ai-expertise

Coverage here centers on how deep human judgment interacts with AI systems: what transfers when expertise gets encoded into models and what quietly disappears. Expect analysis of the gap between explicit method and tacit judgment, why copying outputs is not the same as copying reasoning, and how properties like calibration and robustness survive or degrade. The focus is practical: knowing which parts of your expertise a model can absorb and which parts it cannot.

Glowing head dissolving into particles that rebuild a hollow empty copy, showing what knowledge distillation loses.

knowledge-distillation, tacit-knowledge, model-evaluation, ai-expertise, publishing-expertise

What Actually Gets Distilled: Copying the Method Is Not Copying the Judgment

Knowledge distillation reliably copies a model's outputs and benchmark scores while losing the properties that made them trustworthy: calibration, robustness, and reasoning faithfulness. The same split applies to human expertise absorbed by AI systems -- your explicit method transfers cleanly, but y