🎙️ Ep. 139, From MyCase to Claude: Building a Secure, AI-Ready Tech Stack for Solo and Small Law Firms.

/ The Tech-Savvy Lawyer.Page/

My next guests are Gabriella "Gabby" Cabero, Senior Vice President of Product at 8am — the powerhouse behind MyCase, LawPay, CASEpeer, and DocketWise — and Majo Castro, founder and managing attorney at CastroMand Legal in Austin, Texas. 🌟 Gabby is a 16-year legal tech veteran who co-founded CASEpeer and now drives product strategy across one of the most widely adopted law practice management platforms in the country. Majo is a Venezuelan-born cybersecurity and AI attorney whose solo firm helps growing companies navigate AI implementation, data management, and cybersecurity — and she writes about all of it on her Substack, The Cyber Law Gal. 🛡️ This is a no-fluff, peer-to-peer conversation about the exact workflows that separate a modern LPM from a liability, why the Data Processing Agreement is the most important acronym in your practice right now, and what your employees are almost certainly already doing with AI — whether you've approved it or not.

Join Gabriella Cabero, Majo Castro, and me as we discuss the following three questions and more!

  1. What are the top three integrations or workflows a solo, small, or midsize firm should expect from a modern cloud-based LPM platform like 8am — and what's missing that signals a real red flag around efficiency, cash flow, or security?

  2. As AI gets baked into cloud LPM tools like 8am, what are the top three day-to-day tasks that will change most for solo and small firm lawyers — and what basic security or ethical guardrails should they put in place to use those AI features without putting client data at risk?

  3. For solo and small firms without a CISO or CTO, what are the top three cybersecurity mistakes you see over and over again?

In our conversation, we cover the following:

  • [00:00:00] 🪝 Show Hook — Gabby's critical warning: if your firm hasn't "adopted" AI, your employees probably already have — on free consumer tools

  • [00:00:00] Title read — Episode 139

  • [00:01:00] Host intro: why this conversation goes tactical on AI, security, and LPM workflows

  • [00:02:00] Guest introductions — Gabriella Cabero (8am/MyCase) and Majo Castro (CastroMand Legal / The Cyber Law Gal)

  • [00:03:00] Majo celebrates 1.5 years as a solo practitioner 🎉

  • [00:03:00] Ad: Five-star review request for The Tech-Savvy Lawyer.Page

  • [00:03:30] Tech setups — Gabby's MacBook Air (M4 chip), iPhone Max, Slack, Zoom, Google Drive, Claude Enterprise

  • [00:06:00] Gabby's portable USB-C external monitor for travel (Amazon, highest-rated)

  • [00:09:00] Majo's MacBook Pro 14" M4 (16GB RAM), performance issues, upgrade path discussion

  • [00:10:00] Michael recommends Onyx (free Mac maintenance utility); Michael's Mac Studio M3 Ultra with 256GB

  • [00:11:00] Mac Mini and Mac Studio as desktop alternatives; MacRumors Buyer's Guide tip

  • [00:13:00] Apple Business Account benefits — small discounts + white-glove service

  • [00:15:00] Majo's full setup: iPhone 16 Pro Max, Google Workspace + Gemini (team account with DPA), DJI Osmo Pocket 3, Hollyland wireless mic

  • [00:16:00] Q1: Top three LPM workflows — intake, secure client communication (client portal), and getting paid (trust accounting + automated invoicing)

  • [00:19:00] Majo on switching from QuickBooks to MyCase after discovering QuickBooks mishandles trust accounting

  • [00:20:00] 🎉 Gabby announces: AI case summary features are now LIVE in 8am/MyCase

  • [00:21:00] Cloud vs. local access debate — SaaS uptime, SLAs, and asking vendors for proof

  • [00:23:00] Michael's redundant backup strategy: Backblaze + Dropbox + local Mac Mini

  • [00:25:00] Cautionary tale: ransomware attack converts a server-based firm to the cloud overnight

  • [00:28:00] Majo's Google Drive third-party backup with 2-hour recovery window

  • [00:29:00] Q2: How AI changes daily workflows — drafting, case summaries, surfacing critical info fast

  • [00:30:00] Why reading vendor Terms of Service and activating Data Processing Agreements (DPAs) is non-negotiable

  • [00:31:00] 8am's SOC 2 Type 2 compliance; updated AI terms and opt-in controls coming

  • [00:32:00] SOC 2, HIPAA, end-to-end encryption as baseline vendor security requirements

  • [00:34:00] AI as the great equalizer — leveling the playing field for solo firms vs. BigLaw

  • [00:35:00] Majo's real data: ~12 hours saved last month across 27 consultations using Gemini for proposals

  • [00:36:00] Plaud and Pocket AI recording devices — data retention, PII, and DPA concerns

  • [00:37:00] Majo's stance on wearable AI recorders; Apple Watch comparison; one-party vs. two-party consent

  • [00:39:00] Plaud's terms say no AI training — but it's not a DPA; terms can change without notice 🚨

  • [00:40:00] Google Workspace DPA must be manually activated — most users don't know; creating user friction around protection

  • [00:41:00] Q3: Top cybersecurity mistakes — shadow AI, no MFA, undertrained employees

  • [00:42:00] Majo's checklist: DPA + no model training on client data + enterprise/team-tier subscriptions + MFA

  • [00:43:00] Gabby: employees are the #1 security risk; fractional IT and CISO options for small firms

  • [00:44:00] AI-powered phishing attacks on law firms will only intensify

  • [00:45:00] Majo's training method: positive AI policies + 45-second staff video explainers 🎬

  • [00:46:00] 🚨 Gabby's shadow AI reminder (Show Hook callback): audit your tech stack — your team already has

  • [00:47:00] Episode originally recorded at ABA Techshow; re-recorded after a technical snafu 😅

  • [00:47:00] Where to find Gabby: LinkedIn, X, 8am.com, Kaleidoscope conference (September — banner at 8am.com)

  • [00:48:00] Where to find Majo: LinkedIn (Majo Castro), CastroMand Legal, Substack: The Cyber Law Gal

  • [00:48:30] Outro — michaeldj@thetechsavvylawyer.page | next episode in ~two weeks

RESOURCES

Connect with Gabriella "Gabby" Cabero

Connect with Majo Castro

Mentioned in the Episode

Hardware Mentioned

MTC: Why Rising PC and AI Tool Prices (for Windows and Apple) Should Be on Every Lawyer’s Radar in 2026

/ The Tech-Savvy Lawyer.Page/

Law firms need to plan Windows, Mac, and AI refresh strategy

If you feel like every new laptop quote is 15–20% higher than last year, you are not imagining things. 📈 And if your favorite AI drafting or transcript tool pinged you with a “small” price adjustment this spring, welcome to the club. 🤖

In our December 2025 editorial, “MTC: The 2026 Hardware Hike: Why Law Firms Must Budget for the ‘AI Squeeze’ Now!”, we warned that a perfect storm in the hardware market was forming: DRAM shortages, surging AI infrastructure demand, and shifting trade policy were about to push PC prices up by 15–20% in 2026. 💻 Then, in April 2026’s “MTC: Why 2026’s PC Price Hikes Put Law Firms at Risk (and Why Many Lawyers Are Quietly Switching to Macs)”, we explored how rising Windows laptop prices were reshaping law firm hardware decisions and eroding the old assumption that “Windows is always cheaper than Mac.”

Those forecasts are now reality across both Windows PCs and Macs, and the question I keep hearing from solo and small firm lawyers is simple: Should I be worried?

The short answer is yes—concerned, not paralyzed. The better question is: how do we respond strategically, in a way that respects both our budgets and our ethical obligations under ABA Model Rules 1.1 (Competence) and 1.6 (Confidentiality)?

A quick recap: what’s driving the price surge?

Let’s start with the “why,” because context matters when you sit down with your next-year budget spreadsheet. 📊

Industry analysts now confirm that average PC prices are rising in the 15–20% range for 2026, with memory costs as the biggest driver. AI data centers—those massive server farms powering tools like ChatGPT and other LLMs—are soaking up an estimated majority of advanced DRAM production, leaving less capacity for business laptops and desktops of all flavors, whether they run Windows or macOS. When memory becomes scarce and expensive, everything that relies on it gets pricier.

You can see this in both ecosystems:

Lawyers need t plan their 2026 law firm hardware budget amid rising costs

  • Windows side: In April, Microsoft sharply raised prices across its Surface lineup, including the Surface Pro and Surface Laptop families, many lawyers rely on. Entry-level machines that once started under 1,000 dollars now begin well above that mark, with some configurations jumping several hundred dollars over launch prices and in some cases exceeding roughly comparable MacBook configurations.

  • Apple side: In June, Apple CEO Tim Cook told The Wall Street Journal that Apple will raise prices because the company can no longer absorb skyrocketing memory and storage costs, calling the situation a “hundred-year flood” and saying he has “never seen anything like it in any area in over 40 years,” describing these increases as “unavoidable.” Apple to Raise Prices Due to Memory Chip Crunch, Tim Cook Says.

When both Microsoft and Apple are telling you that memory costs and component shortages are forcing them to push prices up, that is not a platform rivalry story. It is a signal that the entire hardware market—Windows and Mac alike—is being repriced around the AI era.

On top of that, trade policy and tariffs have increased costs for components and final assembly in key manufacturing hubs like China and Taiwan. Vendors have responded by tightening quote windows and baking in risk premiums, which is why the Windows laptop or Mac you priced in Q4 2025 quietly jumped in Q2 2026. 💸

In “MTC: The 2026 Hardware Hike”, we urged firms to accelerate planned refreshes where possible, prioritize RAM over storage, and budget for stronger machines instead of downgrading specs. In the April 2026 editorial, we drilled into how those same forces made some Mac configurations look surprisingly competitive—and why lawyers should stop treating “Windows versus Mac” as a matter of habit and start treating it as a structured evaluation tied to performance, security, and ethical duties. All of that guidance still holds.

Budgeting like a law practice, not a gadget hobby (PC‑neutral framing)

The theme of “MTC: The 2026 Hardware Hike” was simple: treat your tech like a planned, recurring investment—not a last-minute scramble when a laptop dies in the middle of trial prep. The April 2026 follow-up on PC price hikes showed how that planning must now account for both Windows and Mac options, since price gaps have narrowed or flipped depending on configuration.

Here is the approach I recommend for solos and small firms, regardless of platform:

  1. Inventory and classify your devices across platforms.
    Capture which users are on Windows, which are on macOS, and what roles those machines play. Prioritize devices used for active litigation, client communications, and high-sensitivity matters.

  2. Set a realistic refresh cycle that is OS‑aware.
    For most law practices, a 3–5 year cycle for primary laptops and desktops is reasonable, but the exact timing should reflect each platform’s support timeline—Windows 10 reaching end of support, macOS versions aging out, and vendor firmware commitments.

  3. Budget for “competence grade” hardware on both sides.
    As we argued in both the December and April MTC pieces, it is better to buy fewer, well‑specced machines—whether that is a mid-range Surface Laptop or a MacBook Air with sufficient RAM—than to chase the absolute lowest price and end up with systems that choke under AI‑enhanced workflows.

  4. Run a structured Windows vs. Mac evaluation, not a loyalty contest.
    Following the April article’s recommendation, build a simple matrix comparing specific Windows and Mac models on price, RAM, storage, performance, security features (like Secure Boot, Secure Enclave, or TPM), support life, and compatibility with your core practice software. Tie that matrix explicitly to your responsibilities under ABA Model Rules 1.1 and 1.6 so you can show you exercised reasonable diligence.

  5. Cull redundant subscriptions before sacrificing baseline hardware on either platform.
    Before you decide that “Macs are too expensive now” or “Windows machines are out of reach,” examine your monthly AI and SaaS spend. Many firms can free up budget for better Windows or Mac hardware by retiring overlapping tools that deliver marginal benefits.

This is not about declaring a winner in the Windows vs. Mac debate. It is about recognizing that both ecosystems are affected by the same structural forces—AI‑driven memory demand, supply constraints, tariffs—and that your ethical obligations apply regardless of logo. ⚖️

So, should lawyers be worried? (PC‑neutral conclusion)

Concern is justified. Panic is not. 😅

Law firmS of every size need to plan Windows, Mac, and AI refresh strategy

Yes, Windows PC and Mac prices are rising and are likely to remain elevated through at least 2027, given ongoing DRAM constraints and AI demand. Yes, AI and cloud tools are adjusting their pricing and tiers in ways that can catch an unprepared firm off guard. And yes, when Microsoft raises Surface prices, and Tim Cook says he has never seen a memory crunch like this in over 40 years and calls it a “hundred-year flood,” those are market‑wide signals—not platform‑centric marketing talking points.

But you still have levers to pull, no matter which platform you use:

  • Plan your hardware lifecycle instead of reacting to failures.

  • Prioritize “competence grade” devices and security over optional features, whether that is a mid‑range Windows laptop or a MacBook with enough RAM.

  • Rationalize your AI and SaaS stack so you pay for what actually moves the needle.

  • Treat your tech stack as part of your ethics compliance, not just overhead. ⚖️

Lawyers on both Windows and Mac should treat 2026’s hardware and AI price hikes as a market‑wide issue that affects competence, confidentiality, and client service—not as a referendum on one platform. 💻⚖️

MTC

How (To) Lawyers Can Write Better AI Prompts (In Minutes) with PromptCowboy 🤠

/ The Tech-Savvy Lawyer.Page/

today’s Lawyer need to master AI prompts in a modern tech-savvy law office 📚🤖

Large language models (LLMs) are not magic wands. They are very fast, very convincing parrots. When you ask sloppy questions, you get sloppy answers. When you ask clear, structured questions, you start to see real value in your law practice.

That’s why prompt quality is now a lawyering skill, not a party trick—and tools like PromptCowboy can help you build that skill quickly and safely.

In earlier Tech-Savvy Lawyer posts like “🎙️ TSL Lab’s Deep Dive into Our May 18, 2027, editorial, “AI Won’t Replace Solo and Small Firm Lawyers. It Will Supercharge Them”!” and podcast episodes discussing AI workflows, I’ve stressed the same core message: you cannot delegate your professional judgment to an LLM. You can, however, use an LLM to accelerate competent lawyering—if you stay in control of the instructions you give it and the outputs you accept.

Why prompt quality is an ethics issue 💼

The ABA’s technology competence mandate under Model Rule 1.1 now clearly extends to understanding the risks and benefits of generative AI tools. ABA Formal Opinion 512 emphasizes that lawyers may use generative AI to deliver faster and more efficient legal services, but only if they maintain independent professional judgment, supervise results, and comply with duties of confidentiality, candor, and reasonable fees.

That means “prompt engineering” is not a hobby; it’s part of staying reasonably informed about relevant technology and using it responsibly. When you use a tool like PromptCowboy to structure your prompts, you are not outsourcing judgment—you are standardizing how you exercise it.

What PromptCowboy actually does for lawyers 🤠⚖️

PromptCowboy is a guided prompt generator. You type in a rough idea (“help me sanity-check a demand letter” or “summarize this deposition transcript for trial prep”), and it walks you through targeted questions that transform that rough idea into a structured, reusable prompt.

For lawyers, three capabilities matter most:

  • It enforces structure: role, task, context, constraints, and output format.

  • It preserves prompts: you can reuse, tweak, and standardize prompts across matters and teams.

  • It supports multiple LLMs: you can paste the same prompt into your preferred tools (e.g., a legal-specific AI plus a general LLM).

If you’ve ever stared at a blank chat box and thought, “I don’t even know how to ask this,” PromptCowboy is the bridge between your legal brain and the AI chat window.

Why not just type directly into the LLM? 🤔

If you’re comfortable drafting a tight brief from a messy client email, you can learn to write good prompts directly in ChatGPT, Claude, or your preferred tool. The question is not “Can I?”—it’s “Is that the best use of my time and attention?”

PromptCowboy sits between your legal brain and the AI chat box and gives you three advantages that are hard to get from freehand prompting alone.

1. It forces you into best practices by default

Most prompt-engineering guides tell you: be specific, define the role, give context, specify the audience, and tell the model what format you want. When you type straight into an LLM, you have to remember all of that and translate your legal problem into structured instructions.

PromptCowboy automates that discipline:

  • It asks targeted follow-up questions about audience, use case, and output format.

  • Its “improve your prompt” style features can take your “lazy prompt” and suggest refinements, like adding jurisdiction, tone, or specific constraints.

  • It then assembles a complete, structured prompt you can copy into your LLM.

From an ethics standpoint, this matters because better-structured prompts reduce the risk of vague, misleading, or overconfident AI outputs that you might otherwise overlook—helping you meet your competence duty under Model Rule 1.1 and the quality expectations outlined in ABA Formal Opinion 512.

2. It gives you reusable, auditable prompt “precedent”

When you type directly into a chat window, your “good prompts” disappear into the scroll unless you remember to save them elsewhere. Lawyers would never run a litigation practice without templates and prior forms, yet many start from scratch every time they open an AI tool.

PromptCowboy provides:

SOLO AND Small-firm attorneys CAN COMPETE WITH LARGER FIRMS BY CREATING POWERFUL AI prompt templates for clients ⚖️💬

  • Prompt history and private templates in its paid tiers, so you can reuse and iterate on prompts like you do with forms.

  • Centralized prompt management, so a firm can standardize prompts for common tasks (client email drafts, discovery checklists, status updates) and keep everyone using the same baseline instructions.

  • A clean separation between “prompt drafting” and “AI execution,” which makes it easier to document how you instructed the AI if you ever need to explain or audit your process.

That last point goes to Model Rules 5.1 and 5.3—supervision of lawyers and nonlawyer assistants—because LLMs function in practice like a highly automated, but still supervised, assistant. Having standard prompts you can review, update, and roll out to a team is much easier with a dedicated prompt tool than with a dozen scattered screenshots.

3. It speeds up the “iterate and improve” loop

Good prompting is iterative. You try, you see what the AI produces, you refine. That’s true whether you’re drafting in a word processor or prompting an LLM.

PromptCowboy accelerates that loop because:

  • It can generate an initial, detailed prompt from a very short description (“help me draft a discovery checklist for a Virginia PI case”).

  • It automatically suggests follow-up questions whose answers will sharpen the prompt, instead of making you guess what to change.

  • Once refined, you can save that prompt and reuse it as a starting point next time, instead of reinventing the wheel in the LLM chat.

The net effect is less cognitive load. You spend your time reviewing outputs and exercising legal judgment, not handcrafting prompts from scratch—which aligns with the efficiency and cost considerations in Model Rule 1.5 and the access-to-justice benefits emphasized in Formal Opinion 512.

When direct prompting is fine—and when PromptCowboy shines

To keep this honest: there are plenty of scenarios where you can safely type straight into your LLM, like one-off low-stakes tasks or conversational exploration.

PromptCowboy shines when you:

  • Want repeatable workflows (weekly client updates, discovery outlines, intake summaries).

  • Need team-wide standards for how AI should behave and respond.

  • Must document your process for internal policies, insurers, or regulators who may ask how you controlled AI outputs.

Think of typing directly in the LLM as scribbling notes on a legal pad in chambers; using PromptCowboy is more like drafting a form in your document system that the whole firm can rely on.

A simple framework: RICE + I (Role, Instructions, Context, Expectations + Inputs) 🧩

The RICE framework—Role, Instructions, Context, Expectations—is a practical way to structure prompts. Let’s add an explicit “I” for Inputs and walk through how PromptCowboy helps you implement it:

  1. Role – Who is the AI supposed to be?
    Example: “You are a legal writing coach familiar with U.S. civil procedure.”
    PromptCowboy prompts you to define this persona up front, narrowing the output.

  2. Instructions – What task should it perform?
    Example: “Identify ambiguities and tone issues in the following demand letter and suggest specific edits.”

  3. Context – What background does it need?
    Example: “Maryland state court personal injury case involving a rear-end collision, liability admitted, issue is damages only.”

  4. Expectations – How should it respond?
    Example: “Return a bullet-point list, no more than 10 bullets, written at a 10th-grade reading level.”

  5. Inputs – What materials can it see?
    Example: “You will receive the text of the demand letter below this prompt.”

PromptCowboy’s workflow essentially walks you through each of these steps, so you don’t have to remember them every time.

Step-by-step: Building a better legal prompt with PromptCowboy 🛠️

Solo practitionerS CAN craft ethical AI prompts with ABA-focused guidance 🧠📜

Let’s say you want an LLM to help you draft initial discovery requests in a straightforward personal injury case—without crossing ethical lines.

Step 1: Decide what you will do first
Under Model Rule 1.1 and Formal Opinion 512, you must understand the law and facts well enough to supervise any AI assistance. That means you:

  • Identify the jurisdiction and claims

  • Review your client’s key facts

  • Decide what categories of information you need

Only then should you move to the AI.

Step 2: Open PromptCowboy and describe your task in plain English
In PromptCowboy, start with a simple description:

“Help me generate draft interrogatories and requests for production for a rear-end auto collision case in Virginia state court, focusing on damages.”

Step 3: Answer PromptCowboy’s clarifying questions
PromptCowboy will ask for details like:

  • Target audience (you, another lawyer, or a client)

  • Preferred tone (formal, plain language, bullet-point)

  • Output format (numbered list, table, outline)

By answering these questions, you naturally fill in the RICE + I elements without overthinking the jargon.

Step 4: Add ethical guardrails into the prompt
This is where ABA Model Rules meet prompt engineering:

  • Model Rule 1.6 (confidentiality) and Formal Opinion 512 suggest you should avoid disclosing client-identifying information to public LLMs unless you have informed consent and appropriate safeguards.

  • So in the prompt, you write:
    “Do not invent case-specific facts. Use only the generic facts provided. Do not reference any real persons or entities.”

PromptCowboy can store that language so you reuse it in future prompts.

Step 5: Generate, copy, and paste into your chosen LLM
Once PromptCowboy assembles the prompt, you copy it into:

  • A general LLM (e.g., ChatGPT, Claude or Perplexity*) for plain-language drafting, or

  • Your firm’s legal AI platform for case-specific workflows.

Then you review the output like you would a first-year associate’s draft—carefully and critically.

Practical prompt examples you can reuse 🧾

Here are two PromptCowboy-friendly templates you can adapt:

Template 1: Research sanity-check (non-confidential)

“You are a legal research assistant familiar with [jurisdiction].
Task: Summarize the general legal standards for [issue] without citing specific cases.
Context: This is for high-level planning, not court submission.
Expectations: Provide a concise outline with headings and bullet points.
Ethics: Do not fabricate statutes or case names; flag any uncertainty for follow-up research.”

Template 2: Plain-language client explanation (with safeguards)

“You are a communication coach for lawyers.
Task: Rewrite the following explanation of [legal issue] so a layperson can understand it.
Context: This will be used as a draft for a client email.
Expectations: 3–5 short paragraphs, no legalese, no promises of outcomes.
Ethics: Do not add any new legal advice beyond what is given. Flag any unclear sections for attorney review.”

These templates align with Model Rules 1.1 (competence), 1.4 (communication), and 7.1 (avoiding misleading statements), while using PromptCowboy to enforce structure and consistency.

Common mistakes PromptCowboy helps you avoid 🙅‍♂️

PromptCowboy is not a substitute for judgment, but it does reduce some predictable errors lawyers make with LLMs:

  • Vague requests (“Write a brief” with no jurisdiction, facts, or audience)

  • No output format (you get a wall of text you can’t use)

  • Hidden assumptions (AI fills in facts that are wrong or prejudicial)

  • Over-sharing (don’t paste client-identifying facts into a public tool)

By forcing you to specify intent, context, and output, PromptCowboy nudges you toward more disciplined, repeatable AI use.

Bringing it into your practice today 📆

If you are a solo or small firm lawyer, you do not need a full-blown “AI strategy deck” to start. You need one or two well-crafted, reusable prompts for tasks you already handle every week—email drafting, checklists, or content summaries.

📢 Stay Tuned! In a future episode of The Tech-Savvy Lawyer Podcast, we’ll walk through a live PromptCowboy-to-LLM workflow and compare results across different tools. For now, pick one use case, build a prompt with PromptCowboy, and run it through your existing AI stack. Measure whether it saves you time without sacrificing quality or ethics.

Used thoughtfully, PromptCowboy can help bridge the gap between “AI-curious” and “AI-competent”—and that’s exactly where the profession needs to go next. 🚀

Why Macstock 2026 Should Be on Every Tech-Savvy Lawyer’s Calendar (and How to Save $50 with My Code) ⚖️💻

/ The Tech-Savvy Lawyer.Page/

macstock 2026 will be held july 10, 11 & 12, 2026!

If you’re a solo, small-firm, or AI‑curious lawyer who lives in the Apple ecosystem, Macstock 2026 is one of the few conferences that genuinely respects both your time and your tech stack. It’s a three‑day, community‑driven, Apple‑centric event where you can sharpen your skills with your Mac, iPhone, and iPad, and walk away with workflows you can actually deploy on Monday morning.

This year, I’m honored to be speaking at Macstock X on “Podcasting with Apple: From Idea to Launch Using the Gear You Already Own.” We’ll take a practical walk through planning, recording, and publishing a professional‑quality podcast using the same devices you already carry into court, client meetings, and your home office. Whether you want to build a niche show for veterans’ benefits, family law, or small‑business compliance—or simply become a more confident guest on other podcasts—this session is designed to be accessible, concrete, and repeatable. 🎙️

What Makes Macstock Different (and Why Lawyers Should Care)

Macstock isn’t a generic tech expo with a handful of Apple sessions bolted on; it’s an independent, Apple‑focused conference built for people who actually use Apple gear every day. The attendees range from first‑time Mac users to seasoned creators, but everyone shares a common goal: get more from Apple hardware and software without drowning in jargon.

For attorneys, that matters. You’re not trying to become an IT professional. You want to:

  • Capture and organize evidence more efficiently on your iPhone. 📱

  • Draft, annotate, and sign documents on your iPad when you’re away from the office.

  • Automate repetitive tasks on your Mac so you can spend more time on advocacy and less on admin.

learn how to use your mac to podcast!

Macstock’s sessions, hallway conversations, and Creator Camp tracks are all geared toward real‑world workflows—exactly the kinds of workflows I talk about on The Tech-Savvy Lawyer podcast and blog, including episodes like “Ethical AI, Paperless Practice, and Smart Hardware Choices with ABA LTRC Chair Alan Klevan ⚖️🤖” and similar deep‑dives into ethical tech use.

A Time-Sensitive Deal: Save $50 and Support The Tech-Savvy Lawyer

Let’s talk about timing and value. You can use my code TECHSAVVYLAWYER at checkout to save $50 on your Macstock Weekend Pass or Creator Camp Bundle. If you’ve been thinking, “I should go to Macstock one of these years,” this is that year.

For every person who uses the code, Macstock provides me a $25 referral fee. That means:

  • You pay $50 less for a weekend of Apple‑centric, workflow‑rich content.

  • You directly support The Tech-Savvy Lawyer blog and podcast, including future episodes and tutorials.

The code TECHSAVVYLAWYER is not case‑sensitive and is valid through July 8, 2026.

How Macstock Helps You Meet Your Ethical Tech Duties

your The Tech-Savvy Lawyer.Page Blogger and podcaster will be presenting at Macstock x!

Macstock is not marketed as a legal tech conference, but it naturally supports your professional obligations under the ABA Model Rules.

  • Competence — Model Rule 1.1 (Comment 8): You have a duty to keep abreast of the benefits and risks associated with relevant technology. Learning how to securely use Apple devices for uses like document management, client communication, and evidence handling goes directly to your duty of technological competence.

  • Confidentiality — Model Rule 1.6: Many sessions at Macstock touch on system settings, backups, and secure workflows. Understanding how to configure your Apple devices to minimize unauthorized access, especially when using cloud sync and third‑party apps, strengthens your compliance with confidentiality obligations.

  • Communication — Model Rule 1.4: Clear, timely communication often depends on your ability to reach clients where they are—email, secure messaging, or even video updates. The more confidently you use your Apple tools, the more reliably you can keep clients informed.

If there is not a session directly addressing your questions, there are many enthusiastic, friendly attendees and speakers happy to try to help you and your Apple computer needs! 🤗

On The Tech-Savvy Lawyer blog and podcast, we frequently link these ethics points to real tools and scenarios—just as we did in episodes exploring AI, deepfakes, and metadata in digital evidence—and Macstock is a natural extension of that mindset.

Why Lawyers Should Care About Podcasting with Apple

Podcasting can be more than a marketing buzzword. Done right, it can be:

  • A client education channel that answers common questions before they become billable emergencies.

  • A way to build authority in a niche practice area—veterans’ benefits, immigration, special education, you name it.

  • A platform to interview judges, experts, and colleagues in a way that strengthens professional relationships.

My Macstock session, “Podcasting with Apple: From Idea to Launch Using the Gear You Already Own,” is focused on practical, lawyer‑friendly steps. We’ll talk about using your iPhone as a primary microphone, recording with your Mac, organizing episodes in iCloud, and editing in approachable tools—no audio engineering degree required. If you enjoy my conversations with guests on The Tech-Savvy Lawyer podcast, this session will show you what it takes to stand behind the mic yourself.

Community, Not Just Content

One of the things I appreciate most about Macstock is the community. People go back year after year not only because the sessions are strong, but because the hallway track, shared meals, and evening conversations provide real, candid problem‑solving time.

For lawyers—especially solos and small‑firm practitioners—this kind of peer‑to‑peer exchange is invaluable. You’ll find people who:

  • Have already solved a workflow you’re struggling with.

  • Are willing to share templates, shortcuts, and practical advice.

  • Understand the pressure of balancing client work, marketing, and a life outside the office.

If you’ve listened to episodes like my MacVoices “Road to Macstock” appearance in 2024, you’ve heard how much I value that human side of legal tech and Apple tech events.

Ready to Join Me at Macstock?

If you’re serious about making your existing Apple gear work harder for your practice—without overwhelming your staff or your budget—Macstock 2026 is worth the trip. You’ll return with actionable workflows, renewed confidence, and a clearer sense of how to align your technology use with your ethical obligations.

Just don’t wait:

  • Sign up at https://macstockconferenceandexpo.com/register/

  • Use code TECHSAVVYLAWYER (not case‑sensitive) for $50 off your Macstock Weekend Pass or Creator Camp Bundle.

  • For every use of the code, I receive a $25 referral fee that helps sustain The Tech-Savvy Lawyer content you rely on.

I look forward to seeing you at Macstock X— and hopefully hearing your voice in the podcasting space soon. 🎧⚖️

When Your AI Thinks It’s 1930: How Lawyers Must Manage “Frozen” Data Sets Versus the Live Internet 🧠⚖️

/ The Tech-Savvy Lawyer.Page/

AI Legal Research Demands Current Data and Human Judgment

A recent Malwarebytes article profiled “Talkie,” a 13‑billion‑parameter chatbot trained only on English‑language texts published before 1931. This model has no knowledge of anything after the Great Depression—no email, no smartphones, no cybercrime, and certainly no modern e‑discovery. 

For lawyers, Talkie is more than a curiosity. It is a vivid illustration of what happens when an AI’s world stops at an arbitrary date, and why we must understand the difference between isolated data sets and models that continuously ingest the modern internet. That distinction goes straight to your duties of competence, confidentiality, supervision, and candor under the ABA Model Rules. 

On The Tech‑Savvy Lawyer podcast, it is often discussed that “AI is the junior associate you don’t have to hire—but still have to supervise.” Talkie shows us what happens when that junior associate’s legal education ends in 1930. The lesson for your practice is simple: you cannot outsource judgment to any tool, especially one whose view of the world is frozen in time.

What “Vintage AI” Teaches Modern Lawyers 🕰️

Talkie was trained entirely on digitized books, newspapers, legal texts, and other publications in the public domain as of 1930, both to avoid modern copyright headaches and to explore how AI reasons without the internet. In other words, it is a deliberately isolated system: no post‑1930 statutes, no contemporary case law, no modern regulations. 

That design makes Talkie an excellent analogy for every “walled garden” AI lawyers are now being sold—closed research tools, local models trained only on internal firm documents, or court‑approved systems limited to a curated corpus. These tools can be invaluable, but only if you understand three things:

  • What is in the data set.

  • What is deliberately excluded.

  • How often the corpus is refreshed—or if it ever is.

Model Rule 1.1’s duty of technological competence now explicitly includes understanding the “benefits and risks” of relevant technology, which in 2026 squarely includes AI trained on defined corpora. If you do not know what your AI has seen, you cannot competently rely on what it says.

Isolated Data Sets: The Upside for Lawyers ✅

Many solos and small firms are understandably drawn to “closed” or time‑boxed AI systems because they feel safer and more controllable. 😊 Properly designed, those systems can offer real advantages:

  • Predictable scope of authority
    An AI trained only on a vetted body of primary law and secondary sources may be easier to supervise, because you know its universe of materials. You can design workflows where AI research is always checked against the underlying authorities that you recognize and trust. 

  • Reduced confidentiality and IP risk
    Talkie avoids modern copyright disputes by staying within the public domain. Similarly, a local or on‑premises model that does not send data back to a vendor can help you satisfy Model Rule 1.6’s confidentiality obligations—assuming you confirm that the tool does not re‑use your client data to train others’ models. 

  • Consistent, auditable outputs
    With an isolated corpus, it is often easier to log queries, outputs, and the underlying sources, which supports your obligations under Rules 5.1 and 5.3 to supervise both lawyers and non‑lawyer assistants, including AI tools. 

For certain use cases—drafting from your own templates, summarizing client files, or querying only your firm’s knowledge base—a “frozen” or walled‑off model can be exactly the right approach. 

The Hidden Risks of “Frozen” Knowledge 🚨

Lawyers Must Verify AI Case Summaries Before Court

The malware researchers emphasize that Talkie has “no concept” of anything after 1930. That is charming when it tries to explain a “smartphone” using the vocabulary of the telegraph age; it is malpractice waiting to happen if your research tool does the equivalent in a modern brief. 

For lawyers, isolated or out‑of‑date data sets create at least four serious risks:

  • Outdated or incomplete law
    A time‑boxed research tool can miss controlling authority, recent statutory amendments, or new regulations. Under Model Rules 1.1 and 3.3, you cannot rely on a system that stops short of the current law and then present its output as if it were complete.[5][10][3]

  • Distorted factual context
    An AI that has never “seen” modern technology, social conditions, or scientific developments will reason with blind spots that can undermine your factual investigations under Rules 1.1 and 1.3. Think about relying on a pre‑1931 lens for today’s cybersecurity, social media defamation, or veterans’ disability claims involving modern diagnostics. 

  • Invisible bias baked into old texts
    Pre‑1931 materials, like any historical corpus, embed the social, racial, and gender biases of their era. A “vintage” model may reproduce those biases in ways that conflict with your obligations around fairness and anti‑discrimination, and could taint your client‑intake, hiring, or case‑evaluation workflows. 

  • False sense of safety
    Because these systems are “limited,” lawyers may assume they are automatically compliant or “approved.” 😬 But ABA Formal Opinion 512 is clear: the existing rules—competence, confidentiality, communication, candor, supervision, and reasonable fees—apply equally to AI tools, regardless of their training set. 

The message: isolation is not a substitute for judgment. It simply changes the error profile you must manage. 

Live Internet Models: Power With Extra Liability 🌐

At the other end of the spectrum are AI tools connected to the live internet—systems that can pull from statutes, cases, news, and commentary that changed yesterday or this morning. They offer speed and breadth that solos and small firms could only dream of a few years ago. 

But internet‑connected models also present their own set of concerns:

  • Hallucinations blended with real‑time data
    Even when a system claims to be “citing live sources,” you still must verify every authority under Rules 1.1, 3.3, and 5.3. Courts and bars have already disciplined lawyers for filing AI‑generated briefs with fabricated citations. 

  • Ongoing confidentiality exposure
    If the model sends prompts to remote servers, you must analyze data‑handling, retention, and training policies to comply with Rule 1.6. You may need to anonymize prompts, modify your engagement letters, or obtain informed consent for certain uses, as many bars and Formal Opinion 512 recommend. 

  • Dynamic but uncurated sources
    Unlike a curated pre‑1931 corpus, the open web mixes reliable law with marketing pages, blog posts of dubious quality, and outright misinformation. Under Model Rule 1.1, you must treat AI‑surfaced content like any other secondary source: helpful, but never authoritative without independent confirmation. 

The fact that a tool is “up to date” does not relieve you of your duty to be right. It just changes where the landmines are. 😄

Practical Guardrails for AI‑Curious Lawyers 🛠️

In a recent episode of The Tech‑Savvy Lawyer podcast with AI consultant Hamid Kohan, we discussed building an “AI‑ready” practice that treats these tools like supervised, specialized staff—not black boxes. Whether you use a Talkie‑style frozen model, a live internet assistant, or both, consider putting these guardrails in place: 

  1. Inventory your AI tools and their data sources
    For each tool, document what data set(s) it uses (public domain only, commercial databases, firm documents, open web), how often it updates, and how it handles your data. This goes directly to your competence and confidentiality duties under Rules 1.1 and 1.6. 

  2. Define “approved uses” in your firm policies
    Under Rules 5.1 and 5.3, establish written guidance for lawyers and staff: e.g., “Use Tool A only for drafting internal outlines,” or “Use Tool B for brainstorming arguments, but never for final citations.” Train your team accordingly and revisit those policies quarterly. 

  3. Mandate human verification of law and facts
    Require that all AI‑generated citations, quotations, and factual assertions be checked against primary sources and the actual record before leaving the firm. That is how you satisfy Rules 1.1, 3.3, and your supervisory obligations. 

  4. Be transparent with clients and courts
    ABA guidance encourages disclosure of AI use where it is material to the representation or required by court rule. Consider adding a brief, plain‑English AI disclosure to your engagement letters and being prepared to describe, if asked, how you supervise AI‑assisted work. 

  5. Avoid over‑reliance that dulls your own analysis
    California’s guidance warns against delegating your professional judgment to generative AI or letting it replace your own research and critical thinking. Use AI as a springboard, not a crutch—an approach we have explored on The Tech-Savvy Lawyer.Page blog and podcast.

These steps are manageable even for solo and small‑firm lawyers with modest tech skills, and they align neatly with existing ethics frameworks. 💡

Choosing Between “Frozen” and “Live” AI: A Simple Matrix 📊

Frozen AI Data Sets Challenge Modern Legal Research

When should you prefer an isolated corpus, and when do you need the modern web? For many practices—especially for example, disability, administrative, and appellate work—the answer is “both,” but for different tasks. 

  • Use isolated or internal models for:

    • Summarizing your client’s file or medical records.

    • Drafting from your own templates and prior briefs.

    • Issue‑spotting in areas where the governing law is baked into the tool and updated on a known schedule.

    • Use live internet‑connected models (with caution) for:

    • Brainstorming novel arguments and locating secondary sources.

    • Scanning for recent regulatory changes or commentary.

    • Getting “layperson‑level” explanations you then translate into lawyer‑grade analysis.

In every scenario, you remain the final filter. Under the Model Rules, AI can accelerate your work, but it cannot own your judgment. Talkie is a reminder that the scope of what your AI knows is now an ethics question, not just a technical detail. 

Final Thoughts: Don’t Let Your Practice Get Stuck in 1930 ✨

Talkie’s charm lies in its limitations—it is a window into a world before the internet, World War II, and modern computing. Your law practice does not have that luxury. Clients expect you to understand the present, anticipate the future, and choose tools that serve both. 

Whether your AI is frozen in 1930 or streaming 2026 in real time, the obligations are the same: know what it knows, know what it cannot know, and supervise it accordingly. If you do that, you can harness AI’s benefits without letting your ethical obligations slip into the past. 🚀 

🎙️ TSL Lab’s Deep Dive into Our May 18, 2027, editorial, “AI Won’t Replace Solo and Small Firm Lawyers. It Will Supercharge Them”!

/ The Tech-Savvy Lawyer.Page/

📌 Too Busy to Read Our May 18, 2026, Editorial?

Join us for an AI-powered deep dive into the ethical challenges facing legal professionals in the age of generative AI. 🤖 This week’s Tech-Savvy Lawyer Lab’s podcast unpacks my editorial, “AI Won’t Replace Solo and Small Firm Lawyers. It Will Supercharge Them,” and translates it into practical, ethics-aware guidance for solo and small firm professionals navigating AI in real time.

We explore why AI is unlikely to replace lawyers but highly likely to transform how legal work is unbundled, priced, and delivered. We walk through Jevons Paradox, ABA rules on competence, supervision, and confidentiality, and the very real risks of hallucinated filings and careless use of public AI tools. You will see how treating AI as a supervised junior associate can expand your capacity, open new micro‑niches, and make your practice more human-centered, not less. ⚖️

In our conversation, we cover the following:

  • 00:00:00 – Why “doom hype” around AI is targeting the legal profession and why the collapse-of-lawyers narrative falls apart in real life.

  • 00:01:00 – Introducing Michael D.J.’s editorial “AI Won’t Replace Solo and Small Firm Lawyers. It Will Supercharge Them.”

  • 00:02:00 – Setting ground rules: educational discussion only and why this episode is not legal advice.

  • 00:02:30 – Rethinking what a “job” really is and the idea that legal work is a bundle of tasks, not one monolithic activity.

  • 00:03:00 – Comparing big-firm specialization to the tightly packed bundle of tasks handled by solo and small-firm lawyers.

  • 00:03:30 – Why AI can pull on individual threads in that bundle, but cannot run the whole practice for you.

  • 00:04:00 – The solo master-chef metaphor: AI as the kitchen machine doing prep work while the human focuses on taste and judgment. 🍲🤖

  • 00:05:00 – How AI can draft preliminary summaries or case law lists while the lawyer still owns strategy and verification.

  • 00:05:30 – The “mental verification” problem: when typing and thinking used to be the same act for lawyers.

  • 00:06:00 – What changes when AI writes the first draft and why verification must become a separate, deliberate step.

  • 00:06:30 – The risk of hallucinated filings and viral stories of fake cases generated by AI. 😬

  • 00:07:00 – Data points showing the profession is adapting, not dying: more lawyers, more bar-required jobs, rising law school interest.

  • 00:07:30 – Revisiting the e‑discovery panic and predictions that predictive coding would wipe out junior associates.

  • 00:08:00 – How cheaper e‑discovery led to an explosion of data and actually increased demand for legal work.

  • 00:08:30 – Introducing Jevons Paradox and why greater efficiency can increase, not decrease, total demand.

  • 00:09:00 – The widened-highway analogy: more lanes, more traffic, and how that maps onto AI in law. 🛣️

  • 00:10:00 – How AI lets small firms tackle big, complex matters and offer more predictable flat-fee pricing.

  • 00:11:00 – Expanding access to legal services for the middle class and why cheaper legal work grows the market.

  • 00:11:30 – Turning to ethics: ABA Model Rule 1.1 on competence and the duty to understand relevant technology.

  • 00:12:00 – The solo’s burden: you are the IT department and the innovation committee, all at once. ☕💻

  • 00:12:30 – A practical definition of technological competence for solos and small firms.

  • 00:13:00 – Starting small with AI: summaries, first-draft emails, and extracting checklists from dense legislation.

  • 00:13:30 – AI as the “junior associate you don’t have to hire but must supervise” under Rules 5.1 and 5.3.

  • 00:14:00 – Why you remain responsible for AI’s output just as you would for a paralegal or junior lawyer.

  • 00:14:30 – The solo’s question: Does it really make sense to write a formal AI policy for just one person?

  • 00:15:00 – How a short written AI policy creates hard boundaries before you are stressed and rushed.

  • 00:15:30 – Defining approved uses, high‑review tasks, and absolute “no-go” zones for AI in your practice.

  • 00:16:00 – Model Rule 1.6 on confidentiality and the special risk solo and small firms face with cloud tools.

  • 00:16:30 – Why pasting sensitive client facts into a generic consumer chatbot is an ethical minefield.

  • 00:17:00 – How consumer AI tools tokenize your text and use it to train future models.

  • 00:17:30 – The “megaphone in a public square” analogy for pasting confidential data into public AI tools. 📣

  • 00:18:00 – Moving from megaphones to soundproof vaults: using enterprise modes or legal-specific platforms.

  • 00:18:30 – Why a single data breach can be existential for a solo firm and why clients should care about tool choices.

  • 00:19:00 – Legislative inflation: constant growth in complex rules, norms, and regulations across jurisdictions.

  • 00:19:30 – How AI helps solos track regulatory change, generate client alerts, and update templates in real time.

  • 00:20:00 – Carving out lucrative micro‑niches with AI, such as hyper‑specific regulatory domains.

  • 00:20:30 – Pairing niche expertise with SEO and content marketing so a solo can compete at scale.

  • 00:21:00 – The junior lawyer dilemma: what happens to entry-level training when AI eats the grunt work.

  • 00:21:30 – Why firms still need junior lawyers to build a future bench, not just to type memos.

  • 00:22:00 – What AI fundamentally cannot do: build trust in person, join community events, or create referral networks.

  • 00:22:30 – How automation pushes lawyers toward more human-centric, relationship-focused work. ❤️

  • 00:23:00 – The core conclusion: the real existential threat is the AI-literate competitor down the street, not the robot.

  • 00:23:30 – Treating AI as a supervised junior associate while protecting ethics, productivity, and client outcomes.

  • 00:24:00 – Final reflections: mapping your own “bundle of tasks” and deciding what to offload so you can supercharge yourself. ⚡

RESOURCES

Mentioned in the episode

👉 If this episode helps you think more clearly about AI, ethics, and your own “bundle of tasks,” share it with a colleague and subscribe so you never miss a future Tech-Savvy Lawyer deep dive. 🚀

Shout Out! A Thunderstorm, Three Books, and a Room Full of Lawyers: Shout Out from The Lawyer’s Guide to Podcasting Launch 🌩🎙

/ The Tech-Savvy Lawyer.Page/

Seth price 📒 Carolyn Elefant 📒 Mindy Eisenberg 📒 Michael D.J. Eisenberg 📒 Wendy meadows 📒 scott

On May 20 in Bethesda, we launched The Lawyer's Guide to Podcasting: Building Your Brand, Audience, Tech Stack, and Expertise! with exactly the kind of energy I hoped this book would inspire: lawyers and legal professionals showing up for each other even as a serious thunderstorm rolled through the DMV. 🌧️🔥

Whether you braved the weather to come out, this post is for you. If you could not make it, think of this as your inside look at how a group of solos, small-firm lawyers, and AI‑curious professionals came together to talk about using podcasting as a serious business tool—one that fits comfortably within the guardrails of our ethics obligations under ABA Model Rules 1.1 (Competence), 1.6 (Confidentiality), and 7.1–7.3 (Communications about legal services).

A launch party built for working lawyers!

We gathered at the home of Carolyn Elefant in Bethesda—yes, in person, with real conversations and real snacks. 🥂 The goal was simple: make podcasting feel less like a mysterious “tech project” and more like a practical, repeatable part of your practice development strategy.

At the event, I walked through three concrete takeaways that mirror the book:

can’t have a launch party without cake!

  • A simple, lawyer‑tested podcast setup that you can actually keep running on a busy docket. 🎧

  • A short checklist of ethical and confidentiality questions to ask before you hit publish.

  • A set of ready‑to‑use episode ideas tailored to your practice area, so you are never staring at a blank calendar.

If those themes sound familiar, it is because they build on what we have discussed in prior posts and podcasts on the The Tech-Savvy Lawyer.Page. Together, they form the groundwork that became The Lawyer’s Guide to Podcasting: Building Your Brand, Audience, Tech Stack, and Expertise! 🎉

Shout Outs to the people who made the night! ⛈️

seth price and Michael D.J. Eisenberg exchange copies of their current releases!

A launch is never a solo act, even for a solo practitioner. I want to extend a very public, very appreciative shout out to a few people who made the evening special. 🙌

Finally, a heartfelt thanks to my wife and to every colleague, client, and friend who rearranged schedules and drove through a thunderstorm to be there. That kind of support is not just personally meaningful—it is a reminder that legal tech is at its best when it is rooted in community, not gadgets. 💙

Thank you Carolyn for hosting the book launch!

Why a podcasting book for lawyers—and why now?

If you follow the blog or listened to my guest appearance on Ruby Power’s “Power Up Your Practice”, Ep. 104: Legal Podcasting: The New Networking Standard, you have heard me say that podcasting is no longer a fringe experiment for lawyers. For solos, small‑to‑medium firms, and AI‑curious attorneys, a well‑designed podcast is:

  • An ongoing, searchable FAQ for your ideal clients.

  • A trust‑building channel for referral partners.

  • A training and onboarding tool for your own team.

In The Lawyer’s Guide to Podcasting, I walk through the tech stack and workflows that keep this realistic for a law practice, from microphones and recording platforms to editing, show notes, and ethical review. The idea is not to turn you into an audio engineer. The idea is to give you enough structure and competence that you work the basics yourself and delegate confidently without abdicating responsibility—very much in line with the duty of technological competence that is increasingly recognized under ABA Model Rule 1.1 and its state‑level interpretations.

Ethics, AI, and your voice behind the mic!🎙️

Many lawyers have told me that their hesitation about podcasting is not the microphone; it is the ethics. That is a healthy instinct. 👍

  • Model Rule 1.6 (Confidentiality) means no client can recognize themselves in your war stories without informed consent. In the book, I provide red‑flag questions and anonymization strategies you can bake into your outline before you record.

  • Model Rules 7.1–7.3 (Communications and Advertising) remind us that your podcast is marketing, direct or indirect, even when it feels like pure education. We cover how to structure disclaimers, avoid misleading “results‑typical” language, and respect solicitation limits while still giving real‑world examples.

  • For AI‑curious lawyers using tools like transcription, editing assistants, or AI‑drafted show notes, we address how to keep third‑party tools inside a framework that respects confidentiality and your supervisory responsibilities under the Rules.

If this resonates, you might also enjoy revisiting “Shout Out: Carolyn Elefant’s Review of Casetext v. ChatGPT!”, where she looked at AI in legal research through a similar ethics‑first lens. The same mindset applies here: use the tech, but do not outsource your judgment. 🧠

Where we go from here

get your copy of The Lawyers tech guide: The lawyer’s guide to podcasting today on amazon!

The launch party was one evening; the conversation will continue in the weeks ahead on this blog and its podcast as we highlight chapters, interview fellow legal podcasters, and share templates you can adapt for your own show.

If you are a solo, a small‑firm partner, or an in‑house counsel looking for a practical roadmap, you can find The Lawyer's Guide to Podcasting: Building Your Brand, Audience, Tech Stack, and Expertise! on Amazon. My hope is simple: the next time a thunderstorm rolls through the DMV—or your own calendar—you will have a system that keeps your podcast, and your practice development, moving forward. 🌩🎙

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

/ The Tech-Savvy Lawyer.Page/

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

Software & cloud services mentioned

🎙️ Ep. #134 — AI-Powered Legal Writing: How BriefCatch Helps Lawyers Write Smarter, Not Harder with Ross Guberman.

/ The Tech-Savvy Lawyer.Page/

My next guest is Ross Guberman — founder of BriefCatch, nationally recognized legal writing trainer, and author of several acclaimed books on persuasive legal writing. Ross has trained thousands of lawyers and judges across the country. After years of teaching the craft of legal writing, he channeled that expertise into building BriefCatch — a purpose-built AI writing tool that lives right inside Microsoft Word and Outlook, scanning your legal documents using roughly 17,000 rules to help you write cleaner, sharper, and more persuasive work product. Whether you're a solo practitioner or part of a large firm, Ross brings insights that are immediately practical — no matter your tech comfort level. 🚀

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

  1. 🏆 From your vantage point — having trained thousands of lawyers and judges and now running BriefCatch — what are the top three ways lawyers can leverage AI-driven writing tools like BriefCatch inside Word and Outlook to measurably improve the quality and persuasiveness of their briefs without sacrificing their own voice or judgment?

  2. ⚖️ For a tech-curious but time-strapped practitioner, what are the top three everyday workflows beyond traditional brief writing where lawyers are leaving the most value on the table by not using tools like BriefCatch and other legal tech?

  3. 🔮 Looking ahead five years, what are the top three technology competencies every lawyer must develop — not just "nice to have" skills — to collaborate effectively with AI, stay ethically compliant, and turn technology into a genuine competitive advantage rather than a source of risk?

In our conversation, we cover the following:

  • [00:30] 💻 Ross's current tech setup — MacBook Pro M4 Max, macOS, and iPhone 16

  • [01:30] 🔄 Why keeping your OS updated matters — security and performance

  • [03:00] 🖥️ External monitors, portable screens, and traveling with tech

  • [07:00] 📱 Using your iPad as an external monitor via Apple Sidecar

  • [08:30] 🎪 Bonus Question #1 - Ross’s experience in the ABA TECHSHOW Startup Alley

  • [11:00] ✍️ Question #1 — Top 3 ways to use AI writing tools to improve briefs without losing your voice

  • [12:00] 🧑‍⚖️ Using AI to role-play as a skeptical judge or opposing counsel to pressure-test your brief

  • [13:00] 📊 Transforming fact sections into timelines and case law into comparison charts

  • [14:00] 📝 Using AI as a self-check for hyperbole, redundancy, and tone

  • [15:30] 📲 How judges now read briefs on iPads — and what that means for your writing style

  • [17:00] 📂 Using Text Expander to store and deploy your best prompts

  • [18:30] 🎙️ Google Notebook LLM as a learning and podcast creation tool

  • [20:00] 🧩 Bonus Question #2 — What is BriefCatch and why use purpose-built legal AI over general tools?

  • [21:00] 🚀 The origin story of BriefCatch — from side hustle in 2018 to funded legal tech startup

  • [22:30] ⚙️ Workflow, ethics rules, and attorney-specific conventions — why legal-specific AI wins

  • [24:30] 📋 Question #2 — Top 3 underused everyday workflows for lawyers using AI

  • [25:00] 📧 Using AI with your email to surface unanswered messages and unresolved threads

  • [25:45] 📁 Mining your past work product for patterns, style, and reusable language

  • [26:30] 📅 Having AI review your calendar and correspondence for efficiency insights

  • [27:00] 🔒 Data privacy, security settings, and the risks of default AI configurations

  • [28:30] 🏛️ New York State's data protection approach and what more states should do

  • [29:30] 🤖 Question #3 — Top 3 technology competencies every lawyer must master in the next five years

  • [30:00] 🧠 Understanding how LLMs actually "think" — reading the AI's reasoning chain

  • [30:45] 🖊️ Making AI output sound like you — the human voice in an AI-generated world

  • [31:30] 🔧 Integrating AI into your daily workflow while preserving human judgment

  • [32:00] 👏 Closing thoughts and where to find Ross and BriefCatch

RESOURCES

🔗 Connect with Ross Guberman

  • 📧 Email: ross@briefcatch.com

  • 🌐 Website: https://www.briefcatch.com

  • 💼 LinkedIn: Search "Ross Guberman" on LinkedIn at https://www.linkedin.com

📌 Mentioned in the Episode

🖥️ Hardware Mentioned in the Conversation

☁️ Software & Cloud Services Mentioned in the Conversation

MTC: AI may not be your co‑counsel—and a recent SDNY decision just made that painfully clear. ⚖️🤖

/ The Tech-Savvy Lawyer.Page/

SDNY Heppner Ruling: Public AI Use Breaks Attorney-Client PrivilegE!

In United States v. Heppner, Judge Jed Rakoff of the Southern District of New York ruled that documents a criminal defendant generated with a publicly accessible AI tool and later sent to his lawyers were not protected by either attorney‑client privilege or the work‑product doctrine. That decision should be a wake‑up call for every lawyer who has ever dropped client facts into a public chatbot.

The court’s analysis followed traditional privilege principles rather than futuristic AI theory. Privilege requires confidential communication between a client and a lawyer made for the purpose of obtaining legal advice. In Heppner, the AI tool was “obviously not an attorney,” and there was no “trusting human relationship” with a licensed professional who owed duties of loyalty and confidentiality. Moreover, the platform’s privacy policy disclosed that user inputs and outputs could be collected and shared with third parties, undermining any reasonable expectation of confidentiality. In short, the defendant’s AI‑generated drafts looked less like protected client notes and more like research entrusted to a third‑party service.

For sometime now, I’ve warned on The Tech‑Savvy Lawyer.Page has warned practitioners not to paste client PII or case‑specific facts into generative AI tools, particularly public models whose terms of use and training practices erode confidentiality. We have consistently framed AI as an extension of a lawyer’s existing ethical duties, not a shortcut around them. I have encouraged readers to treat these systems like any other non‑lawyer vendor that must be vetted, contractually constrained, and configured before use. That perspective aligns squarely with Heppner’s outcome: once you treat a public AI as a casual brainstorming partner, you risk treating your client’s confidences as discoverable data.

A Tech-Savvy Lawyer Avoids AI Privilege Waiver With Confidentiality Safeguards!

For lawyers, this has immediate implications under the ABA Model Rules. Model Rule 1.1 on competence now explicitly includes understanding the “benefits and risks associated” with relevant technology, and recent ABA guidance on generative AI emphasizes that uncritical reliance on these tools can breach the duty of competence. A lawyer who casually uses public AI tools with client facts—without reading the terms of use, configuring privacy, or warning the client—may fail the competence test in both technology and privilege preservation. The Tech‑Savvy Lawyer.Page repeatedly underscores this point, translating dense ethics opinions into practical checklists and workflows so that even lawyers with only moderate tech literacy can implement safer practices.

Model Rule 1.6 on confidentiality is equally implicated. If a lawyer discloses client confidential information to a public AI platform that uses data for training or reserves broad rights to disclose to third parties, that disclosure can be treated like sharing with any non‑necessary third party, risking waiver of privilege. Ethical guidance stresses that lawyers must understand whether an AI provider logs, trains on, or shares client data and must adopt reasonable safeguards before using such tools. That means reading privacy policies, toggling enterprise settings, and, in many cases, avoiding consumer tools altogether for client‑specific prompts.

Does a private, paid AI make a difference? Possibly, but only if it is structured like other trusted legal technology. Enterprise or legal‑industry tools that contractually commit not to train on user data and to maintain strict confidentiality can better support privilege claims, because confidentiality and reasonable expectations are preserved. Tools like Lexis‑style or Westlaw‑style AI offerings, deployed under robust business associate and security agreements, look more like traditional research platforms or litigation support vendors within Model Rules 5.1 and 5.3, which govern supervisory duties over non‑lawyer assistants. The Tech‑Savvy Lawyer.Page has emphasized this distinction, encouraging lawyers to favor vetted, enterprise‑grade solutions over consumer chatbots when client information is involved.

Enterprise AI Vetting Checklist for Lawyers: Contracts, NDA, No Training

The tech‑savvy lawyer in 2026 is not the one who uses the most AI; it is the one who knows when not to use it. Before entering client facts into any generative AI, lawyers should ask: Is this tool configured to protect client confidentiality? Have I satisfied my duties of competence and communication by explaining the risks to my client (Model Rules 1.1 and 1.4)? And if a court reads this platform’s privacy policy the way Judge Rakoff did, will I be able to defend my privilege claims with a straight face to a court or to a disciplinary bar?

AI may be a powerful drafting partner, but it is not your co‑counsel and not your client’s confidant. The tech‑savvy lawyer—of the sort championed by The Tech‑Savvy Lawyer.Page—treats it as a tool: carefully vetted, contractually constrained, and ethically supervised, or not used at all. 🔒🤖