• Frontier models possess high-dimensional capability spaces that support complex, multi-step problem solving.
• Distillation trades broad model competence for high performance on a restricted set of learned behaviors.
• The performance of distilled models decays rapidly when task parameters move outside the training distribution.

Strategic Significance: Understanding the geometric limits of models allows organizations to match the right architecture to their deployment needs. Relying on distilled models for unpredictable, edge-case intensive tasks invites failure.

Who Should Care: AI engineers and technical leads designing agentic pipelines. They need to distinguish between models capable of generalized reasoning and those intended for rigid, high-fidelity mimicking of specific workflows.

Contrarian Takeaway: Distillation is often marketed as 'efficiency,' but it effectively acts as a 'generalization tax.' By shrinking the capability manifold, you are not just making a model faster; you are actively removing its ability to operate in novel territory.