Matthias Fahn

Professors who view students (PhD or otherwise) as coding agents, display a grave lapse in professional ethos.

My hypothesis is that AI will lead to less dramatic layoffs (e.g., the Block memo) and more a slower drip: companies will continue to fire at similar rates, but will hire at much slower rates. Those who are left will be expected to use AI tools--to figure it out--in order to pick up the slack from those who were let go. This will naturally lead to AI models being adopted effectively throughout organizations. But the labor market implications for the economy are in some ways more dire than the dramatic scenario: exactly the type of slow drip of increasing unemployment and lower labor force participation that policy has the hardest time dealing with (policy is much better when there is a clear demarcated disaster).

AI is a game changer for economic research. We will look back and think: before and after. The junior job market used to place enormous value on technical skills — and rightly so. We wanted to pass on the latest research methods to new PhD students. But the cost of mastering frontier solution techniques has dropped dramatically. I now find myself replicating papers and experimenting with frontier methods in an evening or a few days using Claude Code. That would have taken weeks before — which in practice meant I wouldn’t have done it at all. So what does the new equilibrium look like? Some are pessimistic: ChatGPT can write PhD dissertations, they say. Maybe. But those dissertations won’t push the frontier or generate excitement. I take the other side of that bet. We are in the business of figuring out how the world works and generating new knowledge. There is plenty we don’t understand, and no shortage of questions to answer. AI just accelerates the process. The returns on conceptual thinking and original ideas are now relatively higher compared to the technical grunt work of debugging code and cleaning datasets. I think this is a great development. My guess is it will also erode the monopoly that top US schools — and a handful of others — have long enjoyed. Part of that monopoly rested on access to knowledge that didn’t travel easily. Person-to-person transmission has always been far more efficient than learning from books or published papers — which are outdated by the time they appear, given publication lags. Now knowledge transmission is nearly instantaneous. I find myself using techniques I understood in principle but could never justify the time to implement, because other methods were simply faster. That’s no longer true. The same goes for big data work. One question keeps nagging me though: how should this change how we teach?