Jeff Dean's Last Interview: AI Speed, the 0% Rule, and His Google Exit
In his last interview before leaving Google, Jeff Dean admits his year-old prediction undershot reality: models are improving faster than expected at complex tasks, and agentic systems can now run continuously for weeks. He likens specialized inference hardware to the 2001 'put the search index in RAM' moment, citing 50x latency and 30-80x energy-efficiency gains. A single compute op costs ~1 picojoule; moving data costs 1000x more, which forces batching. The conversation covers context engineering as the new frontier, the napkin math behind TPU, and a founder heuristic: test your domain with the strongest general model — 0% or 1% success is a good sign, 20% is a red flag. Once agents write all the code, he argues, taste — knowing what to have agents solve — becomes the scarce skill. Relevant for AI infrastructure engineers, agent builders, and AI founders.