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What changes for lawyers when an AI model stops writing and starts deciding? In Episode 3 of the LegalQuants podcast, host Victor Wang talks with Dan Sito, head of the Tax practice at General Legal and former partner and first AI Innovation Partner at Perkins Coie. Dan explains Jev, TypeSafe AI's classifier model that answers with a probability instead of generating text, using a Magic 8 Ball he built to understand it. Victor describes how Jev turned his agent-swarm approach to discovery and due diligence into a near-instant first-pass filter. The conversation moves to legal design: why interactive HTML deliverables beat the email-then-call loop, Dan's /legaldesign skill, and Victor's interactive client questionnaires. Then come voice-driven charts, a whiteboard that draws while you talk, software built for one person, and an 83(b) election filled in two seconds. They close on bionic lawyers, why not using these tools is now a choice, and why we still ask a human when the risk is real. Dan Sito at General Legal: https://general.legal/attorneys/dan-sito Jev by TypeSafe AI: https://typesafe.ai/ Dan's /legaldesign skill, in the LegalQuants plugins: https://www.legalquants.com/lqplugins LegalQuants Residency: https://residency.legalquants.com/ LegalQuants: https://www.legalquants.com/ 00:00 Cold open 00:50 Welcome: Dan Sito of General Legal 01:40 Dan's ChatGPT moment 05:04 More legal work, more creativity 07:17 Every model is a paradigm shift 11:51 What is Jev? 12:43 Jev example #1: the Magic 8 Ball 18:25 From agent swarms to Jev 22:44 Legal design: HTML instead of email 30:30 Victor's client questionnaires 33:23 Jev example #2: voice-driven charts 36:52 Jev example #3: the live whiteboard 39:23 Software for one 42:24 Jev example #4: an 83(b) in two seconds 46:33 Speed, focus, and cognitive load 49:54 Bionic lawyers 53:02 Still ask the human

What does it take to build legal AI inside a Big Law firm with a deliberately small team? In Episode 2 of the LegalQuants podcast, host Victor Wang talks with Andrew Simon, Chief AI & Innovation Officer at Weil, Gotshal & Manges. Andrew explains how he evaluates new legal AI products, why he thinks retrieval is one of the hardest problems in legal AI, and why Weil uses both closed frontier models and open-weight models. He describes how lawyers’ prototypes become durable applications, why the firm chose decentralized innovation, and how BenchMark grew out of a client’s question. The conversation then turns to Andrew’s path from Big Law to a startup, supply-chain mathematics, machine learning at BCG, and Weil, before closing on machine-to-machine legal work, access to legal knowledge, raising children in the age of AI, and what he won’t hand over to it. Andrew Simon at Weil: https://www.weil.com/people/andrew-simon LegalQuants Residency: https://residency.legalquants.com/ LegalQuants: https://www.legalquants.com/ Victor Wang: https://vic.law/ In this episode: 00:00 Welcome to the LegalQuants podcast 00:09 Meet Andrew Simon 01:12 How Andrew sizes up a new legal AI product 05:26 ROI when the technology keeps changing 06:37 Why legal retrieval is hard 10:40 Using closed and open-weight models 12:10 What happens when a new model drops 16:40 From a lawyer’s prototype to a real app 21:37 How Weil sees and supports AI use 24:14 Why Weil decentralizes innovation 27:16 Building BenchMark 31:31 How BenchMark caught on 33:07 The LASER name and the three-year goal 37:08 A startup within a Big Law firm 41:04 What Andrew looks for in a hire 44:57 Law school, Big Law, and leaving practice 49:21 Starting a beverage company 51:35 How supply-chain math led to machine learning 55:06 How Andrew came to Weil 58:38 The biggest lesson from BCG 59:26 Legal meaning, retrieval, and confidence 1:02:23 How strong a claim should a product make? 1:04:23 Agent security and machine-to-machine work 1:05:53 The case for an industry standards body 1:08:06 Where machine-to-machine efficiency matters most 1:10:27 Nonlawyers, AI, and knowledge diffusion 1:13:33 Raising kids in the age of AI 1:16:36 Parents, mentors, and curiosity 1:18:13 What he won’t surrender to AI

What happens when OpenAI asks practicing lawyers to help build a canonical set of legal plugins for the OpenAI for Law launch? In Episode 1 of the LegalQuants Podcast, Victor Wang joins Jamie Tso and John Hughes to unpack how the team turned legal expertise into working plugins in just a few weeks. They share the ambitious first idea, the feedback that changed their approach, and what it takes to build something lawyers will actually use. In this conversation, we discuss: 1. How a small WhatsApp group brought together lawyers who had been building alone—and became LegalQuants. 2. Why OpenAI pushed the team beyond an all-purpose “skill creator” toward concrete, customizable legal workflows. 3. How marketplace discovery changed the product: organize around litigators, transactional lawyers, and lawyers learning AI. 4. What makes a flagship skill: a real practice problem, a clear “aha” moment, testing with real documents, and a way to work with other skills. 5. How cite-checking, diligence, and redlining fit into larger acquisition, litigation, and negotiation workflows. 6. Why a legal AI skill needs a clear purpose even after the next model gets better. 7. Where to start: try a skill on work you understand, use legal design to make complex material easier to navigate, and feed what you learn back into the project. 8. Why Jamie sees legal quants becoming an internal capability at law firms—and what the Residency is building toward. Try the LegalQuants plugins: https://www.legalquants.com/lqplugins Where to find LegalQuants: • Website: https://www.legalquants.com/ • Residency: https://residency.legalquants.com/ • LinkedIn: https://www.linkedin.com/company/legalquants/ • Instagram: https://www.instagram.com/legal.quants/ • YouTube: https://www.youtube.com/channel/UCZen0gRIPZZlza8NMIg6otg • Newsletter: https://legalquants.substack.com/ • GitHub: https://github.com/LegalQuants Where to find Victor Wang: • Website: https://vic.law/ • YouTube: https://www.youtube.com/@theviclaw • LinkedIn: https://www.linkedin.com/in/stokebuilder/ • X: https://x.com/stokebuilder • GitHub: https://github.com/stokebuilder In this episode, we cover: 00:00 The OpenAI call 00:23 Welcome to the LegalQuants podcast 00:43 Meet the builders 00:59 How LegalQuants began 03:21 Community milestones and the Residency 04:42 OpenAI's legal plugin invitation 05:29 An ambitious first design 06:48 LQ Architect and feedback from OpenAI 07:38 John's cite-check skill 08:56 From specialized tools to broader practice 09:27 Who would discover the plugins? 10:52 Organizing the build 11:58 What makes a flagship skill 13:20 Three plugin families 14:01 How the skills connect 14:33 Acquisition workflow 15:17 Litigation workflow 15:58 Negotiation workflow 16:32 Open source and evolving skills 18:01 Where LegalQuants goes next 21:12 Practical advice for lawyer-builders 23:19 Thanks and where to follow Referenced: • LegalQuants Litigation, Transactional, and Companion plugins: https://www.legalquants.com/lqplugins • LQ Architect: https://github.com/LegalQuants/LQ-Architect #LegalQuants #LegalTech #OpenAI #Codex