The OECD reported in 2026 that three-quarters of students aged 16 and over used generative AI tools in 2025. At the same time, the World Economic Forum says 39 percent of workers’ core skills are expected to change by 2030, with creative thinking, resilience, and curiosity rising in importance. That combination should force a change in what schools reward, because the definition of useful intelligence is shifting in real time.
For generations, education has largely trained students to solve problems placed in front of them: calculate the answer, write the essay, complete the assignment, pass the test. But when a machine can produce a plausible answer in seconds, reaching the answer can no longer be the highest form of intellectual achievement. One of the most valuable skills may increasingly be identifying the problems worth solving.
By creating problems, I do not mean manufacturing difficulties. I mean noticing what others have overlooked, challenging the premise of a question, identifying a better question, and imposing useful constraints. Innovation rarely begins with someone answering the obvious question faster.
It begins when someone asks why everyone is answering that question in the first place. This is also why the usual debate over whether AI makes young people more or less creative misses the point. It can flatten thinking when students accept its first response as authoritative.
A Microsoft Research study of 319 knowledge workers found that greater confidence in generative AI was associated with less critical-thinking effort. But AI can also accelerate exploration, expose users to alternatives, and make experimentation dramatically cheaper. The danger, then, is not that young people will have too many ideas.
It is that they may never develop the judgment required to know which ideas are good. That judgment is built through friction. A student who struggles with a question, tests an assumption, discovers that an approach fails, and tries again is not wasting time.
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