What “distilling AI” means

What is model distillation, explained in plain English

Distilling AI refers to training a smaller model to mimic the behaviour of a larger, more powerful one.

Instead of learning directly from raw data, the smaller model learns by observing how the bigger model responds. It’s often described as a teacher and student relationship. The larger model acts as the expert, generating answers, while the smaller model learns to reproduce those patterns.

In practice, this means feeding a powerful model lots of questions, recording its responses, and then training a smaller model on those outputs. Over time, the smaller model picks up not just what to say, but how to say it, including elements of structure, tone, and reasoning.

The appeal is practical. Large models are expensive to run, slower to respond, and require significant computing resources. Distilled models are lighter, faster, and easier to deploy in products like apps, enterprise tools, or devices.

But the idea raises a more complicated question.

If a model can learn by copying the behaviour of another, where does that sit between learning and imitation? And if that process can be repeated, with AI models learning from other AI models, what does it mean to “own” how a system behaves?