Google says self-improving AI isn't anywhere close
Google DeepMind says AI has not accelerated its own research despite writing most production code.
Google DeepMind engineer Haydn Belfield said self-improving AI remains far off and that DeepMind has seen no measurable acceleration of AI research by AI. AI now writes more than 75 percent of production code, yet about 88 percent of changes still need human oversight and every change is human-reviewed; fully delegated changes rose from 7.5 to 11.9 percent. Across 1,389 Argon runs, unaided success fell as task complexity rose, and Argon solves about half of 74 RE-Bench tasks compared with 27 percent for Gemini 3.7 Flash. A General Capability Index covering 123 models and 92 benchmarks shows Gemini gaining about 0.1 point per month with no significant acceleration, while other researchers have warned of a possible intelligence explosion.
- DeepMind reports no measurable AI-driven acceleration of its own research.
- AI writes over 75 percent of production code, but humans review every change.
- Fully delegated code changes rose from 7.5 percent to 11.9 percent.
- Argon solves about half of 74 RE-Bench tasks, versus 27 percent for Gemini 3.7 Flash.
- Gemini capability grows about 0.1 GCI point per month, with no significant speedup.
Full article443 words · extracted from the-decoder.com · click to collapse
Google engineer Haydn Belfield says self-improving AI remains far off, even as significant automation is already taking hold.
Google Deepmind has seen no measurable acceleration of AI research driven by AI itself, according to Belfield. That doesn't mean significant automation isn't happening already: AI now writes more than 75 percent of production code. But about 88 percent of code changes still need human oversight or review, and every change is reviewed by a human before submission, according to Belfield. The share of code changes fully delegated to AI rose from 7.5 to 11.9 percent.
The more complex and time-consuming a task gets, the less likely AI is to handle it on its own. Across 1,389 internal Argon agent runs, the success rate without human input dropped sharply as complexity increased. Humans remain essential for creative research, novel experiment design, diagnosing subtle training instabilities, and long-term planning, Belfield said. A survey of twelve senior Deepmind researchers confirmed the pattern, with automation running highest for well-defined, verifiable tasks and lowest for open-ended scientific ideation.
On Deepmind's in-house benchmark RE-Bench, which includes 74 realistic ML engineering tasks, Argon solves about half of them. That's a big jump from Gemini 3.7 Flash at 27 percent, but it's still far from the near-perfect performance full automation would require.
Google's own capability index shows no speedup in AI research
To track progress systematically, the team built a General Capability Index (GCI) covering 123 models and 92 benchmarks. Gemini model capabilities are growing at a steady pace of about 0.1 GCI points per month, with no significant change in speed. The team attributes Gemini 4 Argon sitting slightly above the trend line to more compute and organizational investment rather than AI-driven acceleration.
The team currently rates the risk from AI-driven research automation as low but is building the measurement infrastructure to track signs of acceleration going forward.
Belfield's findings align with recent research, but not everyone in the field agrees. More than 20 AI researchers, including Geoffrey Hinton, Yoshua Bengio, and OpenAI's Jakub Pachocki, recently warned of a possible "intelligence explosion" in a research paper. They argued that AI systems could automate all AI research and development within a few years, compressing what would normally be years of progress into just months.
AI News Without the Hype – Curated by Humans
Subscribe to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive "AI Radar" frontier report six times a year, full archive access, and access to our comment section.