Episode Details

Ever asked an AI for a quick summary and received a dissertation? This episode unpacks the "addition bias" in large language models — the tendency to add complexity when simplicity is needed. We trace it back to training data and reward signals that favor thoroughness, then explore why current architectures lack a "throttle" for task difficulty. From Claude documenting a home network to over-engineered code, we look at the engineering challenges of teaching models to calibrate their effort, and what the shift to unified models means for this problem.