SPEAKER_01: This is Jake Heller. He's the co-founder of CaseText, which sold for $650 million earlier this year. It's one of the mega wins in AI. And today he's going to tell us how he did it and how he built something that reduces weeks of painstaking legal work down into just minutes. And why that turned into million dollar contracts for his startup. Large language models are creating ridiculously huge opportunity. And today we're going to learn about Jake's story. Let's get started.
At YC, one of the things I've learned is that it takes someone who understands the world from a very specific perspective. That person needs to be combined with people who can build incredible technology. Jake's origins actually started in the legal world. I had actually a very early traditional legal career.
SPEAKER_02:
I practiced law at a big law firm. Before that, I clerked for a federal circuit judge. I had the privilege of just for a summer working in Obama's White House as a intern in the White House Counsel's Office. So I was able to see some interesting sides of the practice of law. And it was also pretty apparent early on
that it was an area that could be improved with technology pretty substantially.
It's not any one story, but there are many instances where it was like 2 a.m. or 3 a.m. and I'm trying to just find one piece of information that might help with a case. And we're talking about like a piece of evidence that might swing a billion dollar lawsuit
in one direction or another. Or for our pro bono work, a single legal case
that might help somebody who might go to jail, not go to jail. So like literally life or death or making or breaking a business.
And it was just so hard. And I would then like go on my iPhone and look for like takeout Thai food or something. And that was insanely easy. And so we knew that the technology to do relatively trivial stuff like find nearby restaurants or good reviews of them or what have you was there. And it just felt like this big disconnect where things that really mattered at the end of the day, like whether or not somebody spends their life in prison,
SPEAKER_02: technology wasn't really helping there.
SPEAKER_01: Among the best founders, this feeling that Jake describes is very common.
What does it feel like when you realize technology has made basic consumer actions like ordering my lunch very easy, but my work is so hard? We're still just a decade into this mega trend idea that great software and now AI has only made it into a very, very small percentage of the total pie of GDP, what the world needs. And like Jake mentions, sometimes it's very important, life or death things that are the largest markets.
CaseText was one of the companies I got to work with in my early days in YC in 2013. One of my notes from June, 2013 about Jake, I said, impressed by this team, articulate, accomplished, and good at building software to boot.
SPEAKER_02: So CaseEx changed considerably over the course of our 10 years. What saved the same is we had a vision around applying some of the best technology to the legal profession to make it so that when lawyers are doing their critical work, oftentimes life-changing or saving a business or what have you, that they were getting the best access to the most modern technology. In the early days, the best technology and the ones that we got most excited about were around, for example, crowdsourcing and applying very early and rudimentary versions of natural language processing to help lawyers do legal work.
SPEAKER_01: They started off as a crowdsourced case law library where users would edit and annotate, then have other users upvote or downvote the annotations. Like Wikipedia meets Reddit, but for the law,
2013 to 2023, selling for $650 million, that's literally a 10-year overnight success. That was literally us. It took us basically 10 years to get to a place
SPEAKER_02: where we had a product that was truly, truly amazing. But we also made mistakes. There are times during that path where we thought we had extreme product market fit,
SPEAKER_01: where we thought everything was perfect. At YC, we have a diagram that describes the 10-year overnight success as the process. After the launch, after the adrenaline wears off, you go into the trough of sorrow and the wiggles of false hope.
SPEAKER_02: One early moment, we created a product that a number of enterprise firms seemed really excited about. And this is, you know, back when machine learning and artificial intelligence weren't nearly as powerful, but it could still do pretty incredible things for law firms. Like they could just drag and drop in a set of documents
and it would read through them and say, hey, based on what I've read, here are the things you need to read next. Things you're probably missing that aren't in the documents yet, like cases, regulations, statutes that you have to read next. So we early on had a number of large law firms, enterprise clients, who were super stoked about it. And they started paying us 50,000, 100,000, $150,000 per client. And we got super excited about that. And so we thought, like, just keep on scaling. There's a lot of big law firms globally that just hire better salespeople and watch us grow. And of course it turns out that not all law firms are the same and not all are ready to buy immediately and quickly for new technology. Some aren't early adopters, some are not. And I'm sure this is true,
SPEAKER_01: not just of law firms, but of all customers. So they started with larger clients and then found that they had exhausted that set of customers but then focusing on smaller law firms, it worked again until it didn't. And then we also started focusing later on smaller law firms
SPEAKER_02: who saw extreme value in using artificial intelligence at the time, given how powerful it is today, and applying that to their workflow because they don't have enough people, right? So they're, by definition, a small law firm, not enough people, not enough hours in the day,
anything that can make them work faster was appreciated. And again, went after that market and saw this influx of thousands of customers. And we were celebrating like every day because we had 100 customers a day, 400 customers a day, 1,000 customers in a day. And it felt like, again, we were on fire. And then some of our marketing channels just stopped working as well. And we got the group of people who were responsive to the message and it got harder and harder and harder
SPEAKER_01: to reach that next group and grow slow to get.
SPEAKER_01: But then as the business waxed and waned, they kept at it and then a breakthrough.
So we were really lucky to get early access to GPD4
SPEAKER_02:
before it come out. Like the brief history there is that we've been working on like large language models, actually for the last like five or six years at this point. As soon as the BERT paper came out,
SPEAKER_01: we saw immediate applications to law. They'd been experimenting with AI since 2016, but getting access to GPT-3 and GPT-4 from OpenAI was the moment that allowed them to create their mega product, Co-Council. This is what product market fit feels like.
SPEAKER_02: With this most recent breakthrough of the state of the art in artificial intelligence, we were able to build a really incredible product. One that for our customers was like life-changing. And we saw something that we've never seen before where you start adding millions of dollars of revenue a month. You start working with those same clients from years ago who may have taken nine or 12 or 18 months to make a decision because they're a large enterprise client make a decision in a month. And I think that's when we knew we had something really, really, really special. And frankly, more important than even the numbers, it was just, you can see it
just talking to one of our clients, the way they would light up in a way that we've never seen before in any product we've built. I mean, we were almost certainly gonna triple our revenue per year. And that's after 10 years of building the revenue we had before, right? Again, there are different levels to this kind of product market fit.
SPEAKER_01: Founders are always asking us what product market feels like. The answer is when you hit it like that, you know.
When we got the chance for GPT-4
SPEAKER_02: maybe about six or so months before it was publicly released, we immediately saw that this is different. For us, we saw the raw capabilities of this AI to do human-level work at a superhuman speed.
What that would ultimately mean for our customers is something that's really life-changing, right? To make an AI assistant that lawyers can delegate
complex legal tasks to and have that get done really fast and at the same level of quality and reliability as you'd expect from somebody quite good working for you.
SPEAKER_01: The key pattern we're seeing around the very best LLM-based startups is this, a golden demo that gets people to sign on the dotted line to become large dollar revenue customers immediately because people see the value right away.
SPEAKER_02: We would look at real cases with real data in the past and show how you could have immediately caught the fraud. You upload all of the emails and instant messages and so on from within this organization and ask questions like, which of these emails might evidence potential fraud? And it immediately flags people kind of making jokes. In this case, it's about a case around Enron, failed company that was kind of known in the early 2000s for rampant fraud. All their emails are online
and it would read through all of those and flag like, hey, here's an email where they're talking, and this is literal and like Cookie Monster talk as a joke about hiding assets and avoiding auditors and special purpose vehicles and so on. And another email that sarcastically calls
Kenneth Lay an honest man. And the fact that the AI could pick up on the sarcasm
and highlight that to the lawyer to say, hey, this might be really important evidence was an example. And taking a step back from that, the killer demo was doing that activity that reading a million documents, finding answers and a few other, like maybe half dozen others
doing research, reviewing contracts, et cetera. So at the end of the demo, you say, the last 10 or 15 minutes, I've done like four or five days of work,
if not much more.
And lawyers kind of sat back in their chair and they said, okay, I get it. Like I get how AI can help me do my work better.
SPEAKER_01:
So for those watching, that's what good looks like. A magic demo that shows that you can have five days of work squished down into minutes. That's one pattern that is turning into a repeating one. For every large language model based startup, we see that it's going to win their market and build something huge. The golden magic demo.
One of the main reasons why Jake and CaseTech succeeded is actually their grit and determination. And also they were constantly linking what they had to build to what customers wanted, outcomes to customers. And they did this masterfully for their customers, the lawyers. But it turns out there's a lot of work kind of going from the raw model to a product
SPEAKER_02: that lawyers can actually use. We can upload thousands or millions of documents and have it read all of them and tell you which ones are relevant and not relevant to what you're working on. Or for it to automatically do research for you and find the right answers out of a billion pages of cases, rules, regulations, and statutes. That kind of scale and accuracy is really, really hard. At least still, you know, it's GPD5, so I'll be obviated. But we just kind of really like move fast building that product.
And I think one of the things that never left us from the beginning days of YC is that we are not the only ones that are going to be able to do the same thing that we did with YC, is just a velocity of product and a velocity
SPEAKER_01: of hearing customer feedback, iterating on it. The legendary scientist Louis Pasteur once said, in fields of observation, chance favors
only the prepared mind. And that's what CaseText did. They started originally as a crowdsourced, user-generated content website. And while they were building software over many years for the same customers, the second large language models appeared, they were ready. And the crazy thing is the impact of LLMs might just be getting started. So this is gonna sound probably insane
SPEAKER_02: to most people who hear it,
but I think the GPT technology is underhyped.
Because what we see in GPT-4, specifically as a model, and I'm assuming the same will be true in the state of the art advances,
is a machine that can read and understand and write
and logic to some degree at the level of a pretty good operating postgraduate,
maybe a young paralegal or associate at a law firm
or at McKinsey or what have you.
And once you figure out how to use that technology
with a high level of accuracy and a high level of scale, like review thousands of documents or millions of documents, you can all of a sudden take things that were necessarily human processes
and apply it at scale through technology. And that is just so insanely powerful.
What we're seeing in legal is, for example,
the California Innocence Project
that would get hundreds or thousands of applications
of people who are presently in jail who are looking to prove their innocence.
And the applications are thick.
It's like police reports that are very detailed
and witness reports and trial transcripts and deposition transcripts and much else.
And they would have to personally read
every single page of that individually.
And they still have enough people and not enough time. So they have a four-year backlog to even evaluate cases, right?
And with technology like this, that can read over thousands of pages and minutes and accurately tell you all the details of the case and who's this and whatever, you can cut down that four-year wait time to like one year or one month.
That's like life-changing. People are waiting in jail to prove their innocence, right? And that's just like such a small example
SPEAKER_01:
of what we think is gonna happen in a very big way.
SPEAKER_01:
When you compress the work of dozens or hundreds of people, sometimes the most drudgery-laden type of work, and take it away and give an equivalent or better result, that's magic. A lot of people are going to want that magic and the tooling and developer tools necessary to bring that magic to the world and to the current mega opportunities out there for startups.
SPEAKER_02: There's going to be a really rich ecosystem of technologies that will support builders.
And I think this is gonna look a lot like the cloud, right? To me, GPT and other models is like a base layer
of capability, like cloud computing, that a lot of companies, if not all companies, will plug into to some degree.
And there'll be another layer of technologies that help you get the most out of it. That's a great example. And then there's gonna be an application layer built on top of that.
And each one of these folks provide value and there's great businesses to build in each. What we found kind of at that last mile, right, of course, like GPT-4 itself is really incredible, but how do you engineer it so that you can have thousands of users on the same time and it's reviewing millions of pages of text all at the same time? That's really hard. How do you make sure that it's not inaccurately saying something about a document with so-called hallucinations? That's really hard. How do you test that what you're doing as you make alterations to your code or to the prompts you use when you're talking to the AI doesn't produce the wrong output or hallucination?
That's also really hard. We had to build a lot of that ourselves internally. And who knows, maybe someday some of those things will become products. But I also know that there's gonna be a lot of really fantastic products that'll support companies, just like there were things that built on top of the cloud
SPEAKER_01: like Heroku. So that's the good news. What a time to be alive, even though Jake has reached the top of one peak, he's already encouraging the next adventurer, you, to step up to the mountain.
I think the thing I'd like to pitch right now
SPEAKER_02: is never been a better time to start a company.
And while we're here, apply to YC and get the right advice to start off with. I probably sound a little bit crazy by saying this, but I think it's an under-hyped moment. The more you dig deeper into this technology and what it can do and the kinds of problems that it can solve for people, the more extreme amount of white space you'll likely see. And so I would recommend folks who are kind of sitting on the sidelines or playing around or hacking. I think now is the moment to really consider getting going.
And I think you'll be massively rewarded
SPEAKER_01: for working with this new technology
and being on this ride.
Case text is one of the biggest mega wins in AI. I am so proud to work with Jake early in his YC days, and I can't wait to see what his team does next. I can't wait to see what you do next too. That's it for this time. I'll see you next time.
