For a decade, the story of artificial intelligence was told in parameters — bigger models, bigger datasets, bigger promises. But a quieter shift is now underway: the race is no longer just about scale, it is about reasoning. Labs are teaching models to pause, plan, and check their own work before answering.
The results are striking. On mathematics, coding, and scientific problems, systems that “think” for longer consistently outperform their snap-judgment predecessors. The trade-off is compute: every second of deliberation costs money, which is why the next breakthrough may be economic as much as technical.
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