It seems that your argument is based on high confidence in a METR time-horizon doubling time of roughly 7 months. But the available evidence suggests the doubling time is significantly lower.
In recent years we have observed shorter doubling times:
And what we know about labs’ internal models suggests this faster trend is holding up:
An important piece of evidence is OpenAI’s Gold performance at the International Mathematics Olympiad (IMO):
IMO participants get an average of 90 minutes per problem.
The gold medal cutoff at IMO 2025 was 35 out of 42 points (~83%)
They needed to get 5⁄6 problems fully correct (each question awards a maximum of 7 points), or a number of points equivalent to that.
This is a bit rough, but if their model had a METR-80 greater than 90 minutes then we would expect OpenAI to achieve Gold at least 50% of the time.
OpenAI staff members stated that a publicly released model of this capability could be expected at roughly the end of the year (and our METR trends are of course projections of publicly available models).
So, this implies a METR-80 greater than 90 minutes at December 2025.
The projected METR-80 according to a 3.45 month doubling time is 98 minutes.
So the Gold performance which was a massive surprise to many is actually right on-trend for 3.45 month doubling times. Of course, one might object that OpenAI may have just gotten lucky. But Google also got Gold! so we have two points of data.
And here’s a recent comment from Sam Altman where he states that he expects time-horizons days in length in 2026:
and as these go from multi-hour tasks to multi-day tasks, which I expect to happen next year
Which a 7 month doubling time would not achieve, but which is in line with a doubling-time of 3.45 (that would get us to a time-horizon of roughly 3 days in December, 2026).
And here’s a recent comment from Jakub Pachocki:
Near-Future Fiction III
September 2026
Knowledge worker productivity has become relatively uncoupled from pre-ChatGPT levels, as the hardest technical tasks which these workers did at that point in time in a given working day can now in most cases be carried out autonomously by AI.
Programmers therefore begin to work at a higher level of abstraction, guiding AI workers, managing projects at a higher level.
Meanwhile, much progress is being made in robotics. Full self-driving has been achieved.
And AI has begun making novel breakthroughs. This enables continual learning: the AI’s new discoveries open up many new avenues for further discoveries, which open up many more such avenues, ad infinitum.
Image from a recent OpenAI talk
December 2026
Successful reinforcement learning on the September worker AIs has enabled AI to operate at that higher level of abstraction which software engineers had retreated to. Human knowledge workers are therefore relegated to maintenance work and helping out when the few remaining weak points in these AI systems cause trouble.
The difficulty of progress in AI intelligence relative to human intelligence begins reducing rapidly as time horizons extend beyond a few hours. At horizons of this length, human begin relying on caching tricks, iteration, brute force, etc. rather than, beyond a certain point, making fundamentally more difficult leaps of insight.
Early 2027
Humans are cut out of the loop entirely in knowledge work. The robotics explosion happens. Robots gradually replace humans in physical labour. AI progresses far beyond human-level.
Mid 2027
Humans fully obsolesce. Mind upload is achieved.
Notes
I assume a 3-month METR doubling time. We should expect lower doubling times over time given increased investment in AI, increased contribution by AI to progress, and decreased difficulty per double. Also, OpenAI has communicated that we should expect several major breakthroughs from them in 2026.
We should expect doubling times to decrease even further with time, although in a discontinuous way so it’s impossible to predict with much accuracy when it will happen.