A three-year AI pause could buy 1000 years' worth of AI safety work.

We developed a model of an AI pause that estimates how much AI safety research it produces. We don't know what the pause will look like, so we modeled five durations, four AI capability levels and four levels of trust in AI. We’ll use it to improve Plan A, but it already shows a few things:

We also prepared an explainer for a less technical audience on why even a short AI pause is good.

We are grateful to Eli Lifland, Adam Banksmith, and Misha Yagudin for fruitful discussions.

Capabilities increase quickly, unlike our understanding of alignment. The AI race makes its participants push capabilities, and the most likely solution is some kind of pacing, whether an international deal , domestic regulation or maybe even coordination between the labs .

Imagine that the top three US labs somehow pause the capabilities race and redirect their resources to AI safety research. What would that pause look like? And how much AI safety research would it produce?

AI safety is an overloaded term. Here we mean experimental research that improves our understanding of AI and helps predict its behavior:

We appreciate theoretical work and think it's likely to become a bottleneck once everything else is automated, but it seems to be quite a small part of 2025 AI safety work. Governance is important (that's basically our message), but here we focus on the technical understanding of AI.

These kinds of AI safety experiments are often bottlenecked by GPU compute. Let's calculate how much compute was spent on AI safety in 2025 and compare it with how much compute we would get if the top three AI labs redirected their 2027–2029 compute to AI safety.

In 2025, about 5,000 full-time researchers worked on AI safety and published over 7,000 papers. Their experiments used as much compute as 27,000 H100s running for a year (see our estimates ).

We sum up the AI labs' experimental and training compute for 2027–2029 and divide it by the AI safety compute in 2025.

Why didn't we call this article "10,947 years of AI safety"? Because in the real world:

To account for that, we developed an AI Pause model.

Feel free to jump straight to the Implications section on your first read.

We build on the AI Futures Model ( AIFM ), a semi-endogenous growth model of the AI takeoff. We modified it:

Adding xAI and Meta, or Chinese labs, would bring even more resources and so more years of 2025 AI safety work. Here we model only the three labs together. They don't need to actually merge: it's enough for them to see each other's research results, so they don't duplicate work. There will still be some diminishing returns and stepping on toes, but AIFM already models these inside one lab.

As in AIFM, research combines experiment compute and the coding labor of humans and AI coders, each with diminishing returns. Research effort weights it by the labs' research taste.

AIFM turns the research stock into software efficiency , which is a kind of final output in semi-endogenous growth models. In AIFM, it is software efficiency; in semiconductor research, it could be transistor density ( Bloom et al., 2020 ). In AI safety, the real output is hard to measure: "reducing the risks of AI takeover and misuse" or "improving our understanding of intelligence". Although these are the outputs we actually care about, we can neither measure nor model them, so we model AI safety work instead.

Safety work is not an ideal proxy for risk reduction because of diminishing returns. A thousand years of AI safety work won't reduce risk a thousand times as much as the 2025 safety research did. Still, conditional on continued AI scaling, we think more safety work reduces risk monotonically.

See the AIFM recap and our modifications here .

AIFM's takeoff assumes full trust in AI. That is dangerous, and during a pause, humans would not fully trust AI. We don't know how humans will verify AI-generated research, so we modeled different trust protocols, each adding a new check to the previous one. From here on, we count only the research that humans verified.

AIFM's research is an amorphous mass, but humans verify research in pieces. We cut research into papers: pieces of research that verifiers check.

For papers, we need to decide how much research effort each paper covers. It depends on the paper's compute, the coding labor behind it and the quality of its idea. These are random variables, so a paper's research effort is also a random variable. This distribution and the known total research effort let us estimate the total number of papers.

Humans verify the most important papers first. Each check takes a constant time to verify the paper's result and a growing time to read the prerequisites.

See the full Results Verification model here .

What if it's difficult to figure out which papers are the most important? See this supplement .

Results Verification lets the frontier AI run code on the labs' clusters. Compartmentalization adds separation of duties. Once AI is smarter than the best human AI researchers (SAR level), we no longer let it act as an agent and use it only as an oracle that suggests research plans. We review those plans, and if they seem harmless, we give them to a less capable model (AC level) that implements them.

It's a pretty standard idea in the AI safety community, discussed as AI oracles or AI boxing. Here are some papers with similar protocols:

See the full Compartmentalization model here .

One of the reasons we got interested in modeling the pause was AI 2040's Verification Plan . There, AI companies declare each workload with its full code, and auditors approve it before it runs. To model it, we added auditors who read all the code before letting it run.

Plan A didn't discuss Results Verification or Compartmentalization, so why do we use them? First, Plan A isn't detailed enough to model as is, so we made some necessary assumptions. Second, we aren't sure whether we need Results Verification in this case, but Workload Approval is heavily bottlenecked by auditors, so removing Results Verification barely changes the results (we checked).

Here are some ideas for how these models can be extended.

See how we estimated the input parameters here . See the sensitivity analysis here . To estimate several parameters, we collected a dataset of 2020–2025 AI safety papers. See it here .

If you plan to elicit useful work from AI during the pause, you face a trade-off :

What is the Pareto-optimal setup? How do you make decisions about the trade-off between risk and usefulness?

We developed our main contribution, the AI Pause model, to answer these questions. We are going to use it to come up with optimal setups in future work, but it already gives interesting results.

We don't know how many verifiers the labs would have during a pause, so we model two hiring strategies: the labs keep the staff they had at the start of the pause, or they hire as many verifiers as they need.

With Compartmentalization, the labs' own staff keep up for about three and a half years. By the end of a nine-year pause, they need ~150k more verifiers. That's seven NeurIPS reviewer pools, reading full-time.

But do we need all of them? Humans review the most important papers first. In the most extreme scenario (Results Verification, TED-AI, nine years), hiring all 2.7 million yields only 129% more AI safety research. For shorter pauses and lower capability levels, it changes less. Hiring an additional 130k verifiers in the Compartmentalization scenario (SAR, nine years) adds only 20% more research.

However, if you add external auditors who manually approve all code, they quickly become the bottleneck. In Workload Approval, with 20,000 auditors who review code as fast as Google's engineers do, a three-year pause keeps 33% of Compartmentalization's AI safety years at SAR and 31% at TED-AI. The verifiers stop being the bottleneck, because the labs write so few papers that their own staff check all of them.