Vol. 96, No. 6 Archives – Southern California Law Review

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.

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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.

What’s in a Name? ESG Mutual Funds and the SEC’s Names Rule

April 2024April 2024Jill E. Fisch* & Adriana Z. Robertson†

As investor money flows into environmental, social and governance (“ESG”) mutual funds, regulators have raised growing concerns about greenwashing—specifically that a fund’s name will falsely suggest that the fund invests in companies that meet certain ESG standards. To address these concerns, the Securities & Exchange Commission (“SEC”) proposed amendments to the Investment Company Act (“Names Rule”). The amendments extend the scope of the Names Rule to funds whose names include terms such as ESG, green, or sustainable. If adopted, they will require such funds to invest at least 80% of the value of their assets in companies that meet the standards suggested by these terms.

We interrogate the SEC’s concern about greenwashing and the extent to which the extension of the Names Rule is rationally directed toward addressing that concern. One challenge is that the term ESG is too broad and imprecise to provide an objective basis for determining which companies appropriately fall within an 80% bucket. A second challenge is that the concept of an 80% requirement is in tension with most mainstream ESG investment strategies. Third, and perhaps most problematic, are the limitations of fund names in conveying the extent of information necessary to ensure that a fund meets the expectations of its investors.

We demonstrate these concerns empirically. First, to address the SEC’s concern that investors are not getting a meaningfully different product, we compare the composition of ESG funds with their most closely analogous non-ESG sister funds. Second, through the creation of synthetic Women in Leadership funds, we demonstrate the limitations of fund names in conveying sufficient information about a fund’s investment strategy, even for portfolio criteria that can be measured objectively. Our findings demonstrate that the SEC’s proposal is unlikely to increase investor protection and is likely to impede a variety of legitimate ESG strategies.

We conclude that the SEC’s effort to address greenwashing through the Names Rule reflects an overly simplistic and unworkable approach to characterizing portfolio companies and a narrow perception of plausible ESG investment strategies. Both are at odds with existing market practices and threaten further innovation.

INTRODUCTION

Names matter.¹ Mutual fund names have the power to impact the flow of investor funds substantially.² In recent years, this power has been reflected through large inflows into mutual funds with names conveying a strategy reflecting environmental, social and governance (“ESG”) considerations,³ funds that we will refer to as ESG funds.⁴

Concerned that the shareholders who invest in these funds may not be receiving a product that meets their expectations, and that mutual fund sponsors may inappropriately label their products with ESG names to capitalize on investor demand, in May 2022, the Securities & Exchange Commission proposed changes to Rule 35d-1 (the “Fund Names Rule”), designed to increase the likelihood that mutual funds align more closely with investor expectations.⁵ Specifically, the amendments would, among other requirements, extend the Names Rule to investment strategies and would require that, if a fund’s name suggests “an investment focus in companies that meet certain ESG standards,” at least 80% of the value of the fund’s investments be consistent with that focus.⁶

The proposed amendments reflect a misguided understanding of both the concept of an ESG investment and what it means to use an ESG investment strategy. By extending the Names Rule’s categorization approach to ESG, the SEC introduces an overly simplistic taxonomy into a subject rife with ambiguity. As Elizabeth Pollman explains, the term ESG includes a range of usages that can be applied to both companies and investment strategies, and “consensus on the meaning of ESG does not currently exist.”⁷ That ESG is a “big tent”⁸ makes it appealing to use the term in fund names, but, consequently, its use does not convey very much information. Commentators can, and do, reasonably disagree about whether it is appropriate to characterize a particular portfolio company as ESG or not,⁹ and market-based ratings organizations often differ widely in the ratings they assign to the same company.¹⁰ Notably, because ESG involves a range of factors, a company that appears to fall short in one dimension may nonetheless warrant a high overall score.¹¹

At the same time, investors can implement a variety of approaches to ESG investing.¹² Some strategies are relatively simplistic, such as inclusion—selecting companies based on certain characteristics or screening—excluding certain types of companies.¹³ Other ESG investing strategies can be more complex. They may involve overweighting companies with high ESG scores or underweighting companies with lower scores. An ESG strategy can also seek to maximize diversification by constructing a portfolio consisting of the more highly rated companies in each industry rather than excluding whole industry categories such as oil and gas.¹⁴ An ESG strategy can also be more targeted—such as seeking to obtain a portfolio level of conformity with one or more ESG characteristics such as low carbon emission or board gender diversity. And of course, ESG funds may strive for impact—endeavoring not to choose portfolios of companies with high ESG characteristics, but instead to improve the ESG profile of their portfolio companies.

As a result, there is little logic to policing the use of terms such as ESG through the Names Rule’s 80% investment requirement. In our empirical analyses we demonstrate both the impotency of an 80% requirement preventing the inclusion of companies that some investors would view as problematic from an ESG perspective, as well as the incoherence of applying an 80% requirement to portfolio-wide ESG investment strategies.¹⁵

We start by interrogating the concept of “greenwashing.”¹⁶ The SEC worries that managers are simply naming or renaming funds as ESG in order to capture investment flows without engaging in an ESG investment strategy.¹⁷ For the reasons suggested above, this proposition is difficult to test empirically. Unlike the categories of securities to which the Names Rule has traditionally been applied, such as tax-exempt securities or technology companies, the concept of ESG does not readily lend itself to determining whether a particular security belongs in a fund’s 80% bucket.

To overcome this problem, we adopt a different approach. For each ESG fund in our sample, we identify a “sister fund”—the non-ESG fund in the same fund family most comparable to the ESG fund. We then compare the portfolio composition of each fund pair.

Our comparison spans two dimensions. First, we test the extent to which ESG funds hold different portfolio companies than non-ESG funds. We find that, although a nontrivial number of ESG funds’ holdings overlap significantly with those of their sister funds, the vast majority of ESG funds look quite different. We then compare the performance of ESG funds to their sister funds. We find that ESG funds do not underperform their sister funds, nor do they charge higher fees. This holds both across the distribution of similarity and among ESG funds that are most similar to their sister funds.

While our findings cannot determine whether investors receive what they expect when they purchase an ESG fund, they cast doubt on the claim that ESG funds simply reflect opportunistic rebranding by fund managers. The ESG funds in our sample appear to be meaningfully different from their non-ESG sister funds, and we find no evidence that investors are paying more or receiving inferior returns.

Next, we consider the extent to which a fund name can do the work contemplated by the SEC in conveying sufficient information to meet investor expectations. We create a series of synthetic Women in Leadership funds that seek, through their investment strategy, to promote women in corporate leadership. We identify how modest differences in the implementation of this strategy—all of which are plausibly faithful to the fund name—result in substantial differences in fund composition. Our analysis also reveals how easily a portfolio that is consistent with the strategy conveyed by the fund’s name may nonetheless fail to meet the expectations of some fund investors.

Our findings demonstrate that the SEC’s proposed amendments to the 80% rule are misguided. Not only are they unlikely to reduce the potential for greenwashing, but they are also likely to deter innovation in fund offerings and investment strategies, potentially depriving investors of new products that better align with their investment goals.¹⁸

I. THE REGULATION OF MUTUAL FUND NAMES

A. The Importance of Mutual Fund Names

An emerging finance literature emphasizes the important role of fund names in driving investment decisions. Fund names can convey the characteristics of a fund’s investment portfolio—such as whether the fund invests in equity or fixed income securities.¹⁹ They can seek to attract investor attention by being clever or catchy.²⁰ A trade or generic name may also convey the quality of the fund’s management.²¹

As early as 2005, Cooper, Gulen, and Rau demonstrated that mutual fund name changes had a significant impact on fund flows.²² Responding to a newspaper article reporting that mutual funds were changing their names from growth funds to value funds, the authors found that mutual funds that changed their names to reflect a current hot investment style received substantial inflows whether or not the change was associated with a difference in fund holdings or performance.²³ They argued that investor responsiveness to these “cosmetic” changes is consistent with the fact that “most investors have little knowledge about the products that they are buying.”²⁴

Subsequent research found similar results. For example, Esplenlaud, Haq, and Khurshed studied fund name changes from 2002 to 2011.²⁵ They found that investors responded with increased fund flows to superficial name changes—specifically name changes that did not attempt to reflect the fund’s portfolio composition and that were not accompanied by meaningful portfolio adjustments.²⁶ They further found that these investors gained no benefit from the changes in the form of improved performance or lower fees. In another study, Greene and Stark found that sponsors were successful in attracting inflows by launching funds with trendy names.²⁷

The finding that investors respond to fund names extends to the use of names that convey an ESG-related investment strategy. A substantial number of mutual fund sponsors have repurposed and rebranded funds to take advantage of the popularity of ESG investing.²⁸ In one account that drew substantial media attention, BlackRock changed the name of a fund from impact, to ESG, and then to sustainable.²⁹ The name change purportedly attracted millions of dollars in inflows. Notably, however, the name changes did not appear to be cosmetic—along with changing the fund’s name, BlackRock modified the fund’s investment strategy.³⁰

A recent paper by Aymen Karoui and Sadok El Ghoul looked at twenty-eight funds that changed their names over the period from 2003 to 2018 to convey a sustainability-related strategy.³¹ They found that the name changes were correlated with substantial fund inflows in the first year after the change. Importantly, however, Karoui and El Ghoul investigated the extent to which the name changes were cosmetic and found that the name changes were accompanied by substantial rebalancing.³² They further found that investors were able to distinguish between cosmetic and non-cosmetic name changes, and that only the non-cosmetic changes drew increased asset flows.³³ To examine the potential of ESG fund names to mislead, Candelon, Hasse, and Lajaunie evaluated ESG funds according to their ESG ratings from Morningstar and MSCI.³⁴ They found that although the sample of ESG funds obtained higher average ESG ratings than those obtained by conventional funds, there was a substantial overlap between the two distributions.³⁵

Arguably the documented extent to which investors rely on fund names is problematic. The SEC requires mutual funds to make extensive disclosures about their holdings and strategy in a prospectus and a statement of additional information (“SAI”), as well as to provide periodic disclosure about their holdings and voting records.³⁶ Fund sponsors are required to post these documents on the internet and to make the information contained in them user-friendly. These disclosures provide substantial details on the nature of fund investment strategies and, for the most part, would effectively eliminate the risk that an investor would misunderstand how an ESG fund determines which securities qualify for inclusion in its portfolio.³⁷ The problem, of course, is that investors do not read these documents.³⁸

The situation is further aggravated by the fact that a substantial component of mutual fund investing today takes place through employer-sponsored employee-directed retirement plans.³⁹ In these plans, employers construct a menu of funds that are available to their employees, and employees choose the funds in which their money will be invested.⁴⁰ The standard menu provides employees with a list of fund names and possibly additional information such as asset class and fees, but an investor typically must make an affirmative effort to seek out additional information, and it is unclear how many investors do so.⁴¹

B. Section 35(d) & the Names Rule

Section 35(d) of the Investment Company Act of 1940 makes it unlawful for a mutual fund name to use any word or words that the Commission finds are materially deceptive or misleading.⁴² The statute authorizes the SEC to use its rulemaking authority to designate the circumstances under which a firm name is deceptive or misleading.

In 2001, the SEC used this authority to adopt Rule 35d-1, the Names Rule.⁴³ Rule 35d-1 requires funds using certain types of names to invest at least 80% of the value of their investments in a manner that is consistent with that name.⁴⁴ Under the current form of the rule, the 80% requirement applies to names suggesting investment in certain asset types of industries, names suggesting a focus on investments in a particular country or geographic region, names indicating that fund distributions are exempt from federal or state income tax, and names suggesting guarantee or approval by the United States government. In addition, the rule explicitly states that it does not apply to common investment strategies.⁴⁵ In adopting the rule, the SEC explicitly stated that the 80% requirement is not a safe harbor from liability; a fund may be found to have a misleading name despite its compliance with the 80% requirement.⁴⁶

The SEC has not amended Rule 35d-1 since 2001 although a variety of market developments have occurred in the subsequent two decades.⁴⁷ In particular, after the 2008 financial crisis, the SEC observed that a number of investors appeared to be misled by their investments in target date funds—funds that purported to shift their asset allocation between debt and equity in accordance with the approach of the designated target date. These funds, which are widely used for retirement investing, varied substantially in their asset allocations and glide paths, creating differing levels of risk exposure for investors in the funds.⁴⁸ The SEC’s proposed solution, however, was not to attempt to regulate the use of the term “target date” in a fund’s name, but instead to require changes to the marketing materials for target date funds.⁴⁹ Ultimately, the SEC did not adopt new regulations to address the problem.⁵⁰

C. The 2022 Proposed Amendments

On May 25, 2022, the SEC proposed amendments to the Names Rule.⁵¹ The amendments are explicitly designed to address ESG fund names. As SEC Chair Gary Gensler outlined in the remarks introducing the proposed rule, the release proposes four changes.⁵²

its%20name [https://perma.cc/J69K-FXNA]. First, it extends the scope of the 80% requirement to names that designate specific investment characteristics, including ESG-related names.⁵³ Second, it requires funds that “drift” out of compliance with the 80% requirement to become compliant within 30 days. Third, it requires funds to designate the specific holdings in their portfolios that count toward the 80% requirement.⁵⁴ Finally, it requires funds to use the notional value of derivatives for determining compliance with the 80% requirement.

The proposing release goes further in targeting ESG funds. For example, the proposal would codify the fact that a fund’s name may be materially misleading if the fund’s portfolio contains investments that are inconsistent with its investment strategy, even if it complies with the 80% requirement.⁵⁵ By way of example, the release cites a “fossil fuel-free fund” that makes a substantial investment in fossil fuel reserves.⁵⁶ Similarly the release expressly observes that a fund’s name can be misleading if it invests in an index that is included in the fund’s name, but that index contains “components that are contradictory to the index’s name.”⁵⁷

The proposing release explains the SEC’s justification for the amendments. In particular, the release singles out ESG funds as presenting particular investor protection concerns.⁵⁸ The release observes that the use of ESG terminology may be particularly powerful in attracting investors and that subjecting those funds to the 80% requirement would “help to prevent potential ‘greenwashing.’ ”⁵⁹ The release is explicit in calling out investment advisors for adopting and changing fund names out of “self-interest” in order to attract greater inflows.⁶⁰

II. ESG FUND NAMES AND GREENWASHING

A. The SEC’s Concerns

The proposing release offers some insight into the SEC’s concerns about ESG mutual funds. These concerns can be divided into three categories. The first is that an ESG fund will invest in securities that are not ESG. The SEC illustrates this through its example of a Fossil Free fund that is not, in fact, fossil free because it invests in fossil fuel companies.⁶¹

fossilfreefunds.org/fund/spdr-sp-500-fossil-fuel-reserves-free-etf/SPYX/fossil-fuel-investments/FS0000
C3K4/F00000WAP7 [https://perma.cc/EB9R-AR48]. It is unclear whether the SEC would consider the ETF’s name misleading under the terms of the proposed rule. The greenwashing concern expressed in the media, however, is broader—that a variety of companies in the portfolios of ESG mutual funds are simply inconsistent with an ESG or sustainability investment mandate. For example, the media has questioned whether an ESG fund can legitimately invest in companies in the oil and gas industry.⁶² This is the rationale for requiring funds to designate the securities that fall within their 80% basket—to highlight, and potentially expose a fund for claiming that its investment in Exxon conforms with its ESG investment strategy.⁶³

A related concern is the use of fund names that simply reference an underlying ESG index. Many ESG indexes involve complex strategies similar to those discussed above, including overweighting and underweighting or choosing the companies in each industry with (relatively) higher ESG scores. These strategies may result in an index including companies that the average investor might not view as reflecting ESG values. For example, funds that invest in accordance with the S&P 500 ESG index reflect one of the largest categories of ESG funds in terms of assets under management.⁶⁴ The S&P 500 ESG index is specifically designed to maintain the same industry group weights as the S&P 500 index, meaning that, by necessity, it invests in oil and gas, fossil fuel, and similar industries.⁶⁵

A second, and somewhat different concern, is that an ESG fund’s overall portfolio will not differ sufficiently from a fund that does not bear the ESG name. The SEC’s concern here is that asset managers are using ESG branding to attract asset flows, but not adopting genuine ESG investment strategies.⁶⁶ To this point, the proposing release cites empirical literature indicating that fund name changes are not associated with changes in fund styles.⁶⁷ A related consideration is that ESG funds may cost more than “plain vanilla” funds despite failing to provide additional screening of their investments.⁶⁸ A 2021 Bloomberg article, for example, cited a number of ESG funds with higher fees but with portfolio compositions that closely matched the comparable lower cost non-ESG index funds.⁶⁹

A third concern—the concern most frequently repeated by the SEC—is that ESG funds do not meet investor expectations. It is not entirely clear what this means. One possibility is that the fund manager and investors just disagree about which companies constitute ESG investments. As noted above, there is broad disagreement about this, and it is unlikely that fund names have the capacity to provide greater clarity, a point we interrogate through our empirical analysis below. A different and more subtle point is that investors may overestimate the impact of ESG investing on underlying social issues such as climate change or wealth inequality.⁷⁰

B. How Prevalent Is This Type of Greenwashing?

In this section, we probe the empirical basis for the SEC’s proposed rule. Rather than relying on anecdotes or generalized concerns about greenwashing, we investigate the extent to which ESG funds resemble the other funds that their sponsors offer.

To do so, we identify a non-ESG sister fund for each ESG fund in the market each year. We define the sister fund as the non-ESG fund in the same fund family (funds offered by the same management company) that most closely resembles the ESG fund in terms of the securities in the ESG fund’s portfolio that year.⁷¹ Following the logic of the proposed Names Rule, and, in particular, the proposition that an ESG fund should exclude non-ESG companies, the initial measure of similarity that we adopt—which we call the “portfolio inclusion”—is the percentage of the assets in the non–ESG sister fund’s portfolio that are also in the ESG fund’s portfolio. This measure captures the extent to which the ESG fund excludes assets that its sister fund owns.

Like the proposed Names Rule upon which it is based, portfolio inclusion is binary with respect to each asset in the fund’s portfolio: the asset is either included or it is excluded. Accordingly, this measure does not contemplate the fact that a fund can engage in a tilt-based strategy, which involves over- or under-weighting particular securities.⁷² As discussed in more detail in Part III.A, we view this as an important conceptual limitation of the proposed Names Rule. Accordingly, while we believe this to be a conceptual limitation of the measure in the abstract, we view it as a feature for the purposes of evaluating the Names Rule.

We begin with the universe of mutual funds in the CRSP Survivorship Bias Free database. Consistent with the proposed Names Rule, we identify ESG funds using the fund’s name⁷³ and restrict attention to domestic equity funds.⁷⁴ Because all classes of a particular fund share a common portfolio, we construct the portfolio inclusion measure at the fund (rather than class) level. We perform the analysis for each year between 2015 and 2022. The distribution of portfolio inclusion is presented in Figure 1. In the interest of space, we present the results from 2017 through 2022, but the results from 2015 and 2016 are broadly similar to those from 2017. Because the ESG market is relatively young, we break the results out by year to make it easier to spot trends over time.

Figure 1. Distribution of Portfolio Inclusion, by Year

The results make clear that the large majority of ESG funds are substantially different from their sister funds, based on this measure. In 2021, the average ESG fund excluded securities making up 42% (100%-58%) of its sister fund’s portfolio, with a median of 38%. There is, however, some evidence of “bunching” at the high end. In the “worst” year—2020—29% of ESG funds excluded securities making up less than 20% of their sister fund’s portfolio. Of course, this also means that 71% of funds excluded more than 20%. While there is some evidence that this bunching at the high end (say, 80% or more) increased in recent years, the pattern is not consistent: it dipped between 2017 and 2018 before rising through 2020 and then dipping again in 2021 and 2022. In 2022, 27% of ESG funds held assets making up at least 80% of the assets in their sister fund’s portfolio.

This bunching—the fact that a substantial number of funds only exclude a small percentage of the assets in their sister funds’ portfolios—is, at least superficially, consistent with the SEC’s greenwashing concern. Moreover, the SEC might reasonably conclude that evidence suggesting greenwashing at 27% (or, in 2020, 29%) of ESG funds is more than enough to warrant a regulatory intervention. As an agency primarily charged with enforcing an antifraud regime, the SEC is right to be much more focused on the fact that a relatively large group is concerning than on the attributes of the average fund.

But before jumping to the greenwashing conclusion, it is worth remembering that the portfolio inclusion measure is just part of the story. While excluding assets that are inconsistent with the fund’s ESG strategy is an intuitive, and very common, approach to ESG investing, it is not the only one. For example, rather than excluding companies, an ESG fund might seek out and invest in top ESG companies, based on whatever ESG metric(s) the fund employs. One can imagine an environmental fund, for example, seeking out companies that are producing innovative solar or wind energy products, as opposed to excluding fossil fuel companies. Even if a fund doesn’t exclude many companies, if it is affirmatively seeking out different assets, it is providing investors with something different from its sister fund. Accordingly, we develop a second measure, “portfolio overlap,” which is the percentage of the assets in the ESG fund’s portfolio that are also in the non-ESG fund’s portfolio. We use these two measures in combination to calculate a more refined measure that we call “portfolio similarity” between each ESG fund and its sister fund. This is simply the lesser of the portfolio exclusion and the portfolio overlap. Table 1 illustrates how these measures work.

Table 1. Example of Portfolio Overlap and Portfolio Inclusion
Asset	Portfolio Weight
Asset	Fund A (ESG Fund)	Fund B (ESG Fund)	Fund Z (Non-ESG Fund)
A	25%	0%	30%
B	35%	10%	40%
C	20%	45%	25%
D	0%	45%	5%
E	20%	0%	0%

In the example in Table 1, Fund A’s portfolio overlap with Fund Z is 80%, because 80% of the assets in its portfolio are assets that are also in the Fund Z’s portfolio. By the same logic, Fund B’s portfolio overlap with Fund Z is 100%. This is true even though there are assets in Fund Z’s portfolio that are not in Fund B’s, and even though the two portfolios differ substantially. At the same time, Fund A’s portfolio inclusion with Fund Z is 95%, because assets making up 95% of Fund Z’s portfolio can be found in Fund A’s portfolio (and 5% of the assets in Fund Z’s portfolio as excluded from Fund A’s portfolio). Fund B’s portfolio inclusion with Fund Z is only 70%. Accordingly, Fund A’s portfolio similarity to Fund Z is 80% (the lesser of 80% and 95%), and Fund B’s is 70% (the lesser of 100% and 70%).

We plot the distribution of portfolio similarity in Figure 2. These plots show much less cause for concern. Rather than something in the neighborhood of 30%, we now have fewer than 15% of funds even in the “worst” year whose portfolios are substantially similar to those of their sister funds. More strikingly, Figure 2 makes clear that the overwhelming majority of ESG funds are substantially different from their sister fund (as measured in the ways contemplated by the proposed Names Rule). We emphasize that the sister fund is defined as being the non-ESG fund in the same family that is most similar to the ESG fund—by definition, all other funds in the family are at least as different.

Figure 2. Distribution of Portfolio Similarity, by Year

Nevertheless, recognizing the SEC’s focus on the most concerning funds, we zoom in on the funds at the top of the distribution—those with a portfolio similarity of 80% or more. We present this in Figure 3. Several features stand out from this: first, even in the worst year, this group consists of, at most, 19 funds. It is unclear that this group of potentially concerning funds is large enough to warrant a rulemaking, particularly since the SEC does not need to revise the Names Rule to use its existing enforcement authority against funds with fraudulent and misleading names. Moreover, we note that the very top end—above 95%, say—is even more thinly populated. Using that cutoff, we are down to a total of no more than 4 funds.

Overall, while there are a small number of outliers, the evidence suggests that the overwhelming number of ESG funds are substantially different—based on the logic of the Names Rule—from the other funds in their family. This evidence seems to undercut the SEC’s apparent concern that fund sponsors are just slapping the ESG name onto a fund that is otherwise the same as one of its other offerings.

We stress that nothing in this analysis speaks to the question of whether these funds are giving investor what they want or expect. We simply ask whether they are different from the other funds. In other words, are investors getting something. The answer appears to be yes.

While misleading investors is a harm in and of itself, the concern about greenwashing is generally accompanied by a claim that greenwashing is motivated by a desire to charge investors higher fees. While a higher fee might be warranted if investors were truly receiving a different product, investors should not pay more for a fund that carries the ESG name but does not differ substantially from a non-ESG product. We therefore extend the sister fund analysis to investigate the empirical basis for this concern. Specifically, we investigate the extent to which the ESG funds that are more similar to their sister funds (as measured by portfolio similarity) tend to have higher fees and/or worse risk-adjusted (net of fees) performance than those that are more different.

Figure 3. Distribution of Portfolio Similarity Over 80%

To do so, we estimate a series of ordinary least squares (“OLS”) regression models. We begin by simply asking whether ESG funds tend to be more expensive, and lower performing, than their sister funds, and present the results in Table 2. The dependent variable in columns 1 and 2 is the difference between the ESG fund’s expense ratio and that of its sister fund. The dependent variable in columns 3 and 4 is the difference between the ESG fund’s alpha⁷⁵ and that of its sister fund. A higher expense ratio is generally interpreted as being bad for investors; the opposite is true with respect to alphas.

Table 2. Expenses and Performance of ESG Funds Relative to their Sister Funds
	Expense Ratio		Alpha
	(1)	(2)	(3)	(4)
Mean Difference	-0.10164***	-0.11374**	0.00245	0.01622
Mean Difference	(-4.56)	(-2.98)	(-0.09)	(-0.30)
ESG Fund Size Control	NO	YES	NO	YES
Adjusted R²	0.00000	0.00001	0.00000	-0.00003
N	7,701	7,695	10,176	10,170
Notes: ^aThis table presents results from OLS regression models using data from 2015 through 2022. ^bThe dependent variable in columns 1 and 2 is the difference between an ESG fund’s expense ratio and that of its sister fund (expressed in percent). ^cThe dependent variable in columns 3 and 4 is the difference between an ESG fund’s one-factor alpha (estimated using 12 monthly observations) and that of its sister fund (expressed as monthly alpha, in percent). ^dThese regressions are estimated at the class level, since fees, and therefore alphas, vary by class within a fund. ^eStandard errors are clustered at the (ESG) fund level. + p<0.1, * p<0.05, p<0.01, * p<0.001.

If anything, the ESG funds may be slightly cheaper than their sister funds. This is not to say that they are cheaper than the cheapest funds in the market.⁷⁶ While the point estimates are statistically significant, we note that the magnitudes of the point estimates are small: the standard deviation of the difference in expense ratios is about 0.6 (the standard deviation of the expense ratios of the ESG funds is similar, at about 0.5). The point estimates in columns 1 and 2 therefore represent less than a quarter of a standard deviation. A conservative conclusion is that the expenses of ESG funds do not differ substantially from those of their sister funds. Certainly, we find no evidence that ESG funds are more expensive. The analysis in columns 3 and 4 is consistent with this conclusion: we find no statistically significant difference in the alphas between ESG funds and their sister funds. The point estimates are also very small relative to the standard deviation of the dependent variable.⁷⁷

By construction, these analyses focus on the average. As discussed, the SEC may (reasonably) be more concerned about the most concerning funds than it is about the average funds. We therefore extend the analysis in Table 2 by adding the portfolio difference measure. We present the results in Table 3. In columns 1 to 3 we use an indicator variable equal to one if the portfolio similarity measure is over 80% to focus on funds at the top end of this measure.⁷⁸ In columns 4 to 6 we use the measure itself to explore the relationship between portfolio similarity and fund characteristics across the distribution.⁷⁹ The dependent variable in Panel A is the difference between the expense ratio of the ESG fund and that of its sister fund. The dependent variable in Panel B is the difference between the alpha of the ESG fund and its sister fund.⁸⁰ Because fees and performance are reported at the class level (rather than the fund level), all results are clustered at the fund level.

As in Table 2, we present the baseline relationship with no controls in column 1. In column 2 we include fixed effects for year, fund family (management company), whether the ESG fund is an index fund, and whether the ESG fund is an ETF. In column 3 we further add a control for the natural log of the assets under management in the ESG fund. We use the same pattern of controls in columns 4 through 6. The results are in Panel A of Table 3.

Panel A of Table 3 shows that there is no consistent relationship between portfolio similarity and ESG fund expenses, relative to their sister funds. The point estimates in columns 1 through 3 are small (relative to the standard deviation of the dependent variable, which is about 0.6) and statistically insignificant. Importantly, this analysis compares the funds that are most similar to their sister funds—and are therefore, from the perspective of the SEC, the most concerning—to the rest of the ESG funds in the market. The results in columns 4 through 6, in contrast, suggest that there is, at best, a modest relationship between fees and portfolio difference, although this difference is not statistically significant once we include the full battery of fixed effects. Even in column 4, the practical implications of the point estimates are likely to be small: the results in column 4 imply that going from a portfolio similarity of 0 to 100—the most extreme change possible—is associated with a change in relative expense ratios representing roughly a quarter of a standard deviation, and this is only marginally statistically significant. Once we add controls, this marginal statistical significance vanishes and the point estimates fall. Overall, the results in Panel A provide no evidence that ESG funds that are most clonelike (i.e., most similar to their sister funds) charge systematically higher fees than others.

Table 3. Relationship Between Portfolio Difference and Fund Characteristics (Expenses and Performance)
	(1)	(2)	(3)	(4)	(5)		(6)
	Panel A: Expense Ratio
Portfolio Similarity > 80%	-0.07272	0.01682	-0.03979
Portfolio Similarity > 80%	(-1.25)	(-0.31)	(-0.89)
Portfolio Similarity				0.00135+		0.00037		-0.00005
Portfolio Similarity				(-1.67)	(-0.44)		(-0.06)
Year, Fund Family, Index Fund, & ETF FE	NO	YES	YES	NO	YES		YES
ESG Fund Size Control	NO	NO	YES	NO	NO		YES
Adjusted R²	0.0026	0.13833	0.14307	0.00229	0.13841		0.14300
N	7,701	7,687	7,681	7,701	7,687		7,681
	Panel B: Alpha
Portfolio Similarity > 80%	0.04021	0.09501	0.08173
Portfolio Similarity > 80%	(-1.05)	(-1.15)	(-0.90)
Portfolio Similarity				0.00069	-0.00045		-0.00054
Portfolio Similarity				(-0.75)	(-0.22)		(-0.26)
Year, Fund Family, Index Fund, & ETF FE	NO	YES	YES	NO	YES		YES
ESG Fund Size Control	NO	NO	YES	NO	NO		YES
Adjusted R²	-0.00005	0.23960	0.23967	0.00015	0.23948		0.23962
N	10,176	10,159	10,153	10,176	10,159		10,153
Notes: ^aThis table presents results from OLS regression models. ^bThe dependent variable in columns 1–3 is the difference between an ESG fund’s expense ratio and that of its sister fund. ^cThe dependent variable in columns 4–6 is the difference between an ESG fund’s one-factor alpha (estimated using 12 monthly observations) and that of its sister fund. ^dStandard errors are clustered at the (ESG) fund level. + p<0.1, * p<0.05, p<0.01, * p<0.001.

Turning to Panel B, we again find no statistically significant relationship between portfolio similarity and performance. To some extent, this should not be surprising: 12-month alphas are quite noisy, making statistical inference difficult. We stress that we do not interpret this as evidence either for or against ESG funds as an investment option. Such a determination would require investigating the particular fund’s characteristics and matching those characteristics to an investor’s particular goals. But we do not find an evidentiary basis for the SEC’s specific regulatory intervention. Moreover, to the extent that the SEC believes that a specific fund is misleading investors, it has the power to bring an enforcement action under existing law.

III. SHORTCOMING OF THE PROPOSED RULE

Even if, contrary to the evidence presented in Part II.B, there were a problem with greenwashing of the type that the SEC is concerned about, the Names Rule is a poor solution. In this Part, we highlight two fundamental shortcomings of the proposed rule. First, in Part III.A, we show that the proposed rule ignores a wide variety of legitimate investing strategies. To accommodate these strategies would stretch the proposed Names Rule beyond recognition, but declining to do so would effectively outlaw funds that employ them. Then, in Part III.B, we extend that analysis to show that mutual fund names cannot bear the weight of fully describing a fund’s investment strategy. In other words, the SEC may be expecting too much from a name.

A. The Names Rule Ignores a Wide Variety of Legitimate Strategies

While superficially plausible, the proposed Names Rule takes an artificially narrow view of investing strategies. We discuss this problem analytically, and then illustrate it using a simple example: a synthetic “Women in Leadership” fund. The advantage of focusing on women in leadership—as opposed to more commonly described ESG characteristics such as climate change—is that the number of women on a company’s board of directors or in the executive suite can be quantified objectively—and uncontroversially—in a way that a firm’s environmental sustainability may not.

Underlying the Names Rule is an assumption that whether an investment in a particular security is consistent with the strategy conveyed by the fund’s name is objective and binary—the security either belongs in the fund or it does not. This approach is based on one common approach to ESG investing— the use of screens. Some funds employ exclusive screens, meaning that they eliminate from consideration portfolio companies or securities with certain characteristics. These screens can vary substantially in their stringency, which creates a tradeoff between the breadth of the resulting portfolio and the prioritization of the ESG characteristic(s) at issue. Others employ inclusive screens (a “buy winners” approach), where they target high performers based on the fund’s preferred metric. While different in important respects, both approaches involve restricting the fund’s portfolio to securities that meet certain criteria. As a result, we can think of these strategies as being based on security selection.

The Names Rule polices a strategy based on security selection by limiting the quantity of assets in a fund’s portfolio that do not conform to the selection criteria. This may be sensible in the contexts that the old names rule policed: a security is either tax exempt or it is not. Similarly, although exotic securities theoretically populate a grey area between debt and equity, most of the securities held in mutual funds can easily be characterized as one or the other. With respect to ESG investing, however, as noted above, the characterization of a specific security is less straightforward.

At the same time, common ESG strategies do not necessarily focus on individual securities, but rather on the overall composition of the portfolio.⁸¹ For example, a fund manager could look at the average or target level of the characteristic in question across the whole portfolio. In the carbon emissions setting, for example, a portfolio manager might seek to reduce carbon emissions across an entire portfolio by 10%, while at the same time making individual exclusion choices based on a range of criteria (rather than simply excluding the “dirtiest” companies).

Another option is to deploy an exclusionary (or inclusive) screen within particular portions of the portfolio. For example, the S&P 500 ESG index uses a variation on the basic exclusionary strategy, excluding the worst performing 25% of securities within each industry,⁸² rather than overall. While some have criticized this approach,⁸³ it is a sensible strategy for an investor interested in achieving some level of sustainable investing while retaining the benefit of diversification.

Still other approaches are based on security weighting rather than outright exclusion Under this approach to style investing, the portfolio manager implements her strategy by over-weighting certain securities in the fund’s portfolio and under-weighting others. These are sometimes called “tilt-based” strategies, because the portfolio is tilted towards (or away from) certain characteristics or securities without eliminating them entirely.

Tilt-based strategies are common across the financial markets and are not unique to ESG-based styles. For example, FTSE Russell, the maker of the popular Russell family of indices, offers a variety of tilt-based style indices that have nothing to do with ESG. One example of this is its line of minimum variance indices, which are designed to provide a portfolio that is less volatile than the base index while maintaining full allocation to the relevant market.⁸⁴ We provide a simple example of how tilt can affect a portfolio in Table 4.

Table 4. Examples of Portfolio Tilt
Asset	Portfolio Weight
Asset	Fund A (ESG Fund)	Fund B (ESG Fund)	Fund Z (Non-ESG Fund)
A	22%	5%	25%
B	22%	5%	25%
C	22%	45%	25%
D	22%	45%	25%
E	12%	0%	0%

Finally, an asset manager might use voice to achieve her ESG goals. Such approaches are sometimes called “impact” strategies: the fund makes investments in poorly performing issuers (or just invests broadly across the market), and then uses her clout to advance her ESG priorities. Despite their wide acceptance in the ESG space, the proposed Names Rule does not appear to contemplate their existence: Do poorly performing assets “count” towards the fund’s 80% basket because the manager intends to exert pressure on the issuers to improve? Even under this interpretation, how much pressure must the asset manager exert in order for it to do so? The proposed Names Rule is silent on this.

In Table 5, we illustrate how some of these strategies can be deployed in the ESG context with a very simple example, what we term a synthetic “Women in Leadership Fund.”⁸⁵ Assume the fund manager’s strategy is to use the S&P 500 index as the basis for the portfolio and to implement an investment approach that focuses on female leadership.

We start with five approaches that focus on board-level leadership. The first is a portfolio wide strategy: the fund manager targets a portfolio where the average number of women on the board of the companies in the portfolio is substantially higher than that of the base index. Specifically, she wants the average number of women on boards in her portfolio to be at least the number of women at the 75^th percentile of the base index. The portfolio manager accomplishes this by recording the number of women on the board of all companies on the base index. She then ranks companies by number of women (from smallest to largest) and then by weight in the index (again from smallest to largest). She then eliminates companies one after another until the average in the portfolio of the remaining securities meets her target. We call this the “Portfolio Wide 75^th Percentile” strategy.

The second is a tilt-based strategy. The portfolio manager sorts companies into quartiles by the number of women on their boards. She then under-weights the companies in the lower quartiles relative to the higher quartiles, meaning that she invests more assets, on a relative basis, in the companies with a higher number of women on the board, and fewer assets in the companies with a lower number of women. Specifically, she assigns a factor of 1 to the bottom quartile, a factor of 2 to the second quartile, and a factor of 3 and 4 to the third and fourth quartiles, respectively.⁸⁶ This means that the fund holds every company in the index, but it puts relatively more weight on companies with more women in leadership. We call this the “Board Tilt” strategy.

The final three strategies employ three simple types of exclusionary screens: (i) excluding companies with no women on their boards, (ii) excluding companies with fewer than two women on their boards, and (iii) excluding companies where the number of women on the board is in the bottom quartile of the index. From the perspective of the SEC’s concern about greenwashing, the obvious question is what each of these strategies implies for the companies that make it into the funds. We summarize this in Table 5.⁸⁷

Table 5. Hypothetical Women in Leadership Funds, Selected Characteristics
	Average Number of Women Directors	Percent of Portfolio with ≥50% Women Directors	Percent of S&P 500	Number of Securities
	Panel A: Director Based Strategies
Portfolio Wide 75^th Percentile	4.0	4%	67%	197
Board Tilt	3.9	5%	100%	505
Exclude Bottom Quartile	3.8	3%	84%	369
Exclude Companies with < 2 Women	3.5	3%	99%	490
Exclude Companies with No Women	3.5	3%	100%	505
	Panel B: S&P 500
S&P 500	3.5	3%	100%	505

Panel A in Table 5 demonstrates that the board tilt strategy is extremely effective at increasing the average number of women directors, as well as the proportion of the portfolio invested in high-achieving companies (that is, companies with boards consisting of at least 50% women). On both dimensions, it is comparable to the portfolio wide strategy, and, if the goal is to invest in companies with a substantial number of women directors, it is considerably more successful than the three exclusionary strategies, all while preserving a very wide portfolio (in terms of number of securities).

While the “exclude companies with no women directors” strategy might run into trouble with the Names Rule on other grounds (the criterion doesn’t eliminate any companies, and might therefore be fairly viewed as illusory), it is hard to object to the other two director-focused exclusionary strategies on the basis of their names. In contrast, it is not at all clear that the (more effective) tilt strategy satisfies the Names Rule, or how the Names Rule would even be applied to it. How should the portfolio manager “indicate, with respect to each portfolio investment, whether the investment is included in the fund’s 80% basket[?]”⁸⁸ Should she list the securities that she underweights as “included in the basket” even though they are, by her own estimation, the least consistent with her strategy? Doing so would permit her to include every security in the basket, effectively nullifying the rule. If not, which securities should she exclude, and on what basis? Without further clarity, this approach risks outlawing a large class of legitimate strategies entirely.

B. The Names Rule Expects Too Much Precision from Names

The proposed Names Rule’s limitations might be justified if it were likely to accomplish an important objective. Unfortunately, we fear that it may not be able to deliver on its promises. On March 3, 2022, SEC Chair Gensler released an “Office Hours” video that appeared to illustrate the SEC’s rationale for its proposal.⁸⁹ In the video, Chair Gensler explained that buying a mutual fund should be as easy as buying milk, where you can tell whether milk is fat-free or not simply by looking at the label.

Arguably, mutual funds are somewhat more complex and subject to a greater degree of variation than milk.⁹⁰ As we show in this section, by identifying as a regulatory concern that a fund may not meet an investor’s expectations, the proposed Names Rule is asking names to do too much. To do so, we continue the example of synthetic Women in Leadership funds. We again use this example to demonstrate that a variety of plausible approaches to constructing such a fund—all of which would presumably comply with the proposed Names Rule—would provide investors with very different portfolios. Given the range of possible portfolios that would be consistent with the fund’s name, it is hard to predict what investors might reasonably expect from that name.

To do so, we begin with the two strategies based on exclusionary screening in Table 5 that exclude at least one security from the index. We limit ourselves to exclusionary strategies to keep the exposition as simple as possible, and to make sure that the strategies are as close to “apples-to-apples” comparisons as possible.⁹¹ To this, we add four strategies that focus on corporate officers. In the first, we limit the portfolio to companies with a female CEO. In the second, we expand the portfolio to also include companies with a female CFO. In the third, we exclude companies with fewer than 2 female executives among the top-5 most highly compensated executives (as reported in the proxy statement). The fourth excludes companies with no women in that group.⁹²

We then calculate the extent to which the holdings overlap between these six hypothetical funds and present the results in Table 6. The entries in the table indicate the percentage of the row fund’s portfolio that is also in the column fund’s portfolio. So, for example, the “84%” in the bottom row of the first column means that 84% of the S&P 500 is in the portfolio that excludes companies that have fewer than three women directors (representing the bottom quartile), which corresponds to the information in Panel A of Table 5.

Table 6. Overlap Between Hypothetical Women in Leadership Funds Using the S&P 500 as the Base Index
	Women Directors		Women Executives				S&P 500
	Exclude Bottom Quartile	Exclude Companies with < 2	Require CEO	Require CEO or CFO	Exclude Companies with < 2	Exclude Companies with 0	S&P 500
Exclude Bottom Quartile of Woman Directors	100%	100%	5%	30%	24%	70%	100%
Exclude Companies with < 2 Women Directors	85%	100%	5%	27%	22%	65%	100%
Require Woman CEO	89%	100%	100%	100%	69%	100%	100%
Require Woman CEO or CFO	95%	100%	18%	100%	36%	100%	100%
Exclude Companies with < 2 Women Executives	96%	100%	16%	44%	100%	100%	100%
Exclude Companies with No Women Executives	90%	100%	7%	41%	33%	100%	100%
S&P 500	84%	99%	5%	26%	21%	65%	100%

At the risk of stating the obvious, Table 6 makes clear that these hypothetical funds deliver very different results to investors, yet all could presumably be sold under the name “Women in Leadership Fund” under the proposed Names Rule. For example, the fund that requires that the company have a female CEO—indisputably consistent with women in leadership—contains only 5% of the companies in the fund that excludes companies in the bottom quartile of female directors, another completely respectable approach. Interestingly, this is not solely because the former has a stricter criterion: 11% of the assets in the fund that requires a female CEO are excluded from the fund that excludes companies that are laggards with respect to female board representation. Similar patterns emerge between other pairs of hypothetical funds: 44% of the “require at least two female executives” strategy is also in the “require a female CEO or CFO” strategy; 36% of the “require a female CEO or CFO” strategy is on the “require at least two female executives” strategy. They are, in other words, different strategies. And yet both are clearly consistent with the same fund name.

While firms on the S&P 500 represent a substantial majority of the U.S. public equity market, an analysis that focuses on firms in the S&P 500 may not be representative of the universe of public companies. This is because firms on the S&P 500 have some of the largest institutional ownership and have been the subject of extensive pressure to increase the number of women in their senior leadership. Smaller firms, in contrast, have received significantly less attention. We therefore repeat the analysis using the S&P SmallCap 600 index and present the results in Table 7. If anything, these results are even more striking. For example, 54% of the assets in the “exclude companies with fewer than 3 women directors” strategy are also in the “exclude companies with no women executives” fund. Conversely, only 41% of the assets in the “exclude companies with no women executives” fund are also in the “exclude companies with fewer than 3 women directors” fund. In short, the overlap between these different funds is often minimal. Even in this very simple setting, this example demonstrates that a fund’s name simply cannot provide enough information to give investors even a general sense of which companies are included in the fund’s portfolio.

Table 7. Overlap Between Hypothetical Women in Leadership Funds Using the S&P SmallCap 600 as the Base Index
	Women Directors		Women Executives				S&P SmallCap 600
	Exclude Companies with < 3	Exclude Companies with < 2	Require CEO	Require CEO or CFO	Exclude Companies with < 2	Exclude Companies with 0	S&P SmallCap 600
Exclude Companies with < 3 Women Directors	100%	100%	11%	24%	20%	54%	100%
Exclude Companies with < 2 Women Directors	47%	100%	7%	20%	15%	52%	100%
Require Woman CEO	65%	95%	100%	100%	62%	100%	100%
Require Woman CEO or CFO	49%	85%	33%	100%	50%	100%	100%
Exclude Companies with < 2 Women Executives	54%	88%	27%	68%	100%	100%	100%
Exclude Companies with No Women Executives	41%	84%	12%	38%	28%	100%	100%
S&P SmallCap 600	38%	82%	6%	19%	14%	51%	100%

These differences may well matter to the type of investors interested in a Women in Leadership fund. Such investors may be motivated by a wide variety of different underlying goals.⁹³ Perhaps these investors are interested in ensuring that women are represented on corporate boards, a strategy that is consistent with both the California board diversity statute and the NASDAQ board diversity requirement. In such a case, the investors may be satisfied with any of the director-based strategies, but not necessarily the executive-based ones. Alternatively, perhaps they are focused on gender equality. Strategies that focus on having 2 or 3 women on corporate boards have been successful at producing that level of representation, but the road to gender equality appears to be stalled at near 25%.⁹⁴ Finally, they may be primarily interested in female empowerment, and might therefore care much more about seeing women in senior executive positions (perhaps even CEO or CFO positions) than in minority positions on boards.

C. The Challenges of Complexity and Nuance

The results in Section III.B focus on the extremely simple context of women in leadership. Most ESG-related investing contexts are more complex. We therefore expand our analysis from Section II.B to probe the differences between ESG funds with similar names. To do so, we focus on funds using the same ESG trigger term that also employ the same approach to ESG investing. Specifically, we select funds that use either “sustainable” or “ESG” in their names, and that, according to their prospectus, employ an exclusionary strategy. This allows us to restrict our attention to funds that look, at least superficially, relatively similar to a modestly attentive investor. We then look at the portfolio similarities of all pairs of funds within each of the two groups (that is, within exclusionary “sustainable” funds and within exclusionary “ESG” funds). Recognizing that the industry has been evolving rapidly, we do this only for 2022, so all comparisons are made at the same point in time.

Even within these two relatively narrow universes, we find substantial variability in holdings: in other words, different “sustainable” exclusionary funds turn out to have quite different holdings. Digging deeper, we find that some of this difference comes from the fact that the relevant exclusionary strategy has been layered over very different baseline funds. For example, it should come as no surprise that the holdings of an exclusionary small-cap sustainable fund are very different from those of an exclusionary large-cap sustainable fund. This further highlights the challenges associated with comparing funds under the broad ESG umbrella. Digging deeper still, we find evidence that suggests that fund families have a “house” approach to their various ESG strategies: different sustainable or ESG funds within a fund family appear to be more consistent (other things equal) than such funds across fund families.

The normative implications of this finding are, to some extent, in the eye of the beholder. On the one hand, within-family consistency is consistent with bona fide efforts to implement a coherent strategy, suggesting that asset managers are making good faith efforts to deliver the product that they are promising. On the other hand, the substantial across-family differences suggest meaningful limitations in the amount of information that can be conveyed through a term like ESG or sustainable. This analysis complements the analysis in section III.B. Finally, we think that, given the different approaches, attempts to impose a standardized meaning on ESG trigger words (like “sustainable” or “ESG”) may be ill-conceived.

IV. IMPLICATIONS

What are the implications of our analysis? First, consider the SEC’s concern that funds’ use of ESG names may be inappropriately attracting investor assets. Our empirical findings demonstrate that, using the logic underlying the SEC’s rule, the overwhelming majority of ESG funds differ substantially from their sister funds. At least by this logic, the ESG fund names in our study do not appear to be cosmetic. Nor do they appear to be a means of raising fees on unsuspecting investors. Rather, it seems more likely that fund sponsors are offering ESG funds because that is what investors want, and in doing so are offering a distinct product from their non-ESG offerings. Whether they are doing so in the best way, or even in a sensible way, is beyond the scope of both the proposed Names Rule and our analysis.

Concededly, our results reveal that there is a substantial degree of overlap between ESG and non-ESG funds. It is unclear, however, why that overlap should be viewed as problematic. Mutual funds are designed to provide investors with a diversified market rate of return. A fund that differed too substantially from the overall market could expose an investor to an unacceptably high level of risk, particularly if, in some cases, an ESG investing strategy may sacrifice returns. At the same time, other commentators have argued that some ESG investing considerations are associated with better economic performance. To the extent that they are right, one would expect to see components of an ESG investing strategy in a fund that was managed and marketed exclusively to maximize economic value. We, like the SEC, take no position on whether an ESG-based portfolio is the “right” investment strategy for any particular investor. We also, like the SEC, believe that it is not our place to decide “how ESG” a strategy needs to be in order to use that name. Unlike the SEC, however, we do not believe that the existing evidence warrants additional regulatory intervention.

Our findings also raise questions that we cannot fully explore within the confines of this article, about what it means for an investment to fall within the 80% bucket contemplated by the names rule. From the SEC’s description of the Fossil Free Fund, it doesn’t seem like the SEC really contemplates allowing ESG funds to hold 20% of securities that fall outside the ESG bucket. Rather, the example suggests that ESG funds should be holding 100% of their portfolios in investments that are consistent with their investing strategies. But how does one apply this requirement? In addition to evading precise definition, ESG appears to be more of a range or a spectrum than a binary, and while it may be possible to identify a handful of companies that most investors would characterize as “green” or “brown,” it is likely that the vast majority of publicly traded companies are more accurately described as “gray.”⁹⁵ Both ESG and non-ESG funds will invest primarily in these companies, an intuition that is supported by our tilt analysis. But it would be hard to describe most gray securities as within an 80% ESG bucket.

Finally, our analysis of the Women in Leadership funds suggests the limitations in using fund names to convey detailed information about a fund’s investment strategy. It is reasonable to demand that a fund’s name conveys that the securities in its portfolio comply with some objective criteria. A self-described bond fund should not be investing in equities; a tax-exempt fund should not invest in taxable securities. But what should a MAGA fund, a Magellan fund, or a God Bless America fund invest in? The reason investors rely heavily on fund names is that they convey information. As the ESG investment space continues to evolve, investors will demand, and sponsors will offer, an increasing variety of investment strategies. Although the process of describing those strategies through fund names is imperfect, it is likely far superior to a world in which a fund sponsor offers a series of products named Fund One, Fund Two, and Fund Three.

CONCLUSION

The debate around ESG investing has reached a fevered pitch, with a constant drumbeat of concerns about greenwashing. The SEC has an enormously difficult job, and it is understandable that it would endeavor to reduce fraudulent and misleading marketing. Fortunately, we find little evidence that such greenwashing—at least of the sort that the SEC’s proposed rule could curb—exists. Less fortunately, our analysis also shows that the SEC’s proposed rule is not benign. Because it fails to recognize tilt-based strategies, it is inconsistent with a large class of well-accepted investment strategies. Instead of amending the Names Rule, the SEC should focus its efforts on taking enforcement action against the small number of funds that do, in fact, have fraudulent or misleading names, whatever their purported strategy may be.

96 S. Cal. L. Rev. 1417

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Jill E. Fisch*

* Saul A. Fox Distinguished Professor of Business Law, University of Pennsylvania Carey Law School.

Adriana Z. Robertson†

† Donald N. Pritzker Professor of Business Law, University of Chicago Law School. This article has benefited from comments by participants at the Digital Transformation in Business Symposium at USC law school, the Penn/NYU Symposium on Law & Finance, the Duke Law & Economics Colloquium, the Foundations of Law & Finance LawFin Research Seminar, the University of Virginia Law & Economics Seminar, the BYU Winter Deals Conference, and the American Law & Economics Association Annual Meeting. Levi Haas provided exceptional research assistance. All errors are our own.

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.

Adventure Capital

April 2024April 2024Elizabeth Pollman*

This symposium Article traces the history and rise of venture capital and venture-backed startups in the United States from a business law perspective and explores the current big questions in the field. This examination highlights that after lawmakers shaped the enabling environment for venture capital to flourish, corporate and securities law has responded to the rise of venture-backed startups incrementally but with profound effect. Although business law has not always fit easily with the distinctive features of venture-backed startups, it has provided an enormous space in the private realm for them to order their governance and maneuver with relative freedom. This private realm is a good fit for the needs of startups that drive economic growth and innovation, but their activity can also create lingering issues of social costs and policy that are difficult to address. Grappling with this reality is essential to continuing to foster a vibrant venture capital ecosystem while also developing a coherent business law response to the current wild era of “adventure capital.”

Venture capital has fueled the rise of some of largest businesses in the world.¹ This relatively small asset class produces huge social and economic impact. By one measure, venture capital funds less than one percent of companies started in the United States each year, yet accounts for the backing of nearly half of the companies that enter the public markets.² Among U.S. public companies founded since 1968, venture-backed companies account for 77% of total U.S. market capitalization, 41% of total employees, and 92% of research and development spending.³ Further, the impact of venture capital is not only evidenced in public markets, but also spans the footprint of disruptive startups operating in the private realm and the technology that they generate. From the personal computer you use to access the internet to the search engine by which you navigate it, the innovation fueled by venture capital touches everyday life in innumerable ways.⁴

The industry that produces such outsized social and economic impact is notably of relatively recent vintage and the result of both business and legal transformation. Some of today’s venture capital resembles aspects of the risk-sharing ventures of centuries ago from Genoese merchants to American whalers, but the modern industry began in earnest in the mid-twentieth century—originally coined “adventure capital.”⁵ It was not, however, until the 1980s when venture capital really took off. In 1979, the Department of Labor changed an Employment Retirement Income Security Act (“ERISA”) rule that freed pension fund managers to take portfolio diversification into account in fulfilling their “prudence” standard.⁶ With this change, pension fund managers could allocate a portion of their funds to venture capital even if companies in the venture fund’s portfolio were relatively illiquid or failed.⁷ With a greater influx of investment dollars, the venture capital sector grew quickly,⁸ and in less than half a century has become “the dominant source of financing for high-potential startups commercializing risky new ideas and technologies.”⁹

Although a significant and growing body of scholarly literature examines venture-backed startups, many questions remain open and legal scholars do not often step back to examine the broader landscape of business and legal transformation in this area. How has business law facilitated and responded to the rise of venture capital? What is the social welfare impact of venture capital? Should the law do more to shape the direction of entrepreneurial finance or startup governance? This Article takes aim at stimulating discussion and research about these important questions.

First, the Article traces the history and rise of venture capital in the United States and highlights that venture capital contracting has largely settled upon an established set of practices that create distinctive governance features in startups and the types of companies funded.¹⁰ This discussion illuminates how the rise of the modern venture capital industry evolved to rely on laws that enable private business entities and private markets.

Second, it argues that after lawmakers shaped the enabling environment for venture capital to flourish, in subsequent decades, corporate and securities law has responded to the rise of venture-backed startups incrementally and with some challenges or tensions with the distinctive features of venture capital and startups. The big picture, however, is that the enabling nature of corporate law and the deregulatory trend of securities law have facilitated an enormous space in the private realm for venture-backed startups to order their governance and maneuver with relative freedom. This private realm is highly useful for cultivating startups that drive valuable innovation and create outsized economic impact.

Notably, venture-backed startups also create lingering issues of social costs that have prompted rising concerns in recent years. Therefore, the Article concludes by highlighting two promising avenues for developing a deeper understanding of whether a business law response is warranted: a more systematic study of impacts on stakeholders such as employees and customers or users, and further inquiry into whether and when any governance intervention would be optimal using a realistic understanding of a startup’s timeline in the venture cycle. Building a solid foundation of understanding of these issues in the current era would advance a measured approach to the future of business law, while continuing to promote a vibrant ecosystem of startups and venture capital.

The Article proceeds as follows. Part I traces the rise of venture capital and the distinctive features of venture-backed startups. Following on this background, Part II examines how corporate and securities law has facilitated and responded to venture capital and venture-backed startups. Finally, Part III identifies and starts a conversation about the big questions that the current regime raises.

I. THE RISE OF VENTURE CAPITAL AND DISTINCTIVE FEATURES OF VENTURE-BACKED STARTUPS

Entrepreneurs have long sought financing for risky ventures.¹¹ The venture capital industry emerged in the United States in the mid-twentieth century with its own unique history, and subsequent decades have witnessed its meteoric rise and establishment as a key driver of innovation and economic growth in society. This Part provides an overview of the origins and development of venture capital, as well as an examination of the special characteristics and governance of the startup companies they fund. Although the venture capital industry continually changes, and startup governance varies by individual company, certain patterns have taken shape that can be described by their distinctive features.¹²

A. A Brief History of Venture Capital

Throughout the history of entrepreneurship in the United States, wealthy individuals and family offices have served as notable sources of funding when commercial banks and Wall Street financiers have been unwilling to invest or lend to risky new enterprises.¹³ It was not until the period after World War II, however, that formalized organizational structures started to emerge to provide high-risk, innovative new firms with financing.¹⁴ Startup capital before that time was relatively scarce—representing what many perceived as a funding gap or market failure.¹⁵

Historians pinpoint several key institutions, individuals, and policies that played an important role in catalyzing the modern venture capital industry. A few highlights are worth discussing here to capture how government policy as well as trial and error from enterprising individuals and firms were required before hitting upon the modern formulation of venture capital investing.

Some start the story with one of the most serious early experiments—American Research & Development Corporation (“ARD”).¹⁶ The Boston-based firm made high-risk investments in companies working on technology developed for World War II.¹⁷ The firm, established in 1946 by Harvard Business School professor Georges Doriot and MIT President Karl Compton, was structured as a publicly traded closed-end fund and imbued with public-service motives.¹⁸ It did not ultimately serve as a model organization for later venture investors as the public structure ensnared it in regulation that restricted its ability to invest fresh capital into portfolio companies, calculate the value of its investments, and grant employee stock options.¹⁹ Further, the firm’s public-service ethos that disdained financial incentives disappointed staff and investors, and prevented the firm from productively abandoning underperforming portfolio companies.²⁰

Nonetheless, ARD provided proof of concept of a couple of key ingredients for venture investing. First, a single investment—Digital Equipment Corporation—accounted for the lion’s share of all the gains that ARD generated over a quarter century.²¹ This represented an early demonstration of what later became known as the “power law”²² or “long-tail investing”²³ business model that attracted the attention of others interested in financing technological innovation. A small number of big “hits” can drive a fund’s success despite numerous other failures. Second, the leader of ARD and a key figure in risk capital during this era,²⁴ Doriot, was deeply involved in providing managerial counsel as well as capital to his portfolio companies.²⁵ In his view, founders were the visionary stars and venture capitalists’ role was to provide wisdom and guidance.²⁶

Another important development of the era was the passage in 1958 of the Small Business Investment Company (“SBIC”) Act, which reflected the U.S. government’s effort to respond to the perceived funding gap for entrepreneurial finance.²⁷ Under the SBIC Act, a privately-owned investment fund aimed at making investments in qualifying small businesses would be eligible for favorable tax treatment and a government loan at nominal rates.²⁸ The program engendered debate between advocates of government subsidy to encourage small business formation and those who believed in market-based solutions.²⁹ Neither side received total vindication as the government intervention made clear impact, but it was market players who ultimately pioneered what became the venture capital industry.

Practically speaking, the limitations on SBICs proved too restrictive for pioneering investors in the nascent venture capital industry who sought to finance startups—highly risky innovative ventures with the potential for outsized returns.³⁰ Most SBICs were small and undercapitalized.³¹ SBICs could not exceed a fund size of $450,000 to qualify for maximum assistance, and “could not compensate their investment staff with stock options, nor could they invest more than $60,000 into a portfolio company . . . .”³² SEC registration rules were burdensome and costly, and SBICs and their shareholders were subject to double taxation.³³ In sum, these rules were poorly suited for enabling venture capitalists to provide adequate capital to high-growth, innovative companies, and to compensate the investors for their efforts. Despite these drawbacks, or perhaps thanks to the lessons they generated, the program shed light on the legal policies and financial institutions that would clear the way for venture capital and startup entrepreneurship to thrive.³⁴

The year before the SBIC Act’s passage, and before ARD had financed Digital Equipment, one of the most significant events occurred in the history of venture capital—a tremor that became an earthquake that eventually opened a new landscape in the orange groves around Stanford University. A group of eight young Ph.D. graduates had been recruited by the renowned inventor William Shockley to work at Shockley Semiconductor Laboratory on developing new semiconductor devices at Fred Terman’s new research park.³⁵ These “Traitorous Eight” young engineers quickly became “fed up” with “suffering” under Shockley, the famous Nobel Prize winning “father of the semiconductor,” who acted as a “tyrant.”³⁶ One of the group members, Eugene Kleiner, had a connection through his father to a New York investment firm.³⁷ He wrote and asked if perhaps a financier could find an employer willing to hire all eight as a team.³⁸ Their “act of defection” in 1957 “created the magic culture of the Valley,”³⁹ and it was made possible by an equally visionary young banker, Arthur Rock, who was given the letter by Kleiner’s father.⁴⁰ Rock had already immersed himself in the emerging semiconductor industry and was intrigued by the request from an elite team so he flew out to San Francisco to meet with them. Rock proposed a possibility they had not even imagined: striking out on their own as founders of a new company that he would help finance.⁴¹

Rock’s vision was radical at the time.⁴² And, after trying to raise capital from numerous backers without avail, just one wealthy individual willing to fund the Shockley rebels emerged from Rock’s search—Sherman Fairchild.⁴³ But Fairchild cut a hard bargain—the eight co-founders put up a small amount of cash in return for 100 shares each, the budding venture capitalist bought 225 shares at the same price, 300 shares were set aside for recruiting managers, and Fairchild put in $1.4 million in the form of a loan that came with control via a voting trust and an option to purchase all of the company for $3 million down the line.⁴⁴ The startup, Fairchild Semiconductor, was wildly successful—within two years, each of the Traitorous Eight and Rock received six hundred times what they had invested, but Fairchild, the passive financier, did even better.⁴⁵

Beyond the obvious riches, one important takeaway from the Fairchild Semiconductor adventure was that without dedicated pools of money looking to finance startups, the investors held the bargaining power and the innovators got short shrift. In the 1960s, after a taste of success, Rock moved out to California, and, with a like-minded partner, started to raise a venture fund with a limited partnership structure in which the two general partners would seed the fund with some of their own capital.⁴⁶ By eschewing SBIC loans and public market money, they raised over $3 million from thirty “limited partners”—wealthy individuals who served as passive investors—and avoided the regulatory restrictions that had held back SBICs and ARD.⁴⁷ They had enough money to supply risky yet promising startups with the capital needed to grow aggressively, and they incentivized entrepreneurs and key employees with equity.⁴⁸ After fund raising, they made concentrated bets on a dozen or so companies, respectfully exercised a measure of governance control with the aim of helping the entrepreneurs succeed, and returned a handsome share of profits to limited partners on a set timeline by identifying and nurturing hits that could find an exit by going public or being acquired.⁴⁹

A winning formula for financing risky technology startups was finally found—it involved private business entities and private markets. Venture capitalists in the subsequent period built upon these early lessons, fine-tuning investment and governance practices, and pushing for favorable government policies. By the 1970s, several pioneering venture capital firms emerged alongside the early players—including Sequoia and Kleiner Perkins, among others.⁵⁰ They funded some of the big hits of the era, including Intel, Apple Computer, and Genentech.⁵¹ In this period, the importance of deal flow, repeat entrepreneurship, incentive compensation, and governance support were solidified.⁵² Venture capitalists learned to lower their risk by actively guiding founders and staging financings, with each capital infusion calculated to encourage the company to hit an agreed milestone and leaving open the possibility of abandoning underperforming startups.⁵³ Silicon Valley became an ecosystem of early-stage finance and entrepreneurship with universities, a pool of potential founders, specialized investors, large tech companies and employees, lawyers with dealmaking savvy, and more.⁵⁴

Further, the National Venture Capital Association, a trade association of venture capitalists founded in 1973, lobbied heavily for the legislative change that freed pension funds from previous restrictions in allocating a portion of their capital to venture funds.⁵⁵ Pension funds joined university endowments, insurance companies, and a handful of wealthy individuals in this nascent asset class.⁵⁶ With a supply side boost of capital thanks to supportive policymakers, the venture capital industry rapidly grew in the 1980s.⁵⁷

Over time it became clear that the venture capital industry and the tech firms they financed were subject to boom and bust cycles.⁵⁸ Further, it remained difficult to systematically generate outsized returns from “long-tail” portfolios.⁵⁹ The top-quartile funds have typically outperformed the bottom quartile by a wide margin.⁶⁰ But the overall trendline was one of continued growth and maturation of the industry and the Silicon Valley ecosystem.⁶¹ By the 1980s and 1990s, a set of practices around venture capital investing took shape which fostered a distinctive set of features for venture-backed startups that remain today.

B. Venture-Backed Startup Features and Governance

As the discussion so far has highlighted, the rise of the venture capital industry reflected a historically contingent confluence of business and legal transformation. Pioneers of the industry experimented with a variety of arrangements and then settled on a model that uses the limited partnership form to raise and deploy pools of risk capital over a set period of time. Further, the industry matured through the development of contracting and governance mechanisms aimed at addressing the particular challenges involved in financing and nurturing high-risk and potential high-reward innovative, young companies. The discussion will now turn to those practices and their implications for the kinds of businesses that get funding, and their governance.

It is worth emphasizing at the outset that the most fundamental aspect driving venture capital investing is the “power law.” As noted above, this is not a true law in any sense. Rather it is a phenomenon or understanding that a very small subset of deals typically generates the bulk of the returns for a successful venture capital fund.⁶² As one well-known venture capitalist, Peter Thiel of Founders Fund, has explained, “This is a scary rule, because it eliminates the vast majority of possible investments.”⁶³ Why? As no one knows with certainty in advance which companies will succeed, and as innovative startups are highly risky, many will fail or have mediocre returns. For this reason, “every single company in a good venture portfolio must have the potential to succeed at vast scale.”⁶⁴ Downside risk is limited to the total investment 1x while upside gain could be 100x or more within a relatively short amount of time.⁶⁵ Venture capitalists are therefore not just looking for startups with the possibility of becoming profitable—they are aiming at investing only in startups that have the potential to utterly disrupt or create industries with large addressable markets.⁶⁶ Crystallizing this point, Bill Gurley of Benchmark Capital has remarked, “Venture capital is not even a home run business. It’s a grand slam business.”⁶⁷

Further, the power law operates within an assumption that often goes unarticulated—not only must venture capitalists aim to invest only in potential grand slams, but they also need startups to find an exit within a timeframe that roughly corresponds with the term of their fund. There are just two main paths to a successful exit: sell the company or go public.⁶⁸ As venture capitalists typically use a 10–12 year term for their fund,⁶⁹ this dynamic shapes the type of companies they invest in and the way that they govern them.⁷⁰ One venture capitalist put it succinctly, “I sell jet fuel.”⁷¹ Venture-backed startups must grow fast to succeed.⁷²

This dual reality of power law returns and the need for exit on a relatively short timeframe distinguishes venture capital and the types of startups they invest in from other businesses or stages of a business life cycle. For example, private equity portfolios typically aim to optimize each portfolio company’s performance and leverage the gains.⁷³ They often target existing, underperforming businesses rather than help build new, innovative companies that have a large risk of failure.⁷⁴ Other private businesses include traditional closely held enterprises that range from small mom-and-pop businesses to virtual behemoths such as Cargill and Koch Industries.⁷⁵ These companies often begin as sole proprietorships, partnerships, or family businesses, and organically grow over time without a particular aim at exit.⁷⁶

In turn, because venture capitalists are specialized intermediaries uniquely tailored to financing innovative startups, they have developed contracting and governance mechanisms aimed at addressing the particular constellation of issues that these companies pose: uncertainty, information asymmetry, agency costs, and incomplete contracting.⁷⁷ In the early stages of a startup, its success is highly uncertain—more so even than the usual new business because startups are typically innovative, not replicative.⁷⁸ Entrepreneurs often have more information than investors and their interests are not fully aligned.⁷⁹ Contracts between entrepreneurs and venture capitalists will inevitably be incomplete because of the participants’ bounded rationality and their inability to foresee and resolve all potential contingencies.⁸⁰

These were the challenges faced by the pioneers of venture capital, from Georges Doriot to Arthur Rock. And, since the maturation of the venture industry in the 1980s and 90s, a set of contracting practices for venture capital funds and startup investing has become the norm, creating an “interrelated bundle of incentives and protections” that facilitates the flow of funds to entrepreneurs.⁸¹

Venture capital firms raise capital from passive limited partners, organized in funds with 10–12 year terms, charging an annual management fee and a percentage of profits.⁸² Acting as general partner of the fund, venture capitalists make and monitor investments in a portfolio of startups.⁸³ Startup founders and employees will typically have an incentive-based ownership stake that vests over time and takes the form of common stock or options.⁸⁴ Venture capitalists invest in syndicated, staged financing rounds for convertible preferred stock that come with liquidation preferences and other protections for downside risk and the ability to convert into common on the upside.⁸⁵ Notably, venture contracts separate cash flow ownership from voting and other control rights. Venture capitalists typically participate in board governance and bargain for shareholder voting rights and the right to veto certain major management decisions.⁸⁶ Over the life cycle of a venture-backed startup, as it increases the number of participants with varied interests and claims, the vertical and horizontal tensions among and between common and preferred shareholders tend to multiply.⁸⁷ Ultimately, if a venture-backed startup survives past its early stage, governance complexity increases and pressure builds for the startup to find a liquidity event.⁸⁸

These basic contours of venture capital investing and governance are well understood. Naturally, much more could be said about industry trends and entrepreneurial finance. The point here is to highlight that the basic practices of venture capital in the United States have been established for over four decades now—and have become sticky—fostering a distinctive set of companies and governance in the business world.⁸⁹

With this foundation set, the next Part can take up the big picture view of how business law has enabled and responded to the rise of venture capital and startups, with subsequent discussion to explore the lingering issues of social costs and policy they raise.

II. BUSINESS LAW’S CREATION AND RESPONSE TO THE RISE OF VENTURE CAPITAL AND VENTURE-BACKED STARTUPS

As the previous Part has highlighted, the rise of the modern venture capital industry developed to take advantage of laws that enable private business entities and private markets. Both state business law and federal securities laws facilitated this combination.⁹⁰

Traditionally, business entity formation and governance have been a matter of state law. Under enabling state laws, venture capitalists can form limited partnerships for the purpose of raising and operating venture funds, and founders can form corporations through which to engage in startup entrepreneurship. The internal affairs doctrine provides that the law of a firm’s state of incorporation governs the relationships among the firm, its investors, and managers.⁹¹ Venture capitalists and startup entrepreneurs have predominantly chosen Delaware as their preferred state for formation of limited partnerships and corporations.⁹²

Federal securities law, under the architecture of the Securities Act of 1933 and the Exchange Act of 1934, has partitioned issuers, securities, and offerings into two realms—public and private—with each side bearing distinct privileges and burdens.⁹³ Public company stock, once registered, can be freely issued and traded, but the issuing companies are subject to extensive mandatory disclosure as well as active enforcement mechanisms.⁹⁴ Conversely, the issuance and trading of private company stock must conform to restrictions of registration exemptions, but regulation of private firms is otherwise light.⁹⁵ Investment funds are subject to an analogous public-private divide.⁹⁶ Since Rock started his first fund in the 1960s, venture capital firms have organized their activity to fall on the private side in both the arrangement of their own funds and the portfolio companies in which they invest.

Given the importance of business law to venture capital and venture-backed startups, this Part takes up the question of how the law has responded to the developments traced thus far and describes the big picture of the growth of the private realm and the wild (and sometimes questionable) adventures of startups in it.

A. The Evolution of Corporate and Securities Law in an Age of Venture Capital and Startups

Corporate and securities law have taken vastly different approaches to the rise of venture capital since the industry began to solidify, grow, and mature in the 1980s. They have in common one high-level response: neither creates a legally defined category for venture-backed startups.⁹⁷

While the key corporate law state for startups—Delaware—has been generally enabling and highly regarded, its case law has not been particularly favorable for startup participants as it is not crafted for the distinctive characteristics of these companies and is instead often made in the context of very different public corporations. Despite these tensions, for the most part, startups are able to take advantage of the enabling nature of Delaware corporate law through venture contracting practices and can avoid ex post litigation, so state corporate law ultimately creates an environment for highly flexible governance practices and a stable backstop, albeit sometimes problematic, for the rare disputes that go to court.

By contrast, securities laws have responded to the rise of venture capital and venture-backed startups with enormously favorable provisions and a deregulatory trend that has facilitated a radical transformation of private markets over the past several decades. The combination of these relevant state corporate and federal securities laws, which are discussed further below, work in tandem to foster a growing public-private divide and startup governance challenges that characterize the present era and raise big questions for the role of business law in the future.

1. Corporate Law and Venture-Backed Startups

Delaware famously makes and applies one general corporate law.⁹⁸ Thus, the same statute and case law generally apply whether the corporation at hand is one of the world’s largest public corporations or a brand-new private startup. The rise of venture capital and venture-backed startups has therefore posed interesting and challenging issues of fit between corporate law and startup participants’ interests and needs.

As permitted under Delaware corporate law, venture-backed startups customize their governance arrangements through the organic documents of the corporation (charter and bylaws) and extensive shareholder agreements, typically re-bargaining these arrangements in each round of venture financing.⁹⁹ Delaware’s enabling approach is a boon in this regard. For example, it is mandatory to have a board of directors, but Delaware corporate law requires only one director, allows for different sizes and compositions, and does not impose requirements of independence, qualifications, committees, or the like.¹⁰⁰ When disputes arise, however, Delaware case law takes a highly fact-specific approach and often imposes its most rigorous standard of scrutiny—entire fairness—when venture-backed startups are involved because they generally lack disinterested and independent boards and shareholders.¹⁰¹

Over time, this divergent dynamic between public and private corporations and fiduciary litigation has become more pronounced as federal securities law has added a layer of corporate governance requirements on public companies, requiring majority independent boards.¹⁰² In addition, Delaware corporate law doctrine has developed more pathways to lighter review under the deferential business judgment rule if certain process protections are followed with disinterested and independent board members and/or shareholders.¹⁰³ This means that absent specific conflicts or the like, most public company boards would receive deferential review if their decisions are challenged and could likely dispose of litigation in early motion practice, whereas startup boards would not often be so lucky and might be encouraged to incur costly procedures such as banker fairness opinions or special committees that are not otherwise a fit for the norms or circumstances.¹⁰⁴

Furthermore, some venture-backed startup cases involving fiduciary litigation under the strenuous entire fairness standard have provoked controversy. Most notably, in a case involving a conflict between the interests of the common and preferred shareholders in an M&A exit, In re Trados, the Delaware Court of Chancery declared that directors owe a fiduciary duty to maximize value for the common shareholders as residual claimants.¹⁰⁵ Corporate law scholars have pointed out that this approach can give rise to inefficient outcomes that fail to maximize aggregate welfare.¹⁰⁶ Similarly in cases involving venture capital contracts and the status of preferred shareholder rights, Delaware courts have taken a strict construction approach that has elicited criticism given its potential to disrupt expectations and allocated risks.¹⁰⁷

None of these doctrinal tensions have been insurmountable impediments.¹⁰⁸ On balance, venture-backed startups and their participants have largely learned to take advantage of the freedom for private ordering and to generally avoid litigation. This latter point is likely a matter of practicality—given that failure or economic realities often make litigation less relevant for startups¹⁰⁹—as well as norms in an ecosystem of repeat players and reputational concerns.¹¹⁰ Further, a community of knowledgeable and experienced lawyers has flourished over decades, playing an important role as dealmakers and guides providing counsel to venture-backed startups through contracting practices, conflicts, fiduciary duties, and more.¹¹¹

2. Securities Law and Venture-Backed Startups

While corporate law’s response has been a mix of enabling rules with certain doctrinal tensions in application, securities law has provided a veritable windfall to the venture capital industry. The legal transformation to securities laws regulating private markets and companies has occurred incrementally over many years. In the aggregate, this deregulatory shift has been sufficiently dramatic to raise existential questions about the coherence of the securities law framework and its approach to venture capital and venture-backed startups.

First, in 1988, during a period of rapid growth in funds allocated to venture capital, the Securities and Exchange Commission (“SEC”) adopted Rule 701, permitting private companies to issue equity-based compensation to employees and service providers, in limited amounts, without registering the offering or providing extensive disclosures.¹¹² As the history of venture capital illuminates, equity-based compensation for founders and entrepreneurs has long been understood as a key ingredient for attracting and retaining talent in risky enterprises, and the SEC’s rule added important clarity for startups to offer equity compensation to prospective employees lacking sophistication or high net worth.¹¹³ At the time of adoption, the SEC had come under considerable pressure from scholars, industry representatives, and lawyers to create this special exemption.¹¹⁴ And, under continued lobbying pressure from industry players, the SEC has periodically, across decades, increasingly relaxed various aspects of the remaining Rule 701 restrictions, “turning [a] small exemption into a significant channel of securities offerings to household investors.”¹¹⁵

Second, a long trajectory of additional deregulatory actions since the early 2000s has opened the floodgates to private markets and loosened restrictions.¹¹⁶ The result is larger, more complex private markets and a regulatory environment in which two firms that are virtually identical in value, number of employees and shareholders, access to capital, and size and footprint of operations can be subject to vastly different regulatory obligations and oversight.¹¹⁷ The implicit bargain that venture-backed startups faced through the 1990s of becoming a public company subject to an extensive federal regulatory regime in order to access large and liquid pools of capital has been replaced by a new set of options.

After the dot-com bust and financial accounting scandals of the early 2000s, Congress passed the Sarbanes-Oxley Act of 2002, setting in motion a narrative of “over-regulation” amid a deepening decline in IPO activity.¹¹⁸ While the costs and obligations on public companies concerning governance arrangements, internal controls, and disclosures indeed ratcheted up, a set of market forces including increased M&A activity and greater availability of private capital also took effect.¹¹⁹ The number of IPOs and U.S. publicly traded companies significantly dropped.¹²⁰ Shortly after Congress passed the Dodd-Frank Act of 2010 in the wake of the global financial crisis, further increasing regulatory burdens on public corporations, it passed the JOBS Act of 2012, which deregulated major aspects of the rules concerning venture financings and easing startups’ exit pathway with a new IPO on-ramp.¹²¹ The venture capital industry, startups, and exchanges that stood to gain from the changes lobbied heavily in favor of them.¹²² As the “SEC continued to prioritize the deregulation of the private markets in the name of public capital formation,”¹²³ the tables began to turn—going public changed from a rite of passage that successful startups would go through after a few years in the venture cycle to an idiosyncratic, firm-specific choice that could be significantly delayed.¹²⁴ The pathway to exit via M&A became much more common than IPO.¹²⁵

With massive inflows of private capital and new investors to private markets, the SEC’s rationale for its deregulatory trend took an ironic twist away from capital formation to “democratizing” access to private markets.¹²⁶ Delayed timelines to venture-backed startup exits had effectively allowed startups to grow larger and for much of their growth to occur on the private side of the divide.¹²⁷ “Unicorn” companies that raised venture financing at a private valuation of $1 billion or more exploded.¹²⁸ In 2020, the SEC adopted extensive rule amendments to permit larger and more frequent private offerings to be offered more widely to the general public.¹²⁹

In hindsight, it became evident that a slow-motion series of piecemeal securities law developments transformed the public-private divide and the environment in which startups go through the venture cycle.

B. The Wild Adventures and Misadventures of Venture-Backed Startups in the Private Realm

With an understanding of the business and legal history that has brought about and transformed venture capital, startups, and the regulatory environment that they enjoy, the discussion can now explore the implications of these developments and what the role of business law might be in the future.

The heart of the matter concerns the enormous space that business law creates for venture-backed startups to operate for long periods without significant governance or disclosure requirements. Accountability mechanisms beyond the internal participants’ private ordering are also vastly limited in comparison with those in the public company context.¹³⁰ Private startups are generally free from securities class actions, short sellers, quarterly earnings pressures, public stock prices, and the like. Further, regulators at different levels from federal to local, in different areas of subject matter expertise, face challenging dynamics responding to innovative startups—their activity might fall into unmapped territory, legal gray areas, or regulatory blind spots until egregious circumstances develop.¹³¹ With limited resources, regulators might prioritize oversight and enforcement of large established companies.

This environment of relative freedom to push the envelope fits the needs of venture capital and startups, which are fundamentally “a machine for running experiments.”¹³² Moreover, given the power law, these startup experiments are typically not seeking to hit a single or double—they aim at “disrupting entrenched corporate power” in lucrative markets that could be grand slams.¹³³ As we have seen, venture capitalists are looking for “radical departures from the past.”¹³⁴ Room for maneuvering without disclosures that would prematurely share information with competitors or potential competitors is important for incubating innovative products and services. Longer timelines for staying private enable startups to try moonshots that might take significant time to develop or find product-market fit.

Recent years have witnessed venture-backed startup activity that has increasingly raised concern about the growing public-private divide and startup governance, however. The private space and relative freedom that are embraced by startup entrepreneurs and venture capitalists have given rise to scandals from Theranos to FTX, governance fiascos such as WeWork, and controversial products and services such as Juul’s vaping technology and Uber and Lyft’s ride sharing services. With a massive influx of private capital over the past decade, venture capital has spread sectorially to startups aimed at widespread industries from health to transportation.¹³⁵ And with this long timeline and large footprint have come concerns about harms to customers, employees, and other stakeholders, as well as questions about how society is impacted more generally by venture capital.¹³⁶

A vivid example of this complex dynamic of startups in the private realm comes from the burgeoning artificial intelligence (“AI”) industry. AI is posed to deliver some of the biggest financial hits of the current generation of startups and it threatens to destabilize countless industries and impact social and economic activity globally in unpredictable ways. Sam Altman, the CEO-founder of OpenAI, which has developed ChatGPT, currently valued at $29 billion, has declared that it is better to continue running the company privately so that his decisions are not limited.¹³⁷ Altman remarked, “When we develop superintelligence, we’re likely to make some decisions that public market investors would view very strangely.”¹³⁸

Notably, however, Altman made these statements about staying private while on a world tour of meetings with governments in which he warned them of the existential threat posed by AI. He testified to the U.S. Congress: “I think if this technology goes wrong, it can go quite wrong.”¹³⁹ And although he originally expressed a desire for his company to work with governments on responsibly regulating AI, he threatened that OpenAI would leave Europe in response to new European Union regulations.¹⁴⁰ He later backtracked after EU lawmaker pushback,¹⁴¹ but he could not unring the proverbial bell that raised questions about the dangers of the technology being developed and the sincerity of its stewards’ statements about embracing regulation.

As regulators’ ability to rein in the harms posed by venture-backed startups is often limited as a practical matter, and the protections of public markets are absent, focus has shifted to startup governance and the failures of private ordering to create checks and balances. For over a decade, with more private capital available in a low-interest rate environment and intense competition for venture deals, many venture capitalists adopted “founder-friendly” stances.¹⁴² Some founders have been allowed to act as “monarchs” with “unchecked power.”¹⁴³ Critics have expressed concern that venture capitalists have turned into “hype” people exercising little managerial oversight—“a money-hungry mob” pushing for “hyper growth” instead of the prudent “midwives to innovation” they had been in the past.¹⁴⁴

In some instances, utterly disastrous startup governance has come to light. One example is the collapse of FTX, one of the largest cryptocurrency exchanges, once privately valued at $40 billion.¹⁴⁵ The CEO-founder was “the paragon of crypto,” and vaulted to celebrity status as he led the startup through rocket-ship growth.¹⁴⁶ After troubling reports came to light about potential leverage and solvency concerns, customers attempted to pull out of FTX, precipitating the company’s downfall. Prosecutors and regulators quickly closed in on the CEO-founder, asserting that FTX had been illegally using clients’ deposits.¹⁴⁷ Shortly after, the CEO-founder resigned and the company filed for bankruptcy. One of the biggest unicorns crumbled within days.

In the aftermath, FTX installed a new CEO to handle the bankruptcy—the same person who had handled the cleanup of the massive accounting and audit scandal at Enron that had prompted the passage of the Sarbanes-Oxley Act in 2002.¹⁴⁸ After taking the helm at FTX, he said: “Never in my career have I seen such a complete failure of corporate controls and such a complete absence of trustworthy financial information as occurred here.”¹⁴⁹ The company had no board of directors—none of the well-known venture firms that had financed FTX had taken seats.¹⁵⁰ According to media reports, “control was in the hands of ‘a very small group of inexperienced, unsophisticated and potentially compromised individuals.’ ”¹⁵¹ Company financials were not tracked.¹⁵² Software was reportedly used to conceal the misuse of client money.¹⁵³ The CEO-founder was arrested and charged with multiple criminal counts, and other top executives pleaded guilty and admitted that they knew what they did at the startup was wrong.¹⁵⁴ Potentially billions of dollars in customer funds went missing.¹⁵⁵

Apart from governance scandals is the separate concern that with venture capitalists raising and deploying dramatically larger funds, they might end up funding money-losing companies that are creating “disruption without social benefit.”¹⁵⁶ In some cases, startups might in fact be “destroying social value” and crowding out the development of superior technologies.¹⁵⁷ Venture capital goes to a narrow slice of potential innovators and not necessarily those that would produce the most social value or positive innovation.¹⁵⁸ Even venture capitalists themselves have raised this concern, for example noting, “We wanted flying cars, instead we got 140 characters.”¹⁵⁹ And while the social value that Twitter produced is certainly debatable, it compares favorably to many other startup inventions including robotic pizza makers and “Juicero” juicers.¹⁶⁰ Commentators have also raised concerns that only a relatively small number of venture capital investors shape the direction of a substantial amount of the capital that is financing radical technological change.¹⁶¹

A number of proposals for reform have been offered. These have tended to be somewhat narrowly focused on particular aspects of problematic facets of the public-private divide and startup governance. For example, proposals from scholars and regulators include special disclosure regimes for unicorns,¹⁶² enhanced disclosures for startup employees,¹⁶³ expanded anti-fraud enforcement efforts,¹⁶⁴ facilitating private company stock trading for price accuracy,¹⁶⁵ and reforming the “shareholders of record” trigger for public company status.¹⁶⁶

Without a more systematic approach to these issues, however, it is difficult to know if there is a problem with venture-backed startups and the private realm, and whether private ordering, new lawmaking, or enforcement could provide a solution. The next Part takes up some of the larger issues and promising avenues for future research.

III. THE BIG QUESTIONS

It might be impossible to accurately calculate the social welfare impact of venture capital, but researchers have begun to ask this question.¹⁶⁷ For most industry players, researchers, and others who are involved in some way in startups and venture capital, the answer is intuitively positive—despite drawbacks and harms, the gain is incalculably large as it is a key economic driver of growth and innovation that changes millions of lives.¹⁶⁸ The vibrant U.S. venture capital ecosystem is a jewel of the economy that countries around the world seek to emulate. Innovation is not automatically good for society,¹⁶⁹ but “in the long run, innovation is essential to productivity gains and economic growth.”¹⁷⁰

As one researcher explained: “Venture capitalists generate private value in the form of fund-level returns, but the social value they create surely exceeds that. That social value is equivalent to private value plus all other returns realized from the technological change that venture financing enables.”¹⁷¹ And on the latter point, “[n]umerous innovations developed by VC-backed firms, from memory chips to recombinant insulin . . . have moved society forward—and in turn, stimulated additional waves of technological development with immense collective impact.”¹⁷²

At the same time, even those who have a rosy or optimistic view about the aggregate social value that venture capital produces might be uncomfortable with the lingering impacts on stakeholders that arise in the context of venture-backed startups. Further, the rise of founders with unchecked power and the decline in active corporate governance by venture capitalists is concerning to many observers.

This Article offers two promising avenues for further inquiry for legal scholars and policymakers. Although the social welfare impact of venture capital may be somewhat intractable in the abstract, there are concrete related questions that would benefit from additional legal study and debate.

First, researchers can usefully focus attention on studying which persons impacted by venture-backed startups, if any, are systematically suffering harm. Key areas for additional investigation include startup employees and customers or users.

A number of researchers have raised concerns about startup employees developing incorrect expectations about the value of their equity compensation or suffering losses by exercising vested options and incurring tax consequences.¹⁷³ Assessing the value of startup equity compensation is challenging because of the lack of liquidity and a clear market price.¹⁷⁴ Further, as discussed above, startups rely on Rule 701 to avoid registering compensatory offerings with the SEC, and have limited disclosure obligations.¹⁷⁵ Complex and often opaque capital structures add to the challenges for startup employees to evaluate their equity compensation.¹⁷⁶ Venture capitalists typically receive preferred stock, which comes with contractual protections such as liquidation preferences, and is considerably more valuable than the common stock for which employees are typically granted stock options.¹⁷⁷ In short, an illiquid market, incomplete information, and complex capital structures often make it difficult for startup employees to make informed decisions about their equity compensation.¹⁷⁸ A more systematic study of the issue offers a concrete path for legal reform that could temper some of the harms of startup governance failures or fraud as other startup participants—venture capitalists, founders, and executives—are often better situated to bear the risk or avoid creating the harm in the first place.

Another group of individuals impacted by startups that deserves deeper inquiry are customers or users.¹⁷⁹ Some salient examples of startups that have harmed users in recent times have included the blood-testing company Theranos, vaping pioneer Juul, and the crypto exchange FTX.¹⁸⁰ Determining whether startups pose distinctive risks to customers is difficult, however. Corporate harms and externalities are certainly not unique to startups—closely held and publicly traded companies are involved in a range of pressing social issues from the opioid crisis to environmental pollution and catastrophes. Nonetheless, one could imagine that startups more frequently pose risks, or uncertain impacts, to customers or users stemming from the innovation or technology that is at the heart of venture-backed experiments. And yet, attempting to regulate the harms from innovation through corporate and securities laws may be inefficient or worse. Further work could be done to bring together relevant research across silos of business law, technology and innovation law, and regulatory and enforcement approaches.

Second, a worthy area of legal focus is whether and when a governance intervention is optimal on a startup’s timeline in the venture cycle. Many observers have bemoaned bad startup governance and even blamed it for major scandals, yet little work has been done to examine how the venture contracting and governance model could change to incentivize active monitoring or whether regulatory mandates are necessary.¹⁸¹

For example, would additional disclosure, due diligence, independent directors, audited financials, or some other mechanism improve startup governance? Is there a bargaining or market failure that prevents such a governance mechanism from being used in most startups? As many startups fail, particularly in their early stages, increasing governance or compliance costs may be unwise and impinge on the valuable space for maneuvering in the private realm that fosters a thriving startup ecosystem. Further, as startups mature, they are often navigating increasing potential tensions among a larger number of participants and greater costs of bargaining, while trying to find a path to a successful exit.¹⁸² Therefore, it may be relatively easy to point to startup governance failures as a problem in the abstract, but difficult to find an ideal moment in the timeline to introduce mandatory obligations and to know which solutions, if any, are optimal. As the topic of startup governance garners more attention, it is important to bolster the empirical and theoretical foundations for understanding whether any legal reform is due.

In sum, while the U.S. venture capital ecosystem is a jewel of the economy and a key driver of innovation, it has also catalyzed concerns about social costs which are worthy of further examination. Further inquiry into two key areas—whether any stakeholders systematically experience negative impacts and potential improvements to the venture contracting and governance model—could help illuminate a path for the future of business law in this area.

CONCLUSION

Technology and innovation in the digital era have profoundly transformed business and society. This Article has investigated how law, particularly corporate and securities law, has facilitated and responded to the rise of venture capital that has been the key financial driver of this transformation.

The discussion has explored in particular how, after lawmakers shaped the enabling environment for venture capital to flourish, corporate and securities law has responded to the rise of venture-backed startups incrementally but with profound effect. Although business law has not always fit easily with the distinctive features of venture capital and startups, it has provided an enormous space in the private realm for venture capital and startups to maneuver with relative freedom. This private realm is a good fit for the needs of innovative companies, but their activity creates lingering issues of social costs and policy. Important and promising areas of future research lie ahead to develop a coherent business law response to the current wild era of adventure capital..

96 S. Cal. L. Rev. 1341

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Elizabeth Pollman*

* Professor of Law, University of Pennsylvania Carey Law School. Special thanks to Danny Sokol and the participants and editors involved in the 2023 Symposium of the Southern California Law Review. Additional thanks for helpful comments and valuable discussion to Brian Broughman, Elisabeth de Fontenay, and workshop participants at Seoul National University School of Law, the Center for Advanced Studies on the Foundations of Law and Finance at Goethe University Frankfurt, and Bocconi University.