FEATURE: AI SCALING
AI scaling is slowing down. That might be a good thing
Corey Keyser, Head of AI, Ataccama, says Frontier model gains are tapering – paving the way for the development of more robust, reliable and efficient systems that align with real-world needs
For years, AI progress meant bigger models. The industry’ s recipe was simple. If we add more parameters, feed more data and burn more compute, then performance would predictably climb. Scaling laws, formalized in 2020 by researchers at OpenAI and DeepMind, gave this trajectory a sense of mathematical inevitability. The leap from GPT-3 to GPT- 4 brought striking improvements in reasoning, fluency, and versatility.
That era of scaling-led progress is slowing down. The arrival of GPT-5, while impressive, feels different –“ evolutionary rather than revolutionary” as the Financial Times put it. Independent tracking also shows convergence at the top: the gap among leading systems has narrowed, making each new release less dramatic than the last. Frontier models
Corey Keyser, Head of AI, Ataccama
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