A friend recently asked me to recommend which AI experts they should follow on LinkedIn.
Jennifer Ellis, Carolyn Elefant, Nicole Black, and Thomas G. Martin are can’t-miss choices in the legal AI space.
Venturing beyond the legal domain, Wharton’s Ethan Mollick, author of the best-selling Co-Intelligence: Living and Working With AI, has some great insights on AI generally. He has 400,000+ followers on LinkedIn, but I find it easier to follow him on his One Useful Thing Substack.
There’s an obvious objection to recommending books about AI: timeliness. Between manuscript and publication, details change, and capability claims are overtaken by whatever shipped last week. All of that is true, but none of it matters much.
What a good book does better than a vendor demo or a LinkedIn post is provide a framework for understanding what these systems do well, where they break down, and which failures should worry someone holding a bar card.
My new article Thirteen AI Books For Lawyers: An Opinionated Guide includes ten great books, two you should avoid, and a bonus pick: a 1950 volume that is still relevant today.
The 75 year old book I, Robot shows has much to teach us about rogue AI agents–and manages to be entertaining at the same time.
Many will agree with AI skeptics that lawyers should not use AI in criminal cases. My take is different.
Those lawyers are right about one thing: criminal law deserves the strictest checking discipline in the profession. That is not a prescription for AI abstinence.
The real problem is lawyers filing something without reading it. That sin predates AI by a few centuries.
Suppose a lawyer asks a new law clerk to review his draft memo, then submits it to the court without reading it. How many would conclude from this experience that law clerks are useless?
Hallucination is a genuine risk in legal research. Avoiding them takes work, but it’s not a catastrophic risk when a lawyer knows how to use AI. Nothing the AI model says goes into the brief unless the lawyer includes it.
The reality is that prosecutors, defense counsel, and even judges who know how to use these tools have a large advantage over those who don’t.
Skeptical? Try this. Take a brief you’re proud of, one in your strongest area. Feed it to a frontier model with a one-word prompt: “Critique.”
Every time I run this test, even on subjects I’ve worked with for thirty years, the AI app finds something I missed.
Every time.
Lawyers will never eliminate AI hallucinations with today’s technology. But they can reduce them—sometimes substantially.
Hallucinations arise from fundamental characteristics of current large language models, which generate answers by predicting plausible language rather than independently determining whether every statement is true. Recent OpenAI research adds another wrinkle: AI systems can be rewarded for guessing rather than admitting uncertainty. OpenAI: Why language models hallucinate
Fortunately, there are several ways to reduce hallucination risk. Here are a few of the most promising:
Choose the Right Tool
Purpose-built legal systems such as Thomson Reuters CoCounsel Legal and Lexis+ with Protégé can ground their responses in curated legal databases rather than relying primarily on what the underlying language model learned during training. This is generally known as Retrieval-Augmented Generation, or RAG.
Grounding definitely helps, but it doesn’t make the AI infallible. The model can retrieve the wrong authority, misunderstand what a case says, overlook important contrary authority, or draw an unsupported conclusion from perfectly genuine sources.
The Stanford study Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools demonstrated this problem with earlier generations of commercial legal AI products. The specialized systems hallucinated substantially less than a general-purpose model, but they still produced incorrect or unsupported answers often enough to make lawyer verification indispensable. Stanford Law School: Hallucination-Free?
The products have evolved considerably since that research was conducted. Lexis+ AI, for example, has become Lexis+ with Protégé, and Thomson Reuters introduced a new generation of CoCounsel Legal in 2026. That makes the Stanford results important evidence about the problem, rather than a current scorecard for today’s products.
Use Good Prompts
Lawyers sometimes use instructions such as, “Act as a meticulous appellate attorney who values absolute factual precision over narrative flow.” More granular approaches may get better results. For example
“Do not guess or fill gaps in the evidence. If you do not have sufficient reliable information to answer a question, say so. Identify any material uncertainty. For factual or legal assertions, provide sources that I can independently verify.”
Jennifer Ellis’ Law Practice Today article, Four Strategies for Legal Professionals to Reduce AI Hallucinations, recommends several related techniques, including using specialized tools, restricting the sources the AI should rely on, providing more context, and independently checking its answers.
Other useful overviews include the ABA’s Generative AI for the Legal Profession: A Guide to Tool Selection, Risks, and Rewards and Nature’s excellent AI hallucinations can’t be stopped—but these techniques can limit their damage.
The common theme is that there is no single hallucination cure. Reliability comes from layers: better models, better sources, grounding, carefully designed prompts, opportunities for the AI to admit uncertainty, and—especially in legal work—independent human verification.
One technique deserves more attention than it usually gets: reducing what I call “answer pressure”—the subtle ways our prompts can encourage an AI to give us an answer even when it doesn’t have enough reliable information to do so.
I’ll discuss that in my next post.
Despite the tech industry’s persistent optimism, a growing body of research concludes that generative AI’s tendency to fabricate facts is a structural reality, not a temporary bug. The authors and analysts below argue that entirely eliminating hallucinations remains mathematically and practically impossible.
LawDroid founder and access to justice advocate Tom Martin has seen a lot of organizational organizational inertia in his day:
People keep doing the same things they’re doing. They keep buying from the same company. They keep wanting to use their tools in the same way.
Inertia is powerful indeed. I’ve found one small way to push against this. I tell every new hire:
“Your fresh eyes are valuable. You will notice problems and opportunities that may not be obvious to people who have been here five years. Make a note of them. We’ll talk in a few weeks, and then again in a few months.”
When new employees suggest better approaches, it’s often gold. Encourage them. The ability to see beyond “the way we’ve always done it” is a rare treasure.
Especially when you are willing to listen before those fresh eyes become accustomed to the scenery.
Are blogs passé? I’d argue that blogs are still one of the most effective — and underrated — marketing platforms for lawyers.
Social media is ephemeral. An Instagram post today will be old news tomorrow. A blog offers permanence. It will always be there and always under your control. They make an ideal hub for all your social media marketing. A short social media post can link to a blog post, giving potential clients more information about you and your firm.
Proprietary platforms like Medium and, more recently, Substack appeal to many, but the wise avoid getting locked into a host that can become a trap. Adding a custom domain name can help, but mapping URLs to a new host is a headache, so it’s far from a perfect solution.
Blogs are an easy and reliable way to archive articles, slide decks, and more. You don’t need to worry about a platform that may disappear or become undesirable because the site where you originally published it decides to add a paywall.
Don’t let your valuable content disappear or become lost. Keep your work at your fingertips, not at the mercy of changing platforms or disappearing websites.
David Arato (Legal Content Marketing) has some additional thoughts at Attorney At Work.
Is legal blogging still a viable client acquisition strategy in 2025?
Yes, legal blogging remains viable as a client acquisition strategy in 2025, but its effectiveness depends on execution and integration with your overall marketing approach. While blogging alone might not drive the same volume of direct client inquiries it did a decade ago, it serves critical functions in the marketing ecosystem: establishing authority, supporting SEO efforts, providing content for other channels, and educating potential clients during their decision-making process.
Why does legal blogging still matter in 2025?
Legal blogging still matters in 2025 for several reasons: 1) It demonstrates expertise in a saturated market; 2) SEO value remains significant; 3) it builds trust through consistency; and 4) it integrates with modern marketing channels. Recent data indicates that companies with regularly updated blogs generate 67% more leads than those without.
Colin Levy, one of the smartest and most accomplished people I know, recently observed that he sometimes lacks confidence, reporting he’s “doubting his experience.”
Few things he could say would make me respect him more.
It’s the people who are always certain they are right that are the problem.
Anyone writing seriously about AI and the law learns to watch certain bylines. Michael Murray’s is one of them, and his latest book just moved to the top of my reading list.
Legal Issues of Generative and Agentic Artificial Intelligence (July 2026) is hot off the presses. It tracks the fastest-moving questions in American law, from the ChatGPT moment through the arrival of autonomous agents that research, negotiate, draft, and file on their own.
It’s the third in a series worth owning complete:
Murray is a senior law professor at the University of Kentucky, where he leads its AI and the Law Project. Blessedly, despite the professorship, his prose is highly readable — perhaps because he was a litigator and federal law clerk before he was an academic. He writes for people who practice law, not just people who study it.
Make room for all three on your bookshelf. Highly recommended.
“Human in the loop” has quickly become one of the most reassuring phrases in the modern AI vocabulary. It suggests prudence, restraint, and—above all—control. If a human must approve the system’s actions, what could go wrong?
Human in the loop, often shortened to HITL, describes any arrangement in which a person reviews or authorizes an AI system’s output before it takes effect. In high-stakes professional work, it has become the standard reassurance offered whenever someone worries aloud about AI: there will always be a human checking. The problem is that in many real-world deployments, HITL functions less as a safeguard than as a slogan. Here are some of the reasons why HITL is not everything it is often cracked up to be:
Automation Bias. Humans tend to trust outputs that appear polished, confident, and complete. Modern AI systems excel at producing exactly that kind of output. A well-structured answer, complete with plausible citations and a professional tone, invites acceptance. The features that make these tools useful also make them dangerous.
Mata v. Avianca, the leading case on AI hallucinations, is usually told as a story about an AI inventing cases. The real issue is that the human reviewer was the safeguard that failed.
Cognitive Overload. In practice, users of AI systems are rarely in a position to conduct careful, line-by-line verification of every output. They are busy professionals, often operating under time pressure. When AI tools are integrated into workflows that generate frequent outputs, the review process can degrade into a form of triage: approve unless something obviously looks wrong.
Scope Illusion. Users may believe they are reviewing the entirety of a decision when, in fact, they are only seeing a surface-level summary. The underlying assumptions, intermediate steps, and data sources may remain opaque. The human is “in the loop,” but only within a narrow slice of the process.
Speed Asymmetry. AI systems can generate outputs and take intermediate steps far more quickly than humans can meaningfully evaluate them. As systems scale, the human reviewer becomes a bottleneck. The natural organizational response is to streamline or reduce review, sometimes informally. Over time, scrutiny diminishes as trust increases—a paradox familiar to anyone who has studied risk management.
In high-stakes professional settings like the practice of law, the expectation of human judgment is not going away. These are domains where accountability, context, and ethical reasoning matter in ways that current AI systems cannot fully replicate.
HITL can be valuable if implemented thoughtfully. This includes:
HITL won’t provide much help in the absence of these conditions. Remove any one and you are back to the slogan.
None of this is an argument against human oversight. It is essential.
Human judgment has never been infallible. Errors, biases, and rubber-stamping long predate AI, but AI introduces failure modes of its own, stacking them on top of the old ones. Oversight is fragile in both directions: the human can fail the machine, and the machine can defeat the human.
The question is not whether a human is present. It is whether the conditions that make a human’s presence meaningful are actually met—and whether anyone has checked.