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.