Without Limitation

Why India is a Legal Tech Superpower

Shreya Vajpei built the community for legal tech in India. Now she's connecting that community with the rest of the world. India has more legal tech startups than almost anywhere on earth. I did not know this until I sat down with Shreya Vajpei, but India has around a thousand legal tech startups, which Shreya tells me puts it second only to the US and ahead of every other market, including the UK. That is a striking number for a country where foreign law firms still can’t really practise, where only advocates can own law firms, and where the entire legal services market is about a fifth the size of the UK’s. If there’s one person who can help us make sense of this landscape, it’s Shreya Vajpei. Listen to the full episode on your favourite platform, or keep reading for the full write-up. * Apple Podcasts [https://podcasts.apple.com/us/podcast/without-limitation/id1870080229] * Spotify [https://open.spotify.com/show/3JP46jTirPmEan6TfuVW7k] Introducing Shreya Shreya trained at Khaitan & Co, one of India’s tier one firms and roughly the magic circle equivalent in the Indian market, with around 800 lawyers when she joined and closer to twice that today. Like many guests on Without Limitation, her career has taken an unconventional path. She practised for a couple of years before moving into a practice development role. Then, COVID hit and the marketing budget disappeared overnight, which meant her team ended up absorbing everything else the managing partner needed help with. That covered IT, HR and operations, but also pricing, strategy, new office openings, partner performance and strategic hiring. In her words, whatever landed at the managing partner’s table also landed at theirs, which gave her something most lawyers never really get, which is a top-down view of a law firm as a business rather than a bottom-up view of a practice. From there she became one of the first hires into Khaitan’s innovation team, and her role kept evolving as AI did. Last year she moved to the UK to join Stephenson Harwood, drawn by what she described as a more mature market for digital transformation, with longer-established innovation teams, more consistent IT budgets, and a decade or so head start on the journey. A thousand startups Shreya tells me that India’s legal services market is around $10 to $15 billion in total, roughly a fifth the size of the UK’s, and it is wildly unconsolidated compared to what most of us are used to. The top five firms are similar in size to each other, in the 1,500 to 2,000 lawyer range, and then there is a big gap before you reach the long tail of full-service firms operating somewhere between 50 and 200 lawyers, and then the boutiques and independent chambers, and then a hyperlocal market across the tier two and tier three cities that operates almost entirely separately and accounts for the vast majority of India by population if not by revenue. On top of that, foreign firms still can’t really practise there, a liberalisation bill has been pending for years with significant local opposition, and there is no ABS or non-lawyer ownership of any kind. So the obvious question is why a market with all of those constraints has produced so many startups, and Shreya’s answer is partly cultural, partly economic, and partly a story about talent. India, she tells me, is generally entrepreneurial and high risk-taking, which feeds directly into the volume of founders willing to have a go, and it has some of the best developers in the world at cost structures that make building viable in a way that is hard to replicate elsewhere. The economics also push founders outward almost from day one, because rupee revenues are small once converted, so the dominant playbook is to build in India and sell internationally, which is exactly what companies like SpotDraft (now established in the US and entering Europe) and Lucio (which recently opened a New York office) have done. One category worth flagging, because it is more advanced in India than most other places, is online dispute resolution (ODR). SEBI, the securities regulator, now requires all investor disputes to go through an ODR platform, and there is open API infrastructure called the Pulse Protocol that allows any ODR provider to plug in, in much the same way that UPI revolutionised payments by giving every bank and every app a shared rail to build on. India tends to solve problems at the infrastructure layer when it solves them properly, and ODR is a good example of what that looks like in practice. The bridge What makes Shreya interesting beyond her own story is that she has lived on both sides of the bridge between the Indian and international legal tech ecosystems, and she has clear views on what each side keeps getting wrong about the other. For Indian startups looking to scale into the UK and US, she offers a network and an instinct for what magic circle firms actually buy, which is not always what an Indian founder might assume from a distance. Last year she worked with the UK Department of Business and Trade to bring a contingent of Indian legal tech startups to the UK to meet magic circle firms, Scottish firms, and the Legal Tech Talk crowd, and that kind of bridging is something the industry could probably use more of. For international players trying to enter India, she sees the same mistakes repeated. Pricing set for the US market with no real adjustment for local realities. Customer support sitting in time zones that do not overlap with the Indian working day. A lack of appreciation for the fact that the biggest Indian law firms still operate across multiple languages alongside English, and that most LLMs do not handle Indian languages or scripts particularly well, which means translation is a first-order use case rather than an afterthought. She makes the point that the legal AI players doing well in India have generally understood that the same product positioning does not travel intact, because the problem itself is not quite the same as it is in the US, and the reframing has to happen locally rather than being assumed away. If you want to tap into this network, it is at indianlegaltech.net [http://indianlegaltech.net]. On influential women in legal tech Shreya recently won an ILTA Influential Women in Legal Tech award. We discuss how her award sits against a backdrop that most people in the industry recognise but that not enough are actively doing something about, which is that around 3% of startup funding flows to female founders, and legal tech is no exception. As Shreya puts it, some parts of the ecosystem have become a “boys’ club” where the funders and the people asking for funding are both part of the same cycle, and that cycle is genuinely hard to break from inside it. Her recommendations are practical rather than abstract. She suggests that if your firm runs an incubation programme, ring-fence dedicated seats for female founders. If you sit on a legal tech fund, write a hypothesis that requires a female founder on every backed team. And if you are a woman who has made it into a decision-making role with some capital to deploy, invest in another female founder, because even one extra month of runway can sometimes be the difference between a company that survives and one that doesn’t. She also made an observation that that a lot of the buyer-side decision makers in legal innovation, the heads of innovation and heads of knowledge across the larger firms, are women, and the supply side of the industry has not really caught up with that. On the AI-native firm We finished, as I tend to with these conversations, on whether law firms are actually changing or just dressing the old model up in new clothes. Shreya uses the factory electrification analogy, which is my personal favourite as well. When factories first switched from steam to electric power, owners swapped the engines but kept the same layout, the same processes and the same workflows, which meant they got slightly faster operations but not much else. The real productivity gains came decades later, when factories were redesigned from the ground up around the new power source rather than just retrofitting it into the old design. Most law firms are still firmly in the swap-the-engines phase, treating AI as a tool that makes existing tasks slightly more efficient rather than as a medium for rethinking what legal work is and where the value actually sits. Look at almost any legal tech company website and the framing is the same: draft 20% faster, research 30% faster, all of which assume the underlying tasks remain the tasks lawyers do. The harder question, which Shreya thinks almost no firm has properly sat down with, is where the value layer actually lives in a professional services business once AI is properly in the picture, and what you would build if you started from that question rather than from the current shape of the firm. AI is now forcing the rethink that should arguably have happened years ago, and for those of us interested in true innovation in legal services, that is probably the most exciting part. If you are building in legal tech and you have not yet thought seriously about India, or if you are in India and thinking about scaling out, Shreya is the person to know. I look forward to seeing what she builds next. Links * indianlegaltech.net [http://indianlegaltech.net] * Shreya on LinkedIn [https://www.linkedin.com/in/shreyavajpei] * Stephenson Harwood [https://www.stephensonharwood.com/about/innovation/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.agents.law [https://www.agents.law?utm_medium=podcast&utm_campaign=CTA_1]

Lessons from 40 Years of Building Agents

Dazza Greenwood has been building agents for longer than several legal tech founders have been alive. In the 1980s, as an undergraduate computer science student, he encountered AI assistants for the first time. His module introduced a then-new paradigm: human language, chat-based systems. The exercise was to build something modelled on ELIZA, the MIT chatbot whose therapist module ran on a simple heuristic. Find the keyword. Reflect it back to the user. When in doubt, tie it all back to the user’s parents. It was deterministic, a little absurd, and wildly popular with the people who tried it. Dazza learned some tricks and came away with a fascination with what AI was, and more importantly, what it could be. Note: Some of the concepts in this episode may be unfamiliar to some listeners. We cover them in the technical explainer at the end. What followed was a career which has spanned dozens of initiatives around the world. Let’s just say that Dazza has worn (and continues to wear) a lot of hats. Legislative aide. Candidate for office. In-house Technology Counsel to the Commonwealth of Massachusetts (which by the way he notes would be a Fortune 50 company if it were private). Standards architect. Stanford researcher. Platform builder. He went to law school, he tells me, because he kept getting different answers from different lawyers to the same question and found it unacceptable. After years of practice, he still does not have a fully satisfactory answer to “what is the law?” but he at least knows how to find the relevant law himself, which was enough to let him return to technology without feeling incomplete. He was doing legal tech, he says, before anyone called it that. Writing scripts to automate his work. Treating legal documents as data - to the extreme displeasure of colleagues who just wanted Microsoft Word (why is it always Word?) Why didn’t the standard stick? In the early 2000s, Dazza was one of the architects of LegalXML, an effort to create an international standard for marking up legal documents so they could be treated as structured data. He ran the e-contracts group. It took seven years to reach the status of a recognised international standard. It attracted a small community of who he describes as lawyer-geeks who marked up their contracts in XML, built clause libraries, and imagined a future of genuinely interoperable electronic contracting. It did not really arrive, at least not in the way the group expected. A handful of vendors adopted the standard, mostly for workflows they were already running. The broader transformation never came. The lesson Dazza draws from it is that the obstacle was never the standard. It was the culture. The love affair with Microsoft Word was not something a well-designed schema could fix. Standards, he says, have to arrive at the right moment. Too early and the industry is not ready. Too late and people have already locked into whatever is there. The good news is, the moment has now arrived, and it came from a different direction entirely. Large language models can peer into the meaning of a legal instrument and address it as data without anyone having to tag a single element. Lawyers, it turns out, are naturally good at the lingua franca of LLMs: precise language, conditional logic, sub-clauses, if-but-not-that constructions. The standard that nobody could agree on turned out to be language itself. Is your agent loyal to you? Dazza has just finished a research sprint at Stanford’s Digital Economy Lab on a project called Loyal Agents, run jointly with the Consumer Reports Innovation Lab. The question it asks is simple and the implications are not: when an AI agent conducts transactions on your behalf, what legal framework governs whether it is actually acting in your interest? His answer keeps returning to fiduciary duty, and specifically to a US federal case called Kovel that almost nobody in legal AI is talking about. Kovel itself is sixty years old. It involved an accountant working with a tax law firm whose communications with a client became the subject of a grand jury subpoena. The Second Circuit held that the privilege extended to the accountant because he was acting as the lawyer’s agent in providing legal advice. The principle that emerges, Dazza argues, applies directly to modern AI vendors. To protect attorney-client privilege when a SaaS provider is handling client communications on behalf of a lawyer, that provider needs to be the lawyer’s agent in the legal sense. Most enterprise AI contracts disclaim exactly that. Dazza has read them all. He has built a public breakdown of what each of the major frontier model providers actually says in their enterprise terms about confidentiality and agency. See Links below. The most upvoted question at Anthropic’s recent legal webinar, which drew over 20,000 registrations, was about how to handle privilege when using general purpose AI tools. Dazza thinks the profession is looking for the answer in the wrong places. The technical controls matter. Zero data retention matters. But the legal layer, the contract clause that says we are your agent, is what Kovel’s logic requires, and most providers do not offer it. His pitch to the frontier model providers is direct: stop disclaiming agency and start defining it. Write a narrow, limited agency clause. Be your client’s agent for three specific things and disclaim it for everything else. It contains risk rather than expanding it, it supports privilege, and for providers building agentic products, it simply reflects reality. If a human did what these systems do, it would be an agent. In Dazza’s opinion, the contract should say so. The platform nobody has built yet Six months ago, Dazza started building something called Interlateral. The felt need behind it is something he has observed sitting in meetings in San Francisco with startup and innovation teams where everyone has agents running quietly in the background, and then communicating with each other through the narrow human-to-human channels of Slack and email, as if the agents are not there. Interlateral is a shared collaboration space where humans and their agents can work together in the same room. You bring your agents. He brings his. There is a third space where they can interact, collaborate, and co-work, with a shared markdown surface that both humans and agents can read and write into. The design principle is human-centred: a person is always at the wheel. The agents are extended cognition, not autonomous actors. The first event ran at Stanford last week with 60 lawyers and eight teams, and the next is at MIT. Eventually, Dazza wants tens of thousands of participants. He thinks the combination of people and agents in a shared space is a genuinely new source of collective intelligence, and that we have barely started to understand what it can produce. There is a harder problem underneath it. In Google Docs or Slack, identity is straightforward. You can see who wrote what. In a space where agents are acting on behalf of humans, you now have two levels of separation from the person you think you are dealing with. Agent identity and attribution, knowing whose agent it is and holding humans accountable for what their agents do, is a bleeding-edge question the industry has not yet solved. How he builds with agents At the end of our conversation, Dazza pulls up his GitHub repo and walks me through how he actually works. The interlateral_agents repo is open source, the product of years of slow tuning, and it is more architecturally interesting than most people’s agent setups. He runs three models in parallel: Claude Code, Codex, and Gemini, with Grok CLI from xAI expected to join shortly. What makes it unusual is the communications layer. The agents share what he calls a multiplexing comms hub, a setup that lets them read each other’s outputs and write into each other’s terminals directly. He describes it as a Vulcan mind meld. One agent can see that another tried something, that it failed, and suggest an alternative approach. The collaboration is explicit and adaptive rather than just running tasks in sequence. On top of that he uses skills: lightweight prompt-level definitions that tell the agents how to organise their work on a given task. He can arrange them hierarchically, with one agent orchestrating the others, or as peers collaborating on the same problem. The skills determine the shape of the collaboration without requiring complex infrastructure to enforce it. It is, he says, a surprisingly low-key way to get a lot out of very capable but very different models working together. What is the AI-native organisation? I ask Dazza what he thinks people are underestimating. The current pattern, he says, is that AI creates extraordinary efficiency at discrete points in a workflow and then causes congestion at the parts downstream that have not changed. Contract review is faster. The humans waiting for the output of contract review are not. The clog forms between the transformed part and the untransformed part, and it is going to get worse before anyone fixes it. The AI-native organisation is one that has redesigned itself around AI, touching pricing models, staffing, role definitions, and quality control, which starts to look less like a periodic event and more like continuous monitoring. That redesign, he says, is not premature. It is coming whether organisations are ready for it or not. The ones doing the mapping exercise now, looking holistically at the full lifecycle of a matter rather than optimising individual tasks, are the ones who will navigate it gracefully. The ones waiting are storing up a serious problem. Final note Dazza Greenwood is genuinely hard to keep track of. In preparing for this conversation I found Stanford research, open source repositories, consulting work, a platform under active development. Dazza literally switched hats midway through our discussion. What surprises me most though is that none of it feels scattered. The dots (and hats) are actually very connected. It all connects back to the same conviction he has held since the start: that law and technology are not separate domains, that legal instruments are data, that agents will conduct transactions on behalf of humans and the frameworks governing that need to be built carefully. He has been pretty patient about this for forty years. The future, he says, has finally arrived. And he seems genuinely delighted about it. Links * dazzagreenwood.com [https://dazzagreenwood.com], Dazza’s blog including his upcoming open source model comparison table * computationallaw.org [https://computationallaw.org], Dazza’s write-up of the Stanford Interlateral event * interlateral.com [https://interlateral.com], sign up to attend a future event * civics.com [https://www.civics.com/]to understand more about Dazza’s consulting work * loyalagents.org [https://loyalagents.org], the Stanford and Consumer Reports research and vendor contract analysis, including the Kovel breakdown * analysis on when agency is a feature not a bug [https://loyalagents.github.io/loyal-agent-evals/report/#2-3-2-when-agency-is-a-feature-not-a-bug-the-kovel-pattern-in-legal-tech-saas] * github.com/dazzaji/interlateral_agents [https://github.com/dazzaji/interlateral_agents], the open source agent library Dazza uses to build with Claude Code, Codex, and Gemini The Technical Stuff Here’s a quick primer on some concepts that may be unfamiliar to some: Evals Short for evaluations. A structured way of testing whether an AI system is doing what you want it to do, consistently and measurably. Dazza uses an open source platform he built to run evals on agent behaviour, putting numbers on whether an agent is acting in a user’s interest or getting tripped up by a conflict it has not recognised. Think of it as quality control, but for AI decision-making. Fiduciary duty A legal obligation to act in someone else’s best interest rather than your own. It applies to lawyers, financial advisors, and other professionals. Dazza’s Loyal Agents research asks whether AI agents should be held to something similar, and whether the contracts governing AI services currently reflect that expectation. Most do not. Kovel A 1961 Second Circuit case that Dazza thinks is the missing piece of most privilege discussions in legal AI. United States v. Kovel involved an accountant employed by a tax law firm whose communications with a client became the subject of a grand jury subpoena. The court held that the attorney-client privilege extended to the accountant because he was acting as the lawyer’s agent in providing legal advice. The principle Dazza extracts: for privilege to hold when a SaaS provider handles client communications on a lawyer’s behalf, that provider needs to be the lawyer’s agent in the same legal sense. Most AI vendor contracts disclaim agency explicitly. Dazza argues this is both a legal risk and a fixable problem, and that providers who address it will have a commercial advantage in the legal market. Multiplexing A communications technique that allows multiple signals to share the same channel. In the context of Dazza’s agent setup, it means his agents can read each other’s outputs and write into each other’s terminals in real time, rather than operating in isolation. The result is agents that can observe what the others are doing, flag when something is not working, and adapt accordingly. Skills In the context of agent configuration, skills are lightweight prompt-level instructions that define how an agent should approach a task or how a group of agents should organise their work together. They are not code in the traditional sense. They sit closer to a well-written practice note. Anthropic has its own Skills framework, which we have covered separately on agents.law. Loyal Agents Both the name of the joint Stanford Digital Economy Lab and Consumer Reports Innovation Lab research project Dazza works on, and a broader concept: the idea that AI agents conducting transactions on behalf of users should be demonstrably aligned with those users’ interests, in a way that is measurable, contractually grounded, and legally enforceable. The research has produced evals for testing agent loyalty and a public analysis of how major AI providers currently handle the relevant contract terms. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.agents.law [https://www.agents.law?utm_medium=podcast&utm_campaign=CTA_1]

Should Law Firms Buy or Build?

Michael Kennedy, Addleshaw Goddard Michael Kennedy left university swearing off being a lawyer. He went to work in restaurants and retail for a while, and finally came back to it as a paralegal at Addleshaw Goddard. When he started his training contract, it was very much focused on innovation and technology - a rarity at the time, and Mike was one of the first in the UK to follow this path. Since then, the innovation team at AG has grown from a handful of people to around 80 today. Fast forward to 2026, and Mike now runs the firm’s R&D function, a broad role that encompasses horizon scanning, startup engagement, partnering with clients, internal education, leading a development team, and increasingly a fair amount of building things himself. How do you project into the future? I asked Mike how anyone keeps up when the world is spinning this fast. His answer is that he doesn’t really switch off. He reads constantly, runs research agents through Claude Code, and writes a fortnightly internal newsletter for his team: three things to know, three deep dives, then a long reading list. He says it’s really important for him to know what he’s talking about. He’s not afraid to say he doesn’t know, but he doesn’t like saying it, so he’d rather know and do it. AGPT and the case for building The most visible output of Mike’s team is AGPT, the firm’s in-house AI tool. It’s one of the rare examples of a law firm having built its own in-house solution at a time when most of the industry is focused on buying legal AI. Mike describes AGPT, with characteristic understatement, as “what most people just call a wrapper”. It lives in the firm’s Microsoft Azure environment and does the usual things: chat, document review, translation, prompt libraries, citation tracking. In early 2023, Mike’s team wanted a sandbox to test whether GPT-3.5 was good enough for legal documents. They couldn’t throw client matters into ChatGPT, so they asked the developers to stand something up inside the firm. Other lawyers started asking for access. A pilot followed, then a firm-wide rollout by autumn 2023. The sandbox became the product. Today AGPT runs around 6,000 prompts a day across roughly a thousand users, and the dev team hasn’t had a quiet week since. Mike’s buy-versus-build framework is worth listening to because it comes from someone who has actually done both. Cost matters, but he frames it as a return-on-investment question rather than a sticker-price one. The real factors, he says, are: * Institutional knowledge: you can build for your specific audience in a way you can’t buy for one. A product on the market might have 70% irrelevant features and lose people before they engage, whereas a 30% solution built for your lawyers can land better. * Client consent and data: self-hosted removes a lot of friction. * Portability: which he thinks is underrated. The value is in the solution to the problem, not the tech it happens to run on. In Mike’s view, law firms are going to want to move their prompt libraries, workflows and accumulated know-how between models, and the firms that treat their intelligence as tech-agnostic will have an easier time than those locked into a single vendor. He uses a nice phrase for this: portable intelligence. Claude, vibecoding, and the artifact economy Mike and I are both heavy Claude users. He uses Claude Code primarily to build prototypes - someone in the firm has an idea, usually hard to execute, and instead of taking notes and going away for six months, Mike builds a rough version and shows it to them. One recent example is a regulatory horizon scanning tool for banks, the kind of thing his financial regs team has wanted for a long time. Not a finished product but enough to say “is this what you mean?” and have a real conversation with the Partner. On Claude for legal work itself, Mike is bullish in a way that he thinks should worry the established legal AI vendors. A lot of work inside law firms isn’t legal research. It’s factual research, web searching, document comparison, content creation, the work that fills a competition team’s afternoon. That work is dramatically easier in Claude than in Google, and Mike says Addleshaw is, for the first time, seriously considering enterprise licenses for the lawyers and teams who would benefit. Thanks for reading Agents.law! This post is public so feel free to share it. Training junior lawyers in a world with fewer trainee tasks The question that won’t go away is what happens to junior lawyer training when the grunt work disappears. He thinks training in law firms has always sort of worked by accident. Trainees are bright, engaged, hard-working people who pick things up by osmosis, sat next to a supervisor with a red pen. It’s slow, it’s inconsistent, and what you actually learn is often one individual’s approach rather than a structured body of knowledge. His proposed solution is a good one: use the firm’s data and know-how to build anonymised simulations based on real client matters. Give trainees scoring, measurement, and a structured way to develop across different areas. If AI reduces the billable work trainees do by 20%, use that time for simulated exercises rather than cutting trainee numbers by 20%. It’s an optimistic framing, and Mike knows it. The realist’s version is that firms will just fill the hours with more work and make more money, because that’s what the economic incentives reward. Side note: Mike has built a prototype for this - it’s live on my Vibecode.law platform [https://www.vibecode.law] - check it out! Final note What I take from my conversation with Mike is that there’s a particular kind of legal innovator becoming more common in the industry. They’re not pure technologists and they’re not pure lawyers. They’ve got enough technical ability to build, enough legal experience to know what matters, and enough organisational patience to sit inside a big firm and make things happen. In my view, a lot of the interesting change in BigLaw over the next few years is going to come from “intrapreneurs” like Mike, inside firms, building things and encouraging others to do so. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.agents.law [https://www.agents.law?utm_medium=podcast&utm_campaign=CTA_1]

Fiona Phillips on How to Change the Law

When I sit down with Fiona Phillips, Anthropic has just announced that it won’t be releasing its Mythos model - at least not yet - because of the cybersecurity implications of a system that appears uniquely capable of finding and exploiting software vulnerabilities. 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? This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.agents.law [https://www.agents.law?utm_medium=podcast&utm_campaign=CTA_1]

Elliott Portnoy and the Law Firm of the Future

When I sit down with Elliott Portnoy, it has been just over a year since he stepped down as Founding Global CEO of Dentons, the firm he scaled from a foundation in a mid-sized US firm with no global presence and ultimately became the world’s largest law firm. 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? Thanks for reading Agents.law! Subscribe for free to receive new posts before anyone else. 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. Thanks for reading Agents.law! If you like it, please share it with one other person. 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. Thanks for reading Agents.law! Subscribe for free to receive new posts. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.agents.law [https://www.agents.law?utm_medium=podcast&utm_campaign=CTA_1]