AI DAILY BRIEFING
Zhipu revealed that all production inference for GLM-5.3-Flash now runs on over 100,000 domestic AI accelerators, with a 3.2x end-to-end throughput improvement achieved in under two weeks. Much of the optimization was performed by an AI agent powered by GLM-5.3, which used a 'dense feedback' mechanism to identify and fix three engineering issues: precision drift, a concurrency bottleneck, and redundant computation. CEO Tang Jie stressed that goal setting, feedback environment design, and high-risk change review remain human responsibilities, but the engineer's role is shifting from solving problems to designing feedback. The GLM team described this as an early form of recursive self-improvement, where AI moves from helping humans develop models to participating in building its own successor.
Zhipu's deployment of a GLM-5.3-powered agent to optimize its own inference system demonstrates early recursive self-improvement, with AI participating in building its successor.
The optimization achieved a 3.2x throughput improvement on over 100,000 domestic chips in under two weeks, fixing precision drift, concurrency bottlenecks, and redundant computation.
Human responsibilities remain in goal setting, feedback environment design, and high-risk change review, while engineers shift toward designing feedback.
Watch next: How Zhipu's early RSI progress influences industry discussions on AI self-improvement and safety, and whether other organizations adopt similar agent-driven optimization approaches.