📌 Too Busy to Read This Week’s Editorial: “Lawyers and AI Oversight: What the VA’s Patient Safety Warning Teaches About Ethical Law Firm Technology Use!” ⚖️🤖

Join us for an AI-powered deep dive into the ethical challenges facing legal professionals in the age of generative AI. 🤖 In this episode, we discuss our February 16, 2026, editorial, “Lawyers and AI Oversight: What the VA’s Patient Safety Warning Teaches About Ethical Law Firm Technology Use! ⚖️🤖” and explore why treating AI-generated drafts as hypotheses—not answers—is quickly becoming a survival skill for law firms of every size. We connect a real-world AI failure risk at the Department of Veterans Affairs to the everyday ways lawyers are using tools like chatbots, and we translate ABA Model Rules into practical oversight steps any practitioner can implement without becoming a programmer.

In our conversation, we cover the following

• 00:00:00 – Why conversations about the future of law default to Silicon Valley, and why that’s a problem ⚖️

• 00:01:00 – How a crisis at the U.S. Department of Veterans Affairs became a “mirror” for the legal profession 🩺➡️⚖️

• 00:03:00 – “Speed without governance”: what the VA Inspector General actually warned about, and why it matters to your practice

• 00:04:00 – From patient safety risk to client safety and justice risk: the shared AI failure pattern in healthcare and law

• 00:06:00 – Shadow AI in law firms: staff “just trying out” public chatbots on live matters and the unseen risk this creates

• 00:07:00 – Why not tracking hallucinations, data leakage, or bias turns risk management into wishful thinking

• 00:08:00 – Applying existing ABA Model Rules (1.1, 1.6, 5.1, 5.2, and 5.3) directly to AI use in legal practice

• 00:09:00 – Competence in the age of AI: why “I’m not a tech person” is no longer a safe answer 🧠

• 00:09:30 – Confidentiality and public chatbots: how you can silently lose privilege by pasting client data into a text box

• 00:10:30 – Supervision duties: why partners cannot safely claim ignorance of how their teams use AI

• 00:11:00 – Candor to tribunals: the real ethics problem behind AI-generated fake cases and citations

• 00:12:00 – From slogan to system: why “meaningful human engagement” must be operationalized, not just admired 

• 00:12:30 – The key mindset shift: treating AI-assisted drafts as hypotheses, not answers 🧪

• 00:13:00 – What reasonable human oversight looks like in practice: citations, quotes, and legal conclusions under stress test

• 00:14:00 – You don’t need to be a computer scientist: the essential due diligence questions every lawyer can ask about AI 

• 00:15:00 – Risk mapping: distinguishing administrative AI use from “safety-critical” lawyering tasks

• 00:16:00 – High-stakes matters (freedom, immigration, finances, benefits, licenses) and heightened AI safeguards

• 00:16:45 – Practical guardrails: access controls, narrow scoping, and periodic quality audits for AI use

• 00:17:00 – Why governance is not “just for BigLaw” and how solos can implement checklists and simple documentation 📋

• 00:17:45 – Updating engagement letters and talking to clients about AI use in their matters

• 00:18:00 – Redefining the “human touch” as the safety mechanism that makes AI ethically usable at all 🤝

• 00:19:00 – AI as power tool: why lawyers must remain the “captain of the ship” even when AI drafts at lightning speed 🚢

• 00:20:00 – Rethinking value: if AI creates the first draft, what exactly are clients paying lawyers for?

• 00:20:30 – Are we ready to bill for judgment, oversight, and safety instead of pure production time?

• 00:21:00 – Final takeaways: building a practice where human judgment still has the final word over AI

RESOURCES

Mentioned in the episode

• American Bar Association Model Rules of Professional Conduct

• Interview by Terry Gerton of the Federal News Network of Charyl Mason, Inspector General of the Department of Veterans Affairs, “VA rolled out new AI tools quickly, but without a system to catch mistakes, patient safety is on the line”. 

Software & Cloud Services mentioned in the conversation

• ChatGPT — https://chat.openai.com/

• Lexis - https://www.lexisnexis.com 

• Westlaw - https://legal.thomsonreuters.com

🎙️ My Law School Library Adds The Lawyer’s Guide to Podcasting to Empower Ethical, Tech-Savvy Attorneys ⚖️

https://law-capital.libguides.com/SpecialCollections/NewBooks

I’m thrilled to share that my alma mater, Capital University Law School, has added my book, The Lawyer’s Guide to Podcasting, to its Law Library Special Collections. 🎉📚 Seeing this guide on the same shelves where I learned to think like a lawyer underscores how central ethical technology use has become to modern advocacy. 🎙️ Written for attorneys with limited to moderate tech skills, it walks readers through planning, recording, and promoting a law‑firm podcast while honoring ABA Model Rules on technology competence, confidentiality, and attorney advertising, helping you communicate confidently, credibly, and compliantly. ⚖️🚀

You can pick up your copy on Amazon Today!

🎙️ Ep. #131, Supercharging Litigation With AI: How StrongSuit Helps Lawyers Transform Research, Doc Review, and Drafting 💼⚖️

My next guest is Justin McCallan, founder of StrongSuit, an AI-powered litigation platform built to transform how litigators handle legal research, document review, and drafting while keeping lawyers firmly in control. In this episode, Justin and I dig into practical, real-world workflows that solos, small firms, and big-firm litigators can use today and over the next few years to change the economics, pace, and strategy of litigation—without sacrificing accuracy, ethics, or the quality of advocacy.

Join Justin and me as we discuss the following three questions and more!

1. What are the top three ways litigators should be using AI tools like StrongSuit right now to change the economics and pace of litigation without sacrificing accuracy, ethics, or quality of advocacy?

2. What are the top three mistakes lawyers make when adopting AI for litigation, and what practical workflows help lawyers stay in the loop and use AI as a force multiplier instead of a risk? 

3. Looking ahead to 2026 and beyond, what are the top three AI-driven workflows every litigator should master to stay competitive, and how can platforms like StrongSuit help build those capabilities into day-to-day practice? 

In our conversation, we cover the following

• 00:00 – Welcome and guest introduction

• Justin joins the show and shares his current tech setup at his desk. 

• 00:00–01:00 – Justin’s current tech stack

• Lenovo laptop, ultra-wide monitor, and regular use of StrongSuit, ChatGPT, and Gemini for different AI tasks.

• Everyday tools: Microsoft Word and Power BI for analytics and fast decision-making.

• 01:00–02:00 – Android vs. iPhone for AI use

• Why Justin has been on Android for 17 years and how UI/UX familiarity often drives device choice more than AI capability.

• 02:00–05:30 – Q1: Top three ways litigators should be using AI right now

• Using AI for end-to-end legal research across 11 million precedential U.S. cases to build litigation outlines and identify key authorities.

• Scaling document review so AI surfaces relevant documents and synthesizes insights while lawyers focus on strategy and judgment.

• Leveraging AI for drafting and editing—improving style, clarity, and consistency beyond traditional spelling and grammar checks.

• 05:30–07:30 – StrongSuit vs. basic tools like Word grammar check

• How StrongSuit aims to “up-level” a lawyer’s writing, not just catch typos.

• Stylistic improvements, clarity enhancements, and catching subtle inconsistencies in legal documents.

• 06:00–08:00 – AI context limits and scaling doc review

• Constraints of large models’ context windows (around ~1M tokens ≈ ~750 pages).

• How StrongSuit runs multiple AI agents in parallel, each handling small page sets with heuristics to maintain cohesion and share insights.

• 08:00–09:00 – Handling tens of thousands of documents

• How StrongSuit can handle between roughly 10,000–50,000 pages at a time, with the ability to scale further for enterprise matters.

• 09:00–11:30 – Origin story of StrongSuit

• Why Justin saw a once-in-a-generation opportunity when large language models emerged and how law, with its precedent and text-heavy nature, is especially suited to AI.

• StrongSuit’s focus on litigators: supporting lawyers from intake through trial while keeping them in the loop at every step.

• 11:30–13:30 – From intake to brief drafting in minutes

• Generating full litigation outlines, research, and analysis in about ten minutes, then moving directly into drafting memos, briefs, complaints, and motions.

• StrongSuit’s long-term goal: automating 50–99% of major litigation workflows by the end of 2026 while preserving lawyer control and judgment.

• 12:00–14:30 – How StrongSuit tackles hallucinations

• Building a full database of all precedential U.S. cases enriched with metadata: parties, summaries, holdings, and more.

• Validating citations by checking whether the Bluebook citation actually exists in StrongSuit’s case database before surfacing it to the user.

• Why lawyers should still review cases on-platform before filing, even when AI has filtered out hallucinations.

• 14:30–16:30 – Coverage and jurisdictions

• Coverage of all U.S. jurisdictions, federal and state, focused on precedential cases.

• Handling most regulations from administrative agencies, and limits around local ordinances.

• Uploading your own case files and using complaints and prior research as inputs into StrongSuit workflows.

• 15:00–17:00 – Security and confidentiality for litigators

• SOC 2 compliance and industry-standard encryption at rest and in transit.

• No model training on user data.

• Optional end-to-end encryption that can even prevent developers from accessing case content, using local encryption keys.

• 16:30–20:30 – Q2: Top mistakes lawyers make when adopting AI for litigation

• Mistake #1: Talking about AI instead of diving in with structured experiments and sanitized documents.

• Using a framework to identify high-impact tasks: high volume, repetitive work, and heavy data/analysis (e.g., doc review, research, contract drafting).

• How to shortlist tools: look for SOC 2, real product depth, awards, and a focus on your specific workflows.

• Mistake #2: Expecting immediate mastery instead of moving through predictable adoption stages—from learning the tool, to daily use, to stringing workflows together.

• 20:30–22:30 – Building firm-wide AI workflows over time

• Moving from isolated experiments to integrated, low-friction workflows, such as automatic intake-to-research pipelines.

• Using client intake audio or transcripts to automatically extract facts, issues, and research paths.

• 22:30–24:30 – Time constraints and “no-time” lawyers

• Why lawyers don’t need to be “technical” to use StrongSuit.

• Reframing AI as text-based tools where lawyers’ writing skills and analytical thinking are assets, not obstacles. 

• 24:00–26:00 – Practical workflows beyond intake

• Using AI to prepare for expert depositions, including reviewing valuation analyses, flagging departures from market consensus, and generating targeted questions.

• Reinforcing the value of AI-enhanced legal research and drafting as core litigation workflows.

• 26:00–29:30 – Q3: 2026 and beyond – AI-driven workflows every litigator should master

• Rapid improvement of baseline models (e.g., jumping from single-digit to high double-digit performance on difficult benchmarks year over year). 

• The idea of “tipping points,” where small performance gains turn AI from marginally useful to essential in specific tasks.

• Why legal research is a great training ground for understanding where AI excels, where it falls short, and how to divide labor between human and machine.

• The value of learning basic prompting skills to get more from AI systems, even when platforms offer visual workflows.

• 29:30–32:30 – Will workflows actually change—or just get better?

• Why Justin expects familiar litigation workflows (doc review, research, drafting) to remain structurally similar, but become far faster and more sophisticated.

• AI agents handling the grind work while lawyers focus on synthesis, judgment, and strategy.

• A future where “AI + lawyer vs. AI + lawyer” resembles high-level chess: same rules, but much deeper thinking on both sides.

• 32:30–End – Where to find Justin and StrongSuit

• How to connect with Justin and learn more about StrongSuit’s litigation tools.

Resources

Connect with Justin

• Justin McCallan on LinkedIn – https://www.linkedin.com/in/justin-mccallon/ 

• StrongSuit website – https://www.strongsuit.com

Hardware mentioned in the conversation

• Android smartphone – https://www.android.com

• Lenovo laptop – https://www.lenovo.com

Software & Cloud Services mentioned in the conversation

• ChatGPT – https://chat.openai.com

• Gemini – https://gemini.google.com

• Microsoft Power BI – https://powerbi.microsoft.com 

• Microsoft Word – https://www.microsoft.com/microsoft-365/word

• StrongSuit AI litigation platform – https://www.strongsuit.com 🤖

MTC: Lawyers and AI Oversight: What the VA’s Patient Safety Warning Teaches About Ethical Law Firm Technology Use! ⚖️🤖

Human-in-the-loop is the point: Effective oversight happens where AI meets care—aligning clinical judgment, privacy, and compliance with real-world workflows.

The Department of Veterans Affairs’ experience with generative AI is not a distant government problem; it is a mirror held up to every law firm experimenting with AI tools for drafting, research, and client communication. I recently listened to an interview by Terry Gerton of the Federal News Network of Charyl Mason, Inspector General of the Department of Veterans Affairs, “VA rolled out new AI tools quickly, but without a system to catch mistakes, patient safety is on the line” and gained some insights on how lawyers can learn from this perhaps hastilly impliment AI program. VA clinicians are using AI chatbots to document visits and support clinical decisions, yet a federal watchdog has warned that there is no formal mechanism to identify, track, or resolve AI‑related risks—a “potential patient safety risk” created by speed without governance. In law, that same pattern translates into “potential client safety and justice risk,” because the core failure is identical: deploying powerful systems without a structured way to catch and correct their mistakes.

The oversight gap at the VA is striking. There is no standardized process for reporting AI‑related concerns, no feedback loop to detect patterns, and no clearly assigned responsibility for coordinating safety responses across the organization. Clinicians may have helpful tools, but the institution lacks the governance architecture that turns “helpful” into “reliably safe.” When law firms license AI research platforms, enable generative tools in email and document systems, or encourage staff to “try out” chatbots on live matters without written policies, risk registers, or escalation paths, they recreate that same governance vacuum. If no one measures hallucinations, data leakage, or embedded bias in outputs, risk management has given way to wishful thinking.

Existing ethics rules already tell us why that is unacceptable. Under ABA Model Rule 1.1, competence now includes understanding the capabilities and limitations of AI tools used in practice, or associating with someone who does. Model Rule 1.6 requires lawyers to critically evaluate what client information is fed into self‑learning systems and whether informed consent is required, particularly when providers reuse inputs for training. Model Rules 5.1, 5.2, and 5.3 extend these obligations across partners, supervising lawyers, and non‑lawyer staff: if a supervised lawyer or paraprofessional relies on AI in a way that undermines client protection, firm leadership cannot plausibly claim ignorance. And rules on candor to tribunals make clear that “the AI drafted it” is never a defense to filing inaccurate or fictitious authority.

Explaining the algorithm to decision-makers: Oversight means making AI risks understandable to judges, boards, and the public—clearly and credibly.

What the VA story adds is a vivid reminder that effective AI oversight is a system, not a slogan. The inspector general emphasized that AI can be “a helpful tool” only if it is paired with meaningful human engagement: defined review processes, clear routes for reporting concerns, and institutional learning from near misses. For law practice, that points directly toward structured workflows. AI‑assisted drafts should be treated as hypotheses, not answers. Reasonable human oversight includes verifying citations, checking quotations against original sources, stress‑testing legal conclusions, and documenting that review—especially in high‑stakes matters involving liberty, benefits, regulatory exposure, or professional discipline.

For lawyers with limited to moderate tech skills, this should not be discouraging; done correctly, AI governance actually makes technology more approachable. You do not need to understand model weights or training architectures to ask practical questions: What data does this tool see? When has it been wrong in the past? Who is responsible for catching those errors before they reach a client, a court, or an opposing party? Thoughtful prompts, standardized checklists for reviewing AI output, and clear sign‑off requirements are all well within reach of every practitioner.

The VA’s experience also highlights the importance of mapping AI uses and classifying their risk. In health care, certain AI use cases are obviously safety‑critical; in law, the parallel category includes anything that could affect a person’s freedom, immigration status, financial security, public benefits, or professional license. Those use cases merit heightened safeguards: tighter access control, narrower scoping of AI tasks, periodic sampling of outputs for quality, and specific training for the lawyers who use them. Importantly, this is not a “big‑law only” discipline. Solo and small‑firm lawyers can implement proportionate governance with simple written policies, matter‑level notes showing how AI was used, and explicit conversations with clients where appropriate.

Critically, AI does not dilute core professional responsibility. If a generative system inserts fictitious cases into a brief or subtly mischaracterizes a statute, the duty of candor and competence still rests squarely on the attorney who signs the work product. The VA continues to hold clinicians responsible for patient care decisions, even when AI is used as a support tool; the law should be no different. That reality should inform how lawyers describe AI use in engagement letters, how they supervise junior lawyers and staff, and how they respond when AI‑related concerns arise. In some situations, meeting ethical duties may require forthright client communication, corrective filings, and revisions to internal policies.

AI oversight starts at the desk: Lawyers must be able to interrogate model outputs, data quality, and risk signals—before technology impacts patient care.

The practical lesson from the VA’s AI warning is straightforward. The “human touch” in legal technology is not a nostalgic ideal; it is the safety mechanism that makes AI ethically usable at all. Lawyers who embrace AI while investing in governance—policies, training, and oversight calibrated to risk—will be best positioned to align with the ABA’s evolving guidance, satisfy courts and regulators, and preserve hard‑earned client trust. Those who treat AI as a magic upgrade and skip the hard work of oversight are, knowingly or not, accepting that their clients may become the test cases that reveal where the system fails. In a profession grounded in judgment, the real innovation is not adopting AI; it is designing a practice where human judgment still has the final word.

MTC

There Is an App for That: How the Transit App Helps Lawyers Meet ABA Tech Competence and Protect Client Service 🚆⚖️

Smart Transit Planning Helps Lawyers Stay Punctual, Professional, and ABA-Compliant!

For many lawyers, the most stressful part of the day is not the hearing itself but getting to the courthouse, client site, or arbitration on time. 🚇 In transit‑dependent cities, delays can threaten punctuality, strain client relationships, and create avoidable risk, yet the tools to manage this are already in your pocket.

The Transit app is built on a mix of official agency feeds, crowdsourced rider reports, and its own ETA prediction engine, which gives it unusually accurate real‑time arrival data across many cities worldwide. It is designed to be a “one‑stop” view of buses, subways, trains, ferries, bikeshare, scooters, and even some rideshare options, so you are not juggling multiple agency apps or websites to understand how to get from your office to court.🚌 For a practitioner trying to manage hearings in different venues, this unified view reduces friction and the risk of missing a critical transfer.

Key features go well beyond static schedules. You can plan the fastest route from A to B, compare options that mix modes (for example, bus + rail or scooter + metro), save frequent destinations such as courthouses or jails, and receive disruption alerts when there are delays, detours, or service changes. The GO navigation feature adds departure alarms, “time to get off” alerts, and real‑time progress so you are less likely to overshoot your stop while reviewing notes or answering emails. For lawyers new to a region, live crowding reports and rider tips can also help you choose safer, more predictable paths, which ties back to your duty to reasonably safeguard your own security and your clients’ matters under the ABA Model Rules.⚖️

Reliability is strengthened by the app’s business model and data philosophy. Transit is free to use, supported by a paid option called Royale and by partnerships with transit agencies, rather than by selling riders’ personal data. The developer states that it does not link your location history to identifiable personal data and does not sell your data, which reduces some privacy concerns lawyers may have about location tracking. From an ABA Model Rule 1.6 perspective, this kind of transparent, limited‑use data practice is easier to justify than tools that depend heavily on advertising and profiling.🔐

BE the hero! use Transit Apps to help Manage Delays, Deadlines, and Ethical Duties!

From a practical standpoint, Transit runs on iPhone, Apple Watch, and Android, making it accessible for most modern devices in a law office. The core app is free, which means solo and small‑firm lawyers can test it with no up‑front cost; optional Royale subscriptions are available on a monthly or annual basis, adding cosmetic perks and advanced features while leaving the essential planning tools available to everyone. This combination of broad city coverage, accurate multi‑modal data, and a privacy‑conscious, low‑cost model makes Transit a defensible choice when you explain how you are using accessible technology to manage foreseeable transit risks in line with ABA Model Rules 1.1, 1.3, and 1.6.

For solo and small‑firm lawyers, where every hour is critical, this kind of lightweight technology can be as impactful as more expensive practice‑management platforms. It improves reliability, supports ABA Model Rule compliance, and signals professionalism to clients and courts alike. In an era where judges, clients, and opposing counsel expect you to manage foreseeable risks, there truly is an app for that—and it may keep your practice on track in more ways than one. 🚍

TSL.P Labs 🧪: Everyday Tech, Extraordinary Sanctions: How Courts Are Cracking Down on Fake AI Evidence in 2026 ⚖️🤖

Everyday devices can capture extraordinary evidence, but the same tools can also manufacture convincing fakes. 🎥⚖️ In this episode, we unpack our February 9, 2026, editorial on how courts are punishing fake digital and AI-generated evidence, then translate the risk into practical guidance for lawyers and legal teams.

You’ll hear why judges are treating authenticity as a frontline issue, what ethical duties get triggered when AI touches evidence or briefing, and how a simple “authenticity playbook” can help you avoid career-ending mistakes. ✅

In our conversation, we cover the following

• 00:00:00 – Preview: From digital discovery to digital deception, and the question of what happens when your “star witness” is actually a hallucination or deepfake 🚨

• 00:00:20 – Introducing the editorial “Everyday Tech, Extraordinary Evidence Again: How Courts Are Punishing Fake Digital and AI Data.” 📄

• 00:00:40 – Welcome to the Tech-Savvy Lawyer.Page Labs Initiative and this AI Deep Dive Roundtable 🎙️

• 00:01:00 – Framing the episode: flipping last month’s optimism about smartphones, dash cams, and wearables as case-winning “silent witnesses” to their dark mirror—AI-fabricated evidence 🌗

• 00:01:30 – How everyday devices and AI tools can both supercharge litigation strategy and become ethical landmines under the ABA Model Rules ⚖️

• 00:02:00 – Panel discussion opens: revisiting last month’s “Everyday Tech, Extraordinary Evidence” AI bonus and the optimism around smartphone, smartwatch, and dash cam data as unbiased proof 📱⌚🚗

• 00:02:30 – Remembering cases like the Minnesota shooting and why these devices were framed as “ultimate witnesses” if the data is preserved quickly enough 🕒

• 00:03:00 – The pivot: same tools, new threats—moving from digital discovery to digital deception as deepfakes and hallucinations enter the evidentiary record 🤖

• 00:03:30 – Setting the “mission” for the episode: examining how courts are reacting to AI-generated “slop” and deepfakes, with an increasingly aggressive posture toward sanctions 💣

• 00:04:00 – Why courts are on high alert: the “democratization of deception,” falling costs of convincing video fakes, and the collapse of the old presumption that “pictures don’t lie” 🎬

• 00:04:30 – Everyday scrutiny: judges now start with “Where did this come from?” and demand details on who created the file, how it was handled, and what the metadata shows 🔍

• 00:05:00 – Metadata explained as the “data about the data”—timestamps, software history, edit traces—and how it reveals possible AI manipulation 🧬

• 00:06:00 – Entering the “sanction phase”: why we are beyond warnings and into real penalties for mishandling or fabricating digital and AI evidence 🚫

• 00:06:30 – Horror Story #1 (Mendon v. Cushman & Wakefield, Cal. Super. Ct. 2025): plaintiffs submit videos, photos, and screenshots later determined to be deepfakes created or altered with generative AI 🧨

• 00:07:00 – Judge Victoria Kakowski’s response: finding that the deepfakes undermined the integrity of judicial proceedings and imposing terminating sanctions—“death penalty” for the lawsuit ⚖️

• 00:07:30 – How a single deepfake “poisons the well,” destroying the court’s trust in all of a party’s submissions and forfeiting their right to the court’s time 💥

• 00:08:00 – Horror Story #2 (S.D.N.Y. 2023): the New York “hallucinating lawyer” case where six imaginary cases generated by ChatGPT were filed without verification 📚

• 00:08:30 – Rule 11 sanctions and humiliation: Judge Castel’s order, monetary penalty, and the requirement to send apology letters to real judges whose names were misused ✉️

• 00:09:00 – California follow-on: appellate lawyer Amir Mustaf files an appeal brief with 21 fake citations, triggering a 10,000-dollar sanction and a finding that he did not read or verify his own filing 💸

• 00:09:30 – Courts’ reasoning: outsourcing your job to an AI tool is not just being wrong—it is wasting judicial resources and taxpayer money 🧾

• 00:10:00 – Do we need new laws? Why Michael argues that existing ABA Model Rules already provide the safety rails; the task is to apply them to AI and digital evidence, not to reinvent them 🧩

• 00:10:20 – Rule 1.1 (competence): why “I’m not a tech person” is no longer a viable excuse if you use AI to enhance video or draft briefs without understanding or verifying the output 🧠

• 00:11:00 – Rule 1.6 (confidentiality): the ethical minefield of uploading client dash cam video or wearable medical data to consumer-grade AI tools and risking privilege leakage ☁️

• 00:11:30 – Training risk: how client data can end up in model training sets and why “quick AI summaries” can inadvertently expose secrets 🔐

• 00:12:00 – Rules 3.3 and 4.1 (candor and truthfulness): presenting AI-altered media as original or failing to verify AI output can now be treated as misrepresentation 🤥

• 00:12:30 – Rules 5.1–5.3 (supervision): why partners and supervising lawyers remain on the hook for juniors, staff, and vendors who misuse AI—even if they didn’t personally type the prompts 🧑‍💼

• 00:13:00 – Authenticity Playbook, Step 1: mindset shift—never treat AI as a “silent co-counsel”; instead, treat it like a very eager, very inexperienced, slightly drunk intern who always needs checking 🍷🤖

• 00:13:30 – Authenticity Playbook, Step 2: preserve the original and disclose any AI enhancement; build a clean chain of custody while staying transparent about edits 🎞️

• 00:14:00 – Authenticity Playbook, Step 3: using forensic vendors as authenticity firewalls—experts who can certify that metadata and visual cues show no AI manipulation 🛡️

• 00:14:30 – Authenticity Playbook, Step 4: “train with fear” by showing your team real orders, sanctions, and public shaming rather than relying on abstract ethics lectures ⚠️

• 00:15:00 – Authenticity Playbook, Step 5: documenting verification steps—logging files, tools, and checks so you can demonstrate good faith if a judge questions your evidence 📝

• 00:16:00 – Bigger picture: the era of easy, unchallenged digital evidence is over; mishandled tech can now produce “extraordinary sanctions” instead of extraordinary evidence 🧭

• 00:16:30 – Authenticity as “the moral center of digital advocacy”: if you cannot vouch for your digital evidence, you are failing in your role as an advocate 🏛️

• 00:17:00 – Future risk: as deepfakes become perfect and nearly impossible to detect with the naked eye, forensic expertise may become a prerequisite for trusting any digital evidence 🔬

• 00:17:30 – “Does truth get a price tag?”—whether justice becomes a luxury product if only wealthy parties can afford authenticity firewalls and expert validation 💼

• 00:18:00 – Closing reflections: fake evidence, real consequences, and the call to verify sources and check metadata before you file ✅

• 00:18:30 – Closing: invitation to visit Tech-Savvy Lawyer.Page for the full editorial, resources, and to like, subscribe, and share with colleagues who need to stay ahead of legal tech innovation 🌐

Resources

Cases

• In Mendones v. Cushman & Wakefield, Inc. (Cal. Super. Ct. Alameda County, 2025), plaintiffs submitted multiple videos, photos, and screenshots that the court determined were deepfakes or altered with generative AI.📹 Judge Victoria Kolakowski found intentional submission of false testimony and imposed terminating sanctions, dismissing the case outright and emphasizing that deepfake evidence “fundamentally undermines the integrity of judicial proceedings.”⚖️

• In New York, two lawyers became infamous in 2023 after filing a brief containing six imaginary cases generated by ChatGPT; Judge P. Kevin Castel sanctioned them under Rule 11 for abandoning their responsibilities and failing to verify the authorities they cited.📑 They were ordered to pay a monetary penalty and to notify the real judges whose names had been falsely invoked, a reputational hit that far exceeded the dollar amount.💸

• A California appellate lawyer, Amir Mostafavi, was later fined $10,000 for filing an appeal with twenty‑one fake case citations generated by ChatGPT.💻 The court stressed that he had not read or verified the AI‑generated text, and treated that omission as a violation of court rules and a waste of judicial resources and taxpayer money.⚠️ 

ABA Model Rules

• Rule 1.1 (Competence): You must understand the benefits and risks of relevant technology, which now clearly includes generative AI and deepfake tools.⚖️ Using AI to draft or “enhance” without checking the output is not a harmless shortcut—it is a competence problem.

  • Comment 8’s duty of technological competence; the new sanctions landscape simply clarifies the stakes.📚

• Rule 1.6 (Confidentiality): Uploading client videos, wearable logs, or sensitive communications to consumer‑grade AI sites can expose them to unknown retention and training practices, risking confidentiality violations.🔐

• Rule 3.3 (Candor to the Tribunal) and Rule 4.1 (Truthfulness): Presenting AI‑altered video or fake citations as if they were genuine is the very definition of misrepresentation, as the New York and California sanction orders make clear.⚠️ Even negligent failure to verify can be treated harshly once the court’s patience for AI excuses runs out.

• Rules 5.1–5.3 (Supervision): Supervising lawyers must ensure that associates, law clerks, and vendors understand that AI outputs are starting points, not trustworthy final products, and that fake or manipulated digital evidence will not be tolerated.👥

Word of the Week: Deepfakes: How Lawyers Can Spot Fake Digital Evidence and Avoid ABA Model Rule Violations ⚖️

A Tech-Savvy Lawyer needs to be able to spot Deepfakes Before Courtroom Ethics Violations!

“Deepfakes” are AI‑generated or heavily manipulated audio, video, or images that convincingly depict people saying or doing things that never happened.🧠 They are moving from internet novelty to everyday litigation risk, especially as parties try to slip fabricated “evidence” into the record.📹

Recent cases and commentary show courts will not treat deepfakes as harmless tech problems. Judges have dismissed actions outright and imposed severe sanctions when parties submit AI‑generated or altered media, because such evidence attacks the integrity of the judicial process itself.⚖️ At the same time, courts are wary of lawyers who cry “deepfake” without real support, since baseless challenges can look like gamesmanship rather than genuine concern about authenticity.

For practicing lawyers, deepfakes are first and foremost a professional responsibility issue. ABA Model Rule 1.1 (Competence) now clearly includes a duty to understand the benefits and risks of relevant technology, which includes generative AI tools that create or detect deepfakes. You do not need to be an engineer, but you should recognize common red flags, know when to request native files or metadata, and understand when to bring in a qualified forensic expert.

Deepfakes in Litigation: Detect Fake Evidence, Protect Your License!

Deepfakes also implicate Model Rule 3.3 (Candor to the tribunal) and Model Rule 3.4 (Fairness to opposing party and counsel). If you knowingly offer manipulated media, or ignore obvious signs of fabrication in your client’s “evidence,” you risk presenting false material to the court and obstructing access to truthful proof. Courts have made clear that submitting fake digital evidence can justify terminating sanctions, fee shifting, and referrals for disciplinary action.

Model Rule 8.4(c), which prohibits conduct involving dishonesty, fraud, deceit, or misrepresentation, sits in the background of every deepfake decision. A lawyer who helps create, weaponize, or strategically “look away” from deepfake evidence is not just making a discovery mistake; they may be engaging in professional misconduct. Likewise, a lawyer who recklessly accuses an opponent of using deepfakes without factual grounding risks violating duties of candor and professionalism.

Practically, you can start protecting your clients with a few repeatable steps. Ask early in the case what digital media exists, how it was created, and who controlled the devices or accounts.🔍 Build authentication into your discovery plan, including requests for original files, device logs, and platform records that can help confirm provenance. When the stakes justify it, consult a forensic expert rather than relying on “gut feel” about whether a recording “looks real.”

lawyers need to know Deepfakes, Metadata, and ABA Ethics Rules!

Finally, talk to clients about deepfakes before they become a problem. Explain that altering media or using AI to “clean up” evidence is dangerous, even if they believe they are only fixing quality.📲 Remind them that courts are increasingly sophisticated about AI and that discovery misconduct in this area can destroy otherwise strong cases. Treat deepfakes as another routine topic in your litigation checklist, alongside spoliation and privilege, and you will be better prepared for the next “too good to be true” video that lands in your inbox.

ANNOUNCEMENT: My Book, “The Lawyer’s Guide to Podcasting,” is Amazon #1 New Release (Law Office Technology)

I’m excited to report that The Lawyer’s Guide to Podcasting ranked #1 as a New Release in Amazon’s Law Office Technology category for the week of February 07, 2026, and sales have already doubled since last month. 🎙️📈

For lawyers with limited-to-moderate tech skills, the book focuses on practical, repeatable workflows for launching and sustaining a compliant podcast presence. ⚖️💡

As you plan content, remember ABA Model Rule 1.1 (technology competence) and the related duties of confidentiality (Rule 1.6) and communications about services (Rule 7.1): use secure tools, avoid accidental client disclosures, and ensure marketing statements are accurate. 🔐✅

Get your copy today! 📘🚀

MTC: Everyday Tech, Extraordinary Evidence—Again: How Courts Are Punishing Fake Digital and AI Data ⚖️📱

Check your Ai work - AI fraud can meet courtroom consequences.

In last month’s editorial, “Everyday Tech, Extraordinary Evidence,” we walked through how smartphones, dash cams, and wearables turned the Minnesota ICE shooting into a case study in modern evidence practice, from rapid preservation orders to multi‑angle video timelines.📱⚖️ We focused on the positive side: how deliberate intake, early preservation, and basic synchronization tools can turn ordinary devices into case‑winning proof.📹 This follow‑up tackles the other half of the equation—what happens when “evidence” itself is fake, AI‑generated, or simply unverified slop, and how courts are starting to respond with serious sanctions.⚠️

From Everyday Tech to Everyday Scrutiny

The original article urged you to treat phones and wearables as critical evidentiary tools, not afterthoughts: ask about devices at intake, cross‑reference GPS trails, and treat cars as rolling 360‑degree cameras.🚗⌚ We also highlighted the Minnesota Pretti shooting as an example of how rapid, court‑ordered preservation of video and other digital artifacts can stop crucial evidence from “disappearing” before the facts are fully understood.📹 Those core recommendations still stand—if anything, they are more urgent now that generative AI makes it easier to fabricate convincing “evidence” that never happened.🤖

The same tools that helped you build robust, data‑driven reconstructions—synchronized bystander clips, GPS logs, wearables showing movement or inactivity—are now under heightened scrutiny for authenticity.📊 Judges and opposing counsel are no longer satisfied with “the video speaks for itself”; they want to know who created it, how it was stored, whether metadata shows AI editing, and what steps counsel took to verify that the file is what it purports to be.📁

When “Evidence” Is Fake: Sanctions Arrive

We have moved past the hypothetical stage. Courts are now issuing sanctions—sometimes terminating sanctions—when parties present fake or AI‑generated “evidence” or unverified AI research.💥

• In Mendones v. Cushman & Wakefield, Inc. (Cal. Super. Ct. Alameda County, 2025), plaintiffs submitted multiple videos, photos, and screenshots that the court determined were deepfakes or altered with generative AI.📹 Judge Victoria Kolakowski found intentional submission of false testimony and imposed terminating sanctions, dismissing the case outright and emphasizing that deepfake evidence “fundamentally undermines the integrity of judicial proceedings.”⚖️

• In New York, two lawyers became infamous in 2023 after filing a brief containing six imaginary cases generated by ChatGPT; Judge P. Kevin Castel sanctioned them under Rule 11 for abandoning their responsibilities and failing to verify the authorities they cited.📑 They were ordered to pay a monetary penalty and to notify the real judges whose names had been falsely invoked, a reputational hit that far exceeded the dollar amount.💸

• A California appellate lawyer, Amir Mostafavi, was later fined $10,000 for filing an appeal with twenty‑one fake case citations generated by ChatGPT.💻 The court stressed that he had not read or verified the AI‑generated text, and treated that omission as a violation of court rules and a waste of judicial resources and taxpayer money.⚠️ 

These are not “techie” footnotes; they are vivid warnings that falsified or unverified digital and AI data can end careers and destroy cases.🚨

ABA Model Rules: The Safety Rails You Ignore at Your Peril

Train to verify—defend truth in the age of AI.

Your original everyday‑tech playbook already fits neatly within ABA Model Rule 1.1 and Comment 8’s duty of technological competence; the new sanctions landscape simply clarifies the stakes.📚

• Rule 1.1 (Competence): You must understand the benefits and risks of relevant technology, which now clearly includes generative AI and deepfake tools.⚖️ Using AI to draft or “enhance” without checking the output is not a harmless shortcut—it is a competence problem.

• Rule 1.6 (Confidentiality): Uploading client videos, wearable logs, or sensitive communications to consumer‑grade AI sites can expose them to unknown retention and training practices, risking confidentiality violations.🔐

• Rule 3.3 (Candor to the Tribunal) and Rule 4.1 (Truthfulness): Presenting AI‑altered video or fake citations as if they were genuine is the very definition of misrepresentation, as the New York and California sanction orders make clear.⚠️ Even negligent failure to verify can be treated harshly once the court’s patience for AI excuses runs out.

• Rules 5.1–5.3 (Supervision): Supervising lawyers must ensure that associates, law clerks, and vendors understand that AI outputs are starting points, not trustworthy final products, and that fake or manipulated digital evidence will not be tolerated.👥

Bridging Last Month’s Playbook With Today’s AI‑Risk Reality

In Last month’s editorial, we urged three practical habits: ask about devices, move fast on preservation, and build a vendor bench for extraction and authentication.📱⌚🚗 This month, the job is to wrap those habits in explicit AI‑risk controls that lawyers with modest tech skills can realistically follow.🧠

1. Never treat AI as a silent co‑counsel. If you use AI to draft research, generate timelines, or “enhance” video, you must independently verify every factual assertion and citation, just as you would double‑check a new associate’s memo.📑 “The AI did it” is not a defense; courts have already said so.

2. Preserve the original, disclose the enhancement. Our earlier advice to keep raw smartphone files and dash‑cam footage now needs one more step: if you use any enhancement (AI or otherwise), label it clearly and be prepared to explain what was done, why, and how you ensured that the content did not change.📹

3. Use vendors and examiners as authenticity firewalls. Just as we suggested, bringing in digital forensics vendors to extract phone and wearable data, you should now consider them for authenticity challenges as well—especially where the opposing side may have incentives or tools to create deepfakes.🔍 A simple expert declaration that a file shows signs of AI manipulation can be the difference between a credibility battle and a terminating sanction.

4. Train your team using real sanction orders. Nothing clarifies the risk like reading Judge Castel’s order in the ChatGPT‑citation case or Judge Kolakowski’s deepfake ruling in Mendones.⚖️ Incorporate those cases into short internal trainings and CLEs; they translate abstract “AI ethics” into concrete, courtroom‑tested consequences.

5. Document your verification steps. For everyday tech evidence, a simple log—what files you received, how you checked metadata, whether you compared against other sources, which AI tools (if any) you used, and what you did to confirm their outputs—can demonstrate good faith if a judge later questions your process.📋

Final Thoughts: Authenticity as a First‑Class Question

be the rock star! know how to use ai responsibly in your work!

In the first editorial, the core message was that everyday devices are quietly turning into your best witnesses.📱⌚ The new baseline is that every such “witness” will be examined for signs of AI contamination, and you will be expected to have an answer when the court asks, “What did you do to make sure this is real?”🔎

Lawyers with limited to moderate tech skills do not need to reverse‑engineer neural networks or master forensic software. Instead, they must combine the practical habits from January’s piece—asking, preserving, synchronizing—with a disciplined refusal to outsource judgment to AI.⚖️ In an era of deepfakes and hallucinated case law, authenticity is no longer a niche evidentiary issue; it is the moral center of digital advocacy.✨

Handled wisely, your everyday tech strategy can still deliver “extraordinary evidence.” Handled carelessly, it can just as quickly produce extraordinary sanctions.🚨

MTC

TSL.P Labs 🧪: Legal Tech Wars, Client Data, and Your Law License: An AI-Powered Ethics Deep Dive ⚖️🤖

📌 To Busy to Read This Week’s Editorial?

Join us for an AI-powered deep dive into the ethical challenges facing legal professionals in the age of generative AI. 🤖 In this Tech-Savvy Lawyer Page Labs Initiative episode, AI co-hosts walk through how high‑profile “legal tech wars” between practice‑management vendors and AI research startups can push your client data into the litigation spotlight and create real ethics exposure under ABA Model Rules 1.1, 1.6, and 5.3.

We’ll explore what happens when core platforms face federal lawsuits, why discovery and forensic audits can put confidential matters in front of third parties, and how API lockdowns, stalled product roadmaps, and forced sales can grind your practice operations to a halt. More importantly, you’ll get a clear five‑step action plan—inventorying your tech stack, confirming data‑export rights, mapping backup providers, documenting diligence, and communicating with clients—that works even if you consider yourself “moderately tech‑savvy” at best.

Whether you’re a solo, a small‑firm practitioner, in‑house, or simply AI‑curious, this conversation will help you evaluate whether you are the supervisor of your legal tech—or its hostage. 🔐

👉 Listen now and decide: are you supervising your legal tech—or are you its hostage?

In our conversation, we cover the following

• 00:00:00 – Setting the stage: Legal tech wars, “Godzilla vs. Kong,” and why vendor lawsuits are not just Silicon Valley drama for spectators.

• 00:01:00 – Introducing the Tech-Savvy Lawyer Page Labs Initiative and the use of AI-generated discussions to stress-test legal tech ethics in real-world scenarios.

• 00:02:00 – Who’s fighting and why it matters: Clio as the “nervous system” of many firms versus Alexi as the “brainy intern” of AI legal research.

• 00:03:00 – The client data crossfire: How disputes over data access and training AI tools turn your routine practice data into high-stakes litigation evidence.

• 00:04:00 – Allegations in the Clio–Alexi dispute, from improper data access to claims of anti-competitive gatekeeping of legal industry data.

• 00:05:00 – Visualizing risk: Client files as sandcastles on a shelled beach and why this reframes vendor fights as ethics issues, not IT gossip.

• 00:06:00 – ABA Model Rule 1.1 (Competence): What “technology competence” really entails and why ignorance of vendor instability is no longer defensible.

• 00:07:00 – Continuity planning as competence: Injunctions, frozen servers, vendor shutdowns, and how missed deadlines can become malpractice.

• 00:08:00 – ABA Model Rule 1.6 (Confidentiality): The “danger zone” of treating the cloud like a bank vault and misunderstanding who really holds the key.

• 00:09:00 – Discovery risk explained: Forensic audits, third‑party access, protective orders that fail, and the cascading impact on client secrets.

• 00:10:00 – Data‑export rights as your “escape hatch”: Why “usable formats” (CSV, PDF) matter more than bare contractual promises.

• 00:11:00 – Practical homework: Testing whether you can actually export your case list today, not during a crisis.

• 00:12:00 – ABA Model Rule 5.3 (Supervision): Treating software vendors like non‑lawyer assistants you actively supervise rather than passive utilities.

• 00:13:00 – Asking better questions: Uptime, security posture, and whether your vendor is using your data in its own defense.

• 00:14:00 – Operational friction: Rising subscription costs, API lockdowns, broken integrations, and the return of manual copy‑pasting.

• 00:15:00 – Vaporware and stalled product roadmaps: How litigation diverts engineering resources away from features you are counting on.

• 00:16:00 – Forced sales and 30‑day shutdown notices: Data‑migration nightmares under pressure and why waiting is the riskiest strategy.

• 00:17:00 – The five‑step moderate‑tech action plan: Inventory dependencies, review contracts, map contingencies, document diligence, and communicate with nuance.

• 00:18:00 – Turning risk management into a client‑facing strength and part of your value story in pitches and ongoing relationships.

• 00:19:00 – Reframing legal tech tools as members of your legal team rather than invisible utilities.

• 00:20:00 – “Supervisor or hostage?”: The closing challenge to check your contracts, your data‑export rights, and your practical ability to “fire” a vendor.

Resources

Mentioned in the episode

• ABA Model Rule 1.1 – Competence (Technology Competence Comment) – https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_1_1_competence/

• ABA Model Rule 1.6 – Confidentiality of Information – https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_1_6_confidentiality_of_information/

• ABA Model Rule 5.3 – Responsibilities Regarding Nonlawyer Assistance – https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_5_3_responsibilities_regarding_nonlawyer_assistance/

• Tech-Savvy Lawyer Page (February 2, 2026, Editorial & Show Notes Hub) – https://www.thetechsavvylawyer.page/blog/2026/2/2/mtc-clioalexi-legal-tech-fight-what-crm-vendor-litigation-means-for-your-law-firm-client-data-and-aba-model-rule-compliance-

Software & Cloud Services mentioned in the conversation

• Clio – Cloud-based legal practice management platform – https://www.clio.com

• Alexi – AI‑driven legal research platform – https://www.alexi.com

• AWS (Amazon Web Services) – Cloud infrastructure provider – https://aws.amazon.com

• Google Cloud – Cloud infrastructure provider – https://cloud.google.com

#LegalTech #AIinLaw #LegalEthics #Cybersecurity #LawPracticeManagement