Most AI tools today operate like answer vending machines. Insert a prompt and out comes a polished response. But legal training has long been built around almost the opposite interaction. The Socratic method does not hand students the answer. It questions, challenges, changes a fact, and asks whether the conclusion still holds. The frustration is part of the design. Students learn the rule, its limits and, eventually, which questions matter in the first place. What would it look like for AI to borrow from that tradition?

Law schools are already wrestling with the tradeoff. The University of Chicago is creating AI-free spaces in required first-year classes and exams while introducing AI into legal writing and advanced courses. Stanford students analyze legal problems independently, then critique AI-generated answers. These schools are separating the work that establishes a cognitive foundation from the work where AI can extend it.

This issue often manifests as a conversation about training junior talent in the age of AI. That is at the heart of the problem, but not the entire problem. Judgment is not acquired once. Lawyers keep developing it through unfamiliar facts, changing law, new clients, and the consequences of prior decisions.

And AI can interrupt those feedback loops at every level of seniority.

Another policy reminding lawyers to “think critically” and “check the AI’s work” will not be enough. If an instant answer is on the screen, people will use it. A lawyer who clicks approve is technically in the loop. That does not mean meaningful judgment occurred.

As builders, we have to confront how people actually behave and design around that reality.