AI Tools for Practicing Lawyers

Ron Drescher
  • 5.0 (4)
  • Business

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 what AI can — and cannot — do for lawyers today. No futurism. No speculation. Just practical tools for practicing lawyers. Hosted by Ron Drescher

  1. 3d ago

    Episode 031 AI and Access to Justice

    SHOW NOTES Maryland has about 40,000 lawyers. Roughly half a percent of them work in civil legal aid. Add more lawyers and you still hit a ceiling, because every lawyer can only serve so many people. Reena Shah, Executive Director of the Maryland Access to Justice Commission, joins Ron and Heather to ask what happens when technology breaks through that ceiling, and what happens when the general-purpose chatbots break through it first. What if AI's biggest contribution to law isn't making lawyers faster, but reaching the people who never had a lawyer at all? In this episode: - The civil justice gap in Maryland: no right to counsel in civil cases, 40% of households unable to cover basic needs, and legal aid organizations meeting roughly 20% of demand - Maryland's AI and Access to Justice Summit, one of the first state commission events of its kind, and why it was run as a hands-on workshop instead of a lecture - What a pre-summit survey of 187 legal aid staff revealed: few AI policies, few enterprise tools, and no clear organizational stance on staff use - Why Maryland Legal Aid's Vicki Schultz says policy comes first, and why that policy has to start with the organization's values - Self-represented litigants walking into Court Help Centers with 20-page AI-generated pleadings, and refusing to believe the lawyers who tell them it's wrong - The Innovations in Tiered Legal Services Task Force and its push for a UPL waiver to test safe, closed AI tools in low-risk areas of law - Ron and Heather's disagreement: teach pro se litigants to use AI responsibly, or hand them a vetted tool? And is the teaching itself UPL? - The Nippon Life lawsuit against OpenAI, and why Reena argues the big AI companies crossed the UPL line without ever asking permission - Users vs. builders for legal aid: Maryland Legal Aid's home-grown MLA GPT, rent ledger reviewer, and brief creator versus off-the-shelf tools like NotebookLM - A TurboTax-style expungement tool as a model for scaling high-volume legal help We also discuss: - Anthropic's Claude for Legal launch and the connectors and skills it included for pro bono and legal aid organizations - Whether AI companies should give legal aid organizations pro bono or reduced-cost access, and who is actually asking them - Why AI is a prediction model, not a search engine, and how that distinction shapes training - The People's Law Library and the work of making legal websites accurate enough for AI tools to pull from - Practice Signals: a Reddit lawyer asks where the access-to-justice gaps are, and Ron adds a fourth question about what AI can fill - The AI Doc: Or How I Became an Apocaloptimist, and a preview of the show's 50th episode Key Takeaway Reena's argument is about math, not efficiency. Every lawyer has an upper limit. Technology is the only thing on the table that scales past it, but only if it's built safely and vetted before anyone relies on it. Meanwhile the general-purpose chatbots are already giving legal advice to people who can't tell a good answer from a confident one. Availability is not authority. The pro se litigant arguing with the Court Help Center lawyer hasn't learned that yet. For users, the lawyers running off-the-shelf tools, this episode is a warning about who is sitting across the table: more and more often, it's someone holding an AI-drafted answer they trust more than you. For builders, Maryland Legal Aid shows what a small team with real subject-matter expertise can do at low cost: narrow tools aimed at narrow, repetitive problems. Solo and small firm lawyers live with the same constraint legal aid does. There's more demand than there are hours. Mentioned in This Episode - Reena Shah, Executive Director, Maryland Access to Justice Commission - Heather Gardner, co-host and summit speaker - Maryland AI and Access to Justice Summit - Innovations in Tiered Legal Services Task Force (Maryland Access to Justice Commission, Maryland Judiciary, Maryland State Bar Association) - Maryland State Bar Association - Maryland Legal Aid and MLA GPT - Vicki Schultz, Maryland Legal Aid - John Jeffcott, Maryland Legal Aid - Donald Tobin, former Dean, University of Maryland School of Law - Chief Justice Matthew Fader, Supreme Court of Maryland - Lindsay Bramble - Angela Tripp, National Center for State Courts (formerly Legal Services Corporation) - Legal Services Corporation - Stanford University national summit on AI and access to justice - Maryland Volunteer Lawyers Service (MVLS) - Maryland Court Help Centers and county family self-help clinics - People's Law Library of Maryland - Anthropic and Claude for Legal - OpenAI and ChatGPT - Google Gemini and NotebookLM - Grok - Nippon Life Insurance Company of America v. OpenAI (N.D. Ill.) - The AI Doc: Or How I Became an Apocaloptimist (2026) - Reddit (source of this episode's Practice Signals post) Highlight Reels on YouTube and Instagram YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

  2. Sep 24 ·  Bonus

    Field Note: Materials from NACBA's "Me, Myself & AI" Webinar

    SHOW NOTES Every CLE season produces a stack of materials that make sense in the room and mean nothing six months later. NACBA's "Me, Myself, and AI" program is the exception. Four consumer bankruptcy lawyers — all past guests on this show — didn't just talk about using AI, they handed over the actual prompts and skills they run in their own cases. Ron got NACBA's permission to post it all on the website. The question this field note answers isn't whether you should use AI in a bankruptcy practice. It's which of these four entry points is yours. IN THIS EPISODE: - NACBA's "Me, Myself, and AI" program and how the materials ended up free on our website: https://lawyeraitoolkit.com/deliverables/#row-WoaNrpcH4Y - Jenny Doling's 30-prompt consumer bankruptcy library, organized around the actual workflow: intake, means testing, schedules and petition prep, Chapter 13 plan work, trustee issues, 341 prep, reaffirmations, exemptions - Jenny Doling's firm-built AI skills: a pay stub analyzer, a bank statement analyzer, a debt analyzer, and a tax return analyzer - What the bank statement analyzer actually outputs — an Excel worksheet plus a separate attorney work product memo - Tara Salinas's argument for solving one annoying problem instead of building an AI strategy - Tara Salinas's "Your First Closed File" exercise — a 30-minute AI test run on a case you already know the answer to - Chad Van Horn's shift from prompts to skills, and the four questions every skill needs to answer - Chad Van Horn's petition quality control and pleading reviewer examples - Matt McCune's voice, video, software roadmap for building AI into a practice from the ground up - The NACBA50AI discount code for non-member listeners WE ALSO DISCUSS: - Jenny Doling's document renaming, missing document analysis, email organization, and fee quote builder tools - Tara Salinas's point that you don't need a tech committee — if you can explain an assignment to a good paralegal, you can start - Chad Van Horn's rule: if you've corrected AI twice, stop arguing with it and fix the instructions - Ron's own history with NACBA, going back to a 2012 conference in San Antonio DOWNLOAD: NACBA's "Me, Myself, and AI" Materials Four consumer bankruptcy lawyers posted the actual prompts, skills, and workflows they use in their own practices — not slides, tools. Covers: - Jenny Doling's 30-prompt bankruptcy library and her firm's AI skills (pay stub, bank statement, debt, and tax return analyzers) - Tara Salinas's "Your First Closed File" 30-minute exercise, prompts included - Chad Van Horn's four-part framework for turning a prompt into a repeatable firm skill - Matt McCune's voice, video, and software roadmap Also: NACBA is offering podcast listeners $50 off a new membership with code NACBA50AI, through October 31. KEY TAKEAWAY These aren't hypothetical use cases. They're what four working bankruptcy lawyers actually run in live cases, documented well enough that another lawyer can use them tomorrow. That's the difference between a CLE slide deck and a toolkit. Jenny's build gives Jetsons lawyers a full AI-enabled workflow to study. Tara's "just fix one problem" approach is built for Simpsons lawyers who've dabbled but never found a starting point. And Chad's rule — correct it twice, then fix the instructions — is exactly the governance discipline every Flintstones lawyer will need the moment they stop resisting and start experimenting. MENTIONED IN THIS EPISODE: - NACBA (National Association of Consumer Bankruptcy Attorneys) - "Me, Myself, and AI" (NACBA program) - Jenny Doling - Tara Salinas - Chad Van Horn - Matt McCune - Wispr Flow  - HeyGen - NACBA50AI ($50 discount code - Good until October 31, 2026) Highlight Reels on YouTube and Instagram info@drescherlaw.com

  3. Sep 24

    Episode 030 Workflow Options: Irys

    SHOW NOTES Lawyers are building Claude skills, custom GPTs, and homegrown intake bots faster than ever. Most of what they're building will be obsolete in six months, because the foundation models keep swallowing whole product categories. Sabih Siddiqui left Big Law to co-found Irys.ai, a general-purpose legal AI platform. What started as a $299 off-the-shelf app turned into something closer to a custom infrastructure shop. His view is that a skill is a feature and a platform is a different thing. Should you build your own AI tools, buy them off the shelf, or pay someone to build them for you? In this episode: Why Sabih thinks lawyers are "behind" on AI, and why the conference rhetoric hasn't moved past ROI and basic privacy fearsThe sustainability problem with building on Claude skills when the platform underneath changes every weekAn in-house legal team that ran its intake on Claude skills, and what changed when Irys rebuilt it with 36 engineering hoursIrys's "dirty little secret": the $299 off-the-shelf app is an entryway into custom, white-labeled buildsHow a two-attorney pilot grew to 77 users across legal ops, compliance, and financeGrowing to roughly 400 law firms and in-house teams through word of mouth, Facebook groups, and bar association channels, with no marketing spendHarvey and Legora as first movers, and what Sabih calls "wave 2" of legal AIFlat-fee opinion letters at $3,500 that take one hour, and why clients are paying for decades of judgment rather than the hourIn-house clients demanding proof of how outside counsel uses AI, including shared workspaces and per-client-folder ROI reportsRon and Heather's disagreement over whether rank-and-file lawyers want custom builds or something that simply works off the shelfWe also discuss: Every CLE is about AI now, including Ron's skepticism about who's attending the Subchapter V sessionHeather's pushback that lawyers are racing to the AI CLEs, not implementing anything back at the officeTools versus platforms: whether a firm gets better ROI from mastering Word, Slack, and the phone system or from adopting an AI platformPractice Signals: a PI firm on Reddit losing track of who owns the next step on new cases. Sabih's answer is to set it up in Claude or ChatGPT if you don't want a vendor.Claude for Legal versus OpenAI's legal offering, and why Sabih thinks features are commoditizing while workflows hold the valueToken-based billing and what happens when two attorneys run up a Claude bill in two weeksKey Takeaway Sabih's sharpest point is that a feature isn't infrastructure. A Claude skill gives the model a layer to work on, but it does nothing about ingestion, memory, permissions, or the three systems your documents actually live in. Every builder has to decide whether they're building something durable or something the next model release will make irrelevant. The judgment behind the work stays with the lawyer. Everything else is up for grabs. That split runs straight through the Simpsons crowd. The lawyer who's already building skills at night is on a Jetsons path and needs to know where the ground is stable. The lawyer who shows up for a 30-second hearing and goes back to the office doesn't want to build anything. He wants something that works for $299 a month and someone who answers the phone when it doesn't. Heather and Ron don't agree on which group is bigger, and neither answer is obviously right. Mentioned in This Episode Sabih Siddiqui, CEO & Co-Founder, Irys (irys; formerly Akita / Iqidis)HarveyLegoraClaude, Claude skills, Claude Code, Cowork, Claude for Legal (Anthropic)ChatGPT and OpenAI's legal offering Eve LegalLexis (Harvey integration)Palantir (the "Palantir approach")SkaddenGmail, Google Drive, Slack, Microsoft Teams, Zoom, Skype, Microsoft WordTTAB litigationHighlight Reels on YouTube and Instagram info@drescherlaw.com

  4. Sep 17

    Episode 029 AI and the Rise of the Superpowered Solo

    SHOW NOTES Every lawyer who's ever complained that no software vendor understands their practice has secretly wondered the same thing Tara Salinas actually did something about: what if you just built it yourself? Tara, a consumer bankruptcy lawyer in Denver, didn't buy a client intake system — she built one, on a stack most lawyers have never heard of, and she's convinced you could too. If you've never touched code, does "build it yourself" actually mean anything for your practice — or is it Jetsons talk that only works for people who were already halfway there? In this episode: - How Tara went from a folder of sideways phone photos labeled "scan001" to a Claude-built client portal that auto-identifies, renames, and sorts every document a client uploads - The nuts and bolts of that portal: a Supabase database on a Vercel/Next.js front end, running an API that scores its own confidence and routes anything uncertain to a "needs a human eye" folder - Why Tara moved from ChatGPT to Claude in February 2026 and started building with Claude Code, Projects, and custom skills - ABA Formal Opinion 512 and the argument over what actually counts as a "self-learning" AI tool requiring client disclosure and consent - Individual vs. team AI subscriptions — why sharing one login with staff likely isn't enough to meet the compliance bar, regardless of platform - The difference between a "skill" and an "agent" — Ron's Family Guy arms analogy, and where agentic activity starts creating real risk - Why AI chatbots are steering financially distressed consumers toward debt settlement instead of bankruptcy, and what that means for the profession - NACBA's incoming AI Advantage program: a prompt library, a skill library, and a microlearning track for training the next generation of bankruptcy lawyers - Tara's client AI policy — the privilege risk of running case documents through a third-party AI tool, and why she bills clients who hand her "AI slop" - The Users and Builders framework: what a user does with a business problem versus what a builder does with the same one We also discuss: - Tara's path from a near-miss patent law career and an unfinished electrical engineering degree into bankruptcy law - The bankruptcy bar's demographic gap after 15 quiet years under BAPCPA, and why the profession needs more young lawyers now - Practice Signals: a burned-out seventh-year associate on Reddit weighing solo practice against leaving law to sell AI software - How a bankruptcy lawyer can go solo with volunteer cases, trade groups, and a notoriously collegial bar - Screenshot hoarding and how to actually manage the AI tool tips piling up on your desktop - AI as "durable infrastructure" — Ron's comparison to the internet and the electrical grid Key Takeaway Tara Salinas didn't set out to become a software developer. She set out to stop losing hours to a folder of unnamed scans, and one afternoon with ChatGPT turned into a purpose-built client portal running its own confidence-scored document API. The lesson isn't that every lawyer needs to learn Supabase and Next.js — it's that the line between "using a tool" and "building one" is a lot thinner than most lawyers assume, and crossing it starts with one file, not a development budget. This lands differently depending on where a lawyer sits. Flintstones lawyers need permission just to try the one file Tara keeps preaching. Simpsons lawyers are the ones actually deciding whether to stay users forever or start building small — a bank statement analyzer, a skill, a prompt library entry. Jetsons lawyers like Tara are already past that question, and the show's real pivot this season is that Users and Builders, not Flintstones and Jetsons, is becoming the more useful way to sort the room. Mentioned in This Episode - Tara Salinas, consumer bankruptcy lawyer, Denver, Colorado - NACBA — Me, Myself, and AI webinar; incoming AI Advantage program - ChatGPT - Claude, Claude Code, Claude Projects and skills - ABA Formal Opinion 512 (2024) - Supabase, Vercel, Next.js - Glade.ai - Yvonne Nath and last week's episode on Counsel Commons and skills vs. agents - Jen Grondahl Lee, Lawyers Success Network - Complete Bankruptcy/Team Accelerator (Ron's referenced training program) - Family Guy (referenced for the skills-vs-agents analogy) - ECF and Infusionsoft (referenced in Ron's email-automation story) - DocuSign info@drescherlaw.com Highlight Reels on YouTube and Instagram

  5. Sep 10

    Episode 028 AI Skills and the $1500 Divorce

    SHOW NOTES A legal industry business professional spends her career building expertise, then watches AI automate the most valuable parts of that work. That's not a hypothetical — it's what this week's guest proved could happen. Yvonne Nath, founder of Counsel Commons, joins Ron and Heather to talk about a marketplace where lawyers and legal professionals can buy and sell the AI prompt libraries, skills, and agents they've built. But the conversation goes somewhere bigger: what happens when AI doesn't just make legal work faster, but cheap enough to create an entirely new market for the millions of people who can't afford a lawyer today?    In this episode:    - Yvonne Nath's path from law firm operations and strategy work to founding Counsel Commons, a marketplace where lawyers and legal professionals sell the AI automation tools they've created - How Yvonne productized most of her own   law firm management expertise in about a month, and what that says about the future of legal ops roles - Some distinction between skills and agents, and why Yvonne deliberately builds almost none of the latter - How Counsel Commons splits into two channels — bar-verified attorneys and paralegals versus business professionals — to manage unauthorized-practice-of-law risk - Counsel Commons’ vetting process: AI review for prompt injections and input/output accuracy rather than manually testing every submission - The pricing model for sellers — a flat listing fee for legal professionals who keep nearly all the revenue, versus a revenue split for business-side tools - Yvonne's decision to run her own automations on local, open-weight models rather than through Anthropic or another cloud provider, and the data-privacy reasoning behind it - The $1,500 divorce and the access-to-justice math — why the millions of people priced out of traditional legal help represent an untapped market, not a threat to existing practice - Ron and Yvonne's disagreement over whether productizing legal services cannibalizes a firm's existing client base or reaches an entirely different one - What Yvonne means by an "AI native" law firm, and why she thinks real transformation requires starting from scratch We also discuss: - Counsel Commons' review system, built around objective performance questions rather than star ratings - Heather's team meeting encouraging her paralegals and virtual assistants to build AI literacy now, not later - Ron's case that solo lawyers would get further mastering Word, Excel, and their phone systems than chasing AI tools haphazardly - This week's Practice Signal: a solo domestic-law attorney wondering whether they now need to hire a consultant just to use tools that were supposed to make everything easier - The NACBA webinar where consumer bankruptcy attorneys discussed building their own Claude skills Key Takeaway Yvonne's real point isn't that AI makes legal work faster — it's that legal work becomes a different business (and, hopefully, a more rewarding one) once it's cheap enough for ordinary people to actually buy. A $1,500 divorce or a $50 landlord dispute isn't a discount version of a $20,000 case. It's a client who was never walking through the door in the first place. That lands hardest for lawyers still treating AI as a way to shave hours off existing work rather than a way to reach people who currently have no lawyer at all. It's also this season's Users and Builders split in miniature: Yvonne builds, verifies, and sells; most solo and small firm lawyers just want something proven that works off the shelf — and she thinks that gap is exactly the market Counsel Commons should serve. Mentioned in This Episode - Yvonne Nath, founder of Legal InnovAI - Claude - Qwen the local, open-weight model Yvonne runs on her own hardware - Lawvable / Lawv.ai — an open-source skills library Yvonne referenced as possibly connected to Claude's built-in legal skills - Stripe - NACBA (National Association of Consumer Bankruptcy Attorneys) - divorce.com - Upsolve info@drescherlaw.com Highlight Reels on YouTube and Instagram

  6. Sep 3

    Episode 027 Is it Safe to Go to the Rave?

    SHOW NOTES A lawyer running a massive document production doesn't read every page anymore — she can't, and nobody expects her to. So when does that stop being lawyering and start being blind faith? Jim Casey, Chief Privacy Officer at Akamai Technologies, joins Ron and Heather to argue there's a real answer, one that depends entirely on whether a bad AI output can still be caught before someone acts on it. When is an AI mistake actually reversible, and when have you already handed your judgment to the machine? In this episode: Jim Casey's framework for deciding when a lawyer can trust an AI-generated answer, drawn from his two published pieces On Knowledge in An Agentic World and Agentic Workflows in ComplianceThe "Wilma Simpson" hypothetical — a bankruptcy lawyer using AI to summarize a 180-page court order, and where the real risk actually sitsRon and Jim's sharp disagreement over whether a "reversible" AI mistake stays reversible once a client acts on the lawyer's advice built from itWhy reviewing a massive document production (sorting, summarizing, deduplicating) shifts trust from individual verification to system-level designAn e-commerce company's real-world case study training a model on product-safety complaint data — six months of retraining to reach 90–95% accuracyThe malpractice waiver hypothetical: what an in-house lawyer does when outside counsel wants to disclaim liability for AI-assisted workClient consent and data privacy limits on using AI to process a client's own personal informationThe difference between "human in the loop," "human on the loop," and "human out of the loop" — a framework borrowed from autonomous weapons policy"Compliance theater" — why an audit log nobody actually reviews isn't a safeguardThe "non-delegable duty to think" — what AI can never take off a lawyer's plateWe also discuss: The Flintstones/Simpsons/Jetsons framework applied to matching lawyers with the right clientsA Reddit-sourced Practice Signals case: a kind, hardworking associate who struggles with vocabulary and comprehension — does AI help, or just hide the problem?Chris Ryan's AI training tool for new associates, Benchsim.ai, now SOC 2 compliant and video-capableThe Season 3 shift from Flintstones/Simpsons/Jetsons to a new "Users and Builders" frameworkFirst tips for builders and users: "make failure visible" and "ask and ask again"Key Takeaway Jim Casey's real point isn't that lawyers can or can't trust AI — it's that trust isn't one setting you flip on. The question changes at every step in the chain: is this output feeding the lawyer's own thinking, or is it about to become the advice a client acts on? Reversibility disappears the moment a lawyer hands that answer downstream. Everything else — audit logs, compliance sign-off, vendor demos — is theater unless someone is actually positioned to catch the mistake before it moves. This matters most for Simpsons lawyers who've adopted a tool but haven't thought through where accountability actually sits in their own workflow. Flintstones lawyers aren't there yet, and Jetsons lawyers like Jim are already building the guardrails. But the solo or small firm lawyer running document review or drafting client advice off an AI summary needs a concrete answer to Jim's closing question: what do you do with the confidently wrong answer? Mentioned in This Episode Jim Casey, VP & Chief Privacy Officer, Akamai TechnologiesJim Casey's articles: Knowledge in an Agentic World and Agentic Workflows: A New Frontier for ComplianceChatGPTClaudeHarvey (AI legal tool)Legora (referenced as an enterprise legal AI tool)Chris Ryan and his AI training tool for new lawyers (previously featured on the show)Reddit (source of this episode's Practice Signals story)Highlight Reels on YouTube and Instagram info@drescherlaw.com

  7. Aug 27

    Episode 026 Volunteers and Hostages

    SHOW NOTES Chad Van Horn built an AI petition review process that cut his firm's amendments by 80 percent. He also thinks AI is making lawyers dumber. Both are true in the same law firm, and figuring out how to hold both truths at once is the whole game. If AI can teach a new associate everything a senior partner knows, what happens to the judgment that only comes from doing the reps yourself? In this episode: - How Chad's Claude-built bank statement analyzer replaced his highlighter-and-manual-review process - The "volunteers, not hostages" approach to rolling AI out across a hundred-person firm - Why Chad's AI-powered petition review step reduced amendments by 80 percent - How Chad uses himself as a human "AI agent" to bring reluctant staff along without forcing adoption - What Chad's firm built with GLADE, its all-in-one bankruptcy practice management platform, and how Claude and GLADE connect directly - Why Chad believes AI is making lawyers less capable critical thinkers, even as it makes them more efficient - How Chad trains AI skills to check judges' local rules, procedures, and order formatting, and updates them when orders get kicked back - Why bankruptcy filings are rising sharply and what that means for firm capacity - How Chad and Heather debate what percentage of bankruptcy work is really legal judgment versus process - Chad's prediction that practitioners who don't adopt AI may eventually be unable to compete We also discuss: - The gap between Gemini's early data advantage and its current market position - Whether token pricing stays this cheap once AI vendors need to turn a profit - Vendor reliability risk as new AI legal tools flood the market - The FSJ framework applied to Season 2's document-by-document AI onboarding approach - This week's Practice Signal: a solo practitioner asking an inexperienced legal assistant to build the firm's entire tech stack - Florida's vehicle exemption and how a small asset detail can cost a client their car Key Takeaway Chad's petition review process shows what disciplined workflow gets you: fewer amendments, tighter output, and a documented feedback loop that trains the humans as much as it trains the AI. But Chad's own warning is louder than his results. The same tools that make firms faster are, in his view, quietly eroding the critical thinking lawyers need to catch the case that doesn't fit the pattern. For Flintstones lawyers, GLADE and Chad's petition reviewer are proof that meaningful adoption doesn't require a hundred-person firm to pull off. For Simpsons lawyers building their own skills, Chad's volunteers-not-hostages approach and his habit of weekly skill refinement are a directly usable model. For Jetsons lawyers already running full systems, Chad's warning about critical thinking is the harder, more uncomfortable takeaway. Mentioned in This Episode - Claude - ChatGPT - Gemini and Google Workspace - GLADE, practice management and bankruptcy software (name as heard on the recording, confirm spelling) - Clio - Litify (referenced on the recording as "Lytify," confirm intended name) - Salesforce - HubSpot - Easy Filing - QuickBooks - Best Case - CoCounsel (Westlaw) - David AI - NACBA's "Me, Myself, and AI" webinar - Jenny Doling - Van Horn Law Group - Chad Van Horn, author of The Debt Life and Everything You Need To Know About Bankruptcy in Florida Highlight Reels on YouTube and Instagram info@drescherlaw.com

  8. Aug 20

    Episode 025 Do Lawyers Need New Rules for AI? Judge Elizabeth Gunn Says ... Maybe Not

    SHOW NOTES A federal bankruptcy judge just told us she doesn't personally use generative AI in her own chambers - and that's exactly why this conversation is worth your time. Judge Elizabeth L. Gunn, who sits on the U.S. Bankruptcy Court for the District of Columbia, spent nearly an hour with Ron and Heather working through what AI actually changes about the practice of law, and what it doesn't. Central question: If AI can now do in seconds what used to take a junior associate hundreds of hours, who trains the next generation of lawyers - and who's accountable when the machine gets it wrong? In this episode: - Why Judge Gunn deliberately limits AI use in her own chambers, to protect it as a training ground for law clerks and interns - How judicial opinions on AI vary widely across the bench - "as many opinions as there are black robes" - Whether it matters, evidentiarily, if a summary took an associate 700 hours or an AI tool 700 seconds to produce - How firms could develop internal AI tools for chapter 11 practice, and what foundation they'd need to lay for the court - Why Judge Gunn believes generative AI is hollowing out the mid-level associate pipeline - How fee applications are starting to raise questions about AI subscriptions and engagement agreements - Judge Gunn's real experience with hallucinated cases in her courtroom, including sanctions and a running database of violations - Why she believes a sliding scale of culpability makes sense for different kinds of hallucinations in legal briefs We also discuss: - Whether courts should look behind a flat-fee agreement to question consumer bankruptcy AI tool charges - How Lexis and Westlaw brief-checker tools are used in chambers to catch bad citations - Why Judge Gunn is skeptical the UAE's centralized, nationwide AI judiciary approach would work in the U.S. federal system - Differing philosophies among law schools on AI in the classroom, from banning electronics to teaching practical AI skills - Whether it's the court's role to inquire into AI-related terms of an engagement agreement when reviewing a fee application Key Takeaway: Judge Gunn's core message is that AI doesn't require a parallel set of rules. Rule 9011, the rules of evidence, and existing duties of competence and candor already cover it. What changes isn't the standard - it's how carefully lawyers have to apply it, and how quickly courts have to learn to tell the difference between an inaccurate pin cite and an argument built on a case that doesn't exist. For Flintstones lawyers, that's reassurance: you don't need to master new technology to stay compliant, because your existing ethical obligations already cover AI-created work product. For Simpsons lawyers dabbling with tools like Claude or ChatGPT, Judge Gunn's evidentiary framework is a warning - if you can't establish the foundation for how a summary was created, speed doesn't save you. For Jetsons lawyers building AI into chapter 11 workflows, her comments point to exactly what a judge will want documented before she trusts the output. Mentioned in This Episode: - Judge Elizabeth L. Gunn, U.S. Bankruptcy Judge, District of Columbia - Bad Boys of Bankruptcy podcast (Judge Gunn's podcast) - Claude - ChatGPT - Harvey - Lexis - Westlaw - Best Case (bankruptcy case management platform) - Rule 9011 - Northern Pipeline Construction Co. v. Marathon Pipe Line Co. - Stern v. Marshall - Nancy Rappaport, UNLV School of Law - University of Chicago School of Law - New York and Florida AI ethics rules - DC Bar ethics rule on AI HIGHLIGHT REELS YouTube: https://www.youtube.com/@AIToolsforLawyers Instagram: https://www.instagram.com/aitoolsforlawyers info@drescherlaw.com

See All (48)

5
out of 5
4 Ratings

About

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 what AI can — and cannot — do for lawyers today. No futurism. No speculation. Just practical tools for practicing lawyers. Hosted by Ron Drescher

Information

You Might Also Like