Two of the nation's leading law schools recently responded to the same technological development in strikingly different ways. The University of California, Berkeley School of Law adopted one of the country's most restrictive classroom AI policies. Earlier this year, the law school announced a policy prohibiting students from using AI to conceptualize, outline, draft, revise, translate, or edit work submitted for credit. Students may use AI only to identify potential legal sources for research while remaining responsible for every other aspect of their work. The University of Chicago Law School has largely taken the opposite approach, leaving decisions about AI primarily to faculty judgment.
The obvious question is which institution has the better AI policy. The more interesting question is why two highly respected institutions, confronting the same technology, would reach such different conclusions. The longer I considered that question, the less I believed the answer had much to do with artificial intelligence itself. I found myself becoming less interested in which institution was "getting AI right" than in what each policy revealed about how institutions allocate human capacity when technology changes the economics of work. Viewed that way, the policies began to look less like isolated responses to AI and more like expressions of institutional priorities.
The Human Capacity Allocation Framework
Artificial intelligence changes the economics of producing knowledge work. Research that once required hours may now take minutes. Drafts can be assembled more quickly. Routine administrative work increasingly requires less time than it once did. Whether institutions welcome those changes or resist them, they all encounter the same reality. When the economics of work change, human capacity changes with it.
What happens after that point is no longer determined by the technology. It is determined by the institution. Leaders decide whether newly available capacity will be 
To make those decisions visible, I architected the Human Capacity Allocation Framework. The framework shifts the unit of analysis from artificial intelligence itself to the institutional decisions governing where human capacity should remain central, where it should be redistributed, where it should be developed, and where automation is appropriate.
It does not assume that every gain in efficiency should lead to the same institutional response. Capacity may be preserved when the work itself is developmental, redistributed toward work requiring greater human judgment, developed where people need new capabilities, or released to automation when the work no longer requires sustained human attention. The framework therefore does not ask whether an institution has adopted the "right" AI policy. It asks: What allocation decision is the institution making?
Applying the Framework
Viewed through this lens, the contrast between Berkeley and the University of Chicago reveals more than two positions on artificial intelligence. Berkeley allocates human capacity toward preservation. Its policy protects the developmental process through which students learn to conceptualize, draft, revise, and ultimately form legal reasoning. Chicago allocates capacity toward redistribution. By leaving greater discretion to faculty, it creates room to move student and faculty attention toward learning how to practice, judge, and teach alongside AI. These are not simply different AI policies. They are different judgments about what legal education exists to produce and where human capacity should remain concentrated.
Another law school could reasonably reach a different conclusion. It might encourage AI-assisted drafting while redesigning assessment around oral advocacy, client counseling, negotiation, professional judgment under uncertainty, or other demonstrations of legal reasoning. That institution, too, would not simply be making an AI decision. It would be making an allocation decision about where newly created human capacity should—and should not—be invested.
The same pattern is beginning to appear elsewhere across higher education. Faculty workload, assessment, advising, curriculum design, governance, student support, and professional development all involve decisions about what institutions choose to preserve, what they choose to redesign, and where they choose to invest newly available human capacity.
Capacity and Equity
Thinking about AI as an allocation challenge also changes how we think about equity. Much of the public conversation has focused on whether AI systems themselves produce equitable outcomes. That question deserves continued attention, but it is only part of the conversation. Equity is also shaped by the decisions institutions make after new capacity has been created.
If faculty spend less time developing instructional materials, where should that capacity be invested? If advising becomes more efficient, who benefits from the additional time? If administrative work becomes easier to automate, how will responsibilities be redistributed? If routine tasks consume fewer hours, what new opportunities become possible for students, faculty, and staff? These questions move beyond the technology itself and toward the institutional choices that determine who experiences the benefits of change, who bears its costs, whose work is transformed, and what higher education ultimately chooses to value.
For institutions committed to expanding opportunity, inclusion, and student success, these decisions are especially consequential. AI does not simply change how work is completed. It changes how institutions distribute one of their most valuable resources: human attention. Where that attention ultimately flows will shape educational quality, organizational priorities, equity, and institutional culture long after today's AI policies have been revised.
Looking Ahead
That shift in perspective has changed the questions I ask when I encounter a new AI policy. Rather than asking whether an institution is for or against artificial intelligence, I now find myself asking something else.
What allocation decision is the institution making?
If AI creates new human capacity, where should our institution reinvest it?
Which forms of human judgment should remain central to student learning even if AI can perform them efficiently?
What theory of learning is our AI policy implicitly protecting?
AI policy is therefore never only technology policy. It is a decision about what an institution will preserve, what it will redistribute, what it will develop, and what it will permit technology to absorb. Perhaps that is the deeper opportunity AI presents to higher education: not simply to decide how technology should be used, but to decide, deliberately and transparently, what institutions want human capacity to become.
Sophia Rahming, Ph.D., is an educational futurist, speaker, and consultant who helps colleges and universities design, scale, and evaluate STEM education initiatives while reimagining teaching, learning, and work in the age of artificial intelligence. She is the editor of Black Sisterhoods: Paradigms and Praxis and the author of The Girl Who Loved Math: The Story of Euphemia Lofton Haynes and The Unfinished Days: A 90-Day Practice for Burnout, Healing, and Beginning Again Rooted in Wabi-Sabi. She writes as a private citizen, and the views expressed are her own.
