In 1974, Antony Hewish won the physics Nobel Prize for discovering pulsars. His graduate student, Jocelyn Bell Burnell, had actually spotted the first one in the data; she’d also built parts of the telescope herself, analysed the charts, noticed the anomaly, and helped confirm that it was real. But she didn’t win the prize. At the time, the Nobel committees argued that Hewish had designed the telescope and directed the research programme. The fact that Bell Burnell’s eyes and judgment were the ones that caught the signal didn’t register as the decisive contribution. In fact, in the committee’s apparent view, she was doing what graduate students do: executing a senior scientist’s vision.
Let’s reimagine this scenario by replacing Bell Burnell with an AI, and the question stays the same: when a crucial insight or calculation emerges from something that isn’t the senior scientist’s own brain, how do we decide who ‘made’ the discovery?
Suppose an AI system solves a longstanding problem in mathematical physics — say, the existence and smoothness of the Navier-Stokes equations — and produces a proof. Human mathematicians confirm the proof is correct. Who should win the Nobel Prize?
(“Nobel Prizes” in this article is a stand-in for many prizes of its type, including the Abel Prize, the Wolf Prizes, and the Lasker Awards.)
Understanding the discovery
On February 13, OpenAI announced that its AI model GPT-5.2 had helped a group of scientists “derive a new result in theoretical physics”. The (human) scientists posed the original question. GPT-5.2 suggested a potential solution. Then OpenAI built an internal model that fleshed the solution out as well as – this is important – provided it. The scientists finally verified it (verifiability is also important), and voila.
The first instinct might be to say it should be the humans who asked the question, set up the problem, and knew what would count as a solution. The AI model is just a powerful calculator. When Andrew Wiles proved Fermat’s Last Theorem using computer verification, nobody suggested the computer should share credit; it was only checking cases Wiles had fully specified. But if an AI generates a proof humans can verify but not fully reconstruct, they’re more like curators than coauthors and shouldn’t win the prize. Discovery implies understanding.
So then let’s award the prize to someone that can actually do that. The humans who didn’t just prompt the AI but who supplied the constraints, the sanity checks, the conceptual ideas that made the solution legible as mathematics, etc. That sounds reasonable… right?
The problem is if that sounds reasonable to you, you’ve also admitted there’s a clear line between the intellectual work undergirding the solution and the infrastructure that makes it possible. Why did Hewish alone receive the Nobel Prize instead of the technicians who built the radio receiver? Or the engineers who figured out how to filter atmospheric noise? Because, the story goes, they were all part of the necessary conditions, not of the discovery itself. The discovery was in noticing that the signal was anomalous, something new. That was an intellectual act whereas building the telescope was engineering.
Fine.* But then what about the theoretical physicists in the 1930s who first calculated that neutron stars should exist? Without their work, Hewish and Bell Burnell may not have known what they were looking at. Should they have been co-laureates as well? “Of course not,” you say. Their work was foundational but it was already part of the scientific background. And the Nobel Prizes reward only the final step, not the whole ladder.
Noticing the arbitratiness
However, even this final step is an artefact of how we tell stories. At some point we have to draw a line and say, “these people count as discoverers and all those other people are in the background”. And we need to be mindful that this line will always be arbitrary — a convention rather than some sort of natural joint in reality.
So finally the question becomes: how do we draw this line? People usually draw it in a way that favours those closer to the end of the chain, working in wealthy institutions, in countries with strong intellectual property regimes, and established scientific bureaucracies. The people whose labour is distant — in time, space, and/or the social hierarchy — get written out as the conditions of possibility.
Crucially, when an AI makes a discovery, this arbitrariness becomes impossible to ignore because all the normally invisible labour is evident in the model’s workflow. Hundreds of machine-learning researchers built the model, in the process practically inventing a way to explore mathematics that didn’t exist before. If a new technique to prove something usually gets you credit — mathematicians have won Fields Medals for such work — why doesn’t inventing a machine that invents techniques count?
Then there are the training data and computing resources: the former is accumulated human knowledge from textbooks and research papers annotated by poorly paid data workers whose names appear nowhere, and the latter has been made possible only by a few organisations that can afford to train models at such large scale.
Stories about achievement
The Nobel committees might say all of that matters but it’s not the discovery; that would be only the specific science result. And that the people who should get the prize are the ones who can explain it and take intellectual responsibility for it. But this just pushes the problem back. Taking “intellectual responsibility” is also a social role we’ve invented: in practice it means to be the person who gives talks, writes the papers, gets invited to conferences, has the PhD, and has the faculty position. It means occupying a place in the prestige economy that lets you speak for a result as being “yours”. And this position is itself the product of truckloads of background labour that we’ve already agreed to not count.
But here’s the thing: the Nobel Prizes are already arbitrary. They always have been, less in the sense that they reward the wrong people (though sometimes they do) and more in the sense that the category of ‘primary discoverer’ is a fiction we’ve all agreed to believe in. Science is not done by individual geniuses who’ve had flashes of insight in isolation. It’s done by big, diffuse networks, networks stretching across generations and continents. Every discovery is underwritten by thousands of people whose contributions are individually small but collectively indispensable. So when we give a prize to one person, or three people, we’re just telling a story that makes reality easier to process and reward rather than describing reality itself.
This isn’t necessarily bad. Stories about individual achievement can motivate others to do better. They give people something to aim for. And maybe that pretense is useful even if it’s not exactly true. But it comes at a cost. The story of individual genius erases the infrastructure that makes genius possible. It treats labour as either ‘creative’ and thus deserving of prizes or as mechanical and thus just a cost of doing business. It takes the last person to touch the result and calls them the author, as if the result just popped out of their head with no other dependencies.
Too useful as a signal
The problem may be unfixable since it’s baked into how we think about achievement. We want to be able to say “this person did this thing” but the world doesn’t actually work that way. And maybe that’s just how it has to be. Maybe there’s no way to give out a ‘scarce’ prize without replicating the inequalities that produced the discovery in the first place. Or maybe the prize itself is the problem. Maybe the whole idea of singling out individuals is a mistake — a 19th century relic from back when we could still pretend science was done by lone polymaths working in labs rather than by sprawling global supply chains of human and machine cognition.
But we can’t get rid of the Nobel Prizes: they’re too embedded, too useful as a signal, and — yes — too good at generating headlines. We’re stuck with them and we have to make them work somehow, so the best we can probably do is use the prize as an occasion to talk about everything it doesn’t capture. Every time someone wins a Nobel Prize, we can make it a moment to foreground all the people who didn’t, and not in a “let’s indulge our guilt” way but more in a “here’s how knowledge actually gets made” way. It’s not a solution but at least it’s not a lie.
(* Not fine but fine enough. You get the idea.)
mukunth.v@thehindu.co.in
Published – February 17, 2026 09:09 am IST
