classical ml

The 2026 FDE differentiator: customers ask for LLMs by name, and the engineer who knows when a $0 logistic regression beats a $40k/month LLM pipeline, with the cost math, wins the room.

C3.ai's reported classical-ML screen pair. Textbook recitation passes; what scores is one practical consequence per assumption, and knowing the random-forest failure mode that bites industrial deployments: no extrapolation.

A budget-constrained design problem, not an ideology question. The per-call math that rules an LLM in or out, the cascade that keeps quality while cutting cost 90%, and the SLO arithmetic that decides where the LLM is even allowed to sit.

The margin idea is elegant and worth understanding, and the kernel trick is a clever move. But the question that separates levels is why a model that dominated the 2000s is now a niche pick. The scaling math that killed it for big data, and where it still wins.