Google DeepMind Finds No Significant Acceleration in Machine-Learning R&D

Google DeepMind’s internal assessment found no significant end-to-end acceleration in machine-learning research and development, despite AI writing more than 75% of the lab’s production code. The result separates wider coding automation from faster progress across the full research process. Google DeepMind researcher Haydn Belfield summarized the finding in an Oct. 14 thread: “There is currently no significant end-to-end acceleration of ML R&D, AI-driven or otherwise.” The lab’s GCI v1 index combines scores for 123 AI models across 92 benchmarks. Frontier Gemini capabilities rose by about 0.1 GCI points per month, with no significant change in that pace after April 1, 2026. The index tracks model capability across benchmarks; the acceleration finding concerns whether AI speeds machine-learning R&D as a whole.

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