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Notes -
AI 2040: Plan A
The AI 2027 authors published a follow-up. Scott Alexander also wrote a separate blogpost and although not in the author list contributed.
It's a very speculative and optimistic timeline of AI's future evolution. It presents five ways or "plans" the US government will intervene. Unsurprisingly, the ASI-pilled authors favor strong, global regulation to ensure alignment. Summaries:
Plan A (recommended): the US makes an international treaty with China, pauses AI training (not inference, i.e. no new models but we keep using existing ones), enforces full transparency of future research, then when alignment research advances enough carefully resumes
Plan S: the US makes an international treaty with China and pauses AI training for as long as possible
Plan B: the US regulates AI at home and demands China also regulate, but doesn't negotiate with them, probably leading to a war
Plan C: the US regulates AI and ignores China, so they overtake it and reach ASI first
Plan D: the US doesn't regulate AI, we get ASI in early 2031 and it probably kills everyone
Personally, I just don't share the optimism of these guys in either direction.
I think politicians will prioritize culture war and the failing economy over AI regulation, and at most pass some executive orders suggesting companies be more careful. But I also doubt we'll have ASI that can solve the abstract problems "take over the world" or even "keep existing world leaders in power" (they're getting old and increasingly unpopular, their parties may remain in power but only if their policies significantly shift).
What I expect from AI:
Basically solve legacy code by rewriting entire codebases, applying very niche domain knowledge, and actually finding and handling edge-cases better than humans
Greatly speedup research, leading to new discoveries and inventions. Important but background things like food preservation and medicine will improve from AI-assisted discoveries. Major advancements in math and theoretical physics
Much better and cheaper education, therapy, initial medical/legal appointments, personal repairs...maybe reducing but not eliminating human jobs, because human experts will offer these services "premium"
Won't replace human artists. Some advertisements and infographics will be AI but even some will still be human. At best it will assist them in a way where the human still fully controls the output, e.g. by generating code leading to new and improved software tools to learn, practice, and create art
Used by the vast majority as a personal assistant, but doesn't replace human relations
Maybe someone here can help me with this.
What is the bull case, beyond drawing lines on a graph, for AI achieving superhuman, or even human, performance on tasks that are not quickly verifiable?
AI is quite clearly superhuman at self-contained programming problems. I haven't tried Fable, but I suspect that superhuman open ended software engineering is not far away, though I suspect that humans will have a role in architecture and problem setting as opposed to problem solving for some time more. I expect hardware work will also quickly go down this path, at least to some extent, and really anything that can be RLVR'd. That's enough to account for a huge portion of white collar work and carries serious cyber security risks. Both of those will have serious consequences, politically and militarily.
I am not convinced that AI is improving at anything like this rate for things that can't be RLVR'd, I.e. stuff where you can't generate enormous amounts of useful training data with an answer key. Radiologists continue to do just fine for themselves despite repeated promises of doom. I'm sure someone will chime in to say that the radiologists are there for liability reasons, but it's not as if they are now just hitting thumbs up/thumbs down on AI decisions all day.
Partly this is a sample efficiency question - there simply might not be enough data for them to learn this stuff to human level, and architectural advances that improve sample efficiency may lead to huge gains in quality. But it's not clear to me why people expect this to happen.
I would say Fable is already superhuman at software in general. It's much faster than I am at writing and debugging code and exhibits a high degree of decent taste. The only problem is I run out of tokens so fast. The writing code part is impressive enough but the way it can just look at buggy programs and bang out 50 line test scripts to isolate bugs and test hypotheses is something else entirely. I just watch in astonishment as it does debug cycles that would take me 1-3 hours at a time (plus one coffee) that it does in a minute or two. This is all from my weak user reports like "it doesn't work when I do thing X".
If I were an employer I would definitely pay something like $500-1000/day to arm a senior developer with Fable than I would hire a second senior developer.
But LLMs are getting freakishly good at things they haven't been specifically trained on. Their intelligence does generalize.
Perhaps we only need to RL them in a few more domains to clinch the rest of generalized superintelligence. E.g. you can have them pilot robots and put them in virtual environments and RL fast them there, or real environments like an academy (a warehouse) a bit less fast.
I agree the sample efficiency is terrible and a large limiter and it falls back to RL and we need at least one more architectural breakthrough. But in 2026 I certainly wouldn't bet against AI labs with armies of Fable agents at their disposal and seemingly infinite investment dollars sorting this out.
Such as?
There's been impressive seeming advances in robotics, though I'm not keeping up too closely. I don't see the connection between operating a warehouse and superintelligence though. Certainly the humans operating the warehouse are not superintelligent.
I meant to use "warehouse" to de-hand wave "an academy". Like just put robots in a big space far away from people and give them diverse tasks to train on. I did not mean to literally imply we'd put them to work in a warehouse and simulate them.
The aim is not directly "build better box stacking robots", it's "we're reaching limits on what we can teach by training on words/code/math so maybe we can get the rest of the way there by doing enough different real world tasks and just from having robots amble about in an environment that we unlock general intelligence".
Training on words on the internet has limits so next lets train agents embodied in spaces, virtual and physical.
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