MTC: Judges Will Be Hunting These AI Tricks After Brazil’s Scandal

it is hard to believe that judges will be happy if lawyer insert “code” into their online filings!

Recently, Brazilian court officials uncovered something that should make every tech‑savvy lawyer sit up straight. In a labor court, staff discovered a filing that looked ordinary to the human eye—until they examined it more closely. Hidden in the document was text written in white font on a white background, invisible to anyone casually reading the PDF but fully legible to the court’s AI system.

That invisible text was not a typo. It was an instruction—what technologists call a “prompt injection”—telling the court’s AI software to review the case only superficially and not to challenge the evidence submitted. In other words, the filing was designed to trick the judiciary’s own AI tools into rubber‑stamping a favorable outcome by smuggling in commands that humans would never see.

Fortunately, court staff caught the scheme before it affected the proceedings. But Brazilian authorities immediately recognized the incident as a new species of digital fraud and began discussing safeguards: automatic detection of invisible text, formatting checks before AI processing, and stronger human oversight at every stage. They also raised the prospect of stricter ethics rules and sanctions for lawyers who try to manipulate court AI systems.

For our purposes, the Brazil case does three important things:

1. It confirms that AI now sits inside judicial workflows—not just law firm workflows.

2. It shows that some lawyers will try to game those systems if they think they can get away with it.

3. It gives us a concrete example of what not to do and what to watch for as courts in the U.S. and elsewhere adopt similar tools.

From an ABA perspective, a “white‑text prompt injection” is not clever lawyering—it’s a direct collision with Model Rule 3.3 (candor toward the tribunal) and Model Rule 8.4(c)’s prohibition on conduct involving dishonesty, fraud, deceit, or misrepresentation. And because the Brazil incident exploits the very AI tools that the judiciary is using, it also implicates Model Rule 1.1 and Comment 8: the duty of technology competence now includes understanding how these systems can be abused.

So let’s unpack what we should learn from Brazil—starting with what not to do.

What Not To Do: Hidden Instructions and “Clever” Hacks

The Brazil case is a textbook on the wrong way to think about AI in litigation.

• Do not embed hidden commands in filings (through white‑on‑white text, metadata, or other tricks) with the intent to influence how a court’s AI tools process your case.

• Do not treat court‑side AI as just another system to be “SEO‑optimized” or hacked. Unlike a marketing algorithm, this is part of the machinery of justice; trying to tilt it in your favor crosses a bright ethical line.

• Do not assume that “if the judge doesn’t see it, it doesn’t count.” Malicious prompts aimed at judicial AI are still part of your submission to the tribunal, and they reflect directly on your candor and honesty under Model Rules 3.3 and 8.4.

In short: if you would never say it to the judge in plain black‑and‑white text, you should not whisper it to the court’s AI in invisible text.

What To Watch For: How to Recognize This Behavior

lawyers need to be prepared to vet opposing counsel’s filings for ai injection!

The harder question is how you, as a solo or small‑firm lawyer, can spot similar tactics when others use them—especially when you don’t control the court’s systems.

Here are practical signals and questions:

• Suspicious formatting in PDFs or Word files. Odd spacing, unexpected blank pages, or inconsistent fonts can sometimes signal hidden layers of text. While you won’t always spot white‑on‑white content, unusual formatting should prompt closer inspection.

• Metadata anomalies. If you routinely examine document properties, look for multiple authors, unusual editing histories, or automation tags that do not match the face of the document. These can indicate heavy automated processing or embedded instructions.

• Patterns in AI‑mediated decisions. If certain filings—often from the same party—seem to sail through automated queues or receive unusually favorable, boilerplate orders, you may be seeing the downstream effect of prompt manipulation or aggressive “AI‑targeted” drafting.

Because you usually won’t have direct access to the court’s internal AI, you may need to raise these concerns procedurally: requesting clarification on how filings are screened, asking whether AI systems were involved in certain steps, or moving for relief if you believe your client’s matter was prejudiced by automated processing.

How To Protect Yourself and Your Clients:

Brazil’s experience is a warning shot—not just about bad actors, but about what a healthy response should look like.

Here’s how to translate that into a practical “do this, not that” playbook for your own practice:

1. Assume courts will adopt AI—and plan for it:

Brazil’s judiciary uses AI to prioritize cases, draft reports, and propose decisions in response to massive backlogs. U.S. courts are already experimenting with similar tools, even if not as publicly. Competence under Model Rule 1.1 now includes staying informed about these trends and understanding their implications.

2.     Build “AI integrity” into your litigation strategy.

• Treat any automated system that touches your filings—court e‑filing portals, online forms, AI‑assisted triage tools—as part of the tribunal.

• Resolve that you will never include hidden instructions, misleading metadata, or manipulative formatting in documents submitted to those systems.

3.     Advocate for transparent safeguards.

• In Brazil, authorities responded by exploring automatic detection of invisible text and stronger human oversight.

• When U.S. courts announce AI pilots or tools, comment on proposed rules, advocate for clear notice when AI is used, and request mechanisms for lawyers to challenge AI‑influenced outcomes.

4.     Document your own good‑faith use of AI.

it may be deemed a “fruad upon the court” if a lawyer injects ai into their electronic filings.

• If you rely on AI to format or generate parts of your filings, keep internal records of prompts, outputs, and human review.

• This documentation will help if a court or disciplinary body later asks how you ensured candor and accuracy, especially in a world where Brazil‑style abuses are making judges more skeptical.

Final Thoughts

AI isn’t just something we use; it’s now part of the institutional environment—just like e‑filing, CM/ECF, or digital signatures. The line between legitimate technology use and unethical manipulation is not about whether you use AI, but how you use it and whether you’re honest about it.

MTC

MTC: ChatGPT, Work Product, and Waiver: New Lessons from Tate Group Automotive ⚖️🤖

Tech‑savvy lawyerS need to be able to defend ChatGPT work product before Texas Business Court.

On June 3, 2026, the Business Court of Texas issued a minute entry in Tate Group Automotive, LLC v. Legacy Automotive Capital, LLC that every tech‑curious lawyer should know about. As of today, this is one of the first reported decisions to tackle whether a non‑lawyer’s ChatGPT conversations are protected attorney work product and, if so, whether using a public AI tool waives that protection.

The court’s answer is nuanced but important: generative AI does not automatically destroy work‑product protection, at least where the disclosure is not made to an adversary under Texas Rule of Civil Procedure 192.5(a)(1). For solos and small firms experimenting with AI tools, this is both reassuring and sobering.

What Happened in Tate Group Automotive?

The case arises from a dispute in the Texas Business Court’s Eleventh Division, in which Tate Group Automotive sued Legacy Automotive Capital, The Reynolds and Reynolds Company, and individual defendants. During discovery, the plaintiff withheld “Kris Tate–ChatGPT conversations” on the basis of attorney work‑product protection and submitted them to the court for in camera review.

Defendants challenged that claim. They argued that attorney work‑product protection does not extend to a non‑lawyer’s chats with an AI tool, or alternatively, that any protection was waived when Kris Tate used ChatGPT. They also asked the court to order the plaintiff to identify all discovery materials Mr. Tate or Tate Group had shared with ChatGPT.

Judge Grant Dorfman acknowledged that the issue was “novel,” noting that all case law cited by the parties dated from 2026 and that at least one opinion called the question “a first impression nationwide.” Against that backdrop, he evaluated the ChatGPT conversations under Texas Rule of Civil Procedure 192.5(a)(1), which defines work product and addresses waiver.

The key takeaway from the minute entry—based on the Minerva summary—is that the court concluded a non‑lawyer’s chats with ChatGPT did not automatically waive work‑product protection because the disclosure was not made to an adversary. That is a narrow holding, but it marks a significant moment in the emerging law of AI and privilege.

Why This Ruling Matters for Lawyers Using AI

At first glance, Tate Group may look like a niche discovery dispute. In reality, it answers a question many lawyers have quietly asked: “If my client uses ChatGPT, have we blown work product?”

The court’s answer is “not necessarily.” By focusing on whether the disclosure was made to an adversary, Judge Dorfman signaled that the waiver analysis for AI platforms should track the familiar contours of work‑product doctrine, at least in Texas. That gives practitioners a framework instead of a panic button.

At the same time, this is a minute entry in a specific context—not a blanket blessing for all AI use. The court still treated the issue as novel, still conducted in camera review, and still scrutinized how the AI tool was used. For lawyers, that means AI usage is now part of the discovery and privilege landscape, and courts will expect thoughtful, documented positions—not hand‑waving about “just using a tool.”

From an ABA perspective, this aligns with Model Rule 1.1 and Comment 8: competence now includes understanding the “benefits and risks associated with relevant technology,” including how generative AI intersects with privilege and work product. Model Rule 1.6 (confidentiality) and Rules 5.1/5.3 (supervision of lawyers and non‑lawyers) also come into play when clients or staff use tools like ChatGPT in ways that touch litigation strategy.

Lesson 1: Treat Client AI Use as Discoverable Reality, Not a Side Note

One of the most striking aspects of Tate Group is procedural: the court required in camera review of the ChatGPT conversations and entertained requests that plaintiff identify all discovery materials shared with ChatGPT. That tells us courts are prepared to treat AI interactions as real, reviewable artifacts in discovery.

If your clients or internal teams use AI to draft, summarize, or analyze case materials, those interactions can become part of the discovery conversation, just as drafts, notes, and emails have always been. Under Model Rules 1.1 and 1.6, you cannot stay competent or protect confidentiality if you do not know whether and how AI is being used on your matters.

Practically, that means:

• Ask clients early whether they have used tools like ChatGPT or other AI services to “get help” on their case.

• Document the scope and purpose of any AI use, especially if it involves draft pleadings, strategy, or privileged communications.

• Be prepared to defend or adjust your privilege and work‑product positions in light of those uses, as plaintiff did in Tate Group by asserting work‑product and submitting chats for in camera review.

Lesson 2: Public AI Platforms Are Not Automatic Waiver Machines

Solo attorneys need to protect their privileged work product from risky AI tools.

Defendants in Tate Group argued that a non‑lawyer’s chats with an AI tool either are not work product at all or, at minimum, effect a waiver. The court rejected the idea that simply using ChatGPT automatically destroys protection under Texas Rule 192.5(a)(1) when there is no disclosure to an adversary.

That matters, because there has been a real fear—sometimes stoked by vendors—that “if anyone touches ChatGPT, all privilege is gone.” This ruling shows courts can adopt a more nuanced view, at least under a work‑product framework.

For ABA‑Model‑Rules lawyers, this should not be read as a free pass. Model Rule 1.6 still requires reasonable efforts to prevent unauthorized disclosure of client information, and using a public AI platform can create confidentiality risk even if work product is technically preserved. But Tate Group suggests that waiver analysis will still look to core principles like whether disclosure reached an adversary.

In practice:

• You should not assume that any AI use destroys work product, but you should be ready to explain why your use did not involve disclosure to an adversary or the public.

• Engagement letters and internal policies should clarify whether and how you will use AI tools and what safeguards you apply, consistent with Model Rules 1.1, 1.4, and 1.6.

Lesson 3: In Camera Review Will Become Common for AI Disputes

The court’s process—ordering in camera review of the ChatGPT conversations before ruling—signals a likely pattern for AI‑related privilege disputes. Judges will want to see how AI was used, not just hear generalities, before deciding whether protection applies or has been waived.

That has three implications for practicing lawyers:

• You should assume that AI‑related materials can be reviewed by courts under appropriate safeguards.

• You need internal workflows to collect and present those materials when necessary without scrambling through chat histories.

• You should approach AI use with the expectation that a judge, someday, may read the raw prompts and outputs and ask whether your supervision met the standards of Model Rules 5.1 and 5.3.

This is a shift from treating AI as a “black box” helper to treating it as a discoverable component of your litigation process.

Lesson 4: Non‑Lawyers and AI Need Clear Supervision

In Tate Group, the conversations at issue were between Kris Tate—a non‑lawyer—and ChatGPT, yet they were withheld under an attorney work‑product theory. The court’s willingness to consider work‑product protection in that context underscores a point many of us have made: non‑lawyers can participate in the creation of protected material if they are acting at the direction of counsel.

But it also heightens the importance of supervision. Model Rule 5.3 requires lawyers to ensure that non‑lawyer assistants’ conduct is compatible with the lawyer’s professional obligations. When non‑lawyers use AI tools on client matters, they are effectively acting as an extension of the legal team.

Practical steps include:

• Training non‑lawyers on what they may and may not share with AI platforms.

• Setting clear rules about which tools are approved, for what purposes, and under whose supervision.

• Reviewing AI outputs and underlying prompts when they feed into litigation strategy, to ensure accuracy and compliance with Model Rules 3.3 and 4.1.

As we have discussed in episodes of The Tech‑Savvy Lawyer podcast, AI is not just a “lawyer tool”; it is often a staff and client tool. Your ethical obligations follow it wherever it goes. 💼🤖

Lesson 5: This Is Only the Beginning—But You Can Prepare

Texas judges along with others will be weighing ChatGPT privilege and waiver in generative AI era.

Judge Dorfman noted that all the case law cited by the parties dated from 2026 and that one authority called its ruling a “question of first impression nationwide.” That means we are at the very start of AI‑and‑privilege jurisprudence, not the end.

Every new decision—whether from Texas Business Courts or elsewhere—will refine the analysis. Some may take a stricter view of waiver for public AI tools; others may distinguish between work product and attorney‑client privilege. Regardless, Model Rule 1.1’s technology‑competence requirement demands that we follow these developments and integrate them into our practice.

You do not need to become an AI engineer, but you do need a plan:

• Inventory where AI is used in your matters (by you, your staff, your clients).

• Align that usage with your duties of competence, confidentiality, and supervision.

• Be prepared for in camera review of AI‑related materials, as in Tate Group.

• Update your engagement letters and internal policies to reflect reality, not wishful thinking.

If you approach AI as you approached email, e‑filing, and cloud storage when they were “new,” you will be ahead of many peers—and aligned with the spirit of both the ABA Model Rules and emerging case law.

MTC

MTC: When Your CEO Asks ChatGPT How to Take Over: Lessons for Lawyers on Public AI, Ethics, and Confidentiality 🧠⚖️

Lawyers need to evaluate public AI chatbot against ABA confidentiality and privilege rules

In March 2026, the Delaware Court of Chancery in Fortis Advisors, LLC v. Krafton, Inc. handed lawyers one of the clearest cautionary tales yet about public AI chatbots, corporate governance, and the limits of “move fast and break things.” A South Korean gaming conglomerate, Krafton Inc., used an artificial intelligence chatbot to help devise an internal “Project X” takeover plan against its own studio, Unknown Worlds Entertainment, and then tried to defend the fallout in court. The result: a detailed opinion reinstating the studio’s CEO, extending a $250 million earnout period, and spotlighting how AI misuse can become Exhibit A when things go wrong.

If you’re a solo, a small-firm lawyer, or an AI‑curious practitioner dabbling with ChatGPT or similar tools, this case is your wake‑up call. The message is not “don’t use AI.” The message is: treat public chatbots the same way you treat email, cloud storage, or texting — through the lens of ABA ethics, client confidentiality, and privilege. 😬

In this editorial, I’ll unpack what happened, how the court framed the misuse of a chatbot, and what you should do in your own practice to stay on the right side of the rules.

The Case in a Nutshell: AI as a Takeover Co‑Pilot

Krafton bought Unknown Worlds — the studio behind Subnautica — for $500 million upfront plus up to $250 million in contingent earnout payments, with a contractually guaranteed structure: the founders and CEO (the “Key Employees”) retained operational control and could only be fired for defined “Cause.”  As Subnautica 2 approached early‑access launch, internal projections showed the game would easily trigger a massive earnout.

The CEO of Krafton grew concerned he looked like a “pushover” under the deal and turned to a public AI chatbot for advice on how to avoid paying the earnout and seize control of the studio. The chatbot’s “response strategy” included:

• Locking down publishing rights and code access.

• Crafting messaging to “secure public support” and undermine the “large corporation vs. indie” narrative.

• Preparing a “takeover” path that blended hardball legal tactics with PR framing. 

Krafton’s internal team implemented much of that plan — cutting off the studio’s access to its Steam publishing console, posting unilateral public statements, and ultimately terminating the founders and CEO on a pretext of “premature release” risk.  When sued, Krafton tried to pivot to new justifications, including the executives’ role changes and their defensive downloads of company data. 

The court was having none of it. Vice Chancellor Will held that:

• The terminations were not “for Cause” under the negotiated contract.

• The “Project X” takeover guided by the chatbot was a pretext to avoid the earnout.

• The studio’s CEO, Ted Gill, must be reinstated with full operational control, and the earnout period equitably extended by the length of his ouster. 

In other words, the AI‑assisted takeover strategy became part of the factual narrative of bad faith and breach — not a clever workaround.

Public Chatbots and ABA Model Rules: Three Pressure Points ⚖️

Attorneys must consider ethical AI chatbot use for confidential client case analysis

Even though this is a corporate earnout case, the opinion gives lawyers a concrete frame for thinking about public AI tools under the ABA Model Rules.

1. Confidentiality — Model Rule 1.6

Rule 1.6 requires lawyers to keep “information relating to the representation of a client” confidential, absent informed consent or a specific exception. Public chatbots are not your firm’s Document Management System (DMS) — they’re third‑party services that typically ingest prompts for training, quality, and logging. When Krafton’s CEO ran “Project X” through a chatbot, he was effectively outsourcing high‑stakes strategy to a non‑privileged third‑party system that could store and learn from those prompts. 

For lawyers, the parallels are obvious:

• Dropping fact patterns, names, or deal structures into a public chatbot can mean you’ve disclosed client information to a non‑controlled vendor.

• Even “sanitized” prompts can be re‑identified when combined with other data.

Under 1.6, that’s a potential confidentiality breach unless you’ve vetted the tool, negotiated appropriate terms (including data handling and retention), and obtained informed client consent for that mode of assistance. Emojis and “it’s just drafting help” don’t change that. 😉

2. Privilege — Model Rules 1.1 and 1.4 (Competence and Communication)

Privilege isn’t framed in the Model Rules, but Rule 1.1 (competence) and 1.4 (communication) require you to understand how your technology choices affect the protection of client communications. When you route strategy discussions through a public chatbot:

• You may jeopardize attorney–client privilege by involving a third‑party with no need‑to‑know and no formal role in the representation.

• You may create discoverable records that live outside your control, just as Krafton’s CEO created chat logs he then tried to delete. 

The court noted that relevant chatbot logs were deleted, which did not play well in evaluating Krafton’s narrative.  Privilege analysis is already complex with cloud tools; adding public AI as a “secret co‑counsel” without protections only compounds that risk. 

Competent use of technology now includes understanding whether your AI stack is preserving or eroding privilege and communicating those risks to clients when you propose AI‑assisted workflows.

3. Candor and Misrepresentation — Model Rule 4.1 and 8.4(c) 🚨

Although this case turns on contractual “Cause” and good faith, the court’s language about “pretextual” justifications and manufactured defenses should resonate with litigators. Model Rule 4.1 prohibits knowingly making false statements of material fact to third parties; Rule 8.4(c) bars conduct involving dishonesty, fraud, deceit, or misrepresentation. 

When you:

• Use a chatbot to generate strategic messaging designed to mislead stakeholders.

• Craft public statements or demand letters that you know are pretextual, but you’ve optimized with AI for tone and impact.

… you’re still responsible for the truthfulness of that content. The court saw through Krafton’s attempt to re‑frame events after the fact, and its internal AI‑assisted playbooks did not help. 

For lawyers, the lesson is simple: AI‑generated output is yours once you sign or speak it. If it’s misleading, you own the ethics problem — not “the algorithm.”

Practical Takeaways for Solo and Small‑Firm Lawyers 🧩

So what do you do if you’re a tech‑savvy lawyer who likes AI, but doesn’t want your prompts quoted in an opinion like this?

Here are grounded, practice‑ready steps.

1. Establish an AI Use Policy

Even if you’re a solo, write down what you will and won’t do with public chatbots.

lawyers need to build practical, ethical AI policies for practice.

• No client names, exact fact patterns, or identifiable deal terms in public tools.

• Use AI for structure and language, not for strategy or confidential analysis.

• Prefer client‑specific, non‑logging enterprise tools when handling sensitive material.

Treat this like you treat your cloud storage or remote‑work policy — it’s part of your competence under Model Rule 1.1 and your supervisory obligations under 5.1/5.3 if you have staff.

2. Separate “Public Prompting” from “Privileged Thinking” 🧠

Use public chatbots for:

• Headline and meta description drafting.

• Blog outlines, post ideas, or simple explainer language for non‑client scenarios.

• Rough templates for standard documents that you will heavily edit.

Avoid using them for:

• Fact‑specific case assessments.

• Litigation strategy, negotiation plans, or internal “playbooks” like Krafton’s “Project X.” 

• Anything that feels like the kind of conversation you’d normally have only with a colleague behind closed doors.

This separation keeps your privileged work product inside tools and workflows you control.

3. Vet Vendors Like You Vet e‑Discovery Platforms

If you move beyond public chatbots to paid AI tools, evaluate them as you would any major legaltech vendor:

• Where is data stored?

• Is training on your material disabled by default?

• Can you get a Business Associate Agreement or Data Processing Agreement / Data Protection Impact Assessment that aligns with your jurisdiction’s expectations?

The ABA’s Formal Opinion 477R on secure communications and cloud ethics opinions from state bars all provide analogies: reasonable steps, not perfection, are required — but “type client memo into random website” is not reasonable. 😄

4. Document Client Consent When AI Is Material to the Representation

If you expect to use AI in a way that materially affects how you deliver legal services, communicate that to clients under Rule 1.4:

• Explain benefits (efficiency, faster drafting).

• Explain risks (data handling, reliability, hallucinations).

• Offer an AI‑free option.

Written engagement terms that address AI use can save hard conversations later if something goes sideways.

5. Revisit Your “Bad Facts” Mindset

Reading this Delaware opinion, you see how internal strategy — including AI‑assisted plotting — can become a litigation exhibit.  For lawyers, that’s an invitation to ask: 

“If this prompt or chatbot conversation showed up in an opinion, would I be comfortable defending it under the Model Rules?”

If the answer is no, don’t send it. That simple heuristic scales across tools and platforms.

What This Case Signals for the Next Wave of Legal Tech 🌊

There can be significant legal consequences for AI chatbot misuse in legal disputes.

The opinion in Fortis Advisors v. Krafton is not an ethics decision aimed at lawyers, but it shows courts will:

• Scrutinize AI‑assisted strategies as part of broader narratives about good faith, bad faith, and pretext.

• Expect parties — and by extension, counsel — to maintain and produce AI‑related records where relevant.

• Be unimpressed by attempts to retroactively justify decisions made for economic reasons with thin “quality” or “readiness” arguments. 

As public models get more powerful and more embedded in practice, ABA Model Rules on competence, confidentiality, supervision, and candor apply just as they did when lawyers moved to email, smartphones, and the cloud. AI is just the next tool — but it’s a tool that makes it very easy to generate sophisticated bad ideas quickly.

Your job is to keep your ethical compass steady, even when the chatbot is very persuasive. 🧭

MTC

🎙️ TSL.P Ep. #135: Ethical AI, Paperless Practice, and Smart Hardware Choices with ABA LTRC Chair Alan Klevan ⚖️🤖

My next guest is Alan Klevan, a veteran personal injury lawyer and Chair of the ABA Law Practice Division’s Legal Technology Resource Center (LTRC), known for running one of the first paperless practices in New England and for his clear-eyed approach to AI in law. In this live episode recorded at the ABA Spring Conference in San Diego, Alan and I dig into how solos and small firms can use AI, case management platforms, hardware, and workflows to practice more efficiently while honoring their ethical duties and protecting client confidentiality.

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

• What are the top three ways Alan uses AI and other tech tools to control discovery and document management at scale, protect client confidentiality, and communicate complex case progress to clients who only care that it is accurate and on time?

• As Chair of the ABA Law Practice Division’s Legal Technology Resource Center, what top three technology practices does Alan wish every small or solo lawyer would adopt in the next 12 months?

• What were the three most important technology decisions Alan made early in his career around paperless workflows, practice management, automation, and AI‑powered research—and how can today’s practitioners follow that lead?

In our conversation, we covered the following:

• [00:00:00] Live from the ABA Spring Conference in San Diego, introducing Alan Klevan and the setting of the conversation 🌴

• [00:00:30] Alan’s mirrored bi‑state setup: two Lenovo i7 laptops in Massachusetts and Florida, dual 24" HP HD monitors, two ScanSnap iX1600 scanners, laser printers, and Microsoft OneDrive syncing between offices 💻📠

• [00:01:10] Traveling with a third “road warrior” Lenovo laptop, iPhone as primary smart device, and using the reMarkable 2 tablet for handwritten notes that sync into client and ABA files ✍️

• [00:01:45] Early impressions of the Plaud (AI wearable) device, background-noise muting, and why Alan limits it to non‑critical meetings due to privilege concerns 🎧

• [00:02:20] Judicial skepticism about AI recording tools in court; motion practice, privilege issues, and a New York judge flatly banning AI recorders in the courtroom 🚫

• [00:03:10] AI hallucinations in legal practice, roughly 1,300 known hallucination incidents, and why the real problem is lawyers not checking citations—highlighted by a recent Oregon sanctions case 💸

• [00:04:00] The Oregon lawyer who tried to “fix” hallucinated citations with a motion to refile instead of candor to the court and opposing counsel, and how that became a fraud‑on‑the‑court issue under the Oregon Rules of Professional Responsibility

• [00:04:45] Using Google Scholar as an AI‑prompting “hack” to verify every citation and case suggested by AI tools 🔍

• [00:05:20] Question 1 restated: top three ways Alan uses AI and tech to (1) control discovery, (2) protect confidentiality and ethical duties, and (3) communicate complex case progress to clients

• [00:05:45] Drafting AI and social media policies directly into contingency‑fee agreements so clients do not post about their case or use open‑source AI on case‑related issues 📜

• [00:06:30] Hepner and Warner: open‑source vs enterprise AI, attorney–client privilege, work product concerns, and emerging discoverability questions for public‑facing AI platforms

• [00:07:20] Trap for the unwary: why Alan insists clients notify him before using AI on their case and why he prefers enterprise versions of AI for better protection and governance 🧠

• [00:08:10] The Nippon Life Insurance case: client uploads attorney communications into ChatGPT, asks if her lawyer is gaslighting her, then files 44 AI‑drafted motions—raising product liability and disclaimer questions for AI vendors 🏛️

• [00:09:30] Court pushback on AI disclaimer language, defective product theories, and the infancy of AI‑related legal liability

• [00:10:10] Alan’s big personal‑injury “Aaron Brockovich‑type” case with a deep‑pocket defendant and using AI to level the playing field on litigation management and motion practice ⚖️

• [00:11:00] Feeding facts, parties, defense counsel names, and pleadings into a case management system with a built‑in, highly accurate legal AI component (VL) and generating 50‑state case research for negligent infliction of emotional distress claims 📂

• [00:12:00] Running the same matter through two AI platforms (case management AI and Claude) to compare outputs, reduce hallucination risk, and mold responses to Alan’s writing style and Massachusetts practice

• [00:13:00] Using Claude (enterprise tier) to draft an opposition to a motion to dismiss seven emotional‑distress claims, followed by manual review and cross‑checking in the case management AI—leading to the defendant’s motion being denied ✅

• [00:14:15] Alan’s process for verifying AI outputs: second set of “AI eyes,” Google Scholar citation checks, and lawyer‑level review of every filing

• [00:15:00] Advice for new attorneys: try AI platforms before buying, choose a tool that fits your workflow, avoid shiny‑object syndrome, and do not over‑commit to annual plans while the market is moving fast 🧩

• [00:16:00] Michael’s caution about yearly plans, vendor lock‑in, and ensuring your data is nimble enough to move between AI platforms without costly migrations

• [00:16:45] Alan’s rule: do not chase every AI; become a master of one platform, learn it deeply, and resist the temptation to constantly switch 🧠

• [00:17:10] Both hosts stress “review, review, review”—AI as a law librarian or 3L intern, not as your practicing lawyer, and the concept that AI does not have a JD 🎓

• [00:18:00] Anecdote from 1990: Alan is sent to court unprepared, gets sent out of the courtroom to learn his file, and how that story frames his modern view of AI oversight and responsibility

• [00:19:10] Question 2: as LTRC Chair, Alan’s top three technology practices every small or solo lawyer should adopt in the next 12 months

• [00:19:30] Tech Practice #1: invest in a fast machine (Windows or Mac) with as much RAM and storage as you can reasonably afford, and strip the “crapware” off box‑store Windows machines 🖥️

• [00:20:10] Discussion of Apple vs Windows pricing, the need for more than 16 GB of RAM, multi‑core processors, and why Alan buys Lenovo laptops with 32 GB RAM and expects 3–4 year laptop lifespans 💾

• [00:21:30] Backups and storage: redundant cloud backups, redundant hard drives, using external 5 TB drives from Staples, and keeping active machines “clean” for better AI performance

• [00:22:30] Tech Practice #2: immerse yourself in what is happening with AI and law practice, become a master of one AI platform, and continuously read ethics and disciplinary decisions about AI use 📚

• [00:23:15] Tech Practice #3: your head is your most important piece of technology—using judgment, stepping back to assess risks, and making sure anything submitted to court or client is accurate

• [00:24:00] Economic access, hardware costs, and why Alan still believes lower‑resource attorneys can get workable hardware by being strategic about purchases, specs, and lifecycles

• [00:25:10] Michael’s storage philosophy: lots of local SSD, multiple backups, and revisiting older briefs and arguments (e.g., mailbox‑rule analysis) to build new work more efficiently

• [00:26:10] Disk space versus backup strategy, internal vs external drives, cloud vs local files, and disaster recovery considerations

• [00:27:20] Question 3: top three early technology decisions Alan made around paperless practice, automation, and AI‑powered research

• [00:27:40] Answer #1: going fully paperless in 2005—the first paperless practice in New England—and eliminating almost all postage costs by sending encrypted electronic communications and demand packages ✉️

• [00:28:15] Answer #2: becoming a power‑user of Adobe Acrobat and PDF workflows so he can respond to massive production requests (e.g., 10,000 pages) in seconds instead of hours 📑

• [00:29:00] Answer #3: adopting case management platforms with AI‑driven workflows that automatically assemble record requests, HIPAA authorizations, and certifications for medical providers

• [00:29:45] Dusty hardware: why Alan’s printer and ScanSnap are seeing less use, yet scanners remain necessary for partners who still prefer paper and non‑electronic delivery 🖨️

• [00:30:20] Michael’s own shrinking paper consumption, stamps.com, and transitioning to PDF‑based workflows with secure electronic delivery

• [00:31:00] Adobe Acrobat as “gold standard” for lawyers, why every attorney must understand PDFs deeply, and Alan’s “learn it, love it, live it” mantra 📄

• [00:31:40] Bonus segment: what the ABA Legal Technology Resource Center (LTRC) is, its role as a “delivery board,” and how it serves both the Law Practice Division and the broader ABA membership 🏛️

• [00:32:20] LTRC’s four pillars of law practice management—marketing, technology, practice, and finance—and how it delivers content via Law Technology Today, webinars, podcasts, and roundtables

• [00:33:10] 2024–25 LTRC theme: AI‑centric content from intake through trial, and why Alan believes LTRC may become the ABA’s most important board for practitioners navigating AI

• [00:34:00] Using AI for law‑firm marketing, content creation, case‑law recaps, and SEO—along with warnings about legal advice, PII, and AI‑generated “SEO articles” that sound inauthentic

• [00:35:00] Call to action: join the ABA Law Practice Division and LTRC, become one of roughly 30 tech‑focused thought leaders, and help shape AI guidance for the profession 🙌

• [00:36:00] Where to find Alan: why he is minimizing social presence during a major move and high‑stakes case, and the best way to reach him on LinkedIn

Hardware mentioned in the conversation

• Fujitsu ScanSnap iX1600 scanners – https://www.pfu-us.ricoh.com/scanners/scansnap/ix1600

• HP HD monitors – https://www.hp.com/us-en/shop/slp/hp-monitors/hp-series-3-pro?jumpid=ma_accy_button_na_2_260304

• HP LaserJet printers – https://www.hp.com/us-en/shop/vwa/printers/prnttyp=Laser?jumpid=ma_pr_featured_na_1_250511

• iPhone – https://www.apple.com/iphone

• Lenovo laptops – https://www.lenovo.com/us/en/laptops/

• Mac mini – https://www.apple.com/mac-mini

• reMarkable 2 tablet – https://remarkable.com/store/remarkable-2

Software & cloud services mentioned

• Adobe Acrobat – https://www.adobe.com/acrobat

• ChatGPT – OpenAI ChatGPT – https://chatgpt.com

• Claude – Anthropic Claude – https://www.anthropic.com/claude

• Dropbox – https://www.dropbox.com

• Microsoft OneDrive – https://www.microsoft.com/en-us/microsoft-365/onedrive/online-cloud-storage

• Stamps.com – https://www.stamps.com

• Zoom – https://zoom.us

When AI Falls Short - What Legal Professionals Must Know Before Relying on Microsoft Copilot and Similar Embedded AIs.

AI Errors in Legal Practice Demand Vigilant Attorney Oversight!

Any reader of my blog should realize by now that artificial intelligence is no longer a novelty in law practice; it is embedded in research platforms, document automation, e‑discovery, and now in tools like Microsoft Copilot that appear inside the same Microsoft 365 ecosystem lawyers already live in. Yet Copilot’s own terms of use long described it as being “for entertainment purposes only,” while Microsoft has simultaneously marketed it as an enterprise‑grade productivity assistant and is now backing away from prominent Copilot buttons in several Windows 11 apps. For lawyers who must live under the ABA Model Rules of Professional Conduct, this tension is not an amusing footnote; it is an ethics problem waiting to happen. 

Microsoft’s Copilot terms have advised that the service “can make mistakes,” “may not work as intended,” and should not be relied on for important advice. At the same time, Microsoft has begun removing or rebranding Copilot buttons from Notepad, Snipping Tool, Photos, and Widgets in Windows 11, framing this move as an effort to reduce “unnecessary Copilot entry points” and be “more intentional” about where AI shows up. The features, or at least the underlying AI, are not disappearing entirely; they are simply becoming less conspicuous. For the practicing lawyer, the message is clear: powerful AI is being woven into everyday tools, but its creators still do not want you to rely on it the way you rely on a human associate. 🤖

when AI falls short, it is the lawyer—not the software vendor—who will have to answer to clients, courts, and regulators.

⚠️

when AI falls short, it is the lawyer—not the software vendor—who will have to answer to clients, courts, and regulators. ⚠️

That is precisely where the ABA Model Rules step in. Model Rule 1.1 requires competent representation and, through Comment 8, includes a duty to keep abreast of the benefits and risks of relevant technology. Using AI in law practice is increasingly seen as part of that competence obligation, but competence does not mean blind trust in unvetted outputs from a system whose own terms warn you not to rely on it. A lawyer who treats Copilot’s draft as a finished research memo, brief, or contract without independent verification risks violating the duty of competence every bit as much as a lawyer who never learned to use electronic research tools in the first place.

Model Rule 1.6 on confidentiality presents a second, and in many ways more pressing, concern. Generative AI systems may store, log, or otherwise use prompt content for analysis and improvement, which means uncritical copying and pasting of confidential client information into Copilot can create a non‑trivial risk of exposure. The ABA and commentators have emphasized that before entering client data into a generative AI tool, lawyers must assess whether that data could be disclosed or accessed by others, including through unintended re‑use in future outputs to different users. That risk analysis is not optional; it is part of your obligation to make reasonable efforts to prevent unauthorized access or disclosure.

Fake Citations from AI Tools can Threaten Accuracy and Legal Ethics!

Model Rules 5.1 and 5.3, which govern the responsibilities of partners, managers, supervisory lawyers, and non‑lawyer assistants, also apply to AI use. When you deploy Copilot in your firm, you are functionally introducing a new category of “assistant” whose work product must be supervised like that of a junior lawyer or paralegal. Policies, training, and review procedures are needed so that AI‑drafted content is consistently checked for accuracy, bias, hallucinations, and improper legal conclusions before it ever reaches a client, court, or counterparty. Ignoring Copilot’s disclaimers and Microsoft’s own hedging around reliability is, in effect, ignoring red flags that any reasonable supervising attorney would address.

Model Rule 1.4 on communication adds yet another dimension: transparency with clients about how you are using AI in their matters. Authorities interpreting the Model Rules have stressed that lawyers should keep clients reasonably informed, which includes explaining when and how AI tools are utilized to assist in their cases. This is particularly important where AI may affect cost, turnaround time, or the nature of the work performed, such as using Copilot to generate a first draft instead of assigning that task to an associate. Engagement letters and fee agreements are increasingly incorporating language about AI use, both to set expectations and to align with evolving ethical guidance.

The “for entertainment purposes only” language is more than a curiosity; it is a signal about allocation of risk. Microsoft’s disclaimer mirrors language historically used by psychic hotlines and other services seeking to avoid responsibility for inaccurate advice. When such a disclaimer is attached to a tool you might be tempted to use for legal analysis, the tool is telling you that you assume the risks of errors. Under the Model Rules, those risks ultimately translate into potential malpractice, sanctions, or disciplinary action if AI‑generated errors make their way into filed documents or client counseling.

Recent real‑world incidents involving lawyers who submitted briefs containing AI‑fabricated citations demonstrate how quickly misuse of generative AI can cross ethical lines. In those cases, the core problem was not that AI was used; it was that the lawyers failed to verify the content and then misrepresented fictitious cases as genuine authority to the court. That behavior implicates Model Rules 3.3 (candor toward the tribunal) and 8.4 (misconduct) along with competence. Copilot’s warnings about possible mistakes do not excuse a lawyer from the duty to check every citation, quote, and legal conclusion that AI produces before relying on it.

lawyers must assess whether that data could be disclosed or accessed by others

⚠️

lawyers must assess whether that data could be disclosed or accessed by others ⚠️

For practitioners with limited to moderate technology skills, the answer is not to abandon AI entirely, but to approach it with structured safeguards. A practical workflow might involve using Copilot to outline a research plan or draft a first pass at a contract clause, followed by standard legal research in trusted databases and rigorous review by a human lawyer before anything is finalized. Firms should configure Copilot and other AI tools in ways that minimize data exposure, such as disabling cross‑tenant learning, a feature that lets the system learn from patterns across multiple organizations’ environments, where possible, and restricting which matters and users can access certain features. Training sessions can focus less on technical jargon and more on concrete do’s and don’ts tied directly to the Model Rules, which is the language most lawyers already speak. 🧠

alawys Protect Client Confidentiality When Using AI in Modern Law Practice!

Governance is also essential. Written AI policies should address acceptable use cases, prohibited content for prompts, mandatory review standards, logging and auditing of AI‑assisted work, and incident response if an AI‑related error is discovered. These policies should be backed by regular training and by leadership that models appropriate use, rather than quietly delegating AI experimentation to the most tech‑savvy associates. Vendors’ evolving terms of use—including Microsoft’s move to revise its “entertainment purposes” language and adjust Copilot integration in Windows—should be monitored and incorporated into risk assessments over time.

In short, when AI falls short, it is the lawyer—not the software vendor—who will have to answer to clients, courts, and regulators. Copilot and similar tools can be valuable allies in a modern legal practice, but only if they are treated as fallible assistants whose work must be checked, not as oracles. The ABA Model Rules already provide the framework: competence, confidentiality, supervision, and honest communication. The task for today’s legal professionals is to apply that framework thoughtfully to AI, recognizing both its promise and its very real limitations before letting it anywhere near client work or court filings. ⚖️🤖

Word of the week: “Legal AI institutional memory” engages core ethics duties under the ABA Model Rules, so it is not optional “nice to know” tech.⚖️🤖

Institutional Memory Meets the ABA Model Rules

“Legal AI institutional Memory” is AI that remembers how your firm actually practices law, not just what generic precedent says. It captures negotiation history, clause choices, outcomes, and client preferences across matters so each new assignment starts from experience instead of a blank page.

From an ethics perspective, this capability sits directly in the path of ABA Model Rule 1.1 on competence, Rule 1.6 on confidentiality, and Rule 5.3 on responsibilities regarding nonlawyer assistance (which now includes AI systems). Comment 8 to Rule 1.1 stresses that competent representation requires understanding the “benefits and risks associated with relevant technology,” which squarely includes institutional‑memory AI in 2026. Using or rejecting this technology blindly can itself create risk if your peers are using it to deliver more thorough, consistent, and efficient work.🧩

Rule 1.6 requires “reasonable efforts” to prevent unauthorized disclosure or access to information relating to representation. Because institutional memory centralizes past matters and sensitive patterns, it raises the stakes on vendor security, configuration, and firm governance. Rule 5.3 extends supervision duties to “nonlawyer assistance,” which ethics commentators and bar materials now interpret to include AI tools used in client work. In short, if your AI is doing work that would otherwise be done by a human assistant, you must supervise it as such.🛡️

Why Institutional Memory Matters (Competence and Client Service)

Tools like Luminance and Harvey now market institutional‑memory features that retain negotiation patterns, drafting preferences, and matter‑level context across time. They promise faster contract cycles, fewer errors, and better use of a firm’s accumulated know‑how. Used wisely, that aligns with Rule 1.1’s requirement that you bring “thoroughness and preparation” reasonably necessary for the representation, and Comment 8’s directive to keep abreast of relevant technology.

At the same time, ethical competence does not mean turning judgment over to the model. It means understanding how the system makes recommendations, what data it relies on, and how to validate outputs against your playbooks and client instructions. Ethics guidance on generative AI emphasizes that lawyers must review AI‑generated work product, verify sources, and ensure that technology does not substitute for legal judgment. Legal AI institutional memory can enhance competence only if you treat it as an assistant you supervise, not an oracle you obey.⚙️

Legal AI That Remembers Your Practice—Ethics Required, Not Optional

How Legal AI Institutional Memory Works (and Where the Rules Bite)

Institutional‑memory platforms typically:

• Ingest a corpus of contracts or matters.

• Track negotiation moves, accepted fall‑backs, and outcomes over time.

• Expose that knowledge through natural‑language queries and drafting suggestions.

That design engages several ethics touchpoints🫆:

• Rule 1.1 (Competence): You must understand at a basic level how the AI uses and stores client information, what its limitations are, and when it is appropriate to rely on its suggestions. This may require CLE, vendor training, or collaboration with more technical colleagues until you reach a reasonable level of comfort.

• Rule 1.6 (Confidentiality): You must ensure that the vendor contract, configuration, and access controls provide “reasonable efforts” to protect confidentiality, including encryption, role‑based access, and breach‑notification obligations. Ethics guidance on cloud and AI use stresses the need to investigate provider security, retention practices, and rights to use or mine your data.

• Rule 5.3 (Nonlawyer Assistance): Because AI tools are “non‑human assistance,” you must supervise their work as you would a contract review outsourcer, document vendor, or litigation support team. That includes selecting competent providers, giving appropriate instructions, and monitoring outputs for compliance with your ethical obligations.🤖

Governance Checklist: Turning Ethics into Action

For lawyers with limited to moderate tech skills, it helps to translate the ABA Model Rules into a short adoption checklist.✅

When evaluating or deploying legal AI institutional memory, consider:

1. Define Scope (Rules 1.1 and 1.6): Start with a narrow use case such as NDAs or standard vendor contracts, and specify which documents the system may use to build its memory.

2. Vet the Vendor (Rules 1.6 and 5.3): Ask about data segregation, encryption, access logs, regional hosting, subcontractors, and incident‑response processes; confirm clear contractual obligations to preserve confidentiality and notify you of incidents.

3. Configure Access (Rules 1.6 and 5.3): Use role‑based permissions, client or matter scoping, and retention settings that match your existing information‑governance and legal‑hold policies.

4. Supervise Outputs (Rules 1.1 and 5.3): Require that lawyers review AI suggestions, verify sources, and override recommendations where they conflict with client instructions or risk tolerance.

5. Educate Your Team (Rule 1.1): Provide short trainings on how the system works, what it remembers, and how the Model Rules apply; document this as part of your technology‑competence efforts.

Educating Your Team Is Core to AI Competence

This approach respects the increasing bar on technological competence while protecting client information and maintaining human oversight.⚖️

This approach respects the increasing bar on technological competence while protecting client information and maintaining human oversight.⚖️