A lot of AI discussion focuses on model size.<\/p>\n
I think the bigger long-term moat may be owned specialist datasets<\/strong>.<\/p>\n A good domain dataset does more than add examples. It encodes:<\/p>\n That changes the economics.<\/p>\n Instead of paying frontier-model prices forever for every narrow task, you can use a smaller specialist model trained on data you actually own and understand \u2014 then reserve frontier models for the hard cases.<\/p>\n The interesting part is not \u201csynthetic data\u201d by itself. It is building a measured dataset-engineering process<\/strong> that can repeatedly find holes, fill them, verify the new data, and prove the capability gain on held-out evals.<\/p>\n Models will keep changing.<\/p>\n A high-quality proprietary domain dataset can compound for years.<\/p>\n huggingface.co\/collections\/CompilingThings\/mql5-code-generation<\/a><\/p>\n<\/div>\n submitted by \/u\/Compilingthings <\/a> \n
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