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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 2: So, Tracy, I think like maybe a year and a half ago, we did that episode with Joel Wertheimer talking about AI and law. It does feel like law specifically is one of those areas where it's very easy for the normal person to imagine how AI could be very disruptive.

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Speaker 3: Would you say that's fair, yes, I would say, however, and full disclaimer, So my husband used to be a lawyer, a corporate lawyer. I would say that, like, standardized forms and templates have existed in the legal profession for many, many years, and law has also gone through many many technological revolution so we've gone from like scriveners, you know, to like keyboardists. The advent of like typis was supposed to be this massive hit for not a hit, but like this massive deal for the legal industry, and everything ended up like navigating through it pretty much.

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Speaker 2: I guess they probably each one of those waves. There probably was some extreme period of anxiety about disruption and how would billing still happen? And of course one of the questions that comes to mind and the legal profession is like, why would the lawyers want to reduce the number of hours that they can, you know, do on a case, given that famously they're paid by the hour. I think, actually, are they like paid by the ten minutes? Technically sometimes yeah, but it does feel like we're gods. So maybe, like I'm not saying, and I don't even have an intuition per se that a I would like upend, like a reduce the amount of legal work that needs to get done. But those technological revolutions that you mentioned in the past probably did change the industry quite a bit. So regardless of like total employment or job prospects, et cetera, it feels inevitable that change is going to come at one point. It is probably like a skill to be able to know case law or know how to search lexus and nexus like that almost certainly is going to change.

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Speaker 3: So one thing I've been thinking about, and this gets into the productivity debate, is I'm starting to come around to the idea of a Jeffins paradox in just general for work bureaucracy.

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Speaker 2: Yeah, basically, so.

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Speaker 3: You already have AI that's like being used by insurance companies to push back against claims, and then you have claimants using AI to then push back against the insurance companies. And I feel like the future could just be a bunch of bots like talking to each other and filing different claims, and.

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Speaker 2: I guess he will just sort it all out at the end.

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Speaker 3: Yeah, And maybe I don't get more productive.

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Speaker 4: I don't know.

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Speaker 2: Other people have said this. Everyone. A lot of people like intec coding all of these computer jobs. Right now, a lot of people seem to feel very stressed overworked. Like I don't think you like, look at a situation someone who has a job that primarily involves in their computer. It's like, oh, I'm feeling like I'm really avid eazy. These days, everyone feels very stressed. But anyway, I'm very interested in this topic. Was a big story several months ago. One of the major law firms, Kirkland and ellis like talking about like building out more in house infrastructure. So I think it's like a good time to sort of get a state of the market of like, okay, like we're what almost four years into the post chat GPT world, where do we actually stand in terms of what what AI could do for legal work, and then also just like how it's changing the profession if at all. Yeah, let's do it all right. I'm very excited to say we do, in fact have the perfect guest, someone who's been talking a lot about this, someone who's written about this topic, someone who is at a very substantial law firm where we can actually see some of this taking place in real times. We're gonna be speaking with Gary Wingins. He is the chair of Lowenstein Sandler. So, Gary, thank you so much for coming on odd.

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Speaker 4: Lots delighted to be here. Thanks for having me.

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Speaker 2: Just to set the scene. Why don't you give us a little bit of an idea of what the firm is, how big it is, your role there, and what it's sort of special, what kind of work gets done there?

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Speaker 4: Again, thanks for having me. Loan Science Sandler about four hundred lawyers, primarily located in the New York area.

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Speaker 2: But it's four hundred big law at that point.

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Speaker 4: Four hundred is big law. It's not huge law. You mentioned Kirkland earlier. They're much larger. We very firmly focus on clients in the private capital sector, private equity, venture hedge funds, technology companies and life sciences businesses.

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Speaker 3: Why has the billable hours thing lasted as long as it has because everyone seems to complain about this, Like the lawyers complain that it's terrible, the clients complain that it's terrible, and it does seem to lead to sometimes not the most efficient outcomes.

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Speaker 4: Everyone agrees it's terrible, as you said, yet it remains the dominant model. I have never met a client who has come to me asking to buy billable hours.

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Speaker 3: Right.

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Speaker 4: Nobody ever wants to buy billable hours. They want to buy business solutions, and it happens that billable hours is how we measure what the bill will turn out to be. There has been, at least for the past fifteen years, there's been a pretty strong movement toward what a're called or alternative fear arrangements, where you know you, project based pricing, cap fees, collars, things like that has not really taken hold, which is surprising. We are I think law is one of the last segments in professional services firms that has not moved to some kind of value or project based pricing. Perhaps, and I'm not exactly sure why, Perhaps it's because lawyers and clients don't really trust each other that much. And when we propose a you know, here's how much a cost to do this kind of public offering, sometimes clients say, well, if you're going to propose that amount, I'd rather pay you by the hour because I want to see what you're actually doing. On the other hand, when clients ask for alternative fear arrangements, we almost always offer them.

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Speaker 2: Is there a understanding though of like, okay, maybe it's billable hours and the alternative fear arrangement doesn't get formally written down, But is there a general taskit understanding of like okay, here is a IPO we're going to raise a billion dollars, or here is a you know, venture capital deal where a company is going to ran five raise five hundred million dollars. How much that is there a cost? Like is it well understood what this is? How much this kind of job cost? And if it's wildly different, then some alarm bells would go of yeah.

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Speaker 4: So the IPO market is easy because everybody as part of your disclosure in doing an IPO, you disclose how much you pay the life and so there is a known market and there's a known comparative price, you know, a venture. A round is a little more challenging because there are lots of different details, but there's there's a common understanding about the range of rates and then things go from there if there are funky tax issues or things like that. But for most areas, there's kind of a common range.

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Speaker 3: So getting back to the topic at hand, AI, How much of a step change is this in terms of the actual work process for a corporate lawyer? Because as I mentioned before, like standardized templates exist. Law firms have had, you know, software for due diligence for ages. A lot of them have knowledge libraries where they sort of share information, or you have experts that act like little encyclopedias for law. How big a change is this?

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Speaker 4: I think it is a pretty huge change. So there are two ways it's changing. One is what you're talking about kind of the the process and systems and using AI to become more efficient, just like when you know, when Microsoft Word came out, came out and it was became much more efficient for you to kind of do your own documents or compare Right. When I was a first year associate, I'm old. When I was a first year associate, I was hand black lining documents and clients were paying me by the hour to hand black line documents. Within two years, you know, we had black lining software and they no longer paid me by the hour, and it took roughly ninety seconds to black line a document. So there's there's the efficiency piece which definitely is starting to make it to make the things that clients want to buy less expensive.

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Speaker 2: Were you ever did you.

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Speaker 3: Ever miss a comma that resulted in like five million dollars in extra.

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Speaker 2: Chargees, Not that I'm aware of.

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Speaker 3: Okay, as that's a famous case, right right, right.

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Speaker 4: But I've definitely missed commas every body has, but I'm not aware that it's you know, result in anything bad happening. Hopefully nobody listening to this has the other side of that. The so, but the other way AI is working in law firms right now is it's it's acting as a thought partner, and it is making us better at our jobs and to use a term, acting as a co pilot for all of our lawyers in helping think through problems. So as that thought partner, we've never had that before.

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Speaker 3: Right.

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Speaker 4: That's a dramatic difference in that AI is providing that these other technological advances did not. They only went to the efficiency piece and allowed us to produce what clients want to buy a deal or alligation for a lower total cost. But we were, now I believe, producing better work product from the get go.

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Speaker 2: Okay, let's go back to a VC financing around. So why don't you explain, even to say, pre AI, what was the role of the lawyer or the law firm in a typical financing around?

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Speaker 4: What?

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Speaker 2: Why? Why are their lawyers evolved? What were they what are they brought.

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Speaker 4: In to do?

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Speaker 2: And then maybe sort of give us a concrete example of today what it means or how a lawyer who's in one of these deals can use AI as a quote thought partner.

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Speaker 4: Yeah. So, so first I have to give you a bit of a disclaimer. I'm a structured finance lawyer, not a venture capital lawyer. But we have one of the largest venture practices in the United States in our firm, so I'm you know, I'm familiar. Okay, Right, what's the role of the lawyer? So often when our client is the company side or the founders, they've never done anything like this before, and we are educating our client on how deals work. They rely on us for market knowledge of kind of what typical deal terms are in the adventure financing, whether it's a serious C, the A B C. And we because we are so active in the space, we usually know the other you know, we usually know the funds that are investing, we know their council, and we can add I believe a lot of value to the client in the in the way we can negotiate and structure the deal knowing also what the next round is going to look like, and the round after that and their ultimate exit, whether it's to an IPO or an M and A deal, And we can help them get their structure right from the get go so that they'll get all those future rounds right. And when clients come to us with kind of their chat kept produced documents, I think, go, you know, they're they're missing all of that nuance, the knowledge of the market and the other humans in the deal and what they need for future transactions.

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Speaker 3: So a lot of clients coming to you with chat GPT produced stuff.

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Speaker 4: In the venture space, Yeah, I've right, And in a number of areas. I mean, we have some very sophisticated clients who have the same AI tools we have in the legal profession and some fortune for example, a Fortune fifty client that basically produces their first drafts of whether it's a contract or a complaint or an answer in a litigation. They all produce that using their AI tools first send it to us and want us to take that and run with it. On the other end. In another area of our practice, we do a fair amount of patent prosecution work, particularly out of our West Coast offices. We represent some extremely sophisticated technology companies who will have their AI tools reviewing our work and providing us with AI produced comments. Now we're also using AI tools to create these patents with full knowledge of our clients, so we're almost at the point where their AI agent is talking to our a AI agent. That's an interesting development as well.

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Speaker 3: For sure on the AI copilot ideas, since your expertise is in structured finance, how far does this actually go? As in, could you envision asking an LLM to produce like a brand new structure for I don't know, ABS or cnbs or pick your structured finance poison. Like when you talk about AI out with strategy and complexity.

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Speaker 4: I don't believe that it can come up with a new structure. Okay, But I have a partner who is a real expert in international tax for example, and he will use AI tools to and he's always coming up and he represents a lot of family offices and global businesses, and he will come up with and he spends all of his time structuring you know, how these family offices work. How do you get money from country A to country C with a minimum number of tax hops along the way. He will use AI to test out some of his theories and it will give him some feedback, and he'll work with the AI to come up with new structures. He will then hand the report that he gets out of say Claude, and hand it to an associate and say, okay, now run this down and actually do the research to figure out if this is right. But let's work on this structure together. So it increases the horizons. It validates some stuff. What's the opposite of valid not validate? Knocks out some ideas valid right, and then we still have you know, a real human who has a law degree, run it down and double check and see if we can make it even better. And usually the iterative process between the AI and the human and the associate and the AI and the partner come up with some really cool structures that they wouldn't have thought of on their own, and that the AI wouldn't have thought of on its own.

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Speaker 2: This is gonna sound like a rude question, how do you know? Because the question, like, I think it's like I could see going back and forth with the chat pot as a way to like stress test ideas or sort of like do you think that you know just sort of like you generate these new ideas, and so, like, I guess on some level, I'm not surprised that by interacting, let's say Claude, a really good tax lawyer, could like explore some new potentials or like feel out this potential possibility. But on the other hand, it's also easy to go back and forth with the chatbot and create the illusion that you're finding something new that you also could have like intuitively arrived at yourself, because if you were one of the most brilliant tax lawyers in the world, So how do you sort of establish that this is in fact adding value as opposed to just work ish or work light.

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Speaker 4: So in the example, in the tax example, I just gave you I have no idea, how okay, but I can tell you because I was out in our Palo Alto office just the week before last and was talking to our law I'll go back to the patent example, because that's discrete, right, and it's and and there are discrete set of steps. We are using an AI tool to help us to help our lawyers draft patent patent applications. They have been telling me, we've been doing this for a little over six months, that the primary benefit is that it creates a better patent application because while almost all of our patent lawyers are or engineers of some type, mostly software electrical engineers, the knowledge that the AI tool has is of all engineering, right, and of all sciences and arts, and it's able to bring in a biologist's perspective, perhaps just to use something and create a broader application. I was like, Okay, that's kind of cool. I was then visiting with a couple of our clients out there who we do patent work for and on their own. They have told us that they really appreciate how we're using the technology, that there were that we are ahead of most of the other firms that they're using that they use right now, and that they have noticed over the past six months, how our applications have gotten better and better. So they I didn't ask them that question unprompted, they have said that, so that I was I was really excited about that because that was validation because I asked the same question that you do. It is like, how do you know it makes it any better? And do clients notice? Clients noticing you know makes a difference.

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Speaker 3: One thing I'm really interested in is the impact of AI on actual pricing power for law firms. And you wrote a really good piece for our colleagues over at Bloomberg Law called AI will give Junior Lawyers better work one of our legal insights, and you mentioned a specific number in there. You cite a project where you were going to do it but you decided not to because it was too expensive. But then a year later or so, with the assistance of AI, you said that the cost had come down seventy percent, which is absolutely huge. But I guess my question is if costs are coming down by that much, then why don't clients accrue all of those cost savings? How do lawyers actually protect their margins in that scenario?

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Speaker 4: So a couple a couple of ways. First off, the clients don't necessarily have the same knowledge, deep industry knowledge that they're outside lawyers do in reviewing the work because well, the cost has come down seventy percent hasn't come down one hundred percent, right, So there we're still doing thirty percent of the hours that we might have done a few years ago or something like that, but those hours that we're putting in it are much higher level hours. The project that I was talking about in that article is basically a due diligence project where we were where the assignment was to review thousands of trust agreements for the obligations of various parties in the agreements usually laborious, kind of boring, tedious. And the price we quoted or the cost that we quoted to do it I think it was like three years ago. Was based on you know, humans doing all of the work and a QC team on top of the first round. We can basically take out that first layer or review and assign that to the robot which then puts it all in this beautiful one hundred one hundred columns spreadsheet to show us all of the fields. And we're doing the QC layer, and the QC layer requires legal knowledge of how these transactions work, and some experience that you can spot where the output doesn't make sense and you go back and double check it. So I think the clients still value having a law firm basically certify or you know, say this is good output in a way that they wouldn't want to do themselves. They don't have the staffing to do it themselves, and they want outside eyes on it.

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Speaker 2: So what does this mean specifically though? For early career lawyers? All right, like the fear is or one version of this is, like, that's great for the senior lawyers, pull up the ladder, hire few lawgreds, et cetera, because you can now do this, Like is that? What does that mean for the people who three years ago would have been doing this? You know, as you said, not the most exciting, tedious work.

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Speaker 4: Right again, when I started, I was blacklining documents by hand, and somehow, when the machines took that job away from me, I still had plenty of work to do, and first your lawyers behind me had plenty of work to do. But my life got more interesting.

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Speaker 2: So I think.

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Speaker 4: That the tedious jobs are going away. First, junior lawyers, I think are going to be doing more interesting work sooner. We have to. We have not fully figured out yet and we will solve, but we haven't full yet how to train people without going through that tedious work.

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Speaker 2: So that are you still are you adding? Are you hiring one l's and two or whatever ls? Are they in law school? We are screds like right now.

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Speaker 4: We are hiring summer We have a you know, we have summer associates. This summer we will have a full slate of first year associates who join us in the fall. We have already hired our summer associate class for the summer of twenty twenty seven and we have not changed or hiring patterns we have. We are starting to change the skill set we're looking for and what you need to be successful in this profession. I think we'll change a little bit.

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Speaker 2: Well, can you talk Dan about what is that skill set and what are they going to be doing when they get when they arrived there on their first day after law school? What is this skill set they need to first get in the door, and then what are they going to do once they're in the door.

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Speaker 4: I think, no matter what, you're going to need some training on the area of practice you're in and there's going to be less of the gruntwork kind of training and we're ultimately going to have more simulator type training or you know what we've already started doing. You know, a very basic, a very standard like first year training curriculum at a law firm is like the uh the anatomy of an M and a transaction where you actually go through the provisions of a merger agreement or an A or a stock purchase agreement or an asset purchase agreement and you go through it, you know those provisions. We're now going to What we've started doing is make sure everybody brings their laptop with them to the training and that they have their AI tools open at the at the same time and as part of the training, you're saying, Okay, now take this provision and put it in this tool and see what the responses are. See tell it you're representing a seller, and give me a seller favorable provision to negotiate it. So we're going to do a lot more of that training hands on with the tools where people will get to see that we will have they will have in front of them that they don't have now, but they will very soon our knowledge set of every similar deal that we have done, say in the past couple of years, that have been fully negotiated. They will be able to see the terms of every deal we've recently done. Clients care a lot about our market knowledge. They care what are the reasons that they're willing to pay law firms at the exorbitant rates we charge. Is because you have at the market knowledge that they can't possibly have on their own, because for many of them this is a one time deal. For us, we do it every day, and they expect us to know what the market terms are. Using this technology, I can make sure every junior lawyer has access to all of the knowledge of our entire firm in doing similar types of deals or litigation or bankruptcy proceedings. As opposed to just having a walk through the halls and try to find somebody who's done it before and just talk to them about what they've done. You will now have access to basically a dashboard with all with all prior documents, and I think that makes you much smarter faster. We could have a separate conversation about the cognitive load from that right. There was a lot of value in my having downtime to black line, right, it was like relaxing.

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Speaker 3: This was going to be my next question, which is lawyers have to be very detail oriented.

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Speaker 2: Yes, right.

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Speaker 3: And one of the arguments for having to do all this grunt work like hand deliver is to agreements or review trusts and things like that was that, like it teaches you to focus, Yeah, and it teaches you to look out for that missing comma that's gonna cost you like five million dollars. You know.

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Speaker 2: I was thinking about that as you were saying that dead documentary on Netflix or like fifteen years ago about the sushi guy or it's like all these like chefs she It's like, Okay, I'm gonna teach you to make sushi. First spend fifteen years like learning how like clean rice or whatever it is, and then we like get to sushi. Or like think about musicians, and it's like it's no fun to do scales, right, but you do it and you build some sort of like deep understanding, and it's like that feels like the equivalent of what like the black lining is.

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Speaker 3: Yeah, So what happens if people aren't doing as many repetitive tasks?

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Speaker 4: I worry about that as well. And I'm not sure what the answer is from a behavioral psychology perspective, because I think you're right, doing those scales is important. I don't know, you know, we're talking about simulators to simulate doing a deal. There's know, you know, there's nothing like actually doing a deal right and actually negotiating with somebody on the other side who's a jerk, or somebody on the other side who's really nice but you know, bamboozles you by being so nice. And the human element of this all is still really important, and the human interaction with your client with the adversary, you know, you need to get that experience, and you need to have the touch and feel. With the advances in AI, just over the past, you know, six months or a year, you can see a time when the drafting is largely delegated to the machine. You still have to be careful, as you said, the commas. You know, I can see AI screwing up commas. Ais don't know how to add right and they make silly mistakes all the time, So you got to learn to be careful. I do worry about it becoming too easy to just fall into the trap of trusting what it tells you rather than thinking above it. So I don't know the answer to your question, but I have the same concern you do.

00:28:09
Speaker 2: I want to get into like the various tools and infrastructure you have, you know, because we're Markets and Finance podcast if people want to know what the trade is obviously, but before we get into that specifically, you know, when we did our last episode about AI law, our guest Joel Werdheimer, he's a civil rights lawyer in New York City, and one of the points that he made is that sometimes clients come with him with cases, often maybe like suing the city over like police misconduct or something like that, and the client has a good case, might have a good case on or you're aware of a client might have a good case on paper, but the potential damages are so small that it's an uneconomical to pursue it, even though clearly there's a legitimate case. If we're sort of as Tracy mentioned, the Jeff's parad of all this, do you see this, it's like, okay, you collapse the price of certain types of legal work. Does that expand the number of theoretical cases? Or I guess you're not deals that. Oh let's take a look at this deal. There's some like minimum upfront, cause that's going to be the same. Let's take a look at this deal that then compensates for the reduced number of hours that you charge.

00:29:25
Speaker 4: I think so, I think that the Deevins paradox applies to legal In the example I gave you with the client who had thousands of trust agreements that needs to be reviewed. When we gave them the first quote, we didn't say we didn't want to do it. The client said, at that price, we're not going to do it because the risk, the downside risk isn't high enough to justify the price. But when the price came down by seventy percent, they said, oh yeah, at that price, we'll do it. So at and let's just use fake numbers. Ten million dollars they said, no, we're not going to do it, and we had our revenue was zero. When it came down to three million, they said, that's worth it, and we had three million of revenue.

00:30:11
Speaker 1: Uh.

00:30:12
Speaker 4: So that's the Deevins paradox in action. Over the past twenty five years, E Discovery has ballooned the cost of litigation. To your point, right, Electronic electronic discovery run amok has has made it virtually impossible to bring a sophisticated litigation for under a few million dollars in legal fees and in just the discovery expenses alone, or millions of dollars. If we can bring say two million of discovery costs down to two hundred thousand, it does change the calculus for both on this.

00:30:50
Speaker 2: On this, lawsuits are a lot more economical to file. Everyone good, just what America.

00:30:54
Speaker 4: Everyone is gonna love that aspect of Isn't that fantastic? But you know, you raised it in a virtuous, you know example with a civil rights lawyer. But I think it's also true, you know, for large corporates who were abandoning things before. So yeah, you can debate whether it's a social good to have more litigation, But if I'm speaking on behalf of a law firm that has a large litigation department, I do think that the increased the potential for increased litigation does outweigh the loss of really boring, mundane hours reviewing documents in the I'll go back to the patent example I gave you earlier, where we do see, over time, the cost of being able to prosecute a patent obligation coming down dramatically through the use of AI, and that that same client who told me two weeks ago that they've really noticed an increase in the quality of a work product also told us that because they're using AI in their own work, they're seeing a quadrupling in the number of inventions that their engineers and scientists are coming up with, and the number of patent requests that they're getting internally from their engineering team has quadrupled, and we're going to ultimately be able to lower the cost of each of those, so the clients are able to do more and create more economic activity, which is virtuous, and a lot of that will require some legal work, even though each unit of legal work will cost less, I expect we'll be doing more of it, so you know that should preserve the ability of lawyers to do okay economically.

00:32:48
Speaker 3: Well, how do you see AI shifting? I guess the balance of power or balance of work between external law firms versus in house lawyers. Because you could imagine a scenario where I'm a big company, I have a couple or maybe five big ex big law lawyers who work for me, and because so much of the work is now automated. They can use AI. They don't need to go to a big law firm to do a lot of the grunt work as you put it earlier.

00:33:17
Speaker 4: Yeah, and I think you're going to see the the uh. So the other side of the Devons paradox is you will see larger clients that have dedicated legal teams keeping more of the routine legal work in house. You know, you mentioned is DOES before. Most banks have, for example, have brought all their ISDA and derivatives work in house. You'll probably see more of that, uh and you'll see more end users on the fun side doing that internally rather than sending it out. Although we have a terrific you know, derivatives group. You may see that in some other areas as well. But again, where you really need market preadth and you need market knowledge, I think outside law firms will still be able to provide a lot of value and a lot of outside kind of independent perspective and independent advice that it's hard to do inside.

00:34:30
Speaker 2: Let's talk about your tech stack so to speak. You mentioned lawyers talking to Claude. I know that there are applications or companies like Harvey which are sort of like built on top of some of the frontier models. There are questions about do you need these application layers. I'm also interested in whether, like when it comes to ingesting your own internal data, whether like open source AI models will play a role in that. What do you give us like the overview of your firm, Like what is the sort of essence of your tech stack that you're using?

00:35:09
Speaker 4: So you actually want me to name products?

00:35:11
Speaker 2: Yeah? Sure? Yeah?

00:35:12
Speaker 4: Why not?

00:35:12
Speaker 2: Okay, yeah, you mentioned Claude, but I'm really I did miss Claude.

00:35:15
Speaker 4: But we also so you know, we use a lot of different products at the moment, probably too many, and over time that will narrow. We are we are Harvey shop, Okay, we uh, almost all of our users, our lawyers use Harvey regularly. We also use Microsoft Copilot in the outlook suite or the office.

00:35:37
Speaker 2: Can you for our non lawyer listeners in me and I assume most of us, Like, what does a Harvey offer that is not offered when someone interfaces with the model directly.

00:35:49
Speaker 4: With the model being a frontier model.

00:35:52
Speaker 2: Instead of or GPT five point five. What happens when I use a model that sort of like that powers.

00:35:59
Speaker 4: Hard within Harvey and I think this is true of Legora as well, the other big one. Yeah, you can pick which frontier model you want to use. You can choose Chat or Claude for example, and I think Gemini and some of them as well. But what a Harvey ads is number one, a security layer that is much more robust than the other models, and that is super important for a law firm. We're able to control our own data set and make sure that it's not going up to the Internet, and that our client information is not going out to the internet at all. We have sessions that do not even connect to the Internet. It is much more robust. And one of the things that our clients often don't realize is that if they're using, especially a consumer grade Claude or Chat GPT, they often are losing atturning client privilege by asking a consumer model. There are questions. We are number one focused on the security layer and the ethics layer, and that's super important. And a Harvey or a Legora or you know, West Law has co Council all are very you know, security conscious. So it gives us that it allows us to have playbooks that we can share among among our team so that people can see the projects that we're building, which I think is harder to share in some of these other models directly. And what at least Harvey provides is basically a rag layer on top of the AI frontier.

00:37:38
Speaker 2: Model augmented generation of Wow. Okay, I don't know, yeah.

00:37:42
Speaker 4: I don't know if the I barely even know what it means. But I think it's good to say it's a rag layer, right that the that has been trained on legal stuff, right, so it's much more fine tune to law and out of the box. It comes with some of the things lawyers like to do, like look at clauses a merger and acquisition agreement. It has a better understanding of nuance in reviewing deposition transcripts. But we're seeing, you know, some of the main uses are creating a project for if you're a litigator. I'll give some litigators some airtime too here, not just talk about deals. Loading all of the pleatings, the briefs, all of the transcripts, and the back and forth between lawyers into a project in a Harvey, and then when you are drafting a pleting or a brief, it's able to retrieve for you the quotes in the deposition transcripts that support what you're trying to argue. So rather than spending you know, tens or hundreds of hours trying to find those, it's using the intelligence to find it for you. Claude could do that as well, but Harvey does it I think a little better, and it's collecting knowledge and allowing you to share the playbooks more easily.

00:39:00
Speaker 3: Yeah, I wanted to ask you about the security and privacy aspect of all of this. So how much of AI adoption in the legal industry is actually I mean, law is a very heavily regulated industry itself, So how much AI adoption is I guess constrained by things like malpractice risk, you know, data concerns, accountability. Is that a real limitation for you?

00:39:29
Speaker 4: So two years ago the UH and I in my role, I talked to our malpractice carriers and our underwriters out of Lloyd's of London and go visit with them, and they always ask questions about the things that they're worried about. Two years ago, they were worried about, are you you know, are you letting your lawyers use AI? And you know, is that creating risks?

00:39:56
Speaker 3: For us?

00:39:57
Speaker 4: Now they're asking you like your lawyers use AI right to make sure they're doing things right, right, because it's becoming part of mainstream and it is becoming an assumption that you're not going to be doing some of these tasks without having AI assist you in doing them. So it's going from a A insurers saying, oh, don't use that stuff until it's proven too you have to be using it, and the clients are doing the same thing right. Clients at eighteen months ago were saying, don't use AI for our work and now saying, well, you've got to be using AI to try to cut down the cost of the work. So you know, we've seen that shift over the past two years pretty dramatically. That has changed. But clearly, you know, we emphasize to our lawyers all the time that you got to be double checking this stuff. And there's no excuse for filing a brief or pleading in a court that sites you know, made up hallucinated cases.

00:40:59
Speaker 3: They're just toobarrassing.

00:41:00
Speaker 4: It is embarrassing. We have too many tools at our immediate disposal to be able to catch catch that, and yeah, systems break down and and somebody is trying to draft something and file it you know, in an hour and they get hacinate cases slip in, but it's not.

00:41:21
Speaker 2: So I want to ask you a question I asked Goldenman David Solomons on an episode. One of the things we're seeing actually in a lot of industries, including the legal industry, is like the rise of like these superstar hires, right and you see them like reported and various publications, some big name lawyer gets some crazy salary to go to or signing bonus or whatever kind of like you know, like we see in some of the hedge fund space. When I see these things, and when I think about AI, and you mentioned, it's like, oh, here's this knowledge pool or here's every We're gonna the junior lawyers are going to get access to all these templates. And I'm thinking, like if I'm this guy, like do I want to share all my templates and knowledge with the firm? But if I don't, if everyone thinks that, then these tools don't really work.

00:42:08
Speaker 1: Right.

00:42:08
Speaker 2: If you don't have a culture of like contributing into sight and knowledge back to the firm, there's no franchise value and you're definitely not going to get anything out of AI or in terms of like meaningful. I would think, maybe we'll get some Does this create any tensions? And when you're thinking about the firm, maybe not this year, but down the road. How to make sure that the interests of the firm are aligned with the interests of the partners.

00:42:30
Speaker 4: That is a huge issue. It's not a technological issue, it's a human issue.

00:42:36
Speaker 1: Right.

00:42:36
Speaker 4: Lawyers tend to be highly autonomous creatures, right, and successful law firm partners tend to be even more highly autonomous and generally take the view you're not going to tell me how to practice law.

00:42:50
Speaker 3: My husband used to get calls from when he was a junior lawyer. He would get calls from one of the partners out on their fishing boat.

00:42:56
Speaker 2: Oh yeah, on weekend.

00:42:58
Speaker 3: It's assigning work autonomous.

00:43:00
Speaker 4: Your husband worked at our firm?

00:43:02
Speaker 2: No not. Do you think like an evil you kill environment in many cases? Like yeah, right?

00:43:11
Speaker 4: And you know, many people would say, especially at our firm, that one of the secrets to our success is that we a term we use oftense. We let the horses run, right, you let people go do their thing, and clients love that and love the entrepreneurial energy. And now with AI were saying we needed to share all your knowledge. So the culture the culture clash there is significant interesting and I think that's I think that's a real challenge. Now when you talk about the NBA type hires or NFL type you know salaries that you're seeing, perhaps some of the motivation for that is getting their knowledge into their into the systems. Right if you can, if you can get some of these superstars and kind of put their name on the UH on your AI tools and say that you know, so and so recently did a deal that had these terms. You know, how's the client's going to be super impressed by that? And maybe that's part of the value add UH and maybe that creates even higher comp numbers for superstars.

00:44:20
Speaker 3: I want to go back to I guess my first question about billable hours in the age of AI. If we're seeing pricing actually compressed, if we're seeing a sort of maybe democratization of legal tools, maybe uncertainty over case results starts to collapse as well, would you maybe finally see a shift away from the billable hours thing?

00:44:43
Speaker 4: Maybe so, UH, and you should, right, we should be able to move to project based pricing or outcome based pricing. I think that that that will finally take off, there will still be a lot of instance is where hourly rates apply.

00:45:02
Speaker 2: And I think you're.

00:45:03
Speaker 4: Going to see a continuing huge increase in hourly rates at top tier firms. And I think we were already seeing it. If you look at data from twenty twenty five, as published by the American Lawyer, average hourly rates in twenty twenty five went up by ten point one percent at the largest firms in the United States, and at the top the top twenty firms by profitability, they went up even more. And that doesn't make sense in a year when CPI went up by around three percent, right, I've never seen that kind of gap between CPI and average rates. One of the things that might explain it is that the billable hour has actually become more productive and more valuable because of AI tools, and I think you'll see that continuing at an ever faster pace. So I would expect those hourly rates will continue to go up. Yet the price for the things that clients will actually want to buy, which are solution so the business problems are going to come down because they will take far fewer hours.

00:46:04
Speaker 2: I want to ask you one more question about your your tech stack. You know, there's a couple other things that are sort of happening. One is obviously this idea that well, if you use an open source model unlike say some claude, then you can really like train bake all your knowledge into the model itself and so you're not just sort of doing the retrieval augmented generation where the model essentially is like combined with a search engine, but it's actually like baked in and you can actually do that. And you see, it was a really interesting article paper from Bridgewater et cetera recently where they talked about doing this. And then of course, like just in general setting aside open source versus closed source and model, it's like, we do see this thing, and we mentioned Kirkland Ellis in the beginning. Eventually you hit a scale where it's like no, you want to I just you know, you don't just want to be build your own Harvey or whatever it is and actually internalize some of the infrastructure or spend and like actually truly customize this, etcetera. And arguably it's never been easier to build software et cetera. I'm curious, like, okay, as a four hundred person firm, obviously in your view, getting a lot of value already out of AI, what comes the point where you would think, you know what, we want to start building some technology in house, including perhaps a homegrown version of one of the models trained on your proprietary data.

00:47:40
Speaker 4: So we definitely want to connect these proprietary models to our data and use that to help train the model. But you know, training from scratch a model does it. Kirkland announced they're spending five hundred million dollars over five years. Right, It costs them like a billion and a half dollars to train a model one and a half billion, right, So even that would take Kirkland fifteen years of investment. I don't see us really training our own models. I see us doing some customized solutions on top of existing models. I see us certainly doing our playbooks so that they'll be the Lowenstein Sandler M and a playbook for that's customized for our firm and our market knowledge. But I don't I don't think we're gonna really build our own as much as modify and customize.

00:48:35
Speaker 2: That's not happening.

00:48:36
Speaker 4: We've always I mean, you know, we might white label an existing one to come up with a fancy name, but I don't think that's realistic for a firm of our size. I don't think it's even realistic for a Kirkland. So well, you know, we'll see how that shakes out, but I don't see us building our own I do want to mention one thing though, that's related to both of your points. All of these models that law firms have been using have been at the enterprise level, have been basically all you can eat pricing. Right, they're all going to be switching to token based pricing. You know Claude is doing it first.

00:49:12
Speaker 2: But do you think you could see sticker shocks like, oh, you know what I thought we were getting that. I thought we were able to do all these trusts at thirty percent of the cost, but it turned out that was subsidized. It turns out there's actually gonna be one hundred and thirty percent of the cost or ninety percent of the cost.

00:49:27
Speaker 4: So I don't think we yet know what the actual cost is. That's not if it's not subsidized by outside investors.

00:49:34
Speaker 1: Right.

00:49:34
Speaker 4: I don't know that we really know what the optimal combination of labor and capital and you know, robot capital is because we don't know the true cost, especially if we, you know, kind of hit this energy grid wall where it just gets more and more expensive.

00:49:55
Speaker 2: And today run these July eighth, you do not yet have visibility from any of your partners into like the true cost of this stuff. I mean, if it's like if if they have to make a profit per token.

00:50:06
Speaker 4: Right, we don't know. You know, I know from from my IT person who I was talking to you yesterday that so far this month, I've my use of Claude has used up thirty six dollars. But that's what July fourth weekend in there, right, And it was only this on the seventh, there only three business days that I was actually using it. But so that's not not not so.

00:50:28
Speaker 2: Much tillable time. Yeah, exactly exactly.

00:50:32
Speaker 4: But I don't know where that pricing is ultimately going to go, and uh so we don't we don't know how that story is going to play out two years down the road.

00:50:41
Speaker 2: He Garry wingoins, thank you so much. That was a really helpful conversation. Really appreciate it.

00:50:46
Speaker 4: Great being here, So thanks for having me.

00:51:02
Speaker 2: Tracy. It's really interesting that we really don't even know anything about the economics of AI yet because of like how much like the you know, these all you can eat models and like what how that's all going to shake out? I don't know.

00:51:14
Speaker 3: Well, it reminds me a lot of you know, what happened with Uber and the food delivery apps, where basically venture capital was subsidizing everyone's ability to order food at home, and then when it actually came time to prove the business model and generate some profit, we saw the cost of food delivery go up and use of it go down.

00:51:34
Speaker 2: Yeah. Also same with oil. Yeah, and the fact that like capital markets subsidized losses for years and years with oil and now we're getting you know, similar examples there. And then on the flip side, so let's say that like you know, the historical prices, like token prices continue to drop at a very high rate. On the flip side, we get a lot more lawsuits and so on the flip side, like this is like, okay, this is you know, the Jeffens paradox is Like, good news, it turns out there's going to be plenty of human labor in the future. Bad news, there's going to be one hundred more frivolous lawsuits and patent counter challenges that you never had to do, and that's what we're doing now.

00:52:16
Speaker 3: This is what I worry about on a wider scale, because if we talk about a AI as this productivity enhancement, you could have a situation where AI is just used to expand bureaucracy forever and ever and ever, so not just frivolous lawsuits, but like maybe human resources things like that. I always wanted to write an article about how like human resources ate the US economy.

00:52:38
Speaker 2: Yeah, well think about like, you know, you could imagine like, okay, every worker at a firm gets access to claude or something like that, and on day one, it's like, oh, this is great, Like I just condensed eight hours of labor into two hours of labor, et cetera. But then suddenly, like all of the other people that they're working with, who are fighting for the same promotions also condensed eight hours of labor into two hours, and so it's like, well, I'm trying to get that promotion and you just like find more and more work that scenario in which like all of us suddenly feel like life is easier because of AI. Like I gotta say that feels like you know what I did actually have a I needed to change a flight recently and change a round trip ticket to a three way ticket at the last minute, and that was like a very daunting thing to me to do on the Delta dot com website and so like I asked, like Chadubt the steps like what on and like was really helpful is like go to this page and the bottom right there will be a thing. This is what you click to like change one leg of the thing, and like it worked exactly as it So it's like there was an example where it's like, Okay, this actually eased some psychic attacks. But generally speaking, this future where it's like life just gets much easier because of AI, feels like at a minimum, it's a long way off.

00:53:58
Speaker 3: No, the future is a Jeffens parrot for like administrative overhead and busy works. That's how I feel at the moment. Shall we leave it there?

00:54:05
Speaker 2: Let's leave it there.

00:54:06
Speaker 3: This has been another episode of the Oudlots podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

00:54:12
Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart. Follow our producers Carmen Rodriguez at Kerman armand dash Ol Bennett at dashbot, cal Brooks at Kilbrooks and Kevin Lozano at Kevin Lloyd Losana and from our Odd Lots content. Go to Bloomberg dot com slash odd Lots or of a daily newsletter and all of our episodes and you could chat about all these topics twenty four seven in our discord Discord dot gig slash onlines.

00:54:34
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