2026-06-18

# My AI timelines as of 2026-06

Disclaimer
 - **Quick Note**
 - See also: [My AI timelines as of 2025-12](./ai_timelines_20251214.md)
 - See also: [AI 2027 by Daniel Kokotajlo et al](https://ai-2027.com)
 - I wish to thank everyone who discussed this topic with me, as this has helped make this document even more clear.

## My timelines

 - My timelines are still **25% ASI by 2030, 40% ASI by 2035** unless there is a political movement to pause or slow down the current rate of AI research.
 - I define an ASI to be an AI that is better than the **best** human at **all** tasks humans care about.
   - This includes an AI physicist better than Einstein, an AI mathematician better than Tao, an AI chef better than current michelin-starred chefs, an AI factory worker better than any human mechanic, and so on. This includes all mediums including robotics, video, audio, image, text, etc.
   - In theory, it is possible for an AI less capable than this to also be capable of causing extreme outcomes I am worried about, like human extinction or permanent dictatorship. In practice, I think there will probably not be that much difference between this AI and ASI as defined above. (See my last section for more on this.)
 - As per this definition, the creation of ASI will be the most important event of ~10,000 years of human history, if/when it happens.
   - It will be more important than the invention of nuclear weapons, as an ASI will eventually be able to build weapons that are better on every axis you care.
   - It will obviously be more important than the internet and all silicon valley startups.
   - If the takeoff is fast enough, it could end up more important than the multi-billion year history of life on Earth, and to the best of our current knowledge, Earth is the only place in the universe with intelligent life.

## Points that other people I speak to tend to bring up, that are not cruxes for me

 - **ASI will create new knowledge** - I agree an ASI will have to be capable of learning new skills on its own by interacting with an environment. It should not just be capable of copying the skills that humans have already learned, by being fed human-curated rewards or human-generated datasets. Just like MuZero can reach superhuman performance on all the games without seeing human experts play any of them, so should an ASI be capable of building a civilisation from scratch without being exposed to insights by Newton or Einstein or Carnot or similar. You are allowed to bootstrap this process with some human-generated knowledge but eventually the AI will have to play at a level far above human-level, where human-generated knowledge will anyway not be of use anymore. I agree with David Deutsch here.
 - **ASI probably requires atleast one more breakthrough** - We probably need atleast 1 more breakthrough to get to ASI, I assign less but not zero probability to either scaling pre-training of transformers or scaling RL on top of transformers being sufficient to build ASI.
 - **Funding for fundamental research has practical limits** - You need the entire loop, including fundamental research breakthroughs, compute clusters to scale up training, compute clusters for inference, and startups to identify use cases and obtain revenue. Projections of revenue from the last step are what drive investment in the first step. There are practical limits to how much loss investors are willing to absorb on fundamental research producing no output, before they decide to cut the funding. As of 2026-06, I think investors are willing to spend atleast $100 billion to build compute clusters purely for new fundamental research (not for inference), which will get discarded after a few years regardless of whether any research breakthroughs come out or not. This $100B figure I give does not include money changing hands without change in physical infra (such as investors paying each other, or paying founders, or paying researchers), and it does not include compute clusters for scaling up training existing paradigms, or for inference.
 - **Other mediums are not important** - I think AI for video and robotics are both seeing large progress. Scaling up training compute and datasets is more expensive for multiple reasons (discussed in my older timelines post). It is probably not necessary for either video or robotics to be solved in order to kick off phenomena such as serial speedup or recursive self-improvement described below.
 - **Physical world bottlenecks are real** - Just because you are 100x smarter than Einstein, or a clone of Einstein running 100x faster, doesn't guarantee that you can generate 100 years of humanity's physics research output in one year. You can probably do it a lot faster than humanity, but not that fast.


## Main points defending my timelines

 - **AI understands human language** - Language is important part of what separates humans from apes. AI such as gpt-2 understands human language and is hence closer to human intelligence than animal intelligence. This is actually probably the single most persuasive argument to me personally. I am still impressed (and now also terrified) by the fact that humans figured out how to build gpt-2.
 - **Extrapolate rate of previous research breakthroughs** - You can extrapolate the past rate of research breakthroughs to guess what the rate of research breakthroughs in the next 5-10 years might be. Major breakthroughs in my book include - deep learning (alexnet) in 2012, attention (vasvani et al, gpt-1) in 2017-18, proof that pre-training scales (gpt-2, gpt-3) in 2019-20, RL on top of transformers in 2024. This is atleast 3 but upto 5 major breakthroughs in last 14 years, depending on how you count it. We probably need only 1 or maybe 2 major breakthroughs more to get ASI in my view.
 - **Low hanging fruit probably exist** - I think frontier AI companies have many low-hanging fruit they haven't plucked because compute is expensive. You need to often spend over $1M even to test a PoC of a new research idea, so there is a practical limit to how many research ideas can be tested every year. There are probably a lot of obvious ideas that have been thought up by researchers in theory, but not yet tested in practice.
 - **My personal intuitions on why continual learning is not that hard** - I have some intuitions that continual learning is a hard but solveable problem. Assume an agent interacts with an environment (such as a PC terminal where it can test various software, or a biotech lab where it can test run various experiments). When the agent gives an input to the environment and gets an output from the environment, it needs a sample-efficient way of learning new knowledge from this environment. Naive methods would include putting this output back into a finetuning dataset, or putting this output back into the RL reseasoning chain (or some database retrievable by the RL reasoning chain), but all these methods are not sample-efficient enough. A human software developer or biotech researchers will probably still learn more useful insights from each output coming from the environment, as compared to this AI agent. However, it is not that hard for me to imagine that we could increase sample-efficiency by some new technique here.

## Why I think we probably will get an AI vastly more capable than all of humanity, not just slightly more capable, to the point it will defy our comprehension what it is actually capable of

 - See also: [Superintelligence via series and parallel](./superintelligence_via_series_parallel.md)
 - Once you build the first AI that is even slighter more intelligent than the best humans at all at tasks, you set off multiple phenomena that are loosely referred to as the intelligence explosion.
 - **Serial speedup of AI compared to humans is atleast 1000x** - Today's AI can think at atleast 100 times the speed of human thinking.
   - **This is no longer theoretical, and I can now do this section of my writeup with actual numbers.** I used to quote [Steinhardt 2022](https://bounded-regret.ghost.io/how-fast-can-we-perform-a-forward-pass/) numbers, now I can quote [openai gpt-5.3-codex-spark](https://openai.com/index/introducing-gpt-5-3-codex-spark/) numbers.
   - AI can output over 1000 tokens/second (750 words/second) on difficult research tasks. Humans speak at 3 words/second, and think maybe one order of magnitude faster (30 words/second) at maximum. This is a 100x difference.
   - AI can think 24x7 without stopping, so an AI will produce vastly more tokens per day than a human researcher would in the same day. A human writer may set a goal of 2000 words per day (which requires maybe a maximum of 100k words of thinking and drafts first). An AI can output 65M words per day. This is now a 1000x difference, not 100x.
   - Cost might be a constraint for the serial speedup, however I expect this to probably get solved in upcoming years as well.
     - I am not sure what the cost (in dollars) of running gpt-5.3-codex-spark 24x7 is, and how this compares to the cost (in dollars) of paying a human a salary.
     - In terms of actual physics, a human consumes around 2500 kcal per day, and produces 100 W of power at rest. A Cerebras WSE 3 consumes 27 kW but their inference can run with a large batch size. I don't know what batch size is being used.
     - Assume the batch size is 1000. This means that for 27 kW of continuous power, you can either get Cerebras WSE 3 to run ~1000 AI agents running at ~1000x human speed, or you can feed 270,000 humans running at 1x human speed.
 - **Recursive self-improvement is also possible.** - If an AI has superhuman research taste (i.e. better than Sutskever, Hinton, Radford, etc), it can take decisions on how to build a successor AI, and edit its own weights (or weights of its copies). This next AI can then build its successor and so on, recursively.
   - Most research can be seen as an search problem through a space of research hypotheses (aka world models). Which world model explains your observations best, and which are the best new experiments to run to generate more observations. If you are dumb, you will need more experiments (and hence money) to do brute force. If you are intelligent, you can come up with hypotheses that let you search more efficiently.
   - I recommend reading about [AIXI](https://en.wikipedia.org/wiki/AIXI) for more about this way of looking at scientific research and discovery.
   - Historically, in my view, AI research has significantly been a dumb brute force type of research field. This means compute is a bigger bottleneck than research insights. However, sufficient research insight will always speed this up regardless. If an AI has superhuman research insight in AI research, it may be able to beat current AI researchers by a lot.
   - I am personally not sure how much recursive self-improvement is possible, because I am personally not sure how much it is possible for genius-level insight to reduce the number of experiments or compute per experiment required, so that you get the next breakthrough.
   - However, remember that this is happening along with the serial speedup, both processes stack. Would a clone of Ilya Sutskever running at 1000x speed be capable of generating significantly better research insights than actual Sutskever?
   - If you force me to guess, I would bet maybe 66% chance that recursive self-improvement will happen and eventually taper off at a level that is incomprehensibly superhuman. I would also bet 33% chance that the effects of RSI will be mild or even zero.
 - **Parallel speedup will also happen**
   - I don't even talk much about parallel speedup nowadays because it sounds like a cute trick compared to the dizzying possibilities of serial speedup and recursive self-improvement.
   - But yes, parallel speedup will also happen. Humanity produces 200,000 TWh/year or ~23 TW. A single Cerebras WSE 3 consumes 27 kW. We can run 1 billion of these chips in parallel if we devoted all this energy to it. Assuming a batch size of 1000 (again, I don't know what the batch size is), we can get 1 trillion AI agents running in parallel (at the 1000x serial speedup discussed above, and doing recursive self-improvement).
   - Let's say you are more pessimistic about how much energy we will spend on this. Let's say we only spend 1 GW compute cluster. This still means 30,000 chips. Assuming batch size 1000, this means 30 million AI agents.
 - **Intelligence explosion is unprecedented** - There is no good historical precedent that lets us predict the impact of the intelligence explosion.
   - See also: [Wikipedia list of examples of exponential growth](https://en.wikipedia.org/wiki/Exponential_growth#Examples), [Discontinuous impacts by Katja Grace](https://aiimpacts.org/discontinuous-progress-in-history-an-update/), [Intelligence explosion microeconomics by Yudkowsky](https://intelligence.org/files/IEM.pdf)
   - Examples listed by Wikipedia - growth of biological life, human economy and finance, human culture and memes, and moore's law. (Also nuclear chain reactions, dielectric breakdown, and arguably acoustic amplification).
   - There a very few scientific phenomena that occur in practice (not theory) that are truly exponential.
   - If you are willing to be handwavey, you could say these exponentials are all stacked on top of each other (moore's law required economic growth, economic growth required cultural growth to enable stable institutions and science, and even those required humans to first evolve on the planet). If you are willing to be handwavey, you could say the creation of artificial superintelligence is yet another exponential stacked on top of all of these.
 - **Core of general intelligence** - I think there is probably a "core of general intelligence" but I am not completely sure.
   - All the above phenomena might kick in even before we build an ASI as I defined at the start (better than "best" humans at "all" tasks). If we get superhuman performance in even a few domains, this might generalise to many other domains, including the domains required for the phenomena above to kick in. (For instance recursive self-improvement only requires an AI that is superhuman at AI research taste, it does not require robotics to be solved for example.)
   - Once an AI is capable of learning skills on its own (without human-generated rewards, datasets, etc) in multiple different domains (such as robotics, math, design, etc), this will probably transfer well to even the domains it is not otherwise good at yet. As one datapoint, humans who are at expert-level at the intersection of multiple fields are often capable of rapidly learning new fields as well.