MIT FutureTech director Neil Thompson revisits his group's 2020 computational limits paper and discusses his newer research on how AI automates tasks across the economy, covering the crashing-waves versus rising-tides survey, adoption bottlenecks, fixed-cost economics, apprenticeship risks, AI safety, and the open versus closed model debate.
A discussion on the evolving economics of autonomous vehicles, driven by end-to-end AI, and the growing local opposition to AI data centers due to concerns over resources like water, electricity, and noise.
A deep dive into the architectural and economic foundations of memory for AI agents. The talk explores the core tradeoffs between classical data storage and dynamic agent behavior, introduces a human-inspired framework for memory, and discusses practical strategies and future directions for building reliable, evolving AI systems.