Version Up

Kaj Rozga

One lawyer’s journey to transform his legal practice.

Version Up (versionup.ai) is a podcast about deploying AI and technology in legal practice. Host Kaj Rozga — a practicing lawyer leading innovation inside a legal team at a Global 500 — talks with the practitioners, founders, and operators doing the heavy-duty work of repurposing the practice and business of law for the AI era.

Each episode is a practical conversation about what’s working, what isn’t, and what’s worth paying attention to. The goal is to work towards clarity through an honest dialogue about building and deploying legal technology at law firms and corporate legal departments.

For lawyers, innovation leaders, legal ops professionals, founders and investors who want the bottom-line on the state of play in legal tech.

| Hosted by Kaj Rozga | Music by Brett Ryback | views my own |

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#40
Yesterday · 45 min
The Bull Case for Law Firms in An AI-Disrupted Legal Market
Lev Loukhton joins to discuss where AI creates real business opportunities for law firms—and what it takes to turn legal expertise into a product that clients will buy. We start with the discussion around Freshfields’ internal AI lab and the gap between building a useful tool for your own lawyers and selling it in a competitive market. Lev is optimistic about law firms’ prospects, but cautious about the assumption that an internal tool can become a valuable software business. Selling to other firms means earning their trust, addressing concerns about client data, and demonstrating why they should choose your product over established alternatives. Internal adoption and a supportive budget do not establish that outside buyers will pay. We explore a different opportunity: using AI to become more closely involved in clients’ businesses, anticipate legal needs, and generate more valuable legal work. Lev describes how a firm could help a client identify and protect intellectual property throughout its R&D process. A routine incorporation service might also help a firm spot an upcoming joint venture negotiation. In those cases, the technology could support the legal practice without becoming a separate software business, and a startup might supply the underlying product. The conversation also covers the investment required to make those changes. Lev argues that some firms may need outside capital or accept lower partner distributions while they adapt, while emphasizing that firms face very different circumstances. We discuss specialist practices, pressure on work that can be automated, and whether local relationships and access to standard AI tools will be enough to preserve a firm’s position. Finally, we turn to what Lev looks for in legal tech startups: deep knowledge of legal work, a meaningful advantage over alternatives, and evidence of a repeatable way to win customers. We discuss privacy controls for digital twins as an example of difficult, useful differentiation, why modest time savings can be a weak sales proposition, and why founders need to understand how sales will grow beyond their own personal efforts. Lev Loukhton is a general partner at Grayhair Venture Partners, a specialist legal tech venture capital firm, and a former M&A partner at Linklaters. He brings experience in legal practice and seed investing to assessing how startups can address lawyers’ needs and how firms can adapt their businesses. ALL VIEWS EXPRESSED OUR OWN Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#39
October 27 · 35 min
Humanism in Driving AI Adoption for Legal
How do you actually get lawyers to change the way they work with technology? In this episode of Version Up, Roberto Alarcon, Senior Manager, Innovation and AI at White & Case, comes on to talk about what AI implementation looks like from inside a major law firm. Drawing on more than a decade at the firm—beginning in e-discovery and project management before moving into broader innovation work—Roberto explains why successful legal innovation has shifted from introducing lawyers to new tools toward understanding their workflows, incentives, and problems first. The conversation explores the human side of AI adoption: building trust with lawyers, identifying influential partners and innovation champions, reducing friction, giving lawyers incentives to participate, and meeting different practice groups where they are rather than imposing a single model of technology use. Roberto also discusses White & Case’s partnership with Legora, the role of practice support lawyers and other internal teams, and how law firms can take a best of both worlds approach that leverages what off-the-shelf tools have to offer while connecting and customizing them to drive tailored use cases. Underlying it all is a broader argument for the importance of distinctly human skills in an AI-enabled workplace. Listening, relationship-building, judgment, organizational knowledge, and emotional intelligence are increasingly central to the work of implementing technology effectively. In fact, one theme that emerges is how humanities backgrounds may become more—not less—valuable as AI automates more technical tasks, and why the future of legal innovation may depend as much on understanding people and institutions as on understanding the technology itself. About the guest Roberto Alarcon is Senior Manager, Innovation and AI at White & Case LLP, where he works on technology adoption, legal workflows, and innovation across the firm's practices. Roberto Alarcon on LinkedIn White & Case – AI & Innovation * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#36
October 26 · 57 min
Why Lawyers Are Building: Beyond Off-the-Shelf Legal Tech
**This is an archived episode being re-posted after an erroneous take-down** Salvador Carranza, a former lawyer turned applied AI builder and adviser, joins to talk about a shift that could fundamentally change how legal technology gets made and deployed: lawyers are increasingly able to build solutions themselves. We start with the traditional build-versus-buy decision. For years, buying software was usually the obvious choice. But AI-powered coding tools are lowering the technical barrier to turning an idea into a working prototype, giving lawyers and legal teams new options when off-the-shelf products don’t quite fit their workflows, data, or needs. Salvador makes the case that building is also one of the best ways to actually learn AI. We discuss why lawyers should tinker, experiment, and iterate; how organizations can create safe environments for that experimentation; and why understanding how these systems work can make you a much more sophisticated buyer of legal technology. From there, we get into the bigger implications: the difficulty of measuring AI ROI, data ownership and vendor lock-in, whether today’s legal AI platforms can maintain their position as frontier models improve, and why legal organizations increasingly need people who can turn ideas into proofs of concept. We close by looking at what this means for the business of law. AI gives law firms new opportunities to productize legal services, rethink pricing, and capture more value from efficiency—and gives corporate legal departments the ability to build internal products that change what work they send outside in the first place. At the center of it all is a simple idea: learn AI, use AI, and build with AI—not sequentially, but all at once. Salvador is the founder of PossibLaw, where he writes and hosts a podcast focused on builders and the future of legal, and the founder of Lumen Atlas, where he works with legal teams on applied AI, training, workflow design, and technology decisions. You can follow him on LinkedIn. ALL VIEWS EXPRESSED OUR OWN * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#38
October 14 · 46 min
Legal Tech Unicorns: Reliable AI Agents for Legal Practice
Denisa Kopandi returns to Version Up to discuss what happens when legal AI agents meet the uneven, iterative work of an actual transaction. Drawing on her experience testing legal AI tools and building workflows, she explains why a sequence that looks convincing in a demo can be difficult to trust in practice. We distinguish between agents working behind a chatbot’s answer and workflows intended to carry a legal matter through multiple stages. Denisa describes the gap between a neat progression from intake to analysis to drafting and the reality of documents arriving in batches, incomplete information, and clients changing the structure or timing of a deal. A central theme is the legal analysis that happens before anything gets written down. A lawyer may open an apparently routine document and notice something that changes the entire assessment. That makes it difficult to specify every task or decide in advance where an agent should stop for review. Checking the final legal analysis may not reveal that an earlier agent overlooked a document or extracted the wrong information. We explore a more manageable approach: specialized agents for narrower tasks, with lawyers deciding when to use them and how the results fit together. Examples include assessing a merger notification threshold or preparing a request for information using what is already known about the client. The question is how much useful work can be delegated without creating a verification burden that cancels out the time saved. Denisa also shares an experiment in which an agent began deleting earlier versions of a due diligence report, leaving her to transfer the analysis manually. We discuss what that experience says about reliability, alongside the difficulty of trusting a batch of contract analyses after finding an error in one. There is a training dimension too. Denisa asked interns to use AI for research and submit their full conversations, so she could review how they questioned the model. We consider whether narrower tasks and visible review can help junior lawyers develop judgment, and how quickly improving tools affect the decision to invest in building a workflow. Denisa Kopandi is a senior associate at bpv GRIGORESCU ȘTEFĂNICĂ in Romania, working across technology, competition, and commercial matters. Alongside her client work, she evaluates and implements legal AI tools and explores how they can support specific legal tasks and the development of junior lawyers. You can follow her on LinkedIn. * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#37
October 6 · 50 min
What Decades of Knowledge Management at Elite Law Firms Can Teach Us About Data in the AI Era
Christopher Smith joins to discuss what it takes for law firms to turn their accumulated documents, data, and experience into knowledge that lawyers can actually use. Drawing on his previous work at Sullivan & Cromwell and Freshfields, Chris explains why many of the challenges behind today’s AI ambitions have been familiar to knowledge management teams for decades. We start with the practical problem of collecting information from busy lawyers. Chris describes why matter intake and document profiling need to fit around getting the work done, why firms should avoid asking for the same information twice, and how overly restrictive systems can push lawyers into workarounds. As collaboration spreads across email and other platforms, keeping track of authoritative documents becomes more complicated. That leads to a question I’ve often had when looking at legal AI tools: who is going to make sure they’re working from the right material? Chris sees improving tools for organizing and cleaning data, but someone still has to decide which information is correct. He shares how he brought teams at Freshfields together around shared firm data, with clear responsibility for particular decisions. We also discuss the value of a vetted collection of useful material and why lawyer attention remains a constraint on evaluating what AI finds. The incentives matter just as much. At Sullivan & Cromwell, Chris describes connecting knowledge contributions to performance evaluations and looking at whether other lawyers actually used the submitted material. We explore the tension between building a firm’s intellectual capital and lawyers’ ability to take their experience and client relationships elsewhere. Looking ahead, we consider whether broad legal AI platforms will become a common baseline, with differentiation coming from how lawyers combine tools, data, and judgment. We also discuss the apprenticeship model, emerging roles that combine legal practice and technology, and whether changing client expectations and billing arrangements will create stronger incentives to work differently. Chris closes with a candid explanation of why he is pursuing opportunities outside law firms and the limits he sees in their rewards for people who build technology. Christopher Smith is the former Chief Information Officer and Chief Data Officer at Freshfields and former Chief Knowledge Management and Innovation Officer at Sullivan & Cromwell. **Corrections: TensorFlow was released in 2015. Generative AI emerged in 2022/2023.** ALL VIEWS EXPRESSED OUR OWN * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#35
September 15 · 53 min
Checking In on Legal’s AI Transformation: A Matter of Perspective
Findley Penn-Hughes, a Senior Associate at Mayer Brown, comes on to help provide a practical check-in on where AI actually stands in legal practice. Findley and I compare our experiences adopting AI over the past few years—from early hallucinations that undermined trust to the point where tools like Harvey, Copilot, Claude, and ChatGPT have become part of everyday work. We also dig into why the next step, agentic AI, is proving much harder than the demos suggest. Findley shares his experiments building AI agents to monitor regulatory developments and draft client alerts, along with the technical and reliability problems he encountered. A major part of the discussion is cybersecurity. For law firms and legal departments, the issue is not just whether an AI system produces accurate work, but what happens when it is given access to sensitive client data, internal systems, and the ability to take actions autonomously. We talk about the risks of data leakage, third-party model providers, increasingly capable cyberattackers, and the possibility that more autonomous agents introduce entirely new attack surfaces. Even where those risks can ultimately be managed, the uncertainty itself helps explain why legal organizations are moving more cautiously than the AI hype cycle might suggest. We also spend time on incentives. Lawyers are still largely rewarded for producing billable work, not for spending time building systems that make that work faster or eliminate it altogether. That creates an obvious tension for associates and partners asked to invest non-billable time in AI workflows. Firms may need to rethink billable-hour credit, compensation, staffing, and the role of dedicated AI specialists. And as clients increasingly expect AI-driven efficiencies to translate into lower fees, firms will also have to work out how they price legal work and capture value from the technology they build. From there, we explore other barriers slowing AI adoption across legal: messy and unstructured data, questions about how much proprietary “secret sauce” law firms really have, uncertain economics, and a technology market with no clear winners yet. We close by discussing what AI could mean for law firm business models, competition between firms, junior lawyer training, and the future shape of the profession. Our conclusion is cautiously optimistic: AI is already changing legal practice, but the industry is still much earlier in the transformation than the hype sometimes suggests. https://www.linkedin.com/in/findley-penn-hughes ALL VIEWS EXPRESSED OUR OWN * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#34
July 30 · 43 min
How AI Can Strenghten the Lawyer-Client Relationship
In this episode of Version Up, I am joined by Melanie Hallas Kirsch, a senior in-house lawyer, for a candid look at her personal journey adapting to generative AI. Mel shares how an everyday practitioner in a high-stakes practice area moves from basic curiosity to active experimentation, navigating the overwhelming flood of new tech tools without losing sight of practical realities. Rather than treating AI as a futuristic silver bullet, Mel breaks down how her perspective on legal technology has evolved, offering an honest look at how corporate attorneys can realistically integrate these tools into their daily work. Mel’s journey with technology began long before the recent generative AI boom, drawing on years of using early AI-like tools such as technology-assisted review (TAR) during large-scale document reviews. As general purpose and legal-specific AI tools came into being, she adopted a deliberate strategy to deploying them in her practice. A key highlight of Mel’s journey is her realization that senior legal experience acts as a crucial superpower when adopting AI. Because senior lawyers possess deep practice intuition and can instantly spot when an output fails the "smell test," they can use these tools safely as drafting assistants or advanced search engines. Successful adoption requires changing one's mindset to actively question and push back on the tool rather than accepting its first output. Learning how to leverage AI hasn’t come from generic, click-through corporate webinars, but from continuous self-education—listening to podcasts during long commutes, exchanging practical tips with peers, and taking time to experiment hands-on. In her day-to-day practice, Mel’s biggest productivity gains have come not from chasing niche legal software, but from personalizing general-purpose and off-the-shelf legal AI tools to match her personal workflow. She also openly shares her ongoing experiments and friction points—from attempting to build custom multi-step workflow agents to navigating data connectivity, security boundaries, and information siloing. On how AI will impact the client-attorney relationship, Mel offers a grounded framing. She explains how law firms will continue to do work for corporate clients, but that will move up the value chain to higher-stakes and higher-judgment work. She also outlines the ways that AI can strengthen the client-attorney relationship, whether it's a law firm and their client or an in-house lawyer and their business colleagues within a corporate. Ultimately, Mel’s journey offers a grounded perspective: while AI is an incredible aid for streamlining repeatable work and research, human judgment, risk tolerance, and client relationships remain the true core of legal practice. https://www.linkedin.com/in/melanie-hallas-kirsch/ ALL VIEWS EXPRESSED OUR OWN * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#33
July 24 · 39 min
Educating Tomorrow’s Lawyers for an AI-Disrupted Legal Market
In this episode, I sit down with Professor Kipp Coddington from the University of Wyoming College of Law to discuss how legal education is adapting to the age of AI and what skills tomorrow's lawyers will need to succeed. Kipp teaches an innovative course on AI and Internet Law that combines substantive legal issues with hands-on technology training. Students are expected to use AI throughout the course, experiment with tools such as Gemini Notebook, complete programming exercises, and analyze the legal, policy, and technical challenges emerging from AI adoption. His view is straightforward: lawyers do not need to become software engineers, but they do need to understand and embrace the technologies that are increasingly shaping legal practice. A major theme of our conversation is the debate taking place across legal education about how much AI students should be allowed to use. Kipp argues that law schools are trying to strike a balance between exposing students to transformative technology and preserving the critical thinking, reasoning, and judgment that legal education has always sought to develop. In his view, the goal is not to outsource thinking to machines but to teach students how to use AI responsibly while continuing to strengthen their own "wet neural network" through rigorous analysis and problem solving. What stood out most from the discussion was Kipp's experience teaching students in an AI-enabled environment. Despite concerns that generative AI might make student work more generic or less original, he observed the opposite. In a class assignment where students used AI tools to develop presentations on emerging legal issues, students brought a unique perspective, resulting in a surprisingly diverse range of topics and insights. Rather than producing forty versions of the same answer, students explored issues ranging from healthcare data and privacy to international AI regulation and business risk. Their own judgment, creativity, interests, and experiences shaped the output. That observation led to one of the most important takeaways of the episode: even in an AI-powered world, junior lawyers and law students continue to create value through their perspective. A student or first-year lawyer may not have decades of legal expertise, but they can bring fresh thinking, creativity, problem-solving ability, and a unique lens to legal challenges. AI can accelerate execution, but it does not replace the human judgment that helps lawyers identify risks, generate insights, and develop novel solutions. We also explored what skills law schools should be emphasizing in this environment. Kipp believes the most important traits are not technical. Instead, he highlighted humility, effective listening, empathy, intellectual curiosity, sound judgment, and the ability to operate in legal gray areas. Lawyers are fundamentally risk managers and counselors. They rarely provide simple yes-or-no answers. Their role is to help clients understand risk, evaluate alternatives, and chart a path forward. Those skills become even more important in a world where AI can generate answers but cannot fully replace professional judgment. The conversation also touched on how AI is changing teaching and assessment. Traditional take-home assignments and research papers present new challenges because of the ease with which AI can generate written work. Kipp discussed emerging approaches such as oral defenses, in-person examinations, and more interactive evaluation methods designed to ensure students truly understand the material. While these challenges are real, he remains confident that legal education can adapt and continue to identify and develop talent. Perhaps most striking was Kipp's optimism about the future. While acknowledging that some traditional legal business models may come under pressure, he sees a lot of opportunity for lawyers who embrace technology. He pointed to growing demand for professionals who understand both law and technology, including opportunities within AI companies themselves. Just as the internet created entirely new career paths for lawyers, he believes AI will create new roles, new industries, and new ways for legally trained professionals to add value. If you're a law student, young lawyer, legal educator, or anyone interested in the future of the profession, this conversation offers a thoughtful and optimistic perspective on how legal education can evolve while staying rooted in the timeless fundamentals of legal practice. https://www.linkedin.com/in/kipp-coddington/ ALL VIEWS EXPRESSED OUR OWN * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#32
June 30 · 45 min
The Client-First Approach to AI Adoption at Law Firms
Denisa Kopandi, a senior associate at a leading Romanian law firm, joins to talk about LegalTechTalk 2026. The conversation quickly turns into an exploration of what it means for law firms to prepare for changes to the legal market caused by AI. One key takeaway for me: one of the first steps for law firms looking to prepare for AI's impact on their business is to understand what their clients are doing to deploy AI and what it means for their needs for outside counsel. So our conversation centers around a question that many law firms are still reluctant to confront: what happens when clients can do more legal work themselves? Denisa shares what she heard from in-house teams that are experimenting with AI, legal operations, and workflow automation. We discuss how legal advice may need to evolve from lengthy memos into clearer rules, decision trees, and systems that can be embedded into business processes and AI agents. Along the way, we dig into the risks of that approach, including whether traditional legal advice translates effectively into AI-driven environments and who bears responsibility when it doesn't. We also talk about how lawyers can move up the value chain. Rather than seeing AI as a threat, Denisa argues that it creates an opportunity for lawyers to become more strategic advisors—helping clients shape decisions before problems arise rather than drafting documents after the fact. In terms of the mechanics, one important insight that Denisa shares is that you don't need everyone to become a power user. A few motivated individuals can do a lot to cause the entire team to level up. This should be encouraging news for anyone unsure how to get from a "cold start" to an AI sprint. Another major theme is the future of legal training. It's a topic that is critical to the long-term sustainability of achieving an AI transformation at any legal enterprise. If AI takes over some of the work traditionally performed by junior associates, how do young lawyers develop judgment? Denisa offers a candid view from someone who is actively training junior lawyers today and explains why spotting AI mistakes may be more difficult than many people realize. Finally, we discuss the billable hour, law firm economics, and whether AI will push firms toward new service models that better reflect the value lawyers create for clients. This was a thoughtful conversation about client service, legal training, and the changing role of outside counsel. More than a discussion about technology, it's a discussion about how lawyers can stay relevant—and valuable—as their clients become increasingly sophisticated users of AI. Denisa Kopandi | LinkedIn ALL VIEWS EXPRESSED OUR OWN * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#31
June 17 · 53 min
From BigLaw to Solo Practice: Demystifying What it Means to Launch an AI-Native Law Firm
Dan Sito, former federal tax partner and AI innovation partner at Perkins Coie, joins VersionUp to discuss why he left Big Law to launch Sito PC, an AI-native law firm. Our conversation reveals that going "AI-native" does not mean building a tech company, chasing high-volume/low-value work, or rebuilding a practice from scratch — it means a specialist lawyer using agentic tools to deliver premium work on his own terms. The conversation covers how democratized access to agent harnesses like Claude Code and Codex has erased much of the resource advantage that kept top performers inside large firms, and why enterprise legal AI platforms (Harvey, Legora, Microsoft Copilot) solve for scale that a solo practitioner simply doesn't need. Sito explains how AI powers his legal practice and maps his work — client intake, legal research, drafting, risk allocation — into stages where agents add leverage while the lawyer keeps judgment and verification. He also addresses the questions every lawyer thinking about going solo is asking: competitive advantage versus Big Law, the risk of clients insourcing AI, law firm pricing and whether AI-native means cheaper (he argues for expanding value, not just cutting cost), and business development as a solo. On the tech stack, Sito details running a local open model to keep confidential client data under his own control — converting confidential information to generalized inputs for frontier models — and why data autonomy and subscription pricing matter for a bootstrapped practice. A level-headed, practitioner's view of how AI changes the practice and business of law without disruption mythology. At a time of AI-induced anxiety and fear in the profession, I think lawyers practicing at law firms will find Dan’s experience inspiring and hopeful. In-house lawyers practicing at corporate legal departments will find comfort and familiarity with the transition that Dan’s practice offers when compared to AI-native law firm start-ups, legaltech vendors, and general purpose LLM solutions claiming to be able to fulfill their legal needs. Most importantly, Dan’s experience shows that lawyers are not predestined to be victims to AI disruption to the legal market. They are actors can choose to act. Individually, boot-strapped, and without having to restart their legal practice from scratch. https://www.linkedin.com/in/daniel-sito/ https://www.linkedin.com/company/sito-pc/ https://sitopc.com/ ALL VIEWS EXPRESSED OUR OWN * * * Version Up Members is a free, invitation-only community for lawyers and legal professionals putting AI to work at law firms and legal departments. Request an invitation: versionup.ai/members
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#30
June 8 · 50 min
Commitment Over Curiosity: The Real Drivers of AI Transformation at Law Firms
Abhijat (Ab) Saraswat comes on the podcast to address *the* question that is on the minds of law firm innovation leaders: what makes AI transformation stick? On the surface, it looks like everything is going great. Training sessions, positive usage stats, vibe-coded POCs, vendor demos and pilots, some nice publicity on LinkedIn and at conferences. A law firm going AI-native. But underneath the surface progress is stalling. Cultural constraints, capacity limitations, technical debt, ineffective governance -- hidden icebergs are causing the AI transformation to stall out. Ab says that it all comes down to institutional commitment and use case clarity. It’s a helpful framing that focuses a legal enterprise on the key drivers for whether a law firm is able to deploy AI effectively. These drivers cut across a lot of dimensions: leadership style, organizational structure, build vs. buy, LLM and vendor-agnositic solutions, and building for an agentic enterprise are all part of the formula. These are the things that position legal teams for the future without having to make a “bet” on a constantly changing state of art. It was refreshing to see the conversation go in some directions that I did not expect. Ab is *not* an advocate for the average law firm building the tech stack needed to developing their own bespoke tools. As he sees it, third party solutions are good enough and the added cost of making them great may not be worth it to most legal enterprises. Besides, building out the “middleware” of an AI tech stack that protects a law firm’s “secret sauce” is less about technical architecture and more about achieving the connectivity to tap into enterprise date and practice intelligence. My sit down with Ab was a badly needed sanity check at a moment when things feel extremely fluid in the industry. I’m grateful for him coming on to share his perspective as a legal tech strategist (lupl.com) and thought leader (fringelegal.com). LinkedIn
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#29
May 13 · 33 min
The Digital Brain and the Future of Legal Knowledge
Jamie Tso and Raymond Sun join for a check-in on Legal Quants, their growing community of elite AI-native lawyers who design and deploy AI in their legal practice. A previous episode covered the origin story. This one ends up mostly being about LQ Brain — their adaptation of the "digital brain" concept, applied not to an individual's knowledge but to the collective intelligence of their entire community. The problem they were solving is simple: 700 to 2,000 WhatsApp messages a week, across a global network of highly technical lawyers, with no good way to preserve what gets figured out. A weekly digest helped but had time decay — you read it and toss it, like a newspaper. LQ Brain is the timeless layer on top: a structured knowledge graph built by running agent teams across 12,000 messages, synthesizing them into atomic notes, debates, and insights, cross-linked across eight core themes. It lives in Obsidian, was compiled using Claude Code, and is deployed on their website behind a member password. The conversation is a useful primer on why this approach is different from RAG. The key isn't the retrieval mechanism — it's the compilation step that happens first. When a member queries LQ Brain, they're not searching raw chat exports; they're querying insights that have already been distilled, organized, and interlinked. It works like a reasoning harness: because the notes encode the community's actual debates and disagreements, answers come back with nuance and personality. Several members have said it feels like asking a fellow legal quant. Jamie and Ray also use it internally when thinking through LegalQuants' roadmap — rather than polling the community, he asks the brain how the community would think. Placing Digital Brains in the wider arc of AI, having a second brain is a moat right now, but like agentic coding before it, it will become table stakes fast. The more interesting question is what the playing field looks like once everyone has one. And the second brain is only as good as what feeds it, and the hard problem isn't the technology — it's deciding what context gets captured at all. Calls, meetings, hallway conversations. How much do you record? Who consents? What does systematic capture do to workplace culture? It raises boardroom-level risk questions that are only starting to get asked. Talking to Jamie and Ray tends to feel like seeing around a corner, and this episode is no exception. legalquants.com | Jamie Tso on LinkedIn | Raymond Sun on LinkedIn
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#28
April 30 · 55 min
The AI Wrapper Debate: Does LegalTech Still Add Value Over the Foundation LLMs It’s Built on Top Of?
Does legal tech still add value to the foundation models it is built on top of? Today's episode is a debate on the AI Wrapper -- a topic that's been moving to the foreground as the foundational models that legal tech tools are built on top of improve in effectiveness and promote their capabilities to a legal vertical. Will Chen — former lawyer and developer of Mike, a new open source legal AI platform — joins to make the case that today's leading legal AI tools, including Harvey and Legora, are built on a thin layer of value over the foundation LLMs they run on. Will launched Mike, an open source legal tech tool, in roughly two weeks using AI coding tools. It replicates core features — AI assistant, document projects, and tabular document review — to prove a point: the wrapper layer is becoming commoditized. The conversation covers the real cost difference between paying for branded legal AI versus direct API pricing from Anthropic and OpenAI, the performance gap that many associates already notice between branded products and out-of-the-box Claude or ChatGPT, and the vendor lock-in risk that comes with building proprietary workflows inside someone else's platform. We also dig into what happens when Anthropic and OpenAI push harder into the legal vertical and what that means for legal tech startups trying to maintain distance from their own suppliers. The episode doesn't land on a simple answer. There's a defensible case for legal AI platforms as infrastructure providers for firms without in-house engineering capacity. But the conditions under which that value proposition holds — acceptable performance, controlled costs, no competing legal services ambitions — are narrowing. Law firms need to pay close attention to position themselves with optionality to take maximum advantage of both build and buy in this rapidly evolving landscape. Mike (mikeoss.com) LinkedIn (@wh_chin) and X (@wh_chin500)
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#27
April 28 · 37 min
The Building Blocks of the AI-Native Law Firm: People, Process, and Tech
What are the fundamental building blocks for becoming an AI-native law firm? Julian Gilson comes on the pod to lay out a framework for a successful transformation: people, process, and tech. Julian is founder of IntensifAI, which advises law firms on AI transformation end-to-end. He brings a grounded perspective forged by a product management background to try to answer a question many law firms are asking themselves as they emerge from the fog of war of an AI-disrupted legal services market. The conversation starts with first principles. Julian's framework for AI readiness is three-pronged: digitalization, data quality, and workflow design. The surprising entry point is digitalization — because even in 2026, some of the key work that law firms do (phone calls, internal meetings, client communications) goes uncaptured, unstructured, and therefore unusable. You can't train AI on data you don't have. At the same time, a maximalist approach must be reined in where necessary to meet the confidentiality and security requirements of a regulated legal industry. The data prong goes deeper than most firms realize. Completeness and consistency are the twin failure modes: data fields that exist but aren't reliably filled in, and metadata that's been input by humans in a dozen different formats when it should have been standardized. Julian describes how AI can now be used to manage and generate metadata fields — turning what was once a multi-year, consultant-heavy remediation project into something that can be built into the intake process from day one. The people and process dimensions often get underweighted. Julian is direct: if you're not already a tech company, don't try to become one. The cultural gap between law firms — where failure is stigmatized — and tech companies — where experimentation is the operating mode — is large enough that even well-resourced firms with innovation offices can underestimate it. The CTO you need isn't the one managing vendors and doing data migrations; it's the one who knows how to build. And if you really want to make the transition to AI-native, behind the CTO you will need to a team of technologists (developers, data scientists, etc.) who can build — although AI automation of coding means you may need fewer than you used to. As for the tech stack, set reasonable goals. Don’t look to rebuild from the ground up. And remember that, as Julian puts it, “no on wants another UI,” Instead, build on top of the legacy systems, creating connectivity between them (MCPs, etc.) and a hybrid on-prem/cloud infrastructure that securely leverages your data to develop value-add AI solutions that work. But don’t over-correct your course from buying tools where it makes sense. Being vendor-agnostic gives you flexibility, and vendor lock-in is real. But so is the risk of a non-technical organization trying to own a custom tech stack it can't maintain. His recommendation for most law firms starting out their AI transition: start with the foundation models, hire contractors before you hire full-time, and treat the vendors you do engage as training wheels — valuable for learning what's possible, but not necessarily a permanent solution. versionup.ai IntensifAI | Julian Gilson on LinkedIn
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#26
April 20 · 45 min
The Frontier Labs Go Legal Vertical: What It Means for Law Firms, Vendors, and Investors in LegalTech
The question is no longer whether the frontier AI labs are coming for the legal market. The only remaining question is: how fast and how far do they plan to take it? Claude and others are making significant moves that signal the same thing: LLMs aren't content to be infrastructure that other LegalTech vendors build on top of. They want to service the legal user directly by offering them some of the same core AI features that lawyers have come to expect from legal SaaS providers (redlining, etc.). Horace Wu, founder of Syntheia and a former transactional lawyer, joins the podcast to work through what that actually means. The conversation starts where many of these do — the "wrapper" debate, vendors scrambling to explain why Claude isn't a threat to them — but quickly gets to something deeper. For one thing, the threat from frontier labs moving into legal goes beyond the vendors and extends to law firms. Client insourcing is the linchpin: the moment a client can get a credible answer from Claude on their own, that's a piece of the law firm food pyramid that doesn't come back. But the risk to traditional law firms is amplified by the technical and cultural baggage that they carry. Past technology adoptions in law — productivity tools, practice management software — rewarded a wait-and-see approach. You'd catch up eventually and meet a new baseline. AI is different because it doesn't just make lawyers more efficient; it empowers everyone to compete for the same legal work. And that may start with the lowest value work (NDAs, etc.) but there's no reason to expect it to stop there. The conversation also tackles the data layer -- the third rail of the AI tech stack. As Horace puts it, most of the attention and funding in LegalTech has gone to UI/UX (legal products), and most of the hype has gone to the intelligence layer (the LLMs). The data layer — how documents are structured, indexed, and fed into the context window — gets overlooked. Syntheia's approach is to normalize and index documents in a way that lets the language model reason about what context it actually needs before answering, rather than brute-forcing entire documents through the context window. The accuracy and cost implications are significant: Horace recounts how comparable systems have seen accuracy jump from roughly 50% to over 98% using this approach. Legal users of AI cannot afford to ignore the potential for such gains in efficiency and quality of output. They must solve the data problem otherwise they risk seeing their expensive AI investments flop -- garbage in, garbage out. Horace is a creative and technically-astute commentator on legal tech, and it was a pleasure to have him on. https://syntheia.io/ |https://www.linkedin.com/in/horace-wu
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#25
April 8 · 47 min
Flying on Autopilot: How In-House Lawyers Use AI Agents to Transform Corporate Legal Departments
You could say in-house legal is having its AI moment. Agentic AI enables corporates to not only automate existing work but also to insource additional tasks currently bveing shipped out to external providers. It requires a change in mindset as much as an upgrade in technology. Mathieu Van Assche comes on the podcast to talk about an outcome-driven approach to helping corporate legal departments make the transition from viewing AI as a copilot to viewing it as a way to put work on autopilot. Mathieu does GTM and strategy fpr Flank, a company that deploys AI agents inside enterprise in-house legal teams. Rather than selling a software product and walking away, Mathieu describes the need to guarantee outcomes, operating closer to how a legal service provider would than a traditional SaaS vendor. Mathieu walks through how to tackle the high-volume, lower-complexity work — NDAs, MSAs, first-pass contract reviews, intake Q&A — that quietly consumes most of a legal team's bandwidth. The agents live where lawyers already work (primarily in shared email inboxes), handle tasks asynchronously overnight if needed, and escalate for approval only what genuinely requires human judgment. The goal: get the work off the lawyer's desk without changing how the lawyer operates. Key Topics Covered: **From software to services.** Being an outcome provider rather than a software tool. This mirrors how legal service providers have traditionally been engaged — and is part of a broader market shift that investors like Sequoia are actively discussing. **The copilot vs. autopilot distinction.** There's a line between AI that assists you while you work (copilot) and AI that does the work in the background and only escalates when it needs your judgment (autopilot/agent). The goal: lawyers review and approve; they don't babysit. **Individual AI vs. institutional AI.** Individuals feel the 10x productivity boost from AI long before companies do. Bolting AI onto existing workflows won't move the needle — organizations need to rethink from first principles how work gets done. **The data layer problem.** Unlocking context buried across siloed corporate systems — emails, SharePoints, CLMs, legacy databases. This is where the next wave of agent innovation will come from - but we're not there yet. Permissions, data fidelity, and source-of-truth questions all remain hard problems. **Governance and supervision.** Everything starts supervised. Over time, as clients build trust in the agents' outputs, supervision can be relaxed and more workflows automated. **Setting realistic expectations.** AI need not be 100% accurate before it's useful. No legal department operates at 100% accuracy today — the baseline already includes human errors, missed questions, and outsourced work. About the Guest: Mathieu is Operations & Go-to-Market Lead at Flank ((https://flank.ai)), which focuses on deploying AI agents for enterprise in-house legal teams. His background spans corporate finance, private equity, and chief-of-staff roles at tech scale-ups. *Version Up is hosted by Kaj Rozga. Music by Bretty Ryback*
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#24
March 31 · 59 min
AI Benchmarking: Judging LegalTech on the Merits
Not being able to make apples-to-apples comparisons of AI tools is a major barrier to effective procurement and deployment of AI in legal. But it's also a problem for vendors who struggle to make better-performing products stand out from better-funded ones. Anna Guo and Elgar Weijtmans, seek to solve this problem with Legal Benchmarks. Anna and Elgar joined forces after independently discovering the same problem: legal teams are choosing AI tools on vibes, marketing, and who they know, not on evidence. Their research found that purpose-built legal AI tools were not reliably outperforming general-purpose models. So they developed the Legal AI Evaluation Framework, a community-sourced assessment of AI legal tools that gives buyers a structured, defensible procurement process. Their message is two-fold. For legal enterprises and users, it's more obvious: being able to compare which AI tool is more accurate, safer, quicker will help them make better decision about which tool to buy (or whether to buy one at all). But as compelling is their value proposition for the vendors who sell these tools: improving transparency in the market allows the best products to rise to the top. It's a great collaboration that, like many I've encountered in legal tech, links up diverse capabilities, personalities, and geographies to try to solve an acute legal world problem with a technology-driven solution. I was excited to have them on the pod and look forward to seeing what their framework has in store for the legal industry. https://www.legalbenchmarks.ai/ https://www.linkedin.com/in/anna-guo-255ba7b0/ https://www.linkedin.com/in/weijtmans/
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#23
March 17 · 37 min
AI & Innovation In-House: The Blueprint for Transforming a Corporate Legal Department
As a fellow in-house lawyer, I’m impressed and even a bit jealous of how quickly Amy Posner is able to innovate to supercharge her work at a large technology company. Amy comes on the show to drop some knowledge for in-house legal teams looking to benchmark their efforts and law firms wanting to understand their clients’ expectations. Lots of great takeaways for lawyers who want to know how in-house legal departments at the cutting edge are thinking about the role of innovation and technology in the practice of law. Amy Posner | LinkedIn
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#22
March 7 · 51 min
Going AI-Native: How Law Firms Can Reshape the Delivery of Legal Services
The iPhone didn’t just improve mobile phone, it reshaped entire industries around it. That’s the opportunity law firms have with AI, according to Scott Kveton, CEO and co-founder of CaseMark. From Y2K to GenAI, Scott has been a witness to a lot of change in tech. With that experience, he brings to the conversation some powerful insights on how AI forces law firms to rethink how work is produced, who produces it, and how value is captured. These lessons informed his founding of Casemark, which provides enterprises a secure environment for developing their own applications using AI. The goal is to empower legal to untap the potential of internallly built solutions to the everyday needs of practitioners. This frees law firms from dependencies on third party vendors and false starts that end with zombie software lurking across an enterprise. Going back to the iPhone analogy, this is the app store moment for legal, and Scott is positioning his services at the center of a movement for enterprises to build point solutions. CaseMark - AI for Legal Teams | Professional Class AI case.dev | API Platform for Legal Tech Developers Scott Kveton | LinkedIn
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#21
March 3 · 44 min
Legal Quants: the Elite Legal Practitioners of the AI Era
What is a "legal quant"? According to Jamie Tso, who coined the term, it describes a new breed of elite legal practitioners operating at the frontier of tech who will be the rainmakers of the AI era. Jamie, who is a practicing lawyer, is not a software engineer. But he does identify as a "legal quant". It's a term he coined to describe a small but growing group of lawyers who use the latest in AI to build small and usable -- and sometimes disposable -- solutions to everyday problems they encounter in their practice. But this isn't a hobby. Jamie believes legal quants will transform what it means to be an elite practitioner by leveraging AI to design their own weapons for achieving the best outcome for clients. So he's created a community of like-minded practitioners to meet, exchange ideas, problem solve, and ... most importantly... build things. The driver for this is curiosity and growth. But it's also market realities. AI sets a new baseline. Lawyers who generate work product that is no better than AI-generated output will lag behind. Lawyers who use AI to make better, quicker decisions for their clients will race ahead of those who do not. Organizations who build out the internal capacity and structures needed to translate lawyer ideas and needs into tailored tech solutions will take business from those who do not. Legal quants and other lawyers who are AI-fluent will be the fulcrums for this transformation, and they'll gain professionally (and be rewarded financially) for it. It's a fascinating vision of the future of AI in the practice and business of law. I had a great time talking to Jamie about this moment, and his movement, in AI. Lawyers, students, and professionals alike can learn a lot from how he views the intersection of technology and knowledge work, and what he thinks it will take to reach the top echelons of the legal industry in the AI era. Jamie Tso | LinkedIn LegalQuants
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