Should we slow down AI progress? | MOONSHOTS #288

Artificial intelligence is rapidly accelerating, shifting from a tool for simple tasks to a driver of fundamental scientific breakthroughs. Recent developments, such as the AI-driven solution to the Navier-Stokes equation and the creation of the first AI-designed longevity drug, demonstrate that data curation and algorithmic efficiency now outweigh raw compute power in driving progress. While internal debates persist regarding the probability of catastrophic risks—often termed "PDoom"—the consensus among industry leaders emphasizes that slowing down is impractical and counterproductive. Instead, the focus is shifting toward managing the economic fallout of rapid GDP growth and potential labor displacement. As hardware bottlenecks like high-bandwidth memory persist, algorithmic innovations are enabling smaller, cheaper models to outperform massive, expensive systems, fundamentally altering the landscape of global innovation and economic competition.