I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy. How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes. We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together). We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today. Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment. We are really excited about this paper. Excited to hear people’s thoughts. Link:

Ethan's first book was about AI as chatbots, this one is all about agents. I was lucky enough to get an early copy. If you're interested in agentic AI, get this book. Ethan talks to all the labs, is a critical early tester of the models, and was super early on agentic AI. Hes also an incredible writer, this book was a pleasure to read and learn from.

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:

I’m afraid that speed will make many lessons from history moot in the case of AI. This is why I think references to Industrial Revolution are useful but also very incomplete. And why my confidence intervals so big.

New post with the excellent @FutureEconJacob examining the evidence (so far) on the impact of AI on the labor market. We reviewed 20+ papers and datasets and attempted to synthesize the evidence. Our take: 1) There is no strong signal that AI has had a negative or positive impact on employment in the aggregate. 2) When looking at specific sectors and demographics, the evidence is fairly mixed. While some have argued for negative impact on AI-exposed, early career hiring, others have identified moderators such as remote work or macroeconomic trends. There is also some evidence of positive impacts as well. So has AI hit the labor market yet? When looking at the employment statistics--not really. But that does not mean that jobs are not changing significantly, and that there aren't substantial changes in what skills are becoming less relevant versus in greater demand. That's a topic for another post! Link:

@EconBerger is mandatory reading on the job market. Every week he reviews all available indicators. So far, the job market is getting, if anything, stronger. SWE and Human Resources some of the highest growth sectors. Lots of uncertainty of course, but that’s where we are today.

A few (personal) thoughts on reading empirical AI papers on the economy. Economists have gotten used to reading papers with super clean identification, arguing about the validity of an instrument, making sure parallel trend assumptions are satisfied. This is what gets you into a top journal, and it is *very* important research (no question here). But it also takes years and sometimes decades to get these types of papers right---people often don't find a good instrument to answer a specific causal question decades after the natural experiment. We will eventually have this type of research for AI as well, and it is absolutely necessary. But right we also need signals *right now*, even if they are noisier than what we are used to. We need papers where we can trust that researchers did their best methodologically, while at the same time acknowledging that the space is moving way too fast to wait for perfect identification. This will allow us to accumulate enough signals, coming at the same question using different angles, for example, to say "yes, X is likely happening in the economy". The AI exposure and early career hiring papers are a good example of this. There is no silver bullet paper with super clean identification. But at this point we have several independent teams reaching the same general conclusion, enough where we can say "there seems to be a slow down in AI-exposed, early career hiring."

Second, no single policy is a silver bullet; there are serious tradeoffs across every dimension and durability scenario; policies that are easier to implement and command broad support are not durable to more transformative scenarios. At the same time, the analysis identifies policies that lay on the Pareto frontier of the dimensions we study, which points to a sequence of ``least-regret'' policies, which are triggered based on different economic scenarios. This sequence includes an expanded Unemployment Insurance, modernized Earned Income Tax Credit, and employee-led retraining programs for Mild scenarios; the EITC transitioning to a Negative Income Tax for Moderate scenarios; and a Universal Basic Capital backstop for more extreme, full transformation scenarios. 4/n

Today we are announcing the new DeepMind Institute, a forum dedicated to interdisciplinary, evidence-led debate on the societal and economic questions surrounding AGI. As part of the launch, @JulianDJacobs and I have a new essay and working paper: “Economic Policy for AGI.” 🧵1/n

FWIW I know how annoying economists on this website sound. Techs: well if you automate digital work growth will obviously be 300% and you will be inside the lightcone. Econ *slowly adjusts glasses while looking at old sneakers*: well actually, if you properly model demand to allow for non homothetic preferences, where income elasticity of demand varies across different goods and changes as income changes, then…

If you look at the last 3 years of AI progress, technologists have largely been right about the (massive) increase in capabilities, and economists have largely been right about the economic impact. I don’t expect that to change. Ricardian comparative advantage ftw!

Happy 4th of July everyone. I came here as a kid at 9 years old, not knowing a single word of english, lived on food stamps in section 8 housing. My parents cleaned houses while my sister and I collected cans for recycling. Our clothing was all donated and our furniture was things we found on the curb (here I am in front of our "tv set", the broken one being used as a stand for the little working b/w one). But this country gave us an opportunity--to learn, to work hard and earn something for it. "Grateful" does not begin to cover it. I owe this country and its people everything I have. Thank you America, happy 250 to the idea.

New rule: Anyone who is about to write a "all workers will be replaced by machines" essay needs to first read On Machinery by David Ricardo (yes, that David Ricardo), because he probably wrote your essay...in 1817.

Some news: This week I am starting at @GoogleDeepMind as Director of AGI Economics on @shanelegg’s team. I will be joining the other amazing cross-disciplinary scientists researching AGI there. My team will study how frontier AI could reshape the economy: what happens to work and labor, how wealth and power are distributed, how institutions adapt, how AI agents shape markets, and what kinds of models can help us reason clearly about futures that may look very different from the past. I’m incredibly excited to help build this research agenda. If AGI changes how society operates, economics is going to be critical for shaping our shared future. Many more announcements soon.