Implementation of AI Safety Policies

Most respondents expect at least some substantial policies to be enacted by a jurisdiction hosting a frontier model by 2050: “The EU and China already regulate and states keep passing laws.”; “Twenty-five years without a single substantial measure in any frontier-hosting jurisdiction requires either that AI never produces a salient enough harm to force legislative attention, or that legislatures repeatedly fail to respond when it does. Under my capability assumptions the first is very unlikely, so the status-quo world is essentially the world where regulation is attempted and consistently fails to pass, a real possibility over five years, an implausible one over twenty-five.”

The minority who deem an extension of the status quo likely tend to agree that complete regulatory gridlock is unlikely, but emphasize that “LEAP has set a pretty high bar [by requiring] ‘similar or greater stringency’ compared to those listed in this survey” — and that this high bar is what makes a perpetuation of the status quo, as the survey defines it, likely.

Most respondents who rank this policy as likely by 2030 do so because they perceive it to be “the only policy with genuine near-term momentum: a White House framework asserting federal authority [and] draft bipartisan legislation.” Others see preemption as forming “the bedrock of any national [policy] framework.” As one writes, “if you have something like a [pre-]authorization regime in the US, you actually would want the states not to be able to monkey around with development.” Still others point to historical precedent: “Drawing on the history of industries such as aviation and telecommunications, interstate cloud-distributed AI architectures will inevitably move toward a unified national regulatory ceiling.”

Skeptics emphasize public opposition: “Given the recent incidents (OpenAI Hugging Face, agents escaping their sandboxes during tests, etc.) it’s pretty unlikely that people would vote for a federal preemption on state AI law.”; “Polls show bipartisan opposition to federal preemption of state AI laws”; “The Senate’s 99–1 rejection of the 2025 AI moratorium demonstrates substantial resistance to surrendering state authority.”

Even after the Hugging Face incident, most respondents are skeptical that safety concerns will be enough to prompt a mandatory slowdown of the kind this policy envisions. Many note the bar is particularly high, requiring a “binding cap on compute allocated to frontier R&D, with independent verification” rather than the weaker measures they consider more likely: “A possibility is a timid capability slowdown, where compute will be capped just a little bit below the expected projections, but not as much as to meaningfully affect the actual progress of AI.”

US only. The most common barrier respondents cite is ceding advantage to China: “A compute cap materially below prevailing practice would directly cede ground in a race no participant believes they can afford to lose, so unilateral US action is implausible.” A significant minority disagree. One argues that while a “unilateral capabilities slowdown is not really in the US strategic interest…if [the] risk from RSI [recursive self-improvement] is large enough, it would enact it.” Others note the domestic constituency for slowing AI is growing, and not primarily over safety: “If the political driver is displaced workers rather than safety, then slowing development isn’t the regrettable cost of the policy — it is the point…It may well be adopted under safety framing, but the operative motive would be employment.”

US and China. Most rate this as even less likely than the unilateral version, because neither country trusts the other sufficiently and verification would be exceptionally difficult: “It would require governments to monitor private companies, something arms-control agreements have generally not done.” Some believe a joint measure is more likely precisely because reciprocity removes the competitive element: “I think it’s more likely that both countries come to an agreement than one of them do it on their own.” Many expect “a sub-catastrophic incident or near miss” could force an agreement.

Many consider pre-release authorization more likely than a mandated slowdown, since compliance could require “relatively little sacrifice” from the developer and need not “prohibit internal development.” But that logic applies only to unilateral mandates; few respondents think China or the US will submit to a binding international authority soon.

US only. Respondents frequently note the existing system, while nominally voluntary, is close to what the policy proposes: “This already seems to be in place, at least informally, with pre-release vetting of Mythos and Astra.” One points out that “pre-release authorization gives the government sight of frontier models before anyone else, which is useful for testing its own systems, for offensive cyber capability, and for intelligence work against adversaries. That access is valuable to the state independent of any safety rationale.” Many note historical precedent: “A sufficiently capable AI system eventually being treated more like an aircraft, pharmaceutical, reactor, or other safety-critical technology does not require a particularly large conceptual leap.” Doubters focus on the mandatory component: “Mandatory affirmative authorization, including genuine power to deny deployment, is a large step beyond voluntary review.”

International. Most express deep skepticism: “The likelihood that either, much less both, would agree to subordinate a matter with such grave national security implications to an international body, is slim.”; “Nuclear non-proliferation succeeded because it rested on both broad international consensus and the substantive existence of military enforcement against defectors. Both are absent for AI-specific authorization.” Optimists note “there are precedents for supranational authorization,” in particular “the IAEA template,” and that it only “asks each side to submit releases for review rather than to slow development, which is an easier sell.”

Many think greater liability requirements for AI companies are likely, but distinguish that from the policy as the survey defines it: “Strict liability also seems likely a fairly conventional policy tool, although the particular regulatory regime described here in the survey is disruptive enough that I would not forecast quick adoption of such a regime.”; “The statute must cover every frontier developer, require insurance scaled to capability, and permit punitive damages without malice or recklessness based partly on catastrophic harm that never occurred. Insurability, due process, standing, attribution, political opposition, etc. are major barriers.” Skeptics also expect “tremendous opposition from the US business lobby.”

More optimistic respondents look past the stringency and see a familiar domestic tool: “This condition is common in certain domains with inherent risks, such as demolitions, hazardous chemicals, nuclear power, etc. The potential risks of AI are not exactly minor, and my guess is that eventually, as it diffuses into more and more sectors of the economy, strict liability will be standard for frontier AI models.”

Many give the bundle low odds because all three demanding components must coexist, with one calling it “too conjunctive to be very likely. What are the chances that all three of these things will happen in that particular way?”

Several expect correlated adoption instead: “The bundle is high because one big scare drives all three policies at once.”; “A world in which one of them happens is a world in which the others are also more likely to happen.” A few dispute that, arguing governments might not want all three once one approach is established — one expects an international slowdown would probably indicate some level of nationalization had occurred, and that “we’re no longer in a standard kind of civil liability regime that strict liability would sensibly apply to.”