Biwei Huang (Founder & CEO of Aether AI; Assistant Professor, UC San Diego)…

Brief

Biwei Huang (Founder & CEO of Aether AI; Assistant Professor, UC San Diego) argues that scaling laws depend on data quality: a law-understanding model might need ~200,000 examples versus 1,000,000 for a purely statistical one. He warns that multiplying correlated data (≈5×) can reinforce wrong shortcuts and emphasizes selecting examples that reveal reusable mechanisms for robustness under environment shifts.

"Scaling law has to be tied to data quality. A model that understands the underlying laws may need only 200,000 data points to match what another model gets from a million."
— Biwei Huang, Founder and CEO of Aether AI and Assistant Professor at UC San Diego (十字路口Crossing)

If your dataset only teaches correlations, 5x more examples may buy confidence in the wrong shortcut. The hard operator question is which examples expose the mechanism your model must reuse when the environment changes.

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