AI Tools for Practicing Lawyers

AI Tools for Practicing Lawyers delivers practical, no-nonsense guidance on how attorneys can use artificial intelligence tools in their law practices — right now. This podcast is for practicing lawyers who want real-world answers, not hype. Each episode focuses on clear, understandable explanations of AI tools that can help attorneys work more efficiently, communicate more effectively, and make better business decisions — without requiring technical expertise or coding knowledge. We cover topics such as: • Using AI responsibly and ethically in legal practice • Drafting, research, summarization, and document review tools • Client communication and intake automation • Practice management efficiencies • Emerging AI platforms relevant to law firms • Real examples attorneys can apply immediately Whether you are a solo practitioner, small-firm attorney, or part of a larger practice, this podcast is designed to help you understand w

AI Tools for Practicing Lawyers is a business podcast hosted by Ron Drescher, with 40 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Ron Drescher hosts AI Tools for Practicing Lawyers, a business show with 40 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  AI Tools for Practicing Lawyers

  Field Note: Five Tips for Fighting Pro Se Litigants Who Use AI

  Aug 15, 202618 minS2

  SHOW NOTES An insurance company just spent $300,000 fighting a pro se litigant who had no lawyer — just ChatGPT. That number should worry every practicing litigator. What happens when the other side has access to an inexhaustible junior associate that never bills, never tires, and never stops drafting the next motion? In this episode: - Nippon Life Insurance Company of America's lawsuit against OpenAI over a pro se litigant's AI-generated filings - How Graciella Della Torre used ChatGPT to try to reopen a case that had already settled and been dismissed with prejudice - Why the $300,000 in legal fees is the real story here, not who ultimately wins the Nippon v. OpenAI lawsuit - The new asymmetry AI creates between represented parties and self-represented litigants - Narrowing the battlefield with procedure instead of trying to out-produce an AI on paper - Verifying every citation, quotation, and legal proposition in an AI-assisted filing, not just whether the case exists - The Matthew Elliott case and hidden white-text prompt injection aimed at an AI system that wasn't even part of the court's process - Judge Walter Spader's sanctions against Elliott, and Spader's own disclosure that he used AI to help draft the sanctions order - Whether a pro se litigant's ChatGPT prompts and conversations are discoverable - Warner v. Gilbarco and Tremblay v. OpenAI , and what they say about work product protection for AI use We also discuss: - Matt Lafferman and Rick Shearer's writing on the developing case law around AI and discovery - Why courts are treating AI tools as tools, not persons, for work product purposes - Judge Sarah Smith's public statement on the limits of her own AI use on the bench - The distinction between using AI and committing misconduct with AI - UAE courts' nationwide AI procedures, compared with Connecticut's approach Key Takeaway: Lawyers have spent years asking whether AI will replace lawyers. That's not the immediate problem. AI doesn't need to replace opposing counsel — it just needs to give the person without a lawyer the ability to litigate like they have one. The right response isn't more paper. It's narrower issues, verified authorities, documented patterns, and a clear line between AI use and AI misconduct. This is a Flintstones-lawyer blind spot waiting to happen — the attorney who assumes a pro se opponent's filing quality reflects their legal knowledge, and gets buried in volume. It's also a Simpsons-lawyer governance problem: verification has to become a standing practice, not a one-off gut check. And it's a Jetsons opportunity — a citation-verification workflow built now is the difference between a manageable pro se case and a $300,000 one. Mentioned in This Episode: - 21 Ways AI Can Hallucinate in Your Legal Brief - Nippon Life Insurance Company of America v. OpenAI, (1:26-cv-02448) District Court, N.D. Illinois - Graciella Della Torre - ChatGPT - Claude - Gemini - Matthew Elliott v. New York Bariatric Group - Judge Walter Spader, Connecticut State Court - Ars Technica, Suspecting court of using AI, man injected prompts in filings to try to win case - Reuters - Matt Lafferman, Dentons, Legal AI Lab - Rick Shearer - AI and Privilege: When AI Becomes Evidence - Warner v. Gilbarco, Inc . (2:24-cv-12333) District Court, E.D. Michigan - Tremblay v. OpenAI, Inc. (4:23-cv-03223) District Court, N.D. California - Judge Sarah Smith, Third Judicial Circuit, Illinois, Standing Order Highlight Reels on YouTube and Instagram YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

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  Episode #24

  Episode 024 AI, Wellness and the 4th Bucket

  Aug 13, 202654 minS2

  Show Notes Lawyers wear exhaustion like a badge of honor — but what if the data on your wrist already knows you're running on empty? In this episode, Ron sits down with consultant Megan Grover of MBG Wellness and physician Dr. Kathryn Boling to talk about what happens when you point AI at your own biometric data. The question isn't whether lawyers work too hard — everyone already knows that. The question is: **once you can actually measure the damage, what do you do about it?** In this episode: - Why lawyers (and other high-stress professionals) struggle to recognize their own burnout signals - The difference between wellness data (Oura, Whoop, Apple Watch) and medical data (continuous glucose monitors) - How resting heart rate and HRV (heart rate variability) reveal stress your calendar can't - Why sleep quality matters more than sleep quantity — and how to actually get quality sleep on six hours - Gendered stress load: why women in male-dominated fields like law often carry a heavier physiological burden - Practical entry points for busy lawyers who "don't have time" to exercise - Meal strategies and delivery services for professionals who won't cook - Where AI is genuinely useful for interpreting wearable and lab data — and where it falls short - The Practice Signal segment: a Reddit post about a lawyer who turned down a job requiring 2,700 billable hours a year We also discuss: - The "red light, yellow light, green light" system Ron and Kathryn used to survive her medical residency - VO2 max workouts vs. recovery workouts, and why lawyers tend to over-index on the former - Using ChatGPT to vet supplement interactions (and why it's not a substitute for an actual expert) - Why "looking up your symptoms" online is a bad idea, but structured biometric data is a different story - Building sustainable routines instead of chasing 10,000 steps Key Takeaway: The tools have changed, but the problem hasn't: lawyers don't lack information about their own burnout, they lack a boundary. What wearables and AI add isn't willpower — it's proof. When your HRV and resting heart rate confirm what you've been ignoring, "I'm fine" gets a lot harder to say with a straight face. This one's for the Simpsons and Flintstones lawyers who've never looked at their Apple Watch as anything but a phone-caller-ID. The Jetsons lawyer in this conversation is the one who takes the data seriously enough to feed it to AI and actually change something — not just track it and keep grinding. Mentioned in This Episode: - Oura Ring - Whoop - Apple Watch - Garmin - Stelo by Dexcom (continuous glucose monitor) - ChatGPT - Reddit (source of the Practice Signal case) - Megan Grover, wellness consultant - Dr. Kathryn Boling, physician Highlight Reels on YouTube and Instagram YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

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  Episode #23

  Episode 023 Deepfakes and the Liar's Dividend

  Aug 6, 20261h 5mS2

  Has AI fundamentally changed the law of evidence, or has it just forced us to revisit principles that have always been there? That's the question at the center of this episode, and there's no better person to answer it than retired federal judge Paul Grimm — one of the people who has literally tried to write the new rule. IN THIS EPISODE: - Judge Paul Grimm's background: 25+ years on the federal bench in Maryland, now emeritus director of the Bolch Judicial Institute at Duke Law - The multi-year federal rulemaking process (the Rules Enabling Act) and why it can't keep pace with generative AI - The difference between "acknowledged" and "unacknowledged" AI-generated evidence, and why the distinction matters - Proposed Rule 901(c) for authenticating AI-generated evidence — and why the Evidence Rules Advisory Committee has still declined to publish it after three years - How the existing 51% authentication standard (Rule 901(b)(1)) creates a low bar for admitting evidence, real or fake - The "liar's dividend": how the mere existence of deepfakes lets litigants dismiss real evidence as fabricated - State of Washington v. Puloka (No. 21-1-04851-2 KNT, 2024): a court excludes AI-enhanced video evidence despite a legitimate clarification goal - State of Florida v. Albisu (Broward County, 2025): a defense lawyer uses AI-generated VR goggles to let a judge see a self-defense claim from the client's point of view - Rule 107, the new federal rule distinguishing "illustrative" AI-generated exhibits from actual evidence - Why ABA Formal Ethics Opinion 512 already tells lawyers exactly how to avoid AI hallucination sanctions — and why lawyers keep getting sanctioned anyway WE ALSO DISCUSS: - The "witness washing" problem in facial-recognition photo lineups - How deepfake detection expert Hany Farid's confidence has shifted as the technology improved - Maura Grossman's deepfake-detection research at the University of Waterloo - Judge Grimm's "wow moment" using AI to pressure-test a speech outline - The origin of the term "deepfake" and how fast it entered the mainstream - Why illustrative AI evidence — not fake evidence — may be the bigger near-term risk KEY TAKEAWAY: AI hasn't broken the rules of evidence. It's exposed how thin they always were. A 51% authentication standard and a jury instruction to "disregard" what they just saw and heard were never built for a world where anyone can manufacture convincing fake audio and video for free — and Judge Grimm's own proposed fix has been sitting in committee for three years. For solo and small firm lawyers, the fix isn't waiting on Washington. Notice, disclosure, and pretrial motion practice around AI-generated evidence are tools you can use right now, rule change or not. Lawyers still dabbling with AI without a real verification habit are exposed either way — and the ones building real workflow discipline around it are the ones who'll actually benefit from what these tools can do. MENTIONED IN THIS EPISODE: - Judge Paul Grimm (Ret.), Bolch Judicial Institute, Duke Law - Professor Maura Grossman, University of Waterloo - Hany Farid, Dartmouth (formerly UC Berkeley) - Federal Rules of Evidence 901(b)(1), 901(b)(5), 901(b)(9) - Proposed Rule 901(c) - Federal Rule of Evidence 403 - Federal Rule of Evidence 107 (illustrative evidence) - State of Washington v. Puloka (No. 21-1-04851-2 KNT, 2024) - State of Florida v. Albisu (Broward County, 2025) - ABA Formal Ethics Opinion 512 (August 2024) - "Learned Hand" (AI tool for courts) - ChatGPT, Claude, Gemini - The Rules Enabling Act YouTube: https://youtube.com/@AIToolsforLawyers Instagram: https://instagram.com/aitoolsforlawyers info@drescherlaw.com

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  Episode #22

  Episode 022: The Jetsons Vacation In Florida

  Jul 30, 202644 minS2

  Show Notes How much work has to happen before AI can actually help a law firm? Everyone wants an AI-native practice, but almost nobody asks what has to be true before the AI can do anything useful. This episode answers that question with someone who's already built the answer from the ground up: Leisa Wintz , a Florida family law attorney and founder of Legal Authority Lab, whose firm runs on AI from intake to final judgment — not because she bought a product, but because she spent years teaching her systems everything she knows. In this episode: Why the hardest part of AI adoption isn't the AI — it's teaching the firm what it already knows How Leisa built Legal Authority Lab out of her own Florida family law practice, and where it's expanding next (real estate and title) The difference between AI "users" and AI "builders" — and why builders are the rare exception Why Leisa ran Anthropic's Claude legal skills against her own systems and concluded they were too generic to adopt How her firm delivers AI as Claude skills tied to a GitHub repository, rather than as a standalone app Using Claude as a de facto case management system — and the one thing it still can't do well (a visual dashboard) How Leisa evaluates whether a "Flintstone" firm is even ready for AI, and what she builds first when it isn't Florida's new statewide AI-use disclosure rule, and how it compares to New York's The UAE's AI system for judges, and whether courts are the next frontier for legal AI Week four of the show's AI Build series: turning firm knowledge into Markdown so AI can actually use it We also discuss: Leisa's husband, a computer engineer, joining Legal Authority Lab as a technical partner Discovering and evaluating unfamiliar tools like GoHighLevel on the fly A Reddit "AITA" post about a lawyer paid $100/hour to evaluate AI's legal answers Florida's PDF/A and OCR e-filing requirements, and what they mean for AI-ready documents Whether courts might someday accept Markdown-formatted pleadings Leisa's do-it-yourself legal coaching app for pro se litigants in Florida family court Free Download: AI Build Series — Markdown Examples ( Sample markdown-formatted documents from the show's AI Build series , showing how to turn firm knowledge into a format AI can actually use. Examples of a client intake process rewritten in markdown Templates for Flintstones, Simpsons, and Jetsons-level starting points Guidance on pairing markdown with a clean PDF reference for better AI performance Key Takeaway An AI-native firm isn't built by adding AI — it's built by capturing everything the firm already knows in a form AI can actually use. The bottleneck was never the model. It's the spreadsheet: the categories, the naming conventions, the workflow logic that has to exist before any tool can be useful. Flintstones lawyers shouldn't skip straight to AI — Leisa's first move with an unready firm is basic infrastructure like lead forms and intake, not a chatbot. Simpsons lawyers are the ones already dabbling who need to formalize what they know into structured documents. And Jetsons lawyers — the rare builders like Leisa — are the ones who can package their own systems into reusable skills, and in her case, into a business. Mentioned in This Episode: Legal Authority Lab Claude (Anthropic) and Claude's legal skills GitHub Clio Grow, Smokeball, Cleo, Rocket Matter GoHighLevel JotForm Slack Florida Supreme Court statewide AI-use rule Leisa's LinkedIn Discussion of the Florida Rule New York statewide AI-use rule California pending AI regulation UAE AI judicial assistance platform Highlight Reels on YouTube and Instagram YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

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  AI Tools for Practicing Lawyers

  AI Builds: Gmail Digest

  Jul 23, 202614 minS2

  Most lawyers think about AI as a chat box. Ron argues that's already the old way of thinking — and the real leverage is in systems that run without you, remember what they're supposed to do, and hand you a finished result before you've had your coffee. In this episode: - Why the podcast is shifting focus from prompting skills to persistent, automated AI workflows - The "airport test" — Ron's framework for deciding whether an AI workflow is actually worth adopting - Building a fully automated AI news producer using make.com - The four-step architecture: collector, bundler (text aggregator), producer (persona prompt), and formatter - Using a Gmail label and filter to isolate newsletter content from client and personal email - Why persona prompting ("you are the executive producer") beats a plain summarization request - Why GPT-4.0, an older model, was still the right tool for this specific job - Hitting and solving the "wall of text" formatting problem with a markdown-to-HTML converter - Using Gemini as a build partner because of its familiarity with Gmail - Real examples of what the finished daily digest surfaced, including breaking legal-tech news We also discuss: - Why LinkedIn, properly configured, is a strong AI news source - Newsletter overload and why Ron started ignoring sources he used to value - The PlayStation 3 vs. PlayStation 2 analogy for why newer AI models aren't always necessary - Heather Gardner's role in introducing Ron to this kind of automated workflow thinking - Where this build actually lands on the Flintstones/Simpsons/Jetsons spectrum Key Takeaway: The most useful AI adoption in a small firm doesn't happen in the chat window — it happens when a workflow disappears into the background and just delivers. Ron's four-step build (collector, bundler, producer, formatter) is a repeatable pattern, not a one-off trick, and it didn't require the newest, most expensive model to work. This episode sits right in Simpsons territory: a lawyer who's dabbled with AI tools individually but hasn't yet connected them into something that runs itself. Ron's point isn't "go build this exact thing" — it's that the leap from Simpsons to Jetsons is smaller and less technical than most solo and small firm lawyers assume. Mentioned in This Episode: - make.com - Gmail (labels, search filters) - ChatGPT / GPT-4.0 (OpenAI) - Google Gemini - LinkedIn - Artificial Lawyer (newsletter) - The Rundown (newsletter) - Legal Tech Blogs (newsletter) - Heather Gardner - Apple / OpenAI trade secret lawsuit (Johnny Ive hardware device) - Meta AI-biased layoff algorithm lawsuit info@drescherlaw.com

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