New essay in @ForeignAffairs with James Manyika and @elerianm on steps that can be taken to help AI deliver on its economic promise, drawing on lessons from cooperative game theory as a guide. Most conversations treat AI as a general-purpose technology (GPT), and some compare it to the Industrial Revolution. But we think it is more than that: it is also a method for creating new inventions, like the microscope or the scientific method. Economist Zvi Griliches famously called such discoveries IMIs--inventions of a method of invention. Seen this way, AI carries the potential of the Industrial Revolution and the Enlightenment combined. Yet realizing this potential is a coordination problem. Delivering on the promise of AI requires progress not just on furthering its capabilities, but on building out infrastructure, deep adoption across the economy, and buy-in from many different groups. This is unlikely to happen if people do not feel they have a stake in the upside of the technology. Promises of abstract scientific and medical breakthroughs in the future won't change this when anxieties about near-term disruption are running hot. Rather than treating AI like a zero-sum game, we argue that it is useful to look to cooperative game theory as a guide. Because models, compute, organizational change, and human upskilling are strong complements, AI has the structure of a "convex game": Each group that joins makes everyone else's contributions more valuable. That makes it possible to form stable coalitions of workers, communities, firms, and developers where incentives are aligned and everyone has a stake in the gains. The cooperative approach will of course not be easy, but we will make the case that coalitions can start small and grow as the mutual gains become tangible. We provide concrete examples of how this can be done in practice and at scale. Link to essay here: foreignaffairs.com/guest-pas…

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

Very nice coverage of our piece on how AI is changing science from the FT today: ft.com/content/45088042-61e6…

Nice write up of our AI & Science piece in @sciam. Link to piece: ai.google/static/documents/A… scientificamerican.com/artic…