r/academiceconomics

Richard B. Freeman, Danxia Xie, Hanzhe Zhang, and Hanzhang Zhou, "High and Rising Institutional Concentration of Award-Winning Economists," NBER Working Paper 35875 (2026), https://doi.org/10.3386/w35875.

It's been sad to witness the joy of so many in the field that we seem to have broken free of the need to code, prove theorems, or think rigorously through the validity of the chosen empirical strategy with the advent of AI. I do not think people realize what this portends.

So what is left now?

Terence Tao recently described the future of math as one where mathematicians transition to a role of taste-maker, persuading each other that their results matter, why it matters, and what the results mean. We should be honest from the start that economics is already a field dominated by taste. Otherwise, we would not have Substacks telling PhD students that the manner in which you present your result is the difference between a top 5 and a field journal. What does this mean?

This is possibly a grim end to science as puzzle-solving, which does occur in economics. A lot of economists, myself included, wanted to do research precisely because it is challenging, and intellectually stimulating. But the ground below us has now shifted where the challenge in economics has largely disappeared, since 90% of the technical work can now be handled by AI, and our roles have now turned into babysitting Claude, making sure that it adheres to our particular tastes. The frontier models lately no longer make mistakes on most technical tasks in economics research (though I find it still makes mistakes on technical tasks that are more complex than what you usually see in coding work in economics), provided you give it ample context. I find myself, these days, getting far more intellectual stimulation out of my hobbies than out of research, which is something I'd never thought I would say in my lifetime. I don't think it is challenging or intellectually stimulating to tell a model to cut a subtitle because the editor would not like it. Nor do I think it is challenging or intellectually stimulating to sell my paper by coming up with a story that maximizes its perceived importance or linking it to the current zeitgeist. It's not that economics is not already a field where taste matters greatly. I just think AI has leveled the playing field in a way where technical proficiency no longer matters much in the few remaining "bastions" that valued such, like empirical IO.

If you're outside the t10, you really should not be happy with this state of affairs. Proficiency has always been something that allowed people below t10 to stand out on the job market. Taste, on the other hand, is inherently subjective, and is something that privileges people with close connections to editors and top professors. If we're now a field where everything except taste can easily be achieved with AI, what are the remaining avenues for people who cannot acquire that taste to stand out? Plus, with the proliferation of AI comes the proliferation of papers. People are becoming more productive but journal capacity remains roughly the same (and I do not think that is likely to change since the editors I know still want to read everything manually.) Publishing your papers would become even more competitive, and taste becomes far more likely to be the tie condition.

Let me also assert that tastemaking is simply not a challenging intellectual activity. This comes naturally to most humans, even if matching the particular taste that you expect people at t5 journals to possess could be hard (but that is a knowledge gap, not a thinking gap.) What is difficult is writing and the technical tasks that economists conduct. The clarion calls that AI would cause intellectual decay started with the writers (see, for example, Horowitch.) The process of struggling to put your thoughts on paper helps the writer think more clearly and better; Ezra Klein for examples says that in struggling to write a draft of his essay, it helps deepens his thinking for the future. I've found that struggling to think through the algorithm for a bespoke estimator helps me actually understand what the estimator is doing more properly, and to know that I should certainly not reuse it in the future because of its limitations. Struggling through Rudin when I was in my second year of undergrad allowed me to manipulate abstract ideas in my head with ease years down the line. Struggling through the hard work of research gives you an intuition for how things connect to each other and what the deeper underlying patterns are across papers. Because it is only by going through the weeds yourself that you are forced to play with those ideas in your head for a prolonged period of time. That puzzle-solving also forces your brain to make meaning out of those abstract ideas, and thereby find the deeper truth behind what is written. I often marveled at how my professors seemed to understand the papers they're talking about at a deeper level, seeing beyond its theorems or empirical methods to tell me what the true essence of the paper is -- which paper is it in conversation with, what was the challenge that it solved, and why that challenge was not ex-ante obvious. In outsourcing all of our technical work (and even writing!) to AI, do we sacrifice that understanding? Or are we fine with letting the AI understand our own research for us?

And if you think that is the tradeoff we'll all have to start to make, think about what the end goal is here. Most people reading this are probably not tenured professors at top universities who have secured their future. Why would society need a tastemaker, particularly on the economy? What competitive advantage do you have over people outside academia if you do not understand the mathematical model of your research, you do not understand what the code is doing, and you have not internalized how your research relates to the broader literature at a structural/abstract level? And even if you do understand, if your role is to minimally steer AI and just steer it by taste, why couldn't a layman just replicate what you do in, say, 2028 or 2030 when the models have advanced even further along? Do we as a field really think our taste is what keeps us around?

When I listened to Terence Tao's description of what math may transition into, it is deeply unsettling, because the social contract in which we grant mathematicians the funding and support to pursue their activities is one which presumes that their activities provide social benefit and, importantly, they are something that require sufficient cognitive capacity to even attempt to conduct, which not every individual possesses. If, with AI, mathematicians become mostly ambassadors that explain AI's findings to society, it greatly diminishes the perceived value of funding mathematics.

Economics is in a far more perilous situation, because unlike mathematics, economics is not so abstract and disconnected from people's daily lives. I suspect that people would be just fine to continue funding mathematicians to engage in research they don't quite care about, since it really does not cost that much to the average person. But if anyone can write something passable in a third-tier economics field journal with the help of AI (and agents dealing with the review process), in a way that has the potential to probably be far more viral in society, given that the public's taste for what is considered economically meaningful and important is unlike that of academia's taste, what remains of academic economics' purpose? Do we become storytellers and persuaders, giving the public enough spectacle to justify our own existence? If anyone can frame any arbitrary phenomenon they witness in their personal lives as a Bayesian game, or estimate something they care about with a structural model, at the quality that is passable for most intents and purposes for society, what need do we have for the economists? Perhaps the uppermost echelons at MIT or Harvard or Chicago would preserve their sanctity as the human frontier stays just a step ahead of AI, but let's be so honest with ourselves: most people are not doing frontier, cutting-edge work that is non-existent in the training data. Plus, in just a few months, that frontier work would be in the training data and be accessible to AI for everyone else to use.

We're also riding a tidal wave of economic dissatisfaction, one which the economists have little medicine for -- and not even a coherent diagnosis. If we cannot solve society's problems, even if there is truly nothing we can do about it, what need does society have for the economists? The only respite that we have is that, even if the reputation of higher education has dropped precipitously over the last decade, there is still some shred of prestige left where people for the most part trust academic research. But that is less the case for economics than for physics, as people generally tend to believe they understand their personal lives and things that connect to their personal lives much more than they may truly do. Will we try to persuade the public that their mental model of how the economy impacts them is erroneous, just as some kept trying to persuade the public that inflation is (correctly) coming down to no avail?

I am deeply concerned that economists do not understand that what is beginning to happen to mathematics will come for us with greater wrath. The little joy that you might get that AI can now tackle an algorithm issue that you would previously have struggled for days to fix is nothing compared to the great transformation that will change this field, probably in a way that is unkind to the academics, rather than to the public. Even in the best case scenario, should our funding be spared, we may descend into nothing more than storytellers and persuaders circlejerking each other in our journals as we lose public relevance.