The economics of superstar AI researchers - by Anson Ho
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Gradient Updates<br>The economics of superstar AI researchers<br>What might explain AI researcher pay, and why it matters<br>Anson Ho<br>May 13, 2026
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Gradient Updates shares more opinionated or informal takes on big questions in AI progress. These posts solely represent the views of the authors, and do not necessarily reflect the views of Epoch AI as a whole.
AI is one of those fields where the best winds up much better off than the rest. Superstar researchers at frontier labs earn over ten times more than most of their colleagues, who earn measly million-dollar salaries. They might even earn over a hundred times more than your average AI postdoc:
Ballpark estimates of AI researcher compensation. Postdoc compensation is estimated using NSF report data. For tenure-track professors, I anchor on this Taulbee 2024 survey of computer scientists. Compensation for frontier lab researchers is estimated from Levels.fyi for L4-L5 OpenAI researchers, and news reports for superstars.<br>So why are the differences in pay so large? The naive explanation is that some researchers are just vastly superior. Perhaps the superstar researchers have excellent research taste in designing algorithms and experiments. Or they have a knack for pulling off “yolo runs” — training runs that implement many ambitious changes all at once, relying on deep intuition, whereas most people would need to systematically test the individual changes to make sure they work. Under this framing, superstars are the “10× researchers” that Silicon Valley so deeply reveres, and it’s their quality that makes the difference in pay.1<br>The problem with this explanation is that it’s very incomplete. In reality, we should expect to see big differences in pay even if superstars were only a tiny bit better than your average postdoc. But why?<br>The superstar effect
The short answer is this: there’s a well-known economic dynamic which turns small differences in ability into big differences in pay. Here are two illustrative examples:<br>In the 100-meter sprint, the gold-medallist gets much more reward and attention than the silver-medallist, despite them being quite literally neck-and-neck for most of the race. Consider the London 2012 Olympics, where Usain Bolt won gold. Most people have no idea who won silver, despite finishing just 0.12 seconds behind — do you?
Some musicians earn much more than others. Consider Taylor Swift: last year, she earned $60-70 million from Spotify. I don’t doubt that she’s a “10× singer” compared to me. But it’s very debatable whether she’s that much better than other extremely popular singers like Ed Sheeran, Blackpink, Charli XCX, and Lana Del Rey, who instead earned closer to $5-25 million.
Ballpark estimates of 2025 Spotify earnings of several extremely famous artists. These were estimated by multiplying daily Spotify streams by 365 days, and earnings of $0.004 per stream.<br>Across these two cases, small differences in ability led to big differences in pay some way or another. Economist Sherwin Rosen called this the “superstar effect,” and it kicks in when two conditions hold.<br>One person’s work can reach a big market. Usually this means a market with many people, but a few high-paying people or firms work too. For instance, potentially billions of people watched Usain Bolt win the 100-meter sprint. The more people you can reach, the more pronounced the superstar effects. Across the economy, jobs with broad reach — such as actors, musicians — show far bigger wage dispersion than jobs serving one client at a time, such as plumbers, nurses, and truck drivers:2
Data from the Bureau of Labor Statistics across different occupations, showing the ratio of 90th percentile earnings to the median. If we had data on the extremes (e.g. 99th percentile), I’d guess the difference in wage dispersion would be even larger.<br>Quantity doesn’t easily make up for quality of labor. You can’t have multiple people take the place of a single sprinter, since that would break the rules of the race. And if you like Taylor Swift more than Ed Sheeran, it’s hard to make up for missing a Taylor Swift concert by going to more Ed Sheeran ones.
The first condition means a tiny quality edge captures enormous extra value, making it worth paying a lot for the best — that is, as long as you can’t make up for quality with quantity (the second condition). If you could, you’d just hire a lot more people with lower pay — you wouldn’t need to pay a ton just to hire the cream of the crop.3<br>Why this applies to AI
AI researchers tick both boxes. There’s a huge market: ChatGPT has almost a billion users, served by the same handful of underlying models, so a single researcher’s contribution could scale to every user simultaneously.<br>And in AI, researcher quantity doesn’t easily make up for quality: frontier labs are compute-constrained, so they can only run so many experiments to test new software...