Beyond Scaling Laws: Why "Thinking Longer" Is a Systems Problem, Not a Prompting Trick
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For five years, the answer to "how do we make the model better" was always the same: bigger model, more data, wait for the loss curve to bend. Scaling laws made AI progress feel almost boring — predictable, like compound interest. That knob still works. But it's not the interesting one anymore. The real question teams are fighting with now is: given a model you already have, how much compute should it spend per question? Not per training run. Per question. And the answer turns out to break…
1Key Takeaways
- For five years, the answer to "how do we make the model better" was always the same: bigger model, more data, wait for the loss curve to bend.
- Scaling laws made AI progress feel almost boring — predictable, like compound interest.
- But it's not the interesting one anymore.
- The real question teams are fighting with now is: given a model you already have, how much compute should it spend per question?
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that for five years, the answer to "how do we make the model better" was always the same: bigger model, more data, wait for the loss curve to bend.
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