The first thing AI made abundant wasn't knowledge. It was competent-looking work. And that changes what organizations should value in the people they hire.

I watched it happen in a single assignment.

Early in a business practicum I teach, students build an issue tree: they take a messy client problem, break it into a clean set of questions, and rough out some hypotheses. It is a foundational consulting skill, structured thinking made visible. Students could use AI, provided they were transparent about it. Roughly half did.

The AI-assisted trees were beautiful. Clean structures, tight logic, plausible hypotheses. Graded the old way, they earned high marks. The trees from students who worked without AI looked ordinary: a reasonable start, some muddled thinking, hypotheses that wobbled. Competent, not impressive.

Then the teams presented their findings. The pattern inverted. The groups with the polished issue trees were vague and off-target. The groups with the messy ones were tracking clearly. The difference traced straight back to that first assignment. Where AI had done the structuring, students had skipped the thinking. They had nothing to stand on when the work got hard. Where students had wrestled with the problem themselves, even clumsily, they had built something that carried them.

AI had improved the artifact without improving the understanding. The grade moved. The learning didn't.

That gap is not a classroom curiosity. It is the central management problem of the next decade.

The Signal Just Broke

Research has already shown that generative AI can substantially improve the speed and apparent quality of knowledge work. For as long as knowledge work has existed, we have judged people by what they produce. A sharp memo, a clean model, a tight deck these were hard to make, so making one told you something true about the person who did. The output was a proxy. We rarely inspected the thinking directly........

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