What happened
Researchers found that the expensive way to train AI models (online, real-time feedback) works better than the cheap way (offline, batch data) because of how human brains perceive probability — not because of any fundamental requirement. They built cheaper offline training methods that deliberately encode these human perceptual quirks, matching the performance of expensive online methods while running six times faster.
Why it matters
If this holds up in practice, it collapses a false constraint: the entire AI training industry has assumed you need expensive real-time human feedback loops to get good results, but the bottleneck was never the feedback loop itself — it was not accounting for how humans actually perceive uncertainty.