Here's what's happening in higher education right now, and why you should be concerned about who wins and who loses.
Colleges are racing to launch artificial intelligence programs, certifications, and concentrations faster than institutions can responsibly design them. The incentive structure driving this sprint isn't excellence. It's visibility, enrollment pressure, and the fear of being left behind. That's a problem worth examining.
When federal leaders urge institutional reform, and when accreditors start asking harder questions about program quality, universities should be slowing down. Instead, many are accelerating. Why? Because speed itself has become a competitive advantage in the attention economy. A college that announces an AI degree program gets media coverage. A college that spends two years designing one carefully does not.
This creates a perverse reward system. Institutions are incentivized to say yes to student demand for computer science tracks before they have faculty capacity, before they've determined learning outcomes, before they've thought through whether this new program cannibalizes existing offerings or serves genuine workforce needs. Speed gets rewarded with enrollment and reputation points. Rigor gets rewarded with a slower news cycle.
Consider what this means in practice. A student enrolling in a hastily assembled AI certificate might graduate believing they have skills they don't actually possess. An employer hiring that graduate based on institutional signaling rather than demonstrated competence faces a mismatch. And the college? It hit its enrollment targets and got to say it's "responding to the AI boom." Everyone involved has an incentive to pretend the program delivered what it promised.
The structural problem runs deeper than individual institutions making bad calls. Regional and national rankings reward innovation and program growth. Student loan incentives reward credential accumulation. Donor expectations reward visible expansion. Faculty hiring timelines reward hiring freezes over hiring the right people slowly. None of these forces naturally pushes toward "let's be very careful about this and build something sustainable."
Meanwhile, colleges that do invest in genuine AI literacy across the curriculum, that upgrade existing computer science programs instead of launching new ones, that refuse to pretend nine-month credentials replace meaningful preparation, that actually wait to hire faculty with real expertise, these institutions get no special recognition. They're just doing the work. Their caution looks like falling behind.
This pattern repeats across higher education. When three-year degree programs seemed like efficiency gains, the institutions experimenting with them got attention as innovators. Colleges worried about whether students actually needed that extra year to master material? Their concerns went unheard. The incentive structure doesn't reward institutional skepticism. It rewards institutional boldness, regardless of whether that boldness serves students.
Technology offices are reorganizing to manage digital transformation. Accreditors are asking institutions to commit to reform. But unless the reward system changes, unless colleges face consequences for launching programs they can't sustain or incentives for doing hard work slowly and well, we'll keep seeing this pattern play out.
The stakes are personal. Students taking out loans for AI certifications that don't deliver what they promise. Students turning away from fields they might have loved because they heard computer science was "the future" and found themselves miserable in their fifth coding class. Employers frustrated by credential inflation. Institutions spending money on new programs instead of maintaining existing ones.
Here's the uncomfortable truth: The current system rewards the wrong things, and everyone in higher education knows it. The question is whether that will ever change, or whether the incentive structure is simply too entrenched. Until someone figures out how to make rigor more profitable than speed, expect more of the same.