Small-model evals won't stay a moat for AI monitoring platforms, argues engineer
Shahules786 · x · 2026-10-09
Shahules786 is skeptical that "use a smaller model for evaluation and error analysis" is a durable differentiator for AI monitoring platforms. He tried it in early 2024: post-training a small model for a specific workload did slash costs. But frontier labs can distill those same capabilities into cheaper models — and since evals and error analysis are core to the labs' own training workflows, they have the data, compute, and incentives to keep improving them. The takeaway: it's a capability the labs will absorb, not a standalone moat.
Related event: Developer doubts small-model evals can be a lasting moat(2 posts)→
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