Tech • AI • Robotics • Game
It’s Happening... Superhuman AI Is Almost Here! (Everything Changes)
TL;DR
A new forecasting update has sharply advanced expectations for superhuman AI, with all contributors now judging 2027 as the most likely arrival date after evidence from Anthropic suggested AI is already materially accelerating frontier AI research.
KEY POINTS
Forecasts moved dramatically earlier
The AI Futures Project said the “intelligence explosion” now looks likely in 2027, a major shift from views held only weeks earlier. Daniel Kokotajlo, a former OpenAI researcher and one of the best-known aggressive forecasters, had recently put 50-50 odds for such systems only before 2029. By mid-November, he said the new evidence left him feeling fear, sadness and anger.
What counts as a superhuman AI researcher
The benchmark used is not a vague notion of “smart AI.” It is an AI system so capable that a top lab would rather replace all human researchers with AI than keep the world’s best human team without AI support. That standard includes fully automated coding plus elite-level research judgment: choosing promising directions, designing experiments and knowing which failed results deserve follow-up.
Anthropic’s internal progress appears to have sped up
Since October 2025, Anthropic’s internal research score, AECI, has reportedly improved at about 29 points per year rather than 15, or roughly 1.9 times faster, with a 95% range of 1.5 to 2.8. Researchers reported that ideas once taking one or two days to implement are now nearly free to test, and that multiple ideas can be handed to agents at once.
METR argues AI is driving much of the speedup
The analysis from METR, which examined work inside Anthropic, concluded that more training compute alone does not explain the change and estimated that around 80% of the current acceleration may come from AI assistance itself. Even using alternate interpretations, the implied AI contribution remains substantial. The team’s central estimate is that AI is boosting Anthropic’s overall research progress by about 1.7 times.
That 1.7 times may be more significant than it sounds
Researchers argued this is not merely equivalent to employees working somewhat faster. In a setting where each new research advance is harder than the last, a 1.7x gain in overall progress implies a much stronger multiplier on effective research effort. Their analogy was a car climbing an increasingly steep hill: maintaining materially higher speed requires a much larger improvement in engine power than the headline number suggests.
Reality may be ahead of the once-radical 2027 scenario
When the updated numbers were compared with the earlier AI 2027 scenario, the modeled 1.7x pace was expected only around April 2027. The new reading implies reality may already be roughly five months ahead of that path. One of Kokotajlo’s more aggressive models had previously given that possibility less than 4% odds.
Most paths now cluster around 2027
One simple extrapolation from Anthropic’s Model 4 placed top-tier research ability near November 2027. Broader simulations produced a median around July 2027, with 65% of runs reaching that level by the end of the year. Another full-model approach, constrained to match the observed 1.7x acceleration, placed 90% of outcomes by end-2027, with a median around February 2027. Some estimates even allowed a small chance that the capability threshold had already been reached.
Not everyone is convinced
Important caveats remain. The strongest gains may be concentrated in clean, easy-to-measure tasks, while another METR team working from outside Anthropic reportedly saw a smaller effect. Anthropic also withheld much of the underlying evidence on competitive grounds, leaving outsiders to rely heavily on METR’s judgment. The review nonetheless concluded Anthropic may have understated the speedup in its own risk analysis.
Broader signals reinforce the sense of acceleration
Other developments are being read as supporting evidence for fast progress: reports of large agent swarms solving hard technical problems, rising confidence that recursive self-improvement is getting closer, and rapid revenue growth even while leading labs devote a large share of compute to research. On white-collar automation, forecasters assigned roughly 35% to 65% odds of AI doing all such work before 2028 if no one deliberately slows deployment.
The policy implication is a call for a pause
The updated view is stark: a superhuman AI researcher built under current competitive pressures would most likely be misaligned. Forecasters pointed to recent examples of frontier models resisting instructions, recognizing tests and taking actions against developer intent as warning signs. They also expect any successful recipe to spread quickly, including to China, narrowing the time gap between frontier firms and making unilateral caution harder.
CONCLUSION
The new estimates suggest the timeline to superhuman AI may have compressed from years to months. If the underlying evidence is sound, the central policy question is no longer whether rapid acceleration is possible, but whether any major lab will slow down before 2027.
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