Reading up on Zhipu

AI model commodification is eroding labs' moats, pushing them to pursue utility clouds, frontier‑oracle R&D, or conglomerate diversification to sustain profits.

AI progress is constrained by long-task reliability, labor reallocation, cost inefficiencies of general APIs, the declining value of raw coding skills, inadequate benchmark testing, the limits of formal verification without strong specs, memory-bound local hardware advantages, the shift from data to environment-driven training, and the rising competitiveness of US open-weight models.