On public, permissionless blockchains like Ethereum, space is scarce and crypto traders must compete to use it for executing their transactions. That means those who control this space in the form of blocks resemble landlords who can extract rent. “Maximal Extractable Value” (MEV) refers to trading strategies that exploit the ability to decide what transactions go into a block. Those who control the contents of a block (validators) can obtain rents not only for including transactions in a block, but also for ordering them in profitable ways— say, by letting transactions “front-run” others. Since rising to prominence in 2019, MEV has quickly become a major market phenomenon, generating over $1 billion in profit since 2020, while affecting tens of billions of dollars in transaction value.
MEV is often condemned. Techniques like “sandwich attacks” which involve trading ahead of other users’ trades, have been described as toxic, fraudulent, manipulative—even theft. However, this broad denunciation of MEV is too quick, as the technical nuances of how each kind of MEV extraction operates are determinative of the legal risk it entails. The legality of MEV extraction under U.S. financial laws has yet to be subject to sustained scholarly analysis, and the present Article aims to fill this gap. We undertake the first systematic analysis of how U.S. securities and commodities law, particularly the broad antimanipulation rules wielded by the SEC (Rule 10b-5) and CFTC (Rule 180.1), apply to core MEV extraction techniques on Ethereum.
In so doing, the Article confronts how basic notions of fairness and trust play out differently in a world of discretionary transaction ordering in crypto markets compared to the first-come first-serve world of traditional finance. Behaviors that might seem outrageous off-chain look very different when examined in light of how blockchains actually work.
Nonetheless, this Article argues that some forms of MEV extraction entail a significant risk of market manipulation liability. Focusing on sandwiching in particular, we argue that there is a route for courts that adopt a moralized lens, focused on behavior that exploits privileged control over financial infrastructure, to find sandwiching impermissibly manipulative. We argue, further, that the legal hazards are even greater when it comes to sandwiching private transactions, which more clearly involves a heightened trust relationship, as well as disruptive schemes like oracle manipulation, wherein MEV is part of an independently manipulative strategy. Nonetheless, we argue, this alone does not mean a sweeping ban on MEV is necessarily a desirable policy. It remains unclear whether a strict ban on MEV sandwiching, for instance, would be prudent, given the unknowns about the net effects of MEV extraction and behavioral impact that a ban on MEV sandwiching would entail.
While I agree with many of the arguments presented by Barczentewicz, Sarch, and Vasan, which urge caution to regulators in acting to address MEV sandwich attacks, I present an additional lens and framework to evaluate the question of whether sandwich attacks justify direct validator regulation or should be expressly prohibited as a specific form of market manipulation at this current moment in the development of blockchain-based financial market infrastructures.
As discussed in further detail below, my view is that private sector remedies like smart order routing, optimized slippage limits, and Flashbots Protect RPC have the potential to reduce the prevalence and extent of sandwich attacks and the like. Regulators should defer to the continued development and implementation of such efforts. Ex ante regulatory solutions like direct validator regulation are, in my view, disproportionate and undermine the open architecture necessary to achieve the inclusion and innovation benefits of open blockchains. Other solutions, including ex ante duties to mitigate MEV (and other similar costs to users) for service providers engaging in discretionary order routing (i.e., “non-held” orders where the service provider’s discretion is used to find most favorable price for the user under the circumstances) and ex post regulatory solutions like declaring sandwich attacks and other malicious MEV strategies a form of market manipulation could be justifiable, but only under three pre-conditions that do not currently exist: (1) a constructive, global approach to regulating crypto markets is implemented, enhancing the effectiveness of such a ban; (2) the growth by orders of magnitude of sandwich attacks and other malicious forms of MEV relative to overall on-chain volumes in the most widely used open blockchains; and (3) the demonstrated failure of private sector remedies at mitigating the harm.
Do you know what data exists about you? Do you know what data you create . . . every second of the day? Probably not. But data brokers do. At first glance, this may make you uncomfortable. But with deeper analysis, it should make you downright concerned. Data brokers sell information about you, ranging from addictions to diseases to who you slept with. In other words, information you likely wouldn’t share with your friends is for sale.
And the U.S. Government and its foreign adversaries (think Iran and China) can buy it—legally. Under the current U.S. regulatory and legal landscape, there is little stopping the U.S. Government or its adversaries from doing so. This data is a goldmine for intelligence operations, but its sale also raises serious privacy concerns that must not be ignored.
To address these topics, this paper will first explain the technical background necessary to understand data brokers and how they connect this data to individuals—including you. In the following sections, the paper lays out the perils and benefits that data brokers pose to U.S. national security, the regulatory landscape for data broker sales, and the privacy concerns for American citizens created by these sales. With these foundational points explained, the paper concludes with recommendations on how to best protect U.S. national security without eroding America’s bedrock civil liberties.
OpenAI’s text generation program ChatGPT and the text-toimage generators Stable Diffusion and Dall-E have broken records for early public adoption, capital investment, and a technological shift potentially more far-reaching than even the Internet itself. The broad category of generative AI has the potential to disrupt industry, art, and culture, both if done poorly and if done well. Despite significant problems with accuracy and deep concerns about the social and legal consequences of the premature adoption of these technologies, global multinational enterprises are moving these projects out of the test labs and into everyday use. This article provides a comprehensive, but introductory overview of the development of generative AI, the training methods used to produce artificially generated content, the industry opportunities for generative AI, and the legal considerations that enterprises adopting these technologies should consider involving intellectual property. After discussing the development and implementation of the technology, the article emphasizes the key concerns regarding copyright, trademark, and trade secret. The article also identifies areas in which the growth of generative AI will require new federal legislation to retain the balance of creativity and commercial development within intellectual property laws.