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Data-driven economic ABMs are very interesting. Poledna et al. (2023) is a compelling example: a national-scale economic simulation that rivals traditional VAR and DSGE models in out-of-sample forecasting. doi.org/10.1016/j.euroecorev… Exciting potential for ABMs × AI!

This is true and I have always thought it was a big shame. Part of the reason (as Andrey notes in the replies) is that historically ABMs had not been taken to the data. But this is changing. Here is my colleague @RMaria_drc’s recent work bridging this gap: arxiv.org/abs/2412.16591 ABMs x AI hold a lot of promise for economics, and it’s an active area of research for our team.

Scientific fields where claims are more empirically testable have more equal citation distribution

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New essay for the @GoogleDeepMind Institute with James Manyika on AI x Science, "Bending the Curve of Discovery". While accelerating existing scientific practices is certainly useful, the real promise of AI is its potential to act as an invention of a method of invention (IMI) a la Griliches--e.g., the microscope or statistical inference--which would unlock questions and whole modes of discovery that were previously beyond human reach. Today, LLMs and specialized models like AlphaFold act as economic complements. LLMs handle analysis, coding, and writing, while specialized tools handle domain-specific predictions. Most handoffs between them run through the scientist. What may the future of science look like? LLMs orchestrating those handoffs automatically--prompting specialized models, auditing outputs, and looping until either the question is answered or a non-automated stage is reached (e.g., wet lab testing). The scientist's role shifts from running each step to designing this loop. We saw a glimpse of this workflow with Anthropic’s enzyme discovery a few weeks ago, and we're seeing this in our own work too. This raises foundational epistemic questions: 1. What will scientific understanding look like when discoveries are made by black-box models whose output is increasingly difficult to interpret? 2. How do we extract underlying mechanisms, not just outputs, as science becomes more automated? What will theory look like? 3. If AI automates the writing and junior lab work, how do we train the next generation of scientists to push the frontier? 4. What will motivate scientists in this epistemic reality? There's an economics angle too. Hypothesis generation gets cheap, so bottlenecks move downstream to verification and to choosing which questions are worth asking. Realizing AI’s full potential will take investment in infrastructure and institutional reform--making this as much an organizational challenge as a technical one We don't have the answers yet, and I'd love to hear what others think. Paper here: bit.ly/ai-in-science

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Markdown is the lingua franca of AI docs: this is an excellent move by @GoogleWorkspace colleagues 👏

Really cool to see (well, hear!) our AI in Science work mentioned in this excellent podcast as an example of good research! Paper link here: arxiv.org/abs/2609.28504