An AGI broadly useful for humans needs to be good at general tasks for which currently there is no way of finding legible problem statements (where System 2 reasoning is useful) with verifiable solutions. Currently LLMs are slightly capable at such tasks, and there are two main ways in which they become more capable, scaling and RL.
Scaling is going to continue rapidly showing new results at least until 2026-2027, probably also 2028-2029. If there’s no AGI or something like a $10 trillion AI company by then, there won’t be a trillion dollar training system and the scaling experiments will fall back to the rate of semiconductor improvement.
Then there’s RL, which as o3 demonstrates applies to LLMs as a way of making them stronger and not merely eliciting capabilities formed in pretraining. But it only works directly around problem statements with verifiable solutions, and it’s unclear how to generate them for more general tasks or how far will the capabilities generalize from the training problems that are possible to construct in bulk. (Arguably self-supervised learning is good at instilling general capabilities because the task of token prediction is very general, it subsumes all sorts of things. But it’s not legible.) Here too scale might help with generalization stretching further from the training problems, and with building verifiable problem statements for more general tasks, and we won’t know how much it will help until the experiments are done.
So my timelines are concentrated on 2025-2029, after that the rate of change in capabilities goes down. Probably 10 more years of semiconductor and algorithmic progress after that are sufficient to wrap it up though, so 2040 without AGI seems unlikely.
An AGI broadly useful for humans needs to be good at general tasks for which currently there is no way of finding legible problem statements (where System 2 reasoning is useful) with verifiable solutions. Currently LLMs are slightly capable at such tasks, and there are two main ways in which they become more capable, scaling and RL.
Scaling is going to continue rapidly showing new results at least until 2026-2027, probably also 2028-2029. If there’s no AGI or something like a $10 trillion AI company by then, there won’t be a trillion dollar training system and the scaling experiments will fall back to the rate of semiconductor improvement.
Then there’s RL, which as o3 demonstrates applies to LLMs as a way of making them stronger and not merely eliciting capabilities formed in pretraining. But it only works directly around problem statements with verifiable solutions, and it’s unclear how to generate them for more general tasks or how far will the capabilities generalize from the training problems that are possible to construct in bulk. (Arguably self-supervised learning is good at instilling general capabilities because the task of token prediction is very general, it subsumes all sorts of things. But it’s not legible.) Here too scale might help with generalization stretching further from the training problems, and with building verifiable problem statements for more general tasks, and we won’t know how much it will help until the experiments are done.
So my timelines are concentrated on 2025-2029, after that the rate of change in capabilities goes down. Probably 10 more years of semiconductor and algorithmic progress after that are sufficient to wrap it up though, so 2040 without AGI seems unlikely.