Without Limitation

About Without Limitation

Stories from the people reshaping legal www.agents.law (https://www.agents.law?utm_medium=podcast)

Publisher

Matt Pollins

Category

business · technology

Language

en

Explicit

No

First episode

18 Jan 2026

Latest episode

30 Oct 2026

Latest episodes

2 of 17 episodes have a transcript.

1. 

   30 Oct 2026TRANSCRIPT

   The Unbundling of Legal Work

   Host [Host Name] interviews Liam Brown, founder and CEO of Elevate, about unbundling legal work by applying lessons learned from medicine to drive innovation and efficiency.

2. 

   23 Jul 2026TRANSCRIPT

   What Can Law Learn from TV and Music?

   Host Anthony Rose discusses how the music and broadcasting industries' past disruptions offer lessons for the legal sector's future.

3. 

   4 Jun 2026

   TED and the Future of the Lawyer

   I meet Nishat Ruiter, GC at TED, the world's most influential ideas platform, and learn how TED Law is asking us to question what it means to be a lawyer. What we cover * How TED grew from a conference to become the world’s #1 ideas platform. * Nishat’s unconventional path, via the Netherlands, the Brooklyn family court, and the self-taught IP licensing work that helped launch her GC career. * How organising a local TEDx event gave Nishat a deep understanding of TED’s mission before she ever joined the organisation. * What TED looks like behind the scenes, from the main conference to TEDx, TED-Ed, podcasts, partnerships, licensing, translation and global community-building. * Why Nishat launched TED Law, and what she thinks legal education misses around judgment, professional identity, cultural competence, collaboration and moral courage. * How you can get involved in TED Law (that includes law firms, in-house teams and tech companies). Meet Nishat When I catch up with Nishat Ruiter, it has only been a few weeks since the latest TED conference in Vancouver. The conference itself is over, which sounds like a natural moment to come up for air, but TED is really a year-round organisation of talks, TEDx communities, podcasts, partnerships, education programmes, licences, brand questions, rights questions, publication decisions and all the legal work that has to happen to make sure ideas can safely reach the world and have the maximum impact. Nishat sees that world from an unusual seat. She gets the force of the talks, the audience energy, the spectacle and the sense of possibility, while also leading the back-office legal review that makes the public version possible. A TED Talk may look effortless by the time it reaches the stage or the internet, but behind it sit questions about rights, privacy, defamation, intellectual property, accuracy, context and reputation. That is part of what makes her role so interesting: she is protecting ideas without wanting to smother them. The bigger question in our conversation is what happens when you apply TED’s vision of “Ideas change everything” to the world of law. TED is built around curiosity, clarity, public communication and the belief that a well-framed idea can move people. Law, at its best, is built around judgment, service, justice and the structures that allow human beings to cooperate. Yet the profession has often taught lawyers the rules far more deliberately than it has taught them how to carry those rules into leadership, crisis, culture, technology and moral choice. TED Law is Nishat’s attempt to address that gap. From Dracula to Brooklyn family court Nishat did not grow up with a plan to become a lawyer. Her first love was theatre. In high school, she acted seriously enough to play the lead in Dracula, which I point out should definitely be on her LinkedIn profile. She was drawn to the empathy of acting: understanding a character, getting under the surface, working out why someone acts as they do. A high school director then gave her a blunt assessment of the acting world. If she wanted to pursue it, she should understand the reality of the industry: she was not white, she was not blonde, and she would not fit easily into the box. It was a hard thing to hear, and it became one of the early experiences behind a theme that runs through her career: finding herself outside the expected shape and then building from there. Law arrived almost sideways. In college, she took a course on the legal and social environment of business. The professor posed a simple hypothetical: a student slips on a banana peel at a university. Who is responsible? Everyone wanted to defend the student. Nishat raised her hand to defend the university, less because she had some sophisticated view of liability and more because she was excited by the less obvious side of the argument. She liked the reasoning, the exploration, the challenge of working out the answer. That instinct took her to law school, where she initially imagined a career in international human rights and justice. After graduating, she spent time in the Netherlands, where her husband is from, and explored international work. But law school loans and practical realities eventually pulled her back to the United States. When she returned, she did what many young lawyers do: she applied for what was open. One of those openings was in family law. She worked for a sole practitioner in Brooklyn for two and a half years and saw, up close, the human pressure of matrimonial litigation. Custody. Support. Divorce. Lives being rearranged in courtrooms that had far too many cases and far too little time. At one point, she says, a judge might have 120 cases on the docket and two minutes to hear yours. The work mattered enormously, but the system around it could feel too arbitrary for questions that were so consequential. So she moved back toward intellectual property and technology. This was the dot-com era, before anyone could ask ChatGPT how to become a software contracts lawyer. Nishat went to Barnes & Noble, read everything she could find, and built herself a three-ring binder full of software licensing contracts. When she interviewed for a contracts manager role and they said they were looking for someone with templates, she had them. Then she made the obvious legal career move: if she was going to be the only legal person, perhaps she should be general counsel. That is a very Nishat story. She sees the gap, does the work, builds the thing and then makes it happen. Thanks for reading Agents.law! Subscribe for free to receive new posts and support my work. Finding TED before TED found her Before TED, Nishat built a career across IP, fintech, software, legal tech and a decade as associate general counsel at CA Technologies. The bridge into TED, though, began in Hillsborough, New Jersey, where she and her husband had chosen to live for practical reasons: schools, work, family life. They also cared deeply about community. Her husband is Dutch. Nishat is Pakistani. Between Dutch bluntness and Pakistani directness, she says, there was no shortage of intense discussion at home, but there was also a shared global outlook and an interest in ideas that could bring people together. Nishat was serving on the township library board and felt that the community could do more to surface meaningful ideas. TED had already been part of their family life. Her husband was an early fan, and they would introduce high school students to TED Talks. So they organised a TEDx event through the library. That meant finding speakers, shaping talks, dealing with the intellectual property, building the event and learning what TED actually requires of a local community. It took six months. She did it without any thought that she would one day work for TED, but it gave her a lived understanding of the brand, the mission and what happens when TED’s global idea is translated into a local setting. Then, during a career shift, the pieces came together. Nishat had just accepted a new role in New Jersey. Her children were in high school. She was tired of commuting into New York and wanted to be more present at home. Around the same time, a friend who had recently joined TED in video and TED Talks asked about her work. At the time, TED had no in-house lawyer. Outside firms helped with some matters, but the publication of talks raised exactly the kinds of questions Nishat understood: IP licensing, defamation, privacy, rights, risk and the judgment needed to review content without flattening the idea behind it. She had already accepted another job, so when she sent over her resume she added the caveats: she had family constraints, she did not want a five-day-a-week New York commute, and the timing would be difficult. Chris Anderson wrote back the next day. Within a week, she had met with TED’s leadership and had an offer. It sounds almost too neat, but the fit makes sense. TED needed a lawyer who understood intellectual property and also understood why the ideas mattered. The platform behind the talks Most people know TED through the talks. For many of us, TED is still associated with the canonical talks that seemed to break through the early internet: Sir Ken Robinson on education and creativity, Hans Rosling on population and data, and countless others that made complicated ideas feel alive. But TED is much broader than the stage. It began in 1984 as a conference around technology, entertainment and design. Chris Anderson later helped turn it into a nonprofit and, in 2006, TED began putting talks online. The expectation was modest. Perhaps the talks would reach 100,000 views. They reached around a million in the first month, and TED became a way of bringing ideas to people wherever they were. That required production, publishing, translation, distribution, licensing, brand stewardship and, eventually, an enormous global community. TEDx was another leap. What began as a risky experiment in giving communities a free licence to convene around ideas has become something like 4,000 to 4,500 events around the world. Then there is TED-Ed, with its animated lessons created with teachers and artists. There are podcasts, partnerships, education programmes, the Audacious Project and the many other ways TED tries to make ideas travel. A small legal team supports all of that. Their job is to help TED stay true to itself while operating globally, across formats, partners, communities, rights systems and cultural contexts. It is harder than it looks, and it gives Nishat a useful vantage point on the difference between protecting an institution and preserving the spirit that made the institution worth protecting in the first place. AI and the purpose question We spoke, inevitably, about AI. TED has always been close to the frontier of technology; many of the people shaping what comes next have appeared on the TED stage early, sometimes before their ideas became mainstream. The most compelling use of AI, in her view, is the one that serves humanity: improving education, science, health, access and the ability of human beings to do more meaningful work. She points to global discussions about AI for humanity as the kind of framing that deserves more attention. That becomes the bridge to TED Law, because lawyers are facing the same problem in their own field. A technology that can change the mechanics of work forces a deeper question about the purpose of the work. If AI can draft, summarise, search, structure and increasingly act, the lawyer’s value has to move beyond information retrieval and formal rule application into judgment, courage, context, communication and the ability to make sense of human stakes. Why TED Law exists TED Law grew out of several pressures arriving at once. Some were legal and political. Nishat points to the overturning of major precedent in the United States, including Roe v. Wade, and the broader conversations that followed about rule of law, institutions and what lawyers are meant to do when foundational assumptions are tested. Some were professional. In conversations with general counsel, partners and legal leaders, she kept hearing the same unease: the skills lawyers were using in leadership were often different from the skills they had been taught in law school. Some of it was personal. Nishat remembers staying after class in law school because she wanted to ask why. She wanted to understand why a judge had decided a case in a particular way, what was happening in the community, what the broader context was, and what was going on in the world around the decision. Another strong student once stayed behind too, then asked the professor whether any of that context would be tested. The answer was no. It was just context. The student left. For Nishat, that moment captured something important about legal education. The profession teaches lawyers to read, extract, repeat and advocate. It is less consistent in teaching them to connect law to values, identity, culture, judgment, leadership and courage. That may be manageable in parts of legal practice, but it becomes a serious gap once lawyers move into leadership roles and face moral questions, crisis decisions, compliance failures, rule-breaking, political pressure and business demands. At that point, lawyers sometimes have to push back on the very people they advise. They have to represent an organisation while retaining their own professional judgment. Substantive law still matters, obviously. But Nishat’s point is that it no longer carries the whole burden. TED Law is built around capabilities the profession needs to teach more deliberately: professional identity, critical thinking, cultural competence, intuitive collaboration and the ability to work effectively with AI. The result is a curriculum of roughly 15 to 18 hours, built to pair TED’s ability to open up a subject with the legal rigour needed to examine it properly. The work has been tested against the profession rather than designed from a private theory of what lawyers need. TED Law surveyed law schools, general counsel, managing partners, law students and lawyers across countries. It set up advisory councils. It looked at regulatory changes, including ABA developments around professional identity, client interaction and cultural competence. The gaps Nishat had identified were already showing up in the profession’s own requirements. Teaching lawyers without another terrible slide deck Anyone who has sat through enough legal training will understand part of the problem. Lawyers are cynical and sceptical audiences, and many CLE-style sessions invite exactly that response: dense slides, tiny font, rules read from the screen, and a few seconds at the end for questions so the box can be ticked. You leave with the materials and the credit, but rarely with a changed view of the work. TED’s method gives Nishat a different tool. A talk can change the room. It can lower the guard, create emotional entry and move lawyers from defensiveness into curiosity. From there, you can have a serious legal discussion: a case study, a conversation about ethics, AI, culture, judgment, practice readiness or professional identity. The inspiration is not the whole lesson; it is the opening that lets the lesson breathe. That may sound soft to some lawyers, but the wellbeing data tells a harder story. Nishat is direct about the fact that lawyers experience suicide ideation at around four times the rate of other professions, alongside higher levels of alcohol abuse and drug abuse. Research also suggests that the things that make lawyers feel fulfilled are autonomy, relationships and meaning rather than prestige, rank or power. The legal profession has spent a long time optimising for markers that do not necessarily make lawyers better, healthier or more useful. TED Law asks a different set of questions: why did you come to law in the first place? What did you write in your law school application essay? Was it really about money, status and golden handcuffs, or was there once an idea about justice, service, purpose and doing something useful with a powerful set of skills? The AI-age lawyer All of this has become more urgent because of AI. ChatGPT became publicly available in late 2022, and in the short period since then the legal industry has moved from basic experimentation to agentic AI, automation, workflow redesign and increasingly serious questions about what legal jobs will look like. None of us knows exactly what the profession will look like in two or three years. That uncertainty changes the education question. If the rules, tools and job categories of the near future are still forming, the foundations have to carry more weight: critical thinking, collaboration, creativity, cultural competence, multidisciplinary judgment, moral imagination and the ability to use AI without surrendering human responsibility to it. Nishat is interested in something deeper than skills training. She wants lawyers to examine their role in society and reach for a better version of the profession’s potential. If lawyers do not understand human beings, she asks, how are they supposed to help build the structures that protect them? An incubator with serious ambition TED Law is still in incubator stage, which is an important distinction. The work is being built, tested, refined and rolled out carefully. People can sign up for updates through TED’s legal newsletter at ted.com/law, and Nishat is actively inviting input through surveys and community conversations. There are already concrete paths in motion. TED Law is working with the ACC Foundation, the nonprofit arm of the Association of Corporate Counsel, to bring training to in-house lawyers. It is also working with the American Bar Association, including an in-person training in Chicago in July. Nishat wants lawyers everywhere to understand their professional values and identity, think critically, collaborate intuitively, operate across cultures and use AI effectively. Whether TED Law becomes the vehicle for all of that is secondary to the impact she wants to see. She also wants to change the way society sees lawyers. The old Shakespeare line about killing all the lawyers is usually treated as a joke at the profession’s expense. Nishat points out it actually had a different meaning: remove lawyers and you remove a mechanism for addressing injustice. That is the role she wants lawyers to recover. Final note What struck me most about Nishat is that TED Law feels like the convergence of the whole story. The theatre student who learned to inhabit a character; the young woman told she did not fit the box; the law student who wanted to know why; the Brooklyn family lawyer who saw the human cost of an overstretched system; the IP lawyer who built herself a binder because nobody was going to hand her the answer; the community organiser who brought TEDx to her town; the general counsel who understands that ideas need support. The project is still young, and Nishat would be the first to say it is being built with others: four advisory councils, law schools, GCs, law firms, students, professional bodies and lawyers who know something is missing even if they have not always had the language for it. But the central idea is already clear. The future of law will require lawyers who know what they stand for, understand the people they serve and can build with technology without forgetting why the work matters. That is a big idea, and TED is a fitting home for it. Resources * Sign up for the TED Law newsletter (https://www.ted.com/law) * Sir Ken Robinson and the best TED Talk ever (in my opinion!) (https://www.ted.com/talks/sir_ken_robinson_do_schools_kill_creativity?hsa_acc=7777130675&hsa_cam=218945593&hsa_grp=17379703273&hsa_ad=295607643496&hsa_src=g&hsa_tgt=kwd-16365900407&hsa_kw=best+ted+talks&hsa_mt=b&hsa_net=adwords&hsa_ver=3&gad_source=1&gad_campaignid=218945593) * Follow Nishat on LinkedIn (https://www.linkedin.com/in/nishat-ruiter-1a21362) 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)

4. 

   26 May 2026

   The World’s Best Vibecoder is a Lawyer

   In the latest episode of Without Limitation, I meet Michael T. Brown. When I sit down with Mike, it has been around three months since he won the Anthropic global hackathon, beating 13,000 applicants, most of them career engineers, with a project built in six days from his home in Orange County. We’re also chatting on the day before he takes the main stage at Code with Claude in San Francisco, one of the biggest software events in the world this year, where he will be delivering a thirty-minute keynote. Listen to the full episode on your favourite platform, or keep reading for my take on the conversation. * Apple Podcasts (https://podcasts.apple.com/us/podcast/without-limitation/id1870080229) * Spotify (https://open.spotify.com/show/3JP46jTirPmEan6TfuVW7k) Not a software engineer Mike is, by his own admission, not a software engineer. He has never written a line of code in anger. He is a personal injury attorney by background, with a previous career in Hollywood. Of the top 5 finalists that Anthropic announced, only one was a full-time software engineer. The others included a cardiologist, an electronic musician, an infrastructure worker from Uganda, and Mike, who took the overall crown. So how did a lawyer win the Super Bowl of hackathons? And what does his story tell the rest of us about what is possible with these tools right now? Join me on 17-18 June: We are running what promises to be the biggest vibecode hackathon in law, at LegalTechTalk in London. This is a unique event, in partnership with HSF Kramer and Replit. You’ll get free Replit Pro credits just for participating, and there are some great prizes on offer. But to be involved, you must register (https://www.legaltech-talk.com/experience/legaltechtalk-vibeathon/). Hollywood to law school to a maxed-out credit card Mike didn’t set out to be a coder, or a lawyer for that matter. He spent his twenties in Hollywood doing visual effects work. He did what he describes, with a smile, as the rational response to any burnout: he went to law school. He fell into personal injury practice and genuinely liked it. He spent a few years at California PI firms before hitting the decision point most PI lawyers eventually hit, which is making the decision to go out on his own. That decision happened to land in late 2023, just as ChatGPT was changing what was possible. Faced with the usual choice between hiring paralegals or burning through savings on back office support, Mike did something different. He started building prompt libraries before anyone was calling them prompt libraries, recording intake calls with client permission, transcribing them through Whisper, and feeding the transcripts into ChatGPT to produce his own version of an intake form. The system worked well enough that his cousin, a serial entrepreneur, suggested they productise it. They built a company called Onbreeze, a note-taking phone line for lawyers, on the insight that injured clients don’t book a Zoom call with a lawyer - they call. Vibecoding wasn’t mainstream at this point, and Mike outsourced the entire development process to an external development agency. The product launched in January 2025. Unfortunately for Mike, it crashed the moment a fourth caller hit the line. When the offshore engineering team explained that fixing it would cost roughly the same as building it had, Mike was already maxed out on credit cards. He downloaded Cursor (an AI coding tool) instead. Ten minutes later, by talking to it about what he needed, he had a working solution. He still couldn’t read the code. But the thing worked. That was his first prompt. How Mike learned to build Onbreeze didn’t take off the way Mike hoped. By May 2025 he was at a crossroads: go back to practising law full time, or lean into the strange new fluency he’d developed with vibecoding tools. He chose the tools. But what’s interesting is how he chose them. He didn’t go and learn to code in the traditional sense. He took on a series of consulting projects for friends and family, each of which forced him to learn one new thing. A roll-up project for a private equity contact taught him how to handle data across complex databases. A pickup ordering system for his cousin’s butcher shop in Brooklyn taught him Stripe. Mako, a demand letter generator for personal injury lawyers, taught him how to deploy agents to the cloud using Anthropic’s SDK. He describes it as accidentally building a curriculum. Each project was something he wanted to learn anyway. Each one came with a real user on the other side. And each one added to a stack of skills he could combine later. By the time the Anthropic hackathon was announced in early 2026, Mike was already far more fluent with the technology than most. Picking a problem nobody could solve The hackathon accepted only 500 of the 13,000 applicants, and the brief was specific: they wanted problems that weren’t solvable with previous generation models. The problem was California’s housing crisis, or more precisely, the permits bottleneck inside it. “Everyone thinks California has a housing crisis,” Mike puts it. “We don’t. We have a permit crisis.” His friend builds ADUs (Accessory Dwelling Units), the small backyard cottages California has incentivised as a partial answer to its housing shortage. The laws are favourable, the demand is there, and yet permits routinely take six to nine months, with endless back and forth between builders and city plan reviewers. Mike had a hunch AI could help. He asked Cameron to send him a set of blueprints. Claude couldn’t handle them. The pages were physically too big, the size of a table, and the dense annotations meant traditional OCR pulled the text away from the visual context that gave it meaning. A note about wall thickness only mattered if you knew which wall it referred to. By this point, most people would probably move on, but Mike saw an opportunity. If the most advanced Anthropic model available at the time couldn’t handle this, then it was exactly the kind of problem the hackathon judges were looking for. He submitted, got accepted, and immediately had a small panic about whether he could actually solve it. The lesson buried in this is that problems are way more important than solutions. Most vibecoders jump straight into building things. Mike spent his time finding a problem worth building for, one that mattered to a real user and that pushed the technology to its current limit. Building it in six days Mike decided to call the app CrossBeam. The hackathon ran for a week and by Wednesday night, two days in, Mike was in trouble. He had two disparate approaches, neither of them working, and the hour was getting late. He scrapped the elaborate combination of OCR plus image referencing he’d been wrestling with, and went back to first principles: here are the rules, here are the chunks of blueprint, figure out what’s wrong. That first end-to-end run hit roughly 40% accuracy against the city’s correction letters. Crucially, it ran. Once a system runs end-to-end, even badly, you can benchmark it and improve it. By Thursday afternoon he was at 60 to 70%. Two design choices were key here. The first was treating the model’s context window as a precious resource rather than a dumping ground. Mike used Skills, Anthropic’s open standard (think of them like those files that get uploaded to Neo’s brain in the Matrix so he suddenly knows Kung Fu), to keep his prompts lean. He used parallel sub-agents, each with a narrow job and a fresh context, rather than one bloated agent trying to do everything. He used what he calls adversarial prompting: have one agent plan, a second agent execute, and a third agent test, so that no single agent is grading its own homework. For this, he uses an analogy from legal practice: “When an associate brings you a memo, you don’t ask them if they double checked it. Of course they’re going to say yes. You give it to someone else to check.” The second design choice was testing. He set up command line interfaces on the backend so that one Claude instance could run a new ADU through the system every hour while he worked on something else, tracking accuracy gains in real time. By Friday his accuracy was high enough that he could spend the weekend on the user-facing piece, which itself ended up being shaped by two conversations. The first was with Cameron, his friend, a builder. Mike sent him fresh outputs and Cameron immediately asked if he could run three more blueprints through. Real users behave like that when something works. The second was with his friend Connor Traut, the mayor of Buena Park. Until that conversation, Mike had only been thinking about the builder side. Connor flipped his perspective: cities also need help, on the review side, processing permits faster. So Mike rebuilt the product to serve both sides. Builders drag and drop blueprints and get back a precise action plan in twenty minutes. Cities batch process submissions and generate draft correction letters. Same product, two different audiences. Why lawyers might be unusually good at this Mike’s prompting structure is one most lawyers will recognise immediately. Issue, rule, analysis, conclusion. This is the framework you learn for the bar exam, repurposed as the framework for getting good output from a frontier model. Mike starts with the issue at the top. Then, the relevant rules and constraints. Next, the analysis: why this matters, who it’s for, what good looks like. Conclusion at the end. He often prompts by voice, often for five minutes at a stretch, because typing caps the average user at around 60 words a minute and speech runs closer to 180. (Ahem those of us who took a typing class in the 90s are closer to 120+ but the point stands!) The skillset Mike describes is closer to drafting a memo than to writing code. Lawyers spend years learning to think clearly under pressure, to anticipate counterarguments, to specify exactly what they want and exactly what they don’t. Those skills, plus a working “b******t detector” and a willingness to experiment, are most of what it takes to build with these tools today. The bit lawyers usually need to add is the last part - the curiosity and willingness to experiment. Some legal training does lean towards risk averseness in a way that doesn’t serve help with building new things. We may need to unlearn some things here. Thanks for reading Agents.law! If you’re enjoying this piece, please share it with someone else who may find it useful. On security As we talk, Will Chen’s MikeOSS project has been making waves. It’s an open source version of Harvey/Legora. Mike and I observe that the conversation on vibecoding has become a little polarised into those who seem to believe you no longer need to learn software engineering at all (on the one hand) and those who believe it is a waste of time (on the other). Mike is realistic here, if a little more bullish than I am on AI’s ability to spot and resolve security and compliance issues in production software at the current time. He’s alive to the limitations of vibecoding and shares that he would be open to partnering with software engineers when the project demands it. (Side note: my recommended framework for Responsible Vibecoding is at Vibecode.law/learn (https://www.vibcode.law/learn)). His biggest frustration is people writing about the issues with open source software rather than submitting PRs to resolve the issues. This ties into something else I observe about Mike - his extreme bias towards action. He doesn’t have time for essays about software - he wants to build. Key lessons for vibecoders If we step back from the specifics of permits and ADUs, I think what you end up with is a template for building effectively with AI. Find a problem worth solving. Talk to the people who actually have it and spend as much time with them as you can. The closer you can get to the problem, the better the output. No problem, no solution, no value. Build in short cycles with rigorous testing. Testing is a critical part of software engineering but it’s something that most vibecoders forget. Software can be tested really effectively because it largely either works or it doesn’t. Don’t forget this part. Treat the context window like a budget. Mike learned through experimentation how performance drops off a cliff as you use up the agent’s context window. He manages this carefully with compaction, Skills and just starting new conversations. Prompting still matters. I occasionally read that the era of prompt engineering is coming to an and. I don’t agree on this. Sure, some of the old hacks like “You are a world expert in everything” aren’t very useful these days but Mike’s framework for prompting is effectively about giving the agent the exact context it needs. This leads to better outputs and CrossBeam is proof. Show your work to real users early and let their reactions reshape the product. Don’t be precious about your first design. Most vibecoders spend so long seeking perfection and not enough time getting it in the hands of real users. Finally, be curious. This is the common theme across all episodes on the podcast. People like Mike who are pushing the boundaries of what is possible do so because they are curious about the boundaries. Most people thought the California permitting crisis couldn’t be solved with AI. Mike wondered what would happen if it could. I for one can’t wait to see what Mike builds next. Up next In the next episode, I’ll be sitting down with the amazing Nishat Ruiter, General Counsel of TED and the founder of TEDLaw. Resources & further reading Follow Mike on LinkedIn (https://www.linkedin.com/in/michael-t-brown-034aaa22/) CrossBeam (https://www.crossbeam-permits.com/) Thanks for reading Agents.law! Subscribe for free to receive new posts and support my work. 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)

5. 

   15 May 2026

   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)

6. 

   26 Apr 2026

   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)

7. 

   20 Apr 2026

   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)

8. 

   10 Apr 2026

   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)

9. 

   21 Mar 2026

   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)

10. 

    14 Mar 2026

    Inside the Claude-Native Law Firm

    We discuss how he actually uses AI day to day, how he thinks about the security and privilege considerations, and what happens to the billable hour when you scale your work with AI. Side note: for a demo of Claude on legal use cases, watch this LinkedIn Live recording (https://www.lupl.com/blog/automating-legal-work-with-lupl-and-claude/) 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 (https://www.agents.law/p/how-to-train-your-agent) 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 (https://www.agents.law/p/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 (https://www.lupl.com/blog/automating-legal-work-with-lupl-and-claude/) I posted last week. Links * Rains (rains.law) * Zack Shapiro on X (@ZackBShapiro) * “The Claude-Native Law Firm” (original post on X) (https://x.com/zackbshapiro/status/2027389987444957625) 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)

11. 

    8 Mar 2026

    How AI Could Really Change Things

    In this episode, I meet Dr. Sarah Stephens and learn how an AI Assistant in WhatsApp is solving real problems for women in Tanzania 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 (https://www.gov.uk/government/organisations/online-procedure-rule-committee), leading the Sussex Centre for Law and Technology (https://www.sussex.ac.uk/research/centres/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 (https://www.linkedin.com/in/dr-sarah-moffatt-stephens-68846582/). 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 (https://www.gov.uk/government/organisations/online-procedure-rule-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 (https://www.sussex.ac.uk/research/centres/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 (https://www.linkedin.com/in/dr-sarah-moffatt-stephens-68846582/). Links * Dada Wakili (https://www.sussex.ac.uk/research/centres/sussex-centre-for-law-and-technology/research) * Sussex Centre for Law and Technology (https://www.sussex.ac.uk/research/centres/sussex-centre-for-law-and-technology/) * Access to Justice Foundation (https://atjf.org.uk) * Online Procedure Rules Committee (https://www.gov.uk/government/organisations/online-procedure-rule-committee) * Sarah Stephens on LinkedIn (https://www.linkedin.com/in/dr-sarah-moffatt-stephens-68846582/) 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)

12. 

    5 Mar 2026

    Richard Tromans & The Industrial Revolution for Law

    When I sit down with Richard Tromans, he has just published a piece on the Anthropic legal plugin, along with a flurry of other updates on the day’s legal tech developments - another busy morning in the world of Artificial Lawyer. 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. 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 (https://www.linkedin.com/pulse/ai-powered-law-firm-turing-partners-richard-tromans/) * Driverless cars * AI-powered law firms (heard that term recently) * New leverage models * Big data centre developments Links * Artificial Lawyer (https://www.artificiallawyer.com) * The Robot Times (https://therobottimes.com) * Legal Innovators events (https://www.artificiallawyer.com/legal-innovators-conferences-us-uk/) (London, Paris, New York, California) * Follow Richard on LinkedIn (https://www.linkedin.com/in/artificiallawyer/) 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)

13. 

    22 Feb 2026

    What Happens Next?

    Michael Bommarito ran GPT through the bar exam over Christmas 2022 to prove it wouldn’t pass. 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 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 (https://www.amazon.co.uk/Agentic-Law-Finance-Navigating-Autonomous/dp/B0GH8NXCPZ) by Michael Bommarito and Daniel Martin Katz (2026) * This is Server Country (https://servercountry.org/) 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 (https://royalsocietypublishing.org/rsta/article/382/2270/20230254/112538/GPT-4-passes-the-bar-examGPT-4-passes-the-Bar-Exam) (2023), research papers by Bommarito, Katz, Shang Gao and Pablo Arredondo * 273 Ventures (https://273ventures.com/) / Kelvin (commercial) * ALEA Institute (https://aleainstitute.ai/) (nonprofit research) Connect with Mike Connect with Mike on LinkedIn (https://www.linkedin.com/in/bommarito/). He maintains inbox zero (mostly). 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)

14. 

    9 Feb 2026

    Why YC just backed an AI law firm

    What We Covered * 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 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)

15. 

    31 Jan 2026

    Should law firms encourage vibecoding?

    What We Covered * 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 (https://www.twobirds.com/en/news-and-deals/2025/global/bird-and-bird-announces-partnership-with-vibe-coding-app-development-platform-betty-blocks) 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)

16. 

    24 Jan 2026

    Should clients get a discount if firms use AI?

    What We Covered * Richard’s 40 years of working with law firms on pricing * His viral LinkedIn post (https://www.linkedin.com/pulse/yes-we-now-using-ai-you-getting-discount-richard-usbbe/) 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 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)

17. 

    18 Jan 2026

    Without Limitation (S1 E1): Mary Bonsor

    What we covered * 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 Thanks for reading/listening! If this was useful, please share it. Biggest takeaways * Purpose and profit are not opposites: The best businesses can deliver real commercial outcomes while creating measurable social impact. * 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. * Curiosity is a career superpower: The ability to ask better questions, learn fast, and deeply understand client problems will outperform almost any technical skill. * Human skills are the long-term moat: Judgment, empathy, and trust-building remain the parts of legal work that are hardest to automate. * Networks compound over time: The acquisition story is a reminder that consistent relationship-building creates outcomes years later. * Productisation only works with real client input: Build with customers, pilot early, learn quickly, and iterate before scaling. * 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 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)

Inside a recent episode

The Unbundling of Legal Work

Published 30 Oct 2026 · Transcript excerpt

[…] And what actually happened was we had a little bit more time to talk to every patient about what was going on, understand the context, treat them as a whole person. The result was much better patient care. And I think that was another lesson that I brought into my career later on outside of medicine. […]

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