AI Progress Just Became Predictable. Here’s the Machine Behind It

AI progress has returned to predictable, substantial leaps. The key isn’t just making models bigger. It’s mastering data quality, building synthetic data factories, and creating fast verification loops in domains like math and programming.

Where you can reliably check whether an output is correct (compiling code, solving math problems, running simulations), you unlock rapid compounding improvement.

This is already showing up in frontier models and will accelerate dramatically in verifiable domains.

The data flywheel is now more important than the compute flywheel.

Real-world physical systems (robotaxis, Optimus-style robots) will reach superhuman reliability through massive real-world data collection.

AI + speed + access to all of human knowledge will creates capabilities at peak human level and beyond any individual human where there is the quality data and the means to quickly check and correctly grade the results.