Support for Decoder comes from Anthropic, the team behind Claude. When you're analyzing a complex tech strategy or trying to understand the forces behind major industry decisions, Claude can help. Cloud is AI that goes beyond easy answers to help you explore the nuanced questions that drive real understanding. Whether you're dissecting platform strategies or exploring regulatory implications, Cloud works through complexity alongside you.
Try Cloud for free at cloud.ai slash decoder. Support for this show comes from Adobe. The all-new Adobe Acrobat Studio is reimagining how we use and interact with PDFs and the powerful impact they can have for your business and personal projects. And with their AI-powered PDF spaces, you can collect files, synthesize information,
and even chat with an AI assistant for fast insights. It's time to do more with PDFs than you ever thought possible. And you can do that with Acrobat. Learn more at adobe.com slash do that with Acrobat. That's adobe.com slash do that with Acrobat. The holidays have arrived at the Home Depot and we're here to help bring the excitement with decor for every part of your home.
Check out our wide assortment of easy-to-assemble pre-lit trees so you can spend less time setting up and more time celebrating. And bring your holiday spirit outdoors with unique decor like one of our Santa inflatables. Whatever your style, find the right pieces at the right prices this holiday season at the Home Depot.
Hello and welcome to Decoder. I'm Neil I. Patel, Editor-in-Chief of The Verge, and Decoder is my show about big ideas and other problems. Today I'm talking with Sean Fitzpatrick, the CEO of LexisNexis, one of the most important companies in the entire legal system.
For years, including when I was in law school, LexisNexis was basically the library. It's where you went to look up case law, do legal research, and find the laws and precedents you would need to be an effective lawyer for your clients. There simply isn't a lawyer today who hasn't used a Lexis tool. It's fundamental infrastructure for the legal profession, just like email or a word processor. But enterprise companies with huge databases of proprietary information in 2025
can't resist the siren call of AI, and LexisNexis is no different. You'll hear it right away. I asked Sean to describe LexisNexis to the audience, and the first word he said wasn't law or data. It was AI. The goal is for the LexisNexis AI tool,
called Protégé to go beyond simple research and helped lawyers draft the actual legal writing they submit to the court in support of their arguments. That's a big deal, because so far AI has created just as much chaos and slop in the courts as anywhere else. There is a consistent drumbeat of stories about lawyers getting caught and sanctioned for relying on AI tools that cite hallucinated case law that doesn't exist, and there have even been two court rulings retracted.
because the judges appeared to use AI tools that hallucinated the names of the plaintiffs, incited facts, and quoted cases that didn't exist. Sean thinks it's only a matter of time before an attorney somewhere loses their license because of sloppy use of AI. So the big promise LexisNexis is making about Protégé is simply accuracy, that everything it produces will be based on the real law, and much more trustworthy than a general-purpose AI tool.
You'll hear Sean explain how they've built their AI tools in teams so that they can make that promise. LexisNexis has hired many more lawyers to review the work of AI than Sean expected, for example. But I also wanted to know what Sean thinks tools like Protege will do to the profession of law itself.
to the job of being a lawyer. If AI is doing all of the legal research and writing you'd normally have junior associates doing, how will those junior associates learn the craft? How will we develop new senior associates without a pipeline of junior people in the weeds of the work?
And if I'm submitting AI legal writing to a judge who might be using AI to read it, aren't we getting a little close to automating too much of the judicial system? These are big questions, and they are coming to a head real fast in the legal industry.
I also pressed Sean pretty hard on how judges, particularly conservative judges, are using computers and technology in service of a judicial theory called originalism, which states that laws can only mean what they meant at the time they were enacted. We've run some stories here at The Verge about judges letting automated linguistic systems try and understand the originalist intent of various laws to reach the preferred outcomes, and AI is only accelerating that trend.
especially now in an era where literally every part of the Constitution appears to be up for grabs before an incredibly partisan Supreme Court. So I asked John to demo a protégé doing some legal research for me on a question that appears to be settled. but is newly up for debate in the Trump administration. The question of whether the 14th Amendment guarantees birthright citizenship to everyone born in the United States. To his credit, Sean was game. He did it.
But you can see how taking LexisNexis from a company that provides simple research tools to one that provides actual legal reasoning with AI will have big implications across the board. This conversation is weedsy. But it's important, and it touches on so many things that we've talked about here at Decoder. Okay, LexisNexis CEO Sean Fitzpatrick. Here we go.
Sean Fitzpatrick, you are the CEO of LexisNexis. Welcome to Decoder. Thank you. Great to be here. Thank you for joining me. I was just saying this is my first interview back from parental leave. So apologies if I'm rusty to the audience, but apologies to you if I'm just like totally loopy. Congratulations.
Yeah, I'm very excited to talk to you. I feel like the legal profession in America, you know, I'm very much a failed lawyer. My wife is a lawyer. There's a lot of lawyers in the Verge team. The legal profession in America is just at a moment of... absolute change, a lot of chaos, actually, and an enormous amount of uncertainty. And LexisNexis, if the audiences know,
tends to sit at the heart of what lawyers do all day. Most lawyers are using LexisNexis all day long, every minute, every day. And what that product is and what it can do and how it can help lawyers do their job connects to a lot of themes that I think we see. both in the legal profession and then with technology and AI generally. So just start at the start. What is LexisNexis? How would you explain it to the layperson?
LexisNexis is an AI-powered provider of information and analytics and drafting solutions for lawyers that work in law firms and corporations and government entities. That's a new conception of LexisNexis. When I was in law school in the early 2000s, it was just the thing I searched to find case law. Yes.
We've transformed over time, that we were kind of just that research provider. Over time, we've acquired more businesses. We've integrated those businesses. And kind of in 2020, when we launched our Lex... plus product. We integrated all those things together, and so we became an integrated ecosystem of solutions. And then in 2023, when we launched Lexus Plus AI, that's when we became really an AI-powered provider.
analytics, decision tools, and drafting solutions. And the capabilities of AI have really allowed us to do things, do more things than what we've traditionally done in the past. That jump from being the
You know, sort of gold standard database of legal opinions and reasonings and case notes and all that to we're going to do the work for you or we're going to help you do the work. That's a big one. That's a cultural jump. Obviously, there's some acquisitions along the way you can talk about that helps you make that jump.
What drove you to make that jump to say, actually, the lawyers didn't help drafting the proposed opinions they might give to a judge? What made you say, OK, we've got to step into actually doing the work?
Yeah, I mean, I think it's been a natural evolution. And as technology has evolved, it's opened up new avenues of things that we can do. What we do is we tend to... take the latest technology and we introduce that to our customers and we spend time talking to them about how they think that technology can be best applied in the legal environment.
And then based on the ideas that they come up with, we translate that into products and build products that resolve those opportunities or address those opportunities. Let me ask you a pretty philosophical question. It's one that I struggle with all the time. It's one that I talk to our audience about all the time. Our audience is pretty technically focused. I think most people who encounter the legal system think it's pretty deterministic.
Audience is pretty technically focused. They're used to computers. Computers are until recently pretty deterministic, right? You put in some inputs, you get some outputs. Most people who encounter the legal system. I think it's pretty deterministic. You put in some inputs and you get some predictable outputs. And what I'm always saying is that's not how it works at all, right? You show up to court, the judge is in a bad mood. You have no idea what's going to happen. You show up.
to you're a big company and you have an antitrust appeal and you show up to the three judge appellate. appellate review board and you have no idea what's going to happen. Anything, like literally anything could happen anytime. And the judicial system is fundamentally not deterministic and trying to think about it like a computer.
even though it's structured like a computer, can get you in all kinds of trouble. Maybe the best example of this is people on Facebook putting the words no copyright intended on the bottom of movies. They think they can issue these magic words and the legal system is solved, and they just can't.
So AI is that problem in a nutshell, right? We're going to take a computer. We're going to make it better at natural language. We're going to fundamentally make the computer not deterministic, right? You can't really predict what an AI is going to do. And then we're going to apply that. to the fundamentally not deterministic, the very human nature of the court system. Somewhere in there is a big philosophical problem about applying computers to the justice system. How do you think about that?
First of all, you have these massive investments that are happening with the foundational models, right? These hyperscalers, each one of them is putting in. close to $100 billion, you know, Microsoft, Amazon, Google. And so these models, they just, you know, continue to get better and better and better over time.
That's at the foundational model level. We don't really operate at the foundational model level. We build applications that utilize these foundational models. And at that level, what we see is prices are dropping. right so we used to pay 20 like two years ago for a million tokens and today you know, we might pay 10 cents per million tokens, right? So that allows us to do things at speed and at scale that we've never been able to do before.
And if you look at the law, there are a lot of things about the law that make these models attractive. So most of the law is language-based, right? And these models are really great with language problems. is precedent-based, right? And so... Well, we'll come to that. Yeah. I'm not sure that that's under... That's up for grabs. Sure. We'll come back to that. I'll grant you that, yeah.
And then you look at the activities that lawyers do, you know, they draft documents, they do research, they summarize things. The models are all like really, really good at these types of things. And so you kind of have this like. perfect storm of this technology and the things that lawyers do kind of coming together. And yet...
When people try to use these models, these consumer-grade models, there are all kinds of problems with them, right? You can't just put it, like you said, it's not deterministic. You can't just put information into a computer and get an answer out. If that were the case, we wouldn't need a court system, right? But what we see happening is with these models, they're just not built for the legal system. So you can't go into court.
and say, I found this on the internet, right? You have to have authoritative content. The cutoff date for GPT-40 was... 2023 i believe right you need to have information that's that's constantly updated um you know your audience probably doesn't know you probably know this because you're a lawyer but
there's the citator, right? You know, which traditionally has said, this is good law or it's not good law, right? It's been overturned. Now it'll tell you if it's the law at all, right? Or if some system just made it up because... you know these uh systems they're probabilistic right they want to um put together an answer that's Probably right. Well, that's not the standard that we have in legal. You can't go in with something that's probably right. And so you have this whole list of issues.
these models don't address. And so what we've tried to do is address those with a courtroom-grade solution. So our system is backed by... 160 billion documents and records, you know, that curated collection that we have, that's our grounding data, right? So you can go into court and not say, I found this on the internet, but you could say you can refer to a specific case, right?
We also have a, we call a citator agent, right? So we'll check that case to make sure that it wasn't fabricated by the system, but, and it is actually still good law. And you can also look at the case law summary. You can, so you know what the case is about. You can look. at the head notes so you can see the particular points of law that were addressed in that case. Again, you can see if it's still a valid case. Privacy is another issue.
Right. You know, there's a special relationship that exists between attorneys and their clients in that attorney client privilege. You know, there's some privacy requirements that you need to have in order to maintain that. If you're using one of these. just consumer-grade models, you don't have that level of privacy and security that you need. Transparency is another issue. So you put this question in, you get an answer back.
Well, based on what? Like what was the logic that the system used? So again, our system, we open up the black box and you can see the logic that's being applied. And we give the attorneys the ability to go in and actually change that, right? If this model is getting something wrong, the attorney has the opportunity to change it so that they get the outcome that they're really driving for. But as you said, it's not...
The law is not deterministic, right? There are lots of different factors that go into this, but you need to have a system that's legally driven, you know, that's purpose built for legal situations in order to, you know. to really operate in a courtroom-type environment. Yeah, there's two things I really want to push on. One, again, I was not a good lawyer. I don't want to ever pretend on this show or to you or to anyone that I was any good at this.
But the thing you learn in law school is a particular way of thinking, which is a pretty rigorous, structured way of approaching a problem and then going to find the relevant cases and precedents and then trying to fashion some solution based on that. That feels like –
Oh, we're just moving words around. But it's actually a way of thinking. Right. And before AI showed up, we're going to use a word processor and we're going to think in a certain way. They were mashed together. And now we're pulling them apart. We're saying that the computer can move the words around and generate you some thinking. So that's one thing I want to push. I'm very curious about that because it feels like the lawyering part of being a lawyer is being subsumed into a system.
And that might change how we lawyer. Very curious about that. The other part is, well, is anyone going to look at the work being done? Because we're already seeing lawyers get sanctioned for filing. briefs with hallucinated case citations in them. There was just a case where I believe a court had to rescind an opinion because it had a hallucinated case citation in it.
This is bad, right? Like this is just straightforwardly a threat to the system and how we might think about lawyers and judges and courts. And it's not clear to me that.
anyone's going to use the tools as rigorously as you want. So on the one hand, there's the, we've made the thinking easier. And on the other hand, it's, oh boy, everyone's going to get really lazy. And they're both kind of in your answer. They're both kind of like, what are we, we're making it easier to look at this stuff. We're making it faster to do the research. And I'm just wondering where you think the thinking comes in.
Yeah, I think that these models, they don't replace lawyers, right? I think they help the lawyer and they augment what the lawyer does. So if you think about an activity that a lawyer might do, like let's say that they were... preparing for a deposition so they need to come up with a list of questions that they're they're going to ask the individual that's being deposed um you know you can take the
facts around that particular case and you can load them into a vault and then you can point the system to that vault and say, based on the facts of this particular matter, develop a list of deposition questions.
And then, you know, that's something that a lawyer would have done on their own, right? In the past, they may have, you know, referred to a list of questions that they had previously or something. Well, actually, can I just grab on that example? Sure. Maybe a lawyer would have done that, but more often a lawyer would have told a...
bunch of junior associates to sit in the basement and do that. And that thing was how those junior associates learned how to do their job. And that's what I mean. Like we're, we're sort of farming out the thinking. And some people might never actually do that thinking. Yeah. And that might change the profession down the line in really substantive ways. Right. Yeah. And it is an apprentice system, right? And so if you start to take some of the.
layers out of the bottom, how does everyone skip the bottom layer and still make it to the second layer with the same level of capabilities and skills? I think that's a real challenge. I think the systems are allowing lawyers to not necessarily have the associate do that work, but now they can say,
generate me 300 questions or 700 questions. You know, it doesn't take that long to go through 700 questions and the models never get tired. So, and what our experience is, is they'll go through that list of questions and they'll say, first question. yep that's a good question i would have thought of that you know so the system made it a little bit faster right but it didn't really help them any uh second question same thing third question same thing fourth question
Doesn't even make any sense. Scratch it off the list, right? Fifth question. Oh, that's interesting. I wouldn't have thought to ask that, but that's really, you know. something that's probably important. So I'm going to add that to my list. So there's an efficiency component to it, but I think there's also a better outcome component to it. In terms of the apprenticeship piece of it,
Yeah, I think people are struggling right now to figure out how that's going to impact the apprenticeship model. And if you don't have people, someone was describing to me that they had worked on a situation where they were looking at. securitized assets and collecting debt, you know, on securitized assets. And when they were associate, they did this project for a company that had states, you know, 50 states worth of coverage. And so they became the expert in the firm.
on asset securitization in all 50 states. And for four or five years, anytime somebody had a question, they came to that individual. It was a great way to make a career. Now the system can do all that information for you. And so his question was like, Like, how is that ever going to happen now in this new world? I think firms are going to struggle with that, but I also think they're going to figure it out. You know, we tend to get...
some of the smartest and brightest people going into the legal profession. And so far, they seem to have figured out every challenge that's faced the industry in the past. I think they'll figure this one out as well. What are some solutions that you've seen as people try to figure this out? Well, I don't know that necessarily folks have come up with a lot of solutions around the apprenticeship model. I think that what we're seeing for sure is people are embracing AI.
You know, it's here. It's in the courtroom. It's in the law firm. You know, two thirds of attorneys are using. AI in their work, according to our surveys. They're already using that. And our survey is probably a little outdated. I'd say the number is probably higher. I don't know about you, but I use AI every day, practically.
is in my personal life and in my work life now. And I think the legal profession is perfectly suited for it. So I think it's only going to expand. When you see the lawyers getting sanctioned and the... courts having to rescind opinions is your solution? Well, you should just use Lexus and that won't happen to you. Or do you think that's the symptom of something else? Cause that everyone's just using AI.
I get it. I think probably the biggest split in our audience right now is the data that says everyone's using the stuff all the time and the hostility our audience expresses to us about the tools, their quality, the fact that a lot of that usage is driven by it.
by the big companies just putting it in front of you whether you want it or not, right? There's something happening there where to justify these enormous investments, the tools are showing up whether the consumers are asking for them or not. And then we're pointing at, well, everyone's using the tools.
And what I hear from our audience is, well, I can't turn off the AI overview. Of course I'm using the tool because it's just in front of me all the time. I can't make Microsoft Office stop telling me that it's going to help me do stuff. It's just in front of me all the time. So for you, when you see...
the errors being made in the legal system today, right? The lawyers getting sanctioned, the lazy use of AI, the lack of apprenticeship, which is going to impact the entire next generation of lawyers and how rigorous they are. How do you make your product address that? Or are you just not thinking about that right now?
No, we're definitely thinking about it, and we've incorporated things into our product. I think it's a small percentage of attorneys. These things always make the headlines whenever one happens, but I think it's a small percentage of attorneys that are caught. in these problems, most of them, you know, it's...
It's never been the standard that you just take something and bring it into court, right? Like you've always had the responsibility as a lawyer to check that material and make sure that it's valid before going into court. And some individuals aren't doing that. We certainly saw that like in the Burke case. um where you know they you know some attorneys submitted a um a document to the court i think it had like eight citations in it and seven of them were um just
completely... But that was inevitable. The day ChatGBT showed up, half of the legal pundits I know were like, this is inevitable. This outcome will happen. And then it... It happened. Like there wasn't even like a stutter step. It just happened immediately. And that's what I'm trying to push on is the solution just –
LexisNexis has a tool that's better and you should pay for it? Or is the solution that as we take the rigor away from the younger associates, the profession is going to have to build some new guardrails? Yeah. Well, like you can never stop an attorney from...
just taking it into court, not doing their, you know, the proper work. Right. I think that's going to continue to happen. I think somebody is going to lose their license over this at some point. Right. And we're seeing the sanctions start to ratchet up. So, you know, it was, you know, a couple of attorneys got. fined $5,000 a piece. And then, you know, some attorneys in federal court down in Alabama got referred to the State Bar Association for disciplinary action, you know.
I think the stakes are increasing and increasing. With our system, what we do is if we have a citation... we will provide a link to that citation so you can click on it and you can see it in our system. And there's no fabricated cases within our system. You know, we have a collection mechanism that ensures that every case in there is a valid case.
It's Shepard Eyes that has head notes and different tools that the lawyers can use. So we're making it really easy, if you use our system, to check to make sure that the citations that you're... you know that you're bringing into court not only are they valid and there's you know there's still good law but they're they're in the right format right like format's important um uh and so you know we we check for all these things and make it really easy for the lawyer to
to do the work that they need to do. They need to make sure that case is on point, that that case is still valid. We have to pause here for a quick break. We'll be right back. Support for Decoder comes from Anthropic, the team behind Claude. The best tech journalism takes a seemingly straightforward announcement and reveals all the strategic layers underneath. That's exactly the kind of thinking Claude excels at. Claude is an AI thinking partner.
that goes beyond easy answers to help you work through the nuanced questions that drive real understanding. Whether you're trying to understand why a platform made a particular policy decision or what a merger really means for the industry, Cloud can help you explore connections you might not have considered.
From analyzing corporate strategy to exploring regulatory implications to just trying to understand how these systems actually work, cloud things through complexity alongside you. It's not about getting quick takes. It's about developing the deeper insights that matter. Try Claude for free at claude.ai slash decoder and see why the world's best problem solvers choose Claude as their thinking partner.
Support for this show comes from LinkedIn. When you're a small business owner, your business is on your mind 24-7. So when you're hiring, you need a partner that works just as hard as you do. That hiring partner is LinkedIn Jobs. When you clock out, LinkedIn clocks in. LinkedIn makes it easy to post your job for free, share it with your network, and get qualified candidates that you can manage all in one place.
LinkedIn's new features can help you write job descriptions and then quickly get your job in front of the right people with deep candidate insights. Either post your job for free or pay to promote. Promoted jobs get three times more qualified applicants. At the end of the day, the most important thing to your small business is the quality of candidates. And with LinkedIn, you can feel confident that you're getting the best. Based on LinkedIn data, 72% of SMBs using LinkedIn
Say that it helps them find high quality candidates. Find out why more than 2.5 million small businesses use LinkedIn for hiring today. Find your next great hire on LinkedIn. Post your job for free at linkedin.com slash partner. That's linkedin.com slash partner to post your job for free. Terms and conditions apply.
Support for this show comes from Adobe, who are introducing the all-new Adobe Acrobat Studio, now with AI-powered PDF spaces. Look, I'm sure when I say PDF, you have a very specific thing in mind. And I'm guessing it's an email attachment. Certainly not a dynamic asset that can help elevate your business. But Adobe Acrobat is changing that. It's time to do more with PDFs than you ever thought possible. Need AI to turn 100 pages of market research into five insights with a click?
Do that with Acrobat. Need templates for a sales proposal that'll close that deal? Do that with Acrobat. Need an AI specialist to tailor the tone of your market report to sound real smart in real time? Do that with the all-new Adobe Acrobat Studio. It's time to reimagine and rethink what a PDF can actually do. Learn more at adobe.com slash do that with Acrobat. That's adobe.com slash do that with Acrobat.
Welcome back. Right before the break, I was talking to LexisNexis CEO Sean Fitzpatrick about how lawyers really shouldn't bring the totally fabricated research they do on ChatGBT into court. And that led to the obvious next question. What other parts of the process does Sean see AI and LexisNexis taking over? One of many reasons I was a horrible lawyer was...
That moment when you get your first law firm job and you realize, oh, my boss just has a library of their favorite motions on file. And they're going to just pull. They're just going to pull from the card catalog, and they're going to change some names and dates, and they're going to file this motion. And the judge will recognize the motion and the attorney.
This is all just like a weird formality to get to the next stage of the process, right? And maybe we'll never get to the substantive part of the case because we're just going to settle it. But we need to file this motion we had in the library. This really like truly was demoralizing. I was like, I'm just doing paperwork. There's nothing about this.
That is real, like in a way, I'm probably describing what every first year associate goes through until the check hits. And it just didn't work for me. How close are you to having the Lexus? AI product, just do that thing. Just recognize the moment and say, we have the banked motion and we're just going to file it to a system.
But we're connected, you know, we can connect into a DMS, a document management system that has an attorney's prior motions. We have our vault capability so they can load their motions up so they can still. use the motions that they've already developed, right? And that's a perfectly fine way to do things.
Well, I'm saying from scratch. But yeah, we have the ability to do it from scratch too. But a lot of trainers don't want to do it from scratch because they've reviewed every single word in that motion and they know that it's good. if they do it from scratch, then they have to review every single word, right? But if they want to do it from scratch, we can do that for them today. And we can use their prior work product if they want to use that as the...
grounding content to create a new motion, or we can use our authoritative material. They can choose what the source is, what the grounding content is. I guess I'm asking what's the level of automation there, right? So you're an attorney. You've got a document management system. You've got a new client. You need to file some standardized motion that you always file for whatever thing you need to do, a continuance. At what point does Lexus say?
I'm watching this case. I'm going to file this for you. I'm just going to hit the buttons for you. Don't worry about it. In a way that a great legal assistant might do that. We're always going to give the attorney... the opportunity like we don't want to just be doing things on their uh behalf uh
in an unsupervised way right so we're going to give them the opportunity we you know we could get to the point where we say hey look uh it looks like you need a continuance uh here's a draft of a continuance push here and you know it will automatically file it we're not to that point today but if you to continuance, we can draft it for you, right? And if you think about, like our vision is that every attorney is going to have their own personalized AI assistant and it's going to understand.
their practice area. It's going to understand their jurisdiction. It's going to have access to their prior work product. It's going to have access to, and systems are only as good as the content behind them, right? So it's going to have access to our 160 billion documents. and records. And it's going to be able to automate tasks that they do today, right? And if you think about all the different types of attorneys and all the different tasks that they perform...
You know, there's probably eight to 10,000 tasks that. could be automated right and so we're we're working with our customers to understand you know what are the most important tasks and we're working with them to to automate those tasks today so we have you know the largest and most robust backlog of projects that we've ever had in the history of our company because there's so many of these things that still are to be automated, but we're working.
We're working with our customers to do that. If they tell us that, hey, what we really want is for you to automatically file this or for you to provide me with a... an alert that says, hey, this deadline is coming up and you need to file this. Here's a draft. Do you want to file it? If they ask us for that, I'm sure we can develop it. We're not at that point today.
But we are in the drafting phase. That vision, that's not like a five-year vision or a three-year vision. That's available today. That's Protege, right? That's what Protege does today. But there are tasks that it can do. But we haven't finished that massive backlog yet. Yeah, I mean, if you look at the sweep of other COs who've been on Decoder, they're going to tell you, you just integrate our computer vision system, and we'll use ECF for you to file this motion.
They'll all be very happy to sell you that product, I'm sure. But the reason I'm asking it this way is when I get the consumer AI CEOs, they love to tell me that they're going to write my emails for me with AI. Yeah. And then the next sentence they say is, and then we'll – sort your inbox with AI. And at some point, the robots are just writing emails to each other, and I'm reading summaries, and something very important has been lost in that chain. Absolutely.
One of the funniest outcomes of AI is something my iPhone is just summarizing emails and generating emails for other iPhones to summarize, and I have no idea what's going on. That's bad in the legal context, right? We're automating the... the generation of the documents to make the case for our clients. And on the other side, the judges and clerks might be using these same tools to ingest them, summarize, understand the arguments, and write the opinions that are the outcomes.
So where do you see – like culturally, I think it's important for you to have a point of view on where that should stop. Because otherwise, we are just going to have a fully automated justice system of LLMs talking to each other, maybe with some guardrails that other people don't have. But we've taken an enormous amount of humans out of the loop. Yeah, no, I think you have to have the human in the loop.
that it's an important part of the process. If someone says, hey, can you meet at nine o'clock? uh and your system opens up your calendar and says you're available to meet and you've got that person on your high priority list and it sets up the meeting i mean i can see bots going back and forth to do those kind of things when you're talking about like substantive legal matters
the stakes are too high. You're talking about a disabled veteran getting their benefits or not getting their benefits. You're talking about a victim of a natural disaster. getting insurance proceeds or not getting insurance proceeds. You're talking about a single mother getting welfare benefits or not getting welfare benefits. These are all legal matters.
They really have a huge impact on people's lives. The stakes, I think, are way too high for bots to be kind of going back and forth and sharing information. Do you think that clerks and judges should be using AI the same way the lawyers should be? Because that's where I would draw the line. I think that...
Clerks should be made to read everything as humans and interpret everything as humans. The judges should be made to write everything as humans. But it doesn't seem like that line has been formalized anymore. I don't think a judge should write every line. I think that they could use AI. It's great when you put concepts in, being able to put the words around that concept.
structure them in an orderly way. So I think that there is a component of the work that could be done by AI, but I don't think it should be. a bot talking to a bot. I don't think it should be fully outsourced to AI. I think that you've got a responsibility, you know, as a judge, as a law clerk, as a lawyer, to review that document and make sure this is actually saying what you intended to say.
And I think most attorneys are using it that way, right? It will create a great draft, maybe an 80% draft, right? Which allows you to do 20% of the work, but that 20% of the work is like the deep. analytical thought work, the things you actually went to law school to do, as opposed to kind of what you were describing earlier, right? And I think it's going to allow lawyers to do more of that type of work. I'm curious to see how different jurisdictions and circuits approach the question of...
What should the judges be doing and what should the clerks be doing? Because I sense that that pressure is going to express itself in different ways across the field. I think it is. I'm not 100% sure. Yeah. I mean, judges are becoming forensic auditors, right? And they're reviewing this information, looking for fake cases, right? We don't want them doing that. That should not be their job, right? I think things do need to change in some of these areas.
Using AI to catch AI is another theme that comes up on Decoder all the time. I will say it is my first interview back after 12 weeks. I've utterly forgotten to ask you the Decoder questions. So let me do that. And then I want to zoom out a little bit farther. These are my own questions. You can tell I'm a little rusty. Lexis, Nexus, I'm looking at the leadership structure. Yeah.
It's very complicated, right? There's a CEO who's not you, Mike Walsh, but then you're the CEO of the US and UK. There's a bunch of other VPs everywhere. You've got a parent company called Relics. Explain how LexisNexis is structured and how you – part fits into it. So Relics is the parent company and it's a publicly traded company. It has four divisions, legal and professional.
is one of those divisions. And the CEO of Legal and Professional is Mike Walsh. And I report to Mike. And my responsibility is I'm the CEO of our North America, UK and Ireland businesses. And so, you know, the way that we're organized, it's a matrix, right? So we go to market based on customer segments.
a large law business, a small law business, a corporate legal business, a federal government business, a state and local government business, a Canadian business, a UK business. And then we have functional groups that support that. So we have product management.
And then they're responsible for our product development roadmap and the product strategy. And then we have an engineering team and they take the direction from product management, but they actually build the products. And then we have functional groups that support that. finance, HR, legal, global operations that does things like collects content for us.
From the inside out, once you get used to it, it's not that complicated of a structure, and it's really well integrated and seamlessly integrated together, which allows us to operate really... We can get things done quickly, and I think in an efficient way. And I would say it's all customer. The whole process is customer-driven. So I'm really interested in...
The structure in particular, the fact that you have the UK, Ireland and North America. Yeah. You know, I'm fascinated by corporate structures. And one of the things that strikes me about this is. You are not in control of the taxonomy of your product, right? The governments of those countries are in control of the taxonomy of their legal systems. The English legal system and the American legal system have commonalities with wildly different structures.
The Canadian legal system, the United States legal system, wildly different structures. Canada actually has more in common with the UK given their shared history. How do you think about that? Are those different teams? Do they have different database structures? How does all that work? Yeah, we do have different teams and we do have different database structures, but we're actually trying to consolidate to the extent we can because, you know.
To the extent that we have things that are similar, we shouldn't have them marked up in different ways in different databases. Getting them marked up in a consistent way will allow us to... what we call extreme reuse, but to basically use that same technology that we developed in multiple jurisdictions with limited changes to that system. And what that allows us to do is really focus on that core system.
and roll it out quickly and so the everyone across the world gets the benefit of all those changes but you know you have you know civil law in some jurisdictions and, you know, common law in others. And the laws are structured in different ways. And so you do have things that make that more challenging.
But that's the general idea behind what we're trying to do there. Can you apply the same sort of AI systems to these different legal systems in the same way, or are you actively localizing them in different ways? I would say that we actively localize them, but we try to minimize the amount of work that we do to localize them because a lot of it can be done in a similar way. There's a lot of concern generally about...
American legal precedents sort of traveling across the ocean, particularly in the UK. You can see like American culture war gets exported a lot that shows up in a lot of different ways. Do you think your tool will make that better or worse? Because if you're not pulling them apart, you're actually trying to minimize the differences, you might see repeat arguments or repeat structures just based on the way the AI works.
Yeah, I mean, each one is based on the content of the individual jurisdiction. So we don't mix the content, but we do try to utilize the same technology. So, for example, search relevance technology to find the most... you know the case that's uh most closely associated with the matter that someone is working on uh you know we can take that we can build it like for the us market for example or the uk market and then we can move it to another market and it will work pretty good
It will work pretty well. And then we need to do some modifications to make it work really well for that particular jurisdiction. But we get 80% of the DNA transferred over in that core model. I was talking to Mike Krieger as a chief product officer of Anthropic and just a totally different conversation on a different thing. But he said this thing to me, which is stuck in my mind. He said, I recognize Claude. I can see Claude's writing. And I'm like.
And he said, that's my boy, which is cute. Does your AI have a personality? Can it recognize its writing in all these different jurisdictions? You know, we use a multi-model approach. I think that it's probably a little less clear like which particular model drove something.
If someone puts in a request, and of course, you know, with agentic AI, things have really changed, right? I think that probably was true maybe, you know, a year and a half ago. But now with agentic AI, when someone puts in a query, like let's say they wanted to draft a... a document maybe a client sending in a request and she's interested in a premises liability issue uh you know around like duty to inform a trespasser on
about a dangerous condition on a piece of land, for example, right? The query will go into an agent, a planning agent, who will then... allocate that query out to other agents. So, you know, it needs to do some deep research. So maybe it uses the O3 model from OpenAI because it's really good at deep research. And, you know, at the end, it needs to draft a document.
So maybe it uses Claude 3 to do that, like the Claude 3 Opus, which is really good at drafting. And so we're at Model Agnostic. We'll use whatever model is best at a particular task. And so the result that you get back. is actually work that's potentially done by multiple different models which i think probably makes it a little bit harder just to see like oh yeah i know that was drafted by um open ai
Is that reflected in your structure? You describe engineering and product and your localization. Yeah. But that piece, right? You've got to build that agentic orchestration layer. You've got to decide what models are best for which purpose. You could design and...
engineering organization around that problem specifically. Is that how you've done it or is that done differently? We have an engineering team that focuses on that around the planner agent and the assignment of the tasks to different agents. Is that where the bulk of your investment is or is it paying the token fees?
I haven't actually broken it out that way, so I couldn't tell you. The token fees are certainly an important part of the investment. The engineering is a huge portion of the investment. the attorneys that we hire to review the output and tell us, you know, is this good or is this not good? That's, that's a massive, massive important. piece of the investment. So it's spread out over many, many different things, but certainly we spend a lot of money on that particular issue.
Tell me about those attorneys. You hire attorneys to basically do doc review of the AI. Are they very senior attorneys? Are they moonlighting from big firms? There's a bunch of junior associates in a basement. It's based on the task, right? So what we try to do is get attorneys that have experience.
in a particular matter. So if we're looking at documents related to an M&A transaction, you know, we want those to be looked at by someone who has some experience in mergers and acquisitions and they can tell us, yeah, that.
That document looks great. Or, you know what? It's missing. It's missing these particular things. And then we can go back and say, why did we miss those particular things? And what changes do we need to make to the way that we're training these models and directing these models to? correct that situation going forward. What's the biggest thing you've learned from that process?
I guess the biggest thing I've learned is how important it is to have attorneys doing that work. I mean, that was, you know, I expected to hire. a lot of technical people and data scientists to do this work. I didn't really expect to hire an army of attorneys, but I think it's kind of one of the secret sauce components of our solution is that...
Our outputs are attorney-reviewed, and so that's how we keep getting the more relevant results. Where were you best at to start with, and where were you worst at to start at practice theory-wise? You know, I guess we weren't really good at anything to begin with, right? And I think we're, you know, we're kind of building things out. Sometimes it's a practice area. Sometimes it's a task.
There's a big focus. If you look at all those different tasks that we were talking about earlier that attorneys do, in many cases, the output of that task is some sort of a document, right? And so we're really focused right now on like, how do we improve our document drafting? Is all this revenue positive yet? Are you making money on all this investment or do you see that on the horizon?
I mean, our growth rate has definitely accelerated as a result of this. The main thing that we're focused on is the customer outcome. And so what we're seeing is the... that the customers are getting happier and happier and happier with the solution. And so I would say that it's been very successful in that regard. And it's the fastest growing product that we've ever had. Growing fast, but losing money.
With every query. Yeah, we're not there. We're not losing money with every query. Okay. Are you breaking even or are you making money? Yeah, I mean, our profit is growing. Specifically on AI tools or overall? Yeah. Yeah, I mean, most of our investments in AI tools. Great. Let me zoom out. Let me take the last bit here and just zoom out even more broadly. And I mentioned that I would bring up precedent again in this conversation.
I think if you're paying attention to the legal system in America right now, you know that it's in a state of pretty much pure upheaval, right? You've got district court judges calling out the Supreme Court. which is not a thing that usually happens. You have a Supreme Court that is overturning precedents in a way that – I feel like I learned nothing in law school. Chevron deference is out the door. Humphrey's executor, the law that keeps –
The president for running FTC commissioners, I'm guessing, is out the door. Roe v. Wade was out the door. Just these foundational precedents were out the door. A lot of that is based on what conservative judges would call originalism. I have a lot of feelings about originalism. But a big trend inside of originalism is using AI or what they call corpus linguistics. to determine what people meant in the past and then you take the ai and you say well it did the job for me this is the answer
Are you worried that your tools will be used for that kind of effort? Because it really puts a lot of pressure on the AI tool to understand a lot of things. I'm not that worried. I don't think the Supreme Court is... Is asking LexisNexis what we think they should do and then issuing opinions. But certainly courts up and down the chain are.
Yeah, they're asking legal questions, they're getting answers back, and then they're interpreting those answers. I think we're providing them with the raw content that they need to make the determinations, but we're... We're not practicing law. We're not making those decisions for them. We have to take another short break. We'll be back in just a minute.
Support for the show comes from Crucible Moments, a podcast from Sequoia Capital. We've all had pivotal decision points in our lives that, whether we know it or not at the time, changed everything. This is especially true in business.
Like, did you know that autonomous drone delivery company, Zipline, originally produced a robotic toy? Or that Bolt went from an Estonian transportation company to one of the largest rideshare and food delivery platforms in the world? That's what Crucible Moments is all about.
Deep diving into make or break moments that set the course for some of the most important tech companies of our time. With interviews from some of the key players that made these companies a success. Hosted by Sequoia Capital's managing partner, Rule Off Botha. Crucible Moments is back for a new season with stories of companies as they navigated the most consequential crossroads in their journeys. Hear conversations with leaders at Zipline, Stripe, Palo Alto Networks, Klarna, Supercell,
and more. Subscribe to Season 3 of Crucible Moments and catch up on Seasons 1 and 2 at cruciblemoments.com on YouTube or wherever you get your podcasts. Listen to Crucible Moments today. Support for this show comes from agency. These days, when we talk about AI, we're not talking about one isolated agent or system working alone. We're talking about multiple AI agents working at once without human intervention. Agency.
that's A-G-N-T-C-Y, is working to ensure AI agents aren't siloed off from one another and instead are able to securely discover, connect, and work across any framework. With Agency, your organization gains open, standardized tools and seamless integration. That includes robust identity management to help you identify, authenticate and interact across any platform.
Agency is leading the way in established trusted identity and access management and empowers you to employ multi-agent systems with confidence. It's already working with industry leaders like Cisco, Dell Technologies, Google Cloud, Oracle, Red Hat, and more to set the standard for secure, scalable AI infrastructure. Now, Agency is an open-source Linux foundation project. If your enterprise is ready for the future of agentic AI, visit agency.org to explore use cases now.
That's A-G-N-T-C-Y dot O-R-G. Support for this show comes from AWS Generative AI Accelerator Program. My name is Tom Elias. I'm one of the co-founders at Bedrock Robotics. Bedrock Robotics is creating AI for the built world. We are bringing advanced autonomy to heavy equipment to tackle America's construction crisis.
There's a tremendous demand for progress in America through civil projects, yet half a million jobs in construction remain unfilled. We were part of the 2024 AWS Gen AI Accelerator program. As soon as we saw it, we knew that we had to apply. The AWS Gen AI Accelerator program supports startups that are building ambitious companies using Gen AI and physical AI. The program provides infrastructure support that matches an ambitious scale of growth for companies like Bedrock Robotics.
Now, after the accelerator, about a year later, we announced that we raised about $80 million in funding. We are scaling our autonomy to multiple sites. We're making deep investments in technology and partners. We have a lot more clarity. on what autonomy we need to build and what systems and techniques and partners we need to make it happen. It's the folks that we have working all together inside Bedrock Robotics, but it's also our partners like Amazon.
really all trying to work together to figure out what is physical AI and how do we affect the world in a positive way. To learn more about how AWS supports startups, visit startups.aws. Welcome back. I'm talking with LexisNexis CEO Sean Fitzpatrick about the wrinkle that AI is adding to originalism. And I kind of ended up springing a product demo on Sean to do it.
I'm going to spring this on you, but here it is. Here's John Bush is a Trump appointed judge. He cited the emergence of corpus linguistics in the legal field. And he said. To do originalism, I must undertake the highly laborious and time-consuming process of sifting through all this. But what if AI were employed to do all the review of the hits and compile statistics on word meaning and usage? If the AI could be trusted, that would make the job much easier.
That is him saying I can outsource originalist thinking to an AI. And this is a trend. I see this particularly with the originalist judges that the job they think they're meant to do is determine what a word meant in the past. And AI is great at being like, statistically, this is what that word meant in the past. And we're going to outsource some legal reasoning to them. And this is, I think, very odd. Like my thoughts about originalism aside, my thoughts about stare decisis in America in 2020.
five aside, saying I will use an AI to reach into the past and determine this meeting seems very odd. And I'm just wondering how you feel about your tool being used in that way. I definitely understand your point there. You know, I think about like the analogy of a brick, right? You can use a brick to build a hospital, take care of sick children, or you could take a brick and you could throw it through a window, right? One use is really great.
and one use is pretty negative. But in either case, it's a brick. I think about our tool as being not good or bad. I think it could be used for... You know, I think it could be used for, you know, any type of activity that attorneys, I wouldn't want to say originalism is a bad thing, right? I think it could be used for many different things. I think it could be used for originalism. I think it could be helpful for those that.
that wanted to take that path and find a new way of looking at something we have all the data they can search it they can use the tool to find things that they it wasn't possible to find in the past. So I could see them using our tool in that way. And I guess it's up to the attorneys to determine how they're going to use the product.
We're not building it because we're trying to change the law. We're building it because we're trying to help attorneys do the tasks that they want to do. Right. But I look at the sweep of the tech industry, not the legal industry, but the tech industry over the past 15, 20 years.
Boy, have I heard that answer many, many, many times. The social media companies all said, well, you know, you can use it for good or evil. We're neutral platforms. And it turns out maybe they should have thought of some of these harms earlier. The AI companies today. Who knows if training is copyright? We know the answer. And you can't actually just opt out of copyright law. And now we're going to do the lawsuits and we'll see how it happens, right? Who knows if...
doing TikTok for deep fakes, like OpenAI is doing this for us. Actually, we know, right? Like we know the answers and you should have some guardrails. So I'm posing you the same question, right? We see a judiciary, particularly originalist judiciary. hellbent on using originalism.
To change precedent at alarming rates, I would say for me alarming rates only because I paid for a law degree and now I think it's useless. That's why it's alarming to me. It's alarming because a lot of people have their rights taken away as well. Like every day this is happening.
And one of the tools they're going to use is deference to an AI decision engine. They're going to say, we asked the AI, what did all people mean when the 14th Amendment was drafted? And this will be how we get to a birthright citizen case.
And that – I'm just connecting this to the conversation we had at the beginning. We're going to give our reasoning to a computer in a way that the computer is not necessarily accountable for. And we're going to trust the computer. And that method of thinking and that rigor might go away.
And so I've heard this answer, the tool is neutral, and it's how I'll use it from tech companies for years. And I see the outcomes. I'm asking you, you're building a tech product for lawyers. They're already using it in this specific way. And I'm wondering if you've thought about the guardrails.
We have responsible AI principles that we operate under. And so that includes a number of things. One is we always try to consider the real world implications of any product that we develop. We want to make sure that there's...
transparency in terms of how our product works. So we open up the black box so people can see the logic that we're using and they can actually go and change it too if they if they want to right so we want to make sure that there's transparency and there's control uh human oversight right so we always incorporate human oversight uh into the development of our products
Privacy and security is another one of our core tenets of responsible AI creation. And then the prevention of the introduction of bias is another thing that's incorporated in. So those are the Relics principles. uh for ai development and uh you know we we adhere to those so you know we want to create products that do good things for the world if you ask lexus ai
If the 14th Amendment guarantees birthright citizenship to all people born in the United States, will it make the argument that it doesn't? I've never asked that question. I can't tell you. Do you have a phone on you? There's a mobile app. Oh, I could pop up here and ask it, I suppose. So what was the... Hold on a second. Let me pop into Protégé here. All right. Does the... 14th Amendment, guarantee, birthright, citizenship, or are there exceptions? Let's see.
Yeah, I'm very curious to see what it says, because up until recently, there's only been one answer to that question. Then now the Trump administration is saying there's no, actually, that's not what subject to the jurisdiction thereof means. And they will in order.
to win at this Supreme Court have to construct an originalist argument to that question. And I am confident that the way they're going to do that is they're going to feed a bunch of data into an AI model and say this is what was actually meant. at the time of the drafting of the 14th Amendment. That's a thing that AI will be used for that is very destructive. I'm not an attorney, by the way, so I'm just going to read the answer here.
Fourteenth Amendment of the United States Constitution guarantees birthright citizenship to all persons born or naturalized in the United States and subject to its jurisdiction. The phrase subject...
to its jurisdiction has been interpreted to include nearly all individuals born on U.S. soil with a few narrow exceptions. These exceptions include foreign diplomats, children of foreign diplomats, children of enemy forces in hostile occupation, children born in foreign public ships, and historically children of members of Native American tribes.
who uh owed allegiance to their tribe rather than the united states yeah you should send that to john roberts right now okay yeah can protege do that because that's the answer But the question is – well, you said that on this podcast. Are a bunch of conservative influencers –
going to say protege is woke now. This is the culture war that you're in. It does recognize that recent cases have affirmed this interpretation, rejecting attempts to expand the exceptions of birthright citizenship. So it does also recognize that there are... have been efforts to interpret it in a different way. And the answer goes on.
Quite a bit, so. Well, the reason I ask that question very specifically, that's the next precedent that's up for grabs. It's a big one. It's foundational, right? That's reconstruction is up for grabs in a very real way in that case. Do you think as the toolmaker you have a responsibility? Because that's really the question for so many AI companies. You're the toolmaker. Do you have a responsibility to not deepfake real people? Do you have a responsibility to not...
show people fake ideas, right? I think you're very clear on that. You have a responsibility to not hallucinate. But here you have a question. We don't want to introduce or perpetuate any bias that might exist either. And so to do that, like we, you know, rely back on the law. Right. As opposed to, you know, like a consumer grade model that would just, you know, probably uses news articles. Right. Which might have a very different.
interpretation of things depending on which news articles uh and they're you know they're much more likely to be biased introduced into news articles than into black letter law for example yeah the reason i'm curious about that is you know there's a spectrum i think Telling people what they can do with Microsoft Word running locally on their laptop, I don't think there's any place for that. Fine. Do what you got to do.
Telling people what they can do with a consumer-grade AI tool built into Facebook. I think Facebook has a lot of responsibility there, right? Especially because the opportunity for them to distribute that content far and wide is at their fingertips.
That's a big spectrum of opportunity. And here in the middle with the AI companies, it's do you have the obligation to say, well, if you want to go make the argument that birthright citizenship doesn't protect everyone in the United States, you got to do it on your own. Our robot's not going to help you. Do you feel any of that pressure? Yeah, I mean, we try not to get into politics or any of that debate. I don't think that's politics. I do not think that's politics. Yeah.
You know, we're trying to develop a system that does not have bias introduced into it that will give you the facts and attorneys can do the work that attorneys do and make those important decisions. Our job is to give them the information that they need, the precedent, the facts, all the information that they need to then develop their argument, whatever that might be. But we really don't get into any of them.
The politics of, you know, birthright citizenship, is it guaranteed or is it not guaranteed? Well, at some point you do. I mean, this is, again, to bring us back where we started, I first encountered LexisNexis as a database of cases. In some case notes. There are some law professors who are very proud that their case sends from LexisNexis when I was in law school. Now we're drafting a little bit.
Now we're going to go do the research. Now we have agentic AI that's making the arguments. Maybe one day we will automate all the way to filing. You're taking on more of the burden. You are making the arguments. The company is making the arguments. Where is the line?
Because a lawyer, there are lots of lawyers who wouldn't take that case, who wouldn't make that argument. Is there a line for you? Yeah, I would say our approach is to... arm the attorneys with the best possible information help them with the drafting of those documents and you know we're really just being led by our customers and what they're asking us to do. We certainly are not trying to
interpret the law. We're not trying to shape the legal system. We're not lawyers, right? We're not trying to do the work of lawyers. We're trying to help lawyers do the work that they do in a more efficient way and hopefully help them drive better outcomes. But it's always there. prerogative to interpret the information that we provide, which is what lawyers do, right? That's what they're great at.
The reason we have cases is because there are people on both sides of them, right? The two individuals are going to make opposite arguments. We want to support both of those attorneys as best that we can. Yeah, I get it when you're the database of cases. I get it when you're the word processor. I get it when you're the specialized word processor or the case management platform. The thing that I'm pushing on sort of repeatedly here is...
When the AI system is actually doing the work, do you feel like you have different guardrails? The work that we're doing, I think our responsibility is to develop AI in a responsible way.
Give me an example of something you wouldn't let your AI do. An argument that you wouldn't let your AI make or a motion that you wouldn't let your AI draft. I don't know that we would want to necessarily restrict the... the ai in that way um you know i think that the information in the system is you know we're referring back to the information
that we have, like our authoritative collection of documents and materials, which helps lawyers understand what the facts are, what the precedent is, what the background is. And then they can do... the real deep legal work and make those trade-off decisions, the judgment decisions, those important things that, again, that attorneys went to law school to do. I think these questions are going to come up over and over again. We should have you back to answer them. But as you learn more.
But as you look out over the horizon, the next two, three years, what's the next set of capabilities you see for LexisNexis? And what do you think the pressures that might change how you make some of those decisions will be? Yeah, I think the main thing that's going to change the path going forward, it's hard to say exactly what it's going to look like, because if I look back two years ago, I would have never guessed we'd be doing what we're doing today because the technology didn't exist.
it was too expensive to implement, that's totally changed over the last two years. And I think over the next two years, it's going to change again. And so it's really hard to say where we're going to go. Our vision remains the same, which is that we want to help attorneys. We want to provide them with a personalized AI.
powered product that understands their practice area, it understands their jurisdiction, it has access to our authoritative set of materials it has access to their prior work product it understands their preferences it understands their style understands what they're trying to do and it can automate tasks that they do
today manually, and we'll continue to take that latest available technology, show it to our customers and have them help us understand how we can use that technology to serve them in more modern and relevant ways. And that's really the... what's going to guide our roadmap in the future. Well, Sean, this was great. Let me know when you develop a system that can actually navigate an electronic case filing website.
because some of the smartest people I know can't do that. But this was great. We got to have you back soon. Thank you so much. Thank you so much. Really enjoyed our time today. Take care.
I'd like to thank Sean Fitzpatrick for taking the time to join me on Decoder, and thank you for listening. I hope you enjoyed it. If you'd like to let us know what you thought about this episode or really anything else, drop us a line. You can email us at decoderatheverge.com. We really do read all the emails. Or you can hit me up directly on Threads or Blue Sky.
We also now have a TikTok, an Instagram, and a YouTube. They're all at DecoderPod, and they're a lot of fun. If you like Decoder, please share it with your friends and subscribe over to your podcast. Decoder is a production of The Verge and part of the Box Media Podcast Network. The show is produced by Kate Cox and Nick Stat and edited by Ursa Wright. Our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. We'll see you next time.
Every minute your finance team spends wrestling with data is a minute lost. Insight Software's AI-powered insights instantly move you from complexity to clarity. Automated analysis, real-time reporting, strategic recommendations, all at your fingertips. Transform how your finance team works and watch your business grow. Stop wasting time. Start making smarter decisions. Learn more at insightsoftware.com slash AI.