The GPU Moment

Sometimes an idea is waiting for the right machine.

Training a large neural network is, at its core, a problem of arithmetic. Enormous amounts of arithmetic, repeated billions of times. For years, that arithmetic ran on hardware that was not built for it, on processors designed to handle one thing at a time quickly, when what was actually needed was the ability to handle many things at once, in parallel.

The hardware for that already existed. It had been built for a completely different purpose. The insight was that it could be repurposed.

This is a pattern worth noticing: breakthroughs in AI often come not from new ideas about intelligence, but from unexpected infrastructure arriving at the right moment. The math was ready. The concept was ready. The hardware was what was missing.