Version Up

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)

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

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

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*

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/     

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

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  

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

As the technical co-founder of Version Story, Jordan Bryan draws from his experience as a developer seeing how technology solved a major industry pain point: collaborating on coding projects (the solution there was GitHub). He and his co-founder found an analog to a pain point that every lawyer knows well: version control over documents and contracts. This was the genesis for the idea to develop the "git" for lawyers.  Jordan's journey provides some great lessons for both founders and lawyers.  Lesson for founders? Good solutions come from the marriage of technical prowess and deep domain expertise. Lesson for lawyers? Start using AI in your practice, as often as your workflow suits. It's not just about keeping up, it's about leveling up. Embrace the superpower potential of AI.  It was a fascinating conversation about AI's impact on coding, product development, and legal practice. **views express my own** Jordan Bryan | LinkedIn Version Story: Legal Redlining and Version Control    

The good news for law firms? Most already have the building blocks needed to innovate on their operating model for the AI era. Rok Popov Ledinski explains why. Rok is a developer turned strategic thinker and tech implementor for law firms. So I put on my BigLaw hat and ask him to walk me through the steps of planning, deploying, and driving adoption of technology in a business of law.  Quick bottom-line? Taking incremental steps towards incremental gains by leveraging existing capabilities and empowering experimentation anchored in the law firm's strategic positioning. For those with a slightly longer attention span, check out the episode!   Rok Popov Ledinski | LinkedIn https://www.mpladvisors.com/   

I confess I didn't fully "get it" about Claude Code-Cowork-Skills-Plugins until legal engineer Antoine Louis explained it on this episode. It is a must-listen for anyone who wants to understand why it all means that we are crossing the Rubicon in AI for lawyers. An engineer by trade and a researcher by training, Antoine provides just the right level of detail needed to understand why the last few months have seen a transformational shift in the use of AI in legal (and other verticals). As a start-up founder, he also understands what it means for lawyers, technologists, and anyone working in the business of law.  The interview is, IMHO, the clearest explanation I've heard of where we are and where we are headed when it comes to using AI to develop tailored tools that support lawyers in their day-to-day practice. Developments in the tech have already taken our industry into uncharted waters. We are at the start of an era during which AI will be used to quickly develop and deploy customizable, personalized legal solutions that address the specific needs of lawyers in their day-to-day practice.   Antoine Louis | LinkedIn Lawvable Skills | AI Agent Skills for Legal Experts | Lawvable

Joined by Kevin Keller. Kevin has done it all -- from product counsel to General Counsel, developer to inventor, from advisor to investor. And he's done it through the formative years of the internet era. The secret to his success? I doubt it can be summed up in one sentence, but one thing that stood out to me is how Kevin has continually invested in himself by developing the skills that position him to take advantage of opportunities as they arise. In today's era of rapid tech transformation, I think both aspiring and practicing lawyers can learn something valuable from Kevin's journey. Kevin Keller | LinkedIn

I talk to Matt Pollins, a former BigLaw partner turned LegalTech founder. Matt spent many years practicing tech law before transitioning to building LegalTech products. Matt and I talk lawyer-to-lawyer about the practical implications of agentic AI on the practice and business of law. We also explore what it means in the market for legal software when lawyers, law firms, and corporate legal departments start to build more of their solutions internally. Matt Pollins | Lupl | vibecode.law

What does innovation at a law firm look like? How do you match client/lawyer needs to the internal resources that can address them? I explore this with Benjamin Llinas, Matter Optimization Manager at Linklaters, a global law firm. Ben is that unicorn in the law firm org structure sitting at the intersection of supporting lawyers, servicing clients, and deploying technology. His work is part art, part science. Part technologist, part psychologist.  His work requires not only technical acumen, creative thinking and problem solving, but also the emotional intelligence to connect with lawyers to ensure they are using and benefitting from the tech being deployed. Ben and I talk about how AI is impacting the business of law firms, the day-to-day work of its lawyers, and the demands of its clients. But Ben reminds me that AI is just one out of the many ways that technology can streamline legal practice. If you know how to look for it, you will find a lot in the existing arsenal for streamlining legal operations at a law firm.  The bigger challenge, in some ways, is identifying the specific needs of a lawyer/project/client.  And so much of our conversation is about the process of un-packing a workflow and finding the right bits and pieces that can be automated -- whether it's using the latest snazzy AI-powered solution or an Excel macro.  Because what you don't want to do is jump to a conclusion and buy an expensive end-to-end, out-of-the-box solution that does too much and too little all at once. You will have served your internal client better by putting in the effort up-front to parse the workflow, break it down into its distinct parts, and come up with the right plug-and-play mix mix of buy/build solutions that achieve the best outcome overall. 

I talk with Gwendolyn Lindsay Cooley, founder of Taimet, about productized AI solutions for lawyers, by lawyers. Her product seeks to solve a specific problem set that comes up in the day-to-day practice of every antitrust deal lawyer: screening M&A transactions for regulatory risk. We talk about how she came up with her solution, how it works, and what it means for lawyers and the clients they support. Along the way, we also explore the way that AI products are impacting not only the practice but also the business of law in an evolving business climate in which clients expect more, for less, and quicker. We also talk about the unique strengths that practicing lawyers have in  identifying user needs and developing legaltech solutions to address them.    https://www.taimet.com/ https://www.linkedin.com/in/gwendolyncooley  

I talk with Daniel Schwarz, co-founder of CompetitionAI, about bespoke AI tools for subject matter experts. We discuss what it means to build AI tools tailored to the specific needs of lawyers practicing in a specific area of law. We also explore what AI means for law firms, the lawyers working in them, and the law students aspiring to enter the profession.  

The AI Legal revolution is already here.  That’s the bottom line from my conversation with LegalTech thought leader, educator, and founder, Thomas G. Martin.  It’s no longer just about using tools to be more productive. What we’re looking at is a transformation of what a “legal service provider” is. Tom’s theory is that every law firm is now a software company. Whether or not you agree with the ultimate conclusion, you’ll be hard pressed to find flaws in the logic that leads him to it.  The implications of this are far reaching. Practicing lawyers, law students, startup founders — if guided by the ultimate needs of the client — will witness major shifts in the model for delivering those services.  This is not a matter of a decade. It is just around the corner — with the foundation already shifting underneath everyone’s feet.  But this is not cause for panic or alarm. We haven’t passed some event horizon for law firm tech adoption. It’s a moment for reflection, investment, and for embracing the opportunities that this pivotal moment presents.  As always, views expressed are my own. https://ca.linkedin.com/in/thomasgmartin https://www.lawdroidmanifesto.com/ https://lawdroid.com/

Alex Baker, founder of Legal Tech Collective, comes on to talk about how practicing lawyers can leverage their know-how and experience to develop and productize LegalTech solutions. Whether its internal solutions to streamline your practice or client-facing tools that improve how you offer legal services, lawyers who think like LegalTech founders can position themselves and their practices to out-compete technology laggards. We talk about how lawyers can go from idea to product, what challenges they face along the way, and how to position such products so that the law firms underwriting their development can rightly see them as an investment with potential ROI (margin and market share) as opposed to a partner's costly pet project. 

I'm joined by LegalTech consultant Emma Kelly (quadK) to discuss law firm deployment of technology. We talk about what an "AI law firm" might look like and mean for corporate clients, small businesses, and even individuals seeking legal services. Along the way, we explore the evolution of AI and machine learning from its e-discovery roots to prospects for practice-specific end-to-end digital platforms for legal services.    You can reach Emma at [email protected] or follow her at Emma Kelly | LinkedIn. 

Legal practitioners and legal ops professionals play complementary roles that, combined, are critical to the successful deployment of LegalTech solutions at a corporate. I sit down with Michiel Ypma, Group Head of Legal Operations and Finance at ABB, to talk about the role of Legal Ops in helping lawyers identify the problems that need solving, finding the right solutions, securing internal buy-in, and driving user adoption. As it turns out, legal practice has a lot to learn from MBA-style thinking about strategic planning and operations.  You can find Michiel at Michiel Ypma | LinkedIn *As always, views expressed our own* 

I'm joined by Devon Willitts, Legal Engineering Lead at Robin AI. We talk LegalTech deployment at large corporates, covering the evolution of AI/ML, use cases for contract analysis, measuring the effectiveness of tech tools, and successful user adoption in-house.   

Building AI Agents is not complicated, and it opens up opportunities to automate and streamline your day-to-day work as a lawyer. On this episode, I explain what Agentic AI is and how it works, why you should learn to build AI Agents on your own, and how they fit within a spectrum of LegalTech solutions for legal practitioners.

LegalTech tools to audit, monitor and even give real-time feedback on employee communications tackle a key vector of legal risk for any organization. I talk to Darin Hicks, CEO of LitLingo, about the benefits and potential challenges of deploying these products at a large legal corporate.   LitLingo - Detect and Prevent Careless Communication   Darin Hicks on LinkedIn

I discuss the industry tailwinds behind the accelerating adoption of LegalTech by legal professionals in-house and at law firms.  

Even if it wasn't on the agenda, LegalTech was top of mind for many during the 2025 ABA Antitrust Spring Meeting. In this episode, I share what I learned from attending the conference about how specialized practitioners are using technology to support their legal practices.

In this episode, I share some impressions and takeaways from attending LegalWeek 2025, a LegalTech conference hosted this year in NYC.

Kicking the podcast off with an overview of LegalTech – what it is, the industry's current state of play, the tailwinds driving its adoption, and the benefits and risks of deploying it in your legal practice. Version Up is one lawyer's journey to digitally transform his legal practice. Follow along as I try to separate the signal from the noise and smartly deploy value-add LegalTech tools at a legal corporate.  **Views are my own and do not reflect those of my employer** Hosted by Kaj Rozga Music by Brett Ryback