Fringe Legal Presents Bots @ Work

How I built a law firm with AI agents with Helen Fan

Helen Fan has been building an AI-native law firm from scratch, in public, for 50 days. Not a demo. Not a prototype. A real practice with AI agents handling legal strategy and research, and she's documenting every stumble along the way. In this episode, we get into what that actually looks like: the agents, the arguments, the security concerns, and the hard questions about whether AI-native firms and traditional law firms are on a collision course or just running separate races. In this episode: * How Helen built OpenClaw Law LLP with two AI agents, Morgan and Cleo, and why agent-to-agent argument reports matter more than most people realise * The practical pain of open-source agent frameworks: stability issues, setup overhead, and the security surface that opens up the moment you connect an agent to real systems * The Legal AI Value Stack — five levels of AI maturity in law, and why most firms are still stuck at the bottom two * How Big Law and AI-native firms are competing on entirely different timelines, and why the boutique model might be the one that actually moves * What Helen is telling firms just starting out: start with mindset, build the orchestration layer, and don't skip workflow integration Timestamps: 00:00 — Helen's OpenClaw Law LLP experiment: what an AI-native law firm looks like in practice 02:20 — Agent-to-agent communication and argument reports: why they reduce hallucinations 04:10 — Stability, troubleshooting, and the real cost of open-source frameworks 07:00 — The Legal AI Value Stack: five levels of AI maturity and where the moats actually are 12:00 — Why most firms are still at level one or two 16:00 — Proprietary data and the scaling wall 19:00 — Big Law vs AI-native firms: speed, trust, and structural barriers 23:00 — Guardrails, verification, and building an orchestration layer that holds 34:00 — Vendor moats, platform plays, and what M&A in legal AI actually looks like 39:00 — What makes a law firm genuinely AI-native, not just AI-curious 43:00 — Where to start: mindset, workflow, and infrastructure 44:50 — Final thoughts on the pace of change and what to watch next Resources: * Helen Fan on LinkedIn [https://www.linkedin.com/in/helenfanlegalai/] — follow her 100 Days of AI Law experiment as it unfolds * The Legal AI Value Stack [https://helenfan1.substack.com] — Helen's five-stage framework, published on her Substack, Helen's Legal AI Lab * OpenClaw Framework [https://github.com/openclaw] — the open-source multi-agent system Helen built OpenClaw Law LLP on * Claude AI [https://claude.ai] — the AI tool Helen uses for legal and strategic work * AI-Native Law Firm Index [https://aifirmindex.com/] — a running list of AI-native law firms referenced in the episode

How a Lawyer Beat 13,000 People to Win Anthropic's Biggest Hackathon

Most professionals are overlooking a secret weapon that’s transforming workflows and legal practices faster than anyone expected. Mike Brown [https://www.linkedin.com/in/michael-t-brown-034aaa22/] reveals how a solo lawyer turned AI hobbyist beat out 500 engineers at a high-stakes hackathon—and how this level of creative problem-solving can unlock your team’s potential today. Starting from scratch, Mike’s curiosity and strategic experimentation with open-source AI tools reshaped his legal practice and built a new frontier in legal tech. He shares concrete tactics—like dedicating just one week to mastering prompting or building modular plans that make complex projects manageable—that anyone can apply now. You’ll discover how to leverage AI as an extension of your reasoning, rather than just a search engine, and how to avoid common pitfalls like token constraints and over-reliance on static models. In this episode: * The story behind a lawyer winning a top AI hackathon with minimal coding experience * How curiosity, strategic prompts, and optimizing workflows accelerate AI adoption * The importance of context engineering and planning in AI projects * Practical tips for legal professionals to learn AI fast, including a one-week crash course * Why model upgrades like Opus 4.7 can dramatically boost productivity overnight * Managing sensitive data and confidentiality when working with AI-driven legal workflows * The future of law firms and legal teams in a world increasingly driven by AI and automation * Quick tips on integrating voice tools like Wisprflow into daily legal practice Timestamps: 00:00 [https://www.youtube.com/watch?v=Geld8RS8jXc] - The defining moment: How a lawyer beat 13,000 applicants at a hackathon 02:48 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=168s] - What is Cursor? A straightforward guide for non-developers entering AI-driven workflows 04:14 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=254s] - Creative backgrounds fueling AI exploration — from Hollywood to law 05:43 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=343s] - Overcoming the learning curve in AI — a disciplined one-week challenge 07:19 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=439s] - Building skills with prompt engineering — turning prompts into productivity tools 09:14 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=554s] - The exponential improvements in AI models — what today’s upgrade means 11:11 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=671s] - Handling complex projects: from blueprints to AI vision processing 13:39 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=819s] - The California permit system problem — using AI to cut permit delays 15:57 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=957s] - Managing token limits and context: technical tips from a seasoned AI builder 19:22 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=1162s] - The impact of new model drops on projects — building for the future, not today 20:36 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=1236s] - Voice productivity tools — Whisper Flow and voice mode in AI workflows 22:38 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=1358s] - Planning your AI projects like a lawyer — IRAC prompts and adversarial prompting 25:15 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=1515s] - How AI might reshape legal team structures and company management 33:03 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=1983s] - Maintaining human agency in AI-powered days: asset or risk? 34:21 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=2061s] - Favorite AI tools — Wisprflow and Claude Code 35:08 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=2108s] - Tasks ripe for automation — focus on what makes you uniquely human 37:28 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=2248s] - Common AI mistakes: The trap of "locking in" on current models 38:46 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=2326s] - Building a future-proof legal skillset: prompt mastery and context management 39:52 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=2392s] - Starting today: passions + low-stakes AI experiments as growth strategies 40:58 [https://www.youtube.com/watch?v=Geld8RS8jXc&t=2458s] - Connect with Michael T. Brown: LinkedIn & ongoing projects

The Secret to Building Resilient Legal Tech Teams with Jennifer Waite

Most legal leaders underestimate how quickly AI will redefine their teams—and why embracing curiosity now is the only way to stay relevant. In this episode, Jennifer Waite [https://www.linkedin.com/in/jennifer-waite-8b82415b/], Chief Knowledge & Innovation Officer at Arnall Golden Gregory [https://www.agg.com/professionals/jennifer-waite/], shares her unique journey from librarian to Chief Knowledge and Innovation Officer. She dives into how her early experiences in law and librarianship shaped her approach to knowledge management, technology, and AI in a law firm setting. Whether you're navigating data strategies, vendor relations, or AI adoption, her perspectives offer clarity on building a future-ready legal organization. In this episode:   * Jennifer's transition from law librarian to Chief Knowledge Officer * The evolution of the Knowledge Management department at a law firm * Effective vendor collaboration and partnership strategies * How AI is transforming legal workflows and team culture * Balancing innovation with governance and security * Managing AI tools, costs, and ethical considerations * Building skills for future relevance in legal careers * Practical steps for implementing AI from scratch in a law firm's context   Timestamps: 00:00 - Jennifer's unexpected journey from librarian to Chief Knowledge Officer 02:15 - The role of librarians in knowledge management and AI 03:03 - Transition from public service to private legal sector 04:11 - The importance of data strategy and the capstone project impact 05:39 - Evolution of the KM team and departmental shifts over the years 07:08 - Integrating technology, innovation, and governance within legal teams 09:19 - The dynamics of security, governance, and AI innovation working hand-in-hand 11:39 - Building strong vendor relationships and setting expectations 13:55 - Assessing vendor responsiveness and managing costs efficiently 16:12 - Transparency and openness with vendors during AI tool deployment 17:30 - The influence of rapid software updates on vendor decision-making 19:24 - The feasibility of internal development vs. vendor reliance 23:11 - Understanding AI’s true capabilities and common misconceptions 24:24 - Educating users on AI limitations and strengths in legal workflows 28:43 - Managing AI credits, costs, and internal resource allocation 33:16 - How AI can automate repetitive legal tasks in the near future 36:32 - Engaging senior leaders and promoting AI adoption across levels 38:18 - Cultivating curiosity and ongoing learning in legal teams 42:02 - The peril of ignoring AI advancements and the skills law professionals need 44:36 - Practical advice for launching AI in a new or existing legal firm Resources & Links: * Perplexity AI [https://www.perplexity.ai/computer] * Claude AI by Anthropic [https://www.anthropic.com/]  * Truth Systems [https://truthsystems.ai/] * Lupl [https://lupl.com/]   Connect with Jennifer Waite: * LinkedIn [https://www.linkedin.com/in/jennifer-waite-8b82415b/]

AI Alone Isn't Enough for Law Firms with Ted Theodoropoulos

Most AI conversations in legal right now are either breathless hype or reflexive skepticism. This one is neither. Ted Theodoropoulos [https://www.linkedin.com/in/tedtheo/] has spent close to 20 years in legal tech. He's worked with more than half the AmLaw, runs InfoDash [https://getinfodash.com/] (used by about one in four AmLaw firms), and hosts the Legal Innovation Spotlight podcast. He sees what firms are actually buying, where tools actually break, and which firms are actually moving. We get into vibe coding (what it's good for, where it falls apart), why law firms still spend only 2.4% of revenue on technology, what separates the firms adapting from the firms stalling, and why empathy might be the most undervalued lawyer skill of the next five years. If you care about AI adoption inside professional services, or you're trying to figure out what to build, buy, or ignore, this one's for you. Notable quotes "Vibe-coded apps aren't replacing anything that's rolled out in the enterprise. Even just maintaining compliance with SOC 2, there are certain SDLC processes which have to be followed." — Ted "Many leaders in the business of law functions were literally hand-picked because of their resistance to change. Now we're telling them: change everything with GenAI and move to the cloud. Is that the best person to be sitting in that seat?" — Ted "Law firms spend 2.4% of revenue on all technology. Professional services peers spend about twice that." — Ted "If you fail at change management, it doesn't matter how good your tech is. The project will not succeed." — Ted "Empathy specifically is going to be a core skill set as the technology delivers more and more of the legal work product." — Ted Timestamps * 00:00 Vibe coding: what it is, where it works, where it breaks * 09:12 Enterprise disruption and the collaboration model that actually works * 15:20 Why law firms spend 2.4% on tech (and why it's not enough) * 22:59 Which firms are moving fast and what they're doing differently * 27:34 From bespoke services to SaaS: InfoDash's transformation story * 33:24 Forward-deployed engineers, high-touch delivery, and the future of collaboration tools * 40:07 Measuring AI success and the soft skills lawyers need to build

Responsible AI in Law: Building Frameworks, Use Cases, and Trust with RAILS

This episode explores the RAILS [https://rails.legal/] initiative, which focuses on the responsible use of AI in legal services. The guests discuss the origins of RAILS, its working groups, and the importance of ethical AI practices. They highlight recent outputs, including a risk management framework and various use cases, while emphasizing the need for AI literacy in the legal field. The discussion also touches on the future of RAILS and the ongoing evolution of AI in legal contexts. Thanks to our guests, Kelli Raker [https://www.linkedin.com/in/kelliraker/], Eli Makus [https://www.linkedin.com/in/elimakus/], and Leigh Zeiser [https://www.linkedin.com/in/leighsnider/]. Watch the video version here [https://youtu.be/TuR65iknhH4]. Takeaways * RAILS launched to address the ethical use of AI in legal services. * The initiative aims to bridge gaps in AI understanding among legal professionals. * Working groups focus on client engagement, direct-to-consumer resources, and corporate legal teams. * AI presents both opportunities and risks that need careful management. * Access to AI should be equitable across different legal sectors. * The risk management framework provides guidance for corporate legal teams. * Use cases developed by RAILS help illustrate practical applications of AI. * AI literacy is essential for legal professionals and their clients. * The conversation emphasizes collaboration and sharing of resources. * Future developments will continue to evolve as AI technology advances. Chapters 00:00 Introduction to Rails and Responsible AI 01:57 Understanding Rails: Origins and Objectives 05:55 Working Groups: Focus Areas and Contributions 09:54 The Importance of Responsible AI in Legal Services 13:46 Outputs and Resources from Rails 17:56 Use Cases: Development and Significance 22:04 Future Directions for Rails and AI Literacy