In this episode of The Geek in Review, we welcome Greg Dickason, Chief Technology Officer at LexisNexis, for a wide-ranging conversation on agentic legal AI, Lexis+ AI Protégé, and the movement from AI chat toward AI work. Dickason frames the shift through a simple contrast: earlier legal AI answered questions, while agentic workflows take on multi-step assignments, conduct research, create drafts, verify citations, and move legal professionals closer to finished work product. For law firms and legal departments trying to understand where AI goes next, this episode places agentic AI squarely inside legal workflow, legal research, drafting, and risk management.
A major theme of the conversation is trust. Dickason explains how Shepard’s Verify extends the familiar Shepard’s signal beyond traditional research screens and into uploaded work product. Rather than asking lawyers to rely on AI-generated text without a verification layer, LexisNexis is building citation checking into the workflow, giving lawyers a path to confirm whether cited authority exists, whether authority is still good law, and how later courts treated the cited case. For lawyers worried about hallucinated citations, AI-generated briefs, and unreliable authority, this verification layer becomes part of the product architecture, rather than an afterthought.
The discussion also explores the relationship between LexisNexis and Anthropic, along with the rise of legal AI skills. Dickason describes a market where model choice, orchestration, and legal skills increasingly matter as separate layers. Anthropic, OpenAI, Google, and other model providers offer impressive foundations, yet legal work needs more than general-purpose intelligence. Large law workflows require legal content, expert reasoning, matter-specific playbooks, and firm-defined processes. Dickason notes the ability to upload firm playbooks as skills, giving firms a path to bring their own way of working into Protégé.
Security receives equal billing with accuracy. As firms place client documents into AI vaults and connect work product to legal AI platforms, Dickason explains bring your own key, or BYOK, through a practical office-and-locked-cabinet analogy. The point is control: client content sits encrypted, access depends on the user’s key, and access stops when the key is withdrawn. He also discusses legal chunking, indexing, vector stores, retrieval-augmented generation, and knowledge graphs as part of building AI systems suited for legal documents, rather than generic file handling.
The episode closes with a broader view of legal AI’s impact on junior associates, legal training, and access to law. Dickason does not predict the end of junior lawyers. Instead, he sees AI helping junior lawyers become senior faster through mock trials, mock depositions, and richer training environments. He also warns of risks from agent volume, security vulnerabilities, and legal systems struggling to keep pace with AI-enabled industries. The message is pragmatic and optimistic: agentic legal AI will change legal work, yet the winners will be those who combine trusted content, secure systems, verification, workflow design, and human judgment.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Transcript:
Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review, and I have Sam Moore here from Legal Technology Hub, who’s going to tell us a little bit about analysis of token usage and model selection.
Sam Moore (00:11)
That’s right. Thank you, Marlene. Well, it is tokens, tokens everywhere. I think spurred on by the launch of Claude for Legal, but certainly going back further than that. There’s an issue in the legal industry today around token usage in GenAI tools. And in the Legal Technology Hub advisory team, we’ve had several conversations in the last week or two about this, both in terms of frontier models, but also in terms of the legal AI platforms.
And the topics we’re discussing with clients right now tend to fall into three interconnected topics. First is model selection, because a lot of these products give the users a choice of which model they want to use for a given prompt. But most users of these products really have no idea what the difference is. I’ve seen law firm clients whose users just pick the most sophisticated model for everything, toggle on every optional feature available, and then are confused as to why responses are taking a long time and why they’re hitting token limits very, very quickly.
The second is around model context windows. I’ve had several conversations lately about what a context window even is and how it creates drift when it gets crowded in a chat’s context window, and why that really matters for legal use cases, which often involve uploading quite large documents, which take up a lot of space in those context windows.
And finally, efficient token usage. Law firms and law departments, I think, are generally not accustomed to this kind of pay-as-you-go model in technology. Not unless you’re like me and you recall when the big legal research platforms were on a pay-per-search basis. So now those users are running into high-cost overages on the frontier models in particular, and they’re realizing that low sticker price per month is not their reality, not when their users don’t know how to use those tools efficiently and how to control cost.
So, as well as delivering advisory work on these topics on a one-to-one basis, we’re actually working on a series of articles for LTH Premium about these topics, which will then combine into a sort of playbook for our subscribers to keep handy when they’re working with GenAI tools. And we expect to start putting out that content in early June.
And if people want to know more about LTH advisory and what we do, they can always get in touch with us by going to legaltechnologyhub.com or by finding me on LinkedIn.
Marlene Gebauer (02:36)
Thank you, Sam, for keeping us informed about this important issue.
Sam Moore (02:41)
You’re welcome.
Marlene Gebauer (02:49)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.
Greg Lambert (02:55)
And I’m Greg Lambert, and today we are diving into the rapidly evolving world of agentic legal AI.
Marlene Gebauer (03:19)
Greg, welcome to the show. Greg Two, welcome to the show. No, I think you’re Greg. No, actually you’re Greg One, and Greg Lambert will be Greg Two. How’s that?
Greg Dickason (03:21)
Thanks.
Greg Lambert (03:26)
Yes, yes. But you’ll be Greg with the British accent, and I’ll be Greg recovering from a cold. So, Greg, let’s start off. We talk a lot about agentic AI, agentic workflows, and so I want to define that so we know what we’re talking about here.
Greg Dickason (03:30)
There we go.
Marlene Gebauer (03:31)
The smarter one.
Greg Dickason (03:37)
Gives you a very mellow voice, so it’s good.
Greg Lambert (03:53)
So do you mind breaking down…
Greg Dickason (04:06)
Yeah, absolutely. I think I like to differentiate between sort of the first generation of AI and this generation with agents and agentic workflows, if you like. And what we mean by that is the first generation, you would go and ask it a question, it would give you an answer. You would then have to do something with that answer. You’d have to go and plug it into a Word document or maybe do a bit more research and then go back with another question. It was a bit like the sage sitting on the hill. You walk up the hill, you ask a question, you get your advice, and you wander down again.
Agents, or agentic workflows, you’ve now taken that sage and you’ve put them in your factory floor. And now when you ask them a question, it’s more like you’re telling them to do something. They go and do the research, but then they do something with it. They build something, they produce a document, they realize that they’ve got to do more research, so they have multiple steps. And so it’s a much, much more powerful paradigm. You’re not just asking and getting a response back, you’re actually getting work done for you. And that’s where I think it is the huge shift with agents.
Greg Lambert (04:59)
Yeah. And so how do you look at that when…
Greg Dickason (05:16)
So for us, it’s about understanding that our customers want to get work done. They don’t just want to come and understand something, do some research, and then go off. They want to get work done. They want to produce a document. And in some cases, they don’t always know all the questions they need to ask. So being able to ask a more open-ended question, and then we go off and our agents build on that, ask them questions back, and effectively create a workflow or a long task which produces far closer to the output they want to get.
So we recognize that as much as we’ve got great authoritative content, that’s really powerful when you’re marrying it to the workflow of your customer so they actually know what they want to produce. And a lawyer is not producing an output from an AI. A lawyer is producing an email or a draft or a brief or something like that. And we want to help them get as close as possible to that final output.
Marlene Gebauer (06:05)
So I imagine that agents are very important in tools like Shepard’s. So how do features like Shepard’s Verify operate under the hood to actively cross-check AI-generated text against the LexisNexis database and prevent hallucinated citations?
Greg Lambert (06:23)
Like hallucinations came up in the second question. I like that.
Greg Dickason (06:26)
Yeah. It’s always a theme with AI, isn’t it? Yeah, absolutely. And I think that’s why we think Shepard’s Verify is so important. But everybody knows Shepard’s. Shepard’s is going to tell you, is this good law? It gives you a really strong signal. And that’s on our platform. What we thought is it’s actually good to move that out of our platform and onto your platform and wherever you are.
So if you’ve written a document and you upload it, can we do some citation verification for you? Can we check, does that citation exist? And if it does exist, is it good law? And that’s what Shepard’s Verify is about. It’s about…
Greg Lambert (06:29)
Yeah.
Greg Dickason (06:56)
That trust signal and giving it to you so you can use it where you are. Obviously, in our responses, we always give you a Shepard’s signal so you can click through and check, as well as get the signal to see how good is this law, but also in the documents you upload. So is it even verifiable, and is it still good law? And that’s where it works.
So, how does it work under the covers? We’ve got Shepard’s, we’ve turned it into a really powerful service, and that service is now available inside Protégé, so we can use it against any document. And that goes back to Greg’s earlier question, Greg Two’s earlier question, which is, how is an agent different from an AI? In this case, the agent knows, at this point, I need to verify what I’ve just picked up, or I need to verify this document. So it knows that it can use the Shepard’s Verify tool to do a particular task, which is to give you confidence in the output.
Greg Lambert (07:41)
Do you mind giving us a scenario where, if I’m an attorney and I’m working, how does that process work? Is it smooth, or is it something that I’ve got to purposefully go and do?
Greg Dickason (07:49)
Yeah, so let’s say you’ve got a brief from opposing counsel and you want to check that. You can upload that onto Protégé and we will do the verification checks for you. So you’ll see the signals against your document and be able to see how well the opposing counsel’s citations actually link, whether it’s good law or whether it even exists, as an example.
Greg Lambert (08:16)
And is it verifying the citations only, or does it go a little bit deeper? Does it look at what’s quoted, or how deep does it go?
Greg Dickason (08:24)
Yeah, it looks at whether or not, how well that has been treated by subsequent cases. It doesn’t always go right into the argument, but it does look at how well it is being treated by subsequent cases, and therefore whether this is a good or bad case to use in your particular argument.
Greg Lambert (08:43)
All right. So one of the things, and everyone is now talking about Anthropic. They seem to be the foundational AI model that everyone’s using, and, of course, caused a big stir over the past few months with the SaaS apocalypse and now the legal AI tools, the skill sets that they’re bringing in.
So do you mind talking to us a little bit about what kind of relationship Lexis and Anthropic have? Because I know you guys have used them for a long time. They’ve been underlying a lot of your technology for a long time. So it’s not a new relationship at all. But with them announcing that they’re in legal by releasing these skill sets, how does that relationship work? How are you building on that right now?
Greg Dickason (09:38)
I see the Anthropic one, I’m super excited about working with them, right? The fastest-growing company in history. I mean, you’ve seen what they’ve done this year. It’s pretty amazing. And to your point, we’ve been working with them from before they were really even thinking about how they sold to enterprises. So we had signed an arrangement with them on Amazon Bedrock, which is the way Amazon supports models, before Amazon Bedrock was live. And that was their way to start to work with us. I think we were one of the largest contractors they had in those very early years.
So we’ve got a great relationship with them. It’s been going for a long time. Jeff Bleich, their chief legal officer, was at one of our conferences the other day, and so therefore I see it as largely really collaborative. What’s great about Anthropic is they’re very open. They tell us what they’re doing. They give us early access so we can test against their models. We can test and see their skills. And so that’s a great place to be.
But at the same time, they’re moving very, very fast. And I think what they’re seeing is, how do they enable the knowledge worker in general? So, how can they give the knowledge worker the skills that the knowledge worker needs to get their job done? And they see Claude Cowork as sort of that generic knowledge worker’s interface where you can do some pretty cool stuff.
But what’s great is that what they’re providing is a great model, a good harness, and a set of skills. And I think of those as almost the layers. If you think about old tech, you used to have the database and then the business layer and all the rest. Now you’ve got the model, the harness, which helps that model work in your environment, and then the skills, which tell the model how to think about a particular thing.
All of those are available to us. But at the same time, we also have those available from other parties like OpenAI and Google and others. So we can pick the best of breed for the model, the harness, and the skills, regardless of which provider. And we can do that for whatever use case, for whatever type of lawyer we’re serving at the particular time.
So I think we’re actually in this unique position where we have great content, which we can use to build skills, but we can choose best of breed at all three layers. And we’re working with exciting businesses like Anthropic, which just means that we can innovate very, very fast on what they’re doing. So I don’t see it as too competitive. I think your other question there, Greg, was, you know…
Greg Lambert (11:43)
Yeah, because you hear, like, you hear now, we’re an AI, what’s the phrase, Marlene, that these small firms are? Basically they’re an AI foundational law firm, or I’m not getting it.
Marlene Gebauer (11:51)
AI-powered, AI-forward. AI-native, sorry.
Greg Lambert (12:08)
AI-native. And so I guess, and I think this might be a bit of a softball question, but I’ll throw it out there anyway. What is the value of having that combined?
Greg Dickason (12:23)
First, because the foundational models are tuned for generic solutions. They’re not tuned for what you need. So you need something that layers on top, which understands the law.
Second is that the foundational model is increasingly requiring a harness to work well. So you’re starting to get stuck into that harness because the two are being coupled. Think of it a bit like riding a bicycle. I can be a great athlete, I’m the model, but if I’m on a bicycle that fits me really well, I’m going to be so much better when I’m on my bike. And that’s what’s happening. Increasingly, the harness and the model are working well together. But that’s making you lock in because then it’s only you getting where they’ve tuned that.
So what we can do is we can reverse engineer and work across all of that. So we give you the best harness and the best model for a particular use case. So I think that’s why.
And then the skills is just a really exciting space. Skills are just Word documents, not Word documents, just text documents, which tell the agent how to think. And they can call each other and they can get quite complicated, but they’re basically just a set of text documents. And so if you go to Anthropic, you get a lot of great skills that are focused on just in-house counsel, but they’re not focused on longer-running, harder tasks, particularly in large law. And so, yes, there’s some stuff you can do there, but it’s not a generically strong legal platform like we provide. And we can reuse those skills and our own skills. So I think we can give you the best of all worlds.
Greg Lambert (13:43)
Is there a future where, as a Lexis+…
Greg Dickason (13:55)
That future’s arrived already. You can upload your skills with our new work product. The future’s arrived. But exactly to that point, you have your own way of doing work. You’ve already written it down. You’ve got your playbook. You can turn that into a skill and use that.
Greg Lambert (13:59)
The future is here now.
Marlene Gebauer (14:01)
Hm.
Greg Lambert (14:09)
Okay.
Marlene Gebauer (14:11)
So I’m going to ask another value-related question, sort of what your thoughts are in terms of the value of this. In addition to the Anthropic alliance, you also have now an alliance with Luminance, and I imagine that is going to bring a lot of new document drafting skills, and that it will be combined with the legal research skills of Lexis. Outside of streamlining that process, where do you see the value in that combination in one interface?
Greg Dickason (14:45)
I do think it’s about getting your work product done without having to switch interfaces. So I do think it’s the fact that you can do the research, you can start the draft, then do further research, and it can all happen relatively seamlessly. There might be one click through to check something and then back again, but it’s relatively seamless with things like Shepard’s Verify popping up to tell you, yes, this is right, this is not right.
And I think that’s a lot of, if you listen to good product podcasts, it’s about reducing the friction. It’s reducing how hard it is to do what you want to do, and I think a lot of those kinds of integrations for us are about reducing the friction so that there’s a…
Marlene Gebauer (15:19)
It’s also about getting people comfortable with working in a workspace outside of what they currently do, changing that whole, helping with change management in terms of how they do their work, because people are kind of notorious about not wanting to change that.
Greg Dickason (15:38)
Yes, it’s very hard. And for me, as a product tech guy, that’s one of the hardest things, getting people to change, even the small things. Like when you go into Netflix versus Amazon Prime, they scroll slightly differently. And even that I find is like…
Marlene Gebauer (15:51)
It’s infuriating.
Greg Lambert (15:54)
Yeah. Well, let me ask about this, because I wrote a thing about the future of the UX, and if you’re not developing an interface, an experience that the user likes or works in the way that they work, they’re going to go out and create their own way of accessing it, whether it’s like with…
Marlene Gebauer (16:16)
Or find a workaround or something.
Greg Lambert (16:16)
What Salesforce is doing with a headless interface, or they might use the AI to access the website directly and then pull the information back in for them. So as someone who is on the product side, how do you think about what the future of the user experience is as we move, especially as we move into this agentic period?
Greg Dickason (16:41)
I think it’s increasingly going to be simpler and simpler because the agent’s going to understand your intent. Therefore, one, it’s going to know about you, so it’s going to have memory about you, who you are, what you care about, and then it’s also going to understand the intent of this current thing you want to do. And so you don’t need a complicated UX anymore. What you need is something that’s simple, that’s easy to engage with, but then it might diverge toward a particular use case.
So if you’re doing research, it might ask you some questions. If you’re doing a draft, it might open a document on the side. But ultimately, it’s doing that for you. So it’s very curated for you. I mean, they do talk about AI UI, which is where the UI is actually created by the AI in real time. I think that’s immature, and I don’t think it’s there because then the AI is almost overcomplicating it. I think what we’re going to get down to is a much simpler interface.
Greg Lambert (17:30)
Yeah. I’m curious, because a lot of the web is built for human interaction. And one example is, let’s say I get a web page and it gives me a spreadsheet. Well, it might only give me 50 lines of that spreadsheet, and then I have to click page two, right? Because that’s how a human ingests it. Whereas if it’s an AI interface, it would give them the entire spreadsheet, or it might give them dozens of spreadsheets all at once because it can handle that. So it’s going to be interesting from a product side how you do that.
Greg Dickason (18:04)
Definitely. And we’re looking increasingly, like for our digital side, we’re seeing more and more traffic coming from OpenAI, from ChatGPT, and Anthropic, the actual models, the open models, where users have clicked through. So rather than come through via Google, they’re coming via those channels. And then the question is how much of that is coming from the agent, with agents looking at our website and curating that back for the user. So it’s a really interesting change. I think you’re right. More and more of the web is going to be written for agents, not for users.
Greg Lambert (18:28)
Well, let us know when you figure it out and we’ll bring you back on. You can explain it to us.
Greg Dickason (18:31)
Ha ha ha.
Marlene Gebauer (18:32)
I do have a question about what you’re hearing in terms of feedback from clients. We’ve talked about comprehensive solutions where you can bring in your drafting, you can bring in your research, you can bring in your assistant and all those things, versus point solutions. And I know it will have to do with the actual work that needs to be performed, but there’s also an increasing pressure, I think, for clients regarding the cost of these tools. So I’m curious, what sort of feedback are you getting from clients? Are they leaning one way or the other? Anything that you can offer in terms of that insight?
Greg Dickason (19:16)
I do think increasingly we’re going to start to see consolidation. They want fewer tools. I think there has been a case where they’ve been looking at lots and using point solutions because there have been specific point solutions that have helped for specific use cases. And I do think that’s going to start to collapse, coalesce. So, for example, with our system, we can now load any type of skill, which means you can start to tune our system for your particular matter and how you do your matter, and the agents can pick that up. So I do think that’s going to happen. And I do think that’s what our clients are starting to ask questions about. I think that’s your question, okay?
Marlene Gebauer (19:49)
It is, it is. And I had one other one. In the news, we’ve been hearing about firms making a large investment and building their own AI. And I’m curious, sort of what your take is on that.
Greg Lambert (20:02)
They had an extra $500 million laying around.
Greg Dickason (20:05)
Really? Yes. Look, I think it’s logical for particular workflows. I think for some things it’s not that logical, but for some things it does make a lot of sense, for some workflows, particularly when that is your value proposition that you take into market, that your clients see from you.
And I think in that case, you’re going to need some really good foundational building blocks to help build that. Obviously, we see ourselves as being a key contributor in that kind of space, where you’ve got deep legal research, deep authoritative content. But I don’t see it as being just calling some dumb interface, because you need the reasoning, you need the legal logic that comes with an agent like Protégé.
So it’s not MCP where you’re just being called. It’s A2A, it’s agents talking to agents. And I think that’s probably the emerging space, where you have an expert talking to an expert. They might both be agents to help solve the client’s problem. So we do see a space for that, but I do think it’s agent-to-agent rather than agent-to-MCP.
Greg Lambert (21:01)
And so one of the things that we’re seeing is a lot more of the firm’s data is being uploaded into systems, whether it’s in vaults or whether it’s through the Word document in the plugins, or a number of different ways that the information is being accessed and somewhat commingled, I would say. So, can’t talk AI without also talking about security. And one of the topics that’s being talked about now is the BYOK, or bring your own key.
Greg Dickason (21:51)
I think it’s critical, especially for our larger customers. They have to have it. And the point with bringing your own key is…
Greg Lambert (21:56)
Well, let me stop you there. Do you mind just talking about what it means to bring your own key?
Greg Dickason (22:02)
Sure, sure. So I like to think of it almost like a house. I’ve got a house where you can come and get your work done. You bring your documents, and I’ve got other, well, maybe not a house. I’ve got an office where I’ve got great workers. You can come, you can bring your documents, and you can get stuff done.
Now, what you want to do is bring a lot of documents, so you don’t want to keep bringing them in and out. You want to put them in the vault, right? And you want me to be able to access that so that my experts can give you the right results. But what you don’t want is for me to be looking at your documents when you’re not around, right?
So what I do is I give you a cabinet in my office. You put your documents in the cabinet and you lock it, and you bring your own key and you take that key away. And then you know I can’t access it when you’re not around because I don’t have your key.
And it’s almost exactly the digital equivalent of that. You have a mathematical key which unlocks, and it first of all encrypts and then unencrypts the content I need to do the job for you. But if at any point you withdraw that key, I no longer can do work for you. And that’s provable. And so I think it’s a great model where you can be quite sure that the only time your content is ever accessed is to do work for you.
Greg Lambert (23:11)
So how are you and your customers implementing this with Lexis?
Greg Dickason (23:16)
So exactly as you’re saying, in Vaults, you can now bring your own key. So you lock it. You put your content into the Vault. We index it so it’s all available for the AI to look at and say, okay, this piece of content works with this law to help draft that document for you. But it’s locked. And the only time our AI can look at that is when you’ve actually logged in and you’ve provided your key as part of your login. If you haven’t logged in, we can’t use your content. So it’s built into the Vault and we can prove that, and that helps you from your security posture perspective as a firm.
Greg Lambert (23:47)
And I’m curious if…
Yeah, upload files there.
Greg Dickason (24:16)
So with Claude, typically now you’re having to do it on your own laptop, and you can’t build as strong a vault. So when you upload files with us, we’re not just uploading them, we’re indexing them and we’re chunking them so they’re part of a vector store. And we’re doing that in a legal way. Different models can chunk the content in different ways. We chunk it so that it’s legally relevant. You can’t do that directly with Claude. You have to build your own chunking and your ingestion layer, which properly processes the files, and then your storage layer, which stores them in a way in which they can be easily retrieved for the AI. You might have heard of RAG.
Greg Lambert (24:53)
Yeah. We’ve been talking RAG for…
Marlene Gebauer (25:01)
Last year, year before.
Greg Dickason (25:01)
Yeah. Well, yeah, I mean, that’s like history now, right?
Greg Lambert (25:04)
Yeah, that’s very 2022.
Greg Dickason (25:01)
But to have a really good RAG system, you need to be able to properly chunk and index. And then on top of that, you can build a knowledge graph and other ways in which it makes it easier for your agents to surface.
Marlene Gebauer (25:17)
So it’s good to hear that Lexis is thinking about security, like bring your own key and things like that. What do you find from clients that they are most concerned about? Is it this type of security? Is it the hallucinations that sometimes happen with cases that they see in the news? What type of conversations are you having, and how are you assuring clients that Lexis is focused very much on trustworthy output and absolute security?
Greg Dickason (25:54)
Yeah, completely right. I think it’s both. When we’re talking to the security teams, they’re interested in the mechanics of security. So things like bring your own key, making sure that what they’ve uploaded is properly locked away, that kind of thing.
When you’re talking more to lawyers, they’re more interested in the hallucinations and verifiability, and making sure that they understand how do they know that what we’re giving them is good law, and how easy is it to check it? Because our position is these are non-deterministic models, right? They’re probabilistic models, which means they will always come up with a small probability of saying something that’s not quite right. Now, we’ve got lots of rules and a ton of stuff around to limit that, and we believe we’re best in breed, but you still need to finally be able to verify, to check. And that’s why it’s very easy on our platform to be able to click through and see. You get your Shepard’s signals, and you can easily click through onto the platform.
So I think a lot of our clients are asking us, show us your security model, which we do, and then also show us how we can mitigate any risks of using an AI system to get more efficient, more effective, to provide services. And a lot of that comes down to reduction in hallucinations, reduction in the type of hallucination to almost zero. But then, at the same time, you can always verify. You can click through and verify.
Greg Lambert (27:10)
I’m curious if there’s risk, or things that your customers might not be thinking about now, but maybe they should be thinking about. Is there anything that, I know you’re dealing with some smart customers who are risk-averse, but I’m curious. For example, if you ever get your hands on Mythos, what kind of risks do we think are out there with something like that?
Greg Dickason (27:38)
Well, I do think there are two types of risks. There are risks we’re aware of, but I think where AI is going is pretty mind-blowing. The next six months to two years, I think, is going to be phenomenal. And if you think back to coding, agents were a bit of a toy. You got agents to write code for you, and it was a bit of a toy. Then somewhere around November, December last year, that toy became something real. And a lot of my colleagues in the tech world came back from holidays and said, wow, before the holidays, I wasn’t doing much. Over the holidays, I built five systems that I never even thought I could do. And this is what’s happened.
And I think we’re going to start seeing those types of step changes in other parts of the industry as well. And one of them is Mythos. So I do worry about Mythos because I think that’s going to surface so many security bugs and security vulnerabilities in the next couple of months that we’re going to have the spike of that happening and we’re going to need to make sure we can jump on them. I think we’ll get to a much better state in about six months to a year’s time, but there’s going to be a period of time where we’re all quite vulnerable. And I really like the way Anthropic is trying to roll it out to keep us on.
Where else can we think? I think it’s agents’ ability to overwhelm us. That’s something else I worry about. From a legal perspective, how many briefs can you get, and how much content can you ingest as a human? So I do think we’re going to increasingly need agents to help us mediate the effect of agents in terms of volume, in terms of sheer complexity of work we’re doing.
And then I do think that we’ll start to see new types of industries emerging, new industries that are far more agile and AI-enabled, and that’s going to stress the legal system just like other ways in the past have, even blockchain and new ways of thinking, digital assets and all that. But it’s going to happen faster. And so, how do we keep up? How does legislation keep up? That’s going to be a real societal challenge, Greg. Maybe we’re going a little bit away from Lexis, but you know what I mean.
Greg Lambert (29:25)
Yeah. Yep, exactly. Well, speaking of keeping up, before we get to our crystal ball question, we’ve been asking our guests to talk to us about how they keep up with the industry. Are there certain things that you read or people that you listen to that help you along? What’s a couple of things that you…
Greg Dickason (29:56)
Well, The Geek in Review is a start, of course.
Greg Lambert (29:58)
Of course.
Greg Dickason (29:59)
And then also I read Artificial Lawyer, Law360. So there are a few legal things which are great. I read the Turing Post. It’s quite technical, but it’s a nice email chain that you can get, Turing Post. And then The Information. It’s a technology-focused magazine, but it actually gives you some really good cutting-edge thoughts about where AI is going. That’s not super technical either. So that’s really where I go.
But I also think a lot of the time, I use Claude itself. I say to Claude, what should I know? What’s happened in the last week? And I sort of have an interactive session with Claude to learn. And that’s also quite useful.
Greg Lambert (30:32)
Yeah. It’s one of the things we say here: use the AI to help you AI. So…
Greg Dickason (30:36)
Yeah.
Marlene Gebauer (30:37)
Okay, Greg One, it is time for our crystal ball question. So looking ahead, a few months to a few years, as AI takes over the orchestration of massive document-heavy tasks through tools like Protégé Vault, what do you think is the single biggest shift coming for the traditional role of the junior associate?
Greg Dickason (31:00)
So the simple question is no more junior associates. But I think the answer to that is they’re not junior because they’re not there, they’re junior because they very quickly become senior. And I think we’ll see AI helping us train junior associates, then being able to do mock trials and all the rest very quickly, mock depositions, all of that. And so we’ll see them becoming senior very quickly and learning a lot as a result.
Marlene Gebauer (31:04)
Yeah.
Greg Dickason (31:23)
I think very recently on the podcast, you had somebody who was building their training systems, and that was pretty exciting to hear. And I do think that’s where we’re going to go. So I don’t think we’re going to see fewer lawyers. I think we’re going to see the law being applied in more places. Society’s underserved, and I think it’s going to give us the opportunity to serve more people, which is pretty exciting about where AI can take us.
Greg Lambert (31:44)
Right. I like your vision. So, well, Greg Dickason, CTO there at LexisNexis, I want to thank you very much for joining us.
Marlene Gebauer (31:58)
Thank you, Greg.
Greg Dickason (31:59)
Great to be here. Thanks, Greg. Thanks, Marlene.
Marlene Gebauer (32:01)
And thanks to all of you for listening to The Geek in Review. If you enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn and Substack.
Greg Lambert (32:09)
So Greg, where’s the best place that listeners can find out more about you or about Lexis+ AI with Protégé?
Greg Dickason (32:16)
So jump onto lexisnexis.com/AI. That’s the best place to go. And then happy for you to look me up on LinkedIn, and I think we’ll post the link on this.
Greg Lambert (32:26)
Yes.
Marlene Gebauer (32:26)
And as always, the music here is from Jerry David DeCicca. Thank you, Jerry, and goodbye, everybody.
Greg Lambert (32:31)
Bye.