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This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in October 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.
A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.
A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.
Scientific progress is a key driver of economic growth and prosperity. There is great excitement- but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists.
Scientific progress is a key driver of economic growth and prosperity. There is great excitement- but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists.
The report documents what it calls the scaling era — the five years in which generative AI moved from research preview to a technology used daily by a substantial share of the world's population. The talk traces its findings across twelve standing questions and two deep dives, covering advances in reasoning models and agentic systems, the geopolitics of compute and data, and the contested evidence on AI's effects on work. It closes by revisiting the 2021 panel's predictions, asking why the field's own insiders underestimated how far scaling the transformer would go — and what that suggests about forecasting what comes next.
The One Hundred Year Study on Artificial Intelligence (AI100) is a longitudinal effort administered out of Stanford HAI and managed by a standing committee of AI experts from around the world, chaired by Sheila McIlraith, Professor of Computer Science at the University of Toronto. Its mission is to assess AI not as a snapshot, but as an evolving force across technical, ethical, economic, and societal domains. Every five years, we return to the same standing questions and answer them anew in a changed world.
