in the era of meme trading, retail investors can meaningfully express their support for the company through secondary market purchase of its stock. Their purchases can contribute to meme surges, which would offer the company an opportunity to rake in cash through an ATM offering.

C. Implications for Securities Regulation

Beyond the implications for trading markets, meme trading has important implications for established doctrines in securities regulation. For example, Rule 10b-5 claims under the Securities and Exchange Act of 1934 represent the most common type of securities liability in the United States.⁸⁸ To establish a Rule 10b-5 cause of action, a plaintiff must demonstrate: “(1) a false statement or omission of material fact (2) made with scienter (3) upon which the plaintiff justifiably relied (4) that proximately caused the plaintiff’s injury.”⁸⁹ As we argue below, meme trading arguably undermines each of these four foundations of Rule 10b-5 liability. This could limit the retail investors’ recourse in case of misrepresentations or fraud.⁹⁰ Furthermore, it could reduce the disciplinary effect of litigation risk in curbing managerial misconduct.⁹¹

With respect to the first two elements of 10b-5 liability listed above—a material misstatement or omission and the scienter requirement—the general tumult of meme trading could allow managers to represent their actions as being immaterial or innocuous. For example, AMC’s CEO indulged his company’s committed meme followers online.⁹² He hosted them for a special movie screening, spent an hour every day reading feedback from meme traders on videos streamed on Twitter, and (allegedly) intentionally attended public Zoom meetings without his trousers on.⁹³ Would a securities class action litigant be able to show that the CEO had made a material misstatement in reading supportive messages from meme traders or encouraging them online? After all, the meme investors’ Reddit messages and tweets were already in the public domain and should have been priced in if the market is informationally efficient.⁹⁴ Of course, a plaintiff could argue that the CEO creating hype around his stock is qualitatively different from an existing mass of anonymous Reddit posts doing so. However, the corporate defendant would plausibly have a colorable claim that simply regurgitating the meme investors’ widespread sentiments is neither a material misstatement nor one made with scienter.

On the other hand, meme surges will also complicate how the plaintiff may establish materiality in other settings. For example, if the defendant were to make a rosy but faulty announcement regarding its financials during an extremely volatile meme surge, whose movement is otherwise uncorrelated with the company’s fundamentals, the plaintiff’s expert may have an extremely difficult time establishing that the announcement was material based on an event study.

The unique meme investing scenario also calls into question whether securities plaintiffs can establish reliance or loss causation. As Professor Sue Guan has noted, successive waves of meme activity mean that even if a company, such as AMC, restates its earnings or corrects a misstatement, the stock reaction to the corporate misconduct may be submerged by price movement due to meme trading. This is especially true because meme companies are generally smaller firms whose stock prices can be more easily moved. Reliance is undermined because the lack of a price reaction near the company’s alleged misstatement or omission could imply that traders did not buy shares in reliance on the contested managerial act. Loss causation can similarly be challenged if the defendant can convince the court that its actions did not inflate the price of shares; instead, it can argue that meme trading pushed up the stock price.⁹⁵ Meme traders and their bombastic puffery can thus serve as useful foot soldiers, insulating meme company executives from securities liability.

D. Beyond Trading Markets

If meme trading is here to stay, what can we expect from meme and other retail traders beyond trading markets? A natural question one can ask is whether retail investors participating in meme trades can bring about meaningful changes as retail shareholders. After all, the digital transformations discussed in Part I have brought down the cost of participating in trading, investing, and governance activities. The GameStop saga and the meme stock frenzy of 2021 demonstrated the power of technology to coalesce dispersed individuals who can unite to bring about an impact and provide a check on institutional players. Thus, one interpretation of these events is that future technological developments can allow dispersed individuals to overcome the cost of collective action to further their collective agenda.

One line of predictions says that increased retail access to capital markets will democratize finance and such retail shareholders will embed their “prosocial” preferences on corporate policies.⁹⁶ For example, Professors Sergio Alberto Gramitto Ricci and Christina Sautter observe that the new generation of investors will be more likely to pursue ESG goals rather than focusing on making a profit⁹⁷ and will engage in governance activities by exercising their shareholder rights.⁹⁸ The authors thus predict that meme traders and their activities will lead to a new paradigm for corporate governance. A similar view was echoed by Professor Jill Fisch. While focusing mostly on citizen capitalism’s benefits to economic development, Fisch also notes that “[c]itizen capitalism may also enhance the voice of ordinary citizens in corporate decisions” and argues that retail investors will be able to shape shareholder power.⁹⁹ She acknowledges that while governance measures “must ultimately command the support of institutions as well . . . . [I]n issuers with significant retail ownership, the retail vote can influence the outcome of critical shareholder votes.”¹⁰⁰

On the other hand, there are also reasons to question the link between the distinct transformations in investing and ongoing corporate ownership. For one thing, there are significant differences between meme traders and retail shareholders in terms of their activities, goals, and execution costs. First, their bona fide activities are quite distinct: an investor’s activities include information-gathering and buying and selling; a shareholder’s activities include voting, nominating director candidates, submitting proposals, or running proxy contests. Second, their goals and payoffs may also be very different: a meme trader might trade for profit motives, for the thrill of using game-like apps, or for expressive reasons. Most of these are immediately realized through the act of trading. By contrast, a retail shareholder may recognize that she has a very little chance of affecting any proposal outcomes and many of the changes may not be realized in the short run.¹⁰¹

Third, while digital transformations discussed in Part I largely reduced the participation costs for both meme traders’ activities (trading) and shareholders’ activities (voting), voting on corporate proposals still entails information costs (not present for pure meme trading activities) that have not been eradicated. Finally, meme trading does not take place across all companies. To date, meme surges have been limited to a relatively small set of companies with particular characteristics—such as low stock prices, low market capitalizations, high bid-ask spread, and cultural relevance.¹⁰² Indeed, all eight meme stock companies we analyze are mid- to small-cap companies, valued under $10 billion in market capitalization (and some with a much smaller market capitalization).¹⁰³ But in general, small companies are prima facie less likely to attract significant shareholder activities¹⁰⁴ and less likely to attract shareholder proposals.¹⁰⁵ Meme trading has thus centered on companies whose financial fundamentals do not augur well for sustained shareholder engagement.

For these reasons, a sudden burst of enthusiasm for meme trading may not instantly translate to one for shareholder activities, and meme surges and their impacts may remain orthogonal to shareholder activities. Given this uncertainty in the promise of meme trading, there are important research questions that remain unexplored, to which we now turn.

III. A MEME GOVERNANCE RESEARCH AGENDA

A. Traders and Shareholders

Future work in meme corporate governance should try to disentangle the extent to which sentiment-driven investors sustain their engagement when they become shareholders. The literature review from Part II makes clear that vigilance or activism looks different for investors and shareholders. Activism among retail investors may not necessarily translate to activism among retail shareholders. At the same time, particularly with respect to those retail investors who stayed as shareholders at meme stock companies long past the meme surge (and subsequent crash), one would argue that they are likely to care much more about the company’s governance and long-term performance and become more active in exercising their rights as shareholders. Relatedly, work in empirical corporate finance also finds that passive mutual funds, despite being “lazy investors,” directly or indirectly participate as shareholders. Increased shareholding by these institutional investors leads to greater board independence, fewer takeover defenses, and more equal voting rights.¹⁰⁶

In our companion project,¹⁰⁷ we uncover empirical evidence that meme (and other retail) shareholders may not display the same vigor or aspirations ascribed to them by the literature focused on meme investors. Examining shareholder voting, we find that participation in the proposal process substantially decreased for meme stock companies, like AMC and GameStop, after the abolition of commissions in 2019 and the meme surge in 2021 compared to other companies, even when we control for firm characteristics and include year fixed effects.¹⁰⁸ This can be easily seen in Figure 2, reproduced from our companion project.¹⁰⁹ The dark lines, both solid and dotted, represent the share of non-votes at meme stock companies, on routine and non-routine matters, respectively. Meme stocks are defined as explained in Section I.A.

Figure 2. Average Share of Non-Votes for Meme and Non-Meme Stocks by Proposal Type

Note: This figure presents information on the yearly average percentage of votes that were not voted in shareholder meetings. We define the number of non-votes as Total Outstanding Shares (Votes For + Votes Against + Abstentions). We split the data by meme/non-meme stock as well as proposal type (that is, whether or not it qualifies as “routine” as defined in NYSE Rule 452).

In Figure 2, we can see that, compared to other companies, the non-vote shares increased markedly since 2018. Though it is difficult to infer that the non-votes are all coming from retail shareholders, given the low rate of vote participation among retail shareholders, it would not be unreasonable to infer that the increase in non-vote shares comes from the dramatic shift in shareholder base to retail shareholders. The increase in non-voting at meme companies is especially stark for “non-routine” proposals, for which brokers cannot vote on behalf of their clients.¹¹⁰ What is perhaps surprising is the fact that the non-vote shares seem to be increasing even in 2022, a long time after the meme surge in early 2021, indicating that perhaps even those retail investors who stayed with the companies do not seem to be actively participating in firm governance. Moreover, no shareholder proposals made it onto the proxies of any of the meme companies after 2019.¹¹¹

With respect to indirect measures of corporate governance, we also find that meme companies’ performance on ESG issues as well as board gender diversity either declined or remained the same compared to other firms, once again controlling for firm characteristics and time trends.¹¹² In short, there is so far little evidence to suggest that retail investors have left much of a mark with respect to engaging management or nudging companies in more prosocial directions.

Any work on the meme phenomenon must confront the different incentives and behavioral patterns characterizing retail investors and retail shareholders. Apart from the natural functional disjuncture caused by the purchase of shares, one could also argue that technology and digital transformation plays less of a role for shareholders as compared to investors. While we have seen apps like Robinhood disrupt the PFOF system and “gamify” investing, large chunks of the shareholder experience seem trapped in amber. Corporate voting, for example, depends on a fragile and complex custodial system that is arguably decades out of date with contemporary digital capabilities, making it hard to ensure that shareholders can actually exercise their franchise.¹¹³ Admittedly, as described in Section I.C., some startups are trying to use technology to improve shareholder-management communication. However, until such initiatives become mainstream, the disconnect between twenty-first century investing and the twentieth century shareholding will continue to be an important line of inquiry for researchers.

B. Identifying Sentiment-Driven Stocks

A broader research agenda studying the effect of retail investor sentiment on corporate governance must necessarily define the core variable of interest: which companies could one credibly claim are affected by meme activity or online communities coordinated via social media? The current literature is somewhat reactive in nature, defining meme stocks based on which companies have already experienced meme surges or seen online communities formed around them.¹¹⁴ One concern with such an approach could be whether it is generalizable: are these companies meme stocks solely because of the PFOF system or Reddit discussions, or is there something intrinsically unique about them? Moreover, in comparing meme stocks with other companies, we need to make sure we do not misclassify other companies driven by retail investor sentiment as non-meme companies. For example, some online commentators even called Tesla a meme stock because of its dedicated group of online retail followers, and its chief executive officer’s high visibility on social media.¹¹⁵ However, Tesla has been excluded from most academic analyses of meme stocks since it differs from AMC, GameStop, and others in crucial ways (by, for example, having a credible business model and sufficient analyst coverage that could plausibly explain the stock’s success instead of online coordination by meme investors).¹¹⁶

Nevertheless, the broader point remains: there is a need for a generalizable definition of meme stocks that does not depend on factors that are idiosyncratic to those companies. This concern about endogeneity is a central feature of empirical corporate finance scholarship. For example, for many years, corporate law scholars believed that poison pills (antitakeover devices that directors can use to deter hostile takeovers) depressed firm value. However, more recent work shows that poison pills are adopted in the first place by firms that had suffered decreases in performance. Once we account for these pre-existing performance drops, there is little evidence that poison pills affect firm value.¹¹⁷ Similarly, an externally valid definition of meme stocks could help us rule out other explanatory factors for changes in corporate governance at any given set of companies.¹¹⁸

While this Article does not propose any particular measure of meme stock or retail investor sentiment, we believe there are three potentially promising avenues for finding such a metric. First, researchers could look at media coverage of companies. Firms that feature more prominently in the media and elicit more “emotional” responses (whether positive or negative) may be more likely to emerge as meme stocks. New methods in the textual analysis of data could help make such an empirical measure tractable.¹¹⁹ Second, one could look at how accessible companies are to resource-constrained retail investors. Meme companies, such as AMC and GameStop, were generally smaller (both in terms of market capitalization and trading volume) and had lower share prices.¹²⁰ Companies with such financial features could be more likely to attract retail investors.¹²¹ Finally, meme phenomena can also be closely tied to nostalgia. Many retail investors poured into AMC, for example, because they were millennials who fondly remembered going to the company’s movie theaters and did not want to lose the chain to a COVID-19-induced bankruptcy.¹²² Nostalgia, if amenable to a satisfactory definition, could be a predictor for a company’s attractiveness to millennial meme investors. Whichever definition proves most fruitful, robust empirical examination of the meme stock phenomenon would help us better understand the events of 2021–22.

CONCLUSION

The meme surge of 2020–21 captured the attention of investors, firms, and regulators across the world. In this Article, we have attempted to contextualize this phenomenon within the broader trend of digital transformations in trading, investing, and corporate ownership. The modification of the payment for order flow system through the abolition of commissions radically transformed the trading process, and lowered entry costs for retail investors thinking about entering the stock market or constructing their portfolio. The investing experience was also affected by the emergence of social media platforms that complemented existing online brokerages. These platforms allowed retail investors to exchange notes on investing strategy as well as their expressive likes or dislikes for meme companies (regardless of the quality of information undergirding these preferences). Digital transformation has been most modest, however, in reshaping the ownership or shareholding experience. While some startups have tried to make it easier for shareholders to vote or communicate with managers, many of the processes surrounding shareholder participation do not harness the latest technologies.

Moving from the origins to consequences of these digital transformations, we flag three potential troubling consequences of meme trading that go unaddressed by the current system for public regulation of securities markets. First, it could increase the occurrence of predatory trading (exploiting counterparties’ need to change positions), except that this time the predators could be retail investors. Second, meme surges could induce more ATM offerings by companies keenly followed on social media; firm management would want to timely raise capital while their share prices are inflated. Third, the unique setup of the meme investing ecosystem could undermine a potential securities plaintiff’s claim under Rule 10b-5, and undercut the role played by litigation risk in compensating defrauded investors and disciplining managerial misconduct.

Reviewing the existing literature on the promise of retail investors in corporate governance, we argue that in the absence of further technological disruption affecting the shareholding experience, it is unlikely that meme investing will lead to a “democratization” in governance. For a variety of reasons, it may be hard to transform retail investors into engaged retail shareholders. Finally, we sketch a research agenda for future work on meme stocks. First, future work must disentangle the extent to which non-traditional market participants can make an impact as traders versus as shareholders. Second, there is a need to develop a more objective metric to identify stocks moved by retail investor sentiment, rather than the somewhat idiosyncratic collection of companies that featured in the events of 2021–22.

This Article therefore cautions against viewing the meme surges as simply the product of the COVID-19 pandemic or Reddit social boards. Instead, systematic digital transformations in all facets of the financial markets have allowed retail investors to coordinate their expressive preferences for companies. Meme trading is here to stay. This retail coordination could lead to issues concerning predatory trading, ATM offerings, and reduced litigation liability that our current securities regulation system is ill-equipped to handle. While we lack evidence that these digital disruptions can transform retail investors into engaged shareholders, further research should seek to distinguish investing and shareholding activities, and better define what qualifies as a “meme stock.”

96 S. Cal. L. Rev. 1387

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Dhruv Aggarwal*

* Assistant Professor of Law, Northwestern Pritzker School of Law.

Albert H. Choi†

† Paul G. Kauper Professor of Law, University of Michigan Law School and European Corporate Governance Institute (“ECGI”).

Yoon-Ho Alex Lee‡

‡ Professor of Law, Northwestern Pritzker School of Law.

Technology, Markets, and the Income Tax Frontier

April 2024April 2024Andrew T. Hayashi*

Income tax law and policy are fundamentally intertwined with private markets—causal effects run in both directions. The vitality of public markets can be stifled or invigorated by the way that they are taxed. The power to tax is the power to destroy. In turn, the computation and collection of income taxes depends upon the valuation and liquidity provided by markets. Moreover, the economic properties of tax rules are contingent upon the underlying market structures. Changes to these structures induced by technological innovation can alter the efficiency and equity properties of the prevailing income tax rules. In this Article, I explain how innovations associated with the digital transformation of business will—as an unintended consequence—reduce the administrative barriers to taxing income and improve the economic tradeoffs, thereby making it both feasible and desirable to push outward the frontier of the income tax’s domain.

INTRODUCTION

Tax law operates on a background of individual preferences, cultural norms, institutions, markets, and a complex architecture of other laws and regulations. It is conventional in tax scholarship and policymaking to take those background conditions as fixed, and to consider questions about what to tax, how to tax, and how much to tax, using theoretical models and empirical estimates derived from those background conditions.

This is a sensible division of labor. It is hard to imagine tax scholarship making sustained progress if it could not take those conditions for granted and rely on shared assumptions about how markets operate and how economic behavior is expressed though the laws that regulate it. But, of course, these conditions do change. New markets form and evolve, old markets disappear, laws change, and new technologies enable new business practices. Some of these changes may have little effect on good tax policy, but others might be more significant.

For example, consider the evolution of the U.S. labor force over the last century. One of the most important changes—economically as well as culturally—is the dramatic increase in women’s participation in the paid-labor market.¹ When women’s labor moved from the home to the marketplace, it became visible for both national accounting purposes and tax accounting purposes, resulting in a measured increase in output and taxable income.² The change was also accompanied by an increased demand for market services to replace the work previously done by women without pay,³ creating a market where there had not been much of one before.

Such labor shifts from the private sphere to the public sphere register as an increase in economic activity because of the decision—for reasons of policy or administrative feasibility—to ignore the private sphere in national income and tax accounting. In the same way, renting out a spare room in one’s home gives rise to income, whereas enjoying the benefits of that room oneself does not. And so, as a general feature of national accounting, the relocation of previously private activity to the public sphere and to the marketplace will be reflected as an increase in income and output.⁴ The income tax also follows the private/public distinction. The income tax reaches income that is generated in the public sphere, and it relies on public markets both to measure the amount of income that people have and to provide people with the cash liquidity they need to pay the taxes that they owe. And so, as markets proliferate and more of our time and resources are exchanged on those markets, the reach of the income tax stretches outward and income tax law applies to an ever-larger domain of our lives.⁵

Market proliferation also changes the tradeoffs that must be negotiated when trying to decide how much to tax, specifically the tradeoff between revenue needs and the ways that taxes distort and disturb the choices and plans that people would otherwise have made in the absence of the tax.⁶ It is conventional in the economic literature to measure this disruption by the change in taxable income resulting from a change in tax rates.⁷ For example, increasing the tax on wages and salaries reduces the amount that people work, thereby reducing their taxable income as they shift from paid market labor to unpaid and untaxed leisure or household labor, or as they find other ways to lighten their tax burden.⁸

But the magnitude of this response to a wage tax increase is not a permanent fixture of the world, or even of the United States, over time. It will depend on relative costs and benefits of paid and unpaid labor, and the flexibility that households have to respond to the higher tax—and these conditions evolve. For example, in a world of mostly single-earner married couples, the effect of an increase in this tax rate on a household’s taxable income is almost certainly different than the effect in the world of mostly dual-earner couples that exists today. The difference in the sensitivity of labor supply choices—and taxable income—to higher tax rates implies a new outcome to the tradeoff between the efficiency costs of taxation and the need to raise revenue equitably.

In this Article, I consider how the emerging digital economy will draw more and more of our time, property, and activity into the income tax’s domain. The mechanism for this process is the increasing marketization of activities that currently reside in the private sphere. As we spend more of our time, energy, and resources transacting with other people and technology, more of our lives become observable to—and therefore capable of being regulated and taxed by—the government. Traditional tax policy criteria will generally regard this as a good development, because extending the reach of the income tax will tend to make it more efficient.

I. THE CODEPENDENCY OF TAX AND PUBLIC MARKETS

The federal income tax “base” begins with gross income and then provides a variety of allowances and deductions to arrive at taxable income—the quantity that is subject to tax under a progressive rate structure. What is gross income? The Internal Revenue Code (the “Code”) defines it as “all income from whatever source derived,”⁹ including a list of specific kinds of income, such as gains from dealings in property,¹⁰ dividends,¹¹ and compensation for services.¹² This list is only illustrative, however, and case law has had to draw boundaries around the statutory (and constitutional) meaning of income. For example, what about money that one discovered hidden in a recently purchased piano?¹³ What about debts that are repaid for less than their face amount?¹⁴ What about lodging or meals received from one’s employer?¹⁵

A useful touchstone for the definition of income is sometimes referred to as economic income, or the Haig-Simons definition of income, defined by the economist Henry Simons as “the algebraic sum of (1) the market value of rights exercised in consumption and (2) the change in the value of the store of property rights between the beginning and end of the period in question.”¹⁶ Although a useful place to start, Haig-Simons income is broader than the meaning of income in Section 61 of the Code. Differences between gross income and the Haig-Simons definition of income can generally be grouped into two categories. There is income that is excluded as a deliberate matter of policy, generally with the objective of encouraging people to engage in the kinds of activities that earn that sort of income. For example, interest on certain state and local bonds is excluded from gross income by Section 103 of the Code, notwithstanding that interest is generally included in income.¹⁷ And up to $500,000 of gain on the sale of one’s home can be excluded from gross income,¹⁸ for reasons—mostly obscure—of deliberate public policy.

There would not be any particular difficulty in taxing income from state and local bonds or gains on the sale of one’s home. In the case of interest payments received in respect of bonds, the amount of interest—and therefore the amount that needs to be included in gross income—is easy to observe. The fact that interest is paid from the borrower to the lender, typically through a financial intermediary, makes it easy for the IRS to collect information about the payment by imposing reporting obligations—or even a tax withholding obligation—on the intermediary, and thereby easier to enforce compliance with the taxpaying obligation. In the case of gain on the sale of one’s home, the amount of gross income is again usually easy enough to calculate—as the excess of the amount paid for the property over whatever the seller paid herself for the home.¹⁹ Real estate transactions also generally leave a paper trail (through title transfer systems, for example) that could in principle be used to facilitate accurate income tax reporting.

Moreover, in both the cases of state and local bond interest and gain on the sale of one’s home, the taxpayer’s income typically takes the form of cash, obviating the “liquidity” problem that can arise in other contexts. Income need not take the form of cash. If you win a car at a game show, the fair market value of the car is income to you.²⁰ If a lawyer provides legal services to a client and accepts property or different services performed by the client as payment, the value of the property or those services is income to the lawyer.²¹ The liquidity problem arises when the taxpayer has income, but not the cash to pay the tax.²²

There are no administrative difficulties with taxing interest on state and local bonds or gain on the sale of one’s home. We could do it, but Congress has chosen not to. But there are other kinds of income that we do not tax because of these administrative challenges. For example, an increase in the value of one’s home over the course of a year represents an increase in wealth—and therefore income in the Haig-Simons sense—and yet we do not tax that gain generally until the property is sold.²³ This is the “realization requirement.” There are two reasons that we generally do not tax increases in the value of property until that increase has been crystallized by some transaction, such as a sale of the property. The first is the difficulty of measuring changes in the value of the property over time without the evidence of a market transaction. The second reason is concern about the potential hardship imposed by requiring taxpayers to come up with the cash to pay a cash tax on non-cash income.²⁴

In some cases, the remedies for valuation and liquidity concerns may be worse than the disease. The realization requirement, for example, has been called the original sin of the income tax.²⁵ Because tax on gain is deferred until the gain is realized by some transaction, this naturally creates an incentive to delay the timing of that transaction as long as possible. The ability to defer the recognition of gain in this way not only creates pernicious distributional effects—lowering the effective tax rate on capital income, which tends to be concentrated in the hands of the highest-income taxpayers—but also economic inefficiencies, as taxpayers have an incentive to leave their investment capital locked into underperforming investments to avoid triggering the recognition of taxable gain.²⁶

Invariably, the rules that provide relief to taxpayers from challenges associated with valuation and illiquidity create an attractive nuisance for well-advised taxpayers who steer their activities to transact in forms that benefit from this kind of relief. This nuisance then requires its own response, in the form of anti-abuse rules that prevent these relief provisions from excessively eroding the income tax base or creating too much distortion to economic activity and the distribution of the tax burden. For an example of this kind of rule, consider Section 1259 of the Code, which treats a set of transactions as “constructive sales” of certain financial positions, triggering the taxable recognition of gain (or loss) just as if the positions themselves had been sold.

Barter transactions, in which goods or services are exchanged for other goods or services (rather than cash) give rise to taxable gain or loss, notwithstanding the fact that there may be difficulties valuing the goods or services and collecting a cash tax in respect of the income from the transaction.²⁷ Consider, for example, the lawyer who provides legal services to an artist in exchange for one of her works of art. The market value of the art is income to the lawyer, even though it may be difficult to value.²⁸ The reason why valuation and liquidity concerns cannot be sufficient to exclude such arrangements from the tax base is easy enough to understand: failure to tax barter transactions would create a strong incentive for people to create a barter economy, which would be both less efficient than a cash economy and would allow them to avoid tax on their economic gain and shift the burden of funding government to people who, for whatever reason, cannot connect themselves to a barter network.²⁹

The barter example illustrates a general effect of the income tax. When one kind of income is taxed but another close substitute for that income is not, then people will tend to rearrange their affairs to take advantage of the tax difference. In this way, the income tax law affects which markets flourish and which falter. Consider the fact that health insurance provided by an employer to its employees is generally excluded from gross income,³⁰ even though the insurance has economic value to the employees and that it serves as a form of compensation that would be taxed if it were paid in cash. The effect of this favorable tax treatment has likely played an important role in the amount of resources directed toward the provision of health insurance.³¹ Conversely, an increase in the effective tax rate on income from certain goods or services will tend to suppress the public market for those goods and services, redirecting resources either towards market substitutes or toward a black-market in the goods or service.

Historically, an enormous amount of income from economic activity has been left out of the tax base because the activity takes place within a single household or because the income arises from activities performed by a taxpayer for her own benefit. This latter category of income is known as “imputed income.”³² Recall that Haig-Simons income includes the market value of consumption benefits derived from goods and services. It is irrelevant for this purpose who the provider of the goods and services is. If an employee is given the rent-free use of an apartment by her employer, then the rental value of the apartment is Haig-Simons income to the employee and it is also gross income under the Internal Revenue Code. By contrast, if the employee enjoys the use of an apartment that she herself owns, then the rental value of the apartment, while it is Haig-Simons income to the employee, is not taxable.³³

The same is true of the benefit that people get from using any durable good that they own—but might otherwise rent from somebody else—including washing machines, automobiles, and boats. Similarly, if a barber earns thirty dollars at his job and uses the money to pay for his own haircut, then he has thirty dollars of economic income and thirty dollars of taxable income for federal income tax purposes. But if he cuts his own hair (and we assume that he does just as good a job on his own hair as he would do on another person’s hair) then he has Haig-Simons income of thirty dollars, but we do not, of course, tax the barber on the benefit he gets from cutting his own hair. Needless to say, we have never tried to tax the “consumption” benefit one gets from leisure, whatever the theoretical merits of doing so.

For these reasons, the shift of activity from the private sphere to the public, from self-reliance to interdependent transactions, results in a measured increase in taxable income. If my friend and I care for our own children and clean our own houses, then neither of us has income for federal income tax purposes, but if we watch each other’s children and clean each other’s houses, then the value of our childcare and household cleaning services become subject to tax. Interpersonal transactions make income legible to the tax system. As the number of these barter transactions increases, a market is likely to emerge that uses cash or some other fungible good to function as a currency. The shift from barter to using money to mediate transactions supercharges the market, increasing the volume of transactions and the amount of economic income created. As the number of transactions increases and money is used to price the goods and services exchanged, the amount of income becomes easier to measure and concerns about taxpayer liquidity will diminish. The emergence of a public market may also make it possible to impose information reporting or tax withholding obligations on market participants or intermediaries, thereby facilitating tax collection. In this way, the development of a market, along with the use of money, can increase the amount of taxable income and render previously untaxed activity—childcare and house cleaning, for example—subject to taxation.

This increase in the marketization of the economy, shifting activity from the private sphere where it is untaxed, to the public sphere where it is taxed, not only leads to an increase in taxable income and therefore tax revenue, but it also tends to increase the efficiency of the income tax rules. The efficiency of the income tax is measured in terms of how much it distorts the choices that people make, by inducing them to devote their time, capital, and effort to activities that avoid taxes rather than activities that generate the greatest real economic returns.³⁴ For example, someone facing a 30% tax rate would prefer to purchase a tax-exempt municipal bond paying 6% interest than purchase a taxable corporate bond paying 7% interest, because the tax-exempt bond yields a higher after-tax rate of return, notwithstanding the higher pre-tax rate of return on the corporate bond, which operates as a market signal of the fact that the corporation has a more productive use for the investor’s capital than the municipality.

Or consider the choice of a second earner in a household with small children deciding whether to take paid employment outside the home and pay for childcare, or work in the household where the services he renders to his family are not subject to income tax. This person will forego paid employment opportunities even if he is more productive working outside the home because he must earn a substantially higher wage outside the home to allow him enough after-tax income to pay for the household work that he could otherwise provide. Or consider a homeowner with a spare bedroom that she rarely uses. Although she may derive only a modest amount of personal benefit from the room, in order to make it worthwhile to rent it out to a tenant, she must receive enough in rent such that, after the rental income is taxed, she is compensated for the inconvenience of having someone in her home, complying with whatever laws and regulations may apply to rental properties, advertising her space, processing rental payments, and so on. Increasing the tax rate on corporate bond interest, employment compensation, or rental income, will tend to encourage people to move their capital into tax-exempt investments, move their labor into the private sphere, and use their property for personal consumption rather than to rent it out.

It would be better from an efficiency perspective to tax all income at the same rate,³⁵ thereby encouraging people to use market signals of scarcity and value to allocate their capital, time, and property to where it can generate the highest pre-tax rates of return. But we do not tax all income—including imputed income—at the same rate, or at all. How can market proliferation increase the efficiency of the income tax? The first is simply by providing a high enough rate of return to make remaining in the market worthwhile. Markets that create enough value for participants can induce them to participate and earn taxable income in that market. A modest increase in the tax rate will not push them out of the market if there is enough value created there. Ubiquitous and efficient markets that allow people to earn high pre-tax rates of return from deploying their resources make it possible to increase income tax rates without driving people out of those markets. This effect is well understood in developing economies, where the transition from informal to formal economies and the effects of marketization on tax capacity are starker. For example, there is evidence that as the labor force shifts from contract work toward more stable employment relationships, the standard deduction falls—meaning that the effective tax rate on labor income rises.³⁶ That is, the development of a formal labor market facilitates an increase in labor income tax rates.

II. THE DIGITAL TRANSFORMATION AND MARKETIZATION

The digital transformation of business and the economy increases the marketization of our lives, relocating personal, private activity into an observable transactional space that both creates enormous value—as markets typically do—and converts untaxed imputed income and leisure into taxable income. Time that was previously spent in leisure, volunteerism, or other untaxed activities can now be spent on any number of “side hustles,” running errands, or performing one-off tasks for someone else. Consider the platform Taskrabbit, which connects users with “skilled Taskers to help with odd-jobs and errands.”³⁷ Or consider the widespread use by researchers of Amazon Mechanical Turk³⁸ or survey vendors such as Qualtrics,³⁹ which make it easy for people to spend small amounts of time on their smart phones or digital devices participating in online research studies.

The digital transformation of business enables this easy conversion of unpaid leisure time into paid work time through a variety of mechanisms. Digital platforms make it easy for the buy and sell sides of the short-term labor market to match, lowering search costs dramatically.⁴⁰ Platforms typically provide mechanisms for developing and evaluating the reputations of counterparties, alleviating adverse selection issues.⁴¹ And they provide payment processing, advertising, and legal compliance services that would almost certainly be cost-prohibitive for individual users if they had to obtain them themselves in the marketplace.⁴² As a consequence, it is easier than ever before to perform paid labor during brief periods of downtime.

Not only has digitization made it easier to substitute paid work for leisure, but considering the value placed on data in the digital economy and the way that so many of us spend our time on social media platforms, it is now increasingly difficult to even draw a clear line between the two. The distinction made by economists, tax law scholars, and tax law itself, between work—typically viewed as toil engaged in solely for pecuniary compensation—and leisure—which is its own source of pleasure—has always been a simplification that glosses over the intrinsic enjoyment that people get from some aspects of their jobs.

Tax law generally places property and activity in discrete “buckets” identified by the predominant character of the property or activity. For example, a security may be debt or equity, but not a hybrid of the two, notwithstanding that its economic characteristics may not fit neatly in either category. Individuals are treated as engaging in an activity because of a business or profit motive—in which case the expenses associated with the activity will generally be deductible—or for personal reasons—in which case the expenses are not—but not both.⁴³ But of course certain activities hold out the potential for both profit and personal consumption benefit.⁴⁴

But what is the predominant nature of social media engagement? On the one hand, scrolling though one’s Twitter or Instagram feeds, signaling one’s approval or disapproval of other people’s views or vacation photos, seems to be primarily a leisure activity. Even producing new content—TikTok videos, for example—is something that appears to be a source of enjoyment. But, of course, all of this “leisure” activity is mediated by platforms and there is an implicit barter exchange between users and the platforms, whereby users receive the services of the platform for “free” and the platform, in turn, is enabled to target advertising to the user and generally to collect and perhaps sell the user’s data from interacting with the platform.⁴⁵ Are users engaged in the sale of personal property or the performance of services for the platforms in exchange for their services?

The blurriness of this distinction is most apparent when considering social media “influencers,” individuals who cultivate a platform presence (often characterized by conspicuous consumption or an aspirational lifestyle) that allows them to be compensated for advertising products using that platform. An individual engaged in a trade or business may deduct all the ordinary and necessary expenses paid or incurred in carrying on that trade or business, including travel and meals while away from home.⁴⁶ This naturally encourages influencers to take aggressive positions about whether their lavish travel, personal care products, and meals, are business expenses.⁴⁷

III. THE SHARING ECONOMY

Just as they allow people to monetize their leisure time more easily, digital platforms also make it easier to rent one’s property to others when one is not using it, or when one can get a higher return in the rental market than the imputed return from personal use of the property. In this Section I consider the significance of these new markets for durable-good rentals. I describe some of the important economic features of the so-called “sharing economy,” the market for short term rentals of durable consumer goods such as housing, cars, bicycles, and so on, and then describe some of the consequences of these new markets for tax law and policy.

Consider, for example, using one’s own car to drive for Uber or Lyft, the compensation from which reflects both the cost of the driver’s time (converting leisure into paid work) and the rental value of the car itself. Or consider Airbnb and other short-term property rental platforms, which allow people to rent out a portion of their residence or give landlords the option to rent investment properties on a short-term basis rather than enter long-term leases. Digital platforms provide the infrastructure to match cars and drivers or homes and guests, provide advertising, payment processing and reputational management, all of which make it cost-effective for people to drive during their spare time or rent out a portion of their property. Although the logic is mostly the same for all durable-good rentals, I will focus on the short-term property rental market.

The economic benefits of short-term rental markets are relatively clear, facilitating mutually beneficial transactions that would not otherwise take place, leaving both property owners and would-be renters better off. In economic jargon, there is an increase in consumer surplus in the market for housing services and an increase in the amount of housing services consumed overall.⁴⁸ This is not to say that there are not some new costs associated with the emergence of the short-term rental market. For example, some worry that short-term renters are poor neighbors, imposing noise and other externalities on residential neighborhoods.⁴⁹ There are also concerns that short-term rentals evade laws and regulations,⁵⁰ exacerbate affordable housing issues, and increase home prices.⁵¹ At the same time, the availability of a short-term rental option creates significant benefits for both the renters and property owners that did not exist before.

The economists Apostolos Filippas, John Horton, and Richard Zeckhauser have argued that low-cost rental options can have important implications for the ownership of real estate in the long run.⁵² People who wanted to own a home but previously could not afford it may now be able to become owners by renting out a portion of a property. And people who were reluctant owners, buying property only because there was no other option, may now be able to rent on terms that are more favorable to them. The authors argue that the overall effect of a rental market on the number of owners vis-à-vis renters is ambiguous. Interestingly, whether the number of owners goes up or down, the ease of renting introduces a decoupling of real estate ownership from preferences for the personal enjoyment of real property. In a world where renting is very costly, the people who own durable goods such as homes and automobiles are those who value the consumption benefits the most. But as the cost—regulatory, advertising, payment processing, and taxation—of renting property to others falls, then the relationship between who owns property and who values it the most will weaken.

In the extreme case where it is costless to rent property, there is no correlation at all between who owns the property and how much they value its use.⁵³ The reason is simple: when there are no meaningful costs to renting the property, the people who own the property need not be the people who value it the most—they can simply be people who rent it to the people who value it the most. But this does raise the question of which factors will determine the pattern of real estate and durable good ownership. If it is not the people who value the use of the property the most, then factors such as the cost of financing the acquisition of the property, or maintaining and managing it, may become more important. These are factors that are likely to benefit property owners operating at a large scale, which may suggest greater consolidation of property ownership following the emergence of rental markets.

The substantive tax treatment of income from participating in the sharing economy is relatively straightforward, even as there are challenges with tax enforcement and compliance in these new markets.⁵⁴ But there are ways that tax rules may operate to impede the development of flourishing and efficient rental markets by increasing the after-tax costs of bringing residential properties to the market.⁵⁵ In a few places, tax law places a thumb on the scale of using property that one owns as one’s principal residence, favoring the imputed income from homeownership over the taxable income from renting it.

Most obvious, of course, is the fact that imputed income is not taxed while rental income is taxed. A limited exception exists if one uses a “dwelling unit” as one’s residence during the year and rents it out for less than fifteen days. In that case, the rental income is excluded from gross income, but no deductions otherwise allowable because of the rental use of the property are allowed.⁵⁶ Second, a homeowner who has a separate structure or room that would otherwise qualify as a home office is not entitled to deductions for that office if they rent the space when it is not in use.⁵⁷ And third, under current law, expenses incurred for the production of income—such as the various costs incurred to make space available for short-term rental, including cleaning services, any fees paid to the digital platform, and so on—are not allowable until 2026.⁵⁸ Individuals who are actively engaged in the business of renting out residential property are still able to deduct their expenses, but someone renting out a portion of their home for supplemental income would not be treated as being in the business of being a landlord.⁵⁹

At the very least, the costs of participating in sharing economy markets should be fully deductible to reduce the asymmetry in the taxation of rental income and imputed income and help facilitate the growth of these markets. Not only do these markets directly benefit the participants, but there is also a feedback effect that benefits tax administration as well. Rental income data can provide information to improve home value estimates used in determining assessments for local property tax purposes, and the option to rent one’s property helps alleviate the illiquidity concern.

CONCLUSION

One of the most dramatic changes wrought by the digital economy is the ease with which we can shift our time and property between the private and public spheres, between personal use and the market. This change not only unlocks enormous economic value, but it also expands the income tax’s domain, creating new frontiers for taxation and improving the efficiency of the income tax along the way.

96 S. Cal. L. Rev. 1371

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Andrew T. Hayashi*

* Professor of Law and Nancy L. Buc ’69 Research Professor of Democracy and Equity, University of Virginia School of Law.

Democracy Dies in Silicon Valley: Platform Antitrust and the Journalism Industry

May 2022April 2024Erick Franklund*

Newspapers are classic examples of platforms. They are intermediaries between, and typically require participation from, two distinct groups: on the one hand, there are patrons eager to read the latest scoop; on the other hand, there are advertisers offering their goods and services on the outer edges of the paper in hopes of soliciting sales. More than mere examples of platform economics, however, newspapers and the media industry play an irreplaceable role in the functioning of our democracy by keeping us informed. From behemoths such as the Jeff Bezos–owned Washington Post to outlets like the Hungry Horse News in the small town of Columbia Falls, Montana, the press lets us know what is happening on both the national and local levels. However, the age of the Internet and the corresponding emergence of new two-sided platforms is decimating the media industry.[1] In a world where more users get their news on social media platforms like Facebook than in print,[2] the survival of quality journalism depends in large part on whether the media industry can tap into the flow of digital advertising revenue, the majority of which goes to just two corporations founded around the start of the new millennium.

Facebook and Google, formed respectively in 2004 and 1998, are new types of platforms aiming to accomplish what newspapers have done for centuries: attract a large consumer base and solicit revenue from advertisers. However, unlike the fungible papers newsies once distributed hot off the presses, Facebook and Google connect advertisers and consumers in a more sophisticated, yet opaque manner. Facebook and Google are free to consumers insofar as users do not pay with money to surf the web or connect virtually with their friends. Instead, the companies collect information about users based on their online activity, and complex algorithms connect those users with targeted advertisements.[3] This new method of connecting Internet users and advertisers has been wildly successful, creating a tech duopoly profiting from nearly sixty percent of all digital advertising spending in the United States.[4]

[1]. Throughout this Note, I refer to the journalism industry also as the “media” industry and the “news media” industry. Although there are undoubtedly nuanced differences between journalism and news media, for the purposes of this Note, I draw no distinction between them.

[2]. Elisa Shearer, Social Media Outpaces Print Newspapers in the U.S. as a News Source, Pew Rsch. Ctr. (Dec. 10, 2018), https://www.pewresearch.org/fact-tank/2018/12/10/social-media-outpaces-print-newspapers-in-the-u-s-as-a-news-source [https://perma.cc/5MWY-RSTH].

[3]. Although I may not be interested in an upcoming Black Friday deal for chainsaws posted in a physical publication of the Hungry Horse News, Facebook and Google are—based on my history and activity on the platforms—aware of my affinity for things like antitrust law and coffee, and so their algorithms are likely to present advertisements to me for items such as books written by Herbert Hovenkamp and expensive burr coffee grinders.

[4]. Felix Richter, Amazon Challenges Ad Duopoly, Statista (Feb. 21, 2019), https://
http://www.statista.com/chart/17109/us-digital-advertising-market-share [https://perma.cc/4FPT-RYRV].

* Executive Senior Editor, Southern California Law Review, Volume 95; J.D. Candidate, 2022 University of Southern California, Gould School of Law. I would like to thank Professor Erik Hovenkamp for serving as my advisor. All mistakes are my own.

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