FEATURE: AI SCALING
THE HARDEST AND MOST VALUABLE WORK AHEAD WILL NOT BE TRAINING THE NEXT TRILLION- PARAMETER MODEL. IT WILL BE REWORKING HUMAN PROCESSES TO BE AI-NATIVE.
are still improving, but the gains are now coming in smaller steps.
The signals in the slowdown
The signals are hard to miss. Coverage and investor expectations have cooled, shifting away from the“ hockey-stick” breakthroughs toward smaller, steadier advances. Enterprise buyers are taking longer to evaluate new models, prioritizing reliability, governance, and cost control over headline capability jumps.
Benchmarks echo the change. GPT-4 made a dramatic leap over GPT-3.5 by wide margins. On MMLU – often used as a strong proxy for academic reasoning – scores jumped from 44 % for GPT-3 to 74 % for GPT-4. By comparison, early MMLU scores from GPT-5 are coming in around 87 %, a far smaller jump. That flattening shows up elsewhere, too. On public leaderboards, top models are now bunched more closely together, suggesting less headroom on the tasks those tests capture. This is certainly not a stall. It’ s a change in slope and a change in what will matter next.
Why the gains are smaller
Several forces are shaping this shift. High-quality training data is finite, and much of the public internet has already been mined. Access to copyrighted content is increasingly contested, creating legal and
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