Google DeepMind engineer Haydn Belfield said more than 75% of Google's production code is written by AI, yet about 88% of code changes still require human oversight or review, with fully AI-delegated changes rising from 7.5% to 11.9%. In 1,389 internal Argon agent runs, unsupervised success fell sharply as task complexity rose; on RE-Bench, which contains 74 real machine-learning engineering tasks, Argon solved about half, versus 27% for Gemini 3.7 Flash. The team built a General Capability Index spanning 123 models and 92 benchmarks, finding Gemini capabilities improving about 0.1 GCI point per month with no statistically significant acceleration, and attributed Gemini 4 Argon's slightly above-trend result mainly to more compute and organizational investment rather than AI-driven acceleration. Separately, more than 20 researchers including Geoffrey Hinton, Yoshua Bengio and OpenAI's Jakub Pachocki warned in a paper that automating AI R&D could trigger an intelligence explosion compressing years of progress into months.