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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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.
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Revised Standard 314: Learning-Outcomes Requirements and the August Deadline
The ABA’s revised accreditation standards require law schools to establish measurable learning outcomes for every course, align them to programmatic outcomes, and build formative assessments into the first year, all by the 2026-2027 academic year. Most schools are not staffed for this work. An LLM can help with the drafting. It cannot help with the judgment calls that make the drafting worthwhile.
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Deployer Obligations Under the Colorado AI Act
Colorado’s AI Act takes effect on June 30, and its deployer obligations apply to anyone who uses AI as a substantial factor in consequential decisions, including law firms. “Legal services” is one of the statute’s eight enumerated categories. Most of the legal profession has not grappled with the fact that it is on the regulated side of this law.
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Anatomy of an AI-Contaminated Filing: The Sullivan & Cromwell Errata
Sullivan & Cromwell’s AI-contaminated bankruptcy filing has drawn coverage for the firm’s apology. The three-page errata is more revealing: errors that suggest AI corrupted correct citations during editing, a compliance program that failed despite being rigorous, and a supervision obligation the firm’s letter concedes without naming.
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Upsolve and the Unauthorized-Practice Implications for Legal Chatbots
A federal court dismissed Upsolve’s challenge to New York’s unauthorized-practice-of-law rules, holding that trained non-lawyers cannot give individualized legal advice—even for free, even with safeguards, even with disclaimers. The opinion never mentions AI. But it describes AI legal tools more precisely than any opinion that has.
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New York S7263 and Chatbot Liability for Substantive Professional Advice
New York Senate Bill S7263 would impose civil liability on chatbot proprietors whose systems provide ‘substantive’ responses in areas reserved for licensed professionals, and declares that disclosing the chatbot’s non-human status is not a defense. The bill’s impulse is understandable, but its mechanism confuses information with advice and would suppress exactly the kind of public legal education that existing law permits.
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Building Infrastructure with AI: A Case Study
A law professor with no engineering background used Claude, Cowork, ChatGPT, and Gemini to design and deploy a self-hosted news aggregation pipeline over a weekend. The project worked not because AI eliminated the need for technical skill but because the skills it required turned out to be the ones lawyers already have.
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