AI is capable of revolutionary advances in mathematics. Machine learning research is not different in kind.
No, it's different in kind:
To take your own example, Anthropic's latest research shows that Claude is leading (not autonomously running) 26% of their AI model R&D work as of August 2026. Though that’s increasing pretty quickly, it shouldn't hit 100% before Christmas. In contrast, I think we can safely say that Claude is handling much more than that percentage of their mathematics work (such as it is). We can also see that while mathematics capabilities have advanced tremendously recently, other capabilities such as writing have not advanced as much. There is not full generalization out-of-distribution here. Capabilities remain spiky.
Now, that said, in principle, yes ASI is possible by Christmas. Slow takeoff is not a promise, even if it's been true so far. I think you're right that continual learning, or some comparable research breakthrough, would be the most likely candidate, and it could happen at any time.
I think you're also right that organized swarms of AI agents could contribute to faster-than-usual capabilities gains, but having looked into that a bunch in the past few days, it's not clear to me that we are yet in a place where coordinating swarms meaningfully increase effective compute over parallel agent work. Noam Brown at OpenAI said on Dwarkesh's podcast today that they aren't. Rather, they're still just a way to spend more money to get results faster. That does have RSI implications, but under current limited compute budgets.
I think a good example is the Noam Brown interview with Dwarkesh; they're not compute constrained on running agents in parallel, they're compute constrained on those agents running ML experiments.
Effective long-horizon continual learning is likely the last remaining step to superintelligence. It could be a harder problem than a Millennium Prize problem; I consider that unlikely.
Why do you think this? Cf. https://www.lesswrong.com/posts/sTDfraZab47KiRMmT/views-on-when-agi-comes-and-on-strategy-to-reduce#_We_just_need_X__intuitions
The pace of progress being continuously underestimated is a good point. But it could also be that mathematics research is an especially verifiable case that has historically received far less effort overall than ML research
Intuitively for me, this probability is probably not 0% but it's low. If it does happen my money is on a primarily taste-driven explosion. Basically the AI gains superhuman taste on less-verifiable parts of ML research and is now able to design better and more ingenious algorithms and training methods than any human ever thought of. (Rather than the humans coming up with ideas, the AIs would now come up with their own high-level creative ideas which are superior to the human ones.)
It would also have good enough project management taste to effectively lead the whole project end to end. That way the algorithmic efficiency gains might compound dramatically quickly with the existing amount of compute available at frontier AI labs, and the main bottleneck being sequential agent run time and training time as they design and test ever-better methods and replace themselves with the next model in a loop.
Whether we get "superintelligence by Christmas" or "AI 2027" in that scenario would then depend on by how much the AIs can exceed human judgement and how much algorithmic gains each increased step in judgement contributes to even more increases in judgement (multiplied by training and development speed the AIs can achieve).
I note they don't have AL5 on the graph yet; I think that would need to appear at least a week or two before fully automated RSI. I wonder if they'll keep the dashboard up to date daily.
I think it is plausible a strong form of recursive self-improvement[1] is imminent or already underway, and that we may be on track for superintelligence by Christmas of this year if racing continues.
This is substantially faster than any forecast, including ones like AI 2027 that were considered outrageously fast a year ago. It is faster than I myself expected even a week ago. I don't work at a scaling lab. I don't know more than is public knowledge.
Let me be perfectly clear: what I am saying is absolutely nuts. Extraordinary claims require extraordinary evidence. I claim we have now received said evidence and you should update accordingly.
FOOM should probably should be your *default expectation*.
People have strong status quo bias. Your default expectation should be that things will radically speed up.
We are not at the ceiling of intelligence.
We should probably expect the transition to superintelligence to be incredibly fast. [2]
RSI is a positive feedback loop, so it is inherently (hyper)exponential. Everything is an S-curve eventually, but nothing suggests the ceiling is anywhere near human level, or that it happens at a human timescale.
AI is capable of revolutionary advances in mathematics. Machine learning research is not different in kind.
Navier-Stokes was resolved with a counterexample, which is generically easier than a positive resolution. OpenAI has told the press it has substantial progress on a second Millennium Prize problem. Rumours name the Hodge conjecture for OpenAI [ and Birch–Swinnerton-Dyer for Anthropic. It is difficult to overstate just how incredibly hard these problems are.
Machine learning engineering is not substantially different from mathematics: fundamentally AI lives on computers. There is no or little 'real-world friction'.
A lot is made of progress being confined to domains with verifiable objectives. So the argument goes: AI can solve well-posed problems with a checker, and most of the world has no checker. There is some truth to this but it overstates the case.
These mathematical problems (Millenium prize problems, FrontierMath4) are well-defined but they have no clear gradient: you don't get 30% of a proof for 30% of the work. So this was long-horizon search without a dense reward. Regardless, progress in AI has overwhelmingly come from simple hillclimbing and picking low-hanging fruit.
Effective long-horizon continual learning is likely the last remaining step to superintelligence. It could be a harder problem than a Millennium Prize problem; I consider that unlikely. [3]
The speed of AI progress continues to be underestimated; by superforecasters and even by the researchers themselves.
Even after two years of enormous progress and a lot of updating ('feeling the AGI'), forecasts have still trailed what actually happened. You Should Update On This.
Navier-Stokes and FrontierMath Tier 4 fell substantially ahead of every schedule I know of (numbers below). This should update you toward hyperexponential FOOM scenarios, not 'slow' takeoff.
Actual: 40% at end of 2025, 94% in September 2026; Navier Stokes Millenium prize problem settled last month, with rumors that two more are solved as well. Even the developers of the technology itself are surprised by the speed - Noam Brown, the RL lead of OpenAI says he was taken aback at the pace of progress and hesitates to forecast beyond three months.
Internal models are significantly ahead of released ones;
Intuitions from working directly with publicly available models is may be misleading about the pace of progress. The Navier-Stokes model, which started training on 28 August, is said to be "significantly more capable" than GPT-6 Astra. Anthropic has a model "somewhat more capable" than Mythos 5 it does not plan to release. Claude writes a large majority of the code merged into production. OpenAI's own report of 6 September says its top 10% of researchers now spend $7,000 a day on tokens. That is about $2.5M a year per researcher, against a median new-hire salary of about $1.5M. The median researcher spends $600 a day, up from $162 in July. Agent labour-hours passed human labour-hours inside OpenAI in June and stood at 3.14x by mid-August.
Intuitions about timing from pre-training runs are misleading since most progress comes from RL, unhobbling and algorithmic innovations
In the previous paradigm the pre-trained models were already more than enough; unhobbling and RL was what was left.
One such unhobbling is cooperating agent swarms.
Enter the Swarm
OpenAI and Anthropic seem to have discovered how to use many instances of the same model ['agents'] together much more effectively. This massively multiplies the effective brainpower that can be brought to bear on one problem. 10,000 × 88 hours is 880,000 agent-hours. Navier-Stokes was solved by a swarm of 10,000 agents. As a rule of the thumb firm productivity grows roughly with the square root of the number of employees so my guess is that this buys something like 100x over a single model.
Anthropic's own report states it has 30,000 agents running concurrently, and Claude has completely taken over 26% of all R&D.
Linear extrapolation gives complete RSI summer of 2027. It's beginning to look a lot like FOOM.
There are several interpretations of RSI. The weak version, AI doing most of the coding, is already happening. The strong version would be fundamentally new advances, not just scaling of previous approaches. Eg Strawberry/o1 & reasoning models or Astra's neuralese.
In Hanson's model of growth modes [animal brains, humans, farming, industry] each mode grew about 100x faster than the one before, and each transition took a small fraction of the previous mode's doubling time. On his numbers the next mode doubles every week or two and the transition takes a few years at most. Note that while we have seen a somewhat continuous takeoff so far; that does not preclude FOOM.
Astra reasoning in pure neuralese, with no chain-of-thought, looks like a moderate performance improvement, slightly ahead of cautious trend extrapolation. That may understate it: neuralese is plausibly the key unblocker for effective continual learning.