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

It is valuable, hard-won, and seen by firms and their clients as a key differentiator. It is also notoriously difficult to capture and maintain. Lawyers rarely have time to document how we work, and some are understandably reluctant to give away hard-earned expertise. Plus, there’s always that sense that “every matter is different”. What this means is that when a partner retires or a senior associate leaves, much of that tacit knowledge leaves with them.

A new agentic standard is emerging that I think might change this whole dynamic and it’s called Skills.

Subscribe now

What are Skills?

Skills are a standard introduced by Anthropic – the company behind Claude and the Model Context Protocol (MCP) standard. With Skills, Anthropic is saying - agents are powerful, but real knowledge work requires an agent to follow company-specific processes and precedents.

A Skill is surprisingly simple. It’s a folder that teaches Claude how to do something specific. At its simplest, it contains a SKILL.md file (basically a text file that you or I can read) with instructions. At its most sophisticated, it’s a complete package of expertise: workflows, reference materials, automated scripts, and reusable assets.

Here’s what goes inside:

Fig 1: What’s in a Skill? (Side note: Why is Substack so bad at tables?)

When an agent loads a Skill, it transforms from generalist to specialist. The Skill tells it: this is how we do this type of work, these are our standards, these are our preferences, this is when to flag issues for human attorney, and so on.

One of the cool things about skills is how they help navigate a key limitation of agents – context windows. They use something called progressive disclosure. What this means is that the agent doesn’t load the entire Skill into its context window upfront because that would use up tons of context. Instead, it works in layers, a bit like a lawyer reading the contents page of a long agreement before jumping in. This architecture means you can bundle extensive knowledge into a Skill without overwhelming the agent’s context window every time.

Can Agents Have Multiple Skills?

Yep. Multiple Skills can run simultaneously and the agent automatically identifies which Skills are needed and coordinates their use. (Seems the best practice is separate SKILL.MD files for each Skill. I don’t know if there’s an absolute limit. The Anthropic API has a limit of 8 Skills right now.)

Imagine you’re reviewing a customer contract for a repeat client in a regulated industry. The agent might load a bunch of Skills:

• Your contract review Skill (general negotiation positions)

• A client-specific Skill (this customer’s particular preferences and risk tolerance)

• A regulatory compliance Skill (industry-specific requirements)

• Your document creation Skill, which in turn interacts with an external drafting service (to generate the redline)

Skills vs Prompts vs Tools

Skills sit alongside other ways of customising agents:

• Prompts. One-time instructions for a specific task (or system prompts telling an agent how to behave generally). Skills are a bit like reusable instructions for a type of task that the agent decides when to use (combined with the other bits, like executable code).

• Custom instructions apply universally to everything an agent does for you. They’re broad preferences like “don’t use emdashes ever” or “always ask clarifying questions before starting”. Skills are task-specific and only activate when relevant.

• Tools are actions the agent can take, either in the app in question, or a third party app. They can connect the agent to external systems and data: web search, code execution, database queries, API calls. Tools provide capabilities, while Skills teach the agent how to use those capabilities according to your specific workflows and standards.

• MCP (Model Context Protocol) connects your Agent to external data sources and services through standardised tool interfaces. You might use MCP to connect to your document management system, and use a Skill to teach the Agent how to properly categorise documents, apply your naming conventions, and route them through your firm’s review process.

Show Me Some Skills!

OK we’re there. In theory, almost any task performed by a lawyer could be documented in a Skill. Let me walk through a few examples. (Health warning - these are just from a 30-minute brainstorm this afternoon so go easy on me - they are not production-ready but hopefully provide some ideas).

Contract Review Skill

A contract review Skill could encode your entire negotiation playbook. When an agent reviews third-party paper, it applies your firm’s substantive positions, evaluates deviations against market norms, and suggests alternative language drawn from your own precedents.

contract-review/

├── SKILL.md

├── references/

│ ├── playbook.docx

│ └── market-terms.md

└── assets/

└── clause-library.docx

The SKILL.md file is where the workflow logic lives. The cool thing is it’s human-readable too. It might include instructions like:

## Review Workflow

1. Identify the contract type and governing law

2. For each clause, check against the position in playbook.docx:

- If our preferred position: no markup needed

- If acceptable fallback: note in comments, continue

- If outside tolerance: flag for negotiation, suggest

alternative from clause-library.docx

3. Cross-check jurisdiction-specific requirements in references/

4. Generate summary of key commercial points and recommended

positions

## Escalation Triggers

Flag for partner review if:

- Uncapped indemnities in favour of counterparty

- Liability caps below [threshold]

- Non-standard governing law or jurisdiction

- Assignment rights that conflict with client policy

## Execution

1. Where Partner review needed, assign to Partner via Lupl tool

2. Retrieve contract from DMS via MCP tool

…

Now, this skill could also connect to your document management system via MCP (iManage announced support last year, for example) to save the marked-up version back - with proper metadata, version control, and an audit trail. Another key part is approval routing, so it could integrate with a system like Lupl - if the Skill says an issue must be escalated to a Partner for approval, you could assign that approval to the right person via Lupl and trigger an email or Teams message with the iManage link incorporated.

Due Diligence Skill

We all carry mental models for due diligence: what to look for in a lease, how to spot a change-of-control trap in a material contract, which representations actually matter for the deal at hand. A due diligence Skill might turn this tacit knowledge into explicit, repeatable instructions.

due-diligence-ma/

├── SKILL.md

├── references/

│ ├── review-standards/

│ │ ├── leases.md

│ │ ├── material-contracts.md

│ │ ├── employment.md

│ │ └── ip-assignments.md

│ ├── red-flags.md

│ └── request-list-template.md

└── checklists/

└── closing-checklist.md

The SKILL.md orchestrates the entire process:

## Document Routing

Route each document to the appropriate review standard based

on type:

- Leases → references/review-standards/leases.md

- Employment agreements → references/review-standards/

employment.md

- [...]

## Issue Classification

For each issue identified:

- Critical: Requires immediate escalation. Deal-breaker

potential.

- Material: Include in summary. Likely negotiation point.

- Notable: Log for completeness. Unlikely to affect deal.

- Administrative: Wrong date format.

Flag for cleanup.

## Output Format

Generate:

1. Exception report organised by category

2. Updated checklist with status

3. Draft request list for missing items

## Execution

1. Use Data Room tool to pull files from Data Room

…

Client-Specific Skills

Repeat clients accumulate preferences – e.g., approved fallback language, risk tolerance levels, internal approval thresholds, comment balloon quirks. This knowledge typically lives in partners’ heads or is scattered across email chains.

A client Skill could capture all of it in one place:

clients/acme-corp/

├── SKILL.md

├── references/

│ ├── approved-positions.md

│ ├── internal-contacts.md

│ └── billing-guidelines.md

└── templates/

└── engagement-letter.docx

Every new matter starts with this context already loaded. The agent knows that ACME prefers Delaware law, won’t accept mutual indemnities, requires sign-off from their deputy GC for anything above $500k, and wants comments balloons address to the ACME business unit, not the legal department.

Connect this to your matter management platform, and the right client Skill activates automatically when a new matter opens. The associate doesn’t need to ask around for client preferences - they’re already embedded in the workflow.

But Will Lawyers Actually Create These Skills?

Indeed, don’t all the old issues persist? Let’s look at each objection and why I think Skills may be different.

1. Lawyers are too busy to document know-how.

Guess what – Claude Code already has a “Skill” for “Creating Skills”! Skills don’t require lawyers to stop and document how we work. If an agent is deployed to a matter or triggered at matter closeout it can in theory observe how the matter (or a task) was handled and propose updates to an existing Skill, or even generate a new one. No post-matter KM surveys - this could be a kind of “driverless KM”. (And even if it’s not totally driverless, Skills can easily be vibecoded.)

2. Every matter is different.

Skills are actually a great way to handle variation. They are not fully deterministic (although they can have deterministic executable components or link to deterministic systems). A Skill can encode positions, fallback language, and escalation thresholds, while leaving room for context and judgment. The agent applies that guidance to new documents and new fact patterns, adapting as needed while staying within defined parameters.

3. Lawyers won’t give their knowledge away.

We don’t have to. A Skill doesn’t have to be public (and let’s face it, lawyers are unlikely to rely on public skills because every firm has its own way of practising law) It can sit with a practice group or a team. Access can be controlled and use can be limited. In time, Skills may even become assets that firms package, license, or sell as part of productised services. (I think this is slightly different to the “workflows” Harvey and Legora are trying to productise with their customers, which are more like predefined chatbot interactions, though they will no doubt evolve into this.)

Is there a Practical Law or LexisPSL of Skills?

Not yet. But if Skills become central to how firms work, expect to see Skills Libraries (and maybe even a Practical Law/LexisPSL of Skills?) emerge as a new category of knowledge infrastructure.

A mature Skills Library might include:

• Practice area Skills: Contract review, due diligence, regulatory filings, each encoding your firm’s specific approach and standards

• Client Skills: Major clients’ preferences, risk tolerances, approval thresholds, formatting requirements, automatically loaded on new matters

• Jurisdiction Skills: Local law requirements, filing procedures, regulatory quirks, making it possible for any lawyer to work competently in unfamiliar jurisdictions

• Process Skills: Closing mechanics, discovery workflows, transaction management protocols

Getting Started with Skills

Skills are open, but work best across Claude’s platforms right now, so pick whichever fits your workflow.

Claude.ai or Claude Desktop (Pro, Max, Team, Enterprise)

Go to Settings → Capabilities. You’ll see Anthropic’s pre-built skills (document creation, spreadsheets, etc.) already available. Toggle them on. To add your own, create a folder with a SKILL.md file, zip it, and upload via “Upload skill.” Your Skills appear in the list alongside Anthropic’s.

Claude Code

Skills live as folders in your filesystem. Create a directory at ~/.claude/skills/ (global) or your-project/.claude/skills/ (project-specific). Add a SKILL.md file with your instructions. Claude Code discovers them automatically and invokes them when relevant.

API

Skills work via the code execution tool. Reference pre-built skills by their skill_id (e.g., pptx, xlsx) in the container parameter, or upload custom skills via the /v1/skills endpoints. Custom skills are shared organisation-wide.

The simplest possible skill

Create a folder. Add one file called SKILL.md:

---
name: nda-review
description: Reviews NDAs against firm standard positions. Use when reviewing confidentiality agreements or NDAs.
---

# NDA Review

## Workflow
1. Identify the NDA type (mutual/one-way) and governing law
2. Check each clause against references/standard-positions.md
3. Flag deviations and suggest alternatives
4. Generate sum

Resources

Anthropic has open-sourced example skills at github.com/anthropics/skills. The skill-creator skill (yes, it exists!) can help you build new ones. And agentskills.io documents the emerging standard if you want skills that work across platforms.

The Takeaway

As you’ve probably guessed, I’m long on Skills (and long on Claude Code, though Skills are becoming an open standard). I do think Skills could represent a shift for KM systems from systems of content to systems of action. Not just documented, not just searchable, but actively applied by AI in the course of delivering legal work.

For firms thinking about the multi-channel future, Skills could be a foundational infrastructure.

Of course, there’s a way to go. Skills are built for action, and we’re still early in terms of deploying true agents that take action within legal practices. Chatbots and tabular review are not systems of action. And there are many things we all need to work out from a compliance perspective when deploying true systems of action.

But while we are early, I do think Skills (or their successor standard) will end up being a central part of law firm KM in the not-too-distant future.

If you’re experimenting with Skills in your firm, I’d be interested to hear how it’s going!

But the ground is shifting. Clients are asking about outcomes, not inputs. AI and automation are making it possible to systematise entire processes, not just discrete tasks. New entrants are launching AI law firms built entirely around productised delivery.

In my mind, the direction is clear: legal work is going to be routed, in a much more deliberate way, via distinct delivery channels based on shape, size, and client. High-stakes matters get senior judgment, expertise and bespoke delivery, while repeatable work gets speed, certainty, and predictable costs.

I think the net result is the rise of a new operating model – the “Multi-Channel Law Firm”.

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The Two Primary Channels

Legal work is splitting into two core delivery modes. (There are probably more than two but these are the primary channels that define the model.)

The Productised Channel handles repeatable work as a defined offering. Fixed scope, fixed SLAs, quality designed into the process. Success measured by outcomes, turnaround, reliability. The value proposition is speed, transparency, predictable economics.

To be clear, productised doesn’t mean no humans. Most productised services in BigLaw will be process + people + technology. But they’re priced and structured like products, rather than bespoke engagements.

The Bespoke Channel handles higher-stakes, higher-judgment work. Differentiation comes from expertise, strategy, navigating ambiguity. Value comes from experience, pattern recognition, and judgment through uncertainty.

The bespoke channel isn’t disappearing. But if you believe more work gets automated over time (and it does – more on that below) then more is going to flow through the productised channel. That leaves firms with a decision about where to place their bets. And I think most will settle on a multi-channel model.

Why Now

Automation has been possible for a long time but two things are converging.

Clients are more deliberate buyers. A new generation of general counsel, backed by legal ops teams, are making clearer decisions about what to keep in-house and what to outsource. When they outsource, they come with a point of view about the “how” and the “outcome”. They’re asking: do I need your best judgment, or do I need this done to an acceptable standard, at scale, within 24 hours, at a fixed price, every time?

More legal work is structurally automatable. McKinsey, in a study of agentic AI across industries, estimated in November 2025 that 70% of legal work has automation potential (among the highest of any industry). That number will make lawyers skeptical and we’re of course very far from 70% being automated today. But something has changed. Earlier legal tech automated discrete tasks or individual workstreams, or was exclusively deterministic: document assembly, extraction, etc. The current wave is combining generative AI with workflow systems to automate entire processes end-to-end.

What’s happening:

• Contract review services applying client playbooks at volume, with turnaround measured in hours, including human in the loop.

• Due diligence workflows that route documents through AI review, flag exceptions, escalate only genuine judgment calls

• Regulatory compliance programs that ingest rule changes, map them to client obligations, generate responses, package for sign-off

History tells us that in any industry, what can be automated eventually is automated. It starts with high-volume, low-complexity work but, over time, works its way up the value chain. There is no reason legal will be any different.

Where This Already Happened

There are clear signs of this shift in other parts of professional services already.

Consulting. McKinsey, BCG, Bain run partner-led strategy work alongside implementation arms built on standard playbooks. Accenture derives most revenue from managed services and platforms while keeping a high-end advisory layer. Clients seem to get it - they pay for judgement and design where it matters and expect productised delivery where it doesn’t.

Audit and accounting. The Big Four have long separated professional judgment from industrialised processing. They operate global delivery centres for audit prep, tax compliance, regulatory reporting. Standard methodologies, senior partners focused on risk and sign-off. No client expects partners to manually execute high-volume audit work. They expect systems and outcomes.

The New Competition

2025 has been defined by a new category of law firm – the “AI Law Firm” (or the “AI-First Law Firm”, or “NewMods”, depending on who you ask). I started building a running list here.

Crosby and Tacit offer contract review with rapid turnaround and humans in the loop. Avantia has a productized process for Funds. Landfall is trying to productise patent work. A new firm launches every week. These firms aren’t competing on breadth, headcount, expertise or directory rankings. Their focus is speed, throughput, transparency, cost – and outcomes.

Right now, they’re still relatively small. But they’re growing fast - proving productised legal delivery is viable and that clients will pay for it. They’re also showing clients that work can be delivered effectively through systematised channels - and that expectation doesn’t stay contained to these new entrants.

So, how does BigLaw respond?

Option 1, opt out

Some firms will choose to double down on bespoke work and let others handle the systematisable stuff. This doesn’t mean ignoring AI, but it means integrating it into existing processes and team structures, and focusing on being the best in the world for human judgment and expertise in a given practice area. This is simple, clean, and avoids some of the structural complexities of a multi-channel firm. But it’s also fraught with risk. Some of the work is going to go to other firms – maybe it’s less profitable work, maybe it isn’t. Either way, this strategy means giving up data/intelligence (on all those contracts, disputes, regulatory matters the firm didn’t handle), relationship touchpoints, and cross-sell opportunities.

Option 2, opt in

Doing this requires navigating a challenging path.

Accept Cannibalisation

This is the uncomfortable part – which is the very reason the winners will be the ones who tackle it now. Some productised offerings will replace work currently handled via bespoke delivery. A contract review that took 20 hours might take four in a productised channel, at half the revenue.

But avoiding this just hands the market to competitors who won’t hesitate. Better to cannibalise your own business – the old example about the iPhone cannibalising the iPod.

Perhaps the easiest win is to start by targeting new clients or adjacent work where there’s limited legacy billing to protect. Prove the model, prove the demand, then expand into existing relationships where you’ve built trust and can have honest conversations about value.

Decide Who Owns the Product & Sales

Productised delivery needs someone who owns the product - not the legal output but the end-to-end system that produces it. This person designs intake, defines quality gates, manages SLAs, tracks metrics, and continuously improves the system.

Is this the Partner? Or is it a Delivery Lead, Product Owner, Director of Legal Engineering, Chief Product Officer? I suspect some Legal Engineers will be Partners on the productised side of the business (and or course some will be Partners on the bespoke side too).

And who is in charge of selling these products? Law Firm BD has always been differentiated from “Sales” (in fact, most law firms regard “Sales” as a dirty word) but in this new world of selling products, things like outbound, inbound, lead qualification, pipelines, demos - one can envisage a much more sales-based approach, which may require bringing new skillsets into the firm.

Redesign Incentives

Compensation models that primarily reward bespoke hours don’t create the right incentives for productisation. Partners have little economic reason to route work through a productised channel if it means lower billings and higher relationship risk (because they are less directly in control of the work product), even if it’s better for the client and ultimately more profitable for the firm.

To execute on a productised strategy, firms will need to reward total client value and profitability. This means crediting partners for origination and relationship management even when delivery happens through productised channels, and building bonus structures around client retention, margin improvement, and channel adoption.

Move From Effort to Outcomes

Productised work gets sold and measured on deliverables, quality, and reliability rather than hours spent. This means rethinking pricing, engagement letters, risk sharing, billing systems, SLAs and client reporting. If we’re agreeing to a 24-hour turnaround time, we need a system to route work to the right agent or associate and enforce deadlines systematically. It also means pricing risk differently. In bespoke work, scope creep can sometimes benefit the firm. In productised work, it’s a system design failure that needs to be fixed in the next iteration. Expect to see those Engagement Letter precedents getting amended in 2026!

What are the next steps?

How do firms build out these new productised delivery capabilities? I see a few options:

Option 1: Repurpose the managed service unit. Many firms already have nearshore or offshore centres, primarily staffed with paralegals handling compliance, volume work (e.g., GDPR or leases), and doc review. These were built primarily as labour arbitrage plays - but much of the productised delivery infrastructure is in place. Repurposing a legal service unit would be about evolving it from supporting discrete projects to owning entire productised offerings. This may mean investing in the team with new skillsets, different metrics, different client-facing positioning – maybe even rebranding.

Option 2: Acquire an AI-first law firm. A handful of new entrants have built entire practices around systematised delivery (client intake, workflow automation, fixed pricing, the full stack). I’m aware of a couple of these AI-first law firms already having received investment from BigLaw. The next step would be for an AI-first law firm to be acquired by a BigLaw firm. After that, there’s a question about whether it continues to operate under an independent brand (keeping the main law firm brand for the bespoke work), or gets merged into the main brand.

Option 3: Set up a new unit under a separate brand. Some firms will want separation (different positioning, different pricing, potentially different regulatory structure). They might decide to build, rather than buy, and set up an entirely new “AI Legal Service Unit” or even an “AI Law Firm within a Law Firm”, operating under a separate brand. Maybe they will partner with other organisations on this – consulting, or software for example – to provide a complete business solution, of which legal is only one part.

More than two channels?

While there are two primary channels, I think these will further splinter, which is why I think of it as a “Multi-Channel Law Firm”.

For example, within the productised channel, look out for:

- Self-serve offerings

- Managed outcomes with SLAs

- Variations of the “Lawyers on Demand” model more akin to complete managed legal services – outsourced legal operations

Within the bespoke channel, there’s:

- Premium advisory

- Formal senior sign-off layered over systematised work

- Faster-response yet still with specialist support

- New services like system/workflow design

The Takeaway

I actually think all of this adds up to a huge opportunity for firms.

Yes, there may be difficult conversations about cannibalization, and what work we realistically want to keep – but there are also opportunities to go after whole new categories of work, and build entirely new businesses around it. At a time like this, the biggest risk is standing still.

The next problem is fragmentation. If every vendor invents their own way to plug an agent into tools, pass context, handle permissions, and log actions, we end up with incompatible “agent stacks” that do not work together.

The Linux Foundation has now set up a home for this problem: the Agentic AI Foundation (AAIF), intended to standardize the “agent layer” so the ecosystem can converge on shared building blocks instead of duplicating the plumbing.

What’s being put on the table

The launch involves three founding contributions (with presumably more to follow):

MCP (Model Context Protocol)
Developed by Anthropic and already the industry standard for integration, MCP provides a way for an agent to connect to tools and data sources. Think “one common interface for tool access” rather than bespoke connectors for every agent framework. MCP was already open - so making it part of the AAIF formalises this and helps ensure it remains open and community-driven.

Goose
An open-source agent framework for software engineering tasks, this has been contributed as a practical reference point. The idea is that standards are easier to adopt when there’s real working code.

AGENTS.md
A lightweight convention for coding agents: a markdown file in a repo that tells an agent how to behave there.

Both Goose and Agents.MD have a strong software engineering flavour, which is another clue about where early adoption is strongest right now.

Why standards

This is the boring part of technology that decides whether a wave becomes real. The goal here is:

• fewer one-off integrations

• easier portability across tools and vendors

• more consistent approaches to permissions and policies

• more consistent logging and audit

This is a pretty well-trodden path. Two familiar examples:

• OAuth/OpenID Connect became the standard for delegated access, enabling ecosystems of integrations without every platform inventing its own approach.

• HTTP standardised a simple, universal way for systems to communicate over the web, which is why everything from browsers to APIs can interoperate without bespoke agreements every time.

Does this make agents a commodity then?

Not really. Standards tend to commoditise the plumbing rather than the product. If a common agent layer takes off, “we support MCP” becomes table stakes, kind of like “we have an API.”

Vendors still differentiate on things like:

• Governance: permissions, policy controls, approval workflows, audit trails that stand up to scrutiny.

• Workflow depth: opinionated, domain-specific flows that match how teams actually operate.

• User interface: how you spin up new agents, how they interact with your features.

• Reliability at scale: handling rate limits, retries, failures, observability, and cost controls.

• Agent quality: planning, tool selection, error recovery, and knowing when to stop or ask for human review.

What it means for legal

If agents are going to operate inside legal teams, they need:

• matter-scoped access and ethical walls

• clear approval steps (i.e., handoff to human lawyers)

• auditable records of what was accessed and what changed

• predictable behavior across tools

Standards like MCP are still to really take off in legal. Yes, there are isolated use cases, but it hasn’t yet hit the mainstream.

Why? First, MCP is only the connector. It doesn’t magically solve the enterprise questions that legal cares about. All the usual issues still apply, like:

• Authentication: is the agent acting as a specific user, with delegated access, or as a broad service account?

• Security: what’s exposed through the MCP server, what actions are allowed, what requires approval, and what gets logged?

• Rate limits: how do you prevent an agent from hammering systems, creating throttling cascades, or failing unpredictably under load?

Also, most legal teams live in a few systems of record, like the DMS and the PMS. When those systems formally begin MCP support, I think things will change very quickly. And there are signs this is already happening - iManage announced MCP support earlier this year and will accelerate support in 2026. I’m not sure where the PMS vendors are on this yet but one imagines there will be support at some point.

The practical takeaway

The most interesting part is probably the fact this is happening at all, and the companies behind it. Founding members include Amazon Web Services, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft and OpenAI.

It feels like the agent era might be about to enter a new phase. It is maturing from where we were 12 months ago, “wow, autonomous agents look really cool, here are a few use cases” to “OK this is actually going to happen at scale, how do we make sure the plumbing works?”

This tells us everything we need to know about where these companies think the industry is going - their bet is that agent are going to move from reactive chatbots to proactive teammates. The launch quote captures it well:

“We are seeing AI enter a new phase, as conversational systems shift to autonomous agents that can work together.”

Most of today’s real, practical use cases involve agents as solo contributors: give an agent or assistant a discrete task, let it run, check the output, iterate. That’s a perfectly valid way to use them - and to be completely clear, it’s where almost all the use cases sit today. There are plenty of lower-hanging fruit than multi-agent systems. But let’s for a moment start thinking ahead.

Lessons from coding systems

Other fields are moving towards multi-agent systems. One example is vibecoding tools like Replit and Lovable, which have enjoyed almost unprecedented ARR growth by building multi-agent systems that allow users to develop full-stack software applications.

Instead of pointing one agent at a coding task, they deploy a small coordinated team: a project-management agent breaks work down and keeps the user updated, specialist coding agents handle components, and an architect agent reviews and integrates the final output. A lot of the value comes not because each agent is smart (a lot of this is a relatively thin wrapper on top of LLMs, usually Claude), but from the structure and orchestration around the agent system.

A Replit project management agent calls “the architect” to review its work

Legal work hasn’t really made that shift yet. Most legal uses today are reactive, serving each attorney’s one-off request: summarise this document, extract these facts, draft this clause. There is enormous value in these use cases – we could spend the next few years focused on these and we would barely scratch the surface - but isn’t there a bigger opportunity here?

Types of multi-agent systems

If you’re going to follow one person on agentic systems, follow Dazza Greenwood. Seriously, he built his first agent in the late 1980s! He knows more than almost anyone on the topic and yet still manages to present it in an easily digestible way.

I recently found myself reading Dazza’s excellent article on agent design patterns – an accessible framework for designing interactions between agents – and wondering how each pattern may or may not fit into legal work. So, with full credit to Dazza on the definition of each agent design pattern, I have layered on top my thinking about the pros and cons of each for legal matters.

Sequential Pattern

What the pattern is
A fixed, ordered pipeline: Agent A → Agent B → Agent C. Each performs its step, then hands off. The sequence is predefined.

How it maps to legal work
An example for legal teams might be: gather requirements → draft → review → finalise
For instance, an agent extracts the requirements from an intake form, another drafts a document based on those requirements, a third performs a structured QA review before human approval.

How it might be applied
• Break the matter or workstream into explicit stages with readiness criteria.
• Use agents for well-defined tasks inside each stage.
• Enforce human review between major phases.
• Don’t trigger the next phase until the last one meets its standard.

Pros
• Predictable, auditable and easy to monitor.
• Ideal for standardised, high-volume matters.
• Reduces ambiguity around who does what and when.

Cons
• Inflexible. One error might block the pipeline.
• Poor for matters requiring iteration or backtracking.
• Can feel rigid as real-world facts evolve.

Hierarchical Pattern

What the pattern is
A master–worker model. One agent (the “supervisor”) breaks the work into subtasks and delegates them to worker agents.

How it maps to legal work
Partners set direction → associates break down the tasks → specialists and paralegals execute clearly defined components.

How it might be applied
• Use a planning agent to define the matter plan.
• Assign specialist agents narrow, scoped subtasks (clause drafting, research extraction, chronology building).
• Route ambiguous or missing information directly to humans - not to other agents.
• Treat each delegation like a brief with acceptance criteria.

Pros
• Handles complexity well.
• Enables parallel execution.
• Makes responsibility explicit.

Cons
• Hard to automate good task decomposition.
• Coordination overhead increases with system complexity.
• Mis-scoped tasks propagate downstream errors.

Collaborative Pattern

What the pattern is
Agents work more like peers toward a shared goal. They may contribute to a shared workspace or update a shared state, coordinating without a central arbitrator.

How it maps to legal work
This mirrors an ideal view of multi-disciplinary collaboration: tax reviewers, privacy reviewers, commercial lawyers and local counsel all contributing to a shared document or matter. That said, it is rare in the real world to have such a truly collaborative approach - more often than not, one firm or team acts as quarterback to drive a process.

How it might be applied
• Create a shared workspace for matter contributions.
• Allow multiple agents to propose edits, analyses or suggestions.
• Preserve attribution so humans know which agent proposed what.
• Use collaboration when you need diverse viewpoints, not strict control.

Pros
• Reflects ideal vision of multi-disciplinary collaboration.
• Surfaces diverse expertise simultaneously.
• Works well when specialists need to contribute in parallel.

Cons
• Can become noisy without constraints.
• Hard to maintain coherence without oversight.
• Conflicting contributions can accumulate.

Mediated Pattern

What the pattern is
A mediator agent sits in the middle, receiving inputs from other agents, integrating them, and deciding what to pass forward or reject.

How it maps to legal work
Think of the matter partner or senior associate in Corporate who coordinates a multi-department due diligence effort - receiving drafts, comments and analysis from multiple contributors, then synthesises them into a unified document or plan.

How it might be applied
• Deploy specialist review agents, but have all outputs go through a mediator.
• Use the mediator to merge changes, resolve conflicts, apply style and structure.
• Control quality centrally via the mediator rather than peer-to-peer editing.
• This approach suits documents where a single authoritative voice is required.

Pros
• Produces more coherent outputs.
• Reduces conflict between agents.
• Supports strong governance and version control.

Cons
• Mediator becomes a bottleneck.
• Reduces diversity of perspectives if overly strict.
• Requires strong design of the mediator logic.

Hybrid Patterns

What the pattern is
A combination of patterns. You might use sequential flow for the backbone, hierarchical delegation for complex pieces, and mediated interactions for key decision points.

How it maps to legal work
Legal matters rarely follow one clean pattern. Facts change, stakeholders intervene, tasks run in parallel, regulatory updates appear. Hybrid patterns capture this complexity.

How it might be applied
• Start with a core workflow (intake → requirements → draft → review).
• Add hierarchical decomposition for multi-step tasks.
• Use collaborative agents for specialist input.
• Insert a mediator agent for integration and finalisation.
• Let the orchestration layer route tasks dynamically.

Pros
• Handles complexity, exceptions and branching.
• Allows flexible orchestration around a structured core.

Cons
• Hard to design, test and maintain.
• Requires robust error-handling and state tracking.

Why multiple agents? Why not just one agent prompted to follow multiple steps?

One agent has one chain of thought, one objective at a time, and few mechanisms for separating roles like planning, execution, review, or critique. Multi-agent systems introduce specialization and division of labour: one agent plans, another executes, another reviews, another integrates. This mirrors how real work gets done. It also creates fault-tolerance - if one agent produces weak output, another can catch it. Most importantly, multi-agent systems allow workflows to scale. A single agent forced to plan, execute, evaluate and revise its own work collapses into a monolith that becomes brittle and unpredictable as complexity increases. Multiple agents, each with a narrow, well-defined role, behave more reliably and map more naturally onto the structure of real legal matters.

Conclusions: Integrated teammates or isolated tools?

The single-agent, single-task model isn’t wrong. It’s where we get almost all the reliable value today, and I do not mean to dismiss those use cases as outdated or get too far ahead of where we are today. As always, there are plenty of easier wins.

But that doesn’t mean we can’t start exploring the next level - the harder wins with the potential for more transformational impact. In my opinion, the long-term direction is toward multi-agent systems acting as coordinated teammates inside a structured legal workflow, similar to what’s emerging in coding.

The shift won’t happen instantly. But over time, applying these patterns could move agents from isolated task-doers to something closer to integrated members of the team. And that is when we may start to see agents more actively involved in delivering outcomes, rather than simply discrete task outputs.

Last week, the internet was evangelising the capabilities of Gemini 3. (TLDR: It’s very, very good. If you haven’t already, build something in Google AI Studio. I guarantee you’ll be amazed by what you can build.)

This week, we have Claude Opus 4.5, which promises major advancements in long-horizon task handling, tool use, memory and agentic behaviour.

Let’s look at each one of these claims, and what they may mean for people integrating agents as part of legal service delivery.

Long-horizon task handling

What this means:
The model is better at staying coherent across multi-step tasks without forgetting what happened earlier or veering off-track.

What it might enable in legal work:
Many legal workflows are sequences: intake → triage → draft → review → approval. If the model holds the thread for longer, agents become more reliable inside these sequences instead of stalling after the early steps.

Tool use

What this means:
The model is supposed to be more dependable when interacting with external tools - editing documents, pulling data from spreadsheets, routing information, executing predefined operations - without misinterpreting the tool output or failing mid-flow.

What it might enable in legal work:
Most legal processes span multiple systems. Contracts, schedules, emails, cap tables, templates etc. Improved tool use means an agent can move between these without constant human oversight. That’s essential for anything beyond surface-level automation.

Memory

What this means:
The model can store and retrieve key information from earlier in the workflow more accurately. Not just within one chat, but across longer spans of activity.

What it might enable in legal work:
Legal tasks often depend on state: who approved what, which version is current, which assumptions apply, what deadlines exist. If the model can track and retrieve this state reliably, agents can operate more broadly across a matter lifecycle instead of a single moment.

Agentic behaviour

What this means:
“Agentic” is often vague, but the meaningful version is this: the model is better at following procedures, making conditional decisions, and knowing when to act vs escalate vs stop.

What it might enable in legal work:
Agents for law need to exercise good judgement. This doesn’t mean they must be fully deterministic but, rather, the right blend of deterministic and probabilistic. If 4.5 improves procedural execution, more of the workflow can be delegated without the agent unraveling mid-process.

What this all means

The direction of travel for 2026 is becoming clearer. Firms and legal tech providers have a growing number of tools available to build agents that are genuinely useful, getting closer to the vision of “AI teammates”. 2026 is going to be the year of the “agent”. The next job is to figure out how best to integrate them into legal processes.

A senior Private Equity lawyer once told me, “I have spent most of my adult life in Outlook”.

He was half-joking - then we crunched the numbers and he wasn’t far off - given how much of his adult life was spent at work, and how much of his working day involved sending and reading emails.

Time for a change?

In 2025, you might think things are about to change.

A global pandemic saw massive adoption of Microsoft Teams. Instant messaging and video calling hit the mainstream.

AI transformation is sweeping our industry. Harvey is working towards “Multiplayer AI”, and Legora recently announced the launch of Portal, a platform that enables clients and law firms to collaborate on work product with AI. (Side note: Portals are not new. HighQ has been offering Portals for 20+ years. At Lupl, we did some research on Portals with Above the Law a few years back.)

And yet…Most lawyers, particularly in private practice, still begin and end their days in Microsoft Outlook. (It might be “new Outlook” but more often it’s the “Classic” version that we haven’t quite been able to let go of.) Either way, email is still at the beginning, middle and end of every legal matter.

As Chief Product Officer for a Task Management and Workflow Automation tool, you might think I would be arguing for this to change.

But actually, the more I think about it, the more I think email is and should be central to the future of legal work delivery - I just think its role is going to evolve.

The role of email in Human + AI collaboration

Here is my thinking on where things are headed:

1. AI is integrated into almost every legal process.

2. AI Agents replace humans on some parts of most legal processes.

3. Very few legal processes have zero human involvement.

4. This means the future will be largely “Supervised AI” or “Human + AI”.

5. Human + AI requires a smooth handoff of tasks. Example: A human needs to know when the draft redline is ready for review; someone needs to act as an escalation point for a Triage Agent.

6. That handoff could happen via a notification “ping” via a portal or some other system. But those can get missed. Those pings might or might not be logged. The ultimate fallback - the one thing lawyers do not miss - is an email.

Why email wins - and how its role changes

Lawyers will continue to rely on email because it solves five structural constraints simultaneously:

Interoperability

Law really is going to remain a multiplayer industry. Any client, counterparty, regulator or court can produce and receive structured instructions without shared software. No other medium has equivalent reach or anything close to it.

Latency
Email supports asynchronous review cycles. Not every legal task is or should be a real-time task. Some legal work requires deliberation, record-building and controlled pacing. Email preserves state without requiring real-time review.

Artifact generation
Every message thread becomes a durable audit log of what happened, what was reviewed, what was approved.

Identity and authorization
Email carries implicit authentication via institutional domains and audit trails. Inboxes are trust boundaries.

Protocol flexibility
It encodes requests, approvals, disclaimers, drafts and negotiations without needing explicit schemas. Humans and agents can infer status from natural language and attachments.

Email persists as the Human/Agent API

So, in my view, we can roll out as many portals and workflow tools as we want. But they had better integrate with a lawyer’s inbox and a lawyer’s calendar. Because email is going to persist as the universal connector - and even play a new role as a kind of “API for Human/Agent collaboration”.