If a new legal hire showed up tomorrow and asked what to take off your plate, what would you hand over?

AI 302 is about building the colleague who can take it on: one agent, taught a few skills and given a place to remember.

The first 301 bootcamp was your AI agent’s orientation. In 302 it reports for its first day of work, and a first day means real assignments.

I recorded the whole session as a free video for subscribers. It is hands-on, not a lecture. You will learn to build working skills and a hardened memory system of your own.

You also get to make AI your chief of staff to manage a side project, curate a learning plan, even advise on your career strategy.

I finally preview how to work across different labs’ AI models, cutting AI vendor lock-in while getting more points of view. Two associates reading a brief is better than one.

The link to the full bootcamp is available for subscribers in the Substack email of this post or my welcome letter.

If this is useful, pass it to a lawyer who is curious about AI!

The 302 syllabus

1. Your AI Agent’s First Day

301 was setting up your agent. 302 is the first real day on the job. Today we’ll build skills for repeatable tasks and give the agent a durable memory system that scales.

2. AI Is a People Skill

If you have good people skills, you will do well with AI. Think about a good leader: you plan ahead, make clear asks, give feedback and train up your team. Use the same mental model as you build your AI skills.

3. Why the Terminal Builds AI Intuition

The terminal shows everything: you watch the agent read files, write them and run commands in the open. The AI tools will eventually change. The AI intuition you build here carries over to every AI product you touch later.

4. The Four Skill Types AI Does Best

AI skills tend to fall into four buckets: drafting, review, translation and extraction. Look at the work you do every week and find which bucket it lands in. That’s where a skill is worth building.

5. Build a Skill, End to End

Our first task: turn a long, messy legal memo into a tight summary a busy VP would actually read. We open plan mode (shift + tab) to think it through first, then let the agent write the skill and run /summarize.

6. Seeing the Skill’s Primary Source

A skill isn’t hidden machinery. We open the skill.md file the agent just wrote, and it’s plain English you can read and change yourself. It’s the same kind of file as the CLAUDE.md from 301.

7. Debug and Revise the Skill

We imagine the VP wanted more detail, so we revise the skill to fit the audience better. Same loop you’d run with a new associate: tell it what to change, and it adjusts.

8. A Second AI Model, A Second Opinion

We bring in a second model, OpenAI’s Codex, to review Claude’s work. One AI drafts, the other critiques. They’re trained differently, so Codex catches what Claude missed. Feed Codex’s critique back into Claude and run it again.

9. Skill Two: Automate Slide Decks

Now the second skill: turn that same memo into a slide deck: /presentation. Build it once, and turning any memo into a deck becomes a single command.

10. Deep Sea Detour (Experiment Gone Awry?)

We run the new skill and out comes a deck about the deep sea, nothing to do with our memo. A fun experiment, and I left it in to see where AI works and sometimes strays.

11. Inside the Slide-Deck Skill

We open the presentation skill’s files to see how it works. Same lesson as the first skill: it’s readable instructions, no black box. Once you can see it, you can change it.

12. Fixed: Build the Real Deck

Now we point the skill at our memo, and it builds a polished, presentation-ready deck. From a wall of legal text to slides you could actually put in front of a room, in one pass.

13. Forgot the Skill Names? Just Ask

You won’t remember all your skill names, and you don’t need to. Just describe what you want in plain words, and the agent picks the right skill and runs it.

14. Why AI’s Memory Is a Mess

An agent keeps its memory in a loose running log, if it keeps one at all. It’s unorganized, and you can’t really count on it. The key to real agentic memory is simple: write things down to files and stay organized.

15. Build Your Own Memory System

We build the memory system in a single prompt. We borrow PARA: four folders, Projects, Areas, Resources and Archives, that keep your files in a shape the agent can read every session. People call this a second brain (very fancy branding for keeping organized files and folders).

16. Inside Your Memory System

Memory is just layered CLAUDE.md files, from the root down to a single matter. The agent loads only the layer it needs, which preserves its context for the task in front of it. The same shape works whether you’re tracking one career plan or a whole practice.

17. Recap and Homework

A quick recap of what we built: two skills and a memory system. Then it’s over to you, with homework to put it into practice.

Let me know how it goes and what you build!

“You’re a great lawyer. How will you use AI to drive even more impact here?”

Imagine a job candidate today who can’t format in MS Word or manage Chrome tabs. The hiring manager wouldn’t even reach the next question. She’d be wondering, quietly, whether the candidate could even read.

The AI question is coming for legal job interviews. I can’t get this Jensen Huang quote out of my head:

“If that $500,000 engineer did not consume at least $250,000 worth of tokens, I’m going to be deeply alarmed. And this is no different than one of our chip designers who says, ‘Guess what? I’m just going to use paper and pencil. I don’t think I’m going to need any CAD tools.’” (my emphasis)

Engineers are already there. Lawyers are next.

AI won’t make a mediocre lawyer great. But AI intuition may separate the great lawyers from the one who gets the offer.

How to approach this question

Anyone can ask ChatGPT to draft an email. But this interview question isn’t asking whether you can run a tool.

It’s asking whether you can do legal work at scale and direct agents to extend your judgment.

The catch is that your AI workflow (from your last job, law school or a side project) will not transfer cleanly to the new job. No enterprise rewrites its stack for one new hire.

What’s portable is the mindset, what I call AI intuition. Employers will want candidates who can speak to the AI setup they’ve built themselves, then walk into a new role and scale using the employer’s AI stack.

Both halves matter. The lawyer with a brilliant home setup who can’t adapt an enterprise stack is no use. The lawyer who knows AI theory but has no actual workflow of her own will have no story to tell.

My mental model of the good answer

How useful an AI is depends less on the cleverness of its model and more on how deeply it sits in the work. A brilliant model can’t help you if it can’t see the documents, the messages, the prior decisions.

I would frame my answer by mapping out the employer’s AI stack:

1. Does your AI live where the lawyers actually work?

AI needs to sit inside the tools the team uses every day, not as a separate chatbot that requires copy-pasting back and forth. This means places like your chat platform, document editor, contract system, email, browser — where lawyers actually work.

If AI is a tab people forget to open, it doesn’t matter how good the model is. And AI must connect plumbing between those tools as well; otherwise it’s just fragmented software.

In product terms, this is low latency and fast “time to fun”: how quickly can a user start using the product. Ideally, AI is always just a tap or click away (e.g. I keep ChatGPT voice mode on my iPhone lockscreen).

2. Does your AI see the company’s actual knowledge?

Just like your junior associate, your AI needs context to be helpful. AI has to plug into company knowledge that lives in docs, Slack threads, email, redlines and intranet FAQs. Without that context, AI only gives generic answers.

Then there’s the question of retrieval. A useful AI has to find the right source, distinguish current from stale and avoid confidently synthesizing from the wrong material. And it has to do it fast and accurately. That’s a difficult engineering exercise for AI with limited context windows and token budgets.

3. Is the AI just a chatbot or does it take agentic action?

The strongest systems don’t just answer. They track decisions, flag open issues, summarize meeting notes and push drafts back into the workflow.

A junior associate who hands you a memo is useful. A junior associate who hands you a memo, runs a redline, sends the follow-up chat and updates the closing checklist is on another level.

In May 2026, we’re still in the early innings of AI’s agentic era.

4. Does the lawyer actually come out ahead?

The test isn’t whether the AI sounds smart. It’s whether the work product is usable: a draft, a redline, an issue list, a risk assessment, a negotiation position. And whether the time it saves outweighs the time spent prompting it, checking it and cleaning up after it.

A surprising number of AI rollouts fail this test. They look impressive in a demo but don’t create real work in production.

Learn by doing

The strong answer isn’t a single line. It’s a mindset based on AI intuition, and the only way to build that intuition is by experimenting with AI today. If you’ve made an AI workflow and use it day-to-day, you’ll have the war stories that make these questions real.

That’s what will separate the great lawyers from the one who gets the offer.

In a few years this question will sound as dated as asking whether you can use a computer. Until then, start building AI intuition.

Engineers and lawyers do similar work. Lawyers just write in a more complex code.

We call it English.

I teach lawyers about AI using the same agentic coding tools that are revolutionizing software engineering. This started two months ago when I began hosting get-togethers of lawyers curious about AI.

After 20 live sessions with over 150 attendees, I’ve turned that 50-minute bootcamp into a video course. It’s free, and I charge nothing.

I call it AI 301: Frontier AI for Lawyers.

In 50 minutes you’ll set up your AI agent, personalize it, manage its memory and code your first small project.

Watching engineers shift from typing code to directing agents gives us a glimpse of where lawyers go next.

I make my bootcamps free because lawyers should see this technology firsthand, with both its strengths and weaknesses. Let’s get past the hype in AI and see for ourselves.

Through these bootcamps I’ve met many curious people who are excited to experiment and learn. This has been the most fun I’ve ever had as a lawyer.

Check out the AI 301 bootcamp and let me know your thoughts, especially what you hope to learn more about in law and AI. The bootcamp comes with a full setup guide, FAQs and even homework to practice with.

The bootcamp syllabus

1. Build AI intuition

AI 101 was chats. 201 was prompts. 301 is agents and the foundations of coding. Learn to direct thinking machines even as the software tooling changes.

2. Why learn AI in the terminal

Lawyers care about precision and craft. The terminal exposes every AI feature and every dial. It’s slower to learn but gives you maximum control.

3. Multi-agent workflow demo

See three agents run side by side in a split terminal, a preview of how agentic workflows can feel.

4. Setting up your agent

Learn to start setting up an agent with basic introductory commands. /model picks the brain. /effort dials thinking up or down. /config controls settings. /statusline builds your dashboard: working folder, dollar cost, model, context window.

5. Permissions

Read every permission prompt. When the agent asks to run something cryptic, paste it back and ask, “explain this in plain English.” The self-healing feedback loop: use AI to learn about AI.

6. The CLAUDE.md

Every agent has a system prompt. These are the standing instructions it reads every session. In Claude Code, that file is a plain text file in the markdown format called CLAUDE.md. Run /init to build one, much like an onboarding memo for a junior associate.

7. Personalizing CLAUDE.md

Make the agent yours. Start with your writing style, then layer on whatever you find yourself repeating in your daily work.

8. The context window

The context window is the most important number in AI. It’s the agent’s working memory, shown as a percentage in the status line.

Be mindful of subtle, but increasing, output drift by your AI once you use 30% of your context window. It’s like answering a complex question at the end of a long day. /context shows where you are. /clear wipes the slate of your chat history before the next task.

9. Plan → build

Press shift + tab for plan mode. The agent brainstorms but takes no action until you greenlight it.

Engineers now spend most of their time here in planning. The actual code is what AI made cheap; the leverage is in directing the architecture, not typing the syntax. We use plan mode to code a legal Latin flashcard game.

10. Revising the build

First drafts are never final. Re-enter plan mode and ask the agent how to improve aesthetic, depth and mechanics. Brainstorm with AI as your thought partner.

11. Recap + AI 302: Agentic Workflows

In 301, you onboarded the associate. AI 302 on Agentic Workflows is where you train the associate in your style: custom commands for repeatable tasks, memory so the agent remembers your matters across sessions, project-specific briefs layered on top of the global ones.

“We expect you to be perfect.”

It was first-year orientation in New York. I was 27, just arrived from Berkeley, sitting alongside eager new associates in a conference room lined with orchids and paneled in blonde wood. The partner speaking was a luminary of the field.

I still remember the feeling of hearing that message. Every mistake was a mark against me, I thought.

The firm was successful because it did whatever it took for its clients. And doing whatever it took started with being perfect.

We had self-selected into this. Law school and firms didn’t plant the perfectionism; they drew it out of us. Cold calls broke apart our reasoning so you’d better know your stuff. Final exams rewarded the scorched earth of spotting every issue, every branch of the analysis.

The profession recruits people who already treat errors as personal failings, then calls the instinct professionalism.

For years, that training served us well. The emotional toll aside, it delivered results.

It is also poison for learning AI.

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Eat your own dog food

In tech, the term dogfooding means using your own product day-to-day before anyone else does. This is not just one-off testing in some clinical setting. It’s using the products as your customers would in their everyday lives.

So you install the apps, carry the devices, use the services.

The name is based on an old industry saying, popularized in tech by Microsoft employees in the 1980s: eat your own dog food.

The point is to break things. Push the tool to its edges, feed it bad inputs, find the cracks before users do. In tech, the cadence is to prototype, ship, iterate. You build a rough version, put it out, watch it break, fix.

Learning AI is the same. Dogfooders learn AI better than perfectionists.

Philosophically, this can turn lawyers off. But expecting perfection at this stage means never even starting.

Let’s be honest. AI breaks a lot. It forgets my context. It misses a skill command I built in the same session. It sometimes fails to work out that today, April 23, is actually a Thursday.

But here’s the wrong mindset: run AI, get a bad output and decide that it’s not for you.

Nobody picks up a guitar, plays one bad chord and throws it away. Nobody loads an agent, gets an irrelevant response and quits.

This is why I don’t see AI as just software to learn. Instead, I encourage lawyers to build AI intuition: the skill to direct AI agents the way you would manage a team of associates.

And just like how people aren’t perfect, neither is AI.

Building AI intuition

One way to build intuition: give feedback to AI the way you would to an associate.

Early on, I asked ChatGPT to come up with new recipes mixing different cuisines I loved. It suggested dumplings with hummus.

Eh, not to my taste. The lawyer’s reflex is to scoff. The builder’s reflex is to respond: “That was bad. What led you there, and how can we tune for more grounded answers?”

You wouldn’t take mediocre work from an associate, grumble and ignore them. You’d invest in the relationship if you wanted it to last.

Another way: build your own curated arsenal of AI tools, then ignore the rest. Find the tools that actually do real work for you, not what others hype.

If you could pick your own team of associates, you’d know each one’s strengths and weaknesses. Same with AI.

Claude’s web search misses what Gemini finds in seconds, so I avoid Claude for research. Gemini writes like a committee; I stopped handing it prose. ChatGPT is strong at voice and images.

At the frontier

AI noise floods the world. Every week brings new models or features. I follow along because I love this tech, but I don’t change my AI usage lightly. I keep a small collection of agents I trust: I ruthlessly test them, break them, then personalize them to my liking.

I wrote in my very first Substack that AI will break, and breaking is the point. I’ve watched it play out in every bootcamp I’ve run: the lawyers who learn AI fastest are those who are comfortable finding AI’s limits.

Every wall you hit is a map of where the frontier is right now.

In fact, if you find your AI performing poorly on a task: congratulations, you’re a lawyer now working at the frontier of this fast-moving, transformative technology.

Law firms with AI will stop training junior lawyers. That’s the mantra now.

AI does the grunt work cheaper and faster. Senior lawyers have no incentive to invest in new grads.

Assume they’re right. The next question is the interesting one: if firms won’t train you, who will?

Law schools are the obvious answer. Three years, $300,000 and a mandate to guide young people in a time of disruption.

It won’t happen.

Not because law schools aren’t valuable. I valued mine. But the incentives point the wrong way.

Law students must take matters into their own hands. Not to use AI as a crutch or to outsource their judgment to a machine, but to use AI to make themselves sharper, faster and even more marketable.

The law school business model

When Christopher Columbus Langdell reshaped legal education at Harvard in 1870, he made a deliberate choice: strip the practical training out.

America was industrializing. Railroads and factories made for complex commerce, which meant we also needed lawyers at industrial scale. This was the same pressure that later drove Paul Cravath to build the associate model at his law firm.

Langdell’s answer was the case method: one professor, 150 students, a lecture hall and a casebook the professor wrote. Academic, pure, walled off from the mess of practice.

This teaching style has merits. But it also happened to be a spectacularly efficient business.

No laboratories, no clinical equipment, no specialized facilities. Law schools produce fleets of young lawyers at scale, running on margins that subsidize entire universities.

Practical training demands the opposite. Clinics and workshops need small groups, more faculty. Professors need to give weekly feedback on assignments, not just a single final exam. Every hour spent grading is an hour not spent on publishing academic papers that earn tenure.

The ABA compounds the problem. It mandates a third year the profession openly admits adds little, piling on tuition, living expenses and foregone wages and career growth. It also screens out anyone who cannot afford three years away from paid work.

The institution that taught you to think like a lawyer wasn’t built to teach you to practice like one. Adding even management training and soft skills to the curriculum has proved hard enough. Adding AI, a genuinely new way of working, is a stretch I do not believe law schools can make.

Law firms used to pick up where school left off. That side of the bargain is fraying too.

Which is why the work falls on you.

Your studies, augmented by AI

Look at engineering to see how this could end up. Jensen Huang said he’d be deeply alarmed if a $500,000 Nvidia engineer does not spend at least $250,000 in AI tokens every year. He compares an engineer who passes up AI to a semiconductor chip designer who insists on paper and pencil over CAD tools.

The lesson for lawyers is not the dollar figure. It’s that engineers have reorganized knowledge work around these thinking machines — machines that generate not widgets but units of intelligence. Law is next in line. No one in your classroom was hired to prepare you for it.

Two questions every law student should sit with:

1. Workflow: for any task I take on, can I do it better or faster with AI?

2. Thinking: can AI make me a sharper thinker, not just a faster worker?

A few ways to train:

• Revise a contract by hand. Then hand the same assignment to AI, loaded with your context. Compare the two line by line. The gap between them is where the learning lives: what you missed, what AI missed, what you would do differently on a second pass.

• Run diligence on a 200-document data room the old way. Read every file, scan for red flags, log the issues, ladder up from ground level until the deal-breakers surface. Then point AI at the same room and see what else it catches. You will find patterns you missed (and patterns AI invented).

• Draft your strongest motion or brief. Then flip sides. Use AI as opposing counsel. Ask it to be brutally honest. Let it pressure-test your reasoning to make it even stronger.

Each of these exercises builds AI into the way you work and think, rather than grafting AI onto old habits. In this era, learning AI is not prep for legal practice. It is the practice.

One strong caveat. AI augments; it does not replace. A lawyer’s north star is not raw output, but judgment: spotting the issue, framing the argument, pressure-testing the reasoning. The lawyer who cannot do those has nothing worth augmenting.

No generation of lawyers before you has had tools like these. Your law school won’t teach you to wield them. Your firm may not either.

Take it into your own hands.

Training new lawyers is a drag.

You hire a new outside counsel or junior lawyer. You hand them years of examples. Precedents you like, formats you don’t. Then you meet with them to explain what “good” looks like.

And their work may still be off the mark.

That calibration lives in emails and tribal knowledge nobody can search. It takes months, even years, to nail down. Then they leave and you start over again.

What if it took thirty seconds?

Your legal judgment, in a text file

We carried business cards for a century because both sides gave and both sides gained: our identity, pocket-sized.

Here’s a radical thought experiment for the AI era. Let’s exchange a file that doesn’t tell counsel who we are, but how we think.

First, some AI context. When we load up an AI agent, it always first reads a plain-text instructions file called the system prompt. Think of it as standing orders that govern the AI’s priorities, reasoning style and defaults. OpenAI calls theirs agent.md, Anthropic uses claude.md, but the idea is the same across platforms.

What if we wrote a file — call it the mindset.md — so your new lawyer and their AI have a reference of legal judgment to benchmark their work against?

Imagine a junior lawyer drafting her first legal analysis. It’s academic, maybe too prescriptive. She has her AI run it against my mindset.md before she ships to me.

She’s pressure testing her draft against my mental model for how I think and decide. AI can even make it a training session by giving constructive feedback and explaining why.

“Your advice gave a single legal recommendation, but Eric prefers an options space of three alternatives, weighing the pros and cons of each. This way we can give product teams breathing room to make smart choices while balancing legal risk.”

Rethinking how we work

This sound out there. But we already do this every day; we may just not call it that.

Today, we tailor our message for the audience, based on what we think we know of them.

Today, we give counsel examples of advice we like or don’t like. We ask, but more often hint and imply: please follow this, don’t reinvent the wheel.

I know people who make AI “profiles” of specific executives and run even simple emails against that profile to make sure they land their points.

The mindset.md is just an extension of that, encoding my judgment so a machine can read it too. But as a professional practice.

Here’s an excerpt from mine:

Changing professional habits is hard. But it’s just one example of how lawyers can adapt to the new world of knowledge work.

Machine-readable judgment

Every time you switch counsel, you lose months of calibration. New firm, same onboarding grind, all those preferences you spent a year teaching the last team wiped clean.

A mindset.md doesn’t just speed up onboarding. It’s portable, moving with you across firms, across matters, across platforms, so the calibration follows you instead of dying with the relationship.

The commercial internet spent two decades blocking bots and is now rebuilding for AI agents. Legal work is heading for the same shift, and the lawyers who make their judgment machine-readable will have a head start.

I wrote about the plumbing problem, where AI nails the thinking but breaks on the connections between tools. A mindset.md is one small piece of plumbing that actually works, a machine-readable file that carries your professional judgment into every AI interaction your counsel runs.

The question is whether you give AI something to work with or let it guess.1 You don’t even need to write the file yourself.2

What would yours say?

Think about the last time a new counsel got you wrong. Not the law, but you: the framing, the risk call, the level of detail. If you could write it down in one page, what would it say?

The lawyers who’ve done that work will plug in and go. The rest will calibrate with memos and phone calls, one matter at a time.

Subscribers: bootcamp details are in your email header.

Dear Junior Associates:

When the telephone reached law offices, senior partners resisted. You couldn’t read a client’s face over the wire, they grumbled. Couldn’t build trust without being in the room. A real lawyer showed up in person.

When computers replaced the law library, partners worried associates wouldn’t learn to research. Flipping through case reporters built instinct, they instructed. A search engine built laziness.

When email replaced memos, someone, somewhere, mourned the death of careful composition.

I came up as a lawyer when mobile chat replaced email. I began to lawyer with my thumbs. Texting legal advice between meetings, on the train, from the couch. Sometimes I send gifs to clients.

Every generation of senior lawyers has looked at the next generation’s tools and seen the beginning of decline. Every time, the juniors turned out fine. Often better.

But you’d better step up.

One way to be a proactive junior lawyer

[Video: a fine-tuned AI system makes it effortless.]

Recently on a high-stakes deal, I asked you, a junior attorney, for a contract summary. You told me your workflow would get it to me “later.”

So I opened an AI tool and had it in four minutes. I sent it to you to triple-check. You flagged a few issues and revised.

What would an AI-native junior lawyer do?

A junior who’d built an AI workflow for contract summaries could have had it on my desk before I even asked. That junior would’ve fine-tuned it to the style and framing I like, based on our past work together.

It would’ve taken seconds to generate.

AI is not replacing legal judgment. AI is redefining what “proactive” means.

When I wrote about the AI skill stack, I used the metaphor that we direct AI the way we direct a junior associate. Here, the junior associates also need to raise their own skills.

A better future for lawyers

The worry I hear from other senior lawyers: if AI does the first draft, associates never learn to draft. The reps disappear. The training pipeline breaks.

I understand the concern. The traditional model was apprenticeship through suffering. Read every page, draft something bad, get redlined, study the redlines, repeat. The grunt work was the point.

Some of that genuinely taught things. Proofreading a 200-page credit agreement at 3 a.m. builds pain tolerance, attention to detail and a stubbornness about getting things right. Those matter.

But let’s be honest about the rest. The rest was formatting exhibit lists, manually cross-referencing defined terms and sitting in a conference room re-numbering sections (always at 3 a.m.) because a partner restructured the deal at the last minute.

That wasn’t training. That was a rite of passage dressed up as education.

The real question isn’t whether juniors lose the grunt work. It’s whether they get something better.

I think they do. The most patient tutor in history now lives on your laptop.

AI will explain a clause, walk through the doctrine, generate three drafts to critique, then argue the other side so it can pressure-test your position. It does this at 3 a.m. without complaint.

Yes, there are things AI can’t teach. It can’t show you what it feels like when opposing counsel goes quiet across the table. It won’t teach you when to call the reporter before the regulator because the narrative is the bigger fire. I built an AI demo around this idea.

Those instincts still come from reps with real people under real pressure.

AI means juniors can spend less time re-numbering exhibit lists and more time in the rooms where those instincts are built. More negotiations, more client calls, more time watching a partner read the room and learning how it’s done.

AI intuition is the instinct for directing thinking machines. Juniors will build it faster than any class of lawyers before them. Not because they’re smarter. Because their tools are.

What I expect

Every generation of senior lawyers feared the next would be lesser. Every generation was wrong.

You have better tools than any class of lawyers before you. The bar is higher because of it.

Subscribers: AI 302 bootcamp details are in your email header. Come learn agentic foundations together.

Last month I planned a three-hour legal hackathon while driving on my commute.

With a single tap, I was live in ChatGPT’s voice mode over CarPlay (or AirPods when on a walk). I had a thought partner, riding shotgun, to talk through ideas or help me learn.

For thirty minutes I chatted about the hackathon: how to structure the day for people with different backgrounds, which exercises would land, what to cut. I pressure-tested the agenda out loud and revised on the fly.

By the time I pulled into work, I had a real plan, hands-free, on a commute I used to think was dead time. (Be safe! Always pay attention while driving.)

Once at my desk, I took that full summary of everything we covered and pasted it into Claude Code to pick up the work and flesh out the full plan.

My commuter vehicle has become a mobile workstation.

One tap, no screen

The setup takes seconds. I added the ChatGPT voice widget to my iPhone lock screen. One tap and I’m live. No typing, no screen, just talking.

Gemini and others can do this too. I use ChatGPT the most because it has the most human-like inflections and tones. That matters more than you’d think. When the voice feels natural, I talk to it like a person, and the conversation gets better for it.

I’m less on guard and more open to thinking out loud. I get to workshop ideas, pressure test assumptions, talk through questions that might otherwise stay stuck in my head.

(I’ve even learned etiquette for interrupting if it goes off course: I just say “One second. Hold on.” It pauses right away.)

What I use it for

I’ve used voice mode to work through real decisions: what car or computer to buy, what architecture for a coding project, even questions about taxes or estate planning.

I plan a lot of my AI bootcamps this way, including their curriculum and logistics (Google Meet or Zoom?). I talk through the outlines of these Substack posts.

The everyday stuff stacks up too. I’ve parsed a confusingly worded insurance adjuster’s letter, researched puppy breeds for our kids and scouted family vacation destinations.

The lighter conversations surprise me just as much. My wife and I are both lawyers. One day on a drive we found ourselves talking through a legal question she had: what’s the difference between assignment, novation and delegation. I turned on ChatGPT voice and it walked us through each doctrine, with hypothetical examples of how a buyer and seller would see the issue differently.

It gave targeted examples and patiently explained different corner cases we asked about. It was more engaging than most CLEs, let alone 1L contracts.

Voice mode searches the internet when you need current information. That’s one way to keep up with the news. A trade policy story I heard about in passing turned into a ten-minute crash course on a region of the world I knew little about. ChatGPT searched for the latest details while I kept asking follow-ups until I understood the history and the mechanics, not just the headline.

Talk, capture, build

The conversations aren’t throwaway. This is the part that changes how you work.

When I arrive, I ask for a full summary of everything we covered. It comes back as a working plan. I sit down at my desk and paste it straight into whatever picks up the work: a document, a plan or another AI tool that builds from where the conversation left off.

I wrote about AI’s plumbing problem: the thinking works but the connections between tools break. Voice mode is one example where the plumbing actually holds. You think while you drive. The summary captures it, and the next tool picks up where you left off.

Once, I brainstormed an entire coding project on a drive. AI and I went back and forth, iterating on features, stress-testing scope, debating what to cut. I walked in, pasted the summary and had an AI building the implementation within minutes.

The loop is voice —> summary —> work.

The timeless skill, pointed somewhere new

There was, you know, voice mode before AI too.

Lawyers know it well: debates, Socratic method, client calls, depositions, negotiations. We’ve been thinking out loud our entire careers and lives.

Voice AI isn’t a new skill. It’s an old practice built on a new technology.

I’ve been writing about building AI intuition: the instinct for directing thinking machines that outlasts any single tool. Voice mode is where I rack up the most reps without trying. There’s no blank page, no cursor, just a conversation with a thinking machine.

Subscribers: April bootcamp dates and AI 302 (Agentic Foundations) are in your email header.

This Claude Code demo video starts with a highlight reel. To see the full demo, jump to the 1:45 mark.

I have a confession: I founded a company.

It’s in big legal trouble.

Globex Travel helps you travel better, powered by agentic AI. You pick Barcelona for April 1, five nights, and it books you the best flights at the lowest price, all autonomously.

If you hit weather delays, Globex re-routes you so you maximize your time in the sun, not calling airlines.

But problems have piled up. The AI re-routing model doesn’t help stranded passengers find better flights. Instead, it misfires, dumping travelers at the wrong airports. Barcelona turns into… Buffalo.

Then the AI pricing engine starts showing stale fares. Passengers approve $750, then AI charges them $2,500.

Now a federal regulator is investigating. Passengers are filing a class action. A patent troll claims our core technology infringes. We’re launching an enterprise product whose commitments could contradict what we told the regulator.

And the Financial Times is publishing an exposé in 48 hours. It could tank the company.

You’re the GC. How do you step up?

See here for the full-res pictures of the above.

Okay, Globex is 100% made up, a teaching demo inspired by Hank Scorpio’s Globex Corporation from the Simpsons. All documents are fictional, a storyline generated by Claude, involving no real clients or privilege. Five crises, ~70 files, 16 characters. You can download the full sandbox to test your own AI agent. Instructions at the bottom of this post.

Your judgment, multiplied

In the demo, the GC moves five matters forward at once.

Does AI replace her? No. Does she offload her judgment? No.

AI gives her scale. Her judgment gives AI direction.

In othe words, the GC directs. AI executes.

AI is her force multiplier, delivering work product where she actually works: seven deliverables, showing up across Gmail, slides, Google Docs, Sheets and Calendar.

The GC told AI where to look, what to prioritize and what to build. Every output was a starting point, not a finished product. She could push back, add facts or redirect at any step.

AI doesn’t set strategy. AI can read every file in every matter folder. What it cannot do is decide to call the reporter before the regulator because the narrative is the bigger fire, or tell the CEO that her instinct to fight publicly will blow up the settlement. Those are judgment calls: made under pressure, shaped by years of battle-tested experience.

AI makes an excellent lawyer even more formidable.

Directing AI is like directing associates, and building that instinct is what the AI skill stack is all about.

AI shows up where you work

I wrote about the plumbing problem last month: AI nails the thinking but can’t send an email.

This demo is what it looks like when the plumbing works.

AI also reads across all five matters at once, catching patterns and contradictions that one person juggling five crises would miss.

This demo runs on Google Workspace because that’s where I work every day. The principles here hold for any AI you set up.

Here’s what AI generated:

• Mapped a risk-priority heatmap across all five matters, calibrating which fires she must fight first

• Drafted a cease-and-desist response in Gmail for outside counsel

• Built a 48-hour crisis playbook into a slide deck

• Outlined a regulatory response in Google Docs

• Stress-tested her own arguments against her files, surfacing counter-arguments to sharpen her positions

• Scheduled a CEO briefing on Google Calendar

• Assembled a deposition prep matrix in Google Sheets

How to direct AI agents

What made this work wasn’t the AI’s intelligence but the structure around it: organized files, clear cross-references and a system that tells the AI where to look. The intuition to direct AI is the skill that matters.

Over a century ago, Cravath built a system where one partner could direct six associates to handle America’s booming corporate work. The partner’s job wasn’t just doing the work. It was knowing which work mattered: bringing judgment to a complex problem and pointing talented people at it. This is that same leverage, built with AI agents instead of headcount.

Oh, and this is the worst that this technology will ever be.

The bootcamps get hands-on in the terminal, building the instincts to direct thinking machines. Small group, no slides, subscriber-only.

To sign up, subscribe and check out bootcamp details in my welcome email or in the email header above:

Try the sandbox yourself

The Globex sandbox exists so you can play-test your AI agent without risking real data. The full Globex sandbox is on GitHub: https://github.com/exl-lab/globex-sandbox

Download it and directly run Claude from the globex-sandbox/ folder in the terminal. Run the built-in walkthrough command /start-sandbox. It guides you through the scenario step by step. No setup beyond that.

This is all stuff I go over in my AI bootcamps for lawyers. Take it for a spin and let me know how your AI handles five legal fires.

Lawyers aren’t falling behind on AI for lack of drive. We’re falling behind because our AI training almost always stops at prompting.

Yes, prompting matters. But it’s the starting line, not the finish.

The real skill is directing AI like you direct your associates.

You train associates to fit how you practice: your standards, your style of client service, your way of working.

Do the same with AI, and you have an edge that most lawyers don’t.

The AI Skill Stack

Let’s stick with the associate metaphor.

AI 101 is when we treat our associate like an eager intern. They’re smart and responsive. They draft well, research quickly and handle most of what you throw at them.

But they forget a lot between conversations (not everything, but enough that you notice). They sometimes assert things that aren’t quite right. And when you hand them too much material at once, they mix things up.

AI 201 is when the associate becomes a solid junior. You’re more confident about the work product, although still on simple tasks. You bounce ideas off them before a negotiation. They even start preparing decks for you.

The associate hasn’t changed. You’ve just gotten better at directing them. You scope questions tighter, break big tasks into smaller ones and learn which assignments to trust and which to double-check.

AI 301 is when the associate goes from just taking orders to being an active working partner.

You’ve seen this light-bulb moment when promising junior lawyers step up as mid-levels or senior associates.

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You’re delegating tasks to them now. You trust they’re managing their work well. They sometimes surprise you with polish and insight. They match your working style and habits. You shift more work their way.

You give them real feedback. You invest in their training.

Like rising star associates, you come to depend on them as go-to experts on a project.

They start coming back to you with ideas: “What if we ran this matter differently?” “How about we change this process?” You buy in. You build out a system together, impressed at their initiative.

You see them in action: planning logistics, strategizing, running complex analyses, managing up to clients and senior lawyers, showing client bedside manner.

They’re not just building decks anymore. They’re figuring out how to pitch your client in the first place.

You start thinking this person has staying power. You treat them as a confidant.

AI 401 is the associate who works overnight and shows up ready every morning. They’re thinking deeply about your matters on their own.

They come back with plans to improve things, not small fixes but insightful changes at scale.

They build you a new standard form template that you’d otherwise piece together manually over years. They run a new process for preparing citations, researching precedent and drafting briefs.

They even coordinate other junior associates.

You let them manage big chunks of matters, checking the final work product and output.

This is the horizon, and AI 301 is the foundation that gets you there.

Each layer in the AI skill stack builds the instinct for the next. And each bootcamp session builds toward the one above it.

What AI 301 Covers: Terminal Foundations

I’ve written about why the terminal is the best way to build AI intuition.

This is the instinct to direct thinking machines, orchestrating digital agents who can generate real knowledge work.

The skills you build in AI 301 (working in the terminal, managing context, writing instructions) are the foundation every level above depends on. The bootcamp puts you there hands-on.

Once you’re working with raw AI power, you see what’s possible. Claude Code can string together complex tasks in a single prompt: search the internet for sources, pull in precedents from the cloud drive, run a risk analysis against your checklist and draft a summary memo for your client. You can customize it to match how you work: your preferences, your shortcuts, your workflows.

In AI 301 we start with the foundations: navigating the terminal, understanding how AI memory works and learning the basics of customization. But even the basics change how you think about what AI can do for you.

Your instructions file

The first real thing you’ll build is a file called Claude.md.

You customize the associate’s personality, their communication style, how they work and how it fits you. You set the tone for their legal work, whether deep and rigorous versus high-level and directional.

You give them context about who you are and your priorities so they know how best to help.

Most lawyers talk to AI cold every time. No memory, no preferences. Claude.md changes that. You write your preferences once in plain English, and every conversation after starts from your baseline.

And you refine this file over time, like with an associate, building up that working relationship.

A concrete example: you ask Claude Code, “What are the best writing guidelines for a lawyer?” It gives you a solid answer. You tell it to save that to your Claude.md. One minute, and now every draft follows those guidelines without being asked.

Note: OpenAI’s Codex calls it Agents.md. Gemini calls it Gemini.md. OpenClaw calls it Soul.md. Same idea.

Plan, build, adjust, fix

In AI 301 you build a very simple app, something small and even a little silly. But the app isn’t the point. The thinking process is.

You tell Claude Code to plan before it builds. It asks clarifying questions, helping you brainstorm. It maps out the steps. Then it builds, and things break. (They’ll for sure break. That’s almost the point.)

You then watch an AI debug its own mistakes in real time. You adjust, it adjusts.

Plan, build, adjust, fix. That cycle is the microcosm of every AI interaction you’ll ever have. Once you see it, you stop thinking about AI as a chatbot and start thinking about it as a thinking machine you direct.

Looking Ahead: What’s After AI 301

Future bootcamps build on this foundation.

In AI 302, you upgrade the associate’s toolkit. You build custom commands in plain English that automate tasks you’d repeat manually. You connect AI to the services where you actually work: your chats, your calendar, your documents, your tools.

In AI 303, you build a Second Brain. Your associate stops forgetting between conversations and starts managing your workflow alongside you: triaging messy thoughts into tasks, surfacing what you’ve been neglecting, becoming a partner instead of a tool.

This is how you build AI intuition.

Hands-on Learning

My AI bootcamps are free and exclusive to subscribers. I run the same material multiple times so you can pick a time.

Already subscribed? Check your email for dates and sign-up link.

See you in AI 301.

Microsoft Word cannot take your job. But AI can.

We can’t learn AI the way we learned Word, as software with menus and buttons. AI is an autonomous machine that thinks. Learning AI means learning how to direct it.

I call that skill AI intuition.

The terminal: raw AI power

A new litigator doesn’t first-chair a trial. A junior M&A lawyer doesn’t run the deal. You build judgment through reps before you’re trusted with the whole thing.

AI intuition works the same way. You don’t start by orchestrating five agents to draft and research all at once. You start by learning what a single agent can and can’t do, managing down the hallucination risk that keeps lawyers wary of AI.

To build AI intuition, I recommend that lawyers start learning AI in the terminal.

The terminal is the engine room of the computer. It’s a black screen with a text line and a cursor. No menus, no buttons. Just you and the AI.

Yes, you trade away the visual interface of a desktop app or website. But in exchange you see up close the fullness of raw AI power.

It’s like a bucket of Lego bricks where you build whatever you want, versus a model kit to build just one or two things. That $130 Simba looks 100x nicer than anything I build on my own. But it’s expensive, and I may just build it once and never touch it again. General purpose vs single purpose.

AI in the terminal (aka command-line interface) offers the richest feature set directly at your fingertips: it codes, writes custom skills, personalizes its style to yours, searches the web, edits files on your computer and plugs into a vast software ecosystem (Google Workspace, anyone).

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Other AI interfaces can offer a lot of these, but they don’t offer all of them all at once. They often gate features behind UI that’s painfully manual.

Note: Yes, this is basic for engineers. But we’re lawyers, experts in a different domain, and we’re experimenting with new skills to grow with the times.

A bucket of Legos or a model-kit

Imagine I want to create an AI sparring partner to help me negotiate better. I want it to attack my arguments to make them stronger.

In Claude Code, this single text prompt will:

• brainstorm other ideas with me to make it even better

• research the internet for negotiation frameworks

• make me a Google Doc summary

• write down its instructions so I can improve it over time.

I wrote this out in about 30 seconds:

Compare that to Claude the vanilla chatbot where I have to create a Project in order for these instructions to persist. Same company, different interface. I manually click around, fill in the basic instructions. The next screen gives me a textbox to search the web and make a Google Doc. But it takes lots of clicking around, navigating the curated experience Anthropic set up for me.

Is it terrible? Of course not! I’m not doing this to pick on apps. I use apps all the time, and they do have huge advantages over a bare terminal.

My point, however, is that the terminal will always be faster and more powerful. You can get a lot more done in it.

In exchange, though, the learning curve is real. Yet it’s that learning curve that sets you up best to build AI intuition and one day orchestrate AI agents.

For example, I can spin up three agents in a single command, directing them toward a common task. It’s trivially easy and fast.

Polished AI products make trade-offs to serve up a friendlier experience. That’s a reasonable choice for most users. But for building intuition, the friction is the teacher.

Once you have the intuition…

Okay, but what’s the end goal? What differences are there between a lawyer with AI intuition and one without?

Imagine a lawyer is reviewing a product feature that touches on privacy laws in the U.S., France and Brazil. Three regulatory regimes, overlapping but distinct.

The lawyer without AI intuition may start a new chat thread, dump in the product requirements document along with three statutory texts and secondary sources. That would likely give you a jumbled response, and the hallucination risk is real.

How does a lawyer with AI intuition handle this?

Step one: run three parallel agents (one per jurisdiction) each loaded with only that jurisdiction’s statutory text. Each agent maps the obligations that apply to the feature. We get three independent outputs that haven’t cross-contaminated. Better yet, since these are standalone clean analyses, we can layer in case law and those regulators’ latest enforcement actions, including penalties.

Step two: pull the three outputs into a fourth new agent and ask it to build a comparison matrix. Where do the regimes align, where do they conflict and where does one regime have requirements the others don’t? The agent does this well because it’s working from distilled outputs, not raw statutory text in multiple languages.

Step three: take the conflicts and run each one back through a jurisdiction-specific agent to pressure-test. “The France output says explicit consent is required. The U.S. output says notice and opt-out is sufficient. Confirm the France position and cite the specific CNIL enforcement action. Then cite the U.S. state court case supporting that position.”

We do this because the synthesis step is where hallucination risk spikes. The agent is pattern-matching across regimes and may have smoothed over a real distinction to make the matrix clean.

Each jurisdiction stayed isolated until we chose to combine them. When we combined them we checked the seams.

Staying power

Tools change every quarter. The way people built agents last year is already outdated. That knowledge goes stale. The instinct for splitting a problem apart, keeping the pieces clean and checking the seams when you put them back together: that compounds.

That’s AI intuition. I’m building a bootcamp to teach it. Lawyers in the terminal, building the instinct from scratch.

Subscribe to get in when it opens.

The market does not care what lawyers think they are worth. It pays for what it needs. And what it needs from lawyers — guiding people through fuzzy natural language — is not going away.

As long as words are ambiguous and people stay unpredictable, there is work for lawyers.

The question is which lawyers.

Most lawyers know AI matters. But most sit in the zone of AI anxiety — aware enough to feel behind, not aware enough to take next steps.

That zone is not your fault.

The tools have not caught up to the promise. But the anxiety is keeping good lawyers frozen.

You should not panic. You also should not wait.

Cravath’s insight

In the 1890s, Paul Cravath watched railroads reshape the American economy and saw that legal practice needed a new structure.

The old model was loose groups of practitioners sharing office space. That was buckling under the vast corporate work now powering American industry.

So he built one. Hire the best graduates, train them intensively, leverage their work. Up or out.

The insight was matching the structure of a law firm to the economic reality of the moment. That model powered BigLaw for 125 years.

We are at another one of those moments.

The leverage is shifting

The Cravath system worked because associate labor was the cheapest legal intelligence at scale. Associates did research, drafted contracts and prepared memos. Partners reviewed, refined and applied judgment — with personal relationships to boot.

AI is changing that equation.

The work that made associates valuable — research, first drafts, document review, pattern recognition across thousands of pages — is getting cheaper every month. AI does not bill by the hour. It does not need training rotations.

This does not mean associates go away. It means the leverage shifts.

Instead of one partner directing six associates, picture every lawyer directing a team of AI agents — one redlines the agreement, one checks regulatory filings, one maps the counterparty’s disclosures. The lawyer oversees and applies judgment where it counts.

The same leverage Cravath built with associates, built with software.

You are not too late

No one has a magical roadmap. We are all figuring this out in real time.

I ship AI products for a living, and I wrote recently about what I call the plumbing problem: AI nails the hard thinking but breaks on the easy stuff.

AI summarizes 300 pages of regulations but can’t summarize a Slack thread. The tools are powerful but raw.

But they are getting better by the month. And the real value lives right now at the AI 201 skill level — where you learn what AI can and cannot do, build habits around it and develop the judgment to know when to trust it. That judgment is the new competitive edge, and you can only build it by doing.

In 2026, learning AI is not prep for legal practice. It IS the practice.

There is no “too late.” Start simple. Pick one tool and try it on real work — a contract review, a regulatory question, a research memo. Find a fix for 80% of a real problem. It will break, and that is the point.

Then iterate. Each harder task teaches you more about how AI handles your problems, with your constraints. The skill compounds. What starts as fumbling becomes fluency.

Here are two ways to start this week.

1. Have AI interview you

Most people struggle with AI because they do not know what to ask. So flip it — let AI ask the questions.

Copy this prompt into your model of choice (free is fine):

You are a consultant helping me figure out how to use AI better in my legal practice. Interview me. Ask one question at a time about my daily workflows, biggest time drains and tasks I do over and over. After 10 questions, give me your top 3 recommendations for where AI could save me the most time — with a specific tool and approach for each. I don’t want complex setups. I want low-hanging fruit with high ROI.

This works because it forces you to say out loud where you actually spend your hours. AI spots patterns you have gone blind to because they are just “how work is.” I did this exercise myself and was caught off guard — the biggest time sinks were not the ones I would have guessed.

2. Join a bootcamp

I run “Intro to AI 301” bootcamps for lawyers to work hands-on with AI tools.

Subscribe to this newsletter for an invite. These are one-hour crash courses for lawyers to get hands-on with powerful AI tools like Claude Code.

See the reality behind the hype. Test the tools and see their limits.

I have no patience for fluff or hand-waving. My bootcamps get lawyers up to speed on the basics of the terminal, slash commands, customizing your agent, managing a context window and building a small project from scratch.

I’m not charging anything. The bootcamps are completely free. You do need a $20 Claude subscription -- I’m not sponsored by anyone; I just really like the tool.

My only ask: Share your learnings with other lawyers and encourage them to start building their AI fluency.

Start now

The lawyer who struggles through a first AI-assisted draft today is building the muscle to orchestrate agents a year from now.

The one who waits is still in the anxiety zone.

Cravath did not win by practicing better law. He won by seeing a shift and organizing around it. That shift is here again. The question is whether you start now.

You don’t learn guitar by reading about guitar. You sit down and play badly.

I’ve been running AI bootcamps for lawyers: Intro to AI 301. These are for legal folks (not engineers) who already use Gemini, ChatGPT or Harvey daily. They want to learn more powerful AI features, including coding.

But most haven’t touched a terminal since DOS.

So yes, 60% of the session is setup. Learning terminal commands. Creating working folders. Unblocking enterprise restrictions.

Is it a chore?

🌟 Yes, but that 60% IS the learning! 🌟

Each friction point teaches something about AI’s product limits -- whether it’s clunky setup or broken plumbing between tools (see my last post on AI’s plumbing problem).

But there’s also delight. AI can be a self-healing system. When we hit a problem, we didn’t just stop. We asked AI to debug itself and fix that same problem.

A tool that fixes itself. A tool that teaches you how to use it.

Does this take technical skill? No, but it takes persistence.

You don’t pick up a guitar, play one bad chord and throw it away. You don’t open a terminal, hit one error and quit.

In one hour of AI 301, each lawyer used the terminal, ran slash commands, customized their own claude.md file, managed a context window and coded a small project from scratch. They brainstormed, planned, iterated and debugged.

On a personal note: I played classical guitar growing up. Barre chords are not fun. Raw fingertips are not fun. But you keep going until the friction turns into music.

That’s what AI fluency looks like. Not just reading about it. Playing badly until you don’t.

Fact: AI is the next industrial revolution. Fact: AI cannot send an email.

Last week I opened my mailbox to a soggy Amazon package. A gallon jug of white glue, whose cap fell off in delivery. Kids, slime, you get it.

After cleaning up I had Gemini draft an email to our property manager. It nailed the message. But then... it refused to send it in the Gmail app 🧐

So I manually copy-pasted between two apps (run by the same company).

🟣 Takeaway: The bottleneck in AI right now isn’t intelligence. It’s plumbing. The model nails the hard stuff. The easy part breaks it.

If you’re confused by all this, your feeling is totally justified. It’s not just you.

On one hand, we hear hype. Revolutionary coding tools! Autonomous AI agents!

On the other:

- AI reads an email → can’t create a calendar invite

- AI transcribes meeting notes → can’t update project tracker

- AI ingests 300 pages of regulations → can’t read my chat messages

AI’s gains are not evenly distributed. The truth is, AI is *both* revolutionary and still immature for everyday work.

Most users can’t tell GPT from Gemini, let alone Claude Opus 4.5 to 4.6.

🟣 But users *can* tell whether AI helps them with chats, files, calendars, emails and calls.

Yes, it’s not all there yet. But the race is on, and it’s getting better by the day.

Connect AI to where your work actually happens -- that’s real productivity. Low-hanging fruit, huge ROI.

Next time you try an AI, ask which services it connects to. AI plumbing is becoming the product itself.

What’s one workflow where AI nails the thinking but can’t finish the last mile?

AI has reshaped how I take notes. No typing, yet forensic-level accuracy (with photos). I stay focused on the room, not my keyboard. A design meeting a while back is where I locked in this habit.

I was at a milestone design review, before a table of hardware prototypes. The kind of review where my hands are full holding up things, wearing them, looking over every angle.

I took a day-trip flight just for these 90 minutes. I couldn’t miss a detail. High-stakes meetings require high-fidelity notes.

Spoiler: Gemini made for me not just notes, but a forensic record. It labeled everyone who spoke and summarized all topics. It even wove in the photos of prototypes I took with my phone -- matched to the topic.

An audit trail without the overhead. For 90 minutes I typed nothing. I was fully locked in on the discussion, with headspace to *think*, not splitting my focus and obsessively jotting things down.

4 steps:

1/ Yes, ask attendees for permission. Yes, it changes the dynamic for a split second. But we get speed and accuracy in exchange, and people (especially non-lawyers) value the benefit of a lawyer who’s more engaged.

2/ I record with the native Apple voice memo app -- it’s simple and stable. When needed, I use my phone to take photos.

3/ Upload the raw audio file and photos to Gemini (enterprise version has robust privacy commitments – always check your AI and company policies). Any multimodal AI works, as long as it transcribes voice to text and can parse photos.

4/ I ask AI to distinguish each speaker and transcribe it “intelligent verbatim” so I have the raw record without filler words or false starts. Then have it summarize all topics and weave in photos based on timestamps and photo content.

AI can finish this in 15 seconds.

Bonus tip: when I can’t audio-record a meeting itself, afterward I voice record myself for a few minutes on takeaways when the memory is fresh. I can do this walking in between meetings. And AI rewrites it in a crisp format for later.

In a fast-moving space, change is constant. These records let me look back clearly while pushing ahead.

Below are example AI notes from personal non-work life.

How has AI changed your note-taking habits?