This shift represents a fundamental misunderstanding of human capital. Employers must stop treating junior hiring as a simple operating expense that can be erased by automation. In reality, junior hiring has always been a capital investment mislabeled on the balance sheet. It is the mechanism by which a firm manufactures its own future. While AI can certainly take over the production value of a junior employee, it cannot replace the value of the training. To survive this transition, organizations must become more deliberate about how they fund and structure professional development. This means rethinking training modules, creating intentional job rotations, and prioritizing mentoring and coaching—all with the specific goal of developing the "judgment" that AI cannot replicate.
Universities face a mirror image of this problem. As a former dean and university president, I have seen how the academic apprenticeship is under threat. The Ph.D. process, for example, is traditionally funded by the productive work of researchers in training. However, as the editors of the journal Nature warned recently, early-career researchers are now in danger of having the "crucial training tasks" of science performed by machines.
The evidence from the classroom is equally stark: students learn when they encounter "friction." Learning requires effort, the possibility of failure, and the necessity of working through difficult problems. When AI provides the answers instantly, that friction disappears, and with it, the development of deep understanding. There is a saying from my former boss in Texas: "No friction, no traction." If we allow AI to remove all the struggle from the educational and professional "formation" process, we will produce a generation that knows how to get the right answer but does not understand the work behind it. We must return to the old mantra of the mathematics teacher: "Show your work."
The current trajectory suggests a future where AI handles the routine work, and an entire generation is denied the experiences that turn raw talent into seasoned judgment. This outcome is not an inevitable result of the technology itself; it is a choice made by institutions. It happens if employers view formation as a cost to be cut and if universities continue to certify work that was actually performed by a machine.
The job posting data will eventually recover as the economic cycle turns; it always does. What will not recover on its own is the "old bargain" where the production of work quietly paid for the formation of the professional. Rebuilding that bargain—deliberately, strategically, and with a clear budget—is the most pressing challenge posed by the rise of AI. The question is not whether the machine can do the work, but whether we are willing to continue investing in the humans who must eventually oversee the machine.
