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Terence Tao, UCLA mathematics professor and Fields Medallist, on why AI may be succeeding at science while scientists get worse at it: Tao starts by naming the tension directly: "There is this paradox that on the one hand AIs are becoming more powerful and more capable and making fewer mistakes, and they are ostensibly achieving a lot of the goals that we think scientists are trying to do. They're running experiments. They're analyzing data. They're writing papers." Experiments run, data analyzed, papers written — the exact outputs any university or funding body would cite as proof that science is working. Tao's concern is what those outputs stop telling you: "It may be that it comes at the cost of the AI picks up some skill but no human scientist gets any better at doing the science." The skill still accumulates, just in the wrong place. The system gets more capable while the people operating it stand still, because the work that used to build a researcher is now the work being handed off. And the cost shows up in the one thing scientists are supposed to be able to do: "No human can communicate exactly what just happened and why. This scientific discovery is interesting, why this proof is new and what features it has and how it connects." That final clause is the sharp end of it. Tao is describing a result nobody can place — a proof that arrives with no one able to say what makes it new, how it's built, or how it connects to the rest of the field. Understanding is a separate achievement from getting the answer, and it can quietly disappear while the answers keep arriving on schedule. Which is why he thinks the target itself needs re-examining: "We may have to sort of redesign our conception of what science is and what we actually want out of science. What exactly is science for and what are we trying to do? And is there a danger that we are optimizing the wrong thing when we are pointing our AI tools at science?"

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