The Tech Savvy Lawyer

Join us for an AI-powered deep dive into the ethical challenges facing legal professionals in the age of generative AI. 🤖 We unpack the February 23, 2026, editorial AI may not be your co‑counsel—and a recent SDNY decision just made that painfully clear. ⚖️🤖.  Our Google Notebook LLM hostsbreaks down why a single click on a public AI tool's Terms of Use can trigger a privilege waiver, and what "tech competence" really means in 2026—especially after United States v. Hoeppner and Judge Jed Rakoff's wake-up-call analysis of confidentiality and third-party disclosure risk.
🔗 Read the full editorial on The Tech-Savvy Lawyer.Page and share this episode with a colleague who is experimenting with AI in client matters.
In our conversation, we cover the following
00:00 — The "superhuman assistant" promise, and the procedural nightmare risk. 🧠⚖️
00:01 — The core warning: AI use can "blow a hole" in privilege.
00:02 — Editorial overview: "The AI Privilege Trap" by Michael D.J. Eisenberg.
00:02 — The case: United States v. Hoeppner (SDNY) and why it matters.
00:03 — Why Judge Jed Rakoff's opinion gets attention (tech-literate, influential).
00:03 — The facts: defendant drafts with a public AI tool, then sends outputs to counsel.
00:04 — The court's conclusion: no attorney-client privilege, no work product protection.
00:05 — Privilege basics applied to AI: "confidential + lawyer" and why AI fails that test.
00:06 — The Terms-of-Use problem: inputs/outputs may be collected and shared. 🧾
00:07 — The "stranger on the street" analogy: you can't retroactively make it confidential.
00:08 — PII and client facts: why pasting sensitive data into public AI is high-risk.
00:08 — ABA Model Rule 1.1: competence includes understanding tech risks.
00:09 — ABA Model Rule 1.6: confidentiality and waiver risk with public AI.
00:10 — "Reasonable safeguards": read policies, adjust settings, and know training/logging.
00:11 — Public vs. enterprise AI: why contracts and "walled gardens" matter.
00:11 — Legal research AI examples discussed: Lexis/Westlaw-style AI offerings.
00:12 — ABA Model Rules 5.1 & 5.3: supervise AI like a nonlawyer assistant/vendor.
00:13 — Redefining "tech-savvy lawyer" in 2026: judgment and restraint. 🧭
00:14 — The "straight-face test": could you defend confidentiality after a judge reads the policy?
00:15 — Client-side risk: clients can sabotage privilege before contacting counsel.
00:16 — Practical takeaway: check settings, read the fine print, keep true secrets offline (for now). 🔒
RESOURCES
Mentioned in the episode
ABA Model Rules of Professional Conduct (Rules 1.1, 1.4, 1.6, 5.1, 5.3)
Software & Cloud Services mentioned in the conversation
Lexis (Lexis+ AI category mentioned) — https://www.lexisnexis.com/
Microsoft Word — https://www.microsoft.com/microsoft-365/word
Public generative AI "chatbot" tools (general category) — https://en.wikipedia.org/wiki/Chatbot
Westlaw (Westlaw AI category mentioned) — https://legal.thomsonreuters.com/en/products/westlaw

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 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!
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?
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?
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 🤖

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.👥

📌 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. 🔐
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