The Bitter Lessons (www.hyperdimensional.co)

In "The Bitter Lessons," the author reframes the U.S.–China AI “race” as an unbounded, multi-dimensional competition rather than a straightforward sprint. The U.S. has effectively bet on deep learning and the “bitter lesson” that compute-scaled neural nets drive rapid progress: leading firms and hyperscalers prioritize massive models, chips, and cloud infrastructure. That strategy leverages American strengths in complex software systems, financial engineering, and platform/network effects—areas where sticky user preferences and ecosystems may preserve U.S. advantage even as models advance. China’s approach is complementary and pragmatic: emphasis on embodied AI (robotics, sensors, drones, self-driving), fast-following with open-weight models, and aggressive deployment through manufacturing-scale adoption and data pipelines. Open-weight models lower barriers to inference and can blunt export controls, enabling wide diffusion on low compute budgets. The technical implication is a potential bifurcation—U.S. leads in frontier models and software; China leads in durable hardware, manufacturing, and system integration. The policy takeaway: the U.S. should not focus solely on data centers and chips but rebuild manufacturing and prioritize robotics hardware, because hardware advantages could flip otherwise software-led dominance. If China pivots to a full AGI strategy, geopolitical risk would rise sharply—yet for now the two strategies intensify structural competition rather than produce a clear winner.