Eating the seed corn: Terence Tao and the usefulness of what now looks useless

Tue, 08 Sep 2026 00:00:00 +0000

About: “The paradox at the heart of AI and science” — Terence Tao, Big Think, 3 September 2026. (30 min).

The argument

Tao opens with an analogy about hiking. Someone hears there is a beautiful waterfall out there, sets off with friends to find it, has to draw the map as they go, gets a bit lost, and in getting lost finds something else that seems interesting and makes a note of it; and at some point along the way they see, in the distance, an even more spectacular waterfall they cannot reach yet, but they leave a record so that someone else can get there later. AI tools, he says, are helicopters: they drop you in front of the waterfall and fly you back. The goal was achieved far more efficiently, and nothing was learned about the route and nothing was seen that had not been explicitly requested.

It is worth registering who is saying this, because it changes the weight of what follows. Tao is probably the most widely recognised living mathematician, he uses these tools, and the video is not a warning about the end of mathematics: it is a fairly cold description of what is happening to the practice. His historical reconstruction is clean. First there were theory and experiment (with mathematics almost entirely on the theory side, except for things like the first hundred thousand primes Gauss computed in order to conjecture what we now call the prime number theorem); then came simulation, which let you run a hurricane in a supercomputer instead of waiting for one; then came big data, with the promise of extracting laws from petabytes instead of confirming hypotheses one at a time. Each of those modes required a human to execute it. And today each one has its automated counterpart: labs that run experiments on their own, agents that write the simulation, data analysis that no longer requires anyone to program the analysis, and —this is the new part— automated theory, a system you can ask what follows from these hypotheses and these axioms.

The second move is the most useful part of the video and the least circulated. Tao objects to the one-dimensional framing (easy tasks, hard tasks and very hard tasks, a line where humans stop and another where models stop, and the question of which is higher) and proposes seeing them as complementary. The human expert works in depth: they pick one or two problems that are difficult but not impossible, and the exercise of making a little progress on them produces side findings they can share, and that others build on. The model works in breadth, and is terrible whenever the problem needs a technique that does not exist yet. But point it at a thousand problems and some will be within reach of an already published method, and sometimes the key sits in an obscure paper from a 1970 journal that no expert had the patience to cross with this particular problem. If it solves 5%, that is fifty problems. Sometimes it catches something every human missed, and not out of brilliance but out of not sharing the profession’s prior: everyone assumed the answer was positive and nobody looked seriously at the negative case. The honest sentence comes right after: the fifty problems that get solved may not be the fifty you most wanted solved.

He is equally concrete about his own practice. He does not use them for the problem; he uses them for the secondary work —literature search, checking a proof, writing code, going over his own draft looking for what could be tightened. And the reason he gives is not about capability but about rhythm: with a collaborator of many years you finish each other’s sentences, you pick up a thread abandoned long ago, there is an attunement that was built over time; with a model, even one with simulated memory, the tempo breaks. It is an objection about how it is to work with them rather than about how powerful they are, which makes it more interesting, because it ages differently from capability objections.

From there comes the paradox that gives the video its title. These systems are getting better and better at hitting the targets by which we measure science (they run experiments, they analyse data, they write papers) and it may turn out that the only one learning anything in the process is the model: that no human scientist is left in a position to explain what happened, why that result matters, what it connects to. The full thirty minutes are worth watching — mostly description rather than forecast, and including the best short explanation I have heard of why a mechanism as silly as predicting the next word ends up working at all.

The seed corn

His last point is the one I want to pick up, and the one least likely to be quoted. Tao makes it with an agricultural metaphor that in Argentina needs no translation: the risk is eating our seed corn. The training problems handed to a doctoral student —the ones that produce their first paper, their first bit of recognition, their first experience of having finished something— are exactly the ones a model replicates today. Replace the doctoral student with the model and you get the doctoral-student-level papers and you do not get the doctoral student. And since nobody is then left digesting that output to build the base the next ones stand on (humans and models alike), the system enters something Tao calls stagnation and defines with uncomfortable precision: we will be able to optimise everything that can be done with current technology, and we will stop having original ideas.

Hence his conclusion. We need a much more open discussion about what basic science is and what it is for, about why curiosity-driven research is still necessary, and about why we still need a community of humans that explores, sometimes slowly and sometimes in ways less efficient than this month’s model. And that needs to be told outside the profession, because the public sees the products —the phone, GPS, the internet— and does not see the process, nor the way in which understanding a little maths and science makes the world a good deal less frightening.

It is worth looking at the shape of that argument before its content. What Tao defends is not a discipline, or a result, or a genius: it is an institutional arrangement, meaning the funded position, the slow training, the group that is still there next year. It is the least glamorous possible defence of basic science, and it has a property that makes it politically fragile: the damage of breaking that arrangement does not show up in the metrics used to decide to break it. A system that eats its seed corn keeps publishing for years what was already under way. The output curve does not fall when the budget falls. It falls a decade later, when there is no longer anyone to pin the fall on.

The same accounting, on a different budget

Before moving this argument to another hemisphere it is worth saying where it was made, because a silent transposition is a cheat. Tao speaks from UCLA, where the community exists and the problem is how to protect it from an efficiency gain the system can afford. That is a rich country’s problem, and one of the good ones.

The South’s problem is the same mechanism without the gain. Here the seed corn is being lost by a route that needed no AI to get here. In 2026 Argentine public investment in science and technology comes to 0.140% of GDP, the lowest value since the series began in 1972; CONICET’s budget fell 17.7% in 2024, 14.2% in 2025 and another projected 18.2% for 2026, a cumulative 42.2% in three years; and Law 27,614, which set rising investment targets, is suspended and executed at roughly a quarter of what it establishes.¹

The university chapter deserves the whole sequence, because the detail is more eloquent than any adjective one could attach to it. In August 2025 Congress passed the university funding law; the Executive vetoed it; Congress insisted with a supermajority and the law was enacted; the Executive suspended its application; the rectors went to court and obtained an injunction covering the articles on salary and scholarship updates; the State appealed and the Supreme Court rejected its appeal on 25 June 2026; and in July 2026 the National Interuniversity Council was still asking for the judicial recess to be waived in order to obtain a final ruling, because the injunction was not being complied with either.² The law was not missing, the majority was not missing, the Court’s ruling was not missing. All three exist.

Now put that next to Tao’s point, which is what I wanted on the record. The cuts do not fall on scientific output: they fall precisely on the seed corn. The first things cut are doctoral fellowships, entry into the researcher career, the continuity of a cohort, the possibility that a twenty-eight-year-old can plan ten years of work in the country. This year’s papers come out anyway, because they were already done. It is the same statistical invisibility Tao describes, executed with a different instrument. And what accelerationism and the chainsaw share is a premise about what science is: that science is its outputs and that the process is overhead. Whoever believes that will outsource the process the moment something cheaper produces the outputs, and defund it the moment cash is needed. They are the same measurement on two different budgets, and the paradox in the title, read from here, is an accounting error before it is a philosophical dilemma.

What the standard defence does not cover

The usefulness of the useless has a standard and rather worn form: the number theory that ended up in cryptography, the non-Euclidean geometry that ended up in relativity, Gauss’s hundred thousand primes. The repertoire is always the same, always comes from the hard sciences and always ends in a deferred payoff. And that form has a problem rarely admitted: it concedes the metric while claiming to contest it. “Wait, this will pay off” is not a defence of curiosity-driven research; it is a promise of usefulness on a long timeline, and whoever offers it has already accepted that the criterion is return.

Nor does it cover what is actually under attack in Argentina. The offensive against the social sciences and humanities is not only budgetary: there was a public campaign mocking research project titles —in which a project that was not even funded by CONICET circulated as an example of waste— and there was, in a researcher-intake call, a motion to assign zero positions to the entire area in the general-topics segment.³ The motion did not pass. That it could be tabled at all already set the precedent. And a thesis on sexual dissidence, or on the memory of a provincial repression, has no Fields Medal to display and no future RSA to promise.

Here Tao deserves credit for the best part of his argument, which is exactly what separates it from the cliché. What he defends is not the deferred usefulness of a result but the process and the community that sustains it, and that version covers the humanities with no translation needed: the reason to fund a slow reading is the same as the reason to fund a slow proof. Whether that argument persuades a ministry in the middle of cutting is another matter, and probably not. But it is the right argument, and it is the only one that does not collapse the day the next model solves the top 5% faster.

What remains open

I owe two things. The first is the objection to this very text: it was written in an afternoon, on an automatic transcript, with tools the video is about, and it is exactly the kind of cheap output Tao describes. The defence I can offer is not about craft but the same one I have been making: what made it possible to argue with Tao at all was not the afternoon of writing but the years of reading behind it, and those years were paid for —in my case and in that of almost everyone writing this kind of thing from the region— by a public system that today is in no position to pay the same for anyone starting now.

The second is the question left open. Tao asks for a discussion about what basic science is for and about why a community that works slowly must be sustained. In the North that discussion arrives before the loss, as a precaution against a new tool. Here it arrives with the sector already halved, and arguing from that position carries a cost almost nobody counts: any defence of curiosity-driven research, made by someone paid out of it, sounds like a defence of their own job. The cuts destroyed that too, and it appears on no spreadsheet. Tao can have that discussion with time to spare. Here it has to be had while next year’s budget is being signed.