Reading up on Stanford

Claude AI, guided by a high‑school dropout with no math background, produced a notable new result linked to the Riemann hypothesis, called the most impressive AI math finding yet.

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.

Economists argue that after AGI, scarcity may shift to human-relational services, but the labor share's fate depends on whether new capital varieties prevent satiation.

In the theatrical thriller 'Data,' a Stanford cognitive science graduate dramatizes how tech titans use slick, grandiose language to justify potentially totalitarian AI projects.