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.

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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.

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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.

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This is the first edition of Legal Lab Notebook. Subscribe to receive future posts in your inbox. And if you have a question about AI and law, comment or reply. I read every one.

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?