Congress Should Do Something: The Case for (Fixing) the Great American AI Act

Since the April announcement of Anthropic’s Mythos model and its unprecedented cyber capabilities, there has been a remarkable shift in the artificial intelligence (AI) policy discourse. This has been most noticeable, and most noticed, in the statements and actions of prominent Trump administration officials. After months of dismissing concerns about national security risks from AI and engaging with the issue primarily by attempting to preempt state AI safety laws, the White House recently issued an executive order that called for the establishment of a voluntary predeployment program headed by the National Security Agency to evaluate offensive cyber capabilities of frontier models. This came amid statements from senior administration officials about “striking a … balance between innovation and safety” and even considering a Food and Drug Administration-style mandatory predeployment licensing regime for frontier models.

On June 12, the Trump administration’s concerns about Mythos’s cyber capabilities boiled over into an unprecedented decision to use export control authorities to prohibit Anthropic from allowing foreign nationals to access its Mythos-class Fable 5 model. Practically, this amounted to a mandate that Anthropic revoke public access to the model entirely. Much of the online commentary on this decision devolved into speculation about the administration’s motivations, the alleged behavior of Anthropic’s executives, and other petty interpersonal drama. As intriguing as these are, the more important takeaway from the White House’s decision to abruptly institute a de facto licensing regime for frontier AI systems—as many commentators across the political and safety/innovation spectrums have observed—is that federal legislation to establish a framework for addressing the national security risks posed by the most advanced AI systems is an urgent necessity.

 The Commerce Department’s decision to impose export controls on Fable may or may not have been wise, depending on who you believe about the seriousness of the vulnerability that motivated the decision. But even assuming the decision was justified, the fact that the government was apparently caught by surprise and had to scramble to put together a heavy-handed response based on ad hoc, potentially legally questionable authorities that were not designed with anything like frontier AI systems in mind, with no due process for the affected company, is a serious problem that should be remedied with legislation as soon as possible.

Which brings us, finally, to the subject of this piece—the Great American Artificial Intelligence Act of 2026 (GAAIA), a comprehensive frontier AI safety bill that is the long-awaited product of months of intense negotiation between Rep. Jay Obernolte (R-Calif.) and Rep. Lori Trahan (D-Mass.). Obernolte had tried for months to get a Democrat to sign on to an AI bill that preempted state AI laws before finally persuading Trahan. For her part, Trahan wrote that she was motivated to support the bill by the announcement of Mythos’s groundbreaking cyber capabilities.

The current version of GAAIA is a discussion draft, meaning it has not been introduced yet and is intended to spark a conversation and elicit feedback from stakeholders rather than to become law in its current form. It may seem somewhat strange that a discussion draft sponsored by two relatively junior members of the House, neither of whom appears to have the backing of their party’s leadership, should receive so much attention from the media and from AI policy commentators, but for once the buzz is warranted.

GAAIA is the best attempt to design a federal framework for the governance of frontier AI systems introduced to date. In other words, it is the first serious attempt to actually do the thing that last week’s Fable incident clearly shows is necessary—address the national security risks posed by the most advanced AI systems—in a transparent, legally sound, and democratically legitimate way. While the bill may not pass in the near future—it likely faces opposition from both Democrats and Republicans—the draft can tell us a great deal about what the future of federal and state frontier AI governance efforts may look like.

The bill in its current form falls short in a number of respects and should not be passed. That said, passing a similar bill with narrower preemption of state laws and somewhat stronger federal authorities would be an excellent first step toward a workable federal regime for governing frontier AI systems.

What the Bill Does

GAAIA is a bipartisan compromise, in the truest sense of the phrase, which means that everyone hates it. Obernolte, a longtime advocate for federal preemption of state AI laws who supported last summer’s “moratorium” (which would have preempted all nongenerally applicable state AI laws and replaced them with essentially nothing), has compromised by granting his seal of approval to a number of genuinely consequential affirmative policy proposals. Trahan, who strongly supported increased oversight of AI in the past, has compromised by accepting broad preemption of state AI laws.

The Federal Framework

GAAIA’s four titles contain 45 sections, each of which addresses a significant topic in AI policy. I am an AI safety guy, and my research focuses mostly on serious risks that advanced AI systems might pose to national security and public safety, so this article focuses on the sections of GAAIA that are relevant to those risks. However, GAAIA is not an AI risk bill exclusively. There are also sections on, for example, “Preparing K-12 educators and students for an AI literate future,” “Modernizing access to artificial intelligence-related labor market data,” and establishing a “National artificial intelligence research resource” for improving capacity for AI research in the U.S., among many others. This article does not discuss those sections, not because they’re not important, but because they’re mostly irrelevant to the catastrophic risk concerns that this piece focuses on.

The noteworthy catastrophic risk provisions in GAAIA are:

• Section 102, which codifies and authorizes $100 million in annual funding for the Center for AI Standards and Innovation (CAISI), which would be relocated outside of the National Institute of Standards and Technology (NIST) and given an expanded, quasi-regulatory role as the agency in charge of administering the independent verification organization (IVO) and transparency regimes created by Sections 111 and 112.
• Section 111, which imposes transparency and incident reporting requirements on frontier AI companies similar to the requirements imposed by California’s SB 53 or New York’s RAISE Act.
• Section 112, which authorizes CAISI to establish and administer an IVO auditing regime in which independent third-party companies would regularly evaluate the adequacy of AI companies’ risk mitigation efforts.
• Section 301, which reauthorizes and updates the expiring Cybersecurity Information Sharing Act of 2015.
• Section 411, which directs NIST and the Department of Energy to lead efforts to form “alliances or coalitions” with allied foreign governments in order to facilitate collaboration and cooperation on AI research and development, technical standard-setting, and related issues.

“Transparency,” in this context, means requiring frontier AI companies to publish frontier safety frameworks (documents describing how the company evaluates and addresses catastrophic risks from the company’s most advanced AI models) and model cards (documents accompanying the release of specific models that contain information about the capabilities and limitations of a model and the results of the safety evaluations conducted under the company’s safety framework). “Incident reporting” requirements mandate that companies report “critical safety incidents” (essentially, incidents in which a frontier model’s model weights are stolen or in which a model does something scary that seems catastrophic-risk-ish) to the government and/or to law enforcement. And “auditing” refers to the practice of having an independent third party evaluate the adequacy of a company’s catastrophic risk mitigation practices as well as the company’s compliance with transparency requirements and with its own frontier AI framework. Transparency and incident reporting requirements are intended to provide the information needed for the government and the public to understand how companies think about and address catastrophic risks, and auditing requirements are supposed to ensure that transparency and reporting requirements remain effective rather than being ignored or becoming meaningless box-checking exercises.

As regards GAAIA’s transparency and auditing provisions, one common take is that the risk mitigation benefits of establishing these programs would be marginal because similar requirements already exist at the state level in New York, California, and Illinois. That view is, I think, mistaken in two important respects. For one thing, as Anton Leicht points out, it is vitally important to build up regulatory capacity within the federal government. I co-wrote an essay on this topic a few weeks back. The argument is, essentially:

1. (1) AI might end up being a very big deal with extremely serious national security implications at some point in the next 10 years (and possibly within the next two years).
2. (2) If that happens, we should expect that serious regulatory interventions may be required.
3. (3) That serious regulatory work will almost certainly have to be carried out by the federal government, because the federal government—
   1. (a) has orders of magnitude more regulatory capacity, expertise, and resources to devote to complex regulatory tasks than state governments do; and
   2. (b) is, constitutionally and practically speaking, the only entity that can realistically be entrusted with extremely complex and high-stakes national security projects.
4. (4) Building up the institutional capacity and expertise to competently undertake complex regulatory tasks is difficult and cannot realistically be done in a matter of days or even months.
5. (5) Therefore, it is vitally important that we begin the process of aggressively building up technical expertise and regulatory capacity and know-how within the federal government as soon as possible.

But even setting aside the capacity-building considerations, it is simply not true that existing state catastrophic risk laws are equivalent to GAAIA’s transparency or auditing provisions. These state laws—California’s Transparency in Frontier Artificial Intelligence Act (TFAIA), New York’s Responsible Artificial Intelligence Safety and Education (RAISE) Act, and Illinois’s Artificial Intelligence Safety Measures Act (AISMA)—are an important foundation for future efforts and have been extremely influential. GAAIA itself is clear evidence of this influence; some of Section 111’s transparency provisions are lifted almost verbatim from the transparency provisions of SB 53 (which are substantially identical to the transparency provisions of RAISE and AISMA). But, as groundbreaking as those state laws are, they are still state laws and therefore cannot leverage the resources, institutions, or legal authorities of the federal government in the way that a bill like GAAIA can.

Perhaps the most important institutional advantage that GAAIA leverages is the capacity of federal agencies such as the Department of Commerce to carry out sophisticated rulemaking, a capacity built over decades of administering complex regulatory programs that no state agency can realistically match. GAAIA grants CAISI and the Department of Commerce broad authority to issue regulations fleshing out the auditing and transparency regimes outlined in Sections 111-112. Rulemaking! That word may not sound like the most exciting thing you’ve heard this week, but take my word for it: This is the good stuff.

Take auditing, for example. Because AISMA does not confer any explicit rulemaking authority, Illinois’s auditing regime, when it goes into effect, will be defined solely by the requirements in AISMA’s text. AISMA requires that audits be conducted “consistent with generally accepted auditing standards and best practices” and that auditors possess “demonstrated competence to perform the audit.” These vague requirements, however, aren’t enough to guarantee a functional auditing regime.

Under AISMA, auditors are paid by the AI company that retains them. By default, this system will lead to a race to the bottom in which market forces compel auditors to compete with each other over who can cause the least hassle and difficulty for their customers (frontier AI companies). Rather than ensuring that companies abide by their commitments and hew to responsible risk mitigation practices, this kind of auditing regime will eventually devolve into a system where companies are disincentivized from hiring rubber-stamp auditors only by the uncertain prospect of ex post tort liability.

To be clear, this is not a criticism of AISMA’s auditing provisions, which are well designed. The issue is that Illinois’s state government simply lacks the ability to design and competently administer a complex, technically involved auditing program for out-of-state tech companies. The issue is not that AISMA is insufficiently ambitious but, rather, that Springfield—on its best day—has only a small fraction of the capacity for complex interstate regulatory projects that the federal government has on its worst.

In contrast to AISMA, GAAIA’s auditing section could establish a functional and effective third-party auditing regime. CAISI—reestablished as a regulatory agency separate from the nonregulatory NIST—would be granted broad rulemaking authority. The regulations that CAISI would be required to promulgate would include rules addressing conflict of interest and funding transparency requirements for auditors, requirements for licensing auditors and revoking auditor licenses, minimum requirements for audits and assessments, and “any other rules reasonably necessary to the administration of the IVO oversight and licensing regime.” This is the kind of rulemaking and oversight authority that could, in theory and with competent implementation, actually establish the kind of auditing regime that AISMA gestures at.

GAAIA’s transparency requirements would also be a significant upgrade from the existing state transparency requirements. While GAAIA’s transparency section is similar on its face to existing California, New York, and Illinois transparency statutes, it delegates fairly broad rulemaking authority to the Department of Commerce, which can prescribe regulations governing, among other things, the “form, manner, and minimum quality” of the model cards and safety frameworks that companies are required to publish. In combination with GAAIA’s auditing requirements, which require auditors to regularly evaluate and assess the adequacy of an AI company’s safety framework and its other efforts to identify and mitigate catastrophic risks, GAAIA’s transparency requirements would allow Commerce to ensure that transparency requirements actually result in meaningful transparency.

This rulemaking and minimum-standard-setting authority would allow GAAIA to provide significantly more transparency than existing state laws. Consider California’s TFAIA, which has been in effect for just over five months. TFAIA is a light-touch statute by design and imposes very few obligations on companies. This light-touch approach is, in my opinion, a good thing, but one downside is that companies can technically comply by publishing documents that check the statutorily required boxes without actually saying anything meaningful about the company’s approach to mitigating risks. For example, xAI, despite founder Elon Musk’s frequent public statements about the existential risks posed by superintelligence, complies with TFAIA by publishing a barebones framework that describes xAI’s risk mitigation practices in cursory and general terms.

Under TFAIA, there is no realistic way to require xAI to provide the industry-standard level of transparency that its competitors’ frameworks typically demonstrate. And while the RAISE Act does provide New York’s Department of Financial Services with some rulemaking authority that could in theory be used to give the act’s transparency requirements more teeth, practical and constitutional limitations may prevent a New York state financial services agency from regulating California-based software companies with the same level of rigor and precision that the U.S. Department of Commerce could, in theory, bring to bear.

Of course, the value proposition of GAAIA depends on the assumption that CAISI and the Commerce Department will do a decent job of establishing and administering the proposed transparency and auditing regimes. It’s far from clear that the Commerce Department would view this as a top priority, given that Secretary of Commerce Howard Lutnick has generally signaled skepticism of AI safety concerns. While public reporting in the months since Anthropic’s Mythos announcement has documented a shift among some of Lutnick’s fellow Cabinet members toward taking some of these concerns more seriously, Lutnick’s views have not—at least publicly—evolved along similar lines.

Even if the Commerce Department’s approach is initially ineffective, however, it might still be good to establish the statutory bones of an effective federal oversight regime in case future developments create additional political pressure for governance efforts. Given Mythos’s impact in shifting the Overton window on AI policy, the Trump administration may become more willing to implement serious oversight measures as they receive further concrete evidence of significant national security risks. Of course, it would be better not to have to cross our fingers and hope that the government starts taking risks seriously before anything bad happens. Still, enacting something like GAAIA would, at least, meaningfully improve the legal authorities available to the executive branch when and if the political will to get serious about national security risks from AI manifests itself.

GAAIA’s affirmative provisions are not perfect. The lack of effective whistleblower protections, for example, is a serious defect. The gold standard for federal AI whistleblower legislation is Sen. Chuck Grassley’s (R-Iowa) AI Whistleblower Protection Act (AI WPA). GAAIA’s whistleblower section falls short of that standard because, unlike the AI WPA, it fails to protect AI company employees who disclose information about a “substantial and specific” danger to public health, public safety, or national security to an appropriate government agency. The only AI whistleblowers who are protected from retaliation by GAAIA’s whistleblower section are those who report a violation of federal law—and because state whistleblower laws (including in California, where most of the relevant frontier AI company employees reside) already protect this kind of disclosure, a redundant federal protection adds little value. As I’ve argued before, protection for disclosures about serious risks that don’t involve law violations is crucial because it’s very plausible that perfectly legal frontier AI development activities could lead to serious national security and public safety risks that the government ought to be aware of.

Additionally, GAAIA’s auditing section lacks sufficiently clear enforcement authorities. Auditors are given a wide variety of authorities to assess whether a developer’s practices mitigate risks sufficiently. It’s not entirely clear, however, what happens if the mitigations are inadequate or nonexistent. CAISI is authorized to penalize developers for, for example, failing to retain an auditor or failing to grant timely access to appropriate records, but a developer who checks these boxes may not be required to actually do anything meaningful in response to an auditor’s recommendations. It’s possible that CAISI could use the broad rulemaking authority that the auditing section grants to cobble together a solution to this problem, but given how skeptical courts have been of this kind of broad agency interpretation of congressional delegations of rulemaking authority in recent years, it would be far better to have a clear statutory enforcement hook.

It should be noted that Trahan’s office has signaled potential willingness to remedy both of the specific issues discussed above in future drafts of GAAIA. If this does happen, it will improve my view of the bill’s value significantly; the point of a discussion draft is to bring potential issues like these to light so that they can be hashed out. More generally, a flawed whistleblower or auditing provision is at least marginally better than having no federal AI whistleblower or auditing statute whatsoever. As good as GAAIA’s federal framework is, however, it comes at a steep price: broad preemption of all state laws regulating AI development.

Preemption

In exchange for the substantial federal framework described above, GAAIA preempts—for a period of three years—any state AI law “specifically regulating the development of any artificial intelligence model.” As in prior AI preemption efforts, “generally applicable” laws are exempted, and GAAIA also includes a somewhat opaque exemption for “post-deployment activities.” “Development” is defined to mean

the acts performed or directed by a developer with respect to an artificial intelligence model prior to its deployment, including determining training or fine-tuning objectives; training, fine-tuning, or otherwise substantially modifying the weights or other parameters of an artificial intelligence model; and evaluating and deciding, prior to deployment, whether an artificial intelligence model satisfies applicable safety or capability thresholds for deployment.

There’s currently no consensus regarding exactly which existing or hypothetical state laws or bills GAAIA would preempt. A few commentators have argued that this language would preempt only the few existing state frontier AI safety laws—Illinois’s AISMA, California’s TFAIA, and New York’s RAISE Act—and future state laws in the same vein. If this were true and could be demonstrated clearly, an overwhelming majority of the AI safety community and a number of other factions who currently oppose the bill (including, for example, child safety advocates) would likely change course to support or assume a neutral attitude toward it. One-to-one preemption—eliminating state AI catastrophic risk auditing, transparency, and incident reporting bills in exchange for establishing robust federal AI auditing, transparency, and incident reporting regimes—would be a very reasonable compromise. If Reps. Trahan and Obernolte believe that GAAIA’s preemption section is effectively one-to-one, a clarifying amendment would go a long way toward increasing support for the bill.

Unfortunately, a court would probably not accept such a narrow interpretation. The key phrase is “specifically regulating”—state laws will be preempted if they are deemed to “specifically regulate” development. This phrase does not appear in any existing preemption legislation, meaning that there’s no firm precedent that can be used to accurately predict how courts will interpret the scope of GAAIA’s preemption. Still, a few relatively safe conclusions can be drawn.

It seems clear that “specifically regulating” is a relatively narrow formulation, as preemption triggers go—it certainly preempts less than the broad “relating to,” for example, which would sweep in any state law that had a “connection with” or contained a “reference to” AI. Still, “specifically regulating” is broader than, for example, the CAN-SPAM Act’s “expressly regulates,” which imposes a facial test (i.e., preempts a state law only if the law, on its face, names and regulates the forbidden federal subject). Arguably, GAAIA’s preemption language would instead impose a functional test under which state laws would be preempted if they had the effect of regulating AI development, as defined.

This functional interpretation would be consistent with how courts have treated comparable language in cases examining the (admittedly broader) preemption language in the Energy Policy and Conservation Act, the Clean Air Act, the Federal Meat Inspection Act, and the Federal Aviation Administration Authorization Act. In cases addressing the preemptive effect of the other laws mentioned above, courts have applied functional tests to prevent states from circumventing preemption with clever legislative drafting. For instance, in National Meat Association v. Harris, the Supreme Court held that a provision of California law that regulated the sale of meat from inhumanely raised pigs was preempted by a federal preemption provision that prohibited states from regulating the operation of slaughterhouses because the California provision because “the sales ban … functions as a command to slaughterhouses,” and because “if the sales ban were to avoid the … preemption clause, then any State could impose any regulation on slaughterhouses just by framing it as a ban on the sale of meat produced in whatever way the State disapproved.”

In short, it seems unlikely in light of existing preemption precedents that federal courts will allow states to circumvent GAAIA preemption through creative legislative drafting choices. GAAIA’s express carve-out for “post-deployment activities” and the relatively narrow scope of “specifically regulating” would likely protect some state laws that affect development without explicitly targeting it. But any law that functions primarily to regulate development, or can realistically be complied with only by altering development practices, will likely be preempted even if it purports to regulate deployment.

Because GAAIA’s definition of “development” is quite broad, this functional interpretation would preempt far more than just TFAIA-style catastrophic risk transparency laws. Existing state measures that GAAIA would almost certainly preempt include Texas’s TRAIGA (which, among other development-focused regulations, prohibits developing an AI system that impersonates a child while describing sexual conduct or developing an AI system with the intention of producing child sexual abuse material or illegal deepfakes), California’s AB2013, Colorado’s SB 189 (the law that replaced Colorado’s controversial algorithmic bias bill with less onerous, more business-friendly requirements), and certain California Consumer Privacy Act regulations affecting “automated decisionmaking technology.” State measures that might (or might not) be preempted include the many state child safety chatbot laws, like Idaho’s Conversational AI Safety Act, that purport to regulate deployers or “operators” of AI systems but could realistically be satisfied only via interventions implemented during training or fine-tuning, both of which are “development” under GAAIA.

By far the more important issue with GAAIA’s broad preemption, however, is that, in addition to invalidating these existing laws, it would preempt a broader and more important category of future state laws that would otherwise be passed in 2027, 2028, and 2029. TFAIA, RAISE, and AISMA were always meant to lay the foundation for future efforts rather than to establish a stable end state for state AI policy. As the capabilities of the most advanced AI systems continue to improve, their risks will increase as well. And as risks become more immediate and more difficult to deny, it seems safe to predict that state AI laws will be enacted that would have been politically unrealistic in 2026. The technology’s benefits may outweigh these risks; even so, it would be foolish to assume that AI will be the first transformatively impactful general-purpose technology, the development of which does not create any local harms that state legislation and regulation need to address.

The Takeaway

GAAIA includes by far the best federal framework for frontier AI safety that has been publicly introduced to date, as a discussion draft or otherwise. This being the case, some of the criticisms of GAAIA seem somewhat misguided. The Democratic House AI Commission, for example, has asserted that the bill “does not meet the enormity of the moment.” This may well be true, but if GAAIA’s unprecedentedly ambitious framework does not meet the moment, what bill does? I sincerely hope that there is a tidal wave of enormity-addressing AI legislation waiting in the wings, but the federal legislation that has been introduced thus far (with a few notable exceptions, such as the AI Whistleblower Protection Act and the Artificial Intelligence Risk Evaluation Act) has mostly been notable for its inoffensiveness rather than its moment-meeting boldness. A lot of “convening a multi-stakeholder process to consider the development of a process” for setting up a purely voluntary incident reporting regime, and things of that nature.

Despite this, the discussion draft would, in my judgment, be net-negative if enacted in its current form. As I have argued elsewhere, federal preemption of state AI laws should proceed on a narrow, issue-by-issue basis. Broad preemption of a vaguely defined and poorly understood category of state laws would, in all likelihood, be a disaster, for both political and policy reasons. Politically, it makes no sense to try to preempt broad categories of state AI law that have the backing of politically potent constituencies—developer-focused child safety laws, for example—in exchange for a bill with great frontier AI safety provisions but no child safety provisions. And while state frontier AI safety laws are not an adequate substitute for a robust federal framework, state laws have thus far proved so much easier to pass than federal laws that passing GAAIA in its current state might mean locking in a good-but-ultimately-inadequate 2026 governance framework indefinitely, despite the fact that future capabilities improvements might require more ambitious legislation.

At the same time, I worry many stakeholders who are rejecting GAAIA’s approach out of hand fail to recognize the urgency of the situation. By default, without new legislation, the process for addressing serious risks from frontier systems will be undertaken by the executive branch, the intelligence community, and national security agencies in a haphazard, legally suspect, case-by-case, and increasingly securitized manner, as June’s Fable incident proved. There is, of course, a sense in which serious national security risks being addressed by the nation’s national security apparatus are expected and necessary. But a program that is made up on the fly and administered on a totally discretionary, case-by-case basis, without the resources or structure that only Congress can provide, will never be as effective or as democratically legitimate as a well-designed federal legislative framework could be.

My hope, therefore, is that GAAIA’s authors will narrow its preemption and improve its substance and that GAAIA’s critics will either engage with the process of trying to improve the bill or introduce a serious alternative proposal in the near future. The stakes are high enough, and the issues with the status quo are clear enough, that continuing to do nothing indefinitely is no longer a defensible course of action.