My timelines didn’t notably update overall (I wrote this comment before re-reading the comment you linked). Automation of routine AI R&D is an answer to the question of what specifically causes AGI/RSI in 2026-2027, if it happens this early, but in my model getting a clearer sense of what form this might take doesn’t make AGI in 2026-2027 more likely. Most of my AGI probability is in unknown breakthroughs or scaling outcomes (where quantity becomes quality in a capability that wouldn’t a priori obviously be able to go that far, before the necessary quantity actually arrives). These things are enabled by more compute and then either follow quickly (low-hanging fruit at a given level of compute, unlikely to be accessed earlier even if possible in principle) or take multiple years (when needing human-invented conceptual advancements). As compute grows faster/slower, this directly influences the probability of AGI/RSI per year during the few years after that.
I expect compute buildout (for individual AI companies) to continue at the current pace (of 2-4x more becoming available each year) in 2022-2029, perhaps with low-hanging fruit getting picked through 2032, then slower growth in 2029-2035, and even slower after that (absent AGI). Without AGI by 2035-2045, a lasting ban/pause gets more likely as global cultural attitudes might change. So the highest per-year probability is in 2027-2032, then notably lower in 2032-2038, and even lower after that. And I’m placing the median in 2032-2033. Which means 10% per year in 2027-2032, extending the first 10% to 2026-2027 since there’s some visibility into the very near future that says this probably isn’t happening right now.
New-for-me considerations from mid 2025 to now are a clearer picture of capabilities of RLVR and its implications for AI company revenues, and some details on what might happen around the Rubin Ultra buildout. Turns out RLVR works for IMO gold even with relatively small models (DeepSeek-V3), and there are now some LLM solutions to technical open problems, so it’s probably sufficient for training the deep skills aspect of AGI (it’s more than mere elicitation), especially with bigger models. Though jaggedness still makes it less useful than that suggests. This made 100 billion dollar revenues (2-5 GW training systems) for AI companies before 2030 more likely than o1/o3 suggested on their own. Scaffoldings like Claude Code, especially with better post-deployment adaptation (what’s being foreshadowed as “continual learning”), make even trillion dollar revenues before 2030-2032 plausible (which means 30-50 GW training systems, but it’ll take more time to scale the supply chains and actually build that with hardware of the same generation, as a single system for an individual AI company, probably closer to mid-2030s).
For the Rubin Ultra buildout (2028-2029), individual 5 GW systems don’t seem to be in the works, which previously seemed to suggest it already starts a slowdown in the trend, which then only lasts at the current pace during 2022-2026, and goes 2x slower in 2026-2029. (Trillion dollar revenues only extend the slower part of the trend, after the initial slowdown somewhere in 2026-2029, as the supply chains struggle to catch up to the available funding, and before compute mostly stops growing other than through improved price performance of hardware.) But Nvidia’s bet on FP8 in Rubin makes 2027-2029 hardware 2x-4x more performant per GW than I expected (2x from chips that are faster in FP8 because they no longer care about BF16 as much, and maybe another 2x from the confidence that FP8 is a first-class citizen in training of even the largest models, where this confidence wasn’t already priced in). So even 2 GW Rubin training systems remain on trend, even though the trend previously asked for 5 GW training systems for the same compute. The fact that Nvidia is making this bet means others will likely be doing the same, so this doesn’t necessarily only concern OpenAI.
I, also, would like to hear @Vladimir_Nesov ’s response to this.
Someone recently asked me if I have updated my 2024-2027 estimate for AGI. My answer was that I have adjusted the early side of that estimate (of course), but not the later side. I continue to expect AGI before 2028.
You previously estimated ~10% probability for AGI in 2025–2027 with a median around the mid-2030s. Have your timelines updated since then?
My timelines didn’t notably update overall (I wrote this comment before re-reading the comment you linked). Automation of routine AI R&D is an answer to the question of what specifically causes AGI/RSI in 2026-2027, if it happens this early, but in my model getting a clearer sense of what form this might take doesn’t make AGI in 2026-2027 more likely. Most of my AGI probability is in unknown breakthroughs or scaling outcomes (where quantity becomes quality in a capability that wouldn’t a priori obviously be able to go that far, before the necessary quantity actually arrives). These things are enabled by more compute and then either follow quickly (low-hanging fruit at a given level of compute, unlikely to be accessed earlier even if possible in principle) or take multiple years (when needing human-invented conceptual advancements). As compute grows faster/slower, this directly influences the probability of AGI/RSI per year during the few years after that.
I expect compute buildout (for individual AI companies) to continue at the current pace (of 2-4x more becoming available each year) in 2022-2029, perhaps with low-hanging fruit getting picked through 2032, then slower growth in 2029-2035, and even slower after that (absent AGI). Without AGI by 2035-2045, a lasting ban/pause gets more likely as global cultural attitudes might change. So the highest per-year probability is in 2027-2032, then notably lower in 2032-2038, and even lower after that. And I’m placing the median in 2032-2033. Which means 10% per year in 2027-2032, extending the first 10% to 2026-2027 since there’s some visibility into the very near future that says this probably isn’t happening right now.
New-for-me considerations from mid 2025 to now are a clearer picture of capabilities of RLVR and its implications for AI company revenues, and some details on what might happen around the Rubin Ultra buildout. Turns out RLVR works for IMO gold even with relatively small models (DeepSeek-V3), and there are now some LLM solutions to technical open problems, so it’s probably sufficient for training the deep skills aspect of AGI (it’s more than mere elicitation), especially with bigger models. Though jaggedness still makes it less useful than that suggests. This made 100 billion dollar revenues (2-5 GW training systems) for AI companies before 2030 more likely than o1/o3 suggested on their own. Scaffoldings like Claude Code, especially with better post-deployment adaptation (what’s being foreshadowed as “continual learning”), make even trillion dollar revenues before 2030-2032 plausible (which means 30-50 GW training systems, but it’ll take more time to scale the supply chains and actually build that with hardware of the same generation, as a single system for an individual AI company, probably closer to mid-2030s).
For the Rubin Ultra buildout (2028-2029), individual 5 GW systems don’t seem to be in the works, which previously seemed to suggest it already starts a slowdown in the trend, which then only lasts at the current pace during 2022-2026, and goes 2x slower in 2026-2029. (Trillion dollar revenues only extend the slower part of the trend, after the initial slowdown somewhere in 2026-2029, as the supply chains struggle to catch up to the available funding, and before compute mostly stops growing other than through improved price performance of hardware.) But Nvidia’s bet on FP8 in Rubin makes 2027-2029 hardware 2x-4x more performant per GW than I expected (2x from chips that are faster in FP8 because they no longer care about BF16 as much, and maybe another 2x from the confidence that FP8 is a first-class citizen in training of even the largest models, where this confidence wasn’t already priced in). So even 2 GW Rubin training systems remain on trend, even though the trend previously asked for 5 GW training systems for the same compute. The fact that Nvidia is making this bet means others will likely be doing the same, so this doesn’t necessarily only concern OpenAI.
I, also, would like to hear @Vladimir_Nesov ’s response to this.
Someone recently asked me if I have updated my 2024-2027 estimate for AGI. My answer was that I have adjusted the early side of that estimate (of course), but not the later side. I continue to expect AGI before 2028.