In 2027 the trend that began in 2024 with OpenAI’s o1 reasoning system has continued. The compute required to run AI is no longer negligible compared to the cost of training it. Models reason over long periods of time. Their effective context windows are massive, they update their underlying models continuously, and they break tasks down into sub-tasks to be carried out in parallel. The base LLM they are built on is two generations ahead of GPT-4.
These systems are language model agents. They are built with self-understanding and can be configured for autonomy. These constitute proto-AGI. They are artificial intelligences that can perform much but not all of the intellectual work that humans can do (although even what these AI can do, they cannot necessarily do cheaper than a human could).
In 2029 people have spent over a year working hard to improve the scaffolding around proto-AGI to make it as useful as possible. Presently, the next generation of LLM foundational model is released. Now, with some further improvements to the reasoning and learning scaffolding, this is true AGI. It can perform any intellectual task that a human could (although it’s very expensive to run at full capacity). It is better at AI research than any human. But it is not superintelligence. It is still controllable and its thoughts are still legible. So, it is put to work on AI safety research. Of course, by this point much progress has already been made on AI safety—but it seems prudent to get the AGI to look into the problem and get its go-ahead before commencing with the next training run. After a few months the AI declares it has found an acceptable safety approach. It spends some time on capabilities research then the training run for the next LLM begins.
In 2030 the next LLM is completed, and improved scaffolding is constructed. Now human-level AI is cheap, better-than-human-AI is not too expensive, and the peak capabilities of the AI are almost alien. For a brief period of time the value of human labour skyrockets, workers acting as puppets as the AI instructs them over video-call to do its bidding. This is necessary due to a major robotics shortfall. Human puppet-workers work in mines, refineries, smelters, and factories, as well as in logistics, optics, and general infrastructure. Human bottlenecks need to be addressed. This takes a few months, but the ensuing robotics explosion is rapid and massive.
2031 is the year of the robotics explosion. The robots are physically optimised for their specific tasks, coordinate perfectly with other robots, are able to sustain peak performance, do not require pay, and are controlled by cleverer-than-human minds. These are all multiplicative factors for the robots’ productivity relative to human workers. Most robots are not humanoid, but let’s say a humanoid robot would cost $x. Per $x robots in 2031 are 10,000 more productive than a human. This might sound like a ridiculously high number: one robot the equivalent of 10,000 humans? But let’s do some rough math:
Advantage | Productivity Multiplier (relative to skilled human)
Physically optimised for their specific tasks | 5
Coordinate perfectly with other robots | 10
Able to sustain peak performance | 5
Do not require pay | 2
Controlled by cleverer-than-human minds | 20
5*10*5*2*20 = 10,000
Suppose that a human can construct one robot per year (taking into account mining and all the intermediary logistics and manufacturing). With robots 10^4 times as productive as humans, each robot will construct an average of 10^4 robots per year. This is the robotics explosion. By the end of the year there will be a 10^11 robots (more precisely, an amount of robots that is cost-equivalent to 10^11 humanoid robots).
By 2032 there are 10^11 robots, each with the productivity of 10^4 skilled human workers. That is a total productivity equivalent to 10^15 skilled human workers. This is roughly 10^5 times the productivity of humanity in 2024. At this point trillions of advanced processing units have been constructed and are online. Industry expands through the Solar System. The number of robots continues to balloon. The rate of research and development accelerates rapidly. Human mind upload is achieved.
It’s been 7 months since I wrote the comment above. Here’s an updated version.
It’s 2025 and we’re currently seeing the length of tasks AI can complete double each 4 months [0]. This won’t last forever [1]. But it will last long enough: well into 2026. There are twenty months from now until the end of 2026, so according to this pattern we can expect to see 5 doublings from the current time-horizon of 1.5 hours, which would get us to a time-horizon of 48 hours.
But we should actually expect even faster progress. This for two reasons:
(1) AI researcher productivity will be amplified by increasingly-capable AI [2]
(2) the difficulty of each subsequent doubling is less [3]
This second point is plain to see when we look at extreme cases: Going from 1 minute to 10 minutes necessitates vast amounts of additional knowledge and skill; from 1 year to 10 years very little of either. The amount of progress required to go from 1.5 to 3 hours is much more than from 24 to 48 hours, so we should expect to see doublings take less than 4 months in 2026, so instead of reaching just 48 hours, we may reach, say, 200 hours.
200 hour time horizons entail agency: error-correction, creative problem solving, incremental improvement, scientific insight, and deeper self-knowledge will all be necessary to carry out these kinds of tasks.
So, by the end of 2026 we will have advanced AGI [4]. Knowledge work in general will be automated as human workers fail to compete on cost, knowledge, reasoning ability, and personability. The only knowledge workers remaining will be at the absolute frontiers of human knowledge. These knowledge workers, such as researchers at frontier AI labs, will have their productivity massively amplified by AI which can do the equivalent of hundreds of hours of skilled human programming, mathematics, etc. work in a fraction of that time.
The economy will not yet have been anywhere near fully-robotised (making enough robots takes time, as does the necessary algorithmic progress), so AI-directed manual labour will be in extremely high demand.
But the writing will be on the wall for all to see: full-automation, including into space industry and hyperhuman science, will be correctly seen as an inevitabilit, and AI company valuations will have increased by totally unprecedented amounts. Leading AI company market capitalisations could realistically measure in the quadrillions, and the S&P-500 in the millions [5].
In 2027 a robotics explosion ensues. Vast amounts of compute come online, space-industry gets started (humanity returns to the Moon). AI surpasses the best human AI researchers, and by the end of the year, AI models trained by superhuman AI come online, decoupled from risible human data corpora, capable of conceiving things humans are simply biologically incapable of understanding. As industry fully robotises, humans obsolesce as workers and spend their time instead in leisure and VR entertainment. Healthcare progresses in leaps and bounds and crime is under control—relatively few people die.
In 2028 mind-upload tech is developed, death is a thing of the past, psychology and science are solved. AI space industry swallows the solar system and speeds rapidly out toward its neighbhors, as ASI initiates its plan to convert the nearby universe into computronium.
Why do I expect the trend to be superexponential? Well, it seems like it sorta has to go superexponential eventually. Imagine: We’ve got to AIs that can with ~100% reliability do tasks that take professional humans 10 years. But somehow they can’t do tasks that take professional humans 160 years? And it’s going to take 4 more doublings to get there? And these 4 doublings are going to take 2 more years to occur? No, at some point you “jump all the way” to AGI, i.e. AI systems that can do any length of task as well as professional humans -- 10 years, 100 years, 1000 years, etc.
...
There just aren’t that many skills you need to operate for 10 days that you don’t also need to operate for 1 day, compared to how many skills you need to operate for 1 hour that you don’t also need to operate for 6 minutes.
[4] Here’s what I mean by “advanced AGI”:
By advanced artificial general intelligence, I mean AI systems that rival or surpass the human brain in complexity and speed, that can acquire, manipulate and reason with general knowledge, and that are usable in essentially any phase of industrial or military operations where a human intelligence would otherwise be needed. Such systems may be modeled on the human brain, but they do not necessarily have to be, and they do not have to be “conscious” or possess any other competence that is not strictly relevant to their application. What matters is that such systems can be used to replace human brains in tasks ranging from organizing and running a mine or a factory to piloting an airplane, analyzing intelligence data or planning a battle.
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.
Here’s some near-future fiction:
In 2027 the trend that began in 2024 with OpenAI’s o1 reasoning system has continued. The compute required to run AI is no longer negligible compared to the cost of training it. Models reason over long periods of time. Their effective context windows are massive, they update their underlying models continuously, and they break tasks down into sub-tasks to be carried out in parallel. The base LLM they are built on is two generations ahead of GPT-4.
These systems are language model agents. They are built with self-understanding and can be configured for autonomy. These constitute proto-AGI. They are artificial intelligences that can perform much but not all of the intellectual work that humans can do (although even what these AI can do, they cannot necessarily do cheaper than a human could).
In 2029 people have spent over a year working hard to improve the scaffolding around proto-AGI to make it as useful as possible. Presently, the next generation of LLM foundational model is released. Now, with some further improvements to the reasoning and learning scaffolding, this is true AGI. It can perform any intellectual task that a human could (although it’s very expensive to run at full capacity). It is better at AI research than any human. But it is not superintelligence. It is still controllable and its thoughts are still legible. So, it is put to work on AI safety research. Of course, by this point much progress has already been made on AI safety—but it seems prudent to get the AGI to look into the problem and get its go-ahead before commencing with the next training run. After a few months the AI declares it has found an acceptable safety approach. It spends some time on capabilities research then the training run for the next LLM begins.
In 2030 the next LLM is completed, and improved scaffolding is constructed. Now human-level AI is cheap, better-than-human-AI is not too expensive, and the peak capabilities of the AI are almost alien. For a brief period of time the value of human labour skyrockets, workers acting as puppets as the AI instructs them over video-call to do its bidding. This is necessary due to a major robotics shortfall. Human puppet-workers work in mines, refineries, smelters, and factories, as well as in logistics, optics, and general infrastructure. Human bottlenecks need to be addressed. This takes a few months, but the ensuing robotics explosion is rapid and massive.
2031 is the year of the robotics explosion. The robots are physically optimised for their specific tasks, coordinate perfectly with other robots, are able to sustain peak performance, do not require pay, and are controlled by cleverer-than-human minds. These are all multiplicative factors for the robots’ productivity relative to human workers. Most robots are not humanoid, but let’s say a humanoid robot would cost $x. Per $x robots in 2031 are 10,000 more productive than a human. This might sound like a ridiculously high number: one robot the equivalent of 10,000 humans? But let’s do some rough math:
Advantage | Productivity Multiplier (relative to skilled human)
Physically optimised for their specific tasks | 5
Coordinate perfectly with other robots | 10
Able to sustain peak performance | 5
Do not require pay | 2
Controlled by cleverer-than-human minds | 20
5*10*5*2*20 = 10,000
Suppose that a human can construct one robot per year (taking into account mining and all the intermediary logistics and manufacturing). With robots 10^4 times as productive as humans, each robot will construct an average of 10^4 robots per year. This is the robotics explosion. By the end of the year there will be a 10^11 robots (more precisely, an amount of robots that is cost-equivalent to 10^11 humanoid robots).
By 2032 there are 10^11 robots, each with the productivity of 10^4 skilled human workers. That is a total productivity equivalent to 10^15 skilled human workers. This is roughly 10^5 times the productivity of humanity in 2024. At this point trillions of advanced processing units have been constructed and are online. Industry expands through the Solar System. The number of robots continues to balloon. The rate of research and development accelerates rapidly. Human mind upload is achieved.
This sounds highly plausible. There are some other dangers your scenario leaves out, which I tried to explore in If we solve alignment, do we die anyway?
It’s been 7 months since I wrote the comment above. Here’s an updated version.
It’s 2025 and we’re currently seeing the length of tasks AI can complete double each 4 months [0]. This won’t last forever [1]. But it will last long enough: well into 2026. There are twenty months from now until the end of 2026, so according to this pattern we can expect to see 5 doublings from the current time-horizon of 1.5 hours, which would get us to a time-horizon of 48 hours.
But we should actually expect even faster progress. This for two reasons:
(1) AI researcher productivity will be amplified by increasingly-capable AI [2]
(2) the difficulty of each subsequent doubling is less [3]
This second point is plain to see when we look at extreme cases:
Going from 1 minute to 10 minutes necessitates vast amounts of additional knowledge and skill; from 1 year to 10 years very little of either. The amount of progress required to go from 1.5 to 3 hours is much more than from 24 to 48 hours, so we should expect to see doublings take less than 4 months in 2026, so instead of reaching just 48 hours, we may reach, say, 200 hours.
200 hour time horizons entail agency: error-correction, creative problem solving, incremental improvement, scientific insight, and deeper self-knowledge will all be necessary to carry out these kinds of tasks.
So, by the end of 2026 we will have advanced AGI [4]. Knowledge work in general will be automated as human workers fail to compete on cost, knowledge, reasoning ability, and personability. The only knowledge workers remaining will be at the absolute frontiers of human knowledge. These knowledge workers, such as researchers at frontier AI labs, will have their productivity massively amplified by AI which can do the equivalent of hundreds of hours of skilled human programming, mathematics, etc. work in a fraction of that time.
The economy will not yet have been anywhere near fully-robotised (making enough robots takes time, as does the necessary algorithmic progress), so AI-directed manual labour will be in extremely high demand.
But the writing will be on the wall for all to see: full-automation, including into space industry and hyperhuman science, will be correctly seen as an inevitabilit, and AI company valuations will have increased by totally unprecedented amounts. Leading AI company market capitalisations could realistically measure in the quadrillions, and the S&P-500 in the millions [5].
In 2027 a robotics explosion ensues. Vast amounts of compute come online, space-industry gets started (humanity returns to the Moon). AI surpasses the best human AI researchers, and by the end of the year, AI models trained by superhuman AI come online, decoupled from risible human data corpora, capable of conceiving things humans are simply biologically incapable of understanding. As industry fully robotises, humans obsolesce as workers and spend their time instead in leisure and VR entertainment. Healthcare progresses in leaps and bounds and crime is under control—relatively few people die.
In 2028 mind-upload tech is developed, death is a thing of the past, psychology and science are solved. AI space industry swallows the solar system and speeds rapidly out toward its neighbhors, as ASI initiates its plan to convert the nearby universe into computronium.
Notes:
[0] https://theaidigest.org/time-horizons
[1] https://epoch.ai/gradient-updates/how-far-can-reasoning-models-scale
[2] such as OpenAI’s recently announced Codex
[3] https://www.lesswrong.com/posts/deesrjitvXM4xYGZd/metr-measuring-ai-ability-to-complete-long-tasks?commentId=xQ7cW4WaiArDhchNA
...
[4] Here’s what I mean by “advanced AGI”:
https://web.archive.org/web/20181231195954/https://foresight.org/Conferences/MNT05/Papers/Gubrud/index.php
[5] Associated prediction market:
https://manifold.markets/jim/will-the-sp-500-reach-1000000-by-eo?r=amlt
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.