New untested ideas take unpredictable time to develop. Given the current timeline of pure compute/investment scaling, there is no particular reason for all bottlenecks to be cleared just in time for scaling to continue without slowing down. Hence the possibility of it slowing down at the upcoming possible bottlenecks of natural text data and available-on-short-notice hardware, which are somewhat close together.
Sample efficiency (with respect to natural data) can in principle be improved, humans and some RL systems show it’s possible, and synthetic data is a particular form this improvement might take. But it’s not something that’s readily available, known to subsume capabilities of LLMs and scale past them. Also, straying further from the LLM recipe of simulating human text might make alignment even more intractable. In a universe where alignment of LLMs is feasible within the current breakneck regime, the source of doom I worry about is an RL system that either didn’t train on human culture or did too much reflection to remain within its frame.
Compared to natural text, multimodal data and many recipes for synthetic data give something less valuable for improving model competence, reducing return on further scaling. When competence improvement slows down, and if AGI in the sense of human-level autonomous work remains sufficiently far away at that point, investment scaling is going to slow down as well. Future frontier models cost too much if there is no commensurate competence improvement.
New untested ideas take unpredictable time to develop. Given the current timeline of pure compute/investment scaling, there is no particular reason for all bottlenecks to be cleared just in time for scaling to continue without slowing down. Hence the possibility of it slowing down at the upcoming possible bottlenecks of natural text data and available-on-short-notice hardware, which are somewhat close together.
Sample efficiency (with respect to natural data) can in principle be improved, humans and some RL systems show it’s possible, and synthetic data is a particular form this improvement might take. But it’s not something that’s readily available, known to subsume capabilities of LLMs and scale past them. Also, straying further from the LLM recipe of simulating human text might make alignment even more intractable. In a universe where alignment of LLMs is feasible within the current breakneck regime, the source of doom I worry about is an RL system that either didn’t train on human culture or did too much reflection to remain within its frame.
Compared to natural text, multimodal data and many recipes for synthetic data give something less valuable for improving model competence, reducing return on further scaling. When competence improvement slows down, and if AGI in the sense of human-level autonomous work remains sufficiently far away at that point, investment scaling is going to slow down as well. Future frontier models cost too much if there is no commensurate competence improvement.