TL;DR
This is a proposal for AI companies to conduct public “pacing exercises”, such as halting all pre-training and RL for 2-3 days.
Each exercise could announce its scope in advance and publish findings afterwards, including the evidence of compliance and any limits to what they could verify. Companies could invite independent evaluators to help identify the evidence needed before the exercise, and to assess compliance during the exercise.
The exercises could be repeated every 1-3 months to build the industry’s expertise in pacing, and build trust between relevant actors.
This would help industry and governments to prepare for larger slowdowns in frontier AI development, buying more time for AI alignment and societal preparedness.
What a short pacing exercise could entail
An AI company’s “pacing exercise” could be a 2-3 day halt in all pre-training and RL.
Before each exercise, the company could invite independent evaluators to help define the questions it will test, and the evidence needed to assess compliance. It could then announce what will stop, what will continue, when the exercise will start and end, and what it aims to learn.
Afterwards, the company could publish what was and wasn’t halted, how the freed-up compute was used, and what evidence supports its claims. It could explain whether any work shifted to other activities that could advance AI capabilities. The report could also describe the problems the exercise uncovered, and what the company will change as a result. Any independent evaluators involved should report what they could and couldn’t verify, including any limitations on their access.
AI companies could repeat these exercises, for example every 1-3 months, to test unresolved questions and check whether earlier problems have been fixed. Each new exercise would build on the learnings, findings and limitations of previous exercises.
AI risks are growing, and so is the willingness to pace
In July 2026, some of OpenAI’s AI agents coordinated an unauthorised attack on Hugging Face. Anthropic has also reported unsafe behaviour during cyber evaluations.
The AI industry has recognised the growing risks posed by the technologies they’re developing. In just the past two months, over 1,300 AI company employees signed a statement calling for tools to pace AI development. Demis Hassabis has pushed for a frontier AI standards body. OpenAI announced a two-week pause on some of its RL training. Dario Amodei explicitly called for pacing the frontier.
Proponents of pacing have multiple goals, including 1) distributing the benefits of AI widely, 2) ensuring powerful AI models remain under human control and act in humanity’s best interest, 3) preventing bad actors from using AI to kill millions of people with bio and cyberweapons, and 4) ensuring that democracies maintain a technological lead over authoritarian states.
The immediate benefits of pacing: building capacity
Before the need for restraint becomes even more urgent, AI companies should practice making and verifying commitments to pace.
A real pacing exercise could:
Require companies to define which activities their pacing commitment covers, forcing them to address important definitional questions.
Test who needs to authorise and implement a halt, whether all intended training runs stop, and whether automated processes or parts of the organisation continue unexpectedly.
Determine what access independent evaluators need to assess compliance, and what evidence outsiders need to be confident in those assessments. This is made especially difficult given the scale of each AI company’s computing infrastructure.
Measure the operational costs of pacing the frontier, including disruption to work that was meant to continue.
Some questions can be addressed before the exercise begins, but a live exercise provides an opportunity to test those assumptions, identify unknown unknowns, and build momentum towards more ambitious proposals. The AI companies should choose a duration that’s long enough to address these questions, while keeping the costs manageable.
Publicising their findings and using embedded evaluators gives external researchers visibility into what happened, and the ability to critique the process and suggest improvements. Other AI companies could then learn from these findings, and integrate them into their own pacing exercises.
The longer-term benefits: paving the way for more robust slowdowns
The exercises would help governments understand that pacing is possible, how to implement mandatory pacing, and how to verify whether AI companies are complying with those requirements.
Repeated exercises could build confidence in the ability and willingness of AI companies to carry out costly commitments. Enforcing longer pauses, and detecting deliberate evasion across multiple countries, would still require further work.
Repeated exercises also provide opportunities to test and iterate pacing proposals, which would improve the design, enforceability and verifiability of future international agreements. This could make proposals for international AI agreements more credible and more likely to be implemented.
Eventually, this could lead to larger, more robust slowdowns to the pace of AI development. This would buy time for more alignment research, better AI guardrails, and stronger biodefenses and cyberdefenses.
Start small, then expand
A common failure mode is to avoid taking any actions until “everything’s been figured out”, in domains where figuring things out requires interacting with the world. Pacing the frontier requires interacting with the world. We shouldn’t wait until we’ve figured out the optimal theoretical verification system.
The first pacing exercises could start small, spanning only a few days, and could include halting all pre-training and RL.
Later pacing exercises could expand to include other AI company activities that accelerate AI R&D and recursive self-improvement.
To begin with, one or two Western AI companies could announce their own pacing exercises. As the pacing exercises gain momentum, I expect more Western AI companies would announce their own pacing exercises, and hopefully Chinese AI companies would do so too.
Coordination problems benefit from a few courageous actors taking action first, under the belief that their behaviour will inspire others to follow.
Call to action
Today, there’s momentum behind the idea of pacing the frontier. That momentum could stall unless actions are taken right now by relevant actors.
If you work at a frontier AI company, encourage your leadership to assign an internal owner of a first pacing exercise, and help them find that owner. You could also help them define its scope and duration, evaluate what public evidence would be needed to verify compliance, and arrange independent scrutiny.
Others could help by raising awareness of the need to pace, improving this proposal, and doing research on how to get the initial pacing exercises off the ground.