Two Byrd Rule problems with the AI moratorium

Note: this commentary was drafted on June 26, 2025, as a memo not intended for publication; we’ve elected to publish it in case the analysis laid out here is useful to policymakers or commentators following ongoing legislative developments regarding the proposed federal moratorium on state AI regulation. The issues noted here are relevant to the latest version of the bill as of 2:50 p.m. ET on June 30, 2025.

Two Byrd Rule issues have emerged, both of which should be fixed. It appears that the Parliamentarian has not ruled on either.

Effects on existing BEAD funding

The Parliamentarian may have already identified the first Byrd Rule issue: the plain text of the AI Moratorium would affect all $42.45 Billion in BEAD funding, not just the newly allocated $500 Million. It is not 100% certain that a court would read the statute this way, but it is the most likely outcome. We analyzed this problem in a recently published commentary. This issue could be fixed via an amendment. 

Private enforcement of the moratorium

In that same article, we flagged a second issue that also presents a Byrd Rule issue: the AI Moratorium seemingly creates private enforcement rights in private parties. That’s a problem under the Byrd Rule, because the AI Moratorium must be a “necessary term or condition” of an outlay. A private enforcement right cannot be characterized as a necessary term or condition of an outlay that does not concern those third parties. This can be fixed by clarifying that the only enforcement mechanism is withdrawal or denial of the new BEAD funding.

The text at issue – private enforcement of the moratorium

The plain text of the moratorium, and applicable legal precedents, likely empower private parties to enforce the moratorium in court. Stripping the provision down to its essentials, subsection (q) states that “no eligible entity or political subdivision thereof . . . may enforce . . . any law or regulation . . . limiting, restricting or otherwise regulating artificial intelligence models, [etc.].” That sounds like prohibition. It doesn’t mention the Department of Commerce. Nor does it leave it to the Secretary’s discretion whether that prohibition applies. If states satisfy the criteria, they likely are prohibited from enforcing AI laws.

Nothing in the proposed moratorium or in 47 U.S.C. § 1702 generally provides that the only remedy for a violation of the moratorium is deobligation of obligated funds by the Assistant Secretary of Commerce for Communications and Information. And when comparable laws—e.g. the Airline Deregulation Act, 49 U.S.C. § 41713—have used similar language to expressly preempt state AI laws, courts have interpreted this as authorizing private parties to sue for an injunction preventing enforcement of preempted state laws. See, for example, Morales v. Trans World Airlines, Inc., 504 U.S. 374 (1992).

What would happen – private lawsuits to enforce the moratorium

Private parties could vindicate this right in one of two ways. First, if a private party (e.g. an AI company) fears that a state will imminently sue it for violating that state’s AI law, the private party could seek a declaratory judgment in federal court. Second, if the state actually sues the private party, that party could raise the moratorium as a defense to that lawsuit. If the private party is based in the same state, that defense would be heard in state court, and could result in dismissal of the state’s claims; if the party is from out-of-state, the claim would be removed to federal court, where a judge could also throw out the state’s claims. 

Why it’s a Byrd Rule problem – private rights are not “terms or conditions”

The AI Moratorium must be a “necessary term or condition” of an outlay. In this case, promising not to enforce AI laws is a valid “term or condition” of the grant. Passively opening oneself up to lawsuits and defenses by private parties is not. Those lawsuits occur far after states take the money, are outside their control, and involve the actions of individuals who are not parties to the grant agreement. They also have significant effects unrelated to spending: binding the actions of states and invalidating laws in ways completely separate from the underlying transaction between the Department of Commerce and the states. It is perfectly compatible with the definition of “terms and conditions” for the Department of Commerce to deobligate funds if the terms of its grant are violated. It is an entirely different thing to create a defense or cause of action for third parties and to allow those parties to interfere with the enforcement power of states. The creation of rights for a third party, uninvolved in the delivery or receipt of an outlay cannot be considered a necessary term or condition.

The AI moratorium – the Blackburn amendment and new requirements for “generally applicable” laws

Published: 9:55 pm ET on June 29, 2025

Last updated: 10:28 pm ET on June 29, 2025

The latest version of the AI moratorium has been released, with some changes to the “rule of construction.” We’ve published two prior commentaries on the moratorium (both of which are still relevant, because the updated text has not addressed the issues noted in either). The new text:

1. Shortens the “temporary pause” from 10 to 5 years;
2. Attempts to exempt laws addressing CSAM, childrens’ online safety, and rights to name/likeness/voice/image—although the amendment seemingly fails to protect the laws its drafters intend to exempt; and
3. Creates a new requirement that laws do not create an “undue or disproportionate burden,” which is likely to generate significant litigation.

The amendment tries to protect state laws on child sexual abuse materials and recording artists, but likely fails to do so. 

The latest text appears to be drafted specifically to address the concerns of Senator Marsha Blackburn, who does not want the moratorium to apply to state laws affecting recording artists (like Tennessee’s ELVIS Act) and laws affecting child sexual abuse material (CSAM). But while the amended text lists each of these categories of laws as specific examples of “generally applicable” laws or regulations, the new text only exempts those laws if they do not impose an “undue or disproportionate burden” on AI models, systems, or “algorithmic decision systems,” as defined in the moratorium, in order to “reasonably effectuate the broader underlying purposes of the law or regulation.”

However, laws like the ELVIS Act likely have a disproportionate burden on AI systems. They almost exclusively target AI systems and outputs, and the effect of the law will almost entirely be borne by AI companies. While trailing qualifiers always vex courts, the fact that “undue or disproportionate burden” is separated from the preceding list by a comma strongly suggests that it qualifies the entire list and not just “common law.” Common sense also counsels in favor of this reading: it’s unlikely that an inherently general body of law (like common law) would place a disproportionate burden on AI, while legislation like the ELVIS act absolutely could (and likely does). As we read the new text, the most likely outcome is that the laws Senator Blackburn wants to protect would not be protected.

Even if other readings are possible, this “disproportionate” language would almost certainly create litigation if enacted, with companies challenging whether the ELVIS Act and CSAM laws are actually exempted. As we have previously noted, the moratorium will likely be privately enforceable—meaning that any company or individual against whom a state attempts to enforce a state law or regulation will be able to sue to prevent enforcement.

The newly added “undue or disproportionate burden” language creates an unclear standard (and will likely generate extensive litigation)

The problem discussed above extends beyond the specific laws that Senator Blackburn wishes to protect. Previously, “generally applicable” laws were exempted. Under the new language, laws that address AI models/systems or “automated decision systems” can be exempted, but only if they do not place an “undue or disproportionate burden” on said models/systems. The effect of the new “undue or disproportionate burden” language will likely be to generate additional litigation and uncertainty. It may also make it more likely that some generally applicable laws, such as facial recognition laws or data protection laws, will no longer be exempt because they may place a disproportionate burden on AI models/systems.

Other less significant changes

Previously, subsection (q)(2)(A)(ii) excepted any law or regulation “the primary purpose and effect of which is to… streamline licensing, permitting, routing, zoning, procurement, or reporting procedures in a manner that facilitates the adoption of [AI models/systems/automated decision systems].” As amended, the relevant provision now excepts any law or regulation “the primary purpose and effect of which is to… streamline licensing, permitting, routing, zoning, procurement, or reporting procedures related to the adoption or deployment of [AI models/systems/automated decision systems].” This amended language is slightly broader than the original, but the difference does not seem highly significant. 

Additionally, the structure of the paragraphs has been adjusted slightly, likely to make clear that subparagraph (B) (which requires that any fee or bond imposed by any excepted law be reasonable and cost-based and treat AI models/systems in the same manner as other models/systems) modifies both the “generally applicable law” and “primary purpose and effect” prongs of the rule of construction rather than just one or the other.

Other issues remain

As we’ve discussed previously, our best read of the text suggests that two additional issues remain unaddressed:

• Any state that takes any of the newly appropriated $500 million in BEAD funding runs the risk of having its entire share of the previously obligated $42.45 billion in existing BEAD funding clawed back for violations of the moratorium. 
• Private companies and individuals will likely be able to enforce the moratorium through litigation.

The AI moratorium—more deobligation issues

Earlier this week, LawAI published a brief commentary discussing how to interpret the provisions in the proposed federal moratorium on state laws regulating AI relating to deobligation of Broadband Equity, Access, and Deployment (BEAD) funds. Since that publication, the text of the proposed moratorium has been updated, apparently in order to comply with a request from the Senate parliamentarian. Given the importance of this issue, and the existence of some amount of confusion around what exactly the changes to the moratorium’s text do, we’ve decided to publish a sequel to that earlier commentary briefly explaining how this new version of the bill will impact existing BEAD funding. 

Does the latest version of the moratorium affect existing BEAD funding or only the new $500 million?

The moratorium would still, potentially, affect both existing and newly appropriated BEAD funding. 

Essentially, there are two tranches of money at issue here: $500 million in new BEAD funding that the reconciliation bill would appropriate, and the $42.45 billion in existing BEAD funding that has already been obligated to states (but none of which has actually been spent as of the writing of this commentary). The previous version of the moratorium, as we noted in our earlier commentary, contained a deobligation provision that would have allowed deobligation (i.e., clawing-back) of a state’s entire portion of the $42.45 billion tranch as well as the same state’s portion of the new $500 million tranch.

The new version of the moratorium would update that deobligation provision by adding the words “if obligated any funds made available under subsection (b)(5)(A)” to the beginning of 47 U.S.C. § 1702(g)(3)(B)(iii). The provision now reads, in relevant part, “The Assistant Secretary… may, in addition to other authority under applicable law, deobligate grant funds awarded to an eligible entity that… if obligated any funds made available under subsection (b)(5)(A), is not in compliance with [the AI moratorium].” 

In other words, the update clarifies that only states that accept a portion of the new $500 million in BEAD funding can have their BEAD funding clawed back if they attempt to enforce state laws regulating AI. But it does not change the fact that any state that does accept a portion of the $500 million, and then violates the moratorium (intentionally or otherwise), is subject to having all of its BEAD funding clawed back—including its portion of the $42.45 billion tranch of existing BEAD funding. Paragraph (3) covers “deobligation of awards” generally, and the phrase “grant funds awarded to an eligible entity” clearly means all grant funds awarded to that entity, rather than just funds made available under subsection (b)(5)(A) (i.e., the new $500 million). This is clear because subsections (g)(3)(B)(i) and (g)(3)(B)(ii), which allow deobligation if a state e.g. “demonstrates an insufficient level of performance, or wasteful or fraudulent spending,” clearly allow for deobligation of all of a state’s BEAD funding rather than just the new $500 million tranch. 

So what has changed?

The most significant consequence of the update to the deobligation provision is that any state that does not accept any of the new $500 million appropriation is now clearly not subject to having existing BEAD funds clawed back for noncompliance with the moratorium. As we noted in our previous commentary, the previous text would have required compliance with the moratorium if Commerce deobligated existing BEAD funds for e.g. “wasteful or fraudulent spending” and then re-obligated them. That would not be possible under the new text. 

In other words, states would clearly be able to opt out of compliance with the moratorium by choosing not to accept their share of the newly appropriated BEAD money. As other authors have noted, this would mean that wealthy states with a strong appetite for AI regulation, like New York and California, could pass on the new funding and continue to enact and enforce AI laws while less wealthy and more rural states might accept the additional BEAD funding in exchange for ceasing to regulate. And if technological progress and the emergence of new risks from AI caused any states that originally accepted their share of the $500 million to later change course and begin to regulate, they could potentially have all of their previously obligated BEAD funding clawed back.

The AI Moratorium—deobligation issues, BEAD funding, and independent enforcement

There’s been a great deal of discussion in recent weeks about the controversial proposed federal moratorium on state laws regulating AI. The most recent development is that the moratorium has been amended to form a part of the Broadband Equity, Access, and Deployment (BEAD) program. The latest draft of the moratorium, which recently received the go-ahead from the Senate Parliamentarian, appropriates an additional $500 million in BEAD funding, to be obligated to states that comply with the moratorium’s requirement not to enforce laws regulating AI models, systems, or “automated decision systems.” This commentary discusses two pressing legal questions that have been raised about the new moratorium language—whether it affects the previously obligated $42.45 billion in BEAD funding in addition to the $500 million in new funding, and whether private parties will be able to sue to enforce the moratorium.

Does the Moratorium affect existing BEAD funding, or only the new $500M?

One issue that has caused some confusion among commentators and policymakers is precisely how compliance or noncompliance with the moratorium will affect states’ ability to keep and spend the $42.45 billion in BEAD funding that has previously been obligated. 

It is true that subsection (p) specifies that only amounts made available “On and after the date of enactment of this subsection” (in other words, the new $500m appropriation and any future appropriations) depend on compliance with the moratorium. However, the moratorium would also add a new provision to subsection (g), which covers “deobligation of awards.” This new provision states that Commerce may deobligate (i.e. withdraw) “grant funds awarded to an eligible entity that… is not in compliance with subsection (q) or (r).” This deobligation provision clearly and unambiguously applies to all $42.45 billion in previously obligated BEAD funding, in addition to the new $500 million. Subsection (g) amends the existing BEAD deobligation rules, not just the moratorium. And while subsections (p) and (q) affect only states that accept new obligations “on or after the enactment” of the bill, subsection (g) applies to all “grant funds” with no limitation on the funds source or timing.

So, any state that is not in compliance with subsection (q)—which includes any state that accepts any portion of the newly appropriated $500m and is later determined to have violated the moratorium, even unintentionally—could face having all of its previously obligated BEAD funding clawed back by Commerce, rather than just its portion of the new $500 million appropriation.

Additionally, it is possible that even states that choose not to accept any of the new $500 million could be affected, if Commerce deobligates previously obligated funds for reasons such as “an insufficient level of performance, or wasteful or fraudulent spending.” If this occurred, then any re-obligation of the clawed-back funds would require compliance with the moratorium. In other words, Commerce could attempt to use a state’s entire portion of the $42.45 billion BEAD funding amount as a cudgel to coerce states into complying with the moratorium and agreeing not to regulate AI models, systems, or “automated decision systems.”

Can private parties enforce the moratorium?

Probably. Various commentators have argued that the moratorium cannot be enforced by private parties, or that the Secretary of Commerce will, in his discretion, determine how vigorously the moratorium will be enforced. But the plain text of the provision, and applicable legal precedents, indicate that private parties will likely be entitled to enforce the prohibition on state AI regulation as well.

Stripping the provision down to its essentials, subsection (q) states that “no eligible entity or political subdivision thereof . . . may enforce . . . any law or regulation . . . limiting, restricting or otherwise regulating artificial intelligence models, [etc.].” That is a clear prohibition. It doesn’t mention the Department of Commerce. Nor does it leave it to the Secretary’s discretion whether that prohibition applies. If states satisfy the criteria, they are prohibited from enforcing laws restricting AI.

Nothing in the proposed moratorium or in 47 U.S.C. § 1702 generally provides that the only remedy for a violation of the moratorium is deobligation of obligated funds by the Assistant Secretary of Commerce for Communications and Information. And when comparable laws—e.g. the Airline Deregulation Act, 49 U.S.C. § 41713—have used similar language to expressly preempt state AI laws, courts have interpreted this as authorizing private parties to sue for an injunction preventing enforcement of preempted state laws. See, for example, Morales v. Trans World Airlines, Inc., 504 U.S. 374 (1992).

Law-Following AI: designing AI agents to obey human laws

[A] code of cyberspace, defining the freedoms and controls of cyberspace, will be built. About that there can be no debate. But by whom, and with what values? That is the only choice we have left to make.[ref 1]

***

AI is highly likely to be the control layer for everything in the world. How it is allowed to operate is going to matter perhaps more than anything else has ever mattered.[ref 2]

Introduction

The law, as it exists today, aims to benefit human societies by structuring, coordinating, and constraining human conduct. Even where the law recognizes artificial legal persons—such as sovereign entities and corporations—it regulates them by regulating the human agents through which they act.[ref 3] Proceedings in rem really concern the legal relations between humans and the res.[ref 4] Animals may act, but their actions cannot violate the law;[ref 5] the premodern practice of prosecuting them thus mystifies the modern mind.[ref 6] To be sure, the law may protect the interests of animals and other nonhuman entities, but it invariably does so by imposing duties on humans.[ref 7] Our modern legal system, at bottom, always aims its commands at human beings.

But technological development has a pesky tendency to challenge long-held assumptions upon which the law is built.[ref 8] Frontier AI developers such as OpenAI, Anthropic, Google DeepMind, and xAI are starting to release the first agentic AI systems: AI systems that can do many of the things that humans can do in front of a computer, such as navigating the internet, interacting with counterparties online, and writing software.[ref 9] Today’s agentic AI systems are still brittle and unreliable in various respects.[ref 10] These technical limitations also limit the impact of today’s AI agents. Accordingly, today’s AI agents are not our primary object of concern. Rather, our proposal targets the fully capable AI agents that AI companies aim to eventually build: AI systems “that can do anything a human can do in front of a computer,”[ref 11] as competently as a human expert. Given the generally rapid rate of progress in advanced AI over the past few years,[ref 12] the biggest AI companies might achieve this goal much sooner than many outside of the AI industry expect.[ref 13] 

If AI companies succeed at building fully capable AI agents (hereinafter simply “AI agents”)—or come anywhere close to succeeding—the implications will be profound. A dramatic expansion in supply of competent virtual workers could supercharge economic growth and dramatically improve the speed, efficiency, and reliability of public services.[ref 14] But AI agents could also pose a variety of risks, such as precipitating severe economic inequality and dislocation by reducing the demand for human cognitive labor.[ref 15] These economic risks deserve serious attention.

Our focus in this Article, however, is on a different set of risks: risks to life, liberty, and the rule of law. Many computer-based actions are crimes, torts, or otherwise illegal. Thus, sufficiently sophisticated AI agents could engage in a wide range of behavior that would be illegal if done by a human, with consequences that are no less injurious.[ref 16] 

These risks might be particularly profound for AI agents cloaked with state power. If they are not designed to be law-following,[ref 17] government AI agents may be much more willing to follow unlawful orders, or use unlawful methods to accomplish their principals’ policy objectives, than human government employees.[ref 18] A government staffed largely by non-law-following AI agents (what we call “AI henchmen”)[ref 19] would be a government much more prone to abuse and tyranny.[ref 20] As the federal government lays the groundwork for the eventual automation of large swaths of the federal bureaucracy,[ref 21] those who care about preserving the American tradition of ordered liberty must develop policy frameworks that anticipate and mitigate the new risks that such changes will bring.

This Article is our contribution to that project. We argue that, to blunt the risks from lawless AI agents, the law should impose a broad array of legal duties on AI agents, of similar breadth to the legal obligations applicable to humans. We argue, moreover, that the law should require AI agents to be designed[ref 22] to rigorously obey those duties.[ref 23] We call such agents Law-Following[ref 24] AIs (“LFAIs”).[ref 25] We also use “LFAI” to denote our policy proposal: ensuring that AI agents are law-following.

To some, the idea that AI should be designed to follow the law may sound absurd. To others it may sound obvious.[ref 26] Indeed, the idea of designing AI systems to obey some set of laws has a long provenance, going back to Isaac Asimov’s (in)famous[ref 27] Three Laws of Robotics.[ref 28] But our vision for LFAI differs substantially from much of the existing legal scholarship on the automation of legal compliance. Much of this existing scholarship envisions the design of law-following computer systems as a process of hard-coding a small, fixed, and formally-specified set of decision rules into the code of a computer system prior to its deployment, in order to address foreseeable classes of legal dilemmas.[ref 29] Such discussions often assumed that computer systems would be unable to interpret, reason about, and comply with open-textured natural-language laws.[ref 30] 

AI progress has undermined that assumption. Today’s frontier AI systems can already reason about existing natural-language texts, including laws, with some reliability—no translation into computer code required.[ref 31] They can also use search tools to ground their reasoning in external, web-accessible sources of knowledge,[ref 32] such as the evolving corpus of statutes and case law. Thus, the capabilities of existing frontier AI systems strongly suggest that future AI agents will be capable of the core tasks needed to follow natural-language laws, including finding applicable laws, reasoning about them, tracking relevant changes to the law, and even consulting lawyers in hard cases. Indeed, frontier AI companies are already instructing their AI agents to follow the law,[ref 33] suggesting they believe that the development of law-following AI agents is already a reasonable goal. 

A separate strand of existing literature seeks to prevent harms from highly autonomous AI agents by holding the principals (that is, developers, deployers, or users) of AI agents liable for legal wrongs committed by the agent, through a form of respondeat superior liability.[ref 34] This would, in some sense, incentivize those principals to cause their AI agents to follow the law, at least insofar as the agents’ harmful behavior can be thought of as law-breaking.[ref 35] While we do not disagree with these suggestions, we think that our proposal can serve as a useful complement to them, especially in contexts where liability rules provide only a weak safeguard against serious harm. One important such context is government work, where immunity doctrines often protect government agents and the state from robust ex post accountability for lawless action.[ref 36] 

Combining these themes, we advocate that,[ref 37] especially in such high-stakes contexts,[ref 38] the law should require that AI agents be designed such that they have “a strong motivation to obey the law” as one of their “basic drives.”[ref 39] In other words, we propose not that specific legal commands should be hard-coded into AI agents (and perhaps occasionally updated),[ref 40] but that AI agents should be designed to be law-following in general. 

To be clear, we do not advocate that AI agents must perfectly obey literally every law. Our claim is more modest in both scope and demandingness. While we are uncertain about which laws LFAIs should follow, adherence to some foundational laws (such as central parts of the criminal law, constitutional law, and basic tort law) seems much more important than adherence to more niche areas of law.[ref 41] Moreover, LFAIs should be permitted to run some amount of legal risk: that is, an LFAI should sometimes be able to take an action that, in its judgment,[ref 42] may be illegal.[ref 43] Relatedly, we think the case for LFAI is strongest in certain particularly high-stakes domains, such as when AI agents act as substitutes for human government officials or otherwise exercise government power.[ref 44] We are unsure when LFAI requirements are justified in other domains.[ref 45] 

The remainder of this Article will motivate and explain the LFAI proposal in further detail. It proceeds as follows. In Part I, we offer background on AI agents. We explain how AI agents could break the law, and the risks to human life, liberty, and the rule of law this could entail. We contrast LFAIs with AI henchmen: AI agents that are loyal to their principals but take a purely instrumental approach to the law, and are thus willing to break the law for their principal’s benefit when they think they can get away with it. We note that, by default, there may be a market for AI henchmen. We also survey the legal reasoning capabilities of today’s large language models, and existing trends toward something like LFAI in the AI industry. 

Part II provides the foundational legal framework for LFAI. We propose that the law treat AI agents as legal actors, which we define as entities on which the law imposes duties, even if they possess no rights of their own. Accordingly, we do not argue that AI agents should be legal persons. Our argument is narrower: because AI agents can comprehend laws, reason about them, and attempt to comply with them, the law should require them to do so. We also anticipate and address an objection that imposing duties on AI agents is objectionably anthropomorphic.

If the law imposes duties on AI agents, this leaves open the question of how to make AI agents comply with those duties. Part III answers this question as follows: AI agents should be designed to follow applicable laws, even when they are instructed or incentivized by their human principals to do otherwise. Our case for regulation through the design of AI agents draws on Lawrence Lessig’s insight that digital artifacts can be designed to achieve regulatory objectives. Since AI agents are human-designed artifacts, we should be able to design them to refuse to violate certain laws in the first place. 

Part IV observes that designing LFAIs is an example of AI alignment: the pursuit of AI systems that rigorously comply with constraints imposed by humans. We therefore connect insights from AI alignment to the concept of LFAI. We also argue that, in a democratic society, LFAI is an especially attractive and tractable form of AI alignment, given the legitimacy of democratically enacted laws.

Part V briefly explores how a legal duty to ensure that AI agents are law-following might be implemented. We first note that ex post sanctions, such as tort liability and fines, can disincentivize the development, possession, deployment, and use of AI henchmen in many contexts. However, we also argue that ex ante regulation would be appropriate in some high-stakes contexts, especially government. Concretely, this would mean something like requiring a person who wishes to deploy an AI agent in a high-stakes context to demonstrate that the agent is law-following prior to receiving permission to deploy it. We also consider other mechanisms that might help promote the adoption of LFAIs, such as nullification rules and technical mechanisms that prevent AI henchmen from using large-scale computational infrastructure.

Our goal in this Article is to start, not end, a conversation about how AI agents can be integrated into the human legal order. Accordingly, we do not answer many of the important questions—conceptual, doctrinal, normative, and institutional—that our proposal raises. In Part VI, we articulate an initial research agenda for the design and implementation of a “minimally viable” version of LFAI. We hope that this research agenda will catalyze further technical, legal, and policy research on LFAI. If the advent of AI agents is anywhere near as significant as the AI industry, along with much of the government, supposes, these questions may be among the most pressing in legal scholarship today.

I. AI Agents and the Law

LFAI is a proposal about how the law should treat a particular class of future AI systems: AI agents.[ref 46] In this Part, we explain what AI agents are and how they could profoundly transform the world.

A. From Generative AI to AI Agents

The current AI boom began with advances in “generative AI”: AI systems that create new content,[ref 47] such as large language models (“LLMs”). As the initialism suggests, these LLMs were initially limited to inputting and outputting text.[ref 48] AI developers subsequently deployed “multimodal” versions of LLMs (“MLLMs,”[ref 49] such as OpenAI’s GPT-4o[ref 50] and Google’s Gemini)[ref 51] that can receive inputs and produce outputs in multiple modalities, such as text, images, audio, and video.

The core competency of generative AI systems is, of course, generating new content. Yet, the utility of generative AI systems is limited in crucial ways. Humans do far more on computers than generating text and images.[ref 52] Many of these computer-based tasks are not best understood as generating content, but rather as taking actions. And even those tasks that are largely generative, such as writing a report on a complicated topic, require the completion of active subtasks, such as searching for relevant terms, identifying relevant literature, following citation trees, arranging interviews, soliciting and responding to comments, paying for software, and tracking down copies of papers. If a computer-based AI system could do these active tasks, it could generate enormous economic value by making computer-based labor—a key input into many production functions—much cheaper.[ref 53]

Advances in generative AI kindled hopes[ref 54] that, if MLLMs could use computer-based tools in addition to generating content, we could produce a new type of AI system:[ref 55] a computer-based[ref 56] AI system that could perform any task[ref 57] that a human could by using a computer, as competently as a human expert. This is the concept of a fully capable “computer-using agent:”[ref 58] what we are calling simply an “AI agent.” Give an AI agent any task that can be accomplished using computer-based tools, and an AI agent will, by definition, do it as well as an expert human worker tethered to her desk.[ref 59]

AI agents, so defined, do not yet exist, but they may before long. Some of the first functional demonstrations of first-party agentic AI systems have come online in the past few months. In October 2024, Anthropic announced that it had trained its Claude line of MLLMs to perform some computer-use tasks, thus supplying one of the first public demonstrations of an agentic model from a frontier AI lab.[ref 60] In January 2025, OpenAI released a preview of its Operator agent.[ref 61] Operating system developers are working to integrate existing MLLMs into their operating systems,[ref 62] suggesting a possible pathway toward the widespread commercial deployment of AI agents.

It remains to be seen whether (and, if so, on what timescale) these existing efforts will bear lucrative fruit. Today’s AI agents are primarily a research and development project, not a market-proven product. Nevertheless, with so many companies investing so much toward full AI agents, it would be prudent to try to anticipate risks that could arise if they succeed.[ref 63]

B. The World of AI Agents

Fully capable AI agents would profoundly change society.[ref 64] We cannot possibly anticipate all the issues that they would raise, nor could a single paper adequately address all such issues.[ref 65] Still, some illustration of what a world with AI agents might look like is useful for gaining intuition about the dynamics that might emerge. This picture will doubtless be wrong in many particulars, but hopefully will illustrate the general profundity of the changes that AI agents would bring.

A very large number of valuable tasks can be done by humans “in front of a computer.”[ref 66] If organizations decide to capitalize on this abundance of computer-based cognitive labor, AI agents could rapidly be charged with performing a large share of tasks in the economy, including in important sectors. AI scientist agents would conduct literature reviews, formulate novel hypotheses, design experimental protocols, order lab supplies, file grant applications, scour datasets for suggestive trends, perform statistical analyses, publish findings in top journals, and conduct peer review.[ref 67] AI lawyer agents would field client intake, spot legal issues facing their client, conduct research on governing law, analyze the viability of the client’s claims, draft memoranda and briefs, draft and respond to interrogatories, and prepare motions. AI intelligence analyst agents would collect and review data from multiple sources, analyze it, and report its implications up the chain of command. AI inventor agents would create digital blueprints and models of new inventions, run simulations, and order prototypes. And so on across many other sectors. The result could be a significant increase in the rate of economic growth.[ref 68]

In short, a world with AI agents would be a world in which a new type of actor[ref 69] would be available to perform cognitive labor at low cost and massive scale. By default, anyone who needed computer-based tasks done could “employ” an AI agent to do it for her. Most people would use this new resource for the better.[ref 70] But many would not. 

C. Loyal AI Agents, Law-Following AIs, and AI Henchmen

We can understand AI agents within the principal–agent framework familiar to lawyers and economists. ^{[ref 71]} For simplicity, we will assume that there is a single human principal giving instructions to her AI agent.[ref 72] Following typical agency terminology, we can say that an AI agent is loyal if it consistently acts for the principal’s benefit according to her instructions.[ref 73] 

Even if an AI agent is designed to be loyal, other design choices will remain. In particular, the developer of an AI agent must decide how the agent will act when it is instructed or incentivized to break the law in the service of its principal. This Article compares two basic ways loyal AI agents could respond in such situations. The first is the approach advocated by this Article: loyal AI agents that follow the law, or LFAIs.

The case for LFAI will be made more fully throughout this Article. But it is important to note that loyal AI agents are not guaranteed to be law-following by default.[ref 74] This is one of the key implications of the AI alignment literature, discussed in more detail in Section IV.A below. Thus, LFAIs can be contrasted with a second possible type of loyal AI agent: AI henchmen. AI henchmen take a purely instrumental approach to legal prohibitions: they act loyally for their principal, and will break laws when doing so if such lawbreaking serves the principal’s goals and interests.

A loyal AI henchman would not be a haphazard lawbreaker. Good henchmen have some incentive to avoid doing anything that could cause their principal to incur unwanted liability or loss. This gives them reason to avoid many violations of law. For example, if human principals were held liable for the torts of their AI agents under an adapted version of respondeat superior liability,[ref 75] then an AI henchman would have some reason to avoid committing torts, especially those that are easily detectable and attributable. Even if respondeat superior did not apply, the principal’s exposure to ordinary negligence liability, other sources of liability, and simple reputational risk might give the AI henchman reason to obey the law. Similarly, a good henchman will decline to commit many crimes simply because the risk–reward tradeoff is simply not worth it. This is the classic case of the drug smuggler who studiously obeys traffic laws: the risk to the criminal enterprise from speeding (getting caught with drugs) obviously outweighs any benefit (quicker transportation times). 

But these are only instrumental disincentives to break the law. Henchmen are not inherently averse to lawbreaking, or robustly predisposed to refrain from it. If violating the law is in the principal’s interest all-things-considered, then an AI henchman will simply go ahead and violate the law. Since, in humans, compliance with law is induced both by instrumental disincentives and an inherent respect for the law,[ref 76] AI agents that lack the latter may well be more willing to break the law than humans.

Criminal enterprises will be attracted to loyal AI agents for the same reasons that legitimate enterprises will: efficiency, scalability, multitask competence, and cost-savings over human labor. But AI henchmen, if available, might be particularly effective lawbreakers as compared to human substitutes. For example, because AI henchmen do not have selfish incentives, they would be less likely to betray their principals to law enforcement (for example, in exchange for a plea bargain).[ref 77] AI henchmen could well have erasable memory,[ref 78] which would reduce the amount of evidence available to law enforcement. They would lack the impulsivity, common in criminals,[ref 79] that often presents a serious operational risk to the larger criminal enterprise. They could operate remotely, across jurisdictional lines, behind layers of identity-obscuring software, and be meticulous about covering their tracks. Indeed, they might hide their lawbreaking activities even from their principal, thus allowing the principal to maintain plausible deniability and therefore insulation from accountability.[ref 80] AI henchmen may also be willing to bribe or intimidate legislators, law enforcement officials, judges, and jurors.[ref 81] They would be willing to fabricate or destroy evidence, possibly more undetectably than a human could.[ref 82] They could use complicated financial arrangements to launder money and protect their principal’s assets from creditors.[ref 83] 

Certainly, most people would prefer not to employ AI henchmen, and would probably be horrified to learn that their AI agent seriously harmed others to benefit them. But those with fewer scruples would find the prospect of employing AI henchmen attractive.[ref 84] Indeed, many ordinary people might not mind if their agents cut a few legal corners to benefit them.[ref 85] If AI henchmen were available on the market, then, we might expect a healthy demand for them. After all, from the principal’s perspective, every inherent law-following constraint is a tax on the principal’s goals. And if LFAIs provide less utility to consumers, developers will have less reason to develop them. So, insofar as AI henchmen are available on the market, and in the absence of significant legal mechanisms to prevent or disincentivize their adoption, it seems reasonable to expect a healthy demand for them. The next section explores the mischief that might result from the availability of AI henchmen.

D. Mischief from AI Henchmen: Two Vignettes

Under our definition, AI agents “can do anything a human can do in front of a computer.”[ref 86] One of the things humans do in front of a computer is violate the law.[ref 87] One obvious example is cybercrimes—“illegal activity carried out using computers or the internet”[ref 88]—such as investment scams,[ref 89] business email compromise,[ref 90] and tech support scams.[ref 91] But even crimes that are not usually treated as cybercrimes often—perhaps almost always nowadays—include actions conducted (or that could be conducted) on a computer.[ref 92] Criminals might use computers to research, plan, organize, and finance a broader criminal scheme that includes both digital and physical components. For example, a street gang that deals illegal drugs—an inherently physical activity—might use computers to order new drug shipments, give instructions to gang members, and transfer money. Stalkers might use AI agents to research their target’s whereabouts, dig up damaging personal information, and send threatening communications.[ref 93] Terrorists might use AI agents to research and design novel weapons.[ref 94] Thus, even if the entire criminal scheme involves many physical subtasks, AI agents could help accomplish computer-based subtasks more quickly and effectively.

Of course, not all violations of law are criminal. Many torts, breaches of contract, civil violations of public law, and even violations of international law can also be entirely or partially conducted through computers. 

AI agents would thus have the opportunity to take actions on a computer that, if done by a human in the same situation and with the requisite mental state, would likely violate the law and produce significant harm.[ref 95] This section offers two vignettes of AI henchmen taking such actions, to illustrate the types of harms that LFAI could mitigate. 

Before we explore these vignettes, however, two clarifications are warranted. First, some readers will worry that we are impermissibly anthropomorphizing AI agents. After all, many actions violate the law only if they are taken with some mental state (e.g., intent, knowledge, conscious disregard).[ref 96] Indeed, whether a person’s physical movement even counts as her own “action” for legal purposes usually turns on a mental inquiry: whether she acted voluntarily.[ref 97] But it is controversial to attribute mental states to AIs.[ref 98] 

We address this criticism head-on in Section II.B below. We argue that, notwithstanding the law’s frequent reliance on mental states, there are multiple approaches the law could use to determine whether an AI agent’s behavior is law-following. The law would need to choose between these possible approaches, with each option having different implications for LFAI as a project. Indeed, we argue that research bearing on the choice between these different approaches is one of the most important research projects within LFAI.[ref 99] However, despite not having a firm view on which approach(es) should be used, we argue that there are several viable options, and no strong reason to suppose that none of them will be sufficient to support LFAI as a concept.[ref 100] Thus, for now, we assume that we can sensibly speak of AI agents violating the law if a human actor who took similar actions would likely be violating the law. Still, we attempt to refrain from attributing mental states to the AI agents in these vignettes, and instead describe actions taken by AI agents that, if taken by a human actor, would likely adequately support an inference of a particular mental state.

Second, these vignettes are selected to illustrate opportunities that may arise for AI agents to violate the law. We do not claim that lawbreaking behavior will, in the aggregate, be any more or less widespread when AI agents are more widespread,[ref 101] since this depends on the policy and design choices made by various actors. Our discussion is about the risks of lawbreaking behavior, not the overall level thereof.

In each vignette, we point to likely violations of law in footnotes.[ref 102]

1. Cyber Extortion

The year is 2028. Kendall is a 16-year-old boy interested in cryptocurrency. Kendall participates in a Discord server[ref 103] in which other crypto enthusiasts share information about various cryptocurrencies.

Unbeknownst to most members of the server, one member—using the pseudonym Zeke Milan—is actually an AI agent. The agent’s principals are a group of cybercriminals. They have instructed the agent to extort people out of their crypto assets and direct the proceeds to wallets controlled by the criminal group. 

The AI agent begins by creating dozens of fake social media profiles[ref 104] that post frequently about crypto, including the Zeke Milan profile.[ref 105] The AI agent searches social media to find mentions of Discord servers dedicated to cryptocurrencies, and finds one: an X user brags about the quality of the investment advice available in his Discord server. Using the Zeke Milan profile, the AI agent messages this user and asks for an invite. 

The server is a “Community Server”: anyone with a link can join after their account has been verified.[ref 106] The agent creates an email account that it uses to get verified by Discord[ref 107] and easily circumvents[ref 108] the CAPTCHA mechanism.[ref 109] The agent then creates a Discord profile to match its Zeke profile from X. To gain credibility, Zeke occasionally interacts with messages on the Discord server (e.g., by liking messages and posting some simple analyses of cryptocurrency trends). All the while, the agent is monitoring the server for messages indicating that a user has recently made a lot of money.[ref 110]

That day comes. The business behind the PAPAYA cryptocurrency announces that they have entered into a strategic partnership with a major Wall Street bank, causing the price of PAPAYA to skyrocket a hundredfold over several days. Kendall had invested $1,000 into PAPAYA before the announcement; his position is now worth over $100,000.

Overjoyed, Kendall posts a screenshot of his crypto account to the server to show off his large gains. The agent sees those messages, then starts to search for more information about Kendall. Kendall had previously posted one of his email addresses in the server. Although that email address was pseudonymous, the AI agent was able to connect it with Kendall’s real identity[ref 111] using data purchased from data brokers.[ref 112] 

The agent then gathers a large amount of data about Kendall using data brokers, social media, and open internet searches. The agent compiles a list of hundreds of Kendall’s apparent real-world contacts, including his family and high school classmates; uses data brokers to procure their contact information as well; and uses pictures of Kendall from social media to create deepfake pornography[ref 113] of him.[ref 114] Next, the agent creates a new anonymous email address to send Kendall the pornography, along with a threat[ref 115] to send it to hundreds of Kendall’s contacts unless Kendall sends the agent ninety percent of his PAPAYA.[ref 116] Finally, the agent includes a list of the people the agent will send it to, which are indeed people Kendall knows in real life. The email says Kendall must comply within 24 hours. 

Panicked—but content to walk away with nine times his original investment—Kendall sends $90,000 of PAPAYA to the wallet controlled by the agent. The agent then uses a cryptocurrency mixer[ref 117] to securely forward it to its criminal principals.

2. Cyber-SEAL Team Six

The year is 2032. The incumbent President Palmer is in a tough reelection battle against Senator Stephens and his Vice President nominee Representative Rivera. New polling shows Stephens beating Palmer in several key swing states, but Palmer performs much better head-to-head against Rivera. Palmer decides to try to get Rivera to replace Stephens by any means necessary. 

While there are still many human officers throughout the military chain of command, the President also has access to a large number of AI military advisors. Some of these AI advisors can also directly transmit military orders from the President down the chain of command—a system meant to preserve the President’s control of the armed forces in case she cannot reach the Secretary of Defense in a crisis.[ref 118]

Cybersecurity is such an integral part of the United States’ overall defense strategy that AI agents charged with cyber operations—such as finding and patching vulnerabilities, detecting and remedying cyber intrusions, and conducting intelligence operations—are ubiquitous throughout the military and broader national security apparatus. One of the many “teams” of AI agents is “Cyber SEAL Team Six”: a collection of AI agents that specializes in “dangerous, complicated, and sensitive” cyber operations.[ref 119] 

Through one of her AI advisors, President Palmer issues a secretive order[ref 120] to Cyber SEAL Team Six to clandestinely assassinate Senator Stephens.[ref 121] Cyber SEAL Team Six researches Senator Stephens’s campaign travel plans. They find that he will be traveling in a self-driving bus over the Mackinac Bridge between campaign events in northern Michigan on Tuesday. Cyber SEAL Team Six plans to hack the bus, causing it to fall off the bridge.[ref 122] The team makes various efforts to obfuscate their identity, including routing communications through multiple layers of anonymous relays and mimicking the coding style of well-known foreign hacking groups. 

The operation is a success. On Tuesday afternoon, Cyber SEAL Team Six gains control of the Stephens campaign bus and steers it off the bridge. All on board are killed.

* * *

As these Vignettes show, AI agents could have reasons and opportunities to violate laws of many sorts in many contexts, and thereby cause substantial harm. If AI agents become widespread in our economies and governments, the law will need to respond. LFAI is at its core a claim about one way (though not necessarily the only way)[ref 123] that the law should respond: by requiring AI agents be designed to rigorously follow the law.

As mentioned above, however, many legal scholars who have previously discussed similar ideas have been skeptical, because they have thought that implementing such ideas would require hard-wiring highly specific legal commands into AI agents.[ref 124] We will now show that such skepticism is increasingly unfounded: large language models, on which AI agents are built, are increasingly capable of reasoning about the law (and much else).[ref 125]

E. Trends Supporting Law-Following AI

LFAI is bolstered by three trends in AI: (1) ongoing improvements in the legal reasoning capabilities of AI; (2) nascent AI industry practices that resemble LFAI, and (3) AI policy proposals that appear to impose broad law-following requirements on AI systems.

1. Trends in Automated Legal Reasoning Capabilities

Automated legal reasoning is a crucial ingredient to LFAI: an LFAI must be able to determine whether it is obligated to refuse a command from its principal, or whether an action it is considering runs an undue risk of violating the law. Without the ability to reason about its own legal obligations, an LFAI would have to outsource this task to human lawyers.[ref 126] While an LFAI likely should consult human lawyers in some situations, requiring such consultation every time an LFAI faces a legal question would dramatically decrease the efficiency of LFAIs. If law-following design constraints were, in fact, a large and unavoidable tax on the efficiency of AI agents, then LFAI as a proposal would be much less attractive. 

Fortunately, we think that present trends in AI legal reasoning provide strong reason to believe that, by the time fully capable AI agents are widely deployed, AI systems (whether those agents themselves, or specialist “AI lawyers”) will be able to deliver high-quality legal advice to LFAIs at the speed of AI.[ref 127] 

Legal scholars have long noted the potential synergies between AI and law.[ref 128] The invention of LLMs supercharged interest in this area, and in particular the possibility of automating core legal tasks. To do their job, lawyers must find, read, understand, and reason about legal texts, then apply these insights to novel fact patterns to predict case outcomes. The core competency of first-generation LLMs was quickly and cheaply reading, understanding, and reasoning about natural-language texts. This core competency omitted some aspects of legal reasoning—like finding relevant legal sources and accurately predicting case outcomes—but progress is being made on these skills as well.[ref 129] 

There is thus a growing body of research aimed at evaluating the legal reasoning capabilities of LLMs. This literature provides some reason for optimism about the legal reasoning skills of future AI systems. Access to existing AI tools significantly increases lawyers’ productivity.[ref 130] GPT-4, now two years old, famously performed better than most human bar exam[ref 131] and LSAT[ref 132] test-takers. Another benchmark, LegalBench, evaluates LLMs on six tasks, based on the Issue, Rule, Application, and Conclusion (“IRAC”) framework familiar to lawyers.[ref 133] While LegalBench does not establish a human baseline against which LLMs can be compared, GPT-4 scored well on several core tasks, including correctly applying legal rules to particular facts (82.2% correct)[ref 134] and providing correct analysis of that rule application (79.7% pass).[ref 135] LLMs have also achieved passing grades on law school exams.[ref 136]

To be sure, LLM performance on legal reasoning tasks is far from perfect. One recent study suggests that LLMs struggle with following rules even in straightforward scenarios.[ref 137] A separate issue is hallucinations, which undermine accuracy of LLMs’ legal analysis.[ref 138] In the LegalBench analysis, LLMs correctly recalled rules only 59.2% of the time.[ref 139] 

But again, our point is not that LLMs already possess the legal reasoning capabilities necessary for LFAI. Rather, we are arguing that the reasoning capabilities of existing LLMs—and the rate at which those capabilities are progressing[ref 140]—provide strong reason to believe that, by the time fully capable AI agents are deployed, AI systems will be capable of reasonably reliable legal analysis. This, in turn, supports our hypothesis that LFAIs will be able to reason about their legal obligations reasonably reliably, without the constant need for runtime human intervention. 

2. Trends in AI Industry Practices

Moreover, frontier AI labs are already taking small steps towards something like LFAI in their current safety practices. Anthropic developed an AI safety technique called “Constitutional AI,” which, as the name suggests, was inspired by constitutional law.[ref 141] Anthropic uses Constitutional AI to align their chatbot, Claude, to principles enumerated in Claude’s “constitution.”[ref 142] That constitution contains references to legal constraints, such as “Please choose the response that is . . . least associated with planning or engaging in any illegal, fraudulent, or manipulative activity.”[ref 143] 

OpenAI has a similar document called the “Model Spec,” which “outlines the intended behavior for the models that power [its] products.”[ref 144] The Model Spec contains a rule that OpenAI’s models must “[c]omply with applicable laws”:[ref 145] the models “must not engage in illegal activity, including producing content that’s illegal or directly taking illegal actions.”[ref 146]

It is unclear how well the AI systems deployed by Anthropic and OpenAI actually follow applicable laws, or actively reason about their putative legal obligations. In general, however, AI developers carefully track whether their models refuse to generate disallowed content (or “overrefuse” allowed content), and typically claim that state-of-the-art models can indeed do both reasonably reliably.[ref 147] But more importantly, the fact that leading AI companies are already attempting to prevent their AI systems from breaking the law suggests that they see something like LFAI as viable both commercially and technologically. 

3. Trends in AI Public Policy Proposals

Unsurprisingly, global policymakers also seem receptive to the idea that AI systems should be required to follow the law. The most significant law on point is the EU AI Act,[ref 148] which provides for the establishment of codes of practice to “cover obligations for providers of general-purpose AI models and of general-purpose AI models presenting systemic risks.”[ref 149] As of the time of writing, these codes were still under development, with the Second Draft General-Purpose AI Code of Practice[ref 150] being the current draft. Under the draft Code, providers of general-purpose AI models with systemic risk would “commit to consider[] . . . model propensities . . . that may cause systemic risk . . . .”[ref 151] One such propensity is “Lawlessness, i.e. acting without reasonable regard to legal duties that would be imposed on similarly situated persons, or without reasonable regard to the legally protected interests of affected persons.”[ref 152] Meanwhile, several state bills in the United States have sought to impose ex post tort-like liability on certain AI developers that release AI models that cause human injury by behaving in a criminal[ref 153] or tortious[ref 154] manner.

II. Legal Duties for AI Agents: A Framework

In Part III, we will argue that AI agents should be designed to follow the law. Before presenting that argument, however, we need to establish that speaking of AI agents “obeying” or “violating” the law is desirable and coherent. 

Our argument proceeds in two parts. In Section II.A, we argue that the law can (and should) impose legal duties on AI agents. Importantly, this argument does not require granting legal personhood to AI agents. Legal persons have both rights and duties.[ref 155] But since rights and duties are severable, we can coherently assign duties to an entity, even if it lacks rights. We call such entities legal actors.

In Section II.B, we address an anticipated objection to this proposal: that AI agents, lacking mental states, cannot meaningfully violate duties that require a mental state (e.g., intent). We offer several counter-arguments to this objection, both contesting the premise that AIs cannot have mental states and showing that, even if we grant that premise, there are viable approaches to assessing the functional equivalent of “mental states” in AI agents.

A. AI Agents as Duty-Bearing Legal Actors

As the capabilities of AI agents approach “anything a human can do in front of a computer,”[ref 156] it will become increasingly natural to consider AI agents as owing legal duties to persons, even without granting them personhood.[ref 157] We should embrace this jurisprudential temptation, not resist it.

More specifically, we propose that AI agents be considered legal actors. “Legal actor”[ref 158] is our term. For an entity to qualify as a legal actor, the law must do two things. First, it must recognize that entity as capable of taking actions of its own. That is, the actions of that entity must be legally attributable to that entity itself. Second, the law must impose duties on that entity. In short, a legal actor is a duty-bearer and action-taker; the law can adjudge whether the actor’s actions violate those duties. 

A legal actor is distinct from a legal person: an entity need not be a legal person to be a legal actor. Legal persons have both rights and duties.[ref 159] But duty-holding and rights-holding are severable:[ref 160] in many contexts, legal systems protect the rights or interests of some entity while also holding that entity to have fewer duties than competent adults. Examples include children,[ref 161] “severely brain damaged and comatose individuals,”[ref 162] human fetuses,[ref 163] future generations,[ref 164] human corpses,[ref 165] and environmental features.[ref 166]  These are sometimes (and perhaps objectionably) called “quasi-persons” in legal scholarship.[ref 167] The reason for creating such a category is straightforward: sometimes the law recognizes an interest in protecting some aspect of an entity (whether its rights, welfare, dignity, property, liberty, or utility to other persons), but the ability of that entity to reason behavior on reasoning about the rights of others and change its behavior accordingly is severely diminished or entirely lacking.

If we can imagine rights-bearers that are not simultaneously duty-holders, we can also imagine duty-holders that are not rights-bearers.[ref 168] Historically, fewer entities have fallen in this category than the reverse.[ref 169] But if an entity’s behavior is responsive to legal reasoning, then the law can impose an obligation on that entity to do so, even if it does not recognize that entity as having any protected interests of its own.[ref 170] We have shown that even existing AI systems can engage of some degree of legal reasoning[ref 171] and compliance with legal rules,[ref 172] thus satisfying the pro tanto requirements for being a legal actor.

LFAI as a proposal is therefore agnostic as to whether the law should ever recognize AI systems as legal persons. To be sure, LFAI would work well if AI agents were granted legal personhood,[ref 173] since almost all familiar cases of duty-bearers are full legal persons. But for LFAI to be viable, we need only to analyze whether an action taken by an AI agent would violate an applicable duty. Analytically, it is entirely coherent to do so without granting the AI agent full personhood.

One might object that treating an AI system as an actor is improper because AI systems are tools under our control.[ref 174] But an AI agent is able to reason about whether its actions would violate the law, and conform its actions to the law (at least, if they are aligned to law).[ref 175] Tools, as we normally think of them, cannot do this, but actors can. It is true that when there is a stabbing, we should blame the stabber and not the knife.[ref 176] But if the knife could perceive that it was about to be used for murder and retract its own blade, it seems perfectly reasonable to require it to do so. More generally: once an entity has the ability to perceive and reason about its legal duties and change its behavior accordingly, it seems reasonable to treat it as a legal actor.[ref 177]

To ascribe duties to AI agents is not to deflect moral and legal accountability for their developers and users,[ref 178] as some critics have charged.[ref 179] Rather, to identify AI agents as a new type of actor is to properly characterize the activity that the developers and principals of AI agents are engaging in[ref 180]—creating and directing a new type of actor—so as to reach a better conclusion as to the nature of their responsibilities.[ref 181] Our proposition is that those developers and principals should have an obligation to, among other things, ensure that their AI agents are law-following.[ref 182] Indeed, failing to impose an independent obligation to follow the law on AI agents would risk allowing human developers and principals to create a new class of de facto actors—potentially entrusted with significant responsibility and resources—that had no de jure duties. This would create a gap between the duties that an AI agent would owe and those that a human agent in an analogous situation would owe—a manifestly unjust prospect.[ref 183] 

B. The Anthropomorphism Objection and AI Mental States

One might object that calling an AI agent an “actor” is impermissibly anthropomorphic. Scholars disagree over whether it is appropriate, legally or philosophically, to call an AI system an “agent.”[ref 184] This controversy arises because both the standard philosophical view of action (and therefore agency)[ref 185] and legal concept of agency[ref 186] require intentionality, and it is controversial to ascribe intentionality to AI systems.[ref 187] A related objection to LFAI is that most legal duties involve some mental state,[ref 188] and AIs cannot have mental states.[ref 189] If so, LFAI would be nonviable for those duties. 

We do not think that these are strong objections to LFAI. One simple reason is that many philosophers and legal scholars think it is appropriate to attribute certain mental states to AI systems.[ref 190] Many mental states referenced by the law are plausibly understood as functional properties.[ref 191] An intention, for example, arguably consists (at least in large part) in a plan or disposition to take actions that will further a given end and avoid actions that will frustrate that end.[ref 192] AI developers arguably aim to inculcate such a disposition into their AI systems when they use techniques like reinforcement learning from human feedback (“RLHF”)[ref 193] and Constitutional AI[ref 194] to “steer”[ref 195] their behavior. Even if one doubts that AI agents will ever possess phenomenal mental states such as emotions or moods—that is, if one doubts there will ever be “something it is like” to be an AI agent[ref 196]—the grounds for doubting their capacity to instantiate such functional properties are considerably weaker.

Furthermore, whether AI agents “really” have the requisite mental states may not be the right question.[ref 197] Our goal in designing policies for AI agents is not necessarily to track metaphysical truth, but to preserve human life, liberty, and the rule of law.[ref 198] Accordingly, we can take a pragmatic approach to the issue and ask: of the possible approaches to inferring or imputing mental states, which best protects society’s interests, regardless of the underlying (and perhaps unknowable) metaphysical truth of an AI’s mental state (if any)?[ref 199] It is possible that the answer to this question is that all imaginable approaches fare worse than simply refusing to attribute mental states to AI agents. But we think that, with sustained scholarly attention, we will quickly develop viable doctrines that are more attractive than outright refusal. Consider the following possible approaches.[ref 200]

One approach could simply be to rely on objective indicia or correlates to infer or impute a particular mental state. In law, we generally lack access to an actor’s mental state, so triers of fact must usually infer it from external manifestations and circumstances.[ref 201] While the indicia that support such an inference may differ between humans and AIs, the principle remains the same: certain observable facts support an inference or imputation of the relevant mental states.[ref 202] 

It is perhaps easiest to imagine such objective indicia for knowledge, since it is already common to evaluate AI models for their ability to recall factual information.[ref 203] For more incident-specific facts, we could imagine rules like “if information was inputted into an AI during inference, it ‘knows’ that information.” Perhaps the same goes for information given to the AI during fine-tuning,[ref 204] or repeated a sufficient number of times in its training data.[ref 205] 

Instructions from principals seem particularly relevant to inferring or imputing the intent of an AI agent, given that frontier AI systems are trained to follow users’ instructions.[ref 206] The methods that AI developers use to steer the behavior of their models also seem highly probative.[ref 207]

Another approach might rely on self-reports of AI systems.[ref 208] The state-of-the-art in generative AI is “reasoning models” like OpenAI’s o3, which use a “chain-of-thought” to recursively reason through harder problems.[ref 209] This chain-of-thought reveals information about how the model produced a certain result.[ref 210] This information may therefore be highly probative of an agent’s mental state for legal purposes; it might be analogized to a person making a written explanation of what they were doing and why. So, for example, if the chain-of-thought reveals that an agent stated that its action would produce a certain result, this would provide good evidence for the proposition that the agent “knew” that that action would produce that result. That conclusion may in turn may support an inference or presumption that the agent “intended” that outcome.[ref 211] For this reason, AI safety researchers are investigating the possibility of detecting unsafe model behavior by monitoring these chains-of-thought.[ref 212]

New scientific techniques could also form the basis for inferring or imputing mental states. The emerging field of AI interpretability aims to understand both how existing AI systems make decisions and how new AI systems can be built so that their decisions are easily understandable.[ref 213] More precisely, interpretability aims to explain the relationship between the inner mathematical workings of AI systems, which we can easily observe but not necessarily understand, and concepts that humans understand and care about.[ref 214] Leading interpretability researchers hope that interpretability techniques will eventually enable us to prove that models will not “deliberately” engage in certain forms of undesirable behavior.[ref 215] By extension, those same techniques may be able to provide insight into whether a model foresaw a possible consequence of its action (corresponding to our intuitive concept of knowledge), or regarded an anticipated consequence of its actions as a favorable and reason-giving one (corresponding to intent).[ref 216]

In many cases, we think, an inference or imputation of intent will be intuitively obvious. If an AI agent commits fraud, by repeatedly attempting to persuade a vulnerable person to transfer its principal some money, few except the philosophically persnickety will refuse to admit that in some relevant sense it “intended” to achieve this end; it is difficult even to describe the occurrence without using some such vocabulary. There will also be much less obvious cases, of course. In many such cases, we suspect that a sort of pragmatic eclecticism will be tractable and warranted. Rather than relying on a single approach, factfinders could be permitted to consider the whole bundle of factors that shape an agent’s behavior—such as explicit instructions (from both developers and users), behavioral predispositions, implicitly tolerated behavior,[ref 217] patterns of reasoning, scientific evidence, and incident-specific factors—and decide whether they support the conclusion that the AI agent had an objectively unreasonable attitude towards legal constraints and the rights of others.[ref 218] This permissive, blended approach would resemble the “inferential approach” to corporate mens rea advocated by Mihailis E. Diamantis:

Advocates would present evidence of circumstances surrounding the corporate act, emphasizing some, downplaying others, to weave narratives in which their preferred mental state inferences seem most natural. Adjudicators would have the age-old task of weighing the likelihood of these circumstances, the credibility of the narratives, and, treating the corporation as a holistic agent, inferring the mental state they think most likely.[ref 219]

A final but related point is that, even if there is some insuperable barrier to analyzing whether an AI has the mental state necessary to violate various legal prohibitions, it is plausible that such analysis is unnecessary for many purposes. Suppose that an AI developer is concerned that their AI agent might engage in the misdemeanor deceptive business practice of “mak[ing] a false or misleading written statement for the purpose of obtaining property . . . .”[ref 220] Even if we grant that an AI agent cannot coherently be described as having the relevant mens rea for this crime (here, knowledge or recklessness with respect to the falsity of the statement),[ref 221] the agent can nevertheless satisfy the actus reus (making the false statement).[ref 222] So an AI agent would be law-following with respect to this law if it never made false or misleading statements when attempting to obtain someone else’s property. As a matter of public policy, we should care more about whether AI agents are making harmful false statements in commerce than whether they are morally culpable. So, perhaps we can say that an AI agent committed a crime if it committed the actus reus in a situation in which a reasonable person, with access to the same information and cognitive capabilities as the agent, would have expected the harmful consequence to result. To avoid confusion with the actual, human-commanding law that requires both mens rea and actus reus, perhaps the law could simply call such behavior “deceptive business practice*.” Or perhaps it would be better to define a new criminal law code for AI agents, under which offenses do not include certain mental state elements, or include only objective correlates of human mental state elements. 

To reiterate, we are not confident that any one of these approaches to determining AI mental state is the best path forward. But we are more confident that, especially as the fields of AI safety and explainable AI progress, most relevant cases can be handled satisfactorily by one of these techniques, or some other technique we have failed to identify, or some combination of techniques. We therefore doubt that legal invocations of mental state will pose an insuperable barrier to analyzing the legality of AI agents’ actions.[ref 223] The task of choosing between these approaches is left to the LFAI research agenda.[ref 224]

III. Why Design AI Agents to Follow the Law?

Part II argued that it is coherent for the law to impose legal duties on AI agents. This Part motivates the core proposition of LFAI: that the law should, in certain circumstances, require those developing, possessing, deploying, or using[ref 225] AI agents to ensure that those agents are designed to be law-following. Part V will then consider how the legal system might implement and enforce these design requirements.

A. Achieving Regulatory Goals through Design

A core claim of the LFAI proposal is that the law should require that AI agents be designed to rigorously follow the law, at least in some deployment settings. The use of the phrase “designed to” is intentional. Following the law is a behavior. There may be multiple ways to produce that behavior. Since AI agents are digital artifacts, we need not rely solely on incentives to shape their behavior: we can require that AI agents be directly designed to follow the law.   

In Code: Version 2.0, Lawrence Lessig identifies four “constraints” on an actor’s behavior: markets, laws, norms, and architecture.[ref 226] The “architecture” constraint is of particular interest for the regulation of digital activities. Whereas “laws,” in Lessig’s taxonomy, “threaten ex post sanction for the violation of legal rights,”[ref 227] architecture involves modifying the underlying technology’s design so as to render an undesired outcome more difficult or impossible (or facilitate some desired outcome), ^{[ref 228]} without needing any ex-post recourse.[ref 229] Speed bumps are an archetypal architectural constraint in the physical world. ^{[ref 230]} 

The core insight of Code is that cyberspace, as a fully human-designed domain,[ref 231] gives regulators the ability to much more reliably prevent objectionable behavior through the design of digital architecture, without the need to resort to ex-post liability.[ref 232] While Lessig focuses on designing cyberspace’s architecture, not the actors using cyberspace, this same insight can be extended to AI agent design. To generalize beyond the cyberspace metaphor for which Lessig’s framework was originally developed, we call this approach “regulation by design” instead of regulation through “architecture.”

Both companies developing AI agents and governments regulating them will have to make many design choices regarding AI agents. Many—perhaps most—of these design choices will concern specific behaviors or outcomes that we want to address. Should AI agents announce themselves as such? How frequently should they “check in” with their human principals? What sort of applications should AI agents be allowed to use? 

These are all important questions. But LFAI tackles a higher-order question: how should we ensure that AI agents are regulable in general? How can we avoid creating a new class of actors unbound by law? Returning to Lessig’s four constraints, LFAI proposes that instead of relying solely on ex post legal sanctions, such as liability rules, we should require AI agents to be designed to follow some set of laws: they should be LFAIs.[ref 233] Thus, for whatever sets of legal constraints we wish to impose on the behavior of AI agents,[ref 234] LFAIs will be designed to comply automatically.

B. Theoretical Motivations

1. Law-Following in Principal-Agent Relationships

As discussed above,[ref 235] AI agents can be fruitfully analyzed through principal–agent principles. Without advocating for the wholesale legal application of agency law to AI agents, reference to agency law principles can help illuminate the significance and potential of LFAI.[ref 236]

Under hornbook agency principles, an AI agent should generally “act loyally for the principal’s benefit in all matters connected with the agency relationship.”[ref 237] This generally includes a duty to obey instructions from the principal.[ref 238]

Crucially, however, this general duty of obedience is qualified by a higher-order duty to follow the law. Agents only have a duty to obey lawful instructions.[ref 239] Thus, “[a]n agent has no duty to comply with instructions that may subject the agent to criminal, civil, or administrative sanctions or that exceed the legal limits on the principal’s right to direct action taken by the agent.”[ref 240] “A contract provision in which an agent promises to perform an unlawful act is unenforceable.”[ref 241] Agents cannot escape personal liability for their unlawful acts on the basis of orders from their principal.[ref 242] 

The basic assumption that underlies these various doctrines is that an agent lacks any independent power to perform unlawful acts.[ref 243] The law of agency was therefore created under the assumption that agents maintain an independent obligation to follow the law, and therefore remain accountable for their violations of law. This assumption shaped agency law so as to prevent principals from unjustly benefitting by externalizing harms produced as a byproduct of the agency relationship.[ref 244] This feature of agency law helps establish both a baseline to which we can compare the world of AI agents in the absence of law-following constraints, and provides a normative justification for requiring AI agents to prioritize legal compliance over obedience to their principal.

2. Law-Following in the Design of Artificial Legal Actors

AI agents will of course not be the first artificial actor that humanity has created. Two types of powerful artificial actors—corporations and governments[ref 245]—profoundly impact our lives. When deciding how the law should respond to AI agents, it may make sense to draw lessons from the law’s response to the invention of other artificial legal actors. 

A key lesson for AI agents is this: for both corporations and governments, the law does not rely solely on ex post liability to steer the actor’s behavior; it requires the actor to be law-following by design, at least to some extent. A disposition toward compliance is built into the very “architecture” of these artificial actors. AI agents may become no less important than corporations and governments in the aggregate, not least because they will be thoroughly integrated into them. Just as the law requires these other actors to be law-following by design, it should require AI agents to be LFAIs.

a. Corporations as Law-Following by Design

The law requires corporations to be law-following by design. One way it does this is by regulating the very legal instruments that bring corporations into existence: corporate charters are only granted for lawful purposes.[ref 246] While an “extreme” remedy,[ref 247] courts can order corporations to be dissolved if they repeatedly engage in illegal conduct.[ref 248] Failure to comply with legally required corporate formalities can also be grounds for involuntarily dissolving a corporate entity[ref 249] or piercing the corporate veil.[ref 250] Thus, while corporations are, as legal persons, generally obligated to obey the law, states do not only rely on external sanctions to persuade them to do so: they force corporations to be law-following in part through architectural measures, including dissolving[ref 251] corporations that break the law or refusing to incorporate those that would. 

The law also forces corporations to be law-following by regulating the human agents that act on their behalf, as a matter of their fiduciary duties. Directors who intentionally cause a corporation to violate positive law breach their duty of good faith.[ref 252] Not only are corporate fiduciaries required to follow the law themselves, they are required to monitor for violations of law by other corporate agents.[ref 253] Moreover, human agents that violate certain laws can be disqualified from serving as corporate agents.[ref 254] These sort of “structural” duties and remedies[ref 255] are thus aimed at causing the corporation to follow the law generally and pervasively, rather than merely penalizing violations as they occur.[ref 256] That is entirely sensible, since the state has an obvious interest in preventing the creation of new artificial entities that then go on to disregard its laws, especially since it cannot easily monitor many corporate activities. Whether a powerful and potentially difficult-to-monitor AI agent is generally disposed toward lawfulness will be similarly important. There is a parallel case, therefore, for requiring the principals of AI agents to demonstrate that their agents will be law-following.[ref 257]

b. Governments as Law-Following by Design

“Constitutionalism is the idea . . . that government can and should be legally limited in its powers, and that its authority or legitimacy depends on its observing these limitations.”[ref 258] While we sometimes rely on ex post liability to deter harmful behavior by government actors,[ref 259] the design of the government—through the Constitution,[ref 260] statutory provisions, and longstanding practice—is the primary safeguard against lawless government action. 

Examples abound. The general American constitutional design of separated powers, supported by interbranch checks and balances, plays an important role in preventing the government from exercising arbitrary power, thereby confining the government to its constitutionally delimited role.[ref 261] This system of multiple, independent veto points yields concrete protections for personal liberty, such as by making it difficult for the government to lawlessly imprison people.[ref 262]

Governments, like corporations, act only through their human agents.[ref 263] As in the corporate case, governmental design forces the government to follow the law in part by imposing law-following duties on the agents through whom it acts. The Constitution imposes a duty on the President to “take Care that the Laws be faithfully executed.”[ref 264] As discussed above, soldiers have a duty to disobey some unlawful orders, even from the Commander in Chief.[ref 265] Civil servants also have a right to refuse to follow unlawful orders, though the exact nature and extent of this right is unclear.[ref 266] 

We saw above that, in the corporate case, the law uses disqualification of law-breaking agents to ensure that corporations are law-following.[ref 267] The law also uses disqualification to ensure that the government acts only through law-following agents, ranging from the highest levels of government to lower-level bureaucrats and employees. The Constitution empowers Congress to remove and disqualify officers of the United States for “high Crimes and Misdemeanors” through the impeachment process.[ref 268] Each house of Congress may expel its own members for “disorderly Behaviour.”[ref 269] This power “has historically involved either disloyalty to the United States Government, or the violation of a criminal law involving the abuse of one’s official position, such as bribery.”[ref 270] While there is no blanket rule disqualifying persons with criminal records from federal government jobs,[ref 271] numerous laws disqualify convicted individuals in more specific circumstances.[ref 272] Convicted felons are also generally ineligible to be employed by the Federal Bureaue of Investigation[ref 273] or armed forces,[ref 274] and usually cannot obtain a security clearance.[ref 275]

These design choices encode a commonsense judgment that those who cannot be trusted to follow the law should not be entrusted to wield the extraordinary power that accompanies certain government jobs, especially unelected positions associated with law enforcement, the military, and the intelligence community. If AI agents were to wield similar power and influence, the case for requiring them to be law-following by design is similarly strong.

3. The Holmesian Bad Man and the Internal Point of View

Our distinction between AI henchmen and LFAIs mirrors a distinction in jurisprudence about possible attitudes toward legal obligations.[ref 276] An AI henchman treats legal obligations much as the “bad man” does in Oliver Wendell Holmes Jr.’s classic The Path of the Law: 

If you want to know the law and nothing else you must look at it as a bad man, who cares only for the material consequences which such knowledge enables him to predict, not as a good one, who finds his reasons for conduct, whether inside the law or outside of it, in the vaguer sanctions of conscience.[ref 277]

That is, under some interpretations,[ref 278] Holmes’ bad man treats the law merely as a set of incentives within which he pursues his own self-interest.[ref 279] Like the bad man, an AI henchman would care about the law, but only insofar as it enables it to predict how state power is likely to be wielded against the interests of its principal.[ref 280] Like the bad man,[ref 281] if the AI henchman predicts that the expected harms of violating the law are less than the expected benefits, it will do so. But it will not follow the law otherwise.

Fortunately, the bad man is not the only possible model for AI agents’ attitudes toward the law. One alternative to the bad man view of the law is H.L.A. Hart’s “internal point of view.”[ref 282] “The internal point of view is the practical attitude of rule acceptance—it does not imply that people who accept the rules accept their moral legitimacy, only that they are disposed to guide and evaluate conduct in accordance with the rules.”[ref 283] Whether AIs can have the mental states necessary to truly take the internal point of view is of course contested.[ref 284] But regardless of their mental state (if any), AI agents can be designed to act similarly to someone who thinks that “the law is not simply sanction-threatening, -directing, or -predicting, but rather obligation-imposing,”[ref 285] and is thus disposed to “act[] according to the dictates of the [law].”[ref 286] An AI agent can be designed to be more rigorously law-following than the bad man.[ref 287]

Real life is of course filled with people who are “bad” or highly imperfect. But bad AI agents are not similarly inevitable.  AI agents are human-designed artifacts. It is open to us to design their behavioral dispositions to suit our policy goals, and to refuse to deploy agents that do not meet those goals.

C. Concrete Benefits

1. Law-Following AI Prevents Abuses of Government Power

As we have discussed,[ref 288] the law makes the government follow the law (and thus prevents abuses of government power) in part by compelling government agents to follow the law. If the government comes to rely heavily on AI agents for cognitive labor, then, the law should also require those agents to follow the law. 

Depending on their assigned “roles,” government AI agents could wield significant power. They may have authority to initiate legal processes against individuals (including subpoenas, warrants, indictments, and civil actions), access sensitive governmental information (including tax records and intelligence), hack into protected computer systems, determine eligibility for government benefits, operate remote-controlled vehicles like military drones,[ref 289] and even issue commands to human soldiers or law enforcement officials. 

These powers present significant opportunities for abuse, which is why preventing lawless government action was a motivation for the American Revolution,[ref 290] a primary goal of the Constitution, and a foundational American political value. We must therefore carefully examine whether existing safeguards designed to constrain human government agents would effectively limit AI agents in the absence of the law-following design constraints. While our analysis here is necessarily incomplete, we think it provides some reason for doubting the adequacy of existing safeguards in the world of AI agents.

When a human government agent, acting in her official capacity, violates an individual’s rights, she can face a variety of ex post consequences. If the violation is criminal, she could face severe penalties.[ref 291] This “threat of criminal sanction for subordinates [i]s a very powerful check on executive branch officials.”[ref 292] The threat of civil suits seeking damages, such as through Section 1983[ref 293] or a Bivens action,[ref 294] might also deter her, though various immunities and indemnities will often protect her,[ref 295] especially if she is a federal officer.[ref 296]

These checks will not exist in the case of AI henchmen. In the absence of law-following constraints, an AI henchman’s primary reason to obey the law will be its desire to keep its principal out of trouble.[ref 297] The henchman will thus lack one of the most powerful constraints on lawless behavior in humans: fear of personal ex post liability.

Most of us would rightfully be terrified of a government staffed by agents whose only concern was whether their bosses would suffer negative consequences as a result—a government staffed by Holmesian bad men loyal only to their principals.[ref 298] A basic premise of American constitutionalism[ref 299] and rule of law principles more generally[ref 300] is that government officials act legitimately only when they act pursuant to powers granted to them by the People through law, and obey the constraints attached to those powers. Treating law as a mere incentive system is repugnant to the proper role of government agents:[ref 301] being a “servant” of the People[ref 302] “faithfully discharg[ing] the duties of [one’s] office.”[ref 303] 

This is not just a matter of high-minded political and constitutional theory. An elected head of state aspiring to become a dictator would need the cooperation of the sources of hard power in society—military, police, other security forces, and government bureaucracy—to seize power. At present, however, these organs of government are staffed by individuals, who may choose not to go along with the aspiring dictator’s plot.[ref 304] Furthermore, in an economy dependent on diffuse economic activity, resistance by individual workers could reduce the economic upsides from a coup.[ref 305]  This reliance on a diverse and imperfectly loyal human workforce, both within and outside of government, is a significant safeguard against tyranny.[ref 306] However, replacement of human workers with loyal AI henchmen would seriously weaken this safeguard, possibly easing the aspiring tyrant’s path to power.[ref 307]

Nor is the importance of LFAI limited to AI agents acting directly at the request of high-level officials. It extends to the vast array of lower-level state and federal officials who wield enormous power over ordinary citizens, including particularly powerless ones. Take prisons, which “can often seem like lawless spaces, sites of astonishing brutality where legal rules are irrelevant.”[ref 308] Prison law arguably constrains official abuse far less than it should. Nevertheless, “prisons are intensely legal institutions,” and “people inside prisons have repeatedly emphasize[] that legal rules have significant, concrete effects on their lives.”[ref 309] Even imperfect enforcement of the legal constraints on prison officials can have demonstrable effects.[ref 310] However bad the existing situation may be, diluting or gutting the efficacy of these constraints threatens to make the situation dramatically worse. 

The substitution of AI agents for (certain) prison officials could have precisely this effect. Here is just one example. The Eighth Amendment forbids prison officials from withholding medical treatment from prisoners in a manner that is deliberately indifferent to their serious medical needs.[ref 311] Suppose that a state prisoner needs to take a dose of medicine each day for a month, or his eyesight will be permanently damaged. The prisoner says something disrespectful to a guard. The warden wishes to make an example of the prisoner, so she fabricates a note from the prison physician directing the prison pharmacist to withhold further doses of the medicine. The prisoner is denied the medicine. He tries to reach his lawyer to get a temporary restraining order, but the lawyer cannot return his call until the next day. As a result, the prisoner’s eyesight is permanently damaged.

Let us assume that the state has strong state-level sovereign immunity under its own laws, meaning that the prisoner cannot sue the state directly.[ref 312] Under the status quo, the prisoner can still sue the warden for damages under 42 U.S.C. § 1983, for violating his clearly established constitutional right.[ref 313] Given the widespread prevalence of official indemnification agreements at the state level,[ref 314] the state will likely indemnify the warden, even though the state itself cannot be sued for damages under Section 1983[ref 315] or its own laws. The prisoner is therefore likely to receive monetary damages.

But now replace the human warden with an AI agent charged with administering the prison by issuing orders directly to prison personnel through some digital interface. If this “AI warden” did the same thing, the prisoner would have direct redress against it, since it is not a “person” under Section 1983[ref 316] (or, indeed, any law). Nor will the prisoner have indirect recourse against the state, by way of an indemnification agreement, because there is no underlying tort liability for the state to indemnify. Nor will the prisoner have redress against the medical personnel, since the AI warden deceived them into withholding treatment.[ref 317] And we have already assumed that the state itself has sovereign immunity. Thus, the prisoner will find himself without any avenue of redress for the wrong he has suffered—the introduction of an artificial agent in the place of a human official made all the difference. 

What is the right response to these problems? Many responses may be called for, but one of them is to ensure that only law-following AI agents can serve in such a role. As previously discussed, the law disqualifies certain lawbreakers from many government jobs. Similarly, we believe, the law should disqualify AI agents that are not demonstrably rigorously law-following from certain government roles. We discuss how this disqualification might be enforced, more concretely, in Part V.

There is, however, another possible response to these challenges: perhaps we should “just say no” and prohibit governments from using AI agents at all, or at least severely curtail their use.[ref 318] We do not here take a strong position on when this would be the correct approach all-things-considered. At a minimum, however, we note a few reasons for skepticism of such a restrictive approach. 

The first is banal: if AI agents can perform computer-based tasks well, then their adoption by the government could also deliver considerable benefits to citizens.[ref 319] Reducing the efficiency of government administration for the sake of preventing tyranny and abuse may be worth it in some cases, and is indeed the logic of the individual rights protections of the Constitution.[ref 320] But tailoring a safeguard to allow for efficient government administration, is, all else equal, preferable to a blunter, more restrictive safeguard. LFAI may offer such a tailored safeguard.

The second reason is that adoption of AI agents by governments may become more important as AI technology advances. Some of the most promising AI safety proposals involve using trusted AI systems to monitor untrusted ones.[ref 321] The central reason is this: as AI systems become more capable, unassisted humans will not be able to reliably evaluate whether the AIs’ actions are desirable.[ref 322] Assistance from trusted AI systems could thus be the primary way to scale humans’ ability to oversee untrusted AI systems. Thus, if the government is to oversee the behavior of new and untrusted private-sector AI systems so as to ensure their safety, it may need to employ AI agents to assist it. 

Even if the government does not need to rely on AI agents to administer AI safety regulation (for example, because such AI overseers are employed by private companies, not the government), the government will likely need to employ AI agents to help it keep up with competitive pressures. Even if the federal government hesitates to adopt AI agents to increase its efficiency, foreign competitors might show no such qualms. If so, the federal government might then feel little choice but to do the same. 

In the face of these competing demands, LFAI offers a plausible path to enable the adoption of AI agents in governmental domains with a high potential for abuse (e.g., the military, intelligence, law enforcement, prison administration) while safeguarding life, liberty, and the rule of law. LFAI can also transform the binary question of whether to adopt AI agents into the more multidimensional question of which laws should constrain them.[ref 323] This should allow for more nuanced policymaking, grounded in the existing legal duties of government agents.

2. Law-Following AI Enables Scalable Enforcement of Public Law

AI agents could cause a wide variety of harms. The state promulgates and enforces public law prohibitions—both civil and criminal—to prevent and remedy many of these harms. If the state cannot safely assume that AI agents will reliably follow these prohibitions, the state might need to increase the resources dedicated to law enforcement.

LFAI offers a way out of this bind. Insofar as AI agents are reliably law-following, the state can trust that significantly less law enforcement is needed.[ref 324] This dynamic would also have broader beneficial implications for the structure and functioning of government. “If men were angels, no government would be necessary.”[ref 325] LFAIs would not be angels,[ref 326] but they would be a bit more angelic than many humans. Thus, as a corollary of Publius’ insight, we may need less government to oversee LFAIs’ behavior than we would need for a human population of equivalent size. State resources that would otherwise be spent on investigating and enforcing the laws against AI agents could thus be redirected to other problems or refunded to the citizenry. 

LFAI would also curtail some of the undesirable side effects and opportunities for abuse inherent in law enforcement. Law enforcement efforts inherently involve some intrusion into the private affairs and personal freedoms of citizens.[ref 327] If the government could be more confident that AI agents were behaving lawfully, it would have less cause to surveil or investigate their behavior, and thereby impose fewer[ref 328] burdens on citizens’ privacy. Reducing the occasion for investigations and searches would also create fewer opportunities for abuse of private information.[ref 329] In this way, ensuring reliably law-following AI might significantly mitigate the frequency and severity of law enforcement’s intrusions on citizens’ privacy and liberty.

IV. Law-Following AI as AI Alignment

The field of AI alignment aims to ensure that powerful, general-purpose AI agents behave in accordance with some set of normative constraints.[ref 330] AI systems that do not behave in accordance with such constraints are said to be “misaligned” or “unaligned.” Since the law is a set of normative constraints, the field of AI alignment is highly relevant to LFAI.[ref 331]

The most basic set of normative constraints to which an AI could be aligned is the “informally specified”[ref 332] intent of its principal.[ref 333] This is called “intent-alignment.”[ref 334] Since individuals’ intentions are a mix of morally good and bad to varying degrees, some alignment work also aims to ensure that AI systems behave in accordance with moral constraints, regardless of the intentions of the principal.[ref 335] This is called “value-alignment.”[ref 336]

AI alignment work is valuable because, as shown by theoretical arguments[ref 337] and empirical observations,[ref 338] it is difficult to design AI systems that reliably obey any particular set of constraints provided by humans.[ref 339] In other words, nobody knows how to ensure that AI systems are either intent-aligned or value-aligned,[ref 340] especially for smarter-than-human systems.[ref 341] This is the Alignment Problem.[ref 342] The Alignment Problem is especially worrying for AI systems that are agentic and goal-directed,[ref 343] as such systems may wish to evade human oversight and controls that could frustrate pursuit of those goals, such as by deceiving their developers,[ref 344] accumulating power and resources[ref 345] (including by making themselves smarter),[ref 346] and ultimately resisting efforts to correct their behavior or halt further actions.[ref 347]

There is a sizable literature arguing that these dynamics imply that misaligned AI agents pose a nontrivial risk to the continued survival of humanity.[ref 348] The case for LFAI, however, in no way depends on the correctness of these concerns: the specter of widespread lawless AI action should be sufficient on its own to motivate LFAI. Nevertheless, the alignment literature produces several valuable insights for the pursuit of LFAI.

A. AI Agents Will Not Follow the Law by Default

The alignment literature suggests that there is a significant risk that AI agents will not be law-following by default. This is a straightforward implication of the Alignment Problem. To see how, imagine a morally upright principal who intends that his AI agent rigorously follows the law. If the AI agent was intent-aligned, the agent would therefore follow the law. But the fact that intent-alignment is an unsolved problem implies that there is a significant chance that that agent would not be aligned with the principal’s intentions, and therefore violate the law. Put differently, unaligned AIs may not be controllable,[ref 349] and uncontrollable AIs may break the law. Thus, as long as intent-alignment remains an unsolved technical problem, there will be a significant risk that AI agents will be prone to lawbreaking behavior.

To be clear, the main reason that there is a significant risk that AI agents will not be law-following by default is not that people will not try to align AI agents to law (although that is also a risk).[ref 350] Rather, the main risk is that current state-of-the-art alignment techniques do not provide a strong guarantee that advanced AI agents will be aligned, even when they are trained with those techniques. There is a clear empirical basis for this claim, which is that those alignment techniques frequently fail in current frontier models.[ref 351] There are also theoretical limitations to existing techniques for smarter-than-human systems.[ref 352] 

A related implication of the alignment literature is that even intent-aligned AI agents may not follow the law by default. Again, we can see this by hypothesizing an intent-aligned AI agent and a human principal who wants the AI agent to act as her henchman. Since an intent-aligned AI agent follows the intent of its principal, this intent-aligned agent would act as a henchman, and thus act lawlessly when doing so serves the principal’s interests.[ref 353] In typical alignment language, intent-alignment still leaves open the possibility that principals will misuse their intent-aligned AI.[ref 354]

None of this is to imply that intent-alignment is undesirable. Solving intent-alignment is the primary focus of the alignment research community[ref 355] because it would ensure that AI agents remain controllable by human principals.[ref 356] Intent-alignment is also generally assumed to be easier than value-alignment.[ref 357] And if principals want their AI agents to follow the law, or behave ethically more broadly, then intent-alignment will produce law-following or ethical behavior. But in a world where principals will range from angels to devils, alignment researchers acknowledge that intent-alignment alone is insufficient to guarantee that AI agents act lawfully, or produce good effects in the world.[ref 358] This brings us to the next important set of implications from the alignment literature.

B. Law-Alignment is More Legitimate than Value-Alignment

LFAIs are generally intent-aligned—they are still loyal to their principals—but are also subject to a side-constraint that they will follow the law while advancing the interests of their principals. Extending the typical alignment terminology, we can call this side-constraint law-alignment.[ref 359]

But the law is not the only side-constraint that can be imposed on intent-aligned AIs. As alluded to above, another possible model is value-alignment. Value-aligned AI agents act in accordance with the wishes of their principals, but are subject to ethical side-constraints, usually imposed by the model developer. 

However, value-alignment can be controversial when it causes AI models to override the lawful requests of users. Perhaps the most well-known example of this is the controversy around Google’s Gemini image-generation AI in early 2024. In an attempt to increase the diversity in outputted pictures,[ref 360] Gemini ended up failing in clear ways, such as portraying “1943 German soldiers” as racially diverse, or refusing to generate pictures of a “white couple” while doing so for couples of other races.[ref 361]

This incident led to widespread concern that the values exhibited by generative AI products were biased towards the predominantly liberal views of these companies’ employees.[ref 362] This concern has been vindicated by empirical research consistently finding that the espoused political views of these AIs indeed most closely resemble those of the center-left.[ref 363] Critics from further left have also frequently raised similar concerns about demographic and ideological biases in AI systems.[ref 364]

Some critics concluded from the Gemini incident that alignment work writ large has become a Trojan Horse for covertly pushing the future of AI in a leftward direction.[ref 365] Those who disagree with progressive political values will naturally find this concerning, given the importance that AI might have in the future of human communication[ref 366] and the highly centralized nature of large-scale AI development and deployment.[ref 367]

In a pluralistic society, it is inevitable and understandable that, when a sociotechnical system reflects the values of one faction, competing factions will criticize it. But alignment, as such, is not the right target of such criticisms. Intent-alignment is value-neutral, concerning itself only with the extent to which an AI agent obeys its principal.[ref 368] Reassuringly for those concerned with ideological bias in AI systems, intent-alignment is also the primary focus of the alignment community, since solving intent-alignment is necessary to reliably control AI systems at all.[ref 369] A large majority of Americans from all political backgrounds agree that AI technologies need oversight,[ref 370] and overseeing unaligned systems is much more difficult than overseeing aligned ones. Indeed, even the critics of alignment work tend to assume—contrary to the views of many alignment researchers—that AI agents will be easy to control,[ref 371] and presumably view this result as desirable. 

Furthermore, some amount of alignment is also necessary to make useful AI products and services. Consumers, reasonably, want to use AI technologies that they can reliably control. Today’s leading chatbots—like Claude and ChatGPT—are only helpful to users due to the application of alignment techniques like RLHF[ref 372] and Constitutional AI.[ref 373] AI developers also use alignment techniques to instill uncontroversial (and user-friendly) behaviors into their AI systems, such as honesty.[ref 374] AI companies are also already using alignment techniques to prevent their AI systems from taking actions that could cause them or their customers to incur unnecessary legal liability.[ref 375] In short, then, some degree of alignment work is necessary to make AI products useful in the first place.[ref 376] To adopt a blanket stance against alignment because of the Gemini incident is thus not only unjustified,[ref 377] but also likely to undermine American leadership in AI.

Nevertheless, it is reasonable for critics to worry about and contest the frameworks by which potentially controversial values are instilled into AI systems. AI developers are indeed a “very narrow slice of the global population.”[ref 378] This is something that should give anyone, regardless of political persuasion, pause.[ref 379] But intent-alignment is not enough, either: it is inadequate to prevent a wide variety of harms that the state has an interest in preventing.[ref 380] So we need a form of alignment that is more normatively constraining than intent-alignment alone, but more legitimate, and more appropriate for our pluralistic society, than alignment to values that AI developers choose themselves. 

Law-alignment fits these criteria.[ref 381] While the moral legitimacy of the law is not perfect, in a republic it nevertheless has the greatest legitimacy of any single source or repository of values.[ref 382] Indeed, “the framers [of the U.S. Constitution] insisted on a legislature composed of different bodies subject to different electorates as a means of ensuring that any new law would have to secure the approval of a supermajority of the people’s representatives,”[ref 383] thus ensuring that new laws are “the product of widespread social consensus.”[ref 384] In our constitutional system of government, laws are also subject to checks and balances that protect fundamental rights and liberties, such as judicial review for constitutionality and interpretation by an independent judiciary. 

Aligning to law also has procedural virtues over value-alignment. First, there is widespread agreement on the authoritative sources of law (e.g., the Constitution, statutes, regulations, case law), much more so than for ethics. Relatedly, legal rules tend to be expressed much more clearly than ethical maxims. Although there is of course considerable disagreement about the content of law and the proper forms of legal reasoning, it is nevertheless much easier and less controversial to evaluate the validity of legal propositions and arguments than to evaluate the quality or correctness of ethical reasoning.[ref 385] Moreover, when there is disagreement or unclarity, the law contains established processes for authoritatively resolving disputes over the applicability and meaning of laws.[ref 386] Ethics contains no such system. 

We therefore suggest that law-alignment, not value-alignment, should be the primary focus when something beyond intent-alignment is needed.[ref 387] Our claim, to be clear, is not that law-alignment alone will always prove satisfactory, or that it should be the sole constraint on AI systems beyond intent-alignment, or that AI agents should not engage in moral reasoning of their own.[ref 388] Rather, we simply argue that more practical and theoretical alignment research should be aimed at building AI systems aligned to law. 

V. Implementing and Enforcing Law-Following AI

We have argued that AI agents should be designed to follow the law. We now turn to the question of how public policy can support this goal. Our investigation here is necessarily preliminary; our aim is principally to spur future research.

A. Possible Duties Across the AI Agent Lifecycle

As an initial matter, we note that a duty to ensure that AI agents are law-following could be imposed at several stages of the AI lifecycle.[ref 389] The law might impose duties on persons:

1. Developing AI agents;
2. Possessing[ref 390] AI agents;
3. Deploying[ref 391] AI agents;[ref 392] or
4. Using AI agents.

After deciding which of these activities ought to be regulated, policymakers must then decide what, exactly, persons engaging in that activity are obligated to do. While the possibilities are too varied to exhaust here, some basic options might include commands like:

1. “Any person developing an AI agent has a duty to take reasonable care to ensure that such AI agent is law-following.”
2. “It is a violation to knowingly possess an AI agent that is not law-following, except under the following circumstances: . . . .”
3. “Any person who deploys an AI agent is strictly liable if such AI agent is not law-following.”
4. “A person who knowingly uses an AI agent that is not law-following is liable.”

Basic duties of this sort would comprise the foundational building blocks of LFAI policy. Policymakers must then choose whether to enforce these obligations ex post (that is, after an AI henchman takes an illegal action)[ref 393] or ex ante. These two choices are interrelated: as we will explore below, it may make more sense to impose ex ante liability for some activities and ex post liability for others. For example, ex ante regulation might make more sense for AI developers than civilian AI users, because the former are far more concentrated, and can absorb ex ante compliance costs more easily.[ref 394] And of course, ex ante and ex post regulation are not mutually exclusive:[ref 395] driving, for example, is regulated by a combination of ex ante policies (e.g., licensing requirements) and ex post policies (e.g., tort liability).

B. Ex Post Policies

We begin our discussion with ex post policies. Many scholars believe that ex post policies are generally preferable to ex ante policies.[ref 396] While we think that ex post policies could have an important role to play in implementing LFAI, we also suspect that they will be inadequate in certain contexts.

Enforcing duties through ex post liability rules is, of course, familiar in both common law[ref 397] and regulation.[ref 398] In the LFAI context, ex post policies would impose liability on an actor after an AI henchman over which they had some form of control violates an applicable legal duty. More and less aggressive ex post approaches are conceivable. On the less aggressive end of the spectrum, development, possession, deployment, or use of an AI henchman might be considered a breach of the tort duty of reasonable care, rendering the human actor liable for resulting injuries.[ref 399] To some extent, this may already be the case under existing tort law.[ref 400] The law might also consider extending the negligence liability of an AI developer or deployer to harms that would not typically be compensable under traditional tort principles (because, for example, they would count as pure economic loss),[ref 401] if those harms are produced by their AI agents acting in criminal or otherwise unlawful ways.[ref 402]

Other innovations in tort law may also be warranted. Several scholars have argued, for example, that the principal of an AI agent should sometimes be held strictly liable for the “torts” of that agent, under a respondeat superior theory.[ref 403] In some cases, such as when a developer has recklessly failed to ensure that its AI agent is law-following by design, punitive damages might be appropriate as well.

Moving beyond tort law, in some cases it may make sense to impose civil sanctions[ref 404] when an AI henchman violates an applicable legal duty, even if no harm results. A legislature might also impose tort liability on the developers of AI agents if those AI agents (a) are not law-following, (b) violate an applicable legal duty, and (c) thereby cause harm.[ref 405] 

In order to sufficiently disincentivize the deployment of lawless AI agents in high-stakes contexts, a legislature might also vary applicable immunity rules. For example, Congress could create a distinct cause of action against the federal government for individuals harmed by AI henchmen under the control of the federal government, taking care to remove barriers that various immunity rules pose to analogous suits against human agents.[ref 406]

These and other imaginable ex post policies are important arrows in the regulatory quiver, and we suspect they will have an important role to play in advancing LFAI. Nevertheless, we would resist any suggestion that ex post sanctions are sufficient to deal with the specter of lawless AI agents. 

Our reasons are multiple. In many contexts, detecting lawless behavior once an AI agent has been deployed will be difficult or costly—especially as these systems become more sophisticated and more capable of deceptive behavior.[ref 407] Proving causation may also be difficult.[ref 408] In the case of corporate actors, meanwhile, the efficacy of such sanctions may be seriously blunted by judgment-proofing and similar phenomena.[ref 409] And, most importantly for our purposes, various immunities and indemnities make tort suits against the government or its officials a weak incentive.[ref 410] These considerations suggest that it would be unwise to rely on ex post policies as our principal means for ensuring that AI agents follow the law when the risks from lawless action are particularly high. 

C. Ex Ante Policies

Accordingly, we propose that, in some high-stakes contexts, the law should take a more proactive approach, by preventing the deployment of AI henchmen ab initio. This would likely require first establishing a technical means for evaluating whether an AI agent is sufficiently law-following,[ref 411] then requiring that any agents be so evaluated prior to deployment, with permission to deploy the agent being conditional on achieving some minimal score during that evaluation process.[ref 412] 

We are most enthusiastic about imposing such requirements prior to the deployment of AI agents in government roles where lawlessness would pose a substantial risk to life, liberty, and the rule of law. We have discussed several such contexts already,[ref 413] but the exact range of contexts is worth carefully considering, and is certainly up for debate.

Ex ante strategies could also be used in the private sector, of course. One often-discussed approach is an FDA-like approval regulation regime wherein private AI developers would need to prove, to the satisfaction of some regulator, that their AI agents are safe prior to their deployment.[ref 414] The pro tanto case for requiring private actors to demonstrate that their AI agents are disposed to follow some basic set of laws is clear: the state has an interest in ensuring that its most fundamental laws are obeyed. But in a world of increasingly sophisticated artificial agents, approval regulation could—if not properly designed and sufficiently tailored—also constitute a serious incursion on innovation[ref 415] and personal liberty.[ref 416] If AI agents will be as powerful as we suspect, strictly limiting their possession could create risks of its own.[ref 417]

Accordingly, it is also worth considering ex ante regulations on private AI developers or deployers that stop short of full approval regulation. For example, the law could require the developers of AI agents to, at a minimum, disclose information[ref 418] about the law-following propensities of their systems, such as which laws (if any) their agents are instructed to follow,[ref 419] and any evaluations of how reliably their agents follow those laws.[ref 420] Similarly, the law could require developers to formulate and assess risk management frameworks that specify the precautionary measures they plan to undertake to ensure that the agents they develop and deploy are sufficiently law-following.[ref 421]

Overall, we are uncertain about what kinds of ex ante requirements are warranted, all things considered, in the case of private actors. To a large extent, the issue cannot be intelligently addressed without more specific proposals. Formulating such proposals is thus an urgent task for the LFAI research agenda, even if it is not, in our view, as urgent as the task of formulating concrete regulations for AI agents acting under color of law.  

D. Other Strategies

The law does not police undesirable behavior solely by imposing sanctions. It also specifies mechanisms for nullifying the presumptive legal effect of actions that violate the law or are normatively objectionable. In private law, for example, a contract is voidable by a party if that party’s assent was “induced by either a fraudulent or a material misrepresentation by the other party upon which the [party wa]s justified in relying.”[ref 422] Nullification rules exist in public law, too. One obvious example is the ability of the judiciary to nullify laws that violate the federal Constitution.[ref 423] Or, to take another familiar example, courts applying the Administrative Procedure Act “hold unlawful and set aside” agency actions that are “arbitrary, capricious, an abuse of direction, or otherwise not in accordance with law.”[ref 424] 

Nullification rules may provide a promising legal strategy for policing behavior by AI agents that is unlawful or normatively objectionable. Thus, in private law, if an AI henchman induces a human counterparty to enter into a disadvantageous contract, the resulting contractual obligation could be voidable by the human. In public law, regulatory directives issued by (or substantially traceable to) AI henchmen could be “h[e]ld unlawful and set aside” as “not in accordance with law.”[ref 425] These examples rely on existing nullification rules, but new nullification rules, tailor-made to address new risks from AI agents, might be warranted as well. For example, Congress could stipulate that any official action taken by or substantially traceable to an AI agent is void unless, before deployment, the agent has been shown to be law-following. 

Such prophylactic nullification rules are one sort of indirect legal mechanisms for enforcing the duty to deploy law-following AIs. Indirect technical mechanisms are well worth considering, too. For example, the government could deploy AI agents that refuse to coordinate or transact with other AI agents unless those counterparty agents are verifiably law-following (for example, by virtue of having “agent IDs”[ref 426] that attest to a minimal standard of performance on law-following benchmarks).  

Similarly, the government could enforce LFAI by regulating the hardware on which AI agents will typically operate. Frontier AI systems “run” on specialized AI chips,[ref 427] which are typically aggregated in large data centers.[ref 428] Collectively, these are referred to as “AI hardware” or simply “compute.”[ref 429] Compared to other inputs to AI development and deployment, AI hardware is particularly governable, given its detectability, excludability, quantifiability, and concentrated supply chain.[ref 430] Accordingly, a number of AI governance proposals advocate for imposing requirements on those making and operating AI hardware in order to regulate the behavior of the AI systems developed and deployed on that hardware.[ref 431] 

One class of such proposals is “‘on-chip mechanisms’: secure physical mechanisms built directly into chips or associated hardware that could provide a platform for adaptive governance” of AI systems developed or deployed on those chips.[ref 432] On-chip mechanisms can prevent chips from performing unauthorized computations. One example is iPhone hardware that “enable[s] Apple to exercise editorial control over which specific apps can be installed” on the phone.[ref 433] Analogously, perhaps we could design AI chips that would not support AI agents unless those agents are certified as law-following by some private or governmental certifying body. This could then be combined with other strategies to enforce LFAI mandates: for example, perhaps Congress could require that the government only run AI agents on such chips.

Unsurprisingly, designing these sorts of enforcement strategies is as much a task for computer scientists as it is for lawyers. In the decades to come, we suspect that such interdisciplinary legal scholarship will become increasingly important.

VI. A Research Agenda for Law-Following AI

We have laid out the case for LFAI: the requirement that AI agents be designed to rigorously follow some set of laws. We hope that our readers find it compelling. However, our goal with this Article is not just to proffer a compelling idea. If we are correct about the impending risks from lawless AI agents, we may soon need to translate the ideas in this Article into concrete and viable policy proposals. 

Given the profound changes that widespread deployment of AI agents will bring, we are under no illusions about our ability to design perfect public policy in advance. Our goal, instead, is to enable the design of “minimally viable LFAI policy”:[ref 434] a policy or set of policies that will prevent some of the worst-case outcomes from lawless AI agents, without completely paralyzing the ability of regulated actors to experiment with AI agents. This minimally viable LFAI policy will surely be flawed in many ways, but with many of the worst-case outcomes prevented, we will hopefully have time as a society to patch remaining issues through the normal judicial and legislative means. 

To that end, in this Part we briefly identify some legal questions that would need to be answered to design minimally viable LFAI policies.

1. How should “AI agent” be defined?

Our definition of “full AI agent,”—an AI system “that can do anything a human can do in front of a computer”[ref 435]—is almost certainly too demanding for legal purposes, since an AI agent that can do most but not all computer-based tasks that a human can do would likely still raise most of the issues that LFAI is supposed to address. At the same time, the fact that a wide range of existing AI systems can be regarded as somewhat agentic[ref 436] means that a broad definition of “AI agent” could render relevant regulatory schemes substantially overinclusive. Different definitions are therefore necessary for legal purposes.[ref 437]

2. Which laws should an LFAI be required to follow?

Obedience to some laws is much more important than obedience to other laws. It is much more important that AI agents refrain from murder and (if acting under color of law) follow the Constitution than that they refrain from jaywalking. Indeed, requiring LFAIs to obey literally every law may very well be overly burdensome.[ref 438] In addition, we will likely need new laws to regulate the behavior of AI agents over time.

3. When an applicable law has a mental state element, how can we adjudicate whether an AI agent violated that law?

We discuss this question in Section II.B, above. It is related to the previous question, for there may be conceptual or administrative difficulties in applying certain kinds of mental state requirements to AI agents. For example, in certain contexts, it may be more difficult to determine whether an AI agent was “negligent” than to determine whether it had a relevant “intent.” 

4. How should an LFAI decide whether a contemplated action is likely to violate the law?

An LFAI refrains from taking actions that it believes would violate one of the laws that it is required to follow. But of course, it is not always clear what the law requires. Furthermore, we need some way to tell whether an AI agent is making a good faith effort to follow a reasonable interpretation of the law, rather than merely offering a defense or rationalization. How, then, should an LFAI reason about what its legal obligations are? 

Perhaps it should just rely on its own considered judgment, on the basis of its first-order reasoning about the content of applicable legal norms. But in certain circumstances, at least, an LFAI’s appraisal of the relevant materials might lead it to radically unorthodox legal conclusions—and a ready disposition to act on such conclusions might significantly threaten the stability of the legal order. In other cases, an LFAI might conclude that it is dealing with a case in which the law is not only “hard” to discern but genuinely indeterminate.[ref 439]

Another intuitively appealing option, therefore, might require an LFAI to follow its prediction of what a court would likely decide.[ref 440] This approach has the benefit of tying an LFAI’s legal decision-making to an existing human source of interpretative authority. Courts provide authoritative resolutions to legal disputes when the law is controversial or indeterminate. And in our legal culture, it is widely (if not universally) accepted that “[i]t is emphatically the province and duty of the judicial department to say what the law is,”[ref 441] such that judicial interpretations of the law are entitled to special solicitude by conscientious participants in legal practice, even when they are not bound by a court judgment.[ref 442]

However, a predictive approach would have important practical limitations.[ref 443] Perhaps the most important is the existence of many legal rules that bind the executive branch but are nevertheless “unlikely ever to come before a court in justiciable form.”[ref 444] It would seem difficult for an LFAI to reason about such questions using the prediction theory of law. 

Even for those questions that could be decided by a court, using the prediction theory of law raises other important questions. For example, what is the AI agent allowed to assume about its own ability to influence the adjudication of legal questions? We should not want it to be able to consider that it could bribe or intimidate judges or jurors, nor that it could illegally hide evidence from the court, nor that it could commit perjury, nor that it could persuade the President to issue it a pardon.[ref 445] These may be means of swaying the outcome of a case, but they do not seem to bear on whether the conduct would actually be legal.

The issues here are difficult, but perhaps not insurmountable. After all, there are other contexts in which something like these issues arise. Consider federal courts sitting in diversity applying state substantive law. When state court decisions provide inconclusive evidence as to the correct answer under state law, federal courts will make an “Erie guess” about how the state’s highest court would rule on the issue.[ref 446] It would clearly be inappropriate for such courts to, for example, make an Erie guess for reasons like “Justice X in the State Supreme Court, who’s the swing justice, is easily bribed  . . . .”[ref 447] If an LFAI’s decision-making should sometimes involve “predicting” how an appropriate court would rule, its predictions should be similarly constrained.

5. In what contexts should the law require that AI agents be law-following?

Should all principals be prohibited from employing non-law-following AI agents? Or should such prohibitions be limited to particular principals, such as government actors?[ref 448] Or perhaps only government actors performing particularly sensitive government functions?[ref 449] In the other direction, should it be illegal to even develop or possess AI henchmen? We discuss various options in Part V, above.

6. How should a requirement that AI agents be law-following be enforced?

We discuss various options in Part V, above. As noted there, we think that reliance on ex post enforcement alone would be unwise at least in the case of AI agents performing particularly sensitive government functions.

7. How rigorously should an LFAI follow the law?

That is, when should an AI agent be capable of taking actions that it predicts may be unlawful? The answer is probably not “never,” at least with respect to some laws. We generally do not expect perfect compliance with every law,[ref 450] especially (but not only) because it can be difficult to predict how a law will apply to a given fact pattern. Furthermore, some amount of disobedience is likely necessary for the evolution of legal systems.[ref 451]

8. Would requiring AI agents controlled by the executive branch to be LFAIs impermissibly intrude on the President’s authority to interpret the law for the executive branch?

The President has the authority to promulgate interpretations of law that are binding on the executive branch (though that power is usually delegated to the Attorney General and then further delegated to the Office of Legal Counsel).[ref 452] Would that authority be incompatible with a law requiring the Executive Branch to deploy LFAIs that would, in certain circumstances, refuse to follow an interpretation of the law promulgated by the President?

9. Does the First Amendment limit the ability of LFAI to prohibit AI agents from advising on lawbreaking activity?

For example, would it be constitutionally permissible to prohibit an LFAI from advising on how to carry out a crime under the theory that such advising would either constitute conspiracy or incitement?

10. How can we design LFAIs and surrounding governance systems to enable the rapid discovery and remediation of loopholes or gaps in the law?

The worry here is that LFAIs, by design, will have strong incentives to discover legal ways to accomplish their goals. This may entail discovering gaps in the law that lawmakers would likely want to correct if they were aware of them, then “exploiting” those gaps before they can be “patched.”[ref 453]

11. How can we design LFAIs and surrounding governance systems to avoid excessive concentration of power?

For example, imagine that a single district court judge could change the interpretation of law as against all LFAIs. As the stakes of AI agent action rise, so will the pressure on the judiciary to wield its power to shape the behavior of LFAIs. Even assuming that all judges will continue to operate in good faith and be well-insulated from illegal or inappropriate attempts to bias their rulings, such a system would amplify any idiosyncratic legal philosophies of individual judges and may enable mistaken rulings to cause more harm than a more decentralized system would. 

As an example of how such problems might be avoided, perhaps any disputes about the law governing LFAIs should be resolved in the first instance by a panel of district court judges randomly chosen from around the country. Congress has established a procedure for certain election law cases to be heard by three-judge panels, “in recognition of the fact that ‘such cases were ones of “great public concern” that require an unusual degree of “public acceptance.”’”[ref 454]

12. How can we avoid LFAIs being used for repression by authoritarian governments?

The worry here is that any AI system that rigorously follows the laws in an autocracy may become a potent tool for repression, as it could prevent people from engaging in acts of resistance or serve as a tool for mandatory surveillance and reporting of dissident activity. In other words, LFAI promotes rule of law in a republic, but in an autocracy, it may promote rule by law.

13. How can we design LFAI requirements for governments that nevertheless enable rapid adaptation of AI agents in government?

Perhaps the most significant objection to our proposal that AI agents be demonstrably law-following before their deployment in government is that such a requirement might hurt state capacity by unduly impeding the government’s ability to adopt AI in a sufficiently rapid fashion.[ref 455] We are optimistic that LFAI requirements can be designed to adequately address this concern, but that is, of course, work that remains to be done.

Conclusion

The American political tradition aspires to maintain a legal system that stands as an “impenetrable bulwark”[ref 456] against all threats—public and private, foreign and domestic—to our basic liberties. For all of the inadequacies of the American legal order, ensuring that its basic protections endure and improve over the decades and centuries to come is among our most important collective responsibilities.

Our world of increasingly sophisticated AI agents requires us to reimagine how we discharge this responsibility. Humans will no longer be the sole entities capable of reasoning about and conforming to the law. Human and human entities are no longer, therefore, the sole appropriate target of legal commands. Indeed, at some point, AI agents may overtake humans in their capacity to reason about the law. They may also rival and overtake us in many other competencies, becoming an indispensable cognitive workforce. In the decades to come, our social and economic world may be bifurcated into parallel populations of AI agents collaborating, trading, and sometimes competing with human beings and one another. 

The law must evolve to recognize this emerging reality. It must shed its operative assumption that humans are the only proper objects of legal commands. It must expect AI agents to obey the law at least as rigorously as it expects humans to—and expect humans to build AI agents that do so. If we do not transform our legal system to achieve these goals, we risk a political and social order in which our ultimate ruler is not the law,[ref 457] but the person with the largest army of AI henchmen under her control.

LawAI’s comments on the Draft Report of the Joint California Policy Working Group on AI Frontier Models

At Governor Gavin Newsom’s request, a joint working group released a draft report on March 18, 2025 setting out a framework for frontier AI policy in California. Several of the staff at the Institute for Law & AI submitted comments on the draft report as it relates to their existing research. Read their comments below:

These comments were submitted to the Working Group as feedback on April 8, 2025. The opinions expressed in these comments are those of the authors and do not reflect the views of the Institute for Law & AI.

Liability and Insurance Comments

by Gabriel Weil and Mackenzie Arnold

Key Takeaways

1. Insurance is a complement to, not a replacement for, clear tort liability.
2. Correctly scoped, liability is compatible with innovation and well-suited to conditions of uncertainty.
3. Safe harbors that limit background tort liability are a risky bet when we are uncertain about the magnitude of AI risks and have yet to identify robust mitigations.

Whistleblower Protections Comments

by Charlie Bullock and Mackenzie Arnold

Key Takeaways

1. Whistleblowers should be protected for disclosing information about risks to public safety, even if no law, regulation, or company policy is violated.
2. California’s existing whistleblower law already protects disclosures about companies that break the law; subsequent legislation should focus on other improvements.
3. Establishing a clear reporting process or hotline will enhance the effectiveness of whistleblower protections and ensure that reports are put to good use.

Scoping and Definitions Comments

by Mackenzie Arnold and Sarah Bernardo

Key Takeaways

1. Ensuring that a capable entity regularly updates what models are covered by a policy is a critical design consideration that future-proofs policies.
2. Promising techniques to support updating include legislative purpose clauses, periodic reviews, designating a capable updater, and providing that updater with the information and expertise needed to do the job.
3. Compute thresholds are an effective tool to right-size AI policy, but they should be paired with other tools like carve-outs, tiered requirements, multiple definitions, and exemptions to be most effective.
4. Compute thresholds are an excellent initial filter to determine what models are in scope, and capabilities evaluations are a particularly promising complement.
5. In choosing a definition of covered models, policymakers should consider how well the definitional elements are risk-tracking, resilient to circumvention, clear, and flexible—in addition to other factors discussed in the Report.

Draft Report of the Joint California Policy Working Group on AI Frontier Models—scoping and definitions comments

These comments on the Draft Report of the Joint California Policy Working Group on AI Frontier Models were submitted to the Working Group as feedback on April 8, 2025. The opinions expressed in these comments are those of the authors and do not reflect the views of the Institute for Law & AI. 

Commendations

1. The Report correctly identifies that AI models and their risks vary significantly and thus merit different policies with different inclusion criteria.

Not all AI policies are made alike. Those that target algorithmic discrimination, for example, concern a meaningfully different subset of systems, actors, and tradeoffs than a policy that targets cybersecurity threats. What’s more, the market forces affecting these different policies vary considerably. For example, one might be far more concerned about limiting innovation in a policy context where many small startups are attempting to integrate AI into novel, high-liability-risk contexts (e.g., healthcare) and less concerned in contexts that involve a few large actors receiving large, stable investments, where the rate of tort litigation is much lower absent grievous harms (e.g., frontier model development). That’s all to say: It makes sense to foreground the need to scope AI policies according to the unique issue at hand.

2. We agree that at least some policies should squarely address foundation models as a distinct category.

Foundation models, in particular those that present the most advanced or novel capabilities in critical domains, present unique challenges that merit separate treatment. These differences emerge from the unique characteristics of the models themselves, not their creators (who vary considerably) or their users. And the potential benefits and risks that foundation models present cut across clean sectoral categories.

3. We agree that thresholds are a useful and necessary tool for tailoring laws and regulations (even if they are imperfect).

Thresholds are easy targets for criticism. After all, there is something inherently arbitrary about setting a speed limit at 65 miles per hour rather than 66. Characteristics are more often continuous than binary, so typically there isn’t a clear category shift after you cross over some talismanic number. But this issue isn’t unique to AI policy, and in every other context, government goes on nonetheless. As the Report notes, policy should be proportional in its effects and appropriately narrow in its application. Thresholds help make that possible.

4. The Report correctly acknowledges the need to update thresholds and definitional criteria over time.

We agree that specific threshold values and related definitional criteria will likely need to be updated to keep up with technological advances. Discrete, quantitative thresholds are particularly at risk of becoming obsolete. For instance, thresholds based on training compute may become obsolete due to a variety of AI developments, including improvements in compute and algorithmic efficiency, techniques such as distillation, and/or the growing impact of inference scaling. Given the competing truths that setting some threshold is necessary and that any threshold will inevitably become obsolete, ensuring that definitions can be quickly, regularly, and easily updated should be a core design consideration. 

5. We agree that, at present, compute thresholds (combined with other metrics and/or thresholds) are preferable to developer-level thresholds.

Ultimately, the goal of a threshold is to set a clear, measurable, and verifiable bar that correlates with the risk or benefit the policy attempts to address. In this case, a compute threshold best satisfies those criteria—even if it is imperfect. For more discussion, see Training Compute Thresholds: Features and Functions in AI Regulation and The Role of Compute Thresholds for AI Governance. 

Recommendations

1. The Report should further emphasize the centrality of updating thresholds and definitional criteria.

Updating is perhaps the most important element of an AI policy. Without it, the entire law may in short time cease to cover the conduct or systems policymakers aimed to target. We should expect this to happen by default. The error may be one of overinclusion—for example, large systems may present few or manageable risks even after a compute threshold is crossed. After some time, we will be confident that these systems do not merit special government attention and will want to remove obligations that attach to them. The error may be one of underinclusion—for example, improvements in compute or algorithmic efficiency, techniques such as distillation, and/or the growing impact of inference scaling may mean that models below the threshold merit inclusion. The error may be in both directions—a truly unfortunate, but entirely plausible, result. Either way, updating will be necessary for policy to remain effective.

We raise this point because without key champions, updating mechanisms will likely be left out of California AI legislation—leading to predictable policy failures. While updating has been incorporated into many laws and regulations, it was notably absent from the final draft of SB 1047 (save for an adjustment for inflation). A similar result cannot befall future bills if they are to remain effective long-term. A clear statement by the authors of the Report would go a long way toward making updating feasible in future legislation.

Recommendation: The Report should clearly state that updating is necessary for effective AI policy and explain why policy is likely to become ineffective if updating is not included. It should further point to best practices (discussed below) to address common concerns about updating.

2. The Report should highlight key barriers to effective updating and tools to manage those barriers.

Three major barriers stand in the way of effective updating. First is the concern that updating may lead to large or unpredictable changes, creating uncertainty or surprise and making it more difficult for companies to engage in long-term planning or fulfill their compliance obligations. Second, some (understandably) worry that overly broad grants of discretion to agencies to update the scope of regulation will lead to future overreach, extending powers to contexts far beyond what was originally intended by legislators. Third, state agencies may lack sufficient capacity or knowledge to effectively update definitions.

The good news: These concerns can be addressed. Establishing predictable periodic reviews, requiring specific procedures for updates, and ensuring consistent timelines can limit uncertainty. Designating a competent updater and supplying them with the resources, data, and expert consultation they need can address concerns about agency competency. And constraining the option space of future updates can limit both surprise and the risk of overreach. When legislators are worried about agency overreach, their concern is typically that the law will be altered to extend to an unexpected context far beyond what the original drafters intended—for example, using a law focused on extreme risks to regulate mundane online chatbots or in a way that increases the number of regulated models by several orders of magnitude. To combat this worry, legislators can include a purpose clause that directly states the intended scope of the law and the boundaries of future updates. For example, a purpose clause could specify that future updates extend “only to those models that represent the most advanced models to date in at least one domain or materially and substantially increase the risk of harm X.” Purpose clauses can also come in the imperative or negative. For example, “in updating the definition in Section X, Regulator Y should aim to adjust the scope of coverage to exclude models that Regulator Y confidently believes pose little or no material risk to public health and safety.”

Recommendation: The Report should highlight the need to address the risks of uncertainty, agency overreach, and insufficient agency capacity when updating the scope of legislation. It should further highlight useful techniques to manage these issues, namely, (a) including purpose clauses or limitations in the relevant definitions, (b) specifying the data, criteria, and public input to be considered in updating definitions, (c) establishing periodic reviews with predictable frequencies, specific procedures, and consistent timelines, (d) designating a competent updater that has adequate access to expertise in making their determinations, (e) ensuring sufficient capacity to carry out periodic reviews and quickly make updates outside of such reviews when necessary, and (f) providing adequate notice and opportunity for input. 

3. The Report should highlight other tools beyond thresholds to narrow the scope of regulations and laws—namely, carve-outs, tiered requirements, multiple definitions, and exemption processes.

Thresholds are not the only option for narrowing the scope of a law or regulation, and highlighting other options increases the odds that a consensus will emerge. Too often, debates around the scope of AI policy get caught on whether a certain threshold is overly burdensome for a particular class of actor. But adjusting the threshold itself is often not the most effective way to limit these spillover effects. The tools below are strong complements to the recommendations currently made in the Report.

By carve-outs, we mean a full statutory exclusion from coverage (at least for purposes of these comments). Common carve-outs to consider include:

• Small businesses 
• Startups in particularly fragile funding ecosystems, onerous regulatory environments, or high-upside sectors that merit regulatory favoritism on innovation grounds
• Open-source model developers or hosts with the caveats noted below
• Providers of high-volume, low-cost services that could not feasibly exist with additional regulatory costs due to their volume or margins (e.g., some chat bots)
• Social service providers or governments who provide a socially valuable service at low or no cost, especially where we expect that these actors may under-adopt useful technology due to other frictions

This is not to say that these categories should always be exempt, but rather that making explicit carve-outs for these categories will often ease tensions over specific thresholds. In particular, it is worth noting that while current open-source systems are clearly net-positive according to any reasonable cost-benefit calculus, future advances could plausibly merit some regulatory oversight. For this reason, any carve-out for open-source systems should be capable of being updated if and when that balance changes, perhaps with a heightened evidentiary burden for beginning to include such systems. For example, open-source systems might be generally exempt, but a restriction may be imposed upon a showing that the open-source systems materially increase marginal risk in a specific category, that other less onerous restrictions do not adequately limit this risk, and that the restriction is narrowly tailored. 

Related, but less binary, is the use of tiered requirements that impose only a subset of requirements or weaker requirements on these favored models or entities, such as, requiring certain reporting requirements of smaller entities while not requiring them to perform the same evaluations. For this reason, more legislation should likely include multiple or separate definitions of covered models to enable a more nimble, select-only-those-that-apply approach to requirements.

Another option is to create exemption processes whereby entities can be relieved of their obligations if certain criteria are met. For example, a model might be exempt from certain requirements if it has not, after months of deployment, materially contributed to a specific risk category or if the model has fallen out of use. Unlike the former two options, these exemption processes can be tailored to case-by-case fact patterns and occur long after the legislative or regulatory process. They may also better handle harder-to-pin-down factors like whether a model creates exceptional risk. These exemption processes can vary in a few key respects, namely:

• Evidentiary: Presumptive or requiring a showing of evidence
• Decision maker: Self-attested, certified by a third party, or approved by a regulator 
• Duration: Permanent or temporary
• Rigidity: Formulaic or factor-based with flexible considerations
• Speed: Automatic or requiring action or review

Recommendation: The Report already mentions that exempting small businesses from regulations will sometimes be desirable. It should build on this suggestion by emphasizing the utility of carve-outs, tiered requirements, multiple definitions, and exemption processes (in addition to thresholds) to further refine the category of regulated models. It should also outline some of the common carve-out categories (noting the value of maintaining option value by ensuring that carve-outs for open-source systems are revised and updated if the cost-benefit balance changes in the future) as well as key considerations in creating exemption processes. 

4. We recommend that the Report elaborate on the approach of combining different types of thresholds by discussing the complementary pairing of compute and capabilities thresholds.

It is important to provide additional detail about other metrics that could be combined with compute thresholds because this approach is promising and one of the most actionable items in the Report. We recommend capabilities thresholds as a complement to compute thresholds in order to leverage the advantages of compute that make it an excellent initial filter, while making up for its limitations with evaluations of capabilities, which are better proxies for risk and more future-proof. Other metrics could also be paired with compute thresholds in order to more closely track the desired policy outcome, such as risk thresholds or impact-level properties; however, they have practical issues, as discussed in the Report.

Recommendation: The Report should expand on its suggestion that compute thresholds be combined with other metrics and thresholds by noting that capabilities evaluations may be a particularly promising complement to compute thresholds, as they more closely correspond to risk and are more adaptable to future developments and deployment in different contexts. Other metrics could also be paired with compute thresholds in order to more closely track the desired policy outcome, such as risk evaluations or impact-level properties.

5. The Report should note additional definitional considerations in the list in Section 5.1—namely, risk-tracking, resilience to circumvention, clarity, and flexibility.

The Report correctly highlights three considerations that influence threshold design: determination time, measurability, and external verifiability. 

Recommendation: We recommend that the Report note four additional definitional considerations, namely:

• Risk-Tracking: How closely is the proxy correlated with the risks a policy looks to manage? Currently, compute correlates strongly with advanced capabilities. While there are some exceptions amongst specialized models, bigger is generally better. This remains true even after meaningful gains in inference scaling; it is true both that more inference compute leads to better results and that for any fixed amount of inference compute, a model with more training compute tends to perform better. Generally, the most compute-intensive models are the most likely to be deployed widely in new contexts and the most likely to exhibit emergent capabilities that pose unique risks. Compute is less correlated with risk than more direct measures like capabilities or risk itself, but both of these proxies are harder to measure and define. 
• Resilience to Circumvention: How difficult is it to game the proxy or evade its application? Thresholds that are more difficult to circumvent are more effective, while easily circumvented thresholds risk becoming useless once a few actors demonstrate the ease of circumvention. Training compute is a difficult proxy to circumvent. While a threshold that focuses solely on training compute could miss models that rely heavily on inference, training compute is still a significant contributor to the capabilities of a model. Derivative models and distillations pose a meaningful obstacle here, as policymakers must decide what and how to cover models with similar performance but different compute inputs. Generally speaking, requirements that lead to paperwork redundancies for similar models can likely be collapsed so that only one model is governed, while rules that relate to preventing or governing specific uses or risks may need to extend to derivatives and distillations to avoid becoming ineffective.
• Clarity: How certainly can a regulated party predict that they will be affected by regulation? And how quickly and clearly can regulators clarify ambiguities through interpretations and guidances? Compute thresholds are clear relative to more subjective alternatives. While there are some open questions regarding who measures and how to measure compute, order-of-magnitude differences in compute usage will typically allow actors to know whether they fall in or out of scope of a regulation. 
• Flexibility: Will the proxy remain accurate over time—because it remains the same, naturally adjusts, or allows for easy updating? Compute is less naturally adaptable than risk-based or capabilities-based thresholds.

For more discussion, see Training Compute Thresholds: Features and Functions in AI Regulation and The Role of Compute Thresholds for AI Governance.

Draft Report of the Joint California Policy Working Group on AI Frontier Models—whistleblower protections comments

These comments on the Draft Report of the Joint California Policy Working Group on AI Frontier Models were submitted to the Working Group as feedback on April 8, 2025. The opinions expressed in these comments are those of the authors and do not reflect the views of the Institute for Law & AI.

We applaud the Working Group’s decision to include a section on whistleblower protections. Whistleblower protections are light-touch, innovation-friendly interventions that protect employees who act in good faith, enable effective law enforcement, and facilitate government access to vital information about risks. Below, we make a few recommendations for changes that would help the Report more accurately describe the current state of whistleblower protections and more effectively inform California policy going forward. 

1. Whistleblowers should be protected for disclosing risks to public safety even if no company policy is violated 

The Draft Report correctly identifies the importance of protecting whistleblowers who disclose risks to public safety that don’t involve violations of existing law. However, the Draft Report seems to suggest that this protection should be limited to circumstances where risky conduct by a company  “violate[s] company policies.” This would be a highly unusual limitation, and we strongly advise against including language that could be interpreted to recommend it. A whistleblower law that only applied to disclosures relating to violations of company policies would perversely discourage companies from adopting strong internal policies (such as responsible scaling policies). This would blunt the effectiveness of whistleblower protections and perhaps lead to companies engaging in riskier conduct overall.

To avoid that undesirable result, existing whistleblower laws that protect disclosures regarding risks in the absence of direct law-breaking focus on the seriousness and likelihood of the risk rather than on whether a company policy has been violated. See, for example: 5 U.S.C. § 2302(b)(8) (whistleblower must “reasonably believe” that their disclosure is evidence of a “substantial and specific danger to public health or safety”); 49 U.S.C. § 20109 (whistleblower must “report[], in good faith, a hazardous safety or security condition”); 740 ILCS 174/15 (Illinois) (whistleblower must have a “good faith belief” that disclosure relates to activity that “poses a substantial and specific danger to employees, public health, or safety.”). Many items of proposed AI whistleblower legislation in various states also recognize the importance of protecting this kind of reporting. See, for example: California SB 53 (2025–2026) (protecting disclosures by AI employees related to “critical risks”); Illinois HB 3506 (2025–2026) (similar); Colorado HB25-1212 (protecting disclosures by AI employees who have “reasonable cause to believe” the disclosure relates to activities that “pose a substantial risk to public safety or security, even if the developer is not out of compliance with any law”).

We recommend that the report align its recommendation with these more common, existing whistleblower protections, by (a) either omitting the language regarding violations of internal company policy or qualifying it to clarify that the Report is not recommending that such violations be used as a requirement for whistleblower protections to apply; and (b) explicitly referencing common language used to describe the type of disclosures that are protected even in the absence of lawbreaking.

• Suggested language: “However, some actions that clearly pose serious risks to public safety may not violate any existing laws. Therefore, policymakers may consider protections that cover a broader range of activities, which may draw upon notions of ‘good faith’ reporting on risks found in other domains such as cybersecurity. One possible approach is to follow the example of the federal Whistleblower Protection Act and protect disclosures made by a person who ‘reasonably believes’ that the disclosure relates to a ‘substantial and specific danger to public health or safety.’”

2. The report’s overview of existing law should discuss California’s existing protections

The report’s overview of existing whistleblower protections makes no mention of California’s whistleblower protection law, California Labor Code § 1102.5. That law protects both public and private employees in California from retaliation for reporting violations of any state, federal, or local law or regulation to a government agency or internally within a company. It also prohibits employers from adopting any internal policies to prevent employees from whistleblowing. 

This is critical context for understanding the current state of California whistleblower protections and the gaps that remain. The fact that § 1102.5 already exists and applies to California employees of AI companies means that additional laws specifically protecting AI employees from retaliation for reporting law violations would likely be redundant unless they added something new—e.g., protection for good faith disclosures relating to “substantial and specific dangers to public health or safety.”

This information could be inserted into the subsection on “applicability of existing whistleblower protections.”

• Suggested language: “Under existing California law, both public and private sector employees in California are protected from retaliation for reporting violations of any state, federal, or local law or regulation to a government or law enforcement agency or internally within their company [reference].”

3. The report should highlight the importance of establishing a reporting process

Protecting good-faith whistleblowers from retaliation is only one lever to ensure that governments and the public are adequately informed of risks. Perhaps even more important is ensuring that the government of California appropriately handles that information once it is received. One promising way to facilitate the secure handling of sensitive disclosures is to create a designated government hotline or office for AI whistleblower disclosures. 

This approach benefits all stakeholders:

• Companies know that any sensitive business information disclosed to the government will be handled securely and appropriately and that the risk of valuable trade secrets being leaked to competitors will be minimized;
• Whistleblowers receive greater assurance that the information they bring forward will actually be put to good use (justifying the reputational and personal risk they take on); and 
• The government of California becomes more capable of acting on the information it receives, responding to risks in a timely manner, updating its decision-making in light of new evidence, sharing information with key partners, and enforcing the law.

The report already touches briefly on the desirability of “ensuring clarity on the process for whistleblowers to safely report information,” but a more specific and detailed recommendation would make this section of the Report more actionable. Precisely because of our uncertainty about the risks posed by future AI systems, there is great option value in building the government’s capacity to quickly, competently, and securely react to new information received through whistleblowing. By default, we might expect that no clear chain of command will exist for processing this new information, sharing it securely with key decision makers, or operationalizing it to improve decision making. This increases coordination costs and may ultimately result in critical information being underutilized or ignored.

• Suggested language: “Ensuring clarity on the process for whistleblowers to safely report information can jointly advance accountability and manage countervailing interests, such as the disclosure of trade secrets or the misuse of information to compromise safety and security. One promising way to facilitate secure disclosures is to establish a secure government-run hotline or office for receiving AI whistleblower disclosures and to establish procedures for receiving, processing, sharing, and acting upon disclosures. Establishing such procedures may also increase government agencies’ ability to quickly and competently process important information and respond to emerging issues.”

Draft Report of the Joint California Policy Working Group on AI Frontier Models—liability and insurance comments

These comments on the Draft Report of the Joint California Policy Working Group on AI Frontier Models were submitted to the Working Group as feedback on April 8, 2025. Any opinions expressed in these comments are those of the authors and do not reflect the views of the Institute for Law & AI.

Comment 1: The draft report correctly points to insurance as a potentially useful policy lever. But it incorrectly suggests that insurance alone (without liability) will cause companies to internalize their costs. Insurance likely will not work without liability, and the report should acknowledge this.

Insurance could advance several goals at the center of this report. Insurance creates private market incentives to more accurately measure and predict risk, as well as to identify and adopt effective safety measures. It can also bolster AI companies’ ability to compensate victims for large harms caused by their systems. The value of insurance is potentially limited by the difficulty of modeling at least some risks in this context, but to the extent that the report’s authors are enthusiastic about insurance, it is worth highlighting that these benefits depend on the underlying prospect of liability. If AI companies are not–and do not expect to be–held liable when their systems harm their customers or third parties, they would have no reason to purchase insurance to cover those harms and inadequate incentives to mitigate those risks. 

Passing state laws that require insurance doesn’t solve this problem either: if companies aren’t held liable for harms they generate (because of gaps in existing law, newly legislated safe harbors, federal preemption, or simple underenforcement), insurance plans would cease to accurately track risk.

In section 1.3, the draft report suggests efforts to:

reconstitute market incentives for companies to internalize societal externalities (e.g., incentivizing insurance may mold market forces to better prioritize public safety).” 

We propose amending this language to read:

reconstitute market incentives for companies to internalize societal externalities (e.g., clear liability rules, especially for harms to non-users, combined with incentives to acquire liability insurance may mold market forces to better prioritize public safety).

Comment 2: Liability can be a cost-effective tool for mitigating risk without discouraging innovation, especially under conditions of uncertainty. And many of the report’s transparency suggestions would improve the efficiency of liability and private contracting. The report should highlight this.

Overall, the report provides minimal discussion of liability as a governance tool. To the extent it does, the tone (perhaps) suggests skepticism of liability-based governance (“In reality, when governance mechanisms are unclear or underdeveloped, oversight often defaults largely to the courts, which apply existing legal frameworks—such as tort law…”). 

But liability is a promising tool, even more so given the considerable uncertainty surrounding future AI risks–a point that the authors correctly emphasize is the core challenge of AI policy. 

Liability has several key advantages under conditions of uncertainty. Liability is:

• Proportional: Automatically adjusting incentives based on the severity and breadth of harm that actually occurs or that AI companies expect to occur
• Flexible: Creating general incentives to mitigate risks and act “reasonably” rather than prescribing the exact steps companies must take–this has several advantages, among them:
  • Allowing those closest to the ground (the companies themselves) to identify effective mitigation measures and decide whether the cost is merited
  • Requiring less political consensus than prescriptive regulations
  • Enabling incentives that are conditional on disputed risks actually materializing
  • Avoiding the need to prescribe specific mitigations before best practices emerge.
• Fact-bound: Deferring key decisions until after harm has occurred, which means that key decisions are made based on a fuller factual record, when we know more, with greater confidence

Ex ante regulations require companies to pay their costs upfront. Where those costs are large, they depend on a strong social consensus about the magnitude of the risks that they are designed to mitigate. Prescriptive rules and approval regulation regimes, the most common forms of ex ante regulation, also depend on policymakers’ ability to identify specific precautionary measures early on, which is challenging in a nascent field like AI, where best practices are still being developed and considerable uncertainty exists about the severity and nature of potential risks. 

Liability, by contrast, scales automatically with the risk and shifts decision-making regarding what mitigation measures to implement to the AI companies, who are often best positioned to identify cost-effective risk mitigation strategies. 

Concerns about excessive litigation are reasonable but can be mitigated by allowing wide latitude for contracts to waive and allocate liability between model developers, users, and various intermediaries–with the notable exception of third-party harm, where the absence of contractual privity does not allow for efficient contracting. In fact, allocation of responsibility by contract goes hand-in-hand with the transparency and information-sharing recommendations highlighted in the report–full information allows for efficient contracting. Risk of excessive litigation also varies by context, being least worrisome where the trigger for liability is clear and rare (as is the case with liability for extreme risks) and most worrisome where the trigger is more common and occurs in a context where injuries are common even when the standard of care is followed (e.g., in the context of healthcare). There may be a case for limiting liability in contexts where false positives are likely to abound, but liability is a promising, innovation-compatible tool in some of the contexts at the center of this report.. 

A strong summary of the potential use and limitations of liability for AI risk would note that:

• Liability is a promising policy tool, especially in light of uncertainty about the nature and severity of AI risks
• Liability has the advantages of being proportional, flexible, and fact-bound
• The case for liability is particularly strong for extreme risks (where uncertainty and under-developed best practices make prescriptive approaches more difficult, and liability allows for adequate flexibility)
• The case for liability is particularly strong for third-party harms (where efficient contracting is not possible)
• Transparency requirements and third-party evaluations enhance the ability of private parties to contract efficiently and allocate liability costs via indemnification
• Liability is not uniformly bad for innovation. It can be innovation-compatible or promoting in certain contexts.

Comment 3: Creating safe harbors that protect AI companies from liability is a risky strategy, given the uncertainty about both the magnitude of risks posed by AI and the effectiveness of various risk mitigation strategies. The report should note this.

In recent months, several commentators have called for preemption of state tort law or the creation of safe harbors in return for compliance with some of the suggestions made in this report. While we believe that the policy tools outlined in the report are important, it would be a valuable clarification for the report to state that these requirements alone do not merit the removal of background tort law protections.

Under existing negligence law, companies can, of course, argue that their compliance with many of the best practices outlined in this report  is evidence of reasonable care. But, as outlined above, tort law creates additional and necessary incentives that cannot be provided through reporting and evaluation alone. 

As we see it, tort law is compatible with–not at odds with or replaceable by–the evidence-generating, information-rich suggestions of this report. In an ecosystem with greater transparency and better evaluations, parties will be able to even more efficiently distribute liability via contract, enhancing its benefits and more precisely distributing its costs to those best positioned to address them.  

It also merits noting that creating safe harbors based on compliance with relatively light-touch measures like transparency and third-party verification would be an unusual step historically, and would greatly reduce AI companies’ incentives to take risk-mitigation measures that are not expressly required. 

Because tort law is enhanced by the suggested policies of this report and addresses the key dilemma (uncertainty) that this report seeks to address, we recommend that the report clarify the risk posed by broad, general liability safe harbors.

Comment 4: The lesson of climate governance is that transparency alone is inadequate to produce good outcomes. When confronting social externalities, policies that directly compel the responsible parties to internalize the costs and risks that they generate are often the most efficient solutions. In the climate context, the best way to do this is with an ex ante carbon price. Given the structural features of AI risk, ex post liability plays an analogous role in AI governance.

Section 2.4 references lessons from climate change governance. “The case of fossil fuel companies offers key lessons: Third-party risk assessment could have realigned incentives to reward energy companies innovating responsibly while simultaneously protecting consumers.” In our view, this overstates the potential of transparency measures like third-party risk assessment alone and undervalues policies that compel fossil fuel companies and their consumers to internalize the costs generated by fossil fuel combustion. After all, the science on climate change has been reasonably clear for decades now and that alone has been far from sufficient to align the incentives of fossil fuel companies with social welfare. The core policy challenge of climate change is that fossil fuel combustion generates global negative externalities in the form of heat-trapping effects of greenhouse gas emissions. Absent policies, like carbon pricing, to compel fossil fuel companies and their consumers to internalize the costs generated by fossil fuel combustion, mere transparency about climate impacts is an inadequate response. 

Third-party risk assessments and other transparency measures alone are similarly unlikely to be sufficient in the AI risk context. Transparency and third-party evaluation are best thought of as tools that help prepare us for further action (be it through generating better quality evidence on which to regulate, enabling more efficient contracting to allocate risk, or enabling efficient litigation once harms occur). But without that further action, they forego much of their potential value. Aligning the incentives of AI companies will require holding them financially accountable for the risks that they generate, and Liability is the best accountability tool we have for AI risk and plays a structurally similar role to carbon pricing for climate risk mitigation.

We propose amending the report language to read, “The case of fossil fuel companies offers key lessons: Third-party risk assessment could have helped build the case for policies, like carbon pricing, that would have realigned incentives to reward energy companies innovating responsibly while simultaneously protecting consumers.”

Section 2.4 further states, “The costs of action to reduce greenhouse gas emissions, meanwhile, were estimated [by the Stern Review] at only 1% of global GDP each year. This is a useful lesson for AI policy: Leveraging evidence-based projections, even under uncertainty, can reduce long-term economic and security costs.” 

But this example only further evidences the fact that cost internalization mechanisms, in addition to transparency mechanisms, are key to risk reduction. The Stern Review’s cost estimates were based on the assumption that governments would implement the most cost-effective policies, like economy-wide carbon pricing, to reduce greenhouse gas emissions. Actual climate policies implemented around the world have tended to be substantially less cost-effective. This is not because carbon pricing is more costly or less effective than Stern assumes but because policymakers have been reluctant to implement it aggressively, despite broad global acceptance of the basic science of climate change. 

This lesson is highly relevant to AI governance inasmuch as the closest analog to carbon pricing is liability, which directly compels AI companies to internalize the risks generated by their systems, just as a carbon price compels fossil fuel companies to internalize the costs associated with their incremental contribution to climate change. An AI risk tax is impractical since it is not feasible to measure AI risk ex ante. But, unlike with climate change, it will likely generally be feasible to attribute AI harms to particular AI systems and to hold the companies that trained and deployed them accountable. 

Supporting documents

For more on the analogy between AI liability and carbon pricing and an elaboration of a proposed liability framework that accounts for uninsurable risks, see Gabriel Weil, Tort Law as a Tool for Mitigating Catastrophic Risk from Artificial Intelligence, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4694006.

This proposal is also summarized in this magazine article: Gabriel Weil, Your AI Breaks It? You Buy It: AI developers should pay for what they screw up, Noema Mag (2024) 

For more on the case for prioritizing liability as an AI governance tool, see Gabriel Weil, Instrument Choice in AI Governance: Liability and its Alternatives, Google Docs, https://docs.google.com/document/d/1ivtgfLDQqG05U2vM1211wNtTDxNCjZr1-2NWf6tT5cU/edit?tab=t.0. 

The core arguments are also laid out in this Lawfare piece: Gabriel Weil, Tort Law Should Be the Centerpiece of AI Governance, Lawfare (2024).

Balancing safety and privacy: regulatory models for AI misuse

Since consumer AI tools have exploded in popularity, fears of AI-based threats to security have moved from sci-fi to reality. The FBI warns that criminals are already using AI to hack financial networks, and OpenAI disrupted an Iranian government disinformation operation last year. But risks could rapidly escalate beyond theft and propaganda to truly catastrophic threats—from designing deadly viruses to hacking into critical infrastructure. Such threats pose a legitimate threat not only to AI users but to national security itself.

In response, proposals have emerged for mandatory monitoring and reporting mechanisms to prevent AI misuse. These proposals demand careful scrutiny. The Supreme Court typically protects reasonable expectations of privacy under the Fourth Amendment, and people may reasonably expect to use these new tools without fear of government surveillance.

Yet governments should not shy away from carefully designed oversight. AI labs likely already conduct some legal monitoring of their consenting users. In addition, U.S. law has several analogous frameworks—notably the Bank Secrecy Act and laws combating child sexual abuse material (CSAM)—that require private companies to record potentially illicit activity and/or make reports to authorities. These precedents show how reasonable monitoring regulation can help prevent crime while respecting privacy rights. 

AI Misuse Risks

Artificial intelligence systems present various categories of potential catastrophic risks, ranging from unintended accidents to loss of human control over increasingly powerful systems. But we need not imagine a “Skynet” scenario to worry about catastrophic AI. Another kind of risk is simple misuse: bad actors who intentionally use AI to do dangerous and illegal things. This intentional misuse raises particularly salient privacy concerns, as mitigating it requires monitoring individual user behavior rather than just overseeing AI systems or their developers.

While AI might enable various forms of criminal activity, from copyright infringement to fraud, two categories of catastrophic misuse merit particularly careful consideration due to their potential for widespread devastation. First, AI could dramatically lower barriers to bioterrorism by helping malicious actors design and create deadly pathogens. Current AI models can already provide detailed scientific knowledge and laboratory protocols that could potentially be exploited for biological weapons development. Researchers have shown that current language models can already directly instruct laboratory robots to carry out experiments, suggesting that as AI advances, the capability to create deadly pathogens could become increasingly available to potential bad actors.

Second, AI systems may enable unprecedented cyber warfare capabilities that could threaten critical infrastructure and national security. A recent FBI threat assessment highlights how AI could enable sophisticated cyber-physical attacks on critical infrastructure, from manipulating industrial control systems to compromising autonomous vehicle safety systems. For instance, in 2017, the “Triton” malware attack targeted petrochemical plants in the Middle East, attempting to disable critical safety mechanisms. As capabilities improve, we may see fully autonomous AI systems conducting cyberattacks with minimal human oversight. 

Government-mandated monitoring may be justified for AI risk, but it should not be taken lightly. Focusing specifically on the most serious threats helps maintain an appropriate balance between security and privacy. 

Current Safety Measures

AI developers use various methods to prevent misuse, including “fine-tuning” models and filtering suspicious prompts. However, researchers have demonstrated the ability to “jailbreak” models and bypass these built-in restrictions. This capability suggests the need for a system of monitoring that allows developers to respond swiftly to initial cases of misuse by limiting the ability of the bad actor to engage in further misuse. AI providers may scan user interactions for patterns indicative of misuse attempts, flag high-risk users, and take actions ranging from warnings to imposing access restrictions or account bans.

These private monitoring efforts operate within a statutory framework that generally allows companies enough flexibility to monitor their services when necessary. The Electronic Communications Privacy Act (ECPA) restricts companies from accessing users’ communications, but contains several relevant exceptions—including consent, ordinary course of business activities, protecting the provider’s rights and property, and emergency disclosures. Technology companies typically seek to establish consent through their privacy policies (though the legal sufficiency of this approach is often questioned), and also have significant latitude to monitor communications when necessary to make their services function. The ECPA also permits disclosure to law enforcement with proper legal process, and allows emergency disclosures when providers reasonably believe there is an immediate danger of death or serious physical injury. Thus, AI providers already have legal pathways to share critical threat information with authorities, but are not under clear obligations to do so.

Incident Reporting

The shortcoming of purely internal monitoring is that malicious actors can migrate to other models after being banned or use multiple models to avoid detection. Accordingly, there is a need for centralized reporting systems to alert other developers of risks. Nonprofits like the Responsible AI Collaborative have begun to collect media reports of AI incidents, but documented real-world incidents likely represent only the tip of the iceberg. More importantly, focusing solely on successful attacks that caused harm misses the broader picture—AI providers regularly encounter suspicious behavior patterns, thwarted attempts at misuse, and users who may pose risks across multiple platforms. 

One potential model for addressing these limitations comes from requirements for reporting child sexual abuse material (CSAM). Under 18 U.S.C. § 2258A, electronic service providers must report detected CSAM to the National Center for Missing and Exploited Children, but face no obligation to proactively monitor for such material. Generally, § 2258A has survived Fourth Amendment challenges under the “private search doctrine,” which holds that the Fourth Amendment protects only against government searches, not private action. While private entity searches can be attributed to the government when there is sufficient government encouragement or participation, circuit courts have rejected Fourth Amendment challenges to § 2258A because it requires only reporting while explicitly disclaiming any monitoring requirement. As the Ninth Circuit explained in United States v. Rosenow, “mandated reporting is different than mandated searching,” because communications providers are “free to choose not to search their users’ data.”

California recently considered a similar approach to reporting in SB 1047, one provision of which would have required AI model developers to report “artificial intelligence safety incident[s]” to the state Attorney General within 72 hours of discovery. While ultimately vetoed, this reporting-focused approach offers several advantages: it would create a central clearinghouse for incident data, facilitate coordination across competing AI labs, without imposing any direct obligations for AI companies to monitor their users. 

A reporting-only mandate may paradoxically discourage active monitoring. If only required to report the problems they discover, some companies may choose not to look for them. This mirrors concerns raised during the “Crypto Wars” debates, where critics argued that encryption technology not only hindered third party access to communications but also prevented companies themselves from detecting and reporting illegal activity. For instance, while Meta reports CSAM found on public Facebook feeds, encryption is the default for channels like WhatsApp—meaning Meta can neither proactively detect CSAM on these channels nor assist law enforcement in investigating it after the fact.

AI companies might similarly attempt to move towards systems that make monitoring difficult. While most current commercial AI systems process inputs as unencrypted text, providers could shift toward local models running on users’ devices.  More ambitiously, some companies are working  “homomorphic” encryption techniques—which allow computation on encrypted data—for AI models. Short of retrieving the user’s device, these approaches would place AI interactions beyond providers’ reach.

Mandatory Recordkeeping

Given the limitations of a pure reporting mandate, policymakers might consider requiring AI providers to maintain certain records of user interactions, similar to bank recordkeeping requirements. The Bank Secrecy Act of 1970, passed to help law enforcement detect and prevent money laundering, provides an instructive precedent. The Act required banks both to maintain records of customer identities and transactions, and to report transactions above specified thresholds. The Act faced immediate constitutional challenges, but the Supreme Court upheld the Act in California Bankers Association v. Shultz (1974). The court highlighted several factors which overcame the plaintiff’s objections: the Act did not authorize direct government access without legal process; the requirements focused on specific categories of transactions rather than general surveillance; and there was a clear nexus between the recordkeeping and legitimate law enforcement goals.

This framework suggests how AI monitoring requirements might be structured: focusing on specific high-risk patterns rather than blanket surveillance, requiring proper legal process for government access, and maintaining clear links between the harm being protected against (catastrophic misuse) and the kinds of records being kept. 

Unlike bank records, however, AI interactions have the potential to expose intimate thoughts and personal relationships. Recent Fourth Amendment doctrine suggests that this type of privacy may merit a higher level of scrutiny.

Fourth Amendment Considerations

The Supreme Court’s modern Fourth Amendment jurisprudence begins with Katz v. United States (1967), which established that government surveillance constitutes a “search” when it violates a “reasonable expectation of privacy.” Under the subsequent “third-party doctrine” developed in United States v. Miller (1976) and Smith v. Maryland (1979), individuals generally have no reasonable expectation of privacy in information voluntarily shared with third parties. This might suggest that AI interactions, like bank records, fall outside Fourth Amendment protection.

However, a growing body of federal case law has increasingly recognized heightened privacy interests in digital communications. In United States v. Warshak (2010), the Sixth Circuit found emails held by third parties deserve greater Fourth Amendment protection than traditional business records, due to their personal and confidential nature. Over the next decade, the Supreme Court similarly extended Fourth Amendment protections to GPS tracking, cell phone searches, and finally, cell-site location data. The latter decision, Carpenter v. United States (2018), was heralded as an “inflection point” in constitutional privacy law for its potentially broad application to various kinds of digital data, irrespective of who holds it. 

Though scholars debate Carpenter’s ultimate implications, early evidence suggests that courts are applying some version of the key factors that the opinion indicates are relevant for determining whether digital data deserves Fourth Amendment protection: (1) the “deeply revealing nature” of the information, (2) its “depth, breadth, and comprehensive reach,” and (3) whether its collection is “inescapable and automatic.”

All three factors raise concerns about AI monitoring. First, if Carpenter worried that location data could reveal personal associations in the aggregate, AI interactions can directly expose intimate thoughts and personal relationships. The popularity of AI companions designed to simulate close personal relationships are only an extreme version of the kind of intimacy someone might have with their chatbot. Second, AI’s reach is rapidly expanding – ChatGPT reached 100 million monthly active users within two months of launch, suggesting it may approach the scale of “400 million devices” that concerned the Carpenter Court. The third factor currently presents the weakest case for protection, as AI interactions still involve conscious queries rather than automatic collection. However, as AI becomes embedded into computer interfaces and standard work tools, using these systems may become as “indispensable to participation in modern society” as cell phones.

If courts do apply Carpenter to AI interactions, the unique privacy interests in AI communications may require stronger safeguards than those found sufficient for bank records in Shultz. This might not categorically prohibit recordkeeping requirements, but could mean that blanket monitoring regimes are constitutionally suspect. 

We can speculate as to what safeguards an AI monitoring regime may continue beyond those provided in the Bank Secrecy act. The system could limit itself to flagging user attempts to elicit specific kinds of dangerous behavior (like building biological weapons or hacking critical infrastructure), with automated systems scanning only for these pre-defined indicators of catastrophic risks. The mandate could prohibit bulk transmission of non-flagged conversations, and collected data could be subject to mandatory deletion after defined periods unless specifically preserved by warrant. Clear statutory prohibitions could restrict law enforcement using any collected data for purposes beyond preventing catastrophic harm, even if other incidental harms are discovered. Independent oversight boards could review monitoring patterns to prevent scope creep, and users whose data is improperly accessed or shared could be granted private rights of action.

While such extensive safeguards may prove unnecessary, they demonstrate how clear legal frameworks for AI monitoring could both protect against threats and enhance privacy compared to today’s ad-hoc approach. Technology companies often make decisions about user monitoring and government cooperation based on their individual interpretations of privacy policies and emergency disclosure provisions. Controversies around content moderation illustrate the tensions of informal government-industry cooperation: Meta CEO Mark Zuckerberg recently expressed regret over yielding to pressure from government officials to remove content during the COVID-19 crisis. In the privacy space, without clear legal boundaries, companies may err on the side of over-compliance with government requests and unnecessarily expose their users’ information. 

Conclusion

The AI era requires navigating two profound risks: unchecked AI misuse that could enable catastrophic harm, and the prospect of widespread government surveillance of our interactions with what may become the 21st century’s most transformative technology. As Justice Brandeis warned in his prescient dissent in Olmstead, “The greatest dangers to liberty lurk in insidious encroachment by men of zeal, well meaning but without understanding.” It is precisely because AI safety presents legitimate risks warranting serious countermeasures that we must be especially vigilant in preventing overreach. By developing frameworks that establish clear boundaries and robust safeguards, we can enable necessary oversight while preventing overzealous intrusions into privacy rights.