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 |

  1. 9 h fa

    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/

  2. 30 giu

    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. Denisa Kopandi | LinkedIn 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.

  3. 17 giu

    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/

  4. 8 giu

    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

  5. 13 mag

    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

  6. 30 apr

    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)

  7. 28 apr

    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

  8. 20 apr

    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

Descrizione

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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