AI Tools for Practicing Lawyers

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

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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

• 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

[email protected]

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

To see and download the final outputs from these tools click here.

[email protected]

While AI note-takers grab the headlines, the real issue at stake in this episode is one lawyers have been dodging for decades: "Is AI notetaking actually riskier than everything else already sitting in your practice?"

In this episode:

- Why the claim "AI note-takers turn everything into data" misses the point — meetings were already data, AI just organizes it
- Ron's take on recording everything (calls, meetings, even hallway conversations) as part of the AI-forward way of managing firm information
- The real question lawyers should be asking: not "do I trust AI," but "do I trust this vendor with this information under this security model"
- The information governance questions that actually matter — who has access, where data lives, encryption, retention, whether the vendor can train on your data, whether recording can be disabled
- How the AI adoption debate mirrors the earlier fight over moving client files to the cloud
- Why treating "AI" as fundamentally different from every other cloud service is a mistake
- The shift from "never put client files in the cloud" to firms running on Microsoft 365, NetDocuments, Google Workspace, and Clio

We also discuss:

- Confidentiality, privilege, trade secrets, and personnel discussions as legitimate concerns worth taking seriously
- Everyday, non-AI examples of data risk — email, Dropbox, Google Drive, even the cleaning crew
- Podcast guest Carolyn Elefant's related commentary on AI vs. other cloud tools
- The Fast Company article that prompted this Field Note

Key Takeaway:

The mistake isn't using AI note-takers — it's treating "AI" as a special category that suspends the ordinary due diligence lawyers already owe every vendor. Strip away the letters "AI" and the questions are the same ones firms have been asking about cloud storage and email for twenty years.

This lands hardest for Simpsons lawyers — the ones dabbling with AI tools but without a governance structure behind them. Flintstones lawyers will read this as one more reason to stay away; Jetsons lawyers already have these vendor questions answered. The point of this episode is to move more of the audience from reflexive fear or reflexive adoption into an actual governance conversation.

Mentioned in This Episode:

- Fast Company: [Why You Should Think Twice Before Letting an AI Notetaker in Your Meeting](https://www.fastcompany.com/91571498/ai-notetaker-work-meetings-privacy-data)
- Carolyn Elefant
- Microsoft 365
- NetDocuments
- Google Workspace
- Clio
- Zoom
- Dropbox
- Google Drive

[email protected]

SHOW NOTES

Most AI pitches to law firms start with a product. This one starts with an audit — and a question about whether buying software was ever the right move for a solo or small firm in the first place. Jerry Dearden of David AI joins Ron and Heather to unpack what it actually takes to build AI around a firm instead of forcing a firm into someone else's software. It's the David AI origin story, the pricing, and the pitfalls — no punches pulled.

IN THIS EPISODE:

- How David AI's "fitting" audit works before any code gets written — firm size, niche, workflows, tech stack
- The difference between reactive AI (you prompting a chatbot one task at a time) and proactive AI (an agent that acts on its own and reports back)
- Why firms keep bumping into Harvey and Legora, then walking away because the pricing and scale don't fit solo and small practices
- How David AI builds agents that live wherever a lawyer already works — iMessage, Slack, Teams, or inside existing case management software
- The "company brain" concept: connecting a firm's tools so AI has full context instead of starting from zero every time
- Data protection, DPAs, and how David AI's own contract terms changed after a listener-style question exposed a gap
- Build-versus-buy: when David AI tells a firm to keep its existing software instead of building something new
- Pricing structure — audit fees, build costs, and ongoing maintenance
- A real AI paralegal build for a small Utah firm, including a lawyer-avatar project still in testing
- The insurance defense billing bottleneck, and how AI could translate time entries into carrier-acceptable language automatically

WE ALSO DISCUSS:

- Kirkland & Ellis's $500 million, multi-year investment in a proprietary AI platform, and why Jerry calls building your own LLM "frying an egg on a volcano"
- Claude's connector ecosystem and what it means for firms trying to build a unified knowledge base
- Staff resistance to AI and the "Iron Man suit" framing David AI uses to get buy-in
- This season's ongoing FSJ knowledge-standardization exercise — this week's step is making firm documents AI-readable
- A tease for Season 3: "users and builders"

KEY TAKEAWAY

Jerry's answer to almost every question comes back to the same place: don't buy a tool and force your firm into it, fit the tool to how you actually work. That's a different pitch than most AI vendors make, and it only works if a firm is willing to open up its internal processes to an outside audit first.

This episode's FSJ moment comes from the ongoing build series Ron's been running with listeners since the start of the season. Four weeks in, the assets are identified, organized, and cleaned. This week's step is standardizing them: a Flintstones lawyer writes a plain description at the top of one form, a Simpsons lawyer turns client questions into a consistent Q&A format, a Jetsons lawyer breaks a workflow into sequential steps. It's a reminder that "AI-ready" starts with structure a human puts in place — long before any model touches the data.

MENTIONED IN THIS EPISODE:

- David AI
- Harvey
- Legora
- Claude / Anthropic
- ChatGPT / OpenAI
- Reddit
- Clio
- Microsoft Teams, Slack, iMessage
- Spellbook
- Kirkland & Ellis
- TimeSolv (billing software Ron used in practice)
- Claude's connector ecosystem

[email protected]

SHOW NOTES

What if the smallest law firms are actually the best positioned to win the AI era? That's the provocation Ron opens with this week — and it comes from someone with a genuinely rare vantage point: a solo attorney who also works as a professional AI trainer for the labs building these models.

In this episode:

• Dr. Sara Kubik's path from a gerontology-focused PhD to family law, estate planning, and now legal AI training
• Inside the AI training industry — how OpenAI, Anthropic, Google, and Microsoft contract out trainer roles through staffing layers, and why it's become a multi-billion dollar business
• Why older clients are often faster and more enthusiastic AI adopters than lawyers expect, once they see it's useful
• How different AI models produced very different results on the same creative request, and what that reveals about picking the right tool for the job
• Vibe coding a Medicaid penalty calculator, a community spouse calculator, and a child support chatbot without a formal development background
• The case for solo and small firm lawyers having a structural advantage over Big Law in adopting AI
• University of Chicago Law School's ban on electronics in 1L lectures — and why Sara thinks the policy won't survive the year
• What lawyers consistently get wrong about how AI models work, including training cutoffs and hallucinations

We also discuss:

• ChatGPT's new wave of specialized models and the ongoing comparisons to Claude
• Ron's controversial take on lawyers recording everything as firm data infrastructure
• Practice Signals: a Reddit post from a homeless summer associate, and where AI actually helps — and where it doesn't
• This week's Season 2 AI Build segment: cleaning firm knowledge assets before teaching them to AI, including PII anonymization and "grader guidance"
• The difference between vibe coding and understanding what you're building
• Data-focused stats on solo and small firm representation in the legal market

Key Takeaway

AI adoption was never really about the tool. It's about the pain point. Sara's advice to solos is blunt: stop shopping for tools and start naming what's actually broken in your practice — then go find out what fixes it. That reframe is what makes AI usable instead of overwhelming, and it's also what makes the "solo advantage" argument real: for the first time, small firms have access to something close to the same horsepower as Big Law, without the budget Big Law required to get it.

This one lands hardest for Simpsons lawyers — the ones already poking at ChatGPT on their phones but who haven't connected it to a real problem in their practice yet. It's also a reality check for anyone tempted to treat AI as a universal fix: the practice signals segment on the homeless summer associate is a reminder that some problems are human problems first, and AI is a research tool for finding help — not the help itself.

Mentioned in This Episode

• Dr. Sara Kubik (guest)
• Claude, ChatGPT, Gemini, Grok, Midjourney, DaVinci Resolve
• Codex (ChatGPT)
• Power BI, SQL, Excel
• Clio
• Perplexity
• Cooley Law School
• Purdue University
• University of Chicago Law School
• Indiana State Bar Association
• Reddit

[email protected]

Note: As mentioned in the episode: Is Mitch McConnell's photo real or fake? — Poynter/PolitiFact
Note: This photo has been the subject of online debate regarding its authenticity. We're linking to it as referenced in the episode and take no position on that controversy.

Show Notes

Most bankruptcy lawyers still don't know what their own firm actually makes each month. Jenny Doling built a revenue dashboard in Claude to find out — and then kept going, building analyzer skills for pay stubs, tax returns, bank statements, and Chapter 13 objections that her staff now runs without her. If a nationally recognized bankruptcy attorney and NACBA's incoming president is running her practice this way, what's the excuse for everyone else?

In this episode:

• Jenny Doling's path from paralegal to bankruptcy attorney to NACBA president-elect, and how she got hooked on consumer bankruptcy work back in 2004
• The scale of the current bankruptcy surge — commercial and individual filings both up sharply since 2022 — and what firms without systems risk
• Why Jenny moved her practice from ChatGPT to Claude, and how she uses Claude skills, projects, and Cowork day to day
• The analyzer stack she built: pay stub analyzer, document renamer, bill analyzer, tax return analyzer, and bank statement analyzer
• Her Chapter 13 Opposition Builder skill — loaded with trustee objections, Collier on Bankruptcy material, docket reports, and key case law
• Confidentiality practices: a closed Claude Team environment, a written office AI policy, and PII lockdowns before staff touch any tool
• Why Jenny separates time saved from work-product elevation, and argues the second is the real benefit of AI
• Client-facing AI disclosures in her fee agreements, and why she avoids Zoom's built-in AI transcription specifically
• A frank take on legal tech vendor Glade versus incumbents like Clio, and why responsiveness matters more than market share
• This week's Season 2 build step: organizing the firm knowledge assets identified last week, tiered by Flintstones, Simpsons, and Jetsons lawyers

We also discuss:

• Case law and citation practices for Chapter 13 objections, including Hamilton v. Lanning
• Recording practices — why "team record" doesn't mean "team Zoom record"
• AI transcription and notetaking tools, including Whisper, Granola, and wearable recorders
• NACBA's upcoming AI-focused conference programming in Orlando and Maui
• A Practice Signal segment on a solo PI lawyer's shrinking case volume and how Jenny would advise him to diversify

Key Takeaway

Jenny Doling isn't impressive because she uses AI. She's impressive because she treats her analyzer skills as infrastructure — quality control built in, confidentiality locked down before staff ever touch a tool, and her name still on every pleading that goes out the door. That's the difference between dabbling and running a practice.

This one's for the Simpsons lawyer wondering how far a solo or small firm can actually take this. It gives Flintstones lawyers permission to start small, and it gives Jetsons lawyers something sharper to aim for — the difference between an old-school Jetsons practice and what Jenny calls "AI Jetsons."

Mentioned in This Episode

• NACBA (National Association of Consumer Bankruptcy Attorneys)
• Claude (skills, projects, artifacts, Cowork)
• ChatGPT
• Best Case Desktop
• Jubilee
• Next Chapter
• SharePoint / OneDrive
• Clio
• Glade
• Tara Salinas
• Matt McCune

[email protected]

Show Notes

Episode 018 | Season 2 Premiere | Guest: Jennifer Grondahl Lee

Season 1 taught you how to use AI. Season 2 is going to be harder. The question isn't whether to adopt AI anymore — it's whether your firm's knowledge is organized enough for AI to actually use. When almost every hand in a room full of lawyers goes up to confirm they're using AI, the era of "should I?" is over. What comes next requires something most small firm lawyers haven't done: build the knowledge infrastructure that makes AI work for your practice, not just anyone's.

In this episode:

• Ron announces the shift from Season 1 (learning AI) to Season 2 (teaching AI your firm's knowledge and systems)
• Why the Flintstones/Simpsons/Jetsons framework needs to evolve as AI adoption spreads across the profession
• Jen Grondahl Lee on the trap of the Frankenstein Stack — why firms should design their workflow first, then pick the tools
• The case for charging for consultations — and why free consults are really just sales pitches
• How niching down and turning clients away can actually accelerate a bankruptcy practice
• The FSJ-level homework assignment: Flintstones find your 10 best forms, Simpsons list your 10 most common client questions, Jetsons map your 10 most important firm systems
• AI court orders and the problem of courts overreacting to bad lawyering — not bad AI
• The contradiction hidden in a 3-part judicial AI order: disclose AI use, verify citations — and certify the document wasn't produced by AI

We also discuss:

• Jen's AI-DR blog post — "copy pasta" AI content and how to train your tools to sound like you, not like everyone else
• Heather naming her ChatGPT "Bosley" and why the reference fits
• Claude vs. ChatGPT for brainstorming — and why power users play them off each other
• The AI hiring test: give applicants an unhappy client email and watch whether they improve the AI's draft or just paste it
• Jen's 1,000-hours-saved estimate and how she calculated it
• Bankruptcy Toolbox, Rebel Roundtable, and Jen's course Building a Bankruptcy Practice from Start to Finish

Key Takeaway

Most lawyers using AI are still using it the way they used Google — as a tool they query, not a system they've trained. The difference between a Simpsons lawyer and a Jetsons lawyer isn't which tools they use. It's whether they've done the unglamorous work of documenting what their firm actually knows.

This episode is the on-ramp. The homework isn't hard — find your best forms, write down your most common client questions, name your most important systems. But most Flintstones and Simpsons lawyers haven't done any of it. That's what Season 2 is about.

Mentioned in This Episode:

• Jen Grondahl Lee — Lawyers Success Network 
• Bankruptcy Toolbox — membership community for bankruptcy attorneys 
• Upcoming Events: https://lawyersuccessnetwork.com/events
• Rebel Roundtable — Friday morning sessions with Jen 
• File Bankruptcy and Get Rich — Ron's early lead magnet book
• Financial Recovery for Single Moms — Ron's targeted marketing book
• Jen's AI;DR blog post
• ChatGPT (OpenAI)
• Claude (Anthropic)
• Claude Code
• Gemini (Google)
• Glade AI — bankruptcy-specific AI platform
• Best Case — bankruptcy case management software with AI document collector 
• Grammarly
• Maryland Legal Summit
• Anthropic learning resources Skilljar

[email protected]

Show Notes

What are you actually doing with AI — and does it work?

Ron and Heather put their tools down long enough to talk about what they've been building with them. This isn't a roundup of tools you might try. It's an inside look at two practitioners who have spent serious hours creating AI-powered workflows for their own legal and legal-adjacent businesses — and what those projects revealed about where AI is actually useful for law firms right now.

In this episode:

• Heather's report from the Maryland Legal Summit — how attorney AI adoption shifted dramatically in a single year, and what the fear conversation looks like now
• Why hallucination is more than a citation problem — the 21 ways AI can corrupt a legal brief, from wrong standards of proof to mutated judicial language
• Heather's 11-module Bankruptcy Paralegal Course, built with AI, including a Claude Code-built floating chatbot trained on the course content
• How Heather's paralegal team uses Gemini inside Google Sheets to auto-generate and schedule weekly client status reports — eliminating a manual step entirely
• Ron's markdown workflow system for podcast post-production — built in Claude, portable to any AI environment
• Ron's Case Assessment Pack: a deep-dive workflow that turns a client intake recording into a preliminary liquidation analysis, exemption review, and client-ready deliverable
• This week's Practice Signal — a 9th-year BigLaw associate terrified about making partner and how AI could be the rainmaking engine he hasn't considered
• The FSJ breakdown for capturing firm knowledge — what Flintstones, Simpsons, and Jetsons lawyers each need to do right now to prepare for the agentic AI era

We also discuss:

• OWLL, the recording app Ron just downloaded — and why he thinks lawyers should be recording everything
• Otter.ai as the baseline for meeting intelligence, and how Ron's workflow pack goes further
• The difference between using AI as a Google machine versus as a strategic collaborator
• Why opposing counsel — not the filing attorney — is catching most hallucinated citations right now
• Harvey and Legora showing up at the Maryland Legal Summit — and who's actually using them

Key Takeaway

AI doesn't magically understand your practice. It inherits whatever you've ingested into it. That's the core lesson from everything Ron and Heather describe — the tools work because they were loaded with domain knowledge, firm context, and real workflow logic. Without that, you get a very confident machine that doesn't know what it doesn't know.

For Flintstones lawyers, the move is simple: start documenting. Write down what you do. Create checklists. Capture the firm knowledge that currently lives in your head. Simpsons lawyers need to organize that knowledge — standardize file names, define workflows, build taxonomies. Jetsons lawyers are ready to connect the systems, build repositories, and create governance. The future belongs to firms that treat data as infrastructure. Where you start matters less than whether you start.

Mentioned in This Episode:

• Maryland Legal Summit / Maryland State Bar Association
• Claude (Anthropic)
• Claude Code
• ChatGPT (OpenAI)
• Gemini (Google) — including Google Sheets integration
• Microsoft Copilot
• Harvey
• Legora
• Otter.ai
• OWLL (recording app)
• BusinessGPT
• BenchSimAI (Chris Ryan) 
• Foundation AI
• Reddit (Practice Signal source)
• Cromwell case — hallucinated citation example 
• Heather's Bankruptcy Paralegal Course (Propel AI)
• Ron's AI Builds: Ground Zero episode with the Free Motion to Extend Workflow
• Google Workspace (Sheets, Drive, Docs)

What if you could lose your case to an AI judge tonight, so you don't lose it to a real one tomorrow?

For generations, lawyers learned advocacy the hard way: draft the brief, argue the motion, get knocked down by the judge, learn why you were wrong. Litigation partner Chris Ryan built a different path. BenchSim AI lets lawyers upload their brief and opposing counsel's brief, then argue out loud in real time against an AI judge who pushes back, interrupts, and grades the performance. The question this episode keeps circling: is this the future of how lawyers get their reps in, or is the courtroom apprenticeship something AI can never actually replace?

In this episode:

• Why COVID permanently reduced young lawyers' opportunities to argue in front of a real judge, and what that "reps problem" means for the next generation of litigators
• How Chris built BenchSim AI as a litigation partner with no modern coding background, using "vibe coding" to go from idea to working product in about six weeks
• What vibe coding actually is, and why it's the same process as building a custom GPT, Gem, or Claude skill
• How BenchSim works: upload your brief and opposing counsel's brief, choose a judge temperament (quiet, neutral, or hot bench), and argue out loud
• Why BenchSim deliberately skips video rendering of the judge to avoid latency that would kill the realism of rapid-fire argument
• How the AI judge develops counterpoints from the opposing brief and is programmed to interrupt when an advocate is talking in circles
• The SOC 2 certification process BenchSim is going through before marketing to law firms, and why that matters for adoption
• Whether AI will actually save lawyers time, including the "airport test" framework for evaluating whether a tool is worth the overhead
• Using AI as an adversary instead of a cheerleader — prompting it to argue against your own complaint or brief before opposing counsel does
• The Flintstones/Simpsons/Jetsons breakdown of how to stress-test a brief at every level of AI adoption

We also discuss:

• Chris's recent "wow moment" using AI to play defense counsel against his own drafted complaint
• Heather's experience having Claude Code build software overnight while she sleeps
• Whether AI simulation training could expand beyond litigation into bar exam prep and other legal training
• A Practice Signal segment on a deeply inappropriate mentorship moment a young associate experienced, and whether AI could have helped an older partner communicate the underlying (legitimate) concern without the inappropriate framing
• Chris's plans for BenchSim's feature roadmap, including potential expansion into opening statements and direct examination practice

Key Takeaway

Availability is not authority, and a simulation is not a verdict. BenchSim doesn't tell a lawyer whether they'll win or lose; it tells them where their argument is weak before a real judge finds out for them. That distinction matters. The value isn't in the AI replacing judgment, it's in creating reps that don't exist anymore because courtrooms don't generate them the way they used to.

This episode lands differently depending on where you sit on the FSJ spectrum. A Flintstones lawyer can start by asking any AI tool to summarize their argument and flag weaknesses. A Simpsons lawyer can go further, prompting AI to act as opposing counsel and attack the brief. A Jetsons lawyer is already running full bench simulations, treating AI as an adversary that prepares them for the real fight rather than a cheerleader that tells them what they want to hear.

Mentioned in This Episode:

• BenchSim AI (benchsimai.com)
• Taft (Taft Stettinius & Hollister)
• Harvey
• Legora
• Anthropic Claude / Claude Code
• ChatGPT
• Reddit (Practice Signal segment source)
• MPRE (Multistate Professional Responsibility Examination)

[email protected]