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."

Powerful AI will change our institutions. Yet most of our attention is on aligning or regulating AI, while treating our institutions as fixed. Introducing Pax Machina: a new publication about the institutions we need for powerful AI. It is edited by @MaxKronerDale, @sebkrier, @NoemiDreksler, @LiamPatell, @chelcott9, @synchroaphasia, @ryan_t_lowe, @edelwax, and @klingefjord. The editorial board consists of Peter Railton, @sethlazar, @saffronhuang, @AmmannNora, @xuanalogue, @IasonGabriel, @hamandcheese, and @deanwball. Our goal is to seed rigorous debate about what a world of humans and powerful AI could and should look like. We will publish proposals for new institutions, counterproposals, well-justified design principles, and analyses that change how a class of institutions should be designed.

Project APE update! 🚨 "Verifying the Verifiers: Towards Autonomous Policy Evaluation", new paper with @OlafWillner Reminder: Project APE is an experiment in building a system that learns how to do policy evaluation autonomously. The potential upside is that we could then scale up our understanding of what policies to, cheaply and reliably in many countries. Premise for this paper: With *generation* of plausible-looking research becoming cheap, *verification* is a key bottleneck. But can we automate verification, cheaply and reliably? We need to verify the verifiers, somehow. A thread on what we did so far... 👇

Announcing a 3 wk AGI Governance Fellowship at @JHUBloombergCtr School of Government and Policy. Join me, @ghadfield, @nickacaputo and guests for an intensive schedule of deep dives into AI governance at the frontier, covering topics such as societal resilience, AGI and democracy, and the new institutions that governing in/with AGI will demand; we will range from foundational philosophical questions to the tip of the legislative spear. The arrival of AGI would be challenging enough if our ~250yo institutions were in full health. That they aren’t both increases the scale of the challenge, and creates a unique opportunity to imagine the institutions that will carry us through the next 1/4 millennium. We are looking for ~20 fellows, aiming to attract and shape the people who will start to build those institutions: future leaders in AGI governance. We expect you to come from many different backgrounds, so hope this call will be shared far and wide. Details, eligibility, and how to apply here: sogp.jh.edu/agi-governance-f… Governing the AI transition is going to be a central focus of our new school, and we have many more initiatives coming. And we will be hiring in many different kinds of role, from tenured and tenure track faculty to research scientists to ops and comms and students and beyond.

AI has huge implications for global health and economic wellbeing. So we @coeff_giving are putting out a $10-30M RFP to fund work that helps us prepare for and respond to the potential impacts of AI on economies, health systems, scientific research, and more. 🧵