Why Vertical LLM Agents Are The New $1 Billion SaaS Opportunities

this is their first ever experience talking to this Godlike feeling you know AI that was all of a sudden doing these tasks that would take me when I practice like a whole day and it's being done in a minute and a half the whole company all 120 of us did not sleep for those you know months before gbd4 we felt like we had this amazing opportunity to run far ahead of the market that's why you're the first man on the moon [Music] yeah welcome back to another episode of the light cone I'm Gary this is Jared and Diana Harge is out but he'll be back on the next one and today we have a very special guest Jake heler of case text I think of Jake is a little bit like one of the first people on the surface of the Moon he created uh case tax more than I think 11 12 years ago actually and in the first 10 years you went from0 to $100 million valuation and then in a matter of 2 months after the release of GPT 4 that valuation went to a liquid exit to Thompson Reuters for $650 million so you have a lot of lessons about how to create real value from really like large language models I think you were of um you know our friends in YC one of the first people to actually realize this is a sea change and revolution and not only that we're going to bet the company on it and you were super right so welcome Jake happy to be here one of the cool things I think about Jake story and reason why we wanted to bring him on today is that if you just look at the companies that good Founders are starting now it's a lot of vertical AI agents I mean I was trying to count the ones in s24 we have literally dozens of the YC companies in the last batch for building vertical specific AI agents and I think Jake is the founder who is currently running the most successful vertical AI agent it's by far the largest acquisition and it's actually deployed at scale in a lot of mission critical situations and the inspiration for this was uh we hosted this Retreat a few months ago and Jake gave an incredible talk about how he built it and we thought that it'd be super useful for people who watch the light cone who are interested in this area to hear directly from one of the most successful builders in this area how he did it so how did you do it well first of all like like a lot of these things um there's a certain amount of luck over the course of our decade long uh Journey we started investing very deeply in AI uh and natural language processing and we we became close with a number of different research Labs including some of the folks at open Ai and when it came time for them to start testing early versions uh we didn't realize it was gbd4 at the time but what was what was gbd4 we got a very early kind of like view of it and so you know months before the public release of gbd4 you know we as a company were all under NDA all working on this thing and I I'll never forget the first time I saw it it took maybe 48 hours for us to decide to take every single person of the company and shift what they were working on from what the projects we were then working on at the time to 100% of the company all working on building this new product we call co-counsel based on the Jeep D4 technology how many people was that we're about 20 people at the time so you took like 120 people and completely changed what they were all working on yes yes yes in 48 hours yes and for the people watching uh casex originally I mean had always been in the legal space you're a lawyer and you built something for yourself and you know sort of the first versions of it were actually sort of uh annotated versions of case law actually yeah that's exactly right so in the very early Origins the company the mission of the company what we're always focused on is how can we build something that brings the best of technology to the legal space um I as a lawyer I actually like the job a lot the parts of my job that I hated the most was when I had to interact with the technology that lawyers have to use um regularly to get the job done I remember thinking and this is like 2012 when I was at a law firm if I want want to do something really trivial I had like a new iPhone at the time I can go on Google and find like movie times or where's the closest open Thai restaurant with vegetarian options that was super easy but if I wanted to find the piece of evidence that was going to exonerate my client and and make it so he doesn't have to go to jail for the rest of his life or the um key legal case that will help me win a billion dollar lawsuit well that's going to be like 5 days in a row till 5:00 a. m. every day it's like there's got to be a better way what is the process as a lawyer you would have to read the Stacks and stacks of documents pretty much yeah um right before I started practicing before everything went virtual or like online uh you would literally be in a basement with Bankers box is full of documents reading them one by one by one to try to find you know all the emails in a company like fiser or Google to see if there is potential fraud or um and then if you wanted to find case law slightly before my time you'd literally go to the library and open up books and just start reading and you know new products were coming out that were some of the first web-based research tools but they were pretty clunky it was just hard to find the relevant information you couldn't do contrl F or any of this stuff basically basally not yeah and what was interesting about your is you also happened to be the rare breed of having also computer science training so this must have driven you nuts yeah exactly I mean in the law firm I'll never forget I was building like browser plugins to go on top of the the tools I was using just to make my like life more efficient and effective and actually one of the reasons I left the law firm to start a company and apply to YC was I got in trouble with the general counsel who thought like hey why are you spending all your time you know doing this Tech stuff and also made at the time very clear that that my Law Firm owns all that technology so I decided to do something different so do you want to tell us a little bit about the first 10 years of case text the sort of like long slog in the pre llm era one of the lessons here I think that I took away from that time period is that uh when you start a company you may not get the exact right you may have like the right kind of general direction you know there's a problem you're trying to solve it but it could take a very long time to figure out what the solution is for us for example you know we saw that there was this kind of combined issue of like bad technology in the legal sphere but also like this very like a lot of lawyers use content to do things like research and understand like what the law is and so we thought okay well we can do the technology better but how are we going get this content and we spent like a couple of years trying to get as Gary said lawyers to annotate case law and to provide information so it's like a ugc site like user generated that was a big focus of ours like the kind of onew punch of better technology but also better content um we you know at the time our heroes were like stock overflow and Wikipedia and GitHub and other kind of Open Source or ugc kind of websites and it was a total failure like we could not get lawyers to contribute their time and information and and I think these are just different populations the typical Wikipedia editor has more time on their hands than they know what to do with and so they're adding not all but but many do and they're adding content for free um and and altruistically lawyers bill by the hour their time is incredibly valuable they're always running out of time they had no time to kind of contribute to some ugc site so we had to Pivot and we started investing investing very deeply at the time it was not called AI it's just like natural language processing and machine learning and saw that first of all we didn't need to create all this ugc like to to replicate some of the best benefits of what our competitors had in these big content databases some of it you can basically do even then kind of automated basis and then also uh we were starting to create these user experiences that were you know a lot better than what our competitors could offer based on then at the time what seems kind of quaint like AI stuff like you know the same recommendation algorithm that powers Pandora and spotify's like recommended music you can use they look at basically is how this song relates to that song people listen to this also list to this and this and this right similarly we looked at okay cases that site to you know other cases they all reference earlier opinions you know they they kind of build out this network of citations and we found ways that we can check a lawyer work they'd upload their work so far and be like well everybody who talks about this case talks about this case too and you missed that um so cool experiences like that but the truth is until the very end until co-counsel a lot of what we did were relatively speaking kind of incremental improvements on the legal workflow and one of the things that's kind of weird about this is um when there's just an incremental Improvement it's actually pretty easy to ignore a lot of our clients they never say this literally but you kind this impression you walk into the room their office and you try to pitch them a product and you say this is going to change everything about the way you practice and they go well I make $5 million a year I don't want nothing to change this technology PL it's not I do not want to introduce anything that has the opportunity to make my life at all worse um or potentially worse or potentially more efficient because they build by the hour it was really only after like much later when chat GPD came out you know the time we were privately and secretly working on gp4 chat GPT came out and all of a sudden every lawyer in America probably in the world saw oh my God I don't know exactly how this going to change my work but it's going to change it very substantially like they could feel it and the same you know guys and gals were telling us I make $5 million a year why would it change anything about my life we like I make $5 million a year this is going to change something I need to be ahead of this the technology itself and we'll get into a second really changed what we can build for life employers but also the market perceptions of what was like what was necessary really Chang as well and for the first time in our 10 years you know even before we launched Co counil publicly based on GPT 4 they were calling us like you know we know you work on AI we need to get on top of this what can you know what can you show us what can we what can we work on and I think it's because the change was not incremental anymore it was like fundamental and all of a sudden they had to pay attention they could not ignore it I guess uh the mental model I have for you is there's this concept of the idea maze you know the founder goes in the beginning of the Maze and they're just like feeling around like actually uh in the arena talking to you know customers learning like where are the walls which which path to go should I go left or right like and then um as is actually common for startup Founders in the idea maze you will actually reach a dead end and then usually you have to Pivot yeah and then I think you have a very interesting story because you were sort of towards the end of maybe like one of the uh you know parts that weren't going to get you all the way to product Market fit but then lm's Dr and then it's like the maze got shaken up yeah and then you are actually much closer to product Market fit than absolutely anyone else that's yeah I it's exactly right that's why you're the first man on the moon yeah yeah I think I think there's there's cly something to that and the thing is you know each time we got progressed through that maze it felt like maybe now we're at product Market fit you know we were making real Revenue before we um launched co- counil and we had real customers and they said really great things about us I keep on thinking about this article written by Mark andreon in like the early 2000s uh I think it's called the only thing that matters and in it he describes the what it feels like to have product Market fit he lists things like your servers will go down you can't hire support people and sales people fast enough you're going to eat for a yearfree at bucks the the kind of famous Woodside you know uh Diner where where a lot of VCS will take you the press and I read that early on in my like like you know career and I was like okay well that's like hyperbolic but when we launched Co counil it was literally exactly that our servers were going down we could not hire support people fast enough we couldn't hire sales people fast enough I ate a lot of bucks you know um before we was a really big day if we in the ABA journal or some other you know legal specific uh publication we were on CNN and MSNBC and like you know all of a sudden everything changed and that's real product Market fit looks like I think Mark Mark was even in like 2005 whenever the article came out exactly right about it looked like in 2023 can you talk about that crazy time because it was only two months from when you launched co- counil to getting bought for $650 million so like what happened in those two months well to to be clear the transaction only closed six months after we launched but it was two months the conversation started and so uh so we started building CO counil and and for just just to uh to kind of background purposes the idea we came up with again like 48 hours like a weekend after seeing gp4 was um and it's something that not but kind of still sound crazy today but it's feel felt crazy at the time which is this AI Legal Assistant by which we mean it's like almost like a new member of the firm you can just talk to it um not unlike how you might talk to something like chat GPT today uh and give it tasks like I need you to read these a million documents for me and tell me if there's any evidence of fraud happening in this company and then within a couple of hours it's like I've read all the documents here's what the summary is or summarize documents or do legal research and put together a whole memo after researching you know hundreds or thousands of cases answering the lawyer's initial research question and and so in that sense it was this like really powerful extension of the workforce of these law firms that was the concept from the beginning and we made an very early initial version of it and we started because we couldn't you know under our agreement with open AI we could not be public about this product but they did let us extend the NDA to a handful of our customers and so we started having our customers use it and so you know for months before gb4 was launched publicly we had a number of law firms un like they had no idea they're using GPT for but they were like seeing something really special right this is actually even before chat GPT so they this is their first ever experience talking to this Godlike feeling you know AI that was all of a sudden doing these tasks that would take me when I practiced like a whole day and it's being done in a minute and a half right and and so as you might imagine like it was it was nuts I mean first of all the whole company all 120 of us did not sleep for those you know months before gbd4 was like publicly launch therefore could publicly launch the product we felt like we had this amazing opportunity to run far ahead of the market something really beautiful happens when everybody's working super super hard which is you iterate so quickly past and actually I I still see some companies out there they're stuck where we were in the first month of seeing gbd4 right um and I think it's because they're just not like as intensely focused and engaged as as we were were able to be during those like couple like about 6 months or so before the public launch of gbd4 you kind of uh to do this transition you had to shake the company you kind of went into deep founder mode because there was a lot of uh push back from employees I like oh this thing was working why should we go into throw ourselves into the deep end of AI and tell us about that founder mode moment for you and so first of all like this is especially true you running a business for 10 years because they've seen you wander through that Maze and and bump it to dead ends and a lot of those folks have been there for uh most or all that time watching you know me as the founder saying we're definitely going this direction it's definitely going to work and sometimes it doesn't and you only get so many of those with employees right so this was maybe my last one that I had with some of these folks and they're like here Jake goes again with this crazy new technology and some idea we're gonna invest deeply in and and yeah it took some some a job to convince people and if you imagine like what some of the different roles are if you're in the go to market role if you're if you're selling or marketing a product and we're making you know we're growing 70 80% year-over-year we're between $15 and $20 million in ARR things weren't like terrible right um that's great yeah we're great yeah we but like so they were like what why are we even the board you know some of the members like I get this immediately some of them had to be persuaded right um and about the founder mode moment like one thing that really worked for me is uh I led the way through example I built the first version of it myself um wow even with 120 person company with like a whole bunch of Engineers and lawyers and stuff like mhm before that you like opened up your like IDE and actually built the thing yourself oh yeah and part of it was uh the NDA only extended at first to me and my co-founder that was it that was a blessing though it it turned out to be like perfect and even after the NDA got extended a little bit we kept it pretty small at first for the first like you know little bit of time I made my mind within 48 Hours whole is going to do this but we actually only told the company I think a week and a half afterward first got access during that week and a half like we built the very first version like prototype version of this and and again I I won't I'll never forget this the timing is just so funny like we saw it on like a Friday we had it all weekend long we're working with it and then Monday was an executive offsite where everybody came all my Executives came and they expected we're we're going to be talking about how we're going to hit our sales Target for the next quarter how and it's like guys we're talking about none of that you know we are talking about something totally different right now let me show you something on my laptop you know uh so yeah I I built the first version myself but going through that process me and and and then a handful of other people I think was really helpful and we also brought in customers early and that helped convince a lot of people as soon as like a skeptical sales or marketing or whatever person or even an engineer was on the other line end of a zoom call uh where um a customer was was reacting to the product in real time and giving us their honest reactions and like seeing the look on their face again you have to imagine it's almost hard to imagine that the world was like pre- chat GPT but then there some of these people were seeing that that exact idea for the first time and they were they were just blown away and that really changed Minds quickly I mean we saw people go through like existential crisises live you know on Zoom calls like oh my God you see their expression change exactly in all kinds of ways it's like what am I going to do lot the very common reaction amongst the senior attorneys we showed it to was like well that got up retir soon like you know i' have to deal with this and some of this was um really driven by GPD 4 coming out like you had access to three you had access even to two I think we had access we were we were in a close relationship again with a lot of the labs but including open ey and they kept on showing us stuff kind of early on in its development and they're like well can you build something with this for legal and every time we're like no this sucks like you know by by time we got to three and 3.

5 it was like okay well this is plausible sounding English and sounds kind of like a lawyer so kudos for that but it is just making stuff up wildly like we just didn't it's very hard to connect it to a real use case especially in legal where it's so important that you actually get the facts right the you can't hallucinate um you can't even you know make the wrong kinds of assumptions and we had to do a lot of work with those earlier models to even get them close to usable and they just weren weren't really I mean like one one like totem or like one example along the way is when gbd 3. 5 came out the study was run um and it showed that gbd 3.