Learning Machines
Lawyers are learning to work with artificial intelligence. Artificial intelligence is learning to work with law. This blog explores how — through pedagogy, practice, policy, and the ethical questions that connect them.
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Answer Quality Is Not Learning Impact: The Stanford AI-Tutoring Study and Hybrid Legal Education
Sixteen law professors judged nearly 3,000 blind comparisons between AI-generated and human-written answers to contracts questions, and preferred the AI 75 percent of the time. The finding is hard to dismiss and easy to overread. Hybrid JD programs that deliver most coursework asynchronously might see the study as a case for adopting AI tutors as the primary support mechanism for their distance components. The case is strong as far as it goes. The study measured whether professors preferred the AI’s answers, not whether those answers produce the learning outcomes that legal education is supposed to serve.
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Offensive Prompt Injection as Professional Misconduct
A Brazilian labor court has fined two lawyers for hiding white-text commands in a petition to manipulate the court’s AI, and the Pará bar has suspended them. U.S. bar opinions still treat the lawyer as the user of AI tools and the potential victim when those tools fail. The harder scenario has arrived: the lawyer embedding hidden instructions in a document to manipulate an adversary’s AI workflow. The Model Rules already prohibit the conduct. The bar should say so before a U.S. sanctions order has to.
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Reading the Limitations Section: What the First RCT on AI and Legal Reasoning Actually Shows
A new RCT finds that law students who used AI on an early synthesis task outperformed their peers on a later reasoning task, even without AI. The finding is encouraging, but conditional. The experimental design gave participants curated sources, decomposed tasks, structured prompts, and a selected model—the same institutional scaffolding most firms have not built. The study’s limitations section is a blueprint for the environment that produced the positive result.
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How Lawyers Should Prompt in 2026
Anthropic, OpenAI, and Google all updated their prompt engineering guidance in late 2025 and early 2026, and a striking amount of the advice from 2023 and 2024 now degrades the newer models. This post lists the changes practicing lawyers should know about, with concrete prompt patterns drawn from the three vendors’ current documentation.
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Client-Side AI Recording and NYC Bar Formal Opinion 2025-6
The NYC Bar’s Formal Opinion 2025-6 addresses what happens when clients use their own AI tools to record and transcribe conversations with their lawyers. Read alongside Heppner, the opinion establishes one clear duty (warning clients of AI-related privilege risks) and suggests two further responses (providing privilege-preserving alternatives and redesigning communication channels) that the rules do not yet require but that the foreseeability shift Heppner introduces makes worth taking seriously.
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SB 26-189's Obsolete Exemption: Statutory Categories and Moving Technology
Colorado’s SB 26-189 exempts AI tools used solely to ‘summarize, organize, translate, draft, route, or present information for human review.’ That exemption was drafted for a model of AI use—ask a question, get an answer, review the answer—that the legal technology market has already moved past. The tools law firms are buying don’t summarize information for lawyers. They make the analytical choices that determine what gets summarized, in what order, and with what emphasis.
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Open-Source Legal AI and the Institutional Floor
A former Latham associate reproduced the core features of Harvey and Legora in two weeks and released the code for free. The commentary has focused on what that means for pricing and capability. But Mike is a ceiling story, and the profession’s AI problem is at the floor.
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Agentic AI and the Boundaries of Professional Judgment
Legal technology vendors are marketing AI ‘agents’ that plan, reason, and execute multi-step workflows. These tools can handle information-gathering tasks well, including within legal practice itself. But the line between collecting material for a lawyer’s evaluation and substituting for that evaluation is the line between appropriate delegation and a supervisory problem under Rule 5.1—and the vendors’ incentives push firms to cross it.
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The Disclosure Patchwork
Illinois says courts should not require lawyers to disclose AI use. Florida circuits mandate it on the face of every filing. Hundreds of federal judges have issued individual standing orders, no two identical. The profession has spent three years arguing about whether disclosure is necessary without asking what disclosure is for—and the answer has less to do with catching errors than with enabling the people who review AI-assisted work to do their jobs.
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Citation Sanctions in Q1 2026: The Verification Problem, Quantified
U.S. courts imposed at least $145,000 in sanctions for AI-generated citation errors during Q1 2026 alone, across cases in New York, Kansas, the Sixth Circuit, and Oregon. The sanctioned lawyers share a striking common feature: none of them had functioning AI verification practices in place. That finding complicates the profession’s preferred response to AI risk, but not in the way you might expect.
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