Q3.5 Timelines Update: Looks Like the Intelligence Explosion Will Happen in 2027 After All - By Eli Lifland, Brendan Halstead, Daniel Kokotajlo, Romeo Dean, Thomas Larsen, Lukas Finnveden

From AI Article Readings 2026-11-10 · 3:06:18

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The AI Futures Project team, the authors of AI 2027, revisit their forecasts after METR’s report that AI is now speeding up AI progress at Anthropic. They work through a set of simple extrapolations and their own AI Futures Model to ask when a Superhuman AI Researcher might arrive, look hard at where their earlier model went wrong, and then each of the six authors gives an all-things-considered forecast in their own words, before the team sets out what it would mean for alignment, security and international agreements.* 00:00 - Introduction* 03:35 - Timelines/Takeoff Update Summary* 16:26 - METR’s estimates imply that progress is currently sped up by 1.7x due to AI* 22:32 - 1.7x acceleration of overall progress is a much bigger deal than a 1.7x labor uplift* 27:46 - A 1.7x pace of progress is 5 months ahead of the AI 2027 scenario* 29:42 - Modeling* 29:44 - Operationalization* 32:23 - Simple models* 32:26 - Research taste linear extrapolation* 32:36 - Basic method* 37:46 - One-at-a-time parameter changes* 43:00 - Monte Carlo* 46:16 - Labor uplift extrapolation* 49:01 - Extrapolating Anthropic automation levels* 51:54 - Extrapolating software R&D uplift per unit of ECI* 01:00:32 - Tom Davidson’s toy model* 01:01:24 - Filtered AI Futures Model* 01:06:11 - Filtering for a single acceleration level* 01:11:00 - Filtering for acceleration levels at 2 different dates* 01:13:58 - Hypotheses for why the AI Futures Model was wrong* 01:14:31 - Background* 01:15:18 - Have we achieved AC?* 01:18:27 - Why is the pace of progress acceleration so high?* 01:21:22 - More uplift from near-AC coding skills* 01:28:35 - Research taste is better than anticipated* 01:29:40 - A given level of research taste skill results in more uplift than anticipated* 01:30:16 - Skills besides coding and research taste are driving acceleration* 01:32:35 - Conversion of uplift to pace of progress is too conservative* 01:38:15 - Other considerations* 01:38:18 - How large is the (AI-attributable) acceleration in the current pace of progress, really?* 01:45:45 - Other recent sources of evidence* 01:46:43 - It’s hard to not update much on a 1.7x acceleration* 01:50:32 - Adjustments for what the filtered AIFM could be missing* 01:55:42 - All-things-considered forecasts* 01:55:46 - Forecast table* 01:57:10 - Eli’s forecast* 02:05:25 - Brendan’s forecast* 02:07:49 - Daniel’s forecast* 02:10:47 - Lukas’s forecast* 02:14:13 - Thomas’s forecast* 02:18:23 - Romeo’s forecast* 02:28:30 - Implications* 02:35:42 - Appendix: How our views have changed over time* 02:37:26 - Appendix: Ambiguity in “80% of current acceleration is due to AI”* 02:43:39 - Appendix: More analysis of higher-than-expected near-AC coding uplift* 02:49:05 - Appendix: How much would 1.7x acceleration in inputs to AI progress accelerate AI progress overall?* 02:51:34 - Appendix: A minor fix to the AI Futures Model for better modeling when there’s acceleration in present day* 02:53:33 - Appendix: TasteVal-implied taste slope, AECI basis* 02:56:24 - Appendix: Software R&D uplift extrapolation* 03:02:32 - Appendix: How many years of AI progress in 2027 do the filtered AIFM runs predict?https://open.substack.com/pub/aifutures1/p/q35-timelines-update-looks-like-the?r=67y1h&utm_campaign=post-expanded-share&utm_medium=web This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit askwhocastsai.substack.com/subscribe

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