Had a great conversation with @johncoogan and @jordihays on @tbpn last week about how we think about models at @WeAreLegora. If you break down legal work, there are many different skills you need to be good at: drafting, reviewing, fact checking, legal research, and so on. Different models are good at different things, and the frontier moves almost every week. So what’s interesting to us is where you apply which model, and when, to get the best outcome for our customers. That's the IP we've spent years getting good at. Post-training a model makes sense when we know it gives our customers better performance on a specialized task. Post-training for general intelligence, to get a small performance bump that won't last, doesn't. As a vertical AI company, you need to be positioned to benefit from all the R&D money being spent across the market. Thanks for having me on the show!

Last week, @WeAreLegora reached $200 million in annual recurring revenue (ARR). It took us 18 months to grow from $1 million to $100M ARR. It took us less than 6 months to double it to $200M ARR. To put that in perspective, we are on track to add more ARR this quarter alone than our entire ARR base at the end of 2025. Our positive gross margin improved while we did it. That is what makes the next double possible, and the one after that. The numbers behind the milestone are equally exciting. Our pilot win rate is 75 percent quarter to date, with improvement across every customer segment and geography. More than 40 percent of Q3 new business has come from in-house legal teams. The U.S. is now our largest market by revenue, and more than half of the AmLaw 50 are Legora customers. 130,000 lawyers now use Legora each month across 2,100 firms and legal teams in more than 80 countries. To our customers like @salesforce, @PaloAltoNtwks, @bakermckenzie, @WhiteCase, especially those who bet on us early, and to the entire Legora team, thank you.

Getting from $1M to $100M in ARR took us 18 months. The second hundred took less than six. Last week @WeAreLegora passed $200M. 130,000 lawyers now use Legora on a monthly basis, across 2,100 firms and legal teams in more than 80 countries. Two things from this year I would not have predicted: The first is how fast the US moved. 18 months ago we had no office in North America. Today over half of the AmLaw 50 work with us, out of our offices in New York, Denver, Chicago, Houston, San Francisco and Toronto. The US is our largest market by revenue. The second is where the demand is coming from. More than 40% of our new customers are now in-house legal teams, including Air Canada, Salesforce and Palo Alto Networks. Two thank-yous, before we get back to work. To our customers, who backed a small company from Stockholm with a lot to prove, and who still tell us plainly when we get something wrong. And to the team, engineers and lawyers sitting next to each other, who turn that into something better, faster than any group I have worked with.

There is no best model. There's a lot of noise about models right now. Who is training them, who owns them, where legal intelligence should live. One question actually matters: what produces the best outcome for the legal task in front of you? That's how we decide things at @WeareLegora. We optimize for the end-to-end outcome on a legal task. The model is one layer of that system, not the system. Models are uneven and the frontier changes almost weekly. One model plans a long job well, another runs deep analysis across thousands of documents. Some have to be told exactly what to do, and some are fine with a vague brief. They all break in different ways. So our lawyers write evals and we test them with the Legora BAR, our benchmark for agentic reasoning. Every model takes every test, and the model that wins gets the work. We post-train when we know it buys our customers better performance on a specialized task. Training is a tool we reach for when it helps, nothing more than that. The intelligence that compounds sits in the orchestration layer. Precedents, review standards, client requirements. That knowledge has to stay editable, auditable and portable. In our system, a changed review standard is an edit that takes effect the same day, with no new model training required. No lawyer should have to worry about which model did the work, any more than they think about which chip is in their laptop. They should only care about the quality of the work. That's what we are focused on. If you want the engineering version of this argument rather than the CEO version, our CPO, Bryan Tsao, and CTO, @jacsebl, take it apart in the video below.

Who owns the model? Where should a legal team's intelligence live? These questions are at the center of many conversations in legal AI, but we think the most important question to answer is: what produces the best outcome for every legal task? As CTO @jacsebl and CPO Bryan Tsao explain, there is no best model. Different models lead on different tasks, and the frontier changes almost weekly. At Legora, we use the best available model for each task, and invest in the system, where intelligence compounds and remains editable, auditable, and portable. We post-train when we know it delivers our customers better performance on a specialized task. Training is a tool, not a strategy. No lawyer should have to worry about which model did the work. Just whether the work holds up.

Working with @merritthummer, @Abby__Meyers and the whole @baincapVC team has been great. Congratulation on raising a new fund!

I first came to San Francisco when I was 15, with my dad. He is a programmer. To a teenager from a small island outside of Stockholm, this was the place where the future was being built. We walked into Salesforce East and stood there watching the enormous digital display of moving water. Outside, Salesforce Tower was starting to rise. I remember looking up and thinking it would be pretty sweet to have a tower someday. We are not there yet. What I could not have known is that I would be back years later, having dinner with @Benioff and welcoming @salesforce as a @WeAreLegora customer. Salesforce's Legal and Corporate Affairs organization will now use Legora across North America, EMEA and APAC. Their lawyers keep the judgment calls. We take care of the work around them. We have run on Slack since day one and became Salesforce customers when our sales team outgrew its first CRM, so it is good to have it run in the other direction too. Welcome to Legora, Salesforce. Dad, this one feels special.

For the first time, firms can build compounding engines of institutional knowledge. Those that succeed will become massively more valuable than those that don't.

We are putting a new foundation under legal research in @WeareLegora. Comprehensive data, a full ontology of law, and an AI-native citator. Almost every legal question starts in the same place. What does the law say, and does it still hold. That is also where AI has been least worth trusting, and I think it is the hardest problem in legal AI. Two things have to be true: You have to have the law, and you have to know your way around it. Getting the data is a grind, and a different grind in every country. We partner with publishers where we can. Where nobody will partner, we go and get it ourselves. Manual requests, physical scanning, whatever that jurisdiction takes. We are working through over 100 countries and every type of source. Then the harder half. No agent can reason across hundreds of millions of documents. Something has to choose the sources before the reasoning starts. So we are building an ontology of the law and an AI native citator, compressing corpora of thousands to hundreds of millions of documents into structured data that an agent actually can use to provide reliable output. The publishers did this by hand. 150 years, thousands of attorney editors, every opinion read by a person. We have hired the best of those editors. They set the standard and they call the close ones, but AI does the muscle work. Reading 60 million pages of case law is no longer too expensive to attempt. The ontology is in limited beta now. Generally available in Q4. Full story: legora.com/newsroom/legora-r…

Every journalist asks me a version of the same question. Will AI mean fewer lawyers? Our customers are hiring more of them. Put complexity of the task on one axis and volume on the other. Low-complexity, high-volume work moves to agents, and that pushes the pyramid up. More of the hard work gets done, because someone finally has the capacity to do it. Then there is demand. When something gets faster and better, the world asks for more of it. Jevons noticed it in 1865. Better steam engines made Britain burn far more coal. That was coal, not law, but the mechanism is the same. More M&A, more disputes, more regulatory work, more companies being started. The pie is growing. An independent study interviewed 30 firms across the AmLaw 200, Magic Circle and top international firms. 42% said Legora has helped them win new work, directly or indirectly. 45% said the same about expanding relationships with clients they already had. 39% have taken on matters that would previously have needed more people, more time, or a no. One firm in that study went through a corporate client's entire technology supply chain, every contract, to work out what they were paying and where. A human team would have needed weeks and the cost would have killed the project before it started. With Legora it took four to five days. Their own summary: it was work that we would not have done in the past. The work was not economical before, so nobody did it and nobody billed for it. Now it gets done, and someone has to do it.

Still so much untapped potential in the latest frontier models on the harness level, and each week we keep getting new models. Exciting times to be building out the application layer for legal.

Terrific conversation between @MaxJunestrand and @spdholakia. Highly recommend!

Advancements are constant, surprising, and uneven. There is no best model

Five numbers tell you whether an AI business is a real business. Gross retention.@WeAreLegora is 95%. Customers who bought last year are still here this year. If this one is broken, nothing downstream matters. NRR. Ours is 300%+. Gross retention is the floor. NRR is how much taller customers build on top of it. We don't sell shelf-ware.  DAU/MAU. Ours is north of 50%, and the average active user spends 17 hours a month in the product. A rollout tells you a firm has signed. This indicates the work actually matters, and it moves here before it appears in retention or NRR. Win-rates. Our August pilot closed-won-win rate was 78%. Winning roughly 4 out of 5 competitive pilots is downstream from offering a superior product. Gross margin. The one that matters most. Ours is positive and improving every quarter. Our customers want Legora to be a long-term partner, and this is what makes that possible. There's a shorter route: Price below what it costs to serve, book the logo, and hop on a never-ending fundraise treadmill to pay for it. The top line goes up, everyone claps, and every new customer costs more than they pay. Scaling a negative margin only exaggerates the problem. The whole point of scale is that the margin improves as you go up. Ours does. That's the only version of this business worth building.

@artman please open source the next @linear sync engine. Notion desperately needs your help

Venus Williams told us she liked our tote bags. Then she told us they were too small. You don't argue with someone who has seven Grand Slam singles titles. You get a bigger bag. She's back at the US Open today, playing doubles with Serena. We're in her corner. She could probably fit the corner in the bag. Good luck, Venus. Photo: Gabriel Espinal / BMG

Our Help Center moved today. Probably the least glamorous thing I have posted about, and one of my favourite ships this quarter. support.legora.com used to sit in a separate tool, which is where most companies keep their enablement documentation. From today it lives in our code repo, next to the code it describes. What's nice about that is that docs that live next to code can be maintained like code. We tell legal teams that AI works best when it sits where the work already happens. Hard to say that with a straight face while our own documentation lived somewhere our agent couldn't check it. Three things change: Accuracy. Agents can read the docs against the codebase and flag what is wrong, stale or missing. Before, we found out when a user told us. Speed. An engineer can ship the feature and the doc at the same time. No handover, no copying text between systems needed. Languages. Canadian French went from nothing to a full Help Center, 267 articles, in a day. More languages to come. Documentation is the easy version of a harder problem: keeping everything around a product true while the product changes every week. That gets a lot easier when the two live in the same place. Sweet work by the @WeAreLegora technical writers and Product Ops team.

Jake and the entire Legora engineering team has built remarkable technology from harnesses, routers, and more. Excited to see Jake share more publicly about the underlying technology that has powered Legora to be one of the fastest growing software companies of all time!

Performance and $$ depend hugely on harness design. In vertical AI, the gains from a great specialized harness >>> gains from more expensive model inb4 kimi has quirks - @runta can you try with a diff model also frontierharness.org/