OpenAI research scientist Noam Brown returns to discuss how traditional AI benchmarks fail to account for test-time compute scaling, where model capability increases with the compute budget allocated for thinking. He argues that static benchmark grids are broken and explores how models can reason for extended periods on complex tasks, from building poker solvers to disproving mathematical conjectures.