For most of modern intellectual history, the generalist has been losing. The trend is visible everywhere, including in academia. At my university, Bayes Business School, the finance faculty is divided into groups: banking, asset pricing, M&A, real estate,… These are sub-fields of finance, which is a sub-field of economics, which is a sub-field of the social sciences. And within each group, researchers specialise further still. While this fragmentation is not completely arbitrary, I would argue parts of it are. Disciplines, or sub-sub-(sub-sub…)fields, develop for many different reasons, sometimes by chance: because a set of questions does not fit comfortably inside the mainstream, you get, for example, behavioural economics. Whatever the reasons for this fragmentation, the 18th-century ideal of the “natural philosopher,” equally at home in mathematics, physics, biology, and philosophy, became not just rare but structurally disadvantaged. Being a generalist became increasingly expensive: as each field grew deeper and more technical, the cost of being credible across multiple of them grew beyond what most people could afford.

Large language models are changing this in two ways. First, they lower the cost of acquiring breadth. Learning the basics of a new field (or sub-field, or… you get my point) used to take months of careful reading. But now you can learn its key concepts, frameworks, debates, etc. in a few hours of good conversation with an LLM. Not expertise, of course, but orientation. And orientation matters. The generalist’s traditional handicap is shrinking fast, really fast. Second, and less obviously, LLMs raise the return to genuine cross-domain thinking. This is because of what LLMs are not particularly good at: knowing when a connection between fields is actually deep. They are very good at connections that are already legible in language. They excel at the kind of analogy that is already there in the text. What they miss are the abstract isomorphisms—when an insight from one field reframes a problem in another. Kahneman and Tversky are a good example: the insight that human decision-making under uncertainty follows systematic, predictable patterns was a psychological observation that turned out to be one of the most important ideas in economics of the last fifty years. The fields were asking closely related questions—how do people choose?—in different languages. The LLM makes the cheap connections; the scarce thing is the judgment to know which connections are deep and which are superficial.

None of this is an argument against specialisation. The research frontier in any serious field will continue to be pushed by people with deep, compounding expertise—the kind of calibrated pattern recognition that comes from years of immersion and cannot be shortcut. If anything, LLMs will help specialists too, handling the retrievable parts of their work and freeing them for the genuinely hard problems. The point is not that generalists replace specialists, but that the relative return to breadth is increasing for the first time in a long while.

We may be entering a moment that, in a small way, resembles the age of the natural philosophers—not because any individual can master all of science, which is impossible, but because the person who has thought seriously across multiple fields, and who can spot the connections that no algorithm will surface, is suddenly more valuable than they have been for centuries. The cost of being a generalist is falling. The value of being a genuinely good one is rising. That is an unusual combination. How long will it last?