When I sit down with Mike, it has been around three months since he won the Anthropic global hackathon, beating 13,000 applicants, most of them career engineers, with a project built in six days from his home in Orange County.

We’re also chatting on the day before he takes the main stage at Code with Claude in San Francisco, one of the biggest software events in the world this year, where he will be delivering a thirty-minute keynote.

Listen to the full episode on your favourite platform, or keep reading for my take on the conversation.

• Apple Podcasts

• Spotify

Not a software engineer

Mike is, by his own admission, not a software engineer. He has never written a line of code in anger. He is a personal injury attorney by background, with a previous career in Hollywood. Of the top 5 finalists that Anthropic announced, only one was a full-time software engineer. The others included a cardiologist, an electronic musician, an infrastructure worker from Uganda, and Mike, who took the overall crown.

So how did a lawyer win the Super Bowl of hackathons? And what does his story tell the rest of us about what is possible with these tools right now?

Hollywood to law school to a maxed-out credit card

Mike didn’t set out to be a coder, or a lawyer for that matter. He spent his twenties in Hollywood doing visual effects work. He did what he describes, with a smile, as the rational response to any burnout: he went to law school.

He fell into personal injury practice and genuinely liked it. He spent a few years at California PI firms before hitting the decision point most PI lawyers eventually hit, which is making the decision to go out on his own.

That decision happened to land in late 2023, just as ChatGPT was changing what was possible. Faced with the usual choice between hiring paralegals or burning through savings on back office support, Mike did something different. He started building prompt libraries before anyone was calling them prompt libraries, recording intake calls with client permission, transcribing them through Whisper, and feeding the transcripts into ChatGPT to produce his own version of an intake form.

The system worked well enough that his cousin, a serial entrepreneur, suggested they productise it. They built a company called Onbreeze, a note-taking phone line for lawyers, on the insight that injured clients don’t book a Zoom call with a lawyer - they call.

Vibecoding wasn’t mainstream at this point, and Mike outsourced the entire development process to an external development agency. The product launched in January 2025. Unfortunately for Mike, it crashed the moment a fourth caller hit the line.

When the offshore engineering team explained that fixing it would cost roughly the same as building it had, Mike was already maxed out on credit cards. He downloaded Cursor (an AI coding tool) instead. Ten minutes later, by talking to it about what he needed, he had a working solution. He still couldn’t read the code. But the thing worked.

That was his first prompt.

How Mike learned to build

Onbreeze didn’t take off the way Mike hoped. By May 2025 he was at a crossroads: go back to practising law full time, or lean into the strange new fluency he’d developed with vibecoding tools.

He chose the tools. But what’s interesting is how he chose them. He didn’t go and learn to code in the traditional sense. He took on a series of consulting projects for friends and family, each of which forced him to learn one new thing. A roll-up project for a private equity contact taught him how to handle data across complex databases. A pickup ordering system for his cousin’s butcher shop in Brooklyn taught him Stripe. Mako, a demand letter generator for personal injury lawyers, taught him how to deploy agents to the cloud using Anthropic’s SDK.

He describes it as accidentally building a curriculum. Each project was something he wanted to learn anyway. Each one came with a real user on the other side. And each one added to a stack of skills he could combine later.

By the time the Anthropic hackathon was announced in early 2026, Mike was already far more fluent with the technology than most.

Picking a problem nobody could solve

The hackathon accepted only 500 of the 13,000 applicants, and the brief was specific: they wanted problems that weren’t solvable with previous generation models.

The problem was California’s housing crisis, or more precisely, the permits bottleneck inside it. “Everyone thinks California has a housing crisis,” Mike puts it. “We don’t. We have a permit crisis.” His friend builds ADUs (Accessory Dwelling Units), the small backyard cottages California has incentivised as a partial answer to its housing shortage. The laws are favourable, the demand is there, and yet permits routinely take six to nine months, with endless back and forth between builders and city plan reviewers.

Mike had a hunch AI could help. He asked Cameron to send him a set of blueprints. Claude couldn’t handle them. The pages were physically too big, the size of a table, and the dense annotations meant traditional OCR pulled the text away from the visual context that gave it meaning. A note about wall thickness only mattered if you knew which wall it referred to.

By this point, most people would probably move on, but Mike saw an opportunity. If the most advanced Anthropic model available at the time couldn’t handle this, then it was exactly the kind of problem the hackathon judges were looking for. He submitted, got accepted, and immediately had a small panic about whether he could actually solve it.

The lesson buried in this is that problems are way more important than solutions. Most vibecoders jump straight into building things. Mike spent his time finding a problem worth building for, one that mattered to a real user and that pushed the technology to its current limit.

Building it in six days

Mike decided to call the app CrossBeam. The hackathon ran for a week and by Wednesday night, two days in, Mike was in trouble. He had two disparate approaches, neither of them working, and the hour was getting late. He scrapped the elaborate combination of OCR plus image referencing he’d been wrestling with, and went back to first principles: here are the rules, here are the chunks of blueprint, figure out what’s wrong.

That first end-to-end run hit roughly 40% accuracy against the city’s correction letters. Crucially, it ran. Once a system runs end-to-end, even badly, you can benchmark it and improve it. By Thursday afternoon he was at 60 to 70%.

Two design choices were key here. The first was treating the model’s context window as a precious resource rather than a dumping ground. Mike used Skills, Anthropic’s open standard (think of them like those files that get uploaded to Neo’s brain in the Matrix so he suddenly knows Kung Fu), to keep his prompts lean. He used parallel sub-agents, each with a narrow job and a fresh context, rather than one bloated agent trying to do everything. He used what he calls adversarial prompting: have one agent plan, a second agent execute, and a third agent test, so that no single agent is grading its own homework. For this, he uses an analogy from legal practice: “When an associate brings you a memo, you don’t ask them if they double checked it. Of course they’re going to say yes. You give it to someone else to check.”

The second design choice was testing. He set up command line interfaces on the backend so that one Claude instance could run a new ADU through the system every hour while he worked on something else, tracking accuracy gains in real time. By Friday his accuracy was high enough that he could spend the weekend on the user-facing piece, which itself ended up being shaped by two conversations.

The first was with Cameron, his friend, a builder. Mike sent him fresh outputs and Cameron immediately asked if he could run three more blueprints through. Real users behave like that when something works. The second was with his friend Connor Traut, the mayor of Buena Park. Until that conversation, Mike had only been thinking about the builder side. Connor flipped his perspective: cities also need help, on the review side, processing permits faster. So Mike rebuilt the product to serve both sides. Builders drag and drop blueprints and get back a precise action plan in twenty minutes. Cities batch process submissions and generate draft correction letters. Same product, two different audiences.

Why lawyers might be unusually good at this

Mike’s prompting structure is one most lawyers will recognise immediately. Issue, rule, analysis, conclusion. This is the framework you learn for the bar exam, repurposed as the framework for getting good output from a frontier model.

Mike starts with the issue at the top. Then, the relevant rules and constraints. Next, the analysis: why this matters, who it’s for, what good looks like. Conclusion at the end.

He often prompts by voice, often for five minutes at a stretch, because typing caps the average user at around 60 words a minute and speech runs closer to 180. (Ahem those of us who took a typing class in the 90s are closer to 120+ but the point stands!)

The skillset Mike describes is closer to drafting a memo than to writing code. Lawyers spend years learning to think clearly under pressure, to anticipate counterarguments, to specify exactly what they want and exactly what they don’t. Those skills, plus a working “bullshit detector” and a willingness to experiment, are most of what it takes to build with these tools today.

The bit lawyers usually need to add is the last part - the curiosity and willingness to experiment. Some legal training does lean towards risk averseness in a way that doesn’t serve help with building new things. We may need to unlearn some things here.

On security

As we talk, Will Chen’s MikeOSS project has been making waves. It’s an open source version of Harvey/Legora.

Mike and I observe that the conversation on vibecoding has become a little polarised into those who seem to believe you no longer need to learn software engineering at all (on the one hand) and those who believe it is a waste of time (on the other).

Mike is realistic here, if a little more bullish than I am on AI’s ability to spot and resolve security and compliance issues in production software at the current time. He’s alive to the limitations of vibecoding and shares that he would be open to partnering with software engineers when the project demands it.

(Side note: my recommended framework for Responsible Vibecoding is at Vibecode.law/learn).

His biggest frustration is people writing about the issues with open source software rather than submitting PRs to resolve the issues. This ties into something else I observe about Mike - his extreme bias towards action. He doesn’t have time for essays about software - he wants to build.

Key lessons for vibecoders

If we step back from the specifics of permits and ADUs, I think what you end up with is a template for building effectively with AI.

Find a problem worth solving. Talk to the people who actually have it and spend as much time with them as you can. The closer you can get to the problem, the better the output. No problem, no solution, no value.

Build in short cycles with rigorous testing. Testing is a critical part of software engineering but it’s something that most vibecoders forget. Software can be tested really effectively because it largely either works or it doesn’t. Don’t forget this part.

Treat the context window like a budget. Mike learned through experimentation how performance drops off a cliff as you use up the agent’s context window. He manages this carefully with compaction, Skills and just starting new conversations.

Prompting still matters. I occasionally read that the era of prompt engineering is coming to an and. I don’t agree on this. Sure, some of the old hacks like “You are a world expert in everything” aren’t very useful these days but Mike’s framework for prompting is effectively about giving the agent the exact context it needs. This leads to better outputs and CrossBeam is proof.

Show your work to real users early and let their reactions reshape the product. Don’t be precious about your first design. Most vibecoders spend so long seeking perfection and not enough time getting it in the hands of real users.

Finally, be curious.

This is the common theme across all episodes on the podcast.

People like Mike who are pushing the boundaries of what is possible do so because they are curious about the boundaries.

Most people thought the California permitting crisis couldn’t be solved with AI. Mike wondered what would happen if it could.

I for one can’t wait to see what Mike builds next.

Up next

In the next episode, I’ll be sitting down with the amazing Nishat Ruiter, General Counsel of TED and the founder of TEDLaw.

Resources & further reading

Follow Mike on LinkedIn

CrossBeam

We recently crossed 70 projects on the platform, so I thought now would be a good time to step back from the leaderboard and look at the trends behind what people are building.

Who is building?

62 different people built these 71 projects, and the mix is really interesting. By and large, these are not engineers. The largest identifiable groups are practising lawyers, consultants, legal ops and innovation people, with a handful of students and a couple of GCs.

The takeaway is that most of these are domain experts who taught themselves to build things, not developers who learned some law. Several writeups come from people who describe themselves as non-technical and then mention twelve rounds of security auditing in the next sentence, or admit they were shocked that a feature they assumed was hard had already been done for them. The distance between having an idea and having a working prototype has collapsed.

What are people building?

By studying what people are building, we might get a feel for the problems legal professionals are trying to solve (and which presumably they feel are not already solved by existing commercial applications).

Four categories jump out:

1. Legal Explainers

2. Training Tools

3. Single-Purpose Utilities

4. Research and Drafting Aids

Let’s now take a look at each in more detail.

1. Legal Explainers

The general idea of an explainer app is to take something dense and make it understandable to someone who isn’t legally trained. Some examples:

• Court orders rewritten at an eighth-grade reading level with the deadlines pulled onto a calendar.

• Privacy policies reduced to the few clauses that the data subject needs to know about.

• Rental contracts decoded for students about to sign one.

• Care Act eligibility walked through in plain English, with a letter to the council generated at the end.

All of this goes to making legal information easier to understand. While not every app on Vibecode.law uses AI, most do, and I (personally) think that AI has huge potential to democratise legal information. This has profound implications for access to justice. So, I’m heartened to see that people are building things to help solve this problem.

2. Training Tools

This category is bigger than I expected and it just underlines the growing concern in the profession about how we will train junior lawyers if AI is doing much of the work that juniors have done in the past. Here are some examples:

• A live voice negotiation against an AI counterparty using a firm’s real MSA

• A matter simulator for trainees built inside a large firm

• A tool that plants errors in AI-generated drafts so juniors learn to catch them,

• A card-based recall system for bar study.

I think it’s fair to say that this remains a largely unsolved problem, and I’m looking forward to seeing what people build at The Vibeathon.

3. Single-Purpose Tools

Next up, it’s the small single-purpose tools, which arguably are some of the most useful things on the platform. I like these apps because they typically come from a very specific pain point experienced by the person building the app - and starting from a problem is far better than starting from a solution. Here are some examples of single-purpose tools:

• A tool that unlocks Word documents where track changes has been password-locked.

• A SaaS inflation calculator that pulls live ONS data to compute a contractual uplift.

• A decision-tree builder you can embed in a knowledge base. (Side note: Shoutout to Alex Herrity for bringing this app to life with the UK’s “salmon handling” regulatory framework!)

None of these apps alone will reshape the legal profession. But I think taken together they raise a more interesting possibility - a future in which lawyers don’t just draft documents but actually build solutions to specific problems at the point of need.

4. Research and Drafting

The other submissions fall into the familiar category of research, case search, and drafting. This is the most crowded category, and the one where the established (and well-funded) vendors are perhaps the strongest. Some examples:

• Free Westlaw-style case search

• Citation extraction

• Statute parsers

• Autofill for government forms

• Clause-by-clause contract review.

If anything, I think this category is probably the most interesting to legal tech vendors, because it probably tells them which areas their users feel are mostly unsolved.

Which practice areas?

By practice area, the spread is pretty uneven. Law firm operations is connected to 32 of the 71 projects, then commercial, corporate, regulatory, and technology work. Privacy and data protection, litigation, and IP form a middle tier. Construction, tax, family, and insurance barely register so far.

Perhaps this spread tells us that people feel most comfortable building where (1) they feel the pain themselves and (2) a wrong answer is cheaper. Operations, commercial, and regulatory work is high-volume, more repetitive, and lower-catastrophe when the tool gets something wrong, which is exactly the sort of profile that suits a prototype. The areas that are absent seem to be the ones where the work is bespoke or the cost of error is higher - more on that below.

What aren’t people building yet?

I was also interested to see if there are areas where lawyers aren’t yet building to see if there are untapped opportunities:

First, litigation process coverage is fairly thin. There are plenty of research and case search tools, but very little that helps run a matter through to a hearing: bundling, chronologies, the operational and process side of disputes.

The unglamorous compliance area is missing too. I didn’t see many tools trying to solve for Conflicts, KYC, AML, engagement letters. This work is less exciting to build and harder to test on synthetic data, so perhaps it loses to shinier things that are more fun to build and use. But, in my opinion, there are plenty of unsolved problems in this space.

Access to justice apps shared on the platform are mostly built for the unrepresented person directly, and almost none for the legal aid sector, the law centres, CABs, and duty schemes who serve those people at scale. Nothing wrong with this, but for anyone wanting outsized impact with lower competition, this might be the clearest opening on the board.

The high-stakes regulated area is also mostly empty. I didn’t see much in the way of tax structuring, securities work, clinical negligence. Maybe this is simply because a wrong answer here could be more expensive or dangerous. Interestingly, the tools that do touch higher risk use cases are more careful to frame themselves as preparation before a lawyer gets involved, rather than a substitute.

What about security and compliance?

One of the main reasons we launched Vibecode.law was to bring some balance to the debate on vibecoding. I’d encourage you to check out our Responsible Vibecoding framework.

As a rule of thumb, vibecoded tools are not ready for production rollouts out of the box. In the same way as legal professionals wouldn’t advise their client to vibedraft a Share Purchase Agreement or vibelitigate a dispute, nor should we believe that we can vibecode and maintain a production application without input from a professional.

This isn’t a reason to opt out of building things, but it is a reason to think carefully about who you’re building them for, what data you put into these apps, and how broadly you distribute them.

The vibecode opportunity

As you can probably tell, I am excited about the potential for vibecoding in the legal industry. We are at a moment where anyone can build things and this unlocks huge opportunities.

For legal professionals, we can stop seeing software as something that is done “to” us, and start seeing it as something we can be involved in creating. This doesn’t mean we should vibecode a law firm tech stack overnight, or that we no longer need software engineers, but it does mean we are all now builders, and we can start getting ideas out of our heads and into working prototypes. This is enormously empowering. Let’s just remember our limits and get professional input before we push things more widely.

For vendors, I think it’s time to stop seeing vibecoding as a threat, and start seeing it as an opportunity. How might you open up your platform in such a way that legal professionals can build on top of it? Not only might this make your tool even stickier, but it might help unlock new value or use cases that you hadn’t even considered. (As an aside, there’s rich data on Vibecode.law about what your potential users and customers are already trying to solve for...)

For law students and educators, this opportunity includes you too. I see huge potential for vibecode hackathons in law schools. They’re relatively easy to set up and run (whether or not you have a technical background) and they are probably the best way for students to understand how the underlying technology works. Many of the vibecode platforms now offer free or heavily subsidised credits for educational establishments.

If you do build something, please share it on Vibecode.law. And don’t forget to register for The Vibeathon in June.

Next Up: “The World’s Best Vibecoder is a Lawyer”

In my next post on vibecoding, I’ll share the key takeaways from my discussion with Mike T. Brown, the winner of the global Anthropic/Claude Code Hackathon. In February 2026, Mike saw off competition from 13,000 participants to take home the prize. And he’s a personal injury lawyer who had never written a line of code in his life.

India has more legal tech startups than almost anywhere on earth.

I did not know this until I sat down with Shreya, but India has around a thousand legal tech startups, which she tells me puts it second only to the US.

That is a striking number for a country where foreign law firms still can’t really practise, where only advocates can own law firms, and where the entire legal services market is about a fifth the size of the UK’s. If there’s one person who can help us make sense of this landscape, it’s Shreya.

Listen to the full episode on your favourite platform, or keep reading for the full write-up.

• Apple Podcasts

• Spotify

Introducing Shreya

Shreya trained at Khaitan & Co, one of India’s tier one firms and roughly the magic circle equivalent in the Indian market, with around 800 lawyers when she joined and closer to twice that today.

Like many guests on Without Limitation, her career has taken an unconventional path. She practised for a couple of years before moving into a practice development role. Then, COVID hit and the marketing budget disappeared overnight, which meant her team ended up absorbing everything else the managing partner needed help with.

That covered IT, HR and operations, but also pricing, strategy, new office openings, partner performance and strategic hiring. In her words, whatever landed at the managing partner’s table also landed at theirs, which gave her something most lawyers never really get, which is a top-down view of a law firm as a business rather than a bottom-up view of a practice.

From there she became one of the first hires into Khaitan’s innovation team, and her role kept evolving as AI did.

Last year she moved to the UK to join Stephenson Harwood, drawn by what she described as a more mature market for digital transformation, with longer-established innovation teams, more consistent IT budgets, and a decade or so head start on the journey.

A thousand startups

Shreya tells me that India’s legal services market is around $10 to $15 billion in total, roughly a fifth the size of the UK’s, and it is wildly unconsolidated compared to what most of us are used to. The top five firms are similar in size to each other, in the 1,500 to 2,000 lawyer range, and then there is a big gap before you reach the long tail of full-service firms operating somewhere between 50 and 200 lawyers, and then the boutiques and independent chambers, and then a hyperlocal market across the tier two and tier three cities that operates almost entirely separately and accounts for the vast majority of India by population if not by revenue. On top of that, foreign firms still can’t really practise there, a liberalisation bill has been pending for years with significant local opposition, and there is no ABS or non-lawyer ownership of any kind.

So the obvious question is why a market with all of those constraints has produced so many startups, and Shreya’s answer is partly cultural, partly economic, and partly a story about talent.

India, she tells me, is generally entrepreneurial and high risk-taking, which feeds directly into the volume of founders willing to have a go, and it has some of the best developers in the world at cost structures that make building viable in a way that is hard to replicate elsewhere. The economics also push founders outward almost from day one, because rupee revenues are small once converted, so the dominant playbook is to build in India and sell internationally, which is exactly what companies like SpotDraft (now established in the US and entering Europe) and Lucio (which recently opened a New York office) have done.

One category worth flagging, because it is more advanced in India than most other places, is online dispute resolution (ODR). SEBI, the securities regulator, now requires all investor disputes to go through an ODR platform, and there is open API infrastructure called the Pulse Protocol that allows any ODR provider to plug in, in much the same way that UPI revolutionised payments by giving every bank and every app a shared rail to build on. India tends to solve problems at the infrastructure layer when it solves them properly, and ODR is a good example of what that looks like in practice.

The bridge

What makes Shreya interesting beyond her own story is that she has lived on both sides of the bridge between the Indian and international legal tech ecosystems, and she has clear views on what each side keeps getting wrong about the other.

For Indian startups looking to scale into the UK and US, she offers a network and an instinct for what magic circle firms actually buy, which is not always what an Indian founder might assume from a distance. Last year she worked with the UK Department of Business and Trade to bring a contingent of Indian legal tech startups to the UK to meet magic circle firms, Scottish firms, and the Legal Tech Talk crowd, and that kind of bridging is something the industry could probably use more of.

For international players trying to enter India, she sees the same mistakes repeated. Pricing set for the US market with no real adjustment for local realities. Customer support sitting in time zones that do not overlap with the Indian working day. A lack of appreciation for the fact that the biggest Indian law firms still operate across multiple languages alongside English, and that most LLMs do not handle Indian languages or scripts particularly well, which means translation is a first-order use case rather than an afterthought. She makes the point that the legal AI players doing well in India have generally understood that the same product positioning does not travel intact, because the problem itself is not quite the same as it is in the US, and the reframing has to happen locally rather than being assumed away.

If you want to tap into this network, it is at indianlegaltech.net.

On influential women in legal tech

Shreya recently won an ILTA Influential Women in Legal Tech award.

We discuss how her award sits against a backdrop that most people in the industry recognise but that not enough are actively doing something about, which is that around 3% of startup funding flows to female founders, and legal tech is no exception.

As Shreya puts it, some parts of the ecosystem have become a “boys’ club” where the funders and the people asking for funding are both part of the same cycle, and that cycle is genuinely hard to break from inside it.

Her recommendations are practical rather than abstract. She suggests that if your firm runs an incubation programme, ring-fence dedicated seats for female founders. If you sit on a legal tech fund, write a hypothesis that requires a female founder on every backed team. And if you are a woman who has made it into a decision-making role with some capital to deploy, invest in another female founder, because even one extra month of runway can sometimes be the difference between a company that survives and one that doesn’t.

She also made an observation that that a lot of the buyer-side decision makers in legal innovation, the heads of innovation and heads of knowledge across the larger firms, are women, and the supply side of the industry has not really caught up with that.

On the AI-native firm

We finished, as I tend to with these conversations, on whether law firms are actually changing or just dressing the old model up in new clothes.

Shreya uses the factory electrification analogy, which is my personal favourite as well. When factories first switched from steam to electric power, owners swapped the engines but kept the same layout, the same processes and the same workflows, which meant they got slightly faster operations but not much else. The real productivity gains came decades later, when factories were redesigned from the ground up around the new power source rather than just retrofitting it into the old design.

Most law firms are still firmly in the swap-the-engines phase, treating AI as a tool that makes existing tasks slightly more efficient rather than as a medium for rethinking what legal work is and where the value actually sits. Look at almost any legal tech company website and the framing is the same: draft 20% faster, research 30% faster, all of which assume the underlying tasks remain the tasks lawyers do. The harder question, which Shreya thinks almost no firm has properly sat down with, is where the value layer actually lives in a professional services business once AI is properly in the picture, and what you would build if you started from that question rather than from the current shape of the firm.

AI is now forcing the rethink that should arguably have happened years ago, and for those of us interested in true innovation in legal services, that is probably the most exciting part.

If you are building in legal tech and you have not yet thought seriously about India, or if you are in India and thinking about scaling out, Shreya is the person to know.

Links

• indianlegaltech.net

• Shreya on LinkedIn

• Stephenson Harwood

Legal work has always been hard to price

Legal billing is the history of repeated attempts to solve one persistent problem: how do you price work whose scope is often uncertain, whose value may only become clear long afterwards, and whose quality the client often cannot fully judge themselves?

It turns out, that problem is much older than the billable hour. For most of legal history, lawyers in England and Wales, and later in the United States, were paid based on things other than time. They were paid through gifts, retainers, fixed charges, court-approved costs, statutory tariffs, contingent rewards, negotiated fees, and only much later by the hour.

Each model made legal work priceable in a different way but none solved the underlying problem completely. Fixed fees give certainty but depend on a clear scope, which is often hard to identify at the outset. Contingency fees widen access but tie lawyers’ income to an outcome that may be beyond their direct control. Court assessment offers oversight but can be slow and artificial. Hourly billing records effort but not necessarily value. Alternative fee arrangements promise predictability but often rely on time data behind the scenes.

The legal bill, in other words, is not just an invoice. It is an attempt to price work that is just very difficult to price.

Prelude: Paid Legal Help Before Lawyers (c. 400 BCE-500 CE)

Legal fees began long before common law.

In classical Athens, litigants generally spoke for themselves, but they could pay professional speechwriters, or logographers, to prepare arguments for court. The paid specialist did not necessarily stand up as an advocate in the modern sense. The client delivered the speech. But the economic problem was already recognisable: someone with legal and rhetorical skill could turn a dispute into a more persuasive case, and that skill had a price.

Rome developed a different version of the same thing. Advocacy was associated with honour, status, and public service, yet payment persisted through gifts, and eventually regulated fees. The Lex Cincia of 204 BCE restricted gifts to advocates for pleading cases; later imperial law allowed advocates to receive fees within limits. Long before the common-law solicitor or the American law firm, legal systems were already trying to distinguish honourable assistance from commercial exploitation.

This older history is interesting because it shows that the pricing problem is not a modern invention. But the main story of the modern legal bill begins later, with the English common-law profession and the American legal market that grew from it.

Part One: Gifts and Robes (c. 1100-1500)

The earliest problem was that legal fees were a bit embarrassing.

One part of medieval culture in England was the idea that knowledge was not an ordinary commodity. There was a general maxim that knowledge was a gift from God - and this meant that selling it was seen as morally different from selling tangible commodities like land, cloth, or grain.

No doubt, English common-law practitioners were quite capable of charging for their services. But the moral aspect mattered and this meant that payment for these services was often softened into gifts, robes, hospitality, favours, and (sometimes) retainers. The language let lawyers receive payment while preserving the claim that law was a learned calling, not simply a trade.

This moral unease led to some very practical challenges. For example, in some systems, lawyers could not treat unpaid fees like ordinary commercial debts. The client’s payment was expected, but the professional relationship was not meant to look like a straightforward sale.

The point here is not that billing in this period was purely altruistic. It’s more that it laid the foundation of legal billing being a slightly awkward dance because it positioned legal work as a “calling” and a “profession” as opposed to a simple service.

Part Two: Tasks and Retainers (c. 1300-1700)

By the later medieval and early modern periods, the English legal profession had become a lot more organised.

Over several centuries, England developed various formal ranks and roles: serjeants-at-law, attorneys, solicitors, barristers, clerks, and court officers. These categories did not emerge all at once, and they did not remain stable. The solicitor became recognisable later than the attorney; barristers and solicitors took shape over time; serjeants declined. But the broad pattern in this period was of legal work becoming increasingly organised through professional roles, each with its own customs of payment.

In this period, legal work was often priced by task. A lawyer might be paid for an appearance, a pleading, a writ, advice, travel, waiting, or continuing availability.

Retainers were also central. Wealthy clients, towns, churches, and the Crown used retainers to secure loyalty, priority, and access to expertise. A retainer did not necessarily price a single piece of work but it priced availability and relationship.

One thing we didn’t see much of at this time was hourly billing. It was a world of customary charges, professional rank, procedural acts, and continuing obligations. The price of legal work was effectively tied to what was done, who did it, and for whom.

That world crossed the Atlantic with English legal culture. Early American lawyers inherited many of the same assumptions: retainers, task-based charges, court fees, professional status, and the sense that legal payment was not simply an ordinary services transaction.

Part Three: Rule-Based Fees (c. 1700-1900)

By the eighteenth and nineteenth centuries, the answer became a lot more official, and regulation started to kick in. If clients couldn’t really judge legal value, courts and public rules could help decide what was reasonable.

England developed a strong tradition of scrutinising solicitors’ costs. Bills could be “taxed”, meaning they were reviewed by court officers. I believe the modern term is “detailed assessment”, but the underlying idea is very old: a solicitor’s bill, especially in litigation, is not just a private invoice. It can be examined by the court.

This mattered because English litigation costs were not only about the contract between solicitor and client. They were also about what one party might recover from another. A bill had to be broken down into recognisable items: attendances, documents, hearings, travel, waiting, counsel’s fees, disbursements. The result was a culture of detailed legal costing before the billable hour became dominant.

America leaned more heavily on statutory and bar-backed fee schedules. A will, title examination, writ, adoption, or routine matter might have a customary or published price. These schedules made legal charges more predictable but they also made the profession vulnerable to the perception that it was fixing prices.

The underlying assumption in this period is interesting - it’s that legal fees were effectively too sensitive to leave entirely to private bargaining. The problem of pricing legal work was often treated as a matter for custom, statute, court supervision, or professional control.

Part Four: Lawyer/Client Risk-Sharing (c. 1800-present)

The first major Anglo-American divergence came over contingency fees.

In the United States, contingency arrangements became increasingly accepted during the nineteenth century. They solved an access problem - i.e., a client without money could still bring a claim by promising the lawyer a share of the recovery. The lawyer priced not only labour, but risk: the possibility of doing substantial work and receiving nothing.

The logic made sense - rights are worth little if a person cannot afford the means of enforcing them. American courts and lawyers increasingly accepted that contingency fees could open the courthouse door to clients who could not pay in advance.

England resisted this logic for longer. There was a strong suspicion of litigation funding by someone with a stake in the result. The worry was that such arrangements might encourage speculative litigation, distort justice, or give outsiders improper control over disputes.

That resistance did eventually soften. Conditional fee agreements were enabled under the Courts and Legal Services Act 1990. So, England and Wales reached outcome-linked pricing later, more cautiously, and with a lot more regulatory friction than the United States.

The divergence is a good example of how legal billing is never just accounting. It reflects a culture’s view of access to justice, risk, professionalism, and the proper relationship between lawyer and client.

Part Five: The Timesheet Arrives (c. 1910-1940)

The billable hour didn’t actually begin as the obvious next stage of legal billing. It began in the world of legal aid.

Reginald Heber Smith, a Harvard-trained lawyer, worked at the Boston Legal Aid Society in the 1910s. The Society handled thousands of matters with limited resources. Smith needed to understand where lawyers’ time was going - so the daily timesheet was actually a management tool before it was a billing tool.

When Smith joined Hale and Dorr in 1919, he brought that managerial habit into private practice. Time records helped the firm understand workload, efficiency, staffing, and cost. At first, time was an input into judgment. It did not automatically determine the bill.

The six-minute unit was a small office convenience with large consequences. One tenth of an hour was easy to add and divide. A piece of administrative arithmetic became one of the most recognisable units in modern legal practice.

Smith’s system spread because it made legal work legible. It gave firms records. It gave clients detail. It helped partners understand associate labour. It fitted the increasingly managed law firm of the twentieth century.

The big change came when time stopped being only evidence and became the fee itself. A partner could once look at time records, weigh the result, the client, the difficulty, and the relationship, and then set a bill. By the middle decades of the twentieth century, more firms were moving toward a simpler formula: hours multiplied by rates. This transition turned the timesheet from a management aid into a revenue machine.

Part Six: The Hour Becomes The Bill (c. 1940-1980)

The hour spread because it made legal work look explainable. A bill could say: this lawyer worked this long, at this rate, on this task.

This was of particular importance in the US, where large firms were becoming more leveraged, with partners supervising cohorts of salaried associates. The model most often associated with that change is the Cravath system, named after Paul Drennan Cravath of the New York firm now known as Cravath, Swaine & Moore. It was not really a billing system - it was a way of organising a commercial law firm: recruit elite law graduates, train them through partner-led work and rotations, pay them salaries, promote a small number from within, and expect many others to leave along the way.

That structure changed what firms had to price. A modern firm was no longer only selling the personal attention of a few senior lawyers. It was also selling the organised labour of associate teams. Hourly billing fitted that structure much better. Associate time became measurable, comparable, and billable. It gave firms a way to manage production and gave clients a detailed account of effort.

Then came Goldfarb v Virginia State Bar in 1975. Lewis and Ruth Goldfarb needed a title examination for a house purchase in Fairfax County, Virginia. The local minimum fee schedule set the price at one percent of the property value. The US Supreme Court held that the fee schedule and its enforcement mechanism violated the Sherman Act, describing the arrangement as a “classic illustration” of price fixing.

So, once official price floors became untenable in the United States, hourly billing looked even more attractive: not a cartel price, but a documented calculation. This person worked this long - this is the rate, this is the bill. Who could argue with that?

It also suited the institutions around legal work. Tax authorities, courts, insurers, procurement teams, finance departments, and law firm managers could all understand time-and-rate billing. It was legible, disputable, auditable, and easy to put into systems. That institutional convenience helped lock the hour in place.

England and Wales reached the hour differently. There was no direct Goldfarb moment. The English tradition of costs assessment and itemised bills remained important. But as City firms competed with American firms and served international commercial clients who expected time-based bills, hourly billing became increasingly influential.

By the late twentieth century, the billable hour had become the shared language of commercial legal pricing on both sides of the Atlantic, even though the two systems had reached it by different routes.

Part Seven: The Revenue Machine (c. 1980-2008)

By the late twentieth century, the billable hour had become more than just a pricing method - it became a law firm operating system.

Firms set annual billable-hour targets, associates were assessed by recorded time, and partner profits depended on leverage, rates, utilisation, and realisation. The hour measured not only client cost, but also internal productivity.

This solved some problems - it handled uncertainty, it produced records, and it worked across litigation, transactions, investigations, and advisory work. But it also created new tensions. Most obviously, it priced effort rather than outcome. It shifted much of the risk of inefficiency onto the client. It made legal bills auditable, but also contestable line by line. It explained what lawyers did, but not always what the work was worth.

The internal consequences were just as important. Recorded time became a measure of productivity, status, commitment, and economic contribution. Associate targets rose and the timesheet became part of promotion, compensation, staffing, and firm culture. “How busy are you?” became the conversation starter in law firm corridors from London to New York.

That is why criticism of the billable hour became so persistent. The objection was not simply that it could be expensive but also that it priced input in a profession whose value often lies in judgment, outcome, risk reduction, and trust.

Part Eight: The Hybrid Era (c. 2008-Present)

The present did not begin with the death of the billable hour. It began as a hybrid.

After the financial crisis of 2008, corporate clients pushed harder for budgets, caps, fixed fees, blended rates, retainers, subscriptions, and other alternative fee arrangements. General counsel wanted more predictability, more discipline, and more control over outside legal spend. We saw procurement teams get involved in fee negotiations.

Firms still recorded time, courts still used hourly rates in costs assessments, and clients still received hourly bills. Associate utilisation was still measured in hours. But fixed fees, capped fees, blended rates, retainers, conditional fees, damages-based agreements subscriptions, and other creative models increasingly sat alongside the hour.

Many of these alternatives still depended on time data. A fixed fee was often built from expected hours. A cap assumed a forecast of work. Even when the invoice no longer showed every tenth of an hour, the clock often remained inside the pricing model.

Recent transparency reforms added another layer. Since December 2018, SRA-regulated firms in England and Wales have had to publish price and service information for specified areas such as residential conveyancing, uncontested probate, motoring offences, immigration, employment tribunal claims, debt recovery, and licensing applications. This did not create a general tariff, but it reflected the same recurring concern: clients need help understanding what legal work may cost.

This is why predictions of the billable hour’s death repeatedly proved premature. Clients often dislike its unpredictability, lawyers sometimes criticise its incentives. But time records remained useful because they produced data about effort, staffing, cost, and profitability.

What has really happened in this most recent period is not the disappearance of time. It is the declining assumption that time alone must always be the client-facing price.

Where Next?

Generative AI is already starting to put law firm pricing under pressure.

AI systems can draft, summarise, compare, classify, search, translate, and review at speeds that make hourly billing awkward. If a task that once took five hours takes twenty minutes, the old arithmetic breaks. A lawyer cannot honestly bill five hours merely because the task once required five hours. But it also does not follow that the work is worth only twenty minutes.

That is the new pricing problem. AI compresses production time, but it does not eliminate legal value. The valuable work moves elsewhere: choosing the right question, selecting the right tool, protecting confidential information, checking the output, spotting what is missing, exercising judgment, advising on risk, and standing behind the result.

There isn’t a law firm I speak to these days whose clients aren’t congratulating them on their recent AI press release, while also asking “So tell me about the efficiency saving on my next matter…”

Regulators are already circling the issue. In 2024, the American Bar Association’s Formal Opinion 512 stated that lawyers who bill hourly must bill for actual time spent when using generative AI. It also warned that lawyers generally cannot bill clients for time spent learning tools they should competently know how to use, while leaving room for advance agreement about charges for particular AI tools or workflows. The opinion is advisory rather than legislation, and bar guidance will not be identical everywhere, but it captures the pressure AI puts on hourly billing.

In England and Wales, the Solicitors Regulation Authority has also noted that AI may speed up work that would usually be billed by the hour, and that billable hours could become outdated if technology changes the time required to complete tasks. The SRA’s discussion sits alongside familiar regulatory concerns: accuracy, supervision, confidentiality, data security, transparency, and professional accountability.

This suggests that the next era may put more weight on the idea of assurance (or even insurance). This isn’t new - clients have always bought some mixture of reputation, regulation, professional indemnity, confidentiality, and accountability. What AI perhaps changes is the ratio. If less visible labour is needed to produce a draft, search result, summary, or review (or indeed if clients can self-serve), the fee has to be justified by the lawyer’s supervision, verification, judgment, accountability and possibly insurance.

If the billable hour priced effort, AI forces the profession back to an older question: what is the client actually buying?

The result for now is most likely another hybrid. Routine “run the company” work may move toward fixed fees, subscriptions, productised services, and volume pricing. Complex “bet the company” matters may remain at least partly hourly where uncertainty is high. Some firms may charge for AI-enabled workflows by task, work product unit, or outcome. Courts and regulators will still ask whether charges are reasonable. Clients will ask whether they are sharing in the efficiency gain. And if AI companies increasingly charge by the token (given the expected compute crunch on the horizon), maybe we’ll end up with “The Billable Token” alongside “The Billable Hour”.

Final thoughts

The story of the last 2,000 years suggests caution about predicting that any one approach will win.

Every supposed final answer to legal pricing has turned out to be partial. Legal billing keeps changing because the underlying problem does not go away: how do you price work whose scope is uncertain, whose value may only become clear afterwards, and whose quality the client often cannot fully judge?

I don’t have the answer - but I’m confident lawyers will find ways to capture the value of their work for many years to come.

Sources And Further Reading

• Goldfarb v Virginia State Bar, 421 U.S. 773 (1975)

• WilmerHale, “Reginald Heber Smith and the Birth of the Billable Hour”

• Cravath, “The System’s History”

• The Damages-Based Agreements Regulations 2013

• SRA price transparency guidance

• Solicitors’ guideline hourly rates

• NALP billable-hours material

• ABA Formal Opinion 512 on generative AI tools

• SRA report on serving clients’ needs in a changing legal market

• Thomson Reuters Institute on AI-driven legal pricing

In the 1980s, as an undergraduate computer science student, he encountered AI assistants for the first time. His module introduced a then-new paradigm: human language, chat-based systems. The exercise was to build something modelled on ELIZA, the MIT chatbot whose therapist module ran on a simple heuristic. Find the keyword. Reflect it back to the user. When in doubt, tie it all back to the user’s parents. It was deterministic, a little absurd, and wildly popular with the people who tried it. Dazza learned some tricks and came away with a fascination with what AI was, and more importantly, what it could be.

Note: Some of the concepts in this episode may be unfamiliar to some listeners. We cover them in the technical explainer at the end.

What followed was a career which has spanned dozens of initiatives around the world. Let’s just say that Dazza has worn (and continues to wear) a lot of hats. Legislative aide. Candidate for office. In-house Technology Counsel to the Commonwealth of Massachusetts (which by the way he notes would be a Fortune 50 company if it were private). Standards architect. Stanford researcher. Platform builder.

He went to law school, he tells me, because he kept getting different answers from different lawyers to the same question and found it unacceptable. After years of practice, he still does not have a fully satisfactory answer to “what is the law?” but he at least knows how to find the relevant law himself, which was enough to let him return to technology without feeling incomplete.

He was doing legal tech, he says, before anyone called it that. Writing scripts to automate his work. Treating legal documents as data - to the extreme displeasure of colleagues who just wanted Microsoft Word (why is it always Word?)

Why didn’t the standard stick?

In the early 2000s, Dazza was one of the architects of LegalXML, an effort to create an international standard for marking up legal documents so they could be treated as structured data. He ran the e-contracts group. It took seven years to reach the status of a recognised international standard. It attracted a small community of who he describes as lawyer-geeks who marked up their contracts in XML, built clause libraries, and imagined a future of genuinely interoperable electronic contracting.

It did not really arrive, at least not in the way the group expected. A handful of vendors adopted the standard, mostly for workflows they were already running. The broader transformation never came.

The lesson Dazza draws from it is that the obstacle was never the standard. It was the culture. The love affair with Microsoft Word was not something a well-designed schema could fix. Standards, he says, have to arrive at the right moment. Too early and the industry is not ready. Too late and people have already locked into whatever is there.

The good news is, the moment has now arrived, and it came from a different direction entirely. Large language models can peer into the meaning of a legal instrument and address it as data without anyone having to tag a single element. Lawyers, it turns out, are naturally good at the lingua franca of LLMs: precise language, conditional logic, sub-clauses, if-but-not-that constructions. The standard that nobody could agree on turned out to be language itself.

Is your agent loyal to you?

Dazza has just finished a research sprint at Stanford’s Digital Economy Lab on a project called Loyal Agents, run jointly with the Consumer Reports Innovation Lab. The question it asks is simple and the implications are not: when an AI agent conducts transactions on your behalf, what legal framework governs whether it is actually acting in your interest?

His answer keeps returning to fiduciary duty, and specifically to a US federal case called Kovel that almost nobody in legal AI is talking about. Kovel itself is sixty years old. It involved an accountant working with a tax law firm whose communications with a client became the subject of a grand jury subpoena. The Second Circuit held that the privilege extended to the accountant because he was acting as the lawyer’s agent in providing legal advice. The principle that emerges, Dazza argues, applies directly to modern AI vendors. To protect attorney-client privilege when a SaaS provider is handling client communications on behalf of a lawyer, that provider needs to be the lawyer’s agent in the legal sense. Most enterprise AI contracts disclaim exactly that. Dazza has read them all. He has built a public breakdown of what each of the major frontier model providers actually says in their enterprise terms about confidentiality and agency. See Links below.

The most upvoted question at Anthropic’s recent legal webinar, which drew over 20,000 registrations, was about how to handle privilege when using general purpose AI tools. Dazza thinks the profession is looking for the answer in the wrong places. The technical controls matter. Zero data retention matters. But the legal layer, the contract clause that says we are your agent, is what Kovel’s logic requires, and most providers do not offer it.

His pitch to the frontier model providers is direct: stop disclaiming agency and start defining it. Write a narrow, limited agency clause. Be your client’s agent for three specific things and disclaim it for everything else. It contains risk rather than expanding it, it supports privilege, and for providers building agentic products, it simply reflects reality. If a human did what these systems do, it would be an agent. In Dazza’s opinion, the contract should say so.

The platform nobody has built yet

Six months ago, Dazza started building something called Interlateral. The felt need behind it is something he has observed sitting in meetings in San Francisco with startup and innovation teams where everyone has agents running quietly in the background, and then communicating with each other through the narrow human-to-human channels of Slack and email, as if the agents are not there.

Interlateral is a shared collaboration space where humans and their agents can work together in the same room. You bring your agents. He brings his. There is a third space where they can interact, collaborate, and co-work, with a shared markdown surface that both humans and agents can read and write into. The design principle is human-centred: a person is always at the wheel. The agents are extended cognition, not autonomous actors.

The first event ran at Stanford last week with 60 lawyers and eight teams, and the next is at MIT. Eventually, Dazza wants tens of thousands of participants. He thinks the combination of people and agents in a shared space is a genuinely new source of collective intelligence, and that we have barely started to understand what it can produce.

There is a harder problem underneath it. In Google Docs or Slack, identity is straightforward. You can see who wrote what. In a space where agents are acting on behalf of humans, you now have two levels of separation from the person you think you are dealing with. Agent identity and attribution, knowing whose agent it is and holding humans accountable for what their agents do, is a bleeding-edge question the industry has not yet solved.

How he builds with agents

At the end of our conversation, Dazza pulls up his GitHub repo and walks me through how he actually works. The interlateral_agents repo is open source, the product of years of slow tuning, and it is more architecturally interesting than most people’s agent setups.

He runs three models in parallel: Claude Code, Codex, and Gemini, with Grok CLI from xAI expected to join shortly. What makes it unusual is the communications layer. The agents share what he calls a multiplexing comms hub, a setup that lets them read each other’s outputs and write into each other’s terminals directly. He describes it as a Vulcan mind meld. One agent can see that another tried something, that it failed, and suggest an alternative approach. The collaboration is explicit and adaptive rather than just running tasks in sequence.

On top of that he uses skills: lightweight prompt-level definitions that tell the agents how to organise their work on a given task. He can arrange them hierarchically, with one agent orchestrating the others, or as peers collaborating on the same problem. The skills determine the shape of the collaboration without requiring complex infrastructure to enforce it. It is, he says, a surprisingly low-key way to get a lot out of very capable but very different models working together.

What is the AI-native organisation?

I ask Dazza what he thinks people are underestimating.

The current pattern, he says, is that AI creates extraordinary efficiency at discrete points in a workflow and then causes congestion at the parts downstream that have not changed. Contract review is faster. The humans waiting for the output of contract review are not. The clog forms between the transformed part and the untransformed part, and it is going to get worse before anyone fixes it.

The AI-native organisation is one that has redesigned itself around AI, touching pricing models, staffing, role definitions, and quality control, which starts to look less like a periodic event and more like continuous monitoring. That redesign, he says, is not premature. It is coming whether organisations are ready for it or not. The ones doing the mapping exercise now, looking holistically at the full lifecycle of a matter rather than optimising individual tasks, are the ones who will navigate it gracefully. The ones waiting are storing up a serious problem.

Final note

Dazza Greenwood is genuinely hard to keep track of. In preparing for this conversation I found Stanford research, open source repositories, consulting work, a platform under active development. Dazza literally switched hats midway through our discussion.

What surprises me most though is that none of it feels scattered. The dots (and hats) are actually very connected. It all connects back to the same conviction he has held since the start: that law and technology are not separate domains, that legal instruments are data, that agents will conduct transactions on behalf of humans and the frameworks governing that need to be built carefully. He has been pretty patient about this for forty years. The future, he says, has finally arrived. And he seems genuinely delighted about it.

Links

• dazzagreenwood.com, Dazza’s blog including his upcoming open source model comparison table

• computationallaw.org, Dazza’s write-up of the Stanford Interlateral event

• interlateral.com, sign up to attend a future event

• civics.com to understand more about Dazza’s consulting work

• loyalagents.org, the Stanford and Consumer Reports research and vendor contract analysis, including the Kovel breakdown

• analysis on when agency is a feature not a bug

• github.com/dazzaji/interlateral_agents, the open source agent library Dazza uses to build with Claude Code, Codex, and Gemini

The Technical Stuff

Here’s a quick primer on some concepts that may be unfamiliar to some:

Evals

Short for evaluations. A structured way of testing whether an AI system is doing what you want it to do, consistently and measurably. Dazza uses an open source platform he built to run evals on agent behaviour, putting numbers on whether an agent is acting in a user’s interest or getting tripped up by a conflict it has not recognised. Think of it as quality control, but for AI decision-making.

Fiduciary duty

A legal obligation to act in someone else’s best interest rather than your own. It applies to lawyers, financial advisors, and other professionals. Dazza’s Loyal Agents research asks whether AI agents should be held to something similar, and whether the contracts governing AI services currently reflect that expectation. Most do not.

Kovel

A 1961 Second Circuit case that Dazza thinks is the missing piece of most privilege discussions in legal AI. United States v. Kovel involved an accountant employed by a tax law firm whose communications with a client became the subject of a grand jury subpoena. The court held that the attorney-client privilege extended to the accountant because he was acting as the lawyer’s agent in providing legal advice. The principle Dazza extracts: for privilege to hold when a SaaS provider handles client communications on a lawyer’s behalf, that provider needs to be the lawyer’s agent in the same legal sense. Most AI vendor contracts disclaim agency explicitly. Dazza argues this is both a legal risk and a fixable problem, and that providers who address it will have a commercial advantage in the legal market.

Multiplexing

A communications technique that allows multiple signals to share the same channel. In the context of Dazza’s agent setup, it means his agents can read each other’s outputs and write into each other’s terminals in real time, rather than operating in isolation. The result is agents that can observe what the others are doing, flag when something is not working, and adapt accordingly.

Skills

In the context of agent configuration, skills are lightweight prompt-level instructions that define how an agent should approach a task or how a group of agents should organise their work together. They are not code in the traditional sense. They sit closer to a well-written practice note. Anthropic has its own Skills framework, which we have covered separately on agents.law.

Loyal Agents

Both the name of the joint Stanford Digital Economy Lab and Consumer Reports Innovation Lab research project Dazza works on, and a broader concept: the idea that AI agents conducting transactions on behalf of users should be demonstrably aligned with those users’ interests, in a way that is measurable, contractually grounded, and legally enforceable. The research has produced evals for testing agent loyalty and a public analysis of how major AI providers currently handle the relevant contract terms.

When he started his training contract, it was very much focused on innovation and technology. This was a bit of a rarity at the time, and Mike was one of the first in the UK to follow a legal tech-focused training contract. Since then, the innovation team at AG has grown from a handful of people to around 80 today.

Fast forward to 2026, and Mike now runs the firm’s R&D function, a broad role that encompasses horizon scanning, startup engagement, partnering with clients, internal education, leading a development team, and - increasingly - a fair amount of building things himself.

How do you keep up?

I asked Mike how anyone keeps up when the world is spinning this fast. His answer is that he doesn’t really switch off. He reads constantly, runs research agents through Claude Code, and writes a fortnightly internal newsletter for his team: three things to know, three deep dives, then a long reading list. (Side note: I think this is a great habit to get into. It’s actually one of the reasons I started this blog in the first place!)

He says it’s really important for him to know what he’s talking about. He’s not afraid to say he doesn’t know, but he doesn’t like saying it, so he’d rather know and do it.

AGPT and Buy vs Build

The most visible output of Mike’s team is AGPT, the firm’s in-house AI tool. It’s one of the rare examples of a law firm having built its own in-house AI solution at a time when most of the industry is focused on buying legal AI.

Mike describes AGPT, with characteristic understatement, as “what most people just call a wrapper”. It lives in the firm’s Microsoft Azure environment and does the usual things: chat, document review, translation, prompt libraries, citation tracking.

In early 2023, Mike’s team wanted a sandbox to test whether GPT-3.5 was good enough for legal documents. They couldn’t throw client matters into ChatGPT, so they asked the developers to stand something up inside the firm. Other lawyers started asking for access. A pilot followed, then a firm-wide rollout by autumn 2023. The sandbox became the product. Today AGPT runs around 6,000 prompts a day across roughly a thousand users, and the dev team hasn’t had a quiet week since.

Mike’s buy vs. build framework is worth listening to because it comes from someone who has actually done both - and believes in doing both.

Cost is obviously a factor in decisions, but he frames it as a return-on-investment question rather than a sticker-price one. The bigger factors, he says, are three things:

1. Institutional knowledge: you can build for your specific audience in a way you can’t buy for one. A product on the market might have 70% irrelevant features and lose people before they engage, whereas a 30% solution built for your lawyers can land better. That said, if vendors are willing to partner and co-develop things, that can be a solid option too.

2. Client consent and data: self-hosted removes a lot of friction and makes for easier discussions with clients.

3. Portability: which he thinks is underrated. The value is in the solution to the problem, not the tech it happens to run on. In Mike’s view, law firms are going to want to move their prompt libraries, workflows and accumulated know-how between models, and the firms that treat their intelligence as tech-agnostic will have an easier time than those locked into a single vendor. He uses a nice phrase for this: portable intelligence. We discuss the relevance of standards like Skills as a way to make intelligence portable between vendor and in-house solutions.

It’s worth listening to the full discussion on this as Mike shares more about his buy vs build framework.

Claude, vibecoding, and the artifact economy

Mike and I are both heavy Claude users. He uses Claude Code primarily to build prototypes - someone in the firm has an idea, usually hard to execute, and instead of taking notes and going away for six months, Mike builds a rough version and shows it to them.

One recent example is a regulatory horizon scanning tool for banks, the kind of thing his financial regs team has wanted for a long time. Not a finished product but enough to say “is this what you mean?” and have a real conversation with the Partner.

On Claude for legal work itself, Mike is bullish in a way that he thinks should worry the established legal AI vendors. A lot of work inside law firms isn’t legal research. It’s factual research, web searching, document comparison, content creation, the work that fills a competition team’s afternoon. That work is dramatically easier in Claude than in Google, and Mike says Addleshaw is considering wider licenses for the lawyers and teams who would benefit.

Training junior lawyers in a world with fewer trainee tasks

The question that won’t go away is what happens to junior lawyer training when the grunt work disappears. He thinks training in law firms has always sort of worked by accident. Trainees are bright, engaged, hard-working people who pick things up by osmosis, sat next to a supervisor with a red pen. It’s slow, it’s inconsistent, and what you actually learn is often one individual’s approach rather than a structured body of knowledge.

His proposed solution is a good one: use the firm’s data and know-how to build anonymised simulations based on real client matters. Give trainees scoring, measurement, and a structured way to develop across different areas. If AI reduces the billable work trainees do by 20%, use that time for simulated exercises rather than cutting trainee numbers by 20%. It’s an optimistic framing, and Mike knows it. The realist’s version is that firms will just fill the hours with more work and make more money, because that’s what the economic incentives reward.

Side note: Mike has built a prototype for this - it’s live on my Vibecode.law platform - check it out!

Final note

What I take from my conversation with Mike is that there’s a particular kind of legal innovator becoming more common in the industry. They’re not pure technologists and they’re not pure lawyers. They’ve got enough technical ability to build, enough legal experience to know what matters, and enough organisational patience to sit inside a big firm and make things happen.

In my view, a lot of the interesting change in BigLaw over the next few years is going to come from “intrapreneurs” like Mike, inside firms, building things and encouraging others to do so.

Talk about good timing for a podcast with a leading expert on cybersecurity.

But before we get to that, let’s talk about Fiona’s story, which starts a long way from cybersecurity in the restructuring and insolvency team at a Magic Circle firm, six months before Lehman Brothers collapsed.

She trained at Freshfields, with a six-month stint in The Hague doing arbitration during her training contract. She qualified into restructuring and insolvency, expecting, as she puts it, “a nice quiet corporate support team where I could hide from the really vicious transactional hours.” Then Lehman went down and everything changed. She spent the next stretch of her career working for the administrators of banks and building societies, going in on day one during the most tense period in UK banking history, picking apart what had gone wrong and figuring out how to fix it.

It was fascinating work, but relentless. When HSBC offered her a move in-house, she took it, partly for the quality of life and partly because the bank was so international. She wanted to travel and live abroad, and HSBC delivered on both.

A secondment to Dubai that was meant to last six months turned into four years. She ended up as general counsel for the retail bank across the Middle East and North Africa, dealing with financial crime, M&A across the region, and the complex politics of the Gulf.

The HSBC digital journey

In 2015, Fiona moved to Hong Kong, HSBC’s spiritual home. She tells me that if you get in a taxi in Hong Kong and say “take me to the bank,” you’ll end up at HSBC. She joined the executive committee for the retail and private banks and the team embarked on a serious digital transformation.

The fear at the time was fintechs. Incumbent banks were watching startups build better, faster, more intuitive products, and wondering whether the ground beneath them was about to give way. It’s a dynamic that will sound very familiar to anyone watching legal right now.

HSBC’s response was to go and learn. The exco travelled to Silicon Valley, to China, to Southeast Asia, spending time with big tech companies and innovators. They recruited people from completely different industries. They experimented. They put a team in a WeWork and said: if you were going to disrupt us, what would you build? The lesson, Fiona says, was about giving people inside a big organisation different rules to play by, creating the right environment for experimentation within a business that was built for stability.

As a lawyer watching all of this, she couldn’t help wondering how the same thinking might apply to the legal function. So they tried. And then Fiona became, as she puts it, “really obsessed” with legal design.

Legal tech is the new fintech

When we talk about what law can learn from what happened in banking, Fiona draws a sharp parallel but also flags a crucial difference.

In banking, the fintechs discovered that becoming a bank is hard. Capital requirements, regulatory burden, and consumer expectations around safety and stability all acted as barriers. That’s why the big banks survived. They digitalised fast enough, and the moats held.

In law, those moats may not exist. It’s much easier to become a law firm than a bank. The barriers to entry are low. And clients may not care about the stability and heritage of a big firm if they can get what they need from a tech-enabled alternative. Law, Fiona suggests, may be significantly easier to disrupt than banking was.

The one thing the banking experience made crystal clear, she says, is that you have to obsess about the customer’s point of view. “You have to stop thinking that a customer wants a mortgage. They don’t want a mortgage, they want a house.” The same logic applies to law. Nobody wants a conveyancing lawyer, she says. They want a house. The legal work should be seamless, frictionless, and invisible. If AI-native firms can build that experience from scratch rather than trying to retrofit it onto traditional models, she thinks they may have a genuine structural advantage.

Kill the memo

This leads us to legal design, which Fiona describes simply as making sure that when you deliver a product or service to a client, it’s designed from the beginning for their needs, not yours.

She gives a pointed example. She’s been drafting an AI policy for a client. Most templates she’s seen start with definitions, because they’re written by lawyers for lawyers. Nobody, she says, has ever opened a document as a normal person and thought: what I’d really like first is a dense legal definition. And most AI policies she’s seen are either aggressive or patronising in tone, full of prohibitions and warnings, when what users actually need is clear, practical guidance on a handful of questions. Can I use this tool? What data can I put in? Has the client consented? How do I check the output?

She thinks the legal profession has a deep problem with this. Lawyers don’t think of what they do as a product. They think they give advice. Products feel cheap, beneath them. But if you launched a product in banking or cosmetics, you’d never release it without testing it on users first. The legal profession has, by and large, a complete absence of that kind of testing.

And she’s clear-eyed about the difficulty: making something simple is deceptively hard. Lawyers see a well-designed document and think it looks easy. Actually, she says, getting to simple is a real art, and getting lawyers to respect that is one of the biggest challenges she faces.

At one point in our conversation, we joke about launching KillTheMemo.com. She’s in. I think she’s only half joking.

Back in private practice

After years in-house, Fiona had what she describes as a reflective moment. She went and shadowed a criminal judge for a while. She’d originally wanted to be a criminal barrister and never did, and she wanted to ask herself a basic question: did she still want to be a lawyer?

The answer was yes. She believes in the rule of law. She believes in the power of the law. But she also knew she wanted to be at the cutting edge of where technology was evolving, and she needed to be somewhere that the ethical dimension mattered, somewhere she could say to clients “I don’t think you should do this, even if it’s legal.”

She found that at Marks and Clerk, a 130-year-old IP firm. What drew her in was the people. Patent attorneys, she points out, are the inverse of the usual dynamic: they’re technologists and scientists who became lawyers, rather than the other way around. “It’s kind of the perfect lawyer, in my view.” The firm works at the cutting edge of invention: AI patents, semiconductors, electronics, space. One of her colleagues is on the shortlist to be the UK’s first astronaut.

Within Marks and Clerk, she’s built a new subsidiary focused on cybersecurity, data, AI law, governance, and ethics, with a strong emphasis on education. She describes it as a startup inside a law firm. She doesn’t think she’d have gone back to private practice for traditional transactional work. But she found a place where she can practise law in a way that makes her passionate and lets her build things.

The Anthropic question

The Glasswing announcement has led to a busy week.

She tells me the defenders of companies and governments from cyber attacks are in a constant race with criminals, and the criminals have a structural advantage: they don’t have to comply with any law, go through compliance checks, or worry about whose data they’re using. What Anthropic has said, in essence, is that it has built a model that could be transformative for cyber defence, but devastating if it fell into the wrong hands.

Fiona’s question is about who gets to set the red lines. She thinks it’s admirable that Anthropic has drawn them. But in a functioning democratic society, she asks, should it really be a private company that determines what the government can and can’t do with AI? These companies can enforce limits because they control the tools. But is that how it should work?

She’s not arguing against Anthropic’s decision. She’s arguing that we haven’t built the democratic infrastructure to handle decisions of this magnitude.

Regulation is not the enemy

Fiona pushes back on the common argument that regulation kills innovation. She doesn’t buy it, though she’s thoughtful about proportionality. The question, she says, is whether the most powerful AI models are the equivalent of nuclear technology: capable of enormous good, capable of enormous harm, and therefore requiring intergovernmental rules and collaboration, not just one country’s framework. That top tier of AI, the systems that could orchestrate large-scale cyber attacks, probably warrants that level of seriousness. Your contract review tool does not.

In the meantime, she thinks companies should stop waiting for legislation and start self-regulating on substance, not just process. She’s frustrated by the responsible AI conversation as it currently exists, which she sees as too focused on frameworks and tick-box compliance. She wants companies to take positions: what will you ban? What will you never do? What’s your stance on emotional recognition AI? On AI in HR? On recording every call with a transcription tool?

And she makes a powerful point about existing law. Tort law already provides duties of care that could apply to AI harms. In the absence of legislation, she expects to see a lot more litigation. It’s already happening in the US, with cases involving children harmed by chatbot interactions and bias in hiring tools.

The education gap

Underpinning everything is what Fiona sees as a massive education problem. It’s not just judges who don’t understand the technology.

Many AI vendors can’t clearly explain how their own tools handle data. Companies don’t understand the true value or true risk of their data. Senior executives can’t articulate how their organisations use it. In a world where AI governance is becoming critical, she worries about a repeat of what happened with GDPR: a compliance exercise that generated paperwork without generating understanding.

She and her colleague Eleanor, Marks and Clerk’s data partner, are trying to change this by building educational programmes for in-house lawyers. The goal is about helping people ask the right questions. When someone says “let’s talk about data,” are they talking about prompts, training data, outputs, or something else entirely? Until people can make those distinctions, she says, governance will remain surface-level.

Final note

Fiona Phillips has built a career that most lawyers wouldn’t have the nerve or the curiosity to attempt: Magic Circle to banking to the Middle East to Hong Kong to a startup inside a 130-year-old patent firm. She’s done insolvency, financial crime, digital transformation, legal design, and cybersecurity.

What comes through most clearly in our conversation is a combination of moral seriousness and creative restlessness. She genuinely believes that lawyers have a responsibility to tell clients not just what’s legal, but what’s right. And she thinks the profession’s resistance to rethinking how it delivers its work, from the 30-page memo to the definition-first policy document, is both a failure of imagination and a disservice to clients.

She closes our conversation with a line from Ernest Shackleton, borrowed via Jacinda Ardern: optimism is true moral courage. It’s brave to stay optimistic, she says. But if we don’t, what else have we got?

I love the chart and (depending on how you look at it) the gap between theoretical and actual is a generational opportunity.

But there is one problem with it, and it’s a nuance we keep missing in the narrative around AI in law: law is not one thing.

While this feels like I am stating the obvious, I see it time and again in the discourse. AI will transform law firms. AI will kill the billable hour. Law is resistant to change. Lawyers will be replaced. Or they won’t. All of these claims share the same flaw.

Let’s agree which part of “law” we are talking about

Law is a single profession. Lawyers go to law school and hold practising certificates and tend to work for firms with licences and insurance, or within in-house legal teams. Beyond that, they often don’t have as much in common as you might think.

Consider three lawyers:

• One is running a cross-border M&A deal: coordinating due diligence across fourteen jurisdictions, reviewing dozens of contracts, engaging with the regulator, negotiating an SPA, advising on structure, managing a signing and closing process under time pressure.

• Another is handling a probate matter: guiding a bereaved family through the administration of an estate, valuing assets, coordinating tax return filings, distributing funds, navigating family dynamics.

• A third is a trial lawyer in the middle of a high-stakes bet the company dispute: cross-examining a hostile witness, making submissions to a judge, reading a courtroom in real time, deciding on the fly whether to press a line of questioning or abandon it.

Yes, these three people share a professional qualification. Yes, they share several of the same foundational skills and patterns of thinking. But the overlap in what they do each day is actually quite thin. The cognitive demands, the pace, the relationship with documents, the role of judgment, the industry or sector nuance - all sufficiently different that each lawyer absolutely could not step into the other’s shoes without a significant amount of retraining and unlearning.

And that’s before you get into the difference between working in an AmLaw 20 firm, a UK High Street firm, or the APAC legal department of a Fortune 500 company.

Talking about how “AI will transform law” is a bit like saying “AI will transform sport”. We need to agree which sport we are talking about. All involve people who participate in the sport, most tend to need certain baseline skills such as coordination, reactions, strength or speed, but the impact of AI on Formula 1 is, I would imagine, rather different to the impact of it on Taekwondo or Badminton or Curling. (Yes, we in the UK are still not over the defeat to Canada.)

Practice areas might not be that helpful either

In an effort to draw a distinction between the vast array of jobs that lawyers do, we tend to group things by Practice Area or Sector.

I’m a TMT Lawyer. You’re a Commercial Litigator. This is better than lumping everything under “Law” but I’d argue we should go further when we talk about AI because even within each of those areas, there is enormous variation. Practice areas are effectively bundles of tasks, and each of these tasks is radically different in terms of its exposure to AI.

The M&A lawyer’s week includes tasks that look like project management, due diligence and contract review, drafting, negotiation, pricing and budgeting, relationship management, and so on. And each of those tasks has a completely different relationship with AI.

So I remade the chart

I was curious what it would look like if we remade the Anthropic spider chart but rather than look at impact by industry or practice area, we look at it by legal tasks. I came up with 20 legal tasks and applied the same two dimensions as the Anthropic chart: theoretical AI capability and observed adoption.

The numbers are wrong

These numbers are not the output of a controlled study. Like most things in AI right now, they are best guesses, informed by a limited amount of research gathered rather quickly from Goldman Sachs, Thomson Reuters, Harvard Law School’s Center on the Legal Profession, the VALS Legal AI Report, and several industry surveys from 2025 and early 2026. I should probably cross-check against Michael Kennedy’s excellent legal tech stats engine, which was published today. The precise values and even the variables on the chart are absolutely debatable and I would welcome that debate.

But my point is all about the shape of the chart. It’s that the unevenness, the spikes and collapses, would broadly hold even if every number shifted one way or another. It’s that law is not one thing.

The takeaway from this

My main objective here is just to bring a bit more nuance and clarity to the conversation about AI so we have better discussions.

By thinking in terms of tasks rather than departments or the entire industry, we can:

• Make sure we’re actually discussing the same thing (i.e., apples to apples) rather than make broad predictions which are partly right and mostly wrong

• Make better decisions about where AI can play a part and where it cannot or should not

• Agree which sorts of tasks will lend themselves more to the billable hour, and which will get unbundled and productised

• Identify use cases and learnings from using AI for a legal task that can be applied between departments and teams.

So, next time someone makes a sweeping statement about AI’s impact on law, ask them which legal task they are talking about.

Sources: Goldman Sachs (2023, 2025); Thomson Reuters GenAI in Professional Services Reports (2025, 2026); Harvard Law School Center on the Legal Profession; VALS Legal AI Report (2025); AffiniPay/MyCase Legal Industry Report (2025); US Legal Support (2025); Legalverse Media (2025).

I started tracking these firms about 12 months ago and have been hosting the list on a page at this Substack.

Since then, the rate of press releases announcing new launches and funding rounds has increased, so I decided to spin out the list into a new directory.

You can access it at www.aifirmindex.com. Artificial Lawyer was kind enough to share it this week and that led to a lot of interest and outreach.

The biggest question so far has been:

"Is 'AI-native' just a marketing term, or is there something meaningfully different about how these firms practise law?"

It’s a very fair question. At times, it does feel like the .com days when adding a .com to your company name might 10x your valuation.

But I do think there is real innovation happening too. And I think this should be of interest to all of us, whether you are:

• Founding an AI-native law firm

• Leading a traditional firm and looking at whether/how to adapt

• One of a growing number of SaaS companies starting to think about a services play

Instead of focusing on the wording of the definition (that’s worthwhile but I suspect at some point it all becomes moot because every firm is AI-native), I decided to look at the data on how these firms actually price and deliver work.

I’ve shared my initial findings on 33 law firms into a brief report.

I’d welcome ideas for improvement or collaboration on this topic.

12,000 lawyers, 200+ offices, 87 countries, through 61 mergers in just over ten years. Just think about this for a moment. A merger every two months. More mergers than the rest of Big Law combined.

So, how (and why) did he do it? And if he were taking on a law firm leadership role today, would he do it all again?

Capitol Hill to the City

Elliott didn’t set out to be a lawyer. He spent years working on Capitol Hill, imagining a life in politics and policy. He eventually concluded that the law would give him what he really wanted: a practice at the intersection of politics, policy, and business. He studied as an undergraduate in the US and got his DPhil at Oxford.

His early practice was in public policy and regulatory law, and he loved it. But he’s honest that it was a different era. Washington was more bipartisan then. You could actually get things done, shape legislation, move the dial for clients. He’s grateful his practice years fell when they did. Today, he says, it’s far easier to kill things in politics than to build them.

The firm nobody expected to win

The origin story of Dentons is far more interesting than most people realise. Elliott joined Sonnenschein Nath & Rosenthal, a well-regarded US firm, but one that was, as he puts it, “absolutely indistinguishable from three or four dozen other US law firms.” It had no global presence. It had tried London once before and pulled out.

He and his team saw something others didn’t - an opening - not just to build a global practice, but to build an entirely different kind of global law firm. He calls it “a polycentric one with no dominant culture, no flag flying over the whole thing, no lawyers parachuted in from New York to do work that local partners should be doing”. The insight was radical: clients didn’t want someone who flies in from London wearing a local suit. They wanted the most elite lawyer who actually knew the market, knew the judges, knew the business community. At the time, he felt that no global law firm was genuinely “in and of the communities it served”.

The first deal, in 2010 with Denton Wilde Sapte, was not warmly received in the legal press. Elliott remembers the UK Legal Week headline vividly: it compared the combination to two drunken sailors falling into bed together. He tells me this with a smile. “It was an improbable start to what has been an extraordinarily remarkable journey.”

61 mergers in 10 years

Most law firms do a deal and then pause, sometimes for a decade, sometimes longer, while they fight out whose compensation system wins and whose culture survives. Elliott took a different view: you don’t have to choose between growth and integration. You can do both in parallel.

So they did. For most of the years he led Dentons, the firm completed more M&A than the entire rest of the legal profession combined. They built a dedicated transactions team and a separate integration team, because the skills required are genuinely different. Finding the right partner is nothing like knitting two organisations together, and conflating the two is how most firms end up stalled. At peak, they were travelling around 200 days a year. To do 60 deals, he says, “you have to kiss a lot of frogs”. There may have been 600 conversations for every 60 that completed.

What made it work was the firm’s polycentric model. Elite local firms in South Africa, India, the Philippines, across the Middle East, firms that had spent decades building client relationships and community credibility, could join Dentons and keep their identity while gaining the platform of the world’s largest law firm. Dentons became the first global law firm to combine with a leading firm in China, the first to achieve level one black economic empowerment certification in South Africa. They were the proof of concept for a genuinely different kind of global firm, and they attracted partners that no other firm could.

The three-way combination in 2013, bringing together what had become SNR Denton, Salans in Europe, and FMC in Canada, was another first. Three-way combinations simply didn’t happen in the legal profession, certainly not across continents. But Elliott and his co-architect Joe Andrew had concluded that the pace itself was part of the strategy.

There were law review articles at the time arguing you could never run a law firm with more than 5,000 lawyers. Elliott mentions this with obvious satisfaction. Those articles, he says, have had to be put in the trash heap.

Why the merger wave isn’t slowing down

The current wave of transatlantic mergers, Elliott argues, is different in character from the waves that came before. The 1990s and early 2000s were opportunistic. What’s happening now is existential.

Mid-market firms are getting squeezed from both ends. The top 25 or 30 firms are pulling away, hoovering up the most profitable work and the best talent. And at the other end, small tech-enabled firms are competing for work that used to be safe mid-market territory, because the tools now allow a lean team to do what previously required a large one. The firms caught in the middle, the ones with leaders who can see the problem but are three to five years from retirement, are the ones he worries about most. He puts it plainly: “I hear from a lot of law firm leaders who are just thinking about getting to the end of their runway and letting their successor worry about it. It’s hardly a profile in courage.”

The consequences, he thinks, will be real. Some firms will go out of business. Others will find they’ve left it too long to find a merger partner worth having. The dance music will stop, and if you’ve got no one to dance with, you may not be able to combine. US firms are arriving in London in record numbers and proving to be formidable competitors. In his view, the window for a good deal is open now but it won’t stay open forever.

If he were starting again today

I ask Elliott what he’d put on his to-do list if he were walking in as global CEO of a large law firm today. He doesn’t hesitate.

First, he’d be doubling down on global opportunity. He’s not among those who think geopolitical turbulence is a reason to retreat. He watched Bloomberg the morning we spoke covering the cascade effects of oil prices across agriculture, tech, and supply chains. There’s no going back, he says. Clients don’t retreat from global markets and they need advisors who don’t either.

Second, he’d be all-in on AI and technology - not just the narrow point solutions getting all the coverage, the plugins, the co-pilots, the contract review tools, but genuine tech enablement across the whole business. He thinks McKinsey’s estimate that 70% of legal work is automatable is probably an underestimate. The disruption, he says, is pervasive. And he suspects the billable hour will be the first major casualty, not immediately, but within a few years as the economics become impossible to ignore.

Third, he’d be shifting away from hourly billing entirely, toward alternative fee structures and success-based models that align the firm’s interests with clients.

Private equity and the AI law firm question

Elliott now spends most of his working days advising private equity firms evaluating opportunities in the legal sector, helping with everything from developing an investment thesis through to due diligence, negotiation, and board service once a deal is done. He describes it as work he loves, surrounded by smart, dynamic people who are coming at the legal industry fresh, without the assumptions (or limitations!) that insiders carry around.

He thinks the interest in law from private capital is not sudden - he reminds me that PE has been circling professional services for years, drawn by stable, recurring, profitable revenue in human capital businesses. Accountants, consultants, engineers: and law is just the next one. What changed is the regulatory environment in the US, where the MSO model now offers a workable structure. The MSO bifurcates the professional practice from the business infrastructure of the firm, allowing outside capital into the latter without touching the former. It’s a tried and tested model from healthcare and other sectors, and it’s gathering momentum fast.

By end of 2026, Elliott expects a couple of dozen US law firms to be PE-backed through this structure. By 2027 and 2028, many more. The starting point is consumer and retail-focused firms, personal injury, insurance defence, construction defect, but he expects a steady move up the value chain as investors get more comfortable with the sector and the model matures.

On the AI law firm question, he is measured. Some of the firms spinning out under that banner are genuinely embedding AI into every workflow, rethinking how legal work gets done from intake to billing. Others, he says, are doing what the dot-com era called throwing a .com on the end: branding more than transformation. The multiples being floated in bidding processes are eye-popping, he notes, though by the time due diligence is done they tend to come back to earth.

The university question

Elliott sits on the board of trustees at Syracuse University, which has forged a partnership with Anthropic to give every student and faculty member access to Claude. He sees higher education as facing exactly the same challenge as law: institutions that are leading on the front foot, and institutions that are hoping this just goes away.

It’s not going away. Faculty members, he says, may be the only group he’s encountered who are even more resistant to change than lawyers. But the institutions that embrace technology, rethink their delivery models, and position themselves as homes for lifelong learners rather than just 18 to 22 year olds, those are the ones that will thrive. The ones waiting it out are storing up a serious problem.

Law schools that don’t teach students how to use and think about AI are, he says bluntly, doing those students a disservice. They’ll arrive at law firms, public service, or wherever else they go and be fundamentally less capable of delivering value. The profession has already been disrupted. Sending people into it unprepared is a failure.

Final note

The sheer scale of his achievement at Dentons is hard to fathom, but when you meet Elliott, you can see how he did it.

He is well-prepared. He is considered and confident about where things are going and what it will take for firms to succeed. I also found him to be extremely generous with his time - before, during and after this discussion.

He’s understandably proud of what he achieved at Dentons - but what surprised me most is that he seems more energised than ever about the next chapter and his opportunity to contribute to it in both law and education.

Elliott Portnoy is just getting started.

What we don’t discuss is that almost every aspect of these businesses now interacts with the physical infrastructure of AI: the data centres, the chips, the machines that make the chips, the companies that make the machines, and so on down the supply chain.

Behind every prompt, Word plugin and tabular review sits an incredibly complex and increasingly strained global supply chain, perhaps the most complex in human history. It depends on a handful of companies, most of which you have never heard of, operating in a handful of countries, several of which sit at the centre of intensifying geopolitical rivalry.

On a couple of long train journeys recently, and after listening to this excellent podcast, I found myself going down the rabbit hole. And the more I did, the more I realised that:

1. The hardware and infrastructure side is going to shape what happens next far more than any legal tech company or law firm.

2. Understanding this now can help you see around corners, make better decisions about where to invest, and how to plan for the next few years.

It starts with a prompt

Let’s consider what happens when a lawyer prompts an AI tool to review a contract.

In the two seconds before the response appears, the query hits an API that calls a frontier model running on GPUs in a data centre consuming megawatts of electricity and millions of gallons of cooling water. Those GPUs were fabricated by a single company in Taiwan, on silicon wafers, using patterns printed by a machine that can only be made by one company, operating out of a small town in the Netherlands. That machine relies on mirrors made by a company in Germany that are so precise that if one were scaled to the size of the country, the tallest bump on its surface could not exceed one centimetre.

And every one of those steps has a bottleneck.

Bottleneck 1: The Most Complicated Machine in the World

In Veldhoven, a quiet town near Eindhoven in the south of the Netherlands, is a company called ASML. It barely comes up in discussions about AI. I confess I had never heard of it. But with a market cap of $527 billion, it is now the largest company in Europe, and there is a case for saying it might be the most important company in the world right now. It has no meaningful competitor.

What does ASML do? It makes a machine called the Extreme Ultraviolet lithography system, known as an EUV, a tool for printing the microscopic circuit patterns on silicon wafers that create every leading-edge chip on the planet. Without these machines, not a single advanced Nvidia GPU, Google TPU, Apple processor, or AMD chip gets made. Your AI-powered contract review, your due diligence assistant, your research tool: none of them exist without this machine.

To understand why ASML is the single biggest bottleneck in the AI supply chain, you need to understand what its machine actually does.

The features on a modern chip are a few nanometres wide. To print patterns that small, you need light with an extremely short wavelength, 13.5 nanometres, in the extreme ultraviolet range. This light does not exist in any naturally useful form, so it has to be manufactured. ASML’s machine does this by dropping tiny droplets of molten tin into a vacuum chamber. A laser hits each droplet three times in rapid succession: the first pulse shapes it, the second flattens it into the ideal geometry, the third blasts it at extreme power, exciting the tin atoms until they emit photons at exactly 13.5 nanometres. This happens 50,000 times per second.

EUV light is absorbed by virtually everything, including glass, so conventional lenses are useless. Instead, the light bounces off a series of multilayer mirrors manufactured by Carl Zeiss in Germany. You might recognise that name from your camera lens. Each mirror is built from alternating layers of molybdenum and silicon, deposited atom by atom. The precision is staggering: if one of these mirrors were scaled to the size of Germany, the tallest bump on its surface could not exceed one centimetre. Zeiss produces these optics in quantities of hundreds per year. The process is closer to artisanal craftsmanship than to mass production.

This has been described as the most complicated machine in the world. The whole tool weighs over 150 tonnes, fills the space of a double-decker bus, ships in 40 freight containers, and takes months to install.

Each machine costs $300 to $400 million. ASML currently produces about 70 per year, growing to perhaps just over 100 by the end of the decade.

It will not be enough.

Roughly 3.5 EUV tools are needed per gigawatt of AI compute. Those tools cost about $1.2 billion. The data centre they enable costs $50 billion. The AI revenue generated on top could be $100 billion or more. An astonishing economic pyramid resting on a narrow physical base.

The obvious question is: why not just build more of them? One, because it’s really really hard; and two, because the companies who make these things haven’t yet been convinced to ramp up more aggressively.

An EUV machine is not a product that can be scaled like software or even like a conventional manufacturing line. It is an assembly of roughly 100,000 components sourced from hundreds of suppliers across multiple countries, many of whom are themselves the only company in the world that makes their particular part. The Zeiss mirrors alone take months to produce, require atom-by-atom deposition of alternating layers, and are made in quantities of hundreds per year by a workforce of highly specialised technicians. You cannot train those people quickly. You cannot build a second Zeiss mirror line the way you might open a second factory for car parts. The knowledge is embodied in the people and the processes, not in a blueprint you can hand to a contractor.

The same is true of the Cymer laser sources (the systems that generate the tin-droplet EUV light), and other components in the machine, each of which has its own supply chain. Each one has its own multi-year production constraints, its own specialised workforce, and its own supplier dependencies. Scaling further would require simultaneous expansion across the entire web of suppliers, many of whom see no reason to invest aggressively because they do not share the AI industry’s demand forecasts.

There is also a more fundamental mismatch at the heart of the AI supply chain. The companies at the top of the stack, the AI labs and hyperscalers, are investing at a pace that implies transformative AI is imminent. The companies at the bottom, the ones who would need to expand to meet that demand, seem to be a bit more sceptical. They have seen technology hype cycles before. They are not going to bet their businesses on demand projections from Silicon Valley. The companies at the top see a revolution. The companies at the bottom see a cycle.

Bottleneck 2: The Foundry

ASML makes the tool. But the company that uses the tool (and others) to manufacture the actual chips is Taiwan Semiconductor Manufacturing Company, or TSMC, headquartered in Hsinchu, Taiwan. TSMC is the world’s largest and most advanced chip foundry. It fabricates chips for virtually every major technology company: Nvidia, Apple, AMD, Google, Amazon, Qualcomm, Broadcom, and many others.

TSMC’s manufacturing processes, currently at the 3-nanometre node with 2-nanometre in development, produce the highest yields and best performance in the industry. Samsung has struggled with yields on its most advanced nodes. Intel is aggressively trying to rebuild its foundry business, but it is years behind TSMC in high-volume production. For the foreseeable future, leading-edge AI chips are TSMC chips.

TSMC does not have infinite capacity. It needs to decide what to prioritise. The race for capacity at TSMC has already produced winners and losers. Nvidia saw what was coming and committed to non-cancellable orders years in advance. By 2027, Nvidia is projected to control over 70% of TSMC’s 3nm wafer capacity. Google and Amazon, which design their own AI chips, moved more cautiously and found themselves capacity-constrained when their AI revenue began to surge.

(Side note: Anthropic was similarly conservative about long-term compute contracts from the cloud vendors, wanting to avoid financial risk. OpenAI signed aggressively with every provider it could find. Now OpenAI has more compute at better prices, while Anthropic scrambles to acquire capacity from less established providers at higher cost. This gives OpenAI a huge advantage and I’m not sure people are talking enough about this.)

The lesson at every level of the stack is the same: caution was punished.

TSMC itself prefers to allocate capacity to stable, predictable businesses like CPUs rather than the volatile AI chip market, a conservatism that has further advantaged Nvidia, whose massive pre-commitments made it look like the safe bet. And because chip supply is constrained, what prices a GPU is not the alternative you could buy, but the value you can extract from it. Today’s AI models are dramatically more capable than the models that ran on H100s at launch. Each GPU produces more tokens of a higher-quality model, serving a larger market. Because of the supply constraints, older hardware is actually worth more today than it was a couple of years back, not less.

Bottleneck 3: Memory

An AI chip is two things working together: a processor that does the mathematics, and memory that holds the data the processor needs. Without fast, abundant memory right next to the processor, the most powerful chip in the world doesn’t do very much.

The memory used in AI chips is called high-bandwidth memory, or HBM: stacks of chips layered on top of each other and connected with thousands of tiny wires for maximum data throughput. A single gigawatt of Nvidia’s upcoming Rubin chips requires approximately 170,000 wafers of memory chips.

Two companies dominate HBM production. SK Hynix, a South Korean company, is the market leader and Nvidia’s primary memory supplier. Samsung Electronics is the other major player, though it has trailed SK Hynix in the latest HBM generations. Memory is manufactured in its own fabs, using its own set of EUV lithography, which means it competes for the same scarce ASML machine time as the logic chips it sits beside. Memory vendors are aggressively raising prices, doubling and tripling in some cases, and signing long-term contracts. Nvidia has negotiated three-year deals with memory suppliers to secure allocation.

The same pattern repeats: every component in the AI supply chain ultimately queues behind the same handful of machines in Veldhoven.

Bottleneck 4: Power, Water, and the Physical World

The five largest hyperscalers (Amazon, Google, Meta, Oracle, and Microsoft) collectively plan to spend up to $700 billion in capital expenditure this year, with roughly three quarters going to AI infrastructure. Amazon alone expects to spend $200 billion, a number so large it will likely push the company’s free cash flow negative for the year. Google’s Larry Page has been quoted saying he would rather go bankrupt than lose this race.

To put this in perspective, the entire GDP of Sweden is about $600 billion. The combined AI infrastructure spend of five American technology companies in a single year now exceeds that.

These are not abstract financial commitments in a spreadsheet. They are construction projects. A single hyperscale AI data centre can consume as much electricity as two million US households. Meta is building a facility in Louisiana that could eventually scale to five gigawatts, roughly the output of five nuclear power plants. Microsoft has an $80 billion backlog of Azure orders it cannot fulfil because the power infrastructure does not yet exist.

Data centres are going up fast. Amazon has brought facilities online in as little as eight months. Google has acquired an energy company outright, is putting deposits on gas turbines for 2028 and 2029, and is buying large tracts of powered land. But the physical consequences are becoming harder to ignore, and for lawyers, regulators, and their clients, they are becoming impossible to avoid.

AI data centres are extraordinarily thirsty. Google’s facilities consumed 5.6 billion gallons of water in 2023, a 24% increase over the previous year. In The Dalles, Oregon, Google’s three data centres use more than a quarter of the city’s water supply. In Chile and Uruguay, protests have erupted over planned facilities that would draw from the same reservoirs that supply drinking water. By 2028, data centres across the US could collectively consume as much water as 18.5 million households.

The electricity numbers are equally stark. In 2023, AI and data centres accounted for roughly 4% of US electricity consumption. That share is expected to triple by 2028. Ireland already dedicates over 20% of its national electricity to data centres. In Virginia, the figure is 26%. Microsoft pledged to go carbon negative by 2030. Its total emissions in 2024 were 30% higher than in 2020, driven by AI infrastructure expansion.

Communities are pushing back. Data centres in Northern Virginia, the densest concentration in the world with roughly 300 facilities, are expanding into farmland and generating noise, diesel exhaust from backup generators, and rising utility bills for residents. New York is considering a three-year moratorium on large new projects while it studies their environmental and energy impacts.

Environmental, planning, and regulatory questions are accumulating faster than the legal and policy frameworks needed to answer them. Every one of those questions is potential work. And every one of them is a potential constraint on the buildout that the AI industry is counting on.

The consensus among supply chain analysts is that power in the United States will not be the binding constraint on AI scaling in the near term. Data centres are simpler to build than semiconductor fabs, the money is available, and the engineering is well understood. But adding tens of gigawatts of capacity in a single year places enormous strain on electrical grids, turbine suppliers, transformer manufacturers, and permitting processes. As chip supply eventually catches up with demand, power could resurface as the bottleneck in the late 2020s.

There are more radical ideas on the table. Elon Musk has publicly discussed deploying up to 100 gigawatts of compute in space per year, using solar power in orbit where sunlight is continuous and land constraints vanish. Solar energy in space is roughly seven times more efficient than on the ground, and cooling in the vacuum of space is well understood for satellites. But the concept runs directly into the bottleneck described throughout this article. Even if you could generate unlimited power in orbit, you still need chips to put there, and those chips all have to pass through ASML’s EUV machines and TSMC’s fabrication lines. The power problem might be solved in space. The semiconductor supply chain is earthbound.

Bottleneck 5: Atoms

Behind the supply chain constraints lies a deeper, more fundamental one: the laws of physics.

For sixty years, the semiconductor industry has been governed by Moore’s Law, the observation that the number of transistors on a chip doubles roughly every two years. This was enabled by making transistors smaller with each generation: from micrometres in the 1970s, to hundreds of nanometres in the 1990s, to single-digit nanometres today. An AI chip like Nvidia’s H100 contains roughly 80 billion transistors, each one a few nanometres across, about the width of five silicon atoms.

Jensen Huang, Nvidia’s CEO, has stated it plainly: “We can keep shrinking transistors, but we can’t shrink atoms.” A silicon atom is about 0.2 nanometres wide. At the 2-nanometre node, which TSMC is now entering production on, a transistor is roughly ten atoms across. At these scales, quantum effects become significant: electrons tunnel through barriers they should not be able to pass, causing current to leak. Leaking current means wasted energy and heat. A related rule called Dennard scaling, which promised that smaller transistors would use less power, broke down around 2005. The pace of improvement has slowed from roughly 10x every five years to about 2x every five years.

The industry is buying time through several strategies. New transistor designs like “gate-all-around transistors”, which wrap the gate around the channel on all four sides, give better control over current at small scales. 3D stacking builds up rather than shrinking down, layering chips vertically. Chiplet architectures place multiple smaller dies side by side rather than trying to make one enormous chip.

There is also a more radical possibility. Neuromorphic computing attempts to build chips that work more like the human brain, which runs on roughly 20 watts (about the same as a couple of LED bulbs) while performing cognitive tasks that still exceed AI in many domains. Intel’s Loihi 3 chip, released in January 2026, packs 8 million artificial neurons and operates at a peak of approximately 1.2 watts, compared to 300 or more watts for a GPU doing equivalent real-time inference. IBM’s NorthPole chip achieves up to 25 times the energy efficiency of an H100 for image recognition.

The catch is that neuromorphic chips cannot yet run large language models. They excel at sensory processing and real-time pattern recognition, not the dense matrix multiplication that powers a conversation with Claude. But there is a plausible future in which neuromorphic architectures handle perception and real-time processing while GPUs handle reasoning and language, dramatically reducing the total compute demand on the constrained supply chain. Some researchers, including Yann LeCun (formerly the brains behind Meta’s AI strategy), believe entirely different AI model architectures known as “World Models” focused on understanding the world and fundamental concepts rather than focusing on language may eventually sidestep the GPU bottleneck altogether, at least for robotics use cases.

None of this will change the fundamental picture within the next five years. The industry is locked into the GPU-and-transformer paradigm. But the atomic wall is real, and the long-term future of AI may depend on architectures that work with the physics rather than fighting against it.

Bottleneck 6: Geography and Geopolitics

The overwhelming majority of advanced chip manufacturing capacity is located on an island of 23 million people in the western Pacific. Taiwan’s status, and the security of TSMC’s fabs, is one of the defining strategic questions of the coming decades.

If TSMC’s Taiwan fabs went offline, the consequences would be immediate. Every Nvidia GPU, every Google TPU, every Apple processor in current production depends on them. The chips already deployed in data centres would become irreplaceable and astronomically valuable. AI progress would effectively freeze at whatever capability existed at the time of disruption.

The export control regime coordinated between the US, Netherlands, and Japan uses ASML’s EUV monopoly as the centrepiece of a deterrence strategy: China cannot manufacture advanced chips without EUV tools, and it cannot acquire them. Running a seized TSMC fab without ongoing support from ASML, Applied Materials, Lam Research, and other Western and Japanese suppliers would be effectively impossible. The equipment would degrade and stop producing within months.

TSMC is building fabs in Arizona, Japan, and Germany to diversify. But these are smaller and behind the cutting edge. Meaningful geographic diversification of advanced manufacturing is an early-2030s proposition at best. Until then, the physical infrastructure powering every frontier AI model is concentrated in one of the most geopolitically sensitive locations on Earth.

What this means for legal

I’ll say at the start that there are too many variables right now for anyone to know for sure how all this plays out. But if compute does indeed become more constrained and expensive, there are some potential consequences that firms and vendors should plan for.

Costs are probably going up, not down

All the legal AI vendors build on frontier model APIs and are exposed to the underlying cost of compute. As the labs raise prices to manage demand within increasingly constrained capacity, those costs flow downstream.

Vendors on flat per-seat pricing models (most or all of them right now, I believe) will face pressure to shift toward consumption-based pricing. Firms should obviously budget for legal AI costs to rise when their current fixed term deals (most of them 2-3 years) come to an end.

On a consumption-based model, this also raises the question of whether tokens or compute get billed through to the client like photocopying disbursements.

Context engineering and model routing become critical

A practical consequence of the compute crunch is that how you use the model matters almost as much as which model you use.

Context engineering, the discipline of structuring what you send to the model so that it produces the best possible output with the fewest possible tokens, is becoming a genuine competitive advantage. A well-engineered prompt that gives the model exactly the right context in a compact form gets a better answer at lower cost than a lazy prompt that dumps in everything and hopes for the best. For legal technology vendors, this is an engineering challenge with direct commercial implications. For law firms, it is a skill that might separate effective AI users from expensive ones.

Model routing is the other side of the same coin. Vendors will send simple queries to cheaper, smaller models and reserve frontier models for complex work. This is a rational response to scarcity and cost pressure. But it has implications for quality. A firm may believe it is paying for a frontier model but find that routine queries are being handled by a less capable one. In legal, where a “routine” question can have non-obvious complexity, the distinction matters.

Firms evaluating AI vendors should ask explicitly which models are used for which tasks, whether routing is transparent, and what guarantees exist that high-stakes queries receive frontier-grade processing.

Both of these trends point in the same direction: the era of treating AI as a magic box you throw text into is ending. The firms and vendors that treat compute as a scarce resource to be used skillfully will probably outperform those that treat it as an unlimited commodity.

The human vs AI question continues to be asked

Rising compute costs do not just affect the technology. They affect the fundamental business case for using AI in the first place.

Like it or not, the promise of legal AI has largely rested on the proposition that AI can do certain tasks faster, better and/or cheaper than a human. But “cheaper” depends on the cost of compute. If model costs rise and the tasks that benefit most from AI (complex, multi-step, high-context work) are also the most token-intensive, the calculus might shift. For some workflows, the answer to “should we use AI or a human?” may move back toward the human, at least until the supply chain catches up.

This does not mean AI becomes uneconomic across the board. For the right tasks like high volume doc review, the efficiency gains are large enough to absorb meaningful cost increases. But for bespoke, judgment-heavy work where the AI needs extensive context and multiple reasoning passes, the cost advantage may narrow.

Conclusion

AI is going to transform the legal industry. I don’t think anyone seriously debates that at this point. The models are genuinely capable, improving fast, and rewarding early adoption.

But the physical infrastructure behind them (the silicon, the lithography, the fabs, the memory, the mirrors, the tin droplets, the power) is subject to constraints that no amount of venture capital or algorithmic innovation can immediately overcome, and that has some significant consequences. The most important company in this story is not the one whose name you see on the chatbot interface. It is a company in a small Dutch town, building 70 machines a year, each one a minor miracle of physics and precision engineering, each one indispensable to the future of artificial intelligence.

The next time your AI tool takes two seconds to respond, consider what made those two seconds possible, and what that might mean for your company and its business model in the coming years.

This article draws on reporting and analysis from SemiAnalysis, Dylan Patel’s conversation with Dwarkesh Patel (March 2026), ASML corporate disclosures, Morgan Stanley and Bank of America research, public semiconductor industry data, and Intel, IBM, and Meta research publications.

Side note: for a demo of Claude on legal use cases, watch this LinkedIn Live recording I posted last week.

Introducing Zack

Zack Shapiro went to Yale Law School thinking he’d become an academic. If not law, it would have been a philosophy PhD. He eventually decided that Yale Law offered the same intellectual life with better job security and less time in training.

After law school came a year at Davis Polk, where his timing coincided with the ICO boom. He landed some of the firm’s earliest crypto work. Two federal clerkships followed, first with Judge Engelmeyer on the Southern District of New York, then Judge Lynch on the Second Circuit.

Still not sure he wanted to practise in BigLaw, he joined a friend’s e-commerce startup, BZR, as an operational co-founder. They raised money from Founders Fund, Greycroft, and Abstract Ventures, and then the business got acqui-hired in 2020. The lesson Zack took away surprised him: he never wanted to be a startup founder again. What he enjoyed was being a startup lawyer.

That year he launched a solo practice that grew into Rains. They’ve now advised over 200 clients across corporate law, venture financings, digital asset regulation, and increasingly, AI. Zack also serves as Head of Policy at the Bitcoin Policy Institute.

The post that broke legal Twitter

Before we get to how Zack uses AI, we need to talk about what brought him to most people’s attention: a post on X that hit 7.7 million views. I’m not sure a legal technology post has ever reached that level of virality?

Zack had been experimenting with X’s long-form articles feature. He’d already written two, one on the concept of the “AI centaur” borrowed from the chess world, another on what AI means for intrapreneurs inside larger organisations. Both did reasonably well - but nothing compared to the third. It laid out how he uses Claude as a practising lawyer.

He shares the moment he knew it had gone viral. At around 10,000 views, the notifications started going haywire. He describes it like a slot machine hitting the jackpot. He couldn’t do anything for the next two hours but watch the notification pings come in. Luckily, it was a Friday afternoon.

The piece generated a huge amount of debate and the comments kept rolling in on X and LinkedIn. Some praised it as a practical roadmap; others dismissed it as “productivity theatre” or questioned whether Claude has the enterprise features needed for BigLaw. Either way, it got people talking.

The lesson Zack took from it was that people want the specific, practical examples of what AI looks like in real legal work. And we got into some of that in the discussion.

Why Claude?

Zack points to two features of Claude that he thinks make the difference.

First, Claude can write code on the fly. Before this, he’d use ChatGPT to help think through contract edits, but the best it could produce was a list of redlines he’d then manually apply in Word. The formatting would invariably break. With Claude, he found a way to get it to manipulate documents directly, which he describes as XML under the hood, published as a Word doc with tracked changes.

Second, he found that Claude can create and work with local files. In his view, this addresses the context window limitation that degrades long-running conversations. Instead of relying on the model’s memory and context window, Zack also stores context in markdown files on his computer, effectively creating external memory that can be referenced as needed.

He’s also a huge fan of Skills, the open standard that I’ve written about previously and recommend that all law firms should be experimenting with. TLDR, Skills are simple, human-readable files that explain to an agent how to tackle a particular task. Zack describes it as a zip file you could send to 500 associates, your judgment encoded as a skill file that scales like software.

The Secret Sauce (public version)

Zack sees a clear split online between people who say AI has given them superpowers and people who think the whole thing is productivity theatre. He thinks the gap comes down to two things.

Disciplined input. The model is a fuzzy tool. Fuzzy input produces fuzzy output. Precise, detailed instructions produce much better results. He argues that most legal AI companies are focused on the wrong problem, training models or using variations of RAG for contract and brief templates. In his view, the training data already contains more of those than anyone could need. The bottleneck is the prompting and the context. The good news for lawyers: precision and specificity are skills the profession already selects for.

Reinforcement over time. Once you’ve built up enough back-and-forth with the model, you encode what works into skills. When it does something well, you reinforce it. When it does something poorly, you update the skill. The usefulness compounds. It’s a compelling idea, though it does require a level of discipline and iteration and one wonders if every practitioner will have the time or inclination for that, unless it happens automatically in the background.

A day in the life

Email is still where the work arrives. But the “substantive lawyering” now happens inside Claude.

An engagement letter used to mean opening Word, editing the scope, swapping in the client name and retainer amount. Now it’s a one-sentence instruction: engagement letter for this company, addressed to this person, here’s the retainer, standard scope. The letter comes out the other end.

He’s also built a custom tool combining Claude Code with ElevenLabs that reads long documents aloud, which is helpful as Zack has a health condition making it hard to read longer documents on screen. But for working with Claude itself, he types. Long prompts, often 2,000 words, written like essays. He finds that typing without worrying about grammar or spelling is faster than voice, and being redundant about the things that matter is a feature, not a bug.

Zack says the drudgery is gone and the work feels more joyful.

On vibecoding and the future of legal deliverables

Long-time readers will know I’m big on vibecoding. I asked Zack whether he’s started vibecoding things as a way of delivering advice. Dashboards, interactive maps, visual tools. His answer was no.

He’s sceptical of anything that intermediates between a client’s intent and the lawyer’s delivery. Take the classic 50-state regulatory review. Version one is the memo. Version two might be an interactive visual. But Zack’s thinking about version three: what if you deliver the answer in the format the client actually needs? Not a memo about sales tax rules, but the code to make their sales engine compliant across all 50 states.

It’s an interesting provocation, though it raises its own questions about where legal advice ends and software engineering begins, and who’s responsible when the code is wrong.

On security

Information security is probably the question Zack gets most in X threads.

On privilege: he thinks it’s easier than people assume. Many of the negative reactions to his article cited the Heppner case, the February 2026 ruling from Judge Rakoff in the Southern District of New York. But Zack argues that case is distinguishable. In Heppner, a criminal defendant used a consumer version of Claude, on his own initiative and not at counsel’s direction, to research legal strategy. The privacy policy allowed training on inputs. Judge Rakoff found no reasonable expectation of confidentiality and no privilege. A law firm using an enterprise AI tool with training turned off, generating attorney work product at counsel’s direction, is a different posture in Zack’s view. Whether the courts will draw that line clearly remains to be seen; Judge Rakoff himself noted that the analysis “might differ” if counsel had directed the AI use.

On data confidentiality: more nuanced, and requiring case-by-case judgment. The spectrum runs from cloud-hosted with zero data retention, through custom DPAs, local inference, and encrypted AI, to simply not putting certain data into any model.

Zack reserves his sharpest words for some legal AI vendors, who he sees as “selling fear”. He believes there are companies pushing expensive platforms with checkbox workflows that, in his view, ultimately aim to automate away the lawyers buying them. He’d rather lawyers engage with the ethical rules and the technology directly and build things themselves. Not everyone will agree; some firms will conclude that a managed platform is the most practical way to meet their compliance obligations, but Zack believes that is more fear and hype than reality.

Pricing in a post-AI world

Zack tells me that Rains charges hourly rates at roughly half the cost of Big Law, with overall service costs landing at about a quarter, the additional reduction coming from AI-driven efficiency. Most clients are on subscriptions denominated in a cap of human hours but calculated to be functionally all-you-can-eat. The long-term goal is flat subscriptions, but the technology isn’t reliable enough yet to remove the human-attention safeguard.

The tension Zack identifies is that the value of the work product is becoming untethered from the hours spent producing it, but the capacity to exercise judgment is still measured in human time. Overextending means falling into the temptation of not checking the AI’s output.

Scaling through Claude, not headcount

Rains already runs multiple Claude chat and Cowork sessions in parallel (all on screen for now!) In his opinion, one lawyer plus Claude can replace a partner plus a team of associates on certain matters.

But taking on more clients doesn’t scale the same way, because each one requires human judgment. To grow that side, he’d need to hire lawyers who use AI the way he does. That’s a small pool right now.

He’s thinking about what comes next: training for larger firms, forward deployment into in-house teams, possibly selling his agentic workflow to a tech company. He sees two possible futures for the profession. One where everyone ends up inside opinionated platforms and everything becomes a process. Another where lawyers use AI directly and the profession opens up.

On venture capital and AI law firms

Y Combinator’s latest batch included two AI-native law firms: General Legal, and LegalOS, and another legal service platform, Arcline. VC money is flowing into the space more broadly. (Take a look at my list of AI law firms here.) For now at least, Zack isn’t rushing to take any.

His concern is that the incentives of a venture-backed AI law firm push towards automating everything, including the judgment, and delivering what he bluntly believes is slop. In his view, you need good lawyers doing the lawyering, with automation built around that. He points to Atrium, the hybrid law firm and legal tech company that raised $75 million before imploding in 2020, as a cautionary tale.

The tools are already here, he argues. He doesn’t need $60 million to keep building skills. But he’s open to conversations.

How to get involved

Zack is open to conversations with Big Law managing partners, in-house leaders, and tech companies thinking about the future of legal work. Reach him on X at @ZackBShapiro or email info@rains.law.

For a live demo of Claude on legal use cases, watch this LinkedIn Live recording I posted last week.

Links

• Rains (rains.law)

• Zack Shapiro on X (@ZackBShapiro)

• “The Claude-Native Law Firm” (original post on X)

Introducing Dr. Sarah Stephens

Dr. Sarah Stephens isn’t your typical legal technologist.

She started her career on a traditional path, training at the global law firm, Linklaters.

But a summer volunteering on grassroots access to justice projects in Kenya, working with children and widows navigating life-limiting legal situations, set her on a very different path.

Nearly two decades later, she’s running an AI-powered legal empowerment platform in Tanzania, sitting on the UK’s Online Procedure Rules Committee, leading the Sussex Centre for Law and Technology, and launching a new AI law lab.

I’ll confess, I have struggled with the term “access to justice”. It feels like one of those terms that mean different things to different people and it can sometimes feel abstract. This conversation and Sarah’s work helped me think about it differently.

Meet Dada Wakili

After Linklaters, Sarah moved through Kennedy’s, picked up a master’s in human rights law, and took a case to the Human Rights Court with Coram Children’s Legal Centre and won.

Then came the opportunity to relocate to East Africa, where she went in-house with KPMG in Tanzania. That’s where it all clicked.

In Tanzania in 2015, Sarah watched M-Pesa, the mobile money platform, transform financial inclusion through mobile phones and asked herself: why can’t we do the same for legal services?

That question ultimately became Dada Wakili - dada meaning sister, wakili meaning lawyer.

It’s an AI-powered chatbot on mobile phones, integrated with WhatsApp, that guides women through the justice issues they encounter in daily life.

The focus on women came from the field research. Tanzania has a pluralistic legal system, with statutory, religious, and customary law all interplaying, and the gaps fall hardest on women. A husband dies, the family invokes customary rights, and the widow and children lose their home. It’s unconstitutional. But nobody tells them that. That’s where Dada Wakili comes in.

The design challenges are real: laws still written in English from the colonial era, training data split across two languages, feature phones in rural areas with no smartphone access. The team is Tanzanian-led, engineers, lawyers, partners, and the whole thing is free to users. It’s currently grant-funded by Irish Aid and the FCDO, but finding sustainable financing remains the hardest problem to crack.

How to get involved

Sarah is actively looking for law firms, legal tech companies, and organisations interested in supporting Dada Wakili or collaborating on the Sussex AI Law Lab. Connect with her on LinkedIn.

What “Access to Justice” really means

Sarah pushes back, gently, on the phrase. Access to what, exactly? In the UK we tend to think courts, advice, enforceable remedies. In Tanzania, justice might mean resolution within a customary system. She prefers the frame of legal empowerment: building someone’s individual capability to act on information they’ve received. And she’s clear that AI won’t solve the access to justice crisis, because the crisis isn’t one thing. It’s a web of policy failures, funding gaps, and poverty.

New rules for online courts

Appointed by the Lord Chancellor, Sarah sits on the committee writing the rules for England’s online court system. The inclusion framework she’s been working on provides principles for legal tech builders across the digital justice ecosystem, covering pre-action advice tools, online dispute resolution, and self-help tools, with the vision of end-to-end data flow from early advice through to court. The rules are deliberately short and plain. No white book.

Sussex, students, and the skills question

The Sussex Centre for Law and Technology teaches AI literacy, innovation, and building. Sarah’s view is that lawyers need wider life experience and tech fluency more than ever, but also that AI critical literacy matters as much as AI enthusiasm.

Her students are asking hard questions about bias, data, and environmental cost. Some refuse to use generative AI because of its water consumption. Every tool, she feels, should carry an environmental statement.

The new Sussex AI Law Lab (SAILL) will run real use cases from the university’s legal clinics and partnerships with organisations like Citizens Advice, and get students actually building. (I’m hoping to support by providing a course of vibecoding!)

Where next for Dada Wakili?

After a big week at the Legal Tech for Access to Justice East Africa conference and a national TV appearance (far more challenging than my podcast I’m sure!), Dada Wakili is expanding from smartphones to feature phones via USSD and SMS, reaching more remote communities across Tanzania.

Partners from other countries are already asking when it’s coming to them.

If you’re inspired by her work, please reach out to Sarah directly on LinkedIn.

Links

• Dada Wakili

• Sussex Centre for Law and Technology

• Access to Justice Foundation

• Online Procedure Rules Committee

• Sarah Stephens on LinkedIn

He started the business in 2016 after a career in journalism and consulting - inspired by the change he saw coming and wanted to be a part of shaping.

Nearly a decade later, he says things are finally moving.

In this conversation, we cover his diverse background (he’s done more jobs than most people you know!) and how that informs his writing. We cover what is different about the latest developments in legal tech, whether the law firm pyramid is about to be replaced by something else, and whether AI risks making us all a bit dumber.

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A decade of travel that still shapes his work today

Richard left university with one goal: never have a proper job. While his peers were lining up training contracts and summer placements, he went in the opposite direction. A decade of travel, factory shifts, bar work, cinema tickets, salad prep, and even a stint as a telephone tarot card reader (yes really), which he quit once he realised the callers were people who couldn’t afford a therapist.

He doesn’t see those years as squandered. Working in factories taught him how different parts of the economy actually operate and the assembly line gave him a perspective on the economics of law firms. Growing up in the Black Country, the historic heartland of the Industrial Revolution, shaped everything. His school history teacher skipped Waterloo and the Napoleonic Wars and spent years teaching the Industrial Revolution instead, in a town where it had literally happened a few doors down.

By the time Richard eventually landed in the City and then legal tech, he had a completely different lens.

From journalist to consultant

Richard’s first real job in law was as the world’s first international legal reporter at Legal Week magazine, starting in 1999. His beat was everything happening outside London: globalisation, the expansion of the magic circle firms, the BRICs coming online, the EU taking shape.

The job meant sitting down with managing partners of enormous firms and asking them to explain their strategy. He says the first couple of years he probably didn’t understand what they were saying and his articles probably weren’t much good either. But he got deeper and deeper into the business of law. About seven years in, a managing partner stopped their interview mid-conversation because he said Richard now knew more about the subject than he did. That was his first consultation. It was free, because he was still a journalist.

He spent the next decade or so as a strategy consultant in the City. By 2015 he was running his own practice, strangely dissatisfied, and increasingly intrigued by AI - which, he admits, he initially mocked. He says the scepticism masked a deep-seated interest. He just wasn’t getting good answers.

Then someone invited him to see it work. A company called RAVN (later acquired by iManage) showed him their contract analysis tool in action. He watched it zip through real estate documents and pull out key clauses. And he thought: this is the industrial revolution of the legal world. He changed his LinkedIn title to “legal industrialist” in homage to his Black Country forefathers, and launched Artificial Lawyer.

The billable hour problem that never went away

Richard tells a story from early in Artificial Lawyer’s life. He was giving a speech to hundreds of people at a law firm network event in Berlin, right next to the Brandenburg Gate. It had gone well. Then a woman at the back raised her hand and said: there’s one problem. I sell time for a living, and this will destroy my business.

Every head in the room turned. Richard admits he hadn’t thought deeply enough about the billing question at that point. He was more amazed by the technology. But she was right. She owned a private business, she wasn’t a charity, and this thing was not going to help her.

That was nearly a decade ago, and the problem hasn’t gone away. Almost every issue anyone ever raised about AI in law — training juniors, the billable hour, time to value — still exists. The base technology has changed dramatically, but the structural environment hasn’t.

Why firms aren’t rushing to change

On whether big law is productising its work, Richard is blunt: no, and we shouldn’t expect it. Even Big Law, he points out, contains about twenty different constituencies. Shipping firms in London are nothing like private equity firms in Manhattan. They share a pyramid structure, but they’re radically different businesses.

The economics are too compelling. An equity partner can work out on the back of a napkin what a workstream will produce in billable hours, at what rates, for what profit. It must be incredibly reassuring, Richard says, to know at the end of each quarter that you and your team have made millions doing essentially what you did last year. Without much client pushback, without much threat from new entrants.

He’s not surprised they’re not queuing up to disrupt themselves. Some firms are doing interesting things at the edges. If you went to an equity partners’ annual meeting and proposed a radical redesign of the business, why would they vote for it? There’s no need yet. They don’t feel it yet.

Richard thinks we’re waiting for a Cravath moment: a leading firm or small group of firms that seize the moment, change the model, and everyone else lines up behind them. He doesn’t know whether it will come from the US, the UK, or somewhere else. But he thinks it will take three or four years before partners start truly feeling it — losing clients, being told by buyers that they won’t pay for anyone below eight years’ PQE.

The pyramid is eternal, but it will operate differently

Richard pushes back on the idea that the pyramid is going away. The pyramid, he says, is the eternal structure of all human labour and organisation. Even organisations that say they’re flat are kidding themselves.

But the way it operates is going to change. He makes an interesting historical point: law firms were one-to-one for hundreds of years. A partner and an apprentice. It was technology - Word, email, the internet - that allowed leverage to scale. The question now is whether technology shrinks it back down again.

What won’t change is that equity partners are owners with client followings or irreplaceable niche skills. How they design their businesses around that is up to them. He wonders how many partners are actually having that conversation, versus how many are simply behaving as if this is the way it always was.

Cognitive surrender and the risk of getting dumber

Richard raises a concern he calls cognitive outsourcing. He shares a recent experience where ChatGPT confidently told him that a type of AI plugin didn’t exist - and nearly convinced him, until he pushed back and the model admitted it was wrong. He’s seen it with health questions too: the model assured him an edge case was extremely unlikely, and it turned out to be the correct diagnosis.

The danger, he says, isn’t just hallucination. It’s that when AI tries to be clever, it leads you down the wrong street entirely. And if people - or governments - outsource their thinking to systems that aren’t good enough yet, the consequences could be severe.

What’s next

Richard plans to keep Artificial Lawyer going for at least another twenty years. He can’t wait to see what legal tech looks like then. The Legal Innovators events are expanding - London, Paris, New York, California, which he calls the perfume bottle lineup. And he’s quietly launched The Robot Times, covering the intersection of robotics, business, and law, because he believes the robotics industry will become huge in the next decade and will need its own specialist legal ecosystem.

Through it all, the thread is the same one that started in the Black Country: how do complex systems change? He’s been watching this one for a decade, and he thinks we’re finally passing through a gate into something new.

Final Note: Turing & Partners, an AI Law Firm (in 2016)

In preparing for this discussion, I stumbled upon this post from 2016 about an AI law firm. I did a double-take when I saw the date!

Turing & Partners, an AI Law Firm

• Driverless cars

• AI-powered law firms (heard that term recently)

• New leverage models

• Big data centre developments

Links

• Artificial Lawyer

• The Robot Times

• Legal Innovators events (London, Paris, New York, California)

• Follow Richard on LinkedIn

He’d been working with language models for years and had never had a moment where he thought they were really useful. Dan Katz kept asking. A little eggnog was involved. So Mike did it to shut him up. By the time they published the second paper a few months later, it had passed.

That story kicked off one of the most cited moments in legal AI history. But it's just one chapter in a career that keeps moving.

In this conversation, we trace the arc from that Christmas break experiment through to OpenClaw and agentic AI, the future of the Cravath pyramid, what we should be teaching our kids, and the trillion-dollar data centre buildout that's reshaping rural communities.

Mike sees the same story playing out everywhere: a growing identity crisis at every level, from lawyers, to rural communities, to humanity itself.

Keeping up with Mike

Mike is a modern polymath and that makes him hard to keep up with. My head was spinning just trying to prepare for the interview.

He started as a classics major studying Latin and Greek at Michigan before pivoting to maths and financial engineering. A PhD in political science followed, which is where he met Dan Katz in the Center for the Study of Complex Systems. He left academia for a hedge fund, then landed in legal tech in 2013. LexPredict, the company he co-founded with Katz, was doing predictive analytics and NLP for litigation years before the rest of the industry caught up.

Today he splits his time across 273 Ventures and Kelvin on the commercial side, and the ALEA Institute, a nonprofit where he funds research, builds models and datasets, and runs projects like Leaky, an open source tool for detecting whether text was in a model’s training data.

The real story behind “GPT Takes the Bar Exam”

The first paper, GPT Takes the Bar Exam (fondly remembered as GPT Fails the Bar Exam), showed GPT-3.5 passing some sections or coming close. But the real drama came during preparation for the second paper with OpenAI. Mike and the team discovered that the bar exam they’d tested on was in the training data. The “oh sh*t” moment wasn’t that it had passed. It was that the results might not be scientific, that they couldn’t separate memorisation from actual ability.

They had to find a new exam, transform it into a format the model could process, and run it again. Only after multiple reads with clean data did they have confidence the results held. Pablo Arredondo from CaseText was involved. The second paper, GPT Passes the Bar Exam, made it onto The Late Show, the New York Times, and into conversations around the world.

Agents aren’t new

Mike’s latest book, Agentic AI in Law and Finance, makes the case that the word “agent” didn’t appear out of nowhere. Agent-based modelling goes back to the 1970s across economics, political science, and cognitive science. Schelling’s segregation models, Monte Carlo simulations, basic behavioural heuristics programmed into interacting routines. Mike and Dan grew up intellectually in that world during their PhDs. Then the foundation model companies picked up the term and most of that history got forgotten overnight.

The book grounds the current hype in 50 years of research and asks what it means for governance in highly regulated industries. Mike’s short definition of an agent: a doer with a to-do. Beyond that, he says, it’s still a mess. And governance hasn’t kept pace. He pointed to Dario Amodei’s candid admission that nobody appointed the foundation model companies as leaders of this. The lack of governance runs all the way from the top of the AI industry down to individual firms making buying decisions, about half of which, Mike argues, are driven by FOMO rather than strategy.

The pyramid is changing

On law firm business models, Mike is direct: status quo is certainly not the right answer. He and Katz are writing a new book on transforming legal and financial organisations, and the working cover image is the Cravath pyramid being reshaped, its base becoming mechanical or cybernetic. He’s hearing anecdotally about firms slowing junior hiring and seeing large back-office reductions at large global firms.

Mike feels the question firms need to ask themselves is what their clients are actually buying. Most buyers can’t answer that consistently, in his view. Some buy big law for insurance, some for relationships, some because they believe they get the best results. Until that’s clear, nobody can say what the new model looks like. But the old one isn’t surviving this.

What should we teach our kids?

Going a bit deeper than your usual legal tech podcast, we got into the question of what we should teach our kids, in a world where AI can out-perform humans in a growing number of tasks.

Mike and his wife homeschool all three of his kids and he says he doesn’t know whether they’ll go to college. He’s built custom AI tools for their education, and the gap between what you can deliver at home with today’s technology and what even the best schools offer is, in his words, huge.

The problem runs deeper than curriculum. In the US, the cost of legal education isn’t commensurate with the expected value of the degree at most institutions. Faculty are naturally resistant to redesigning programmes in ways that might not include them. And the mismatch between what law schools will try to continue doing and what firms and clients will need is only going to widen.

Mike sees some hope in states like Texas and Florida, where regulatory innovation untethered from ABA standards might allow for more practical, technical training.

But the question extends well beyond law. If productised AI tools can deliver a better education than a classroom, what does that mean for public institutions where education is one of the primary services? What happens when you don’t need a teacher for every 15 to 30 students? These aren’t hypothetical questions for Mike. He’s living the answer with his own family every day.

The real risk isn’t AGI

The thread that ran through everything was bigger than legal. Mike’s deepest concern isn’t the terminator scenario. It’s that the global middle class expanded on the back of knowledge work that can be done over the internet by someone with basic language skills and a computer. A trillion and a half dollars is now racing to convert that exact work into “pure electricity”. If the expanding middle class is what kept the world relatively peaceful, what happens when that contraction starts?

That question led us to his other new book, This is Server Country, about the physical infrastructure buildout reshaping rural communities. As we spoke, Mike was about to join a court hearing over the Oracle/OpenAI Stargate data centre project in Saline Township, Michigan, a small town of a couple thousand people where, as he put it, everything about the identity and experience they’ve had is being destroyed.

Mike doesn’t hold back at this point: “We’re replacing people’s interactions with each other with tokens and audio that’s not real. We’re replacing physical landscape with something that’s not natural. We’re replacing labour in the economy with something that’s not actually labour”.

Whether it’s a lawyer drawing a line around their profession, a community drawing a line around their town, or humans drawing a line between themselves and machines, Mike sees the same thing everywhere: an identity crisis, at every level, that will dominate politics for the foreseeable future.

Books and links

• Agentic AI in Law and Finance by Michael Bommarito and Daniel Martin Katz (2026)

• This is Server Country by Michael Bommarito (2026)

• Upcoming book on transforming legal and financial organizations (Bommarito and Katz, in progress)

• GPT Takes the Bar Exam (2022) and GPT Passes the Bar Exam (2023), research papers by Bommarito, Katz, Shang Gao and Pablo Arredondo

• 273 Ventures / Kelvin (commercial)

• ALEA Institute (nonprofit research)

Connect with Mike

Connect with Mike on LinkedIn. He maintains inbox zero (mostly).

A new study from UC Berkeley and published by Harvard Business Review gives us a glimpse of where things are heading. Researchers Aruna Ranganathan and Xingqi Maggie Ye spent eight months inside a 200-employee tech company studying how AI agents actually affect the people using them. Their finding was interesting: AI doesn’t necessarily lighten workloads. It increases the intensity of work.

This matters for legal because our industry is about to adopt AI and agentic teammates at a pace that will make the last two years look like a slow start. And legal work, with its long hours, high stakes, and culture of availability, may be uniquely susceptible to exactly the kind of intensification this research documents.

What did the researchers find?

The Berkeley researchers documented three patterns of work intensification:

1. Task expansion. Workers absorbed jobs previously done by others or outsourced. Product managers started writing code. Researchers took on engineering tasks they’d never touched.

2. Dissolved boundaries. Work bled into lunch breaks, evenings, and early mornings, because AI chat feels like conversation rather than work, so it can happen anytime, anywhere. A lot of these tools also have a strong dopamine loop where you get instant output from a prompt.

3. Constant multitasking. Employees juggled multiple AI-mediated tasks simultaneously, bouncing between agent sessions during meetings, while waiting for files, between calls. Claude Code fans, think about those moments when Claude is “Combobulating”. What do you do during these “Discombobulation Breaks”? It seems most of us context switch continuously.

The consequences were predictable: cognitive fatigue, burnout, declining quality, and turnover.

What does this mean for lawyers?

We’re already seeing this play out in legal. As AI makes tasks like contract review, due diligence, and legal research faster and cheaper, lawyers aren’t really doing “less work”. They’re becoming responsible for supervising an ever-growing volume of outputs, tasks, and risks.

Categories of agreement that were previously excluded from DD reviews can now be included. SaaS agreement reviews can be outsourced at scale with agreed SLAs. Clients expect continuous real-time regulatory alerts, not a quarterly risk review.

Where a lawyer once deeply owned a handful of matters, they might now oversee a portfolio of a hundred mini-tasks and sub-matters handled by machines and human helpers. The level of involvement changes. The role becomes more supervisory. But the net result is that lawyers touch more things, more often, with less time and depth for each one.

Meanwhile, expectations for output quality stay constant or increase. In addition to increasing expectations around rapid turnaround times, it raises the fidelity bar. Work must be fast and good.

Why the human context window is struggling

To state the obvious, humans have cognitive capacity limits. AI agents expand what’s possible to do. They don’t inherently expand human capacity to track everything, evaluate everything, organise everything.

Cognitive psychologist George Miller’s research established that human working memory can hold approximately 5 to 9 unrelated items at once, a finding widely known as the “7 ± 2” rule. More recent studies suggest the true capacity may be closer to 3 or 4 items. Atul Gawande made a similar observation in The Checklist Manifesto (this is a great book btw), arguing that the complexity of modern knowledge work has already exceeded what a single professional can reliably manage within their working memory and attention span.

Right now, most lawyers can keep the status of their matters in their head. A client calls on a Friday wanting to know where things stand across their portfolio, and the lawyer can synthesise a summary blending the big picture with key details.

But as each lawyer becomes personally responsible for more matters, contracts, jurisdictions, and clients, this gets harder. It’s like reading a hundred novels in parallel, one paragraph at a time, and being asked to keep track of what’s happening in each.

Consider the associate who once reviewed one complex agreement per day. With AI document review, that same associate might now handle thirty. The AI flags issues, generates summaries, handles the mechanical work. But the human still evaluates context, assesses risk, and makes judgement calls.

The effects then start to compound, with more context switching as lawyers bounce between quick AI-assisted tasks. Continuity suffers because each matter carries its own logic, timeline, and risks. Decision fatigue sets in as work becomes a rapid series of small calls: edit this clause, approve that draft, escalate that risk, fine-tune that agent.

So what do we do about it?

Ranganathan and Ye propose what they call “AI Practice,” structured norms that protect humans from intensification. Their recommendations seem sensible and include:

1. Build in pauses after AI-assisted sprints

2. Resist the urge to parallel process everything

3. Keep humans connected to humans for quality control and peer review

4. Set boundaries on agent use outside work hours.

Beyond those principles, I think two other things are worth acting on now.

Get status tracking out of your head

If AI is going to multiply the number of matters, tasks, and decisions a single lawyer is responsible for, we need systems that externalise the tracking. We just can’t keep it all in our heads.

The lawyer who can see in one place who is doing what, what’s been done, and what needs to happen next is in a fundamentally different position from the one trying to hold it all in working memory.

Full disclosure: this is exactly what we’ve built at Lupl, so I’m biased. But the broader point stands and it’s one reason task and project management in legal is having its moment.

Take a “Discombobulation Break”

OK this isn’t a real term. I just invented it. I’m talking about what you do when your AI agent is off doing its thing, which can be anywhere from 30 seconds to 30 minutes or longer.

With mixed results, what I’m personally trying to do is resist the temptation to immediately start another task or check my phone. The temptation to fill every gap is strong. But perhaps the most productive thing we can do while the agent is working is…nothing at all? (Or maybe a quick walk and some fresh air!)

Final Thoughts

None of this is to say AI is a bad thing. I’m bullish on its potential to improve outcomes in our industry. But it is introducing a whole new paradigm for how we work, and it’s happening now, in real-time, faster than we can devise systems to adapt.

It feels to me like there’s a structural mismatch when one person, augmented with AI, is expected to increase their output or area of responsibility by ten times, because it still takes one human brain to understand and take responsibility for the overall outcome.

Ranganathan and Ye have provided an early piece of evidence. It’s now up to us to figure out how to adapt to this new normal.

Source: This post draws from “AI Doesn’t Reduce Work, It Intensifies It” by Aruna Ranganathan and Xingqi Maggie Ye of UC Berkeley, published in Harvard Business Review, February 2026.

• General Legal’s founding story and its roots in Casetext

• JP’s unusual career path from iOS developer to Harvard Law to Big Law (WilmerHale, Cooley) to legal tech engineer

• What makes a firm “AI native” versus a traditional firm that’s adopted AI tools - and the corporate structure and reinvestment philosophy that distinguishes the two

• The practical workflow: how clients engage General Legal via Slack, send contracts, and receive AI-assisted attorney-reviewed markups within a three-hour SLA

• Pricing model: $250 for documents under three pages

• The “attorney attention engine” concept - AI handles first-pass review and context gathering, directing lawyer focus to the provisions that actually matter

• How General Legal differentiates from Atrium by targeting “run the company” work (MSAs, NDAs, DPAs) rather than “bet the company” work (priced rounds, M&A)

• The competitive landscape: not directly competing with Big Law or in-house teams, but filling a gap where neither wants to operate

• The YC experience, the $4.2M pre-seed, and the ambition to build the largest law firm in the world

• Forward-looking topics including MCP-compatible law firms, clients pre-processing contracts with ChatGPT, and the blurring line between engineers and attorneys

Key Takeaways

• The defining question for an AI native firm: are you willing to reinvest virtually all profits back into efficiency rather than distributing them to partners?

• Run the company legal work (routine commercial contracts) is ripe for AI disruption; bet the company work (M&A, priced rounds) still demands top-tier human strategic advice

• The percentage of work done by AI versus humans isn’t fixed; it depends entirely on the matter - a DPA draft might be 90% AI, while advising on GDPR compliance is 98% human

• Traditional law firms spend only 1-2% of profits on efficiency tools, which J.P. believes structurally limits their ability to compete with firms that take outside capital and reinvest aggressively

• The most important hiring criterion is still excellent lawyering - you don’t need engineer-attorneys, you need client-obsessed commercial lawyers who are willing to adopt AI workflows and help shape the tools

• Slack-first client communication is a meaningful efficiency gain over email, even before any AI enters the picture

• The corporate structure mirrors Atrium’s model: a separate law firm entity employing attorneys alongside a partner technology company, sidestepping ABS restrictions

• The long-term play is horizontal expansion across practice areas - starting with commercial contracts to earn client trust, then expanding into regulatory, litigation, and broader transactional work

• The rise of vibecoding in the legal industry

• Bird & Bird’s recent rollout of a vibecoding solution within the firm

• Governance and compliance considerations, including how to give everyone a safe sandbox to prototype, with clear pathways to enterprise deployment with the appropriate safeguards when something proves valuable

• The maintenance question

• Opportunities for vendors to lean into vibecoding rather than see it as a competitive threat

• The shifting training needs toward product thinking

• The skills needed to sell products rather than services

Key Takeaways

• Velocity excites everyone, but someone has to handle sustainability, governance, and scale.

• Vibecoding works, but not at scale yet. It’s brilliant for prototyping and individual problems, but no one has solved managing proliferating micro-applications.

• The polarised debate misses reality. Truth sits between “I built Harvey in 30 minutes” and “vibe coding is just a hobby.” For the right use cases with proper controls, it delivers genuine value.

• Recreating a feature is easy; creating a company is very hard. Weekend projects that replicate one capability shouldn’t be confused with sustainable products.

• The forest of mushrooms problem. Apps sprouting everywhere, some great, some poisonous, creates fragmentation in already-fragmented law firms.

• Trust must transfer to platforms before agents scale. Clients need to trust the technology enough to upload documents without a human in the middle.

• Reward failure in innovation. Three days vibe coding something that goes nowhere still teaches you something. That learning has value even when the app doesn’t ship.

Links

Bird & Bird announces partnership with vibe-coding app development platform Betty Blocks

• Richard’s 40 years of working with law firms on pricing

• His viral LinkedIn post from January 2026 that sparked debate: a fictional partner’s letter explaining why AI may not mean lower fees - we get into the arguments for and against

• Richard’s view that transparency and benefit-sharing between firms and clients is the only sustainable path forward

• The difference Richard has observed between what clients say they want (lowest price) and what actually drives their buying decisions

• How productised legal services like Littler’s employee classification tool represent a new pricing paradigm

• The “creative destruction” mindset firms need to avoid being disrupted

• Richard’s journey from managing partner to pricing consultant, and the Aderant acquisition of Virtual Pricing Director

Key Takeaways

• Richard believes firms that have invested heavily in AI tools deserve ROI on that investment, not a race to the bottom on fees

• When GCs were asked to prioritize price factors, less than 10% chose “lowest price” as most important

• Richard believes the winning formula is transparent benefit-sharing: if AI reduces delivery cost from £100k to £70k, billing £85k splits the value fairly

• Clients aren’t just buying legal advice. They’re buying security, reassurance, and the firm’s professional indemnity coverage

• If you don’t destroy your own business model, someone else will

• The legal profession’s greatest pricing limitation is lack of confidence. As one senior partner told Richard: “If you don’t think you’re worth it, why should anyone else?”

• Legal work will grow, not shrink. Life and commerce are getting more complex, and AI itself creates new advisory opportunities

• Technology alone won’t transform pricing. Sustainable change requires addressing people, process, and technology together

• Mary’s journey from law student and litigator to founder, sparked by her own struggle to secure a training contract and the obvious disconnect between eager junior talent and firms needing support

• How she made the leap into entrepreneurship, including raising external funding to create a real proof point before leaving practice

• What Flex Legal is and how it evolved: from a platform focused on paralegals to a broader model supporting lawyers and in-house teams

• The social mobility mission behind Flex Legal, including the impact of the SQE route and the creation of training contract pathways

• The real impact of AI on junior legal careers: why Mary is optimistic, what’s changing in role requirements, and why junior lawyers still matter in an AI-enabled workflow

• The skills that will define successful lawyers in 2026 and beyond: curiosity, judgment, EQ, relationship-building, and commercial awareness

• The story behind the Mishcon acquisition, and why relationships and long-term networks matter more than people think

• Mary’s new role as GC Relationships Director and how it reframes law firm client relationships through a “customer success” lens

• The shift toward productised legal delivery: breaking work into strategic vs BAU components, combining people/process/tech, and designing pricing that works for both sides

• Why client feedback and pilots are essential to successful innovation, especially when firms are building new service lines

• The GC Academy: a structured programme designed to build financial literacy, leadership, legal ops and legal tech skills for in-house leaders

• Lessons from 10 years of building: staying optimistic through the lows, maintaining energy, and treating startup life as a marathon

Biggest takeaways

1. Purpose and profit are not opposites
   The best businesses can deliver real commercial outcomes while creating measurable social impact.

2. AI is changing job specs faster than it’s changing demand
   The work juniors do will evolve, but the need for people who can operate with judgment and quality control is only increasing.

3. Curiosity is a career superpower
   The ability to ask better questions, learn fast, and deeply understand client problems will outperform almost any technical skill.

4. Human skills are the long-term moat
   Judgment, empathy, and trust-building remain the parts of legal work that are hardest to automate.

5. Networks compound over time
   The acquisition story is a reminder that consistent relationship-building creates outcomes years later.

6. Productisation only works with real client input
   Build with customers, pilot early, learn quickly, and iterate before scaling.

7. Founding a company requires durable optimism
   You need enough energy and belief to keep going through the inevitable difficult moments.

Book recommendations

• Patrick Lencioni (especially The Five Dysfunctions of a Team)

• Stephen R. Covey — The 7 Habits of Highly Effective People