AI 2027 authors now forecast superhuman AI researchers in 2027 — five months ahead of schedule

The authors of AI 2027 just moved the intelligence explosion forward. In a timelines update published on Tuesday, all six authors of the AI Futures Project's forecast now say superhuman AI researchers will likely arrive in 2027 — and they put a 35–65% chance on AI that can automate all white-collar work arriving before 2028.

The reversal is the story. For more than a year the authors had been pushing their own dates later: their February grading put real progress at roughly 65% of the scenario's pace, and by August it was 70–90%. What changed their minds is a single outside measurement — METR's November 5 assessment of AI research and development at Anthropic, which found the lab improving its models about twice as fast as its historical rate, with roughly 80% of that acceleration coming from AI itself.

What METR found#

METR, an independent evaluator, spent eleven weeks inside Anthropic — August 12 to October 29 — with access to internal data and interviews with about fifteen staff. Its headline finding: as of October 2026, Anthropic is likely improving the capabilities of its models on well-specified tasks at around twice the rate it managed between March 2024 and October 2025. METR says it would not be surprised if the true figure were anywhere from 1.3x to 3x.

The hard number behind that judgment comes from AECI, Anthropic's internal version of the Epoch Capabilities Index. Benchmark scores had been rising about 15 points a year until October 2025; since then they have risen about 29 a year — a 1.9x slope, with a 95% confidence interval of 1.5x to 2.8x. Anthropic's researchers also told METR, in informal polls, that AI assistance was making them about 12 times faster by October 2026 — self-reported, and METR flags the figure as loose evidence.

The AI Futures authors translate METR's numbers into their own terms: if about 80% of the current acceleration is due to AI, the AI-attributable speed-up in the pace of progress is about 1.7x — which puts the world roughly five months ahead of where the AI 2027 scenario assumed it would be.

The new forecast, author by author#

The update is striking less for any single number than for the unanimity. Previously, Daniel Kokotajlo — the most bullish of the authors — assigned only a 50% chance of a superhuman AI researcher before 2029. In the group's last update, in mid-August, his 50% marks were an automated coder in December 2027 and a superhuman researcher in December 2028. Now every author puts the superhuman researcher in 2027.

The group's combined call: a 35–65% chance that top-expert-dominating AI — systems better than top human experts at virtually all tasks, able to automate all white-collar work — arrives before 2028. SAR, for the authors, is roughly the point where a company would rather fire all its human researchers and rely solely on automated AI researchers than keep a team of the best humans with no AIs.

A model trajectory — not a prediction#

The post also shows one example trajectory from the group's AI Futures Model: an automated coder on November 27, 2026, a superhuman AI researcher on April 12, 2027, and superintelligence on July 1, 2027. That is a single illustrative run of the model, not the authors' forecast — their own medians are slower, and the post stresses that the analysis is preliminary and may contain errors. But it shows what the model's logic implies once you feed it a 1.7x acceleration: the climb from automated coder to superintelligence compresses into about seven months.

The underlying index tells the same story. The Epoch Capabilities Index has been growing 1.28 times faster than its 2024-to-mid-2025 pace since May, and 1.77 times faster since November 1 — a steepening the authors did not expect to see this soon.

The caveats are doing a lot of work#

Read the update carefully and the confidence limits are everywhere. METR's most important evidence is evidence the public cannot see: Anthropic redacted a majority of what METR found most informative, and the 80% attribution figure — the number the whole 1.7x calculation rests on — has no comparable public support. Anthropic's own system card for Fable 5.2 is ambiguous on the point: it says only that Claude Mythos 5.2 “does not double” the internal rate of AI research and development, and that the lab will discuss overall trends in a future report.

The authors are candid that their modelling could be wrong in either direction — their most aggressive distribution, from less than three months ago, assigned under a 4% chance to the pace of progress being sped up by 1.7x in November 2026. On Reddit, where four threads in r/singularity and one in r/accelerate carried the update through the day, the reception was mostly endorsement, with the sharpest doubts aimed at the slowdown call rather than the numbers — though the top comment on one thread called a sub-a-year timeline “insane unlikely”, and the original poster on another raised the possibility that METR's report overstates the case.

Why the authors want a pause#

The update does not stop at forecasting. If superhuman researchers arrive in 2027, the authors write, it is “incredibly urgent to pause or substantially ‘pace’ frontier AI development” — ideally an industry-wide pause followed by an international agreement. Their reasoning is blunt: companies are not on track to align a superhuman researcher, and using a misaligned one to do recursive self-improvement would put humanity “in an extremely bad position”. They add a second warning: if such a system is trained soon, China will probably be able to steal the training recipe, and possibly even the model weights.

That is a harder sell than the forecast. One of the most-upvoted replies in the largest thread argued there will be no slowdown “unless a catastrophic event occurs” — and that such an event would trigger “an even more dangerous and aggressive race”. The authors' own history cuts both ways: they have been right often enough that the shift is hard to dismiss, and wrong often enough — their timeline has moved in both directions — that the exact dates deserve skepticism.

What to watch#

Three things will test the new timeline fastest. First, whether Anthropic releases enough of the redacted METR evidence for outsiders to check the 2x finding — the authors explicitly urge it to. Second, whether the automated-coder milestone the model puts at November 27, 2026 actually lands on schedule; it is dated within weeks, so the wait is short. Third, whether any lab or government takes up the pacing call, or whether the race simply accelerates into the window the authors now think is open. The forecast is a warning as much as a prediction — and its authors are now saying the window is months, not years.