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Sam Altman defends LLM scaling, says past researchers underestimated its power

Sam Altman says a generation of researchers held the field back by underestimating what scaling can achieve for large language models.

In detail

  • Altman spoke at Stanford and pushed back on critics such as Yann LeCun.
  • He argues that skepticism about scaling slowed progress in the field.
  • OpenAI cites examples — including a model that disproved a long-standing mathematical conjecture — as evidence LLMs can generate new knowledge.
  • Altman acknowledges LLMs perform worse than humans on very long‑horizon tasks needing high judgment.

Why it matters

Altman's stance indicates OpenAI will keep prioritizing scale, which affects vendor strategies, R&D funding and the kinds of products businesses can expect; it’s the strategic frame for many corporate AI decisions.

For you Review vendor plans for scaling versus targeted capability improvements and ask for concrete ROI, safety mitigations and temperature for judgment‑heavy tasks.

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