Latest Interviews

What are we scaling?

Toby Ord, Beren Millidge

Baron Millage argues that current Reinforcement Learning strategies rely on inefficiently pre-baking skills into models due to a fundamental misunderstanding of their ability to learn like humans, which keeps AI revenue far below the potential of knowledge work automation. While the industry anticipates a 2030 surge in continual learning revenue reaching the hundreds of billions, the lack of generalizable on-the-job capabilities and the immense compute requirements for RL scaling suggest AGI remains distant despite incremental progress. This perspective challenges the "superhuman researcher" narrative by emphasizing that solving the core learning problem requires a shift from specialized training loops to systems capable of semantic, self-directed adaptation.