Was about to write this but Ben beat me to it. Econ is unique among the sciences in that it is several pretty different fields living under one umbrella. Empirical econ is the biggest subfield and its closest to biology or chemistry: it’s about designing or—even better—finding a natural experiment, or documenting interesting and important patterns in data. Most of the arguments in that field are about methods, eg do you have identification. More and more are starting to study AI now but obviously this won’t be transformative AI because, well, there is no data on transformative AI. Then there are macro economists. They work with a set of pretty standard models and mainly expand them by slowly adding/subtracting different sectors or micro founding factors of production in different ways. Many of the arguments are on the parameter values that we should assume when simulating the economy. Early research on transformative AI started there—as demonstrated recently by Chad, Anton et al, the different levels of transformation are due to different assumptions on parameters rather than some new type of economics. The same model can predict basically no impact of AI vs huge transformation, it’s all about assumptions on parameters. Finally there is micro theory. This is where decision theory, game theory, mech design, org econ (sort of) live. This field works by setting out a set of assumptions and then *deriving* the expected results; alternatively one could start with a result and work backwards to derive the necessary and sufficient assumptions. There is a ton of potential here to do work on transformative AI: start with assumptions on how AI differs than human agents (eg relax basic assumptions on constraints, on scarcity, introduce completely different objective functions), then derive implications. But this is where there is the least amount of work, and it’s important for that to change (and I believe that it already is). I think we can make a ton of progress from these three subfields on better understanding the potential AGI and post AGI transformation. But there is another scenario, post strong ASI, where I don’t think economics has much to say about. Here I agree with @Jabaluck that the economy (if you can even call it that) and the agents in it will be so vastly different and beyond our imagination that I wouldn’t know where to start modeling it. Not to discourage others, but post singularity seems like pure epistemic uncertainty to me. A lot of the critiques I hear about AI and economics come from people who don’t quite get what economists do. They look around at different results and if they don’t like some of them, they say “oh economics isn’t “pilled” or something. But economists don’t debate results, they *derive* results from assumptions. Thats where the debate is. Saying “but you don’t understand a world with 15 billion AI agents”. What does that mean for the economy? What are those agents doing, who is buying what they are producing, what do property rights look like? Depending on those assumptions you’ll get vastly different economic scenarios, from 2% growth and high human labor share to completely alien centralized systems with no prices or markets.

New Substack post with @soumitrashukla9. Work has never been just about the income earned sans effort costs, as typical in economic models. Key aspects of people’s identity, their meaning, and sense of agency are all wrapped up with what they do for a living. There has been a lot of conversation lately about how technological change, particularly AI, may disrupt this relationship, from Ezra Klein's column on his own mathematician father’s grapplings with losing meaning in mathematics, to Francis Fukuyama's post on AI, agency, and recognition. In this piece we take a historical perspective on how technological transformation has disrupted identity and meaning by affecting jobs, and how workers and society responded to these changes. Much of the debate around AI and jobs has centered on wages and material consequences, but losing one's work is also intertwined with how people view their place in society. Most people know about the Luddites—skilled weavers and knitters—who protested the devaluation of their mastery. What’s less known is that the Luddites were actually experts in using the machines as well, and that their protests prompted prominent economists like David Ricardo to rethink the role of automation in society, and later inspired the Arts and Crafts movement. A century later, when recorded sound displaced 20,000 cinema musicians, their campaign against "canned music" and two-year recording ban pressured labels into funding live performance. Soumitra and I have been working on versions of this essay for months. He led the piece, and we’re both really excited to get it out there. Link to the post here: aleximas.substack.com/p/tech…