> It does not assume that progress will be in LLMs
If that's the case then there's not as much reason to assume that this progress will occur now, and not years from now; LLMs are the only major recent development that gives the AI 2027 scenario a reason to exist.
> You have have 2 AIs, then 4, then 8.... then millions
The most powerful AI we have now is strictly hardware-dependent, which is why only a few big corporations have it. Scaling it up or cloning it is bottlenecked by building more data centers.
Now it's certainly possible that there will be a development soon that makes LLMs significantly more efficient and frees up all of that compute for more copies of them. But there's no evidence that even state-of-the-art LLMs will be any help in finding this development; that kind of novel research is just not something they're any good at. They're good at doing well-understood things quickly and in large volume, with small variations based on user input.
> But the thought experiment doesn't seem indefensible.
The part that seems indefensible is the unexamined assumptions about LLMs' ability (or AI's ability more broadly) to jump to optimal human ability in fields like software or research, using better algorithms and data alone.
Take https://ai-2027.com/research/takeoff-forecast as an example: it's the side page of AI 2027 that attempts to deal with these types of objections. It spends hundreds of paragraphs on what the impact of AI reaching a "superhuman coder" level will be on AI research, and on the difference between the effectiveness of an organizations average and best researchers, and the impact of an AI closing that gap and having the same research effectiveness as the best humans.
But what goes completely unexamined and unjustified is the idea that AI will be capable of reaching "superhuman coder" level, or developing peak-human-level "research taste", at all, at any point, with any amount of compute or data. It's simply assumed that it will get there because the exponential curve of the recent AI boom will keep going up.
Skills like "research taste" can't be learned at a high level from books and the internet, even if, like ChatGPT, you've read the entire Internet and can see all the connections within it. They require experience, trial and error. Probably the same amount that a human expert would require, but even that assumes we can make an AI that can learn from experience as efficiently as a human, and we're not there yet.