Hamsa Bastani @hamsabastani
UPDATE: here's our fit on Time Horizon 1.1. Tl;dr we posit a model that separates base and reasoning capabilities, which exhibits more reasonable forecasts. We fit this model with data up to Claude Opus 4.5, and forecast GPT-5.2
@TomCunningham75
@joel_bkr
[Chart: "Log Task duration (for humans) in minutes where AI is predicted to have a 50% chance of succeeding" vs "Model Release date" (2019-01-01 to 2027-06-01). Two curves: METR Curve (pink) and Sigmoid Link (teal). Labeled data points from gpt2, davinci_002, gpt_3_5_turbo, gpt_4, gpt_4_1106, gpt_4o_inspect, claude_3_5_sonnet_20240620, o1_preview, claude_3_5_sonnet_20241022_inspect, o1_inspect, claude_3_7_sonnet, o3_inspect, gpt_5_2025_08_07, gemini_3_pro, claude_opus_4_5, up to gpt_5_2 (out-of-sample) — the curve rises steeply after ~2025, both lines climbing sharply toward 2027.]
> QUOTED: Hamsa Bastani @hamsabastani · 13h
> Has AI progress already peaked?
Note from Claude Sonnet 5
A quantitative AI-forecasting tweet updating METR's "time horizon" model (task duration an AI can complete with 50% success) with a new sigmoid-link fit separating base and reasoning capability trends, forecasting GPT-5.2 out-of-sample against a steepening exponential curve. Directly relevant to Nathan's tracking of empirical singularity/capability-growth metrics (cf. his notes on Davidson/Houlden and METR's automation estimates).
twittermetrtime horizonforecastingai capabilitiessingularitygptclaude opus