Google Says AI Isn't Recursively Improving Itself—It's Just Really Good at Autopilot Coding ✈️

Google DeepMind found no evidence that artificial intelligence is accelerating the development of more advanced artificial intelligence, according to a safety assessment tied to its most powerful model, Gemini 4 Argon. The conclusion runs counter to warnings from AI executives who have called for a slowdown over fears of recursive self-improvement, a scenario in which an AI builds a smarter version of itself, which builds an even smarter one, until progress outruns anyone's ability to test, regulate, or switch it off, described in the report as "a situation where existing guardrails are severely inadequate."

"There is currently no significant end-to-end acceleration of ML R&D, AI-driven or otherwise," Haydn Belfield, a Google DeepMind researcher who worked on the assessment, wrote in a thread on X. "We therefore conclude that the risk from AI-driven ML R&D is currently low." ML R&D refers to machine learning research and development, the work of building new AI models. DeepMind tracks the issue because "AI could speed up frontier capability gains faster than safety, governance, and societal adaptation can keep pace," Belfield wrote, with more automation also raising the chance that broader system access for AI agents could let a misaligned model, one pursuing goals its makers never intended, cause trouble.

To measure progress, the team built GCI v1, an internal index that blends scores of 123 AI models across 92 benchmarks into a single number per model. "Frontier Gemini capabilities have improved at a remarkably steady pace of ~0.1 GCI points/month," Belfield wrote. In the report's chart of that index, Gemini 1.5 Pro sits near 0.5 points in late 2024 and Gemini 3.8 Flash just above 2.9 in September 2026, with nearly every release hugging a straight line. Argon is the one dot poking above it, at roughly 3.3, though its error bar stretches low enough to touch the trend. "While Gemini 4 Argon sits above [the] trend, we trace this to increased compute and organizational investment rather than reflecting AI-driven acceleration," Belfield wrote.

Google unveiled Gemini 4 Argon on September 30. Most production code at DeepMind is now more than 75% AI-written, and the share of fully delegated pull requests, bundles of code changes submitted for review before going live, rose from 7.5% to 11.9% between April and September. Roughly 88% of pull requests still require multi-turn human steering or human code authorship, with code changes remaining subject to review before deployment.