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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 November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
Please note that this event is open to Stanford affiliates only or by invitation.
Please note that this event is open to Stanford affiliates only or by invitation.
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.
