Forecasting Research Institute × LEAP

Support for AI Safety Policies

Support for AI Safety Policies

Four of the seven policies win majority support in every group; federal preemption is the only one no group backs.

Every bar is Experts’ answers. Bars show the share of each group answering yes.

Support by policy

Share of experts answering yes for each policy

policyYes
P1. Federal Preemption
28.9%
P2.a Capability Slowdown (US)
33.5%
P2.b Capability Slowdown (US/China)
56.9%
P3.a Pre-Release Authorization (US)
68.6%
P3.b Pre-Release Authorization (international)
74.6%
P4. Strict Liability
60.3%
P5. Bundled (2-4)
45.5%

Click any cell to compare

Rationale analysis

The main lines of reasoning driving the spread in estimates

Support is conditional, opposition is not absolute

Although respondents gave yes-or-no answers, their rationales often indicate that their support is conditional and their opposition is not absolute. “I support the policy only if…” is a common sentiment, as is “I don’t support the policy as written, but would if…” The qualifications typically concern acceptable costs versus benefits, the competence of potential regulators, international participation, and the alternative against which enactment is judged.

Federal preemption

Opposition is the majority view, but much of it is centered on this specific version of preemption — which sets a ceiling but no floor — rather than the general concept: “I would change this answer if the policy were paired with a substantive federal standard”; “If it came bundled with a genuine federal standard I’d flip to yes, but as defined, no.” Among the substantial minority who support it, many cite the same concern when qualifying that support: “I support preemption if tied to strong federal AI security and control legislation.”

Where opposition is unconditional, the focus tends to be the value of state-level experimentation: “The individual state laws can continue to act as both incubators of new ideas and also as warning systems.” Unconditional support tends to focus on the value of uniform rules: “Better to have one set of federal rules than fifty different state ones.”

Capability slowdown

Supporters and opponents alike consider how much progress should be delayed, if at all, and which activities to constrain, rather than simply whether to back a slowdown. Many supporters acknowledge it could carry real costs, and many opponents accept it could buy useful time for safety work. There is a near-consensus that a bilateral version is preferable to a unilateral one.

US only. Opponents nearly always argue a unilateral cap would “allow China to get ahead in the race, in which case the risk from AI would likely increase drastically.” Others worry “domestic researchers [will] lose capacity for beneficial and defensive work.” Yet many opponents also express concern about the current pace: “A unilateral slowdown is not ideal in the long-term…But insofar as we're entering an exceptionally risky period of AI development right now (i.e., on the cusp of RSI), and the US is driving most of that risk, I think there are situations where a unilateral slowdown would be beneficial.” A recurring affirmative argument is that it preserves stronger options rather than working alone: “The risk reduction is small, but it could serve as precedent to make future more impactful policies more likely.”

US and China. Support is broad but typically conditioned on verification: “Only if it could be done in a manner that was reciprocal, verifiable, and narrowly tailored to focus on genuinely dangerous frontier capabilities.” Opponents tended to signal that their rejection was driven by verification concerns rather than the general merits: “[This policy] is inadvisable because it cannot be adequately verified.”

Pre-release authorization

Many favor an independent check before dangerous models become widely available, yet many also condition support on the quality of the reviewer: “Do I want pre-authorization if unqualified judges are exercising very subjective judgement? No.”

US only. Supporters emphasize that their support depends on the nature of the authorizing entity: “[Only] provided the reviewing bodies possess genuine technical competence and authority to block dangerous deployments”; “[Only] provided the process is based on clear capability and risk thresholds.” Opponents point to the same variable: “[It] relies on federal government being a competent judge”; “[It risks] political bias leakage into model vetting.” As one writes, “I desperately want such a policy to be implemented. I just don’t think the quality is likely to be that good.”

International. Most who support the US version express even more support for an international regime, usually because it “would reduce the incentive for companies to move development to jurisdictions with weaker rules.” Skepticism is still common: “I don’t fully trust how it would work in practice. But I’d accept it as part of the wider package.” Several opponents support international cooperation but not the entity described: “International coordination is valuable, but [shared] standards and reciprocal verification are more workable than a centralized body with sweeping authorization power.” Unconditional opposition often centers on concentration of power: “Pre-release authorization risks entrenching a small number of approved developers, reducing model diversity and concentrating correlated failures.”

Strict liability

Responses to this proposal are particularly polarized, but much of the polarization stems from the policy details rather than the principle. Supporters emphasize that it “gives the AI companies the financial incentives to create safe products [and] forces them to focus on real-world outcomes instead of jumping through regulatory hoops”; “is the only policy that creates no new agency, but instead can be managed by courts and insurers”; and “conscripts private capital into risk assessment, pricing danger that regulators are too slow to see.”

A substantial share of the opposition nods toward those effects but objects that “this survey’s version allows damages for expected unrealized harms and mandates insurance,” adding, “that could become effectively prohibitory, so I would vote ‘No’ on this formulation.” Others write: “No to the full package as written.”; “Too harsh on AI providers, would completely destroy them.” Some supporters doubt it would be enough: “Strict liability I think is probably good, but I don’t think it actually guards against catastrophic risk - at that point, who cares if the company goes under?”

The bundle

Many reject the full bundle because it includes a single policy they refuse to support: “[Strict liability] is a disqualifier”; “No, because I disagree with [international authorization].” Rather than rejecting it outright, some opponents describe the package they would accept: “I would support a revised bundle with workable financial assurance and proportionate, contestable remedies.”; “I would go ‘Yes’ on a bundle with [pre-authorization] and [strict liability], but not with the inclusion of a developmental slow down.” One respondent who answers No adds, “But…I would take this package if it was [the only thing] on the table.”

Support for the full bundle often reflects acceptance of an imperfect compromise. One respondent distrusts international authorization but would accept it if it helped secure “a joint slowdown and stronger accountability.” Another would accept the bundle “although I think strict liability is slightly harmful.”

Rationale examples

High-quality rationales from forecasters across the range

Pro-enactment respondents

I support capability slowdown policies, provided that they are targeted, risk-based and reversible. The main rationale is to increase the time available for safety research, institutional adaptation and the development of effective governance. A coordinated US-China slowdown is preferable to unilateral restraint because it reduces incentives for competitive acceleration and regulatory arbitrage.

The insurance component is potentially valuable because it introduces another party with financial incentives to question optimistic safety assumptions. There is something appealingly clarifying about asking an insurer to put actual capital behind the proposition that a frontier model is adequately controlled. I would, however, want the system designed so that insurance remains a genuine risk-pricing mechanism rather than becoming a prohibition disguised as actuarial science. If lawmakers demand coverage for hypothetical losses so large and poorly characterized that no insurer can price them, then the market has not discovered that the activity is unsafe; the legislature has simply banned it indirectly while making Lloyd’s of London deliver the news.

I would also support international pre-release authorization in principle, although I would be much more cautious about institutional design. An international body that includes both the United States and China could reduce regulatory arbitrage and create a shared mechanism for handling capabilities that are globally consequential regardless of where they were developed. The obvious danger is creating an organization whose authority is broad, whose technical capability is weak, and whose decision-making process becomes hostage to geopolitical bargaining. Humanity has produced several institutions in this general genre already, so this is not entirely a hypothetical concern. Still, if frontier systems eventually become powerful enough that a deployment decision in one country creates material risk everywhere else, some form of international governance becomes difficult to avoid.

I am a narrow ‘Yes’ on a U.S.–China slowdown because reducing race pressure around autonomous cyber systems, nuclear command-and-control, and premature military deployment has independent value, though I would prefer targeted agreements in those areas…[But] I am wary of broad policies giving governments open-ended ex ante authority to slow or block a general-purpose technology based on fuzzy capability benchmarks. Such powers could create distorted incentives, be repurposed for surveillance or geopolitical control, entrench incumbents, and suppress defensive uses of AI alongside dangerous ones. I would prefer narrow rules tied to concrete harms and specific high-risk applications.

Anti-enactment respondents

I oppose federal preemption in its current form, not because state AI law is well designed, much of it is not, but because preempting without substituting a federal standard removes the only…constraint that currently exists. I oppose a US-only slowdown for the obvious reason that it relocates frontier work rather than stopping it, and I oppose US-only pre-release authorization for the same reason plus a second one: a single national gatekeeper becomes a chokepoint that will be captured or routed around, and it converts a fast-moving technical judgment into an administrative process that will lag capabilities by years. The bilateral and international versions I support conditionally, conditional on verification being real and obligations being symmetric, because an unverifiable agreement is worse than none, giving the appearance of a constraint while the actual development continues unobserved.

The natural answer is to support those policies that might lead to a smaller probability of disaster. But then it is not so easy because most of these policies also favor regulatory capture and concentration of power in a few AI companies. If the productivity of intellectual work depends on AI access, and this access is highly controlled and monitored for the sake of safety, then what will happen is that white collar workers will amount for very little if whoever controls the AI decides to cut their access to it. In such circumstances political power will likely move from the middle class to the owners of AI…The prosperity of countries will not depend on the hard work of their people, but on compute. This is analogous to countries whose economies depend on a natural resource, which do not tend to be democracies.

I have answered No throughout, but this reflects uncertainty rather than opposition. Each of these policies has effects that depend heavily on how capabilities develop over the next decade, and I do not think anyone can currently predict that well enough to endorse binding commitments through 2050. Pre-release authorization could be sensible or could become the main bottleneck on beneficial development. Strict liability could internalise risk properly or could concentrate the field in the few firms able to carry the insurance. Unilateral US slowdown seems clearly worse than useless given China's position. I would rather see reversible, adaptive measures than the fixed regimes described here, and I would want any of them stress-tested against the case where the capability picture in 2035 looks nothing like the one now.

If I'm going to decline all five, I should offer something. The mechanism I find most promising isn't on this list. I'd want a structured channel through which competing firms can investigate and report risks in each other's models, with findings routed to a government agency that holds enforcement authority. The rationale is a comparative-advantage argument, and I think it's the strongest thing I have to offer here.