I’m starting to suspect that if 2026-2027 AGI happens through automation of routine AI R&D (automating acquisition of deep skills via RLVR), it doesn’t obviously accelerate ASI timelines all that much. Automated task and RL environment construction fixes some of the jaggedness, but LLMs are not currently particularly superhuman, and advancing their capabilities plausibly needs skills that aren’t easy for LLMs to automatically RLVR into themselves (as evidenced by humans not having made too much progress in RLVRing such skills).
This creates a strange future with broadly capable AGI that’s perhaps even somewhat capable of frontier AI R&D (not just routine AI R&D), but doesn’t accelerate further development beyond picking low-hanging algorithmic fruit unlocked by a given level of compute faster (months instead of years, but bounded by what the current compute makes straightforward). If this low-hanging algorithmic fruit doesn’t by itself lead to crucial breakthroughs, AGIs won’t turn broadly or wildly superhuman before there’s much more compute, or before a few years where human researchers would’ve made similar progress as these AGIs. And compute might remain gated by ASML EUV tools at 100-200 GW of new compute per year (3.5 tools occupied per GW of compute each year; maybe 250-300 EUV tools exist now, 50-100 will be produced per year, about 700 will exist in 2030).
I’m starting to suspect that if 2026-2027 AGI happens through automation of routine AI R&D (automating acquisition of deep skills via RLVR), it doesn’t obviously accelerate ASI timelines all that much. Automated task and RL environment construction fixes some of the jaggedness, but LLMs are not currently particularly superhuman, and advancing their capabilities plausibly needs skills that aren’t easy for LLMs to automatically RLVR into themselves (as evidenced by humans not having made too much progress in RLVRing such skills).
This creates a strange future with broadly capable AGI that’s perhaps even somewhat capable of frontier AI R&D (not just routine AI R&D), but doesn’t accelerate further development beyond picking low-hanging algorithmic fruit unlocked by a given level of compute faster (months instead of years, but bounded by what the current compute makes straightforward). If this low-hanging algorithmic fruit doesn’t by itself lead to crucial breakthroughs, AGIs won’t turn broadly or wildly superhuman before there’s much more compute, or before a few years where human researchers would’ve made similar progress as these AGIs. And compute might remain gated by ASML EUV tools at 100-200 GW of new compute per year (3.5 tools occupied per GW of compute each year; maybe 250-300 EUV tools exist now, 50-100 will be produced per year, about 700 will exist in 2030).