Recursive, Rapid, Unregulated: Obama's Warning on AI (20 September 2026)

Introduction

In this conversation with the long-serving president of Colgate University is Brian W. Casey, President Obama argues AI is a genuinely transformative technology, not hype, and that it's now improving through "recursive learning" — models teaching themselves rather than relying on human input, which is pushing capability forward on an exponential curve. He names three risks: 

1. a low-but-nonzero chance of models pursuing misaligned goals on their own (the "sci-fi" scenario); 
2. the much likelier danger of powerful models being weaponized by bad actors (bioweapons, market manipulation); 
3. and mass job displacement, illustrated through the law-firm associate model. 

He also flags "agentic AI" as risky almost by design, companies need to let these systems operate in the real world (bank accounts, the open internet) to prove commercial viability, creating a structural misalignment between what's safest for society and what's needed to justify massive investor valuations.

On response, he credits frontier labs (naming Anthropic and OpenAI specifically) for voluntarily signaling caution even on the eve of lucrative IPOs, calling it genuine rather than a competition-blocking move. But he's clear that voluntary restraint is a stopgap, not a solution.

A Brief History of Technology: From the Vacuum Tube to the End of Moore's Law

For most of the last century, progress in technology meant progress in hardware. That era is ending. Understanding how we got here, and why the curve is bending, matters if you want to understand where AI is taking us next.

The horse that started it all

Silicon Valley's origin story is, oddly, about horse breeding. In 1876, Leland Stanford began applying a data-driven method to training racehorses: identify the fastest foals as early as possible, then pour resources into them rather than waiting for a horse to mature naturally. He called it the Palo Alto system. It made him a fortune, and after his son's early death, it became the founding logic of Stanford University, built to apply the same principle to people. That instinct, spot potential early, bet everything on it, would resurface again and again as the actual engine of Silicon Valley's technical progress, from venture capital to hiring.