LexisNexis CEO says the AI law era is already here

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28:04Welcome 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?

28:25One 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. And 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.

28:57This 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? Well, we're connected. 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.

29:37so they can still use the motions that they've already developed, right? And that's a perfectly fine way to do things because... 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. And 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.

30:17Or 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. continuance. At what point does Lexis 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.

30:51We don't want to just be doing things on their behalf in an unsupervised way, right? So we're going to give them the opportunity. We could get to the point where we say, hey, look, it looks like you need a continuance, here's a draft of a continuance push here and it will automatically file it. We're not to that point today, but if you need a 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.

31:25It'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 8 ,000 to 10 ,000 tasks that could be automated, right? And so we're working with our customers to understand, you know, what are the most important tasks? And we're working with them to automate those tasks today.

32:04So 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, you know, 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 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.

32:43That 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 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 CEOs 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.

33:20Yeah. 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. And this is 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 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.

34:03Where 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. You know, if someone says, hey, can you meet at nine o 'clock 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.

34:35I 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 know, you're talking about, you know, a disabled veteran, you know, getting their benefits or not getting their benefits. You know, you're talking about a victim of a natural disaster, you know, getting insurance proceeds or not getting insurance proceeds. You're talking about a single mother, you know, being, you know, getting welfare benefits or not getting welfare benefits. These are all legal matters and they're, they really have a huge impact on people's lives.

35:08The 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, is 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 that they should, like, I don't think a judge should write every line. I think that they could use AI. You know, it's great when you put concepts in, being able to put the words around that concept and, you know, structure them in an orderly way.

35:48So 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 percent draft. Right. Which allows you to do 20 percent of the work. But that 20 percent 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.

36:30Right. 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. 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. I think things do need to change in some of these areas.

37:03Using 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. 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 Relix. Explain how LexisNescus is structured and how your part fits into it.

37:36So Relix 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. So we have 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.

38:18And 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. So it's 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 quickly.

39:03You know, we can get things done quickly and I think in an efficient way. And I would say it's all customer. You know, 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. Trevor Burrus 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 but wildly different structures.

39:39Right? The Canadian legal system and 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 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 develop in multiple jurisdictions with limited changes to that system.

40:24And what that allows us to do is really focus on that core system and roll it out quickly. And so everyone across the world gets the benefit of all those changes. But you have civil law in some jurisdictions and 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. Do you think you 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.

41:14There'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.

41:49So, for example, search relevance technology to find the case that's most closely associated with the matter that someone is working on. We can take that, we can build it for the U.S. market, for example, or the U.K. 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.

42:24But 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. And so I think that it's probably a little less clear, like, which particular model drove something. So, you know, like, 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.

43:02But now with Agentec AI, when someone puts in a query, like let's say they wanted to draft a document, maybe a client sending a request and she's interested in a premises liability issue, you know, around duty to inform a trespasser about a dangerous condition on a piece of land, for example, right?

43:28the 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 Cloud 3 to do that, like the Cloud 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 OpenAI.

44:20Is 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 an 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? You know, I haven't actually broken it out that way, so I couldn't tell you.

44:56You know, the token fees are certainly an important part of the investment. The engineering is a huge portion of the investment.

45:05The attorneys that we hire to review the output and tell us, you know, is this good or is this not good, that's a massive, massive 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, we want those to be looked at by someone who has some experience in mergers and acquisitions.

45:48And they can tell us, yeah, 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.

46:25I 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 area-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. You know, I think that there's a big focus. You know, 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?

47:13And 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 know our growth rate is definitely accelerated as a result of this. You know, the main thing that we're focused on is the customer outcome. And so what we're seeing is 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. It's the fastest growing product that we've ever had. Growing fast but losing money with every query.

47:49Yeah, 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.

48:25You 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 from running FTC commissioners, I'm guessing is out the door. Roe v. Wade was out the door. Like just these foundational precedents are 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.

49:03And 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 asking LexisNexis what we think they should do. And then - 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.

49:43But 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.

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52:46Welcome 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.

53:20Right? 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 starry decisis in America in 2025 aside, saying I will use an AI to reach into the past and determine this meaning seems very odd.

53:57And 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 to take care of sick children, or you can take a brick and you can 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. Like, you know, I think about our tool as being like not good or bad. I think it could be used for good. You know, I think it could be used for, you know, any type of activity that attorneys want.

54:39I 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 want 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 you know it wasn't possible to find in the past so i could see them you know using our tool in that way and i guess it's you know it's up to the attorneys to determine how they're going to to use the product we're not building it because we're trying to change the law right we're building because we're trying to help attorneys do the tasks that they want to do.

55:22Right. 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? Like we know the answer. Yeah. 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.

55:56Actually, we know. We know the answers and you should have some guardrails. So I'm posing you the same question. 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?

56:33And 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, you know, 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.

57:04We 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 want to, right? So we want to make sure that there's transparency and there's control. Human oversight, right? So we always incorporate human oversight into the development of our products. Privacy and security is another one of our core tenets of responsible AI creation.

57:48And then the prevention of the introduction of bias is another thing that's incorporated in. So those are the relics principles for AI development. And, you know, 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 your phone on you? There's a mobile app. I could pop up here and ask it, I suppose. So what was the... Hold on a second.

58:23Let me pop into Projet 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 nope actually that's not what subject to the jurisdiction thereof means and they will in order to win at this report 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.

59:14That'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. 14th 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 owed allegiance to their tribe rather than the United States.

59:57Yeah, you should send that to John Roberts right now. Okay, yeah. Can Protégé 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 Protégé is woke now? Yeah, I mean, it does recognize— 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 have been efforts to interpret it in a different way. And the answer goes on quite a bit. Well, the reason I ask that question very specifically, that's the next precedent that's up for grabs.

1:00:36It'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 deep fake real people? Do you have a responsibility to not show people fake ideas? I think you're very clear on that. You have a responsibility to not hallucinate. But here you have a – I'm wondering if the question is – We don't want to introduce or perpetuate any bias that might exist either. And so to do that, we rely back on the law, right, as opposed to a consumer-grade model that would just probably use as news articles, right, which might have a very different interpretation of things depending on which news articles.

1:01:26And 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 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.

1:02:01And 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. Like 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 the – I don't think that's politics. I want to be 100%. I do not think that's politics. Yeah. 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.

1:02:39Our job is to give them the information that they need, the precedents, 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 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. And some case notes, right? There are some law professors who are very proud that their case notes were in LexisNexis when I was in law school. Now we're drafting a little bit.

1:03:18Now we're going to go do the research. Now we have a Gentic 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 we're really just being led by our customers and what they're asking us to do.

1:03:54We 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 their prerogative to interpret the information that we provide, which is what lawyers do, right? That's what they're great at. And the reason we have cases is because there are people on both sides of them, right? And they're making the two individuals are going to make opposite arguments. We want to support both of those attorneys as best that we can.

1:04:33Yeah, 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. And so I think, you know, we – So 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.

1:05:08I don't know that we would want to necessarily restrict the AI in that way. I think that the information in the system is, 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, they're 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?

1:06:03Yeah, 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 or 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. So 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.

1:06:42It 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. It 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 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.

1:07:20Because some of the smartest people I know can't do that. But this was great. We've got to have you back soon. Thank you so much. Thank you so much. Really enjoyed our time today. Take care.

1:07:31I'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 your podcast. Decoder is a production of The Verge and part of the Box Media Podcast Network.

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