“Research note: A simpler AI timelines model predicts 99% AI R&D automation in ~2032” by Thomas Kwa

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Recorded12 Feb 2026

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Audio note: this article contains 31 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. In this post, I describe a simple model for forecasting when AI will automate AI development. It is based on the AI Futures model, but more understandable and robust, and has deliberately conservative assumptions. At current rates of compute growth and algorithmic progress, this model's median prediction is >99% automation of AI R&D in late 2032. Most simulations result in a 1000x to 10,000,000x increase in AI efficiency and 300x-3000x research output by 2035. I therefore suspect that existing trends in compute growth and automation will still produce extremely powerful AI on "medium" timelines, even if the full coding automation and superhuman research taste that drive the AIFM's "fast" timelines (superintelligence by ~mid-2031) don't happen. Why make this? The AI Futures Model (AIFM) has 33 parameters; this has 8. I previously summarized the AIFM on LessWrong and found it to be very complex. Its philosophy is to model AI takeoff in great detail, which I find admirable and somewhat necessary given the inherent complexity in the [...] ---Outline:(01:22) Why make this?(02:23) Scope and limitations(04:08) The model(07:07) Parameter values(08:42) Graphs(11:00) Observations(13:13) Discussion(14:39) More on modeling choices(14:43) List of differences from the AIFM(17:21) How could we better estimate the parameters?(18:16) Why is automation logistic?(18:43) Why are labor and compute Cobb-Douglas?(19:49) Why is there no substitutability between tasks? --- First published: February 12th, 2026 Source: https://www.lesswrong.com/posts/uy6B5rEPvcwi55cBK/research-note-a-simpler-ai-timelines-model-predicts-99-ai-r --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

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