Fine-tuned small models can't keep up with frontier AI — clean your data instead
mattbeane · x · 2026-09-14
Ethan Mollick argues the enterprise "data sovereignty" movement will hit a hard truth: maintaining fine-tuned small models that continually beat large general-purpose models is expensive and difficult as long as AI capability growth stays exponential.
Afinetheorem amplifies the point: it's far more valuable to spend time digitizing and cleaning SOPs, pipelines, and internal data sources — a hundreds-of-millions fine-tune will fall behind the curve very quickly, while well-curated context stays useful forever.
The exchange frames a core enterprise AI debate: build small-model moats, or turn internal knowledge into context assets that ride on frontier models.
Related event: Ethan Mollick: Fine-tuned small models can't outrun frontier AI growth(2 posts)→
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