Webinar Video: Congress and the Future of AI: Model Testing, Preemption, and Open Weight Policy
The Bottom Line
Artificial intelligence presents real risks, but poorly designed regulation can create risks of its own. Congress should replace opaque and improvised policymaking with transparent rules, invest in independent technical expertise, and provide national clarity without displacing the states’ traditional authority. Above all, lawmakers must avoid turning legitimate safety concerns into an open-ended permission system that protects incumbent firms from competition.
On Aug. 28, the Committee for Justice convened a panel to examine three questions at the center of the congressional AI debate: how advanced models should be tested, whether federal law should preempt state AI regulations, and why continued access to open-weight models matters for American innovation.
CFJ Executive Director Ashley Baker moderated the discussion with Kevin Frazier, director of the AI Innovation and Law Program at the University of Texas School of Law; Neil Siefring, senior fellow at Alliance for the Future; and Paul Steidler, senior fellow at the Lexington Institute.
The panel did not produce a single regulatory blueprint—and that was one of its strengths. Its members agreed that AI policy must become more transparent, technically informed, and nationally coherent. They differed, however, over where assurance testing should end and mandatory government review should begin.
Congress Has Moved From “Whether” to “What”
As Frazier explained, Congress has spent several years asking whether it should legislate on AI. It is now confronting the harder question of what it should legislate.
He divided current proposals into three broad categories. The first concerns frontier-model governance: who may release the most advanced models, under what conditions, and with what assurances. The second addresses AI’s economic effects, including workforce disruption, small-business adoption, and access to computing resources. The third deals with physical and digital infrastructure, from research computing to data centers and electricity demand.
Despite the volume of proposals, Frazier was skeptical that Congress would enact major AI legislation during the current session. That delay does not mean policymaking has stopped. It means consequential decisions may instead be made through executive action, voluntary agreements, state legislation, and private negotiations.
That is precisely why institutional design matters. A policy can address an immediate problem and still be the wrong long-term governing structure.
Testing Without an FDA for AI
All three panelists recognized a legitimate role for AI testing. Their disagreement concerned the form that testing should take and the legal consequences that should follow.
Steidler focused on models that could create imminent catastrophic risks, particularly through cyberattacks against financial institutions, hospitals, utilities, and other critical infrastructure. He supported a mandatory, time-limited review of the most sophisticated frontier systems before release.
At the same time, Steidler rejected an FDA-style approach to AI. Drug approval is famously prolonged, expensive, and restrictive. Importing that structure into AI could delay beneficial products, limit individual choice, and turn technical evaluation into a general government license to innovate.
Frazier also supported a testing framework. He emphasized that advanced models can display capabilities that were not apparent before deployment and that reliable evaluation is technically difficult, expensive, and resource-intensive. Testing is therefore necessary both to discover hazards and to give prospective users better information about systems they may place in sensitive environments.
But Frazier argued that any federal system must be authorized by Congress and administered transparently. Secret or ad hoc arrangements between executive officials and selected technology companies are not a substitute for public, prospective, and predictable law.
Siefring added another essential caution: testing should focus on identifiable threats and actual uses, not treat the mere existence of a model as proof of danger. Congress must also examine the effects of compliance requirements throughout the AI ecosystem. Large laboratories can hire specialized lawyers and absorb substantial fixed costs. Startups and smaller developers often cannot.
As Siefring put it, “Regulation can become a moat.”
That insight should shape every AI proposal. A rule may be facially neutral yet still protect incumbent firms by increasing the cost of entry. A safety regime that only the largest companies can navigate may reduce the very experimentation and competitive pressure that help expose weaknesses and produce better systems.
Expertise Without a New Bureaucracy
Congress cannot write detailed technical requirements that will remain current as AI develops. By the time lawmakers define a particular model, benchmark, or testing procedure in statute, the technology may already have changed.
Frazier therefore urged Congress to set the governing framework while relying on institutions capable of updating technical practices. He pointed to the Rules Enabling Act as one possible analogy: Congress establishes authority and procedures but does not itself write every operational detail.
He also called for greater investment in independent expertise. As leading researchers leave universities and enter a small number of frontier laboratories, knowledge about the technology becomes increasingly concentrated inside the companies government is supposed to evaluate. Frazier described this as the rise of a “Silicon Tower.”
The panelists saw a possible role for the Center for AI Standards and Innovation, which works within the National Institute of Standards and Technology on AI measurement, testing, voluntary standards, and national-security risks. Steidler favored strengthening the center and drawing on outside evaluators. Siefring agreed that government needs better access to expertise but cautioned against reflexively creating another bureaucracy. Congress should first use existing institutions, university partnerships, fellowships, and private expertise more effectively.
The proper division of labor is critical. Congress should establish public objectives, legal limits, oversight, and accountability. Technical experts can then develop and revise measurement practices without requiring Congress to amend a statute whenever the technology changes.
Preemption Without Erasing the States
AI systems operate across state lines. A model is not developed in California, deployed in Arizona, and then stopped at the New Mexico border. Fifty inconsistent development regimes would impose particularly heavy costs on smaller companies and could make the rules of the most restrictive states national by default.
Siefring therefore argued for meaningful federal preemption. But he emphasized that preemption does not mean laissez-faire, nor does it mean eliminating the states’ traditional police powers. States would continue to enforce generally applicable laws governing fraud, discrimination, contracts, consumer protection, property, and personal injury.
Frazier proposed a related distinction between AI development and AI deployment. The federal government would establish nationally uniform rules for development-level questions, such as training requirements, pre-deployment evaluations, and model-release standards. States would retain greater authority over how AI is deployed in particular settings, such as hospitals, schools, professional services, or state agencies.
That division is a promising starting point, although it will require careful drafting. “Development” and “deployment” are not self-defining categories, and nominally local requirements can sometimes affect how a product must be designed nationwide.
The goal should be a federal framework clear enough to provide real national certainty, yet narrow enough to preserve state authority over local harms and context-specific uses. Preemption should not become immunity, just as state regulation should not become a backdoor national licensing system.
Why Open Weights Matter
Open-weight models allow researchers and developers to download, study, modify, and build on a model’s underlying parameters. Siefring argued that maintaining access to those models is essential to American technological leadership.
The international competition over AI is not merely a contest over which country produces the most powerful model. It is also a contest over which country creates the most attractive ecosystem for researchers, entrepreneurs, and developers.
Restricting open weights will not end experimentation. It may simply determine where that experimentation occurs. If researchers cannot build on advanced models in the United States, they will look for jurisdictions and platforms where they can.
Open models can present legitimate security questions, but those concerns should be addressed through evidence-based and risk-specific measures. A categorical approach would sacrifice competition, independent research, and decentralized experimentation without guaranteeing that dangerous capabilities disappear.
Data Centers Are Not AI Regulation
The panel also addressed the political backlash against data centers. Steidler stressed that data-center siting is fundamentally a local land-use question, even though public anxiety about AI increasingly shapes those disputes.
The distinction is relevant because concerns about a proposed facility’s water use, electricity demand, noise, or effects on neighboring property should be evaluated on their own facts. General unease about AI should not become a substitute for that analysis.
Likewise, Congress should not use infrastructure policy as an indirect means of restricting AI development that lawmakers have chosen not to regulate directly. Limiting the electricity, computing resources, or facilities available to developers can function as regulation “through the loading dock”—with less transparency and fewer procedural protections than a direct law.
Rules That Can Learn
The webinar revealed a broad area of agreement beneath the panelists’ differences.
Congress should not tolerate an indefinite system of secret, voluntary, and improvised executive policymaking. It should strengthen access to independent expertise, focus federal attention on demonstrable risks, provide greater national certainty, and preserve the states’ ability to address traditional legal harms. It should also resist moratoria and hyper-detailed statutory requirements that could become obsolete almost immediately.
The hardest question remains whether mandatory pre-release review can be confined to truly extraordinary risks without evolving into a general permission system. If Congress chooses that path, the scope, criteria, deadlines, transparency requirements, and avenues for review must be unmistakably clear.
AI policy does not require a choice between ignoring risk and freezing innovation. It requires institutions capable of learning as quickly as the technology changes. The objective should be rules that help discover problems, allocate responsibility, and permit correction—not regulatory moats that presume government can certify uncertainty away.
Learn more about the webinar and its panelists here.
