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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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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Sycophancy as a Failure Mode in AI-Assisted Legal Reasoning
Hallucination gets the headlines, but sycophancy may be the more dangerous failure mode for lawyers. An LLM that systematically validates your reasoning instead of challenging it functions as a mirror, not a counsel.
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A Delegation Framework for AI-Assisted Legal Work: Delegating the Task, Not the Judgment
LLMs are good at generating options, structuring information, and doing legwork. They are not good at weighing what they generate. The most common mistake lawyers make with AI is asking it to exercise judgment lawyers should be exercising themselves.
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Context-Window Degradation as a Practice Risk
LLMs degrade predictably as their context windows fill: losing track of middle-document content, dropping earlier conversation history, and producing confident output built on incomplete inputs. For lawyers using these tools on long documents, the question is not whether it happens but how to structure your work to prevent it.
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The Duty to Inform: Client AI Use as a Known Hazard After Heppner
Heppner established that consumer AI conversations are not privileged. But the case also raises an uncomfortable question for practicing lawyers: if a known hazard to the privilege now exists, do you have a duty to warn your clients about it? The answer, under existing ethics rules, is almost certainly yes.
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API Access and the Limits of Data-Handling Compliance
Using an LLM through an API rather than a consumer chatbot improves your data-handling posture, sometimes dramatically. But an API alone does not satisfy FERPA, HIPAA, or any other regulatory framework, and treating it as though it does mistakes a technical control for a legal one.
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Privilege and the Consumer Chatbot: Data Handling Across Claude's Tiers After Heppner
A comparison of Anthropic’s data-handling policies across Claude’s consumer and commercial tiers, and why the distinction now carries legal consequences after the SDNY’s decision in United States v. Heppner.
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