My AI Opinions - by Scott Alexander - Astral Codex Ten

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My AI Opinions<br>...

Scott Alexander<br>Jun 11, 2026

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I recently had a minor spat over someone misinterpreting my AI beliefs (see section marked “Update” at the bottom here), so I thought I would list them in one place, so I can refer people when they ask.<br>Timelines1

Define AGI as AI intelligent enough to do 90% of knowledge work jobs. I think there’s a 25% chance of AGI by 20272, a 50% chance by 2034, and a 75% chance by 2045.

Basic argument: In a certain sense, AI is already “smart” enough for this (eg it can answer quantum physics problems, which require higher IQ than most knowledge work). Its remaining limitations are that it’s confused, unagentic, lacks situational awareness, and tends to hallucinate. The METR time horizon graph, and several other related benchmarks/experiments/intuition pumps, suggest it’s improving on time horizons at an (exponential) rate that lets it cross human-level performance sometime around the early end of the schedule above, and subjectively it feels like harder-to-measure constructs like situational awareness are improving about as fast.<br>Arguments for earlier: recursive self-improvement causes a speedup compared to the trend. This is one of the biggest blank spots in my model: I don’t know how fast RSI will progress, and I don’t think anyone else does either. There’s some function mapping a combination of AI talent and compute to progress, and we don’t know how it behaves in the domain when there’s far more talent than compute available. It could fizzle out completely for lack of compute, or it could go vertical. The AI Futures Project has done some of the best work trying to model this, but even they have low confidence.<br>Arguments for later: AI hits some kind of wall, or existing AI is fundamentally unsuitable for jobs in some way currently disguised by its other limitations. For example, it might be much harder to improve at the top of the human range than the bottom (since there are less training data). Or AI could become bottlenecked on continuous learning/memory in a way that hackish scratchpads can’t compensate for. Or the upcoming world compute bottleneck (about ~2028) could prevent further progress more than expected (because in fact algorithmic progress depended on compute to a greater degree than I expected).<br>Arguments for very late dates, past 2045: a residual uncertainty that maybe I’m fundamentally wrong about everything. Also contributing is a naive overapplication of the Nothing Ever Happens heuristic, and an attempt to leave space for the Outside View argument (ie that some smart people like the AI As A Normal Technology Team seem to think this is possible).<br>Define the diffusion gap as the time between the AI that could do 90% of knowledge work jobs, and the time when AI does do even half of knowledge work jobs. The diffusion gap covers the time it takes to release AGI, diffuse it through society, overcome regulatory hurdles, and onboard/train it for specific use cases. This could go very fast (the AI quickly becomes superintelligent at orchestrating AI diffusion) or very slowly (there are regulatory barriers, and AI isn’t smart enough to plow through them). I think there’s a 25% chance the diffusion gap is less than 3 years, and a 50% chance it’s less than 10 years. The 75% number is irrelevant because it’s past the point where other changes make the concept of “diffusion” obsolete.

Basic argument: diffusion is very hard. Everyone agrees diffusion is very hard. The whole field of AI economics is smart experts shouting “You fools who think AI will diffuse quickly don’t understand that diffusion is very hard!” On the other hand, the personal computer diffused in about 20 years (that is, from the time PCs became invaluable for most jobs, it was only about 20 years before they were used at most jobs). So far early-stage AI has diffused faster than the PC in nearly every way (for example, AI companies’ revenue has grown faster than PC companies’ revenue at the same stage in their corporate life cycle), so 10 years is probably a naive median estimate here that won’t make the smart experts shout at me too hard.<br>Arguments for shorter gap: AI can orchestrate its own diffusion. Adopting computers is hard because a company need an IT department, cybersecurity experts, specialist software, etc, and it might not want to hire all these people. AGI can itself do all of that work, so that you can sign a contract with the AI company today and have the AI start working on integrating itself with your systems tomorrow. The AI can even come up with a plan to train your human employees in how to use it! Once AI reaches superintelligence, this consideration dominates.<br>Arguments for longer gap: Regulation. This is a very strong argument, and responsible for much of the greater-than-3-years probability and almost all the greater-than-10-years probability. But even Waymo has...