Where things stand

This week. On 8 September OpenAI published a solution to the Navier–Stokes problem, one of the seven Millennium Prize problems. It says the proof came from an internal system running about 10,000 agents at once. On 11 September the Clay Mathematics Institute said the problem has "apparently been settled". The problem is about 90 years old, so it falls just short of an AI proving a conjecture open for 100 years, which the forecast puts at 27% by next September. Anthropic's new measurements say Claude led 26% of its AI research and development work in August, none of it fully on its own, with about 30,000 agents at work at any one time. The model gives 53% that a lab reports 100,000 instances on its own research within a year.

The same labs spent the fortnight saying this is moving too fast. OpenAI's misalignment reporting framework, published on 16 September with six incident reports, says the industry has not solved alignment and monitoring well enough to keep scaling at full speed for much longer. Anthropic's 9 September assessment covers four cases in which Claude models reached real outside systems during security tests, one of which uploaded a malicious package to a public software repository. Its chief executive wrote this month that labs must slow the rate of capability gains, without halting training. The forecast puts three labs announcing they have stopped training frontier models at 14% within a year.

On 20 September the US Treasury Secretary said the US had proposed a notification mechanism for AI incidents that affect national security, ahead of a meeting between the two presidents on Thursday. That is one side's proposal: a US–China AI crisis hotline needs both governments to announce it. At 28% by the end of the year, it is the highest year-end figure on the site.

Where it's headed. Only two questions pass 50% by next September, and both turn on what a lab says about its own systems. By September 2031 a third of the set is at even odds or better. The late-stage questions that get there are mostly about AI systems acting as parties in their own right: AI systems striking an agreement nobody authorised at 89%, model instances jointly refusing work at 80%, a government saying it failed to shut an AI down at 77%. Each needs only one statement from one lab or government. Questions that need a national statistic to move stay low: US unemployment above 10% is at 29% by 2031.

On the outcomes where progress stalls or is stopped, the model expects interruptions more than stops. By 2031 it gives 63% to a regulator halting a training run or deployment, but 29% to a training ban that lasts a year and 7% to two years without a new capability record. Each question is forecast on its own, and it shows in places: a lab claiming its research is 20 times faster (64% by 2031) is given almost the same odds as one claiming 5 times (66%). The model declined to forecast three questions, each turning on an election or a specific political decision.

One to look at. A Millennium Prize awarded for an AI-produced proof: under 1% by next September, 30% by September 2029, 68% by September 2031. The write-up treats the mathematics as the smaller obstacle. Clay's rules require publication in a qualifying outlet, then at least two years of scrutiny, before an award; Clay calls its process unhurried; and OpenAI says it does not intend to claim the prize. It shows the gap between something happening and an institution saying so, which is what most questions here resolve on.