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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: Tracy, I was at my cousin's wedding this weekend, and

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Speaker 2: I have a lot of lawyers in my family, and

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Speaker 2: every time I know that, yeah, yeah, and my extended

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Speaker 2: family a lot of lawyers in there, and I'm always

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Speaker 2: asking them, like what they do and stuff. Within like

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Speaker 2: five seconds of any conversation, I'm like, tell me about

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Speaker 2: trials and techniques like swathe the jury, etc. The stuff

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Speaker 2: I see on TV. I am aware though that that

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Speaker 2: is actually not that big of a part of many

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Speaker 2: lawyers like day to day.

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Speaker 3: Well, I have a lawyer in my family too, an

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Speaker 3: ex lawyer, my husband, who did big law, big corporate law,

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Speaker 3: absolutely hated it and got out of it luckily. And

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Speaker 3: so I have a feeling in this particular episode, I'm

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Speaker 3: just going to ask a lot of work life balance

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Speaker 3: questions based on my own experience of my husband's experience. Yeah,

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Speaker 3: but it is a huge industry, and it's one of

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Speaker 3: those industries that I think all financial journalism actually doesn't

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Speaker 3: do a good job of covering along with accounting. So

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Speaker 3: we should talk about it.

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Speaker 2: We definitely should talk about it. We should talk about

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Speaker 2: it way more than we do. Within the AI conversation, specifically,

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Speaker 2: you hear a lot about the prospects of this being

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Speaker 2: either like a force multiplier or a labor reducer, because

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Speaker 2: my understanding is that a lot of like, especially like

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Speaker 2: junior lawyers, they're up all night. What are they like

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Speaker 2: looking for misplaced commas because those could be a big

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Speaker 2: deal in contracts, right, and so in theory, and again, Tracy,

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Speaker 2: I'm sure you know way more about this than I do.

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Speaker 2: But in theory that sort of seems like something a

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Speaker 2: I might be good at, Like, oh no, if the

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Speaker 2: is not here and it's there, then that totally changes

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Speaker 2: the definition of this clause or whatever.

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Speaker 3: There's a famous case involving a commo that did exactly that.

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Speaker 3: So many points to make here. First of all, I

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Speaker 3: am vastly in favor of anything that makes corporate lawyers

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Speaker 3: lives slightly more efficient and happier, so AI could be

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Speaker 3: useful here. But I also wonder, you know, the lawyer profession,

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Speaker 3: they have things like templates and standard forms and stuff

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Speaker 3: like that. So I also wonder how much of an

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Speaker 3: improvement AI will be here. So I'm excited to talk

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Speaker 3: about it.

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Speaker 2: You know that, Tracy, when they get the AI tools

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Speaker 2: really in place, they're just going to give them more work.

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Speaker 3: Yeah, well that's what I suspect.

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Speaker 1: Yeah, yeah, no, one's.

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Speaker 3: Actually by the way, I have a lot of stories

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Speaker 3: of like partners calling from their fishing boats on the

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Speaker 3: weekend demanding that junior lawyers go into the office and

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Speaker 3: like fix a comma and things like that.

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Speaker 2: All Right, Well, I'm really excited to say we do

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Speaker 2: have the perfect get. Someone that I've known for a

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Speaker 2: very long time, someone who just knows about a lot

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Speaker 2: of stuff. Someone who is lawyer. We're going to be

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Speaker 2: speaking with Joel Worthheimer. He's currently at Worthheimer Fleet LLP

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Speaker 2: of civil rights firm here in New York City. He's

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Speaker 2: previously at the White House. He's done big law in

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Speaker 2: the past. He knows a lot about a lot of stuff.

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Speaker 2: He knows a lot about poker, he knows a lot

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Speaker 2: about AI. Someone I think we had just talked to

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Speaker 2: about anything. He knows a lot about elections. He just

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Speaker 2: sort of knows everything. Joel, Thank you so much for

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Speaker 2: coming on Odd Laws thrilled to have you here.

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Speaker 4: Thank you for having me.

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Speaker 2: Why do you real? Quickly tell us, like who you are,

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Speaker 2: what do you do? Why are we talking to you.

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Speaker 5: I run my own civil rights law firm. I just

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Speaker 5: partnered up, and we sue the government mostly for violations

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Speaker 5: of civil rights. Sometimes that's false arrests or wrongful convictions.

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Speaker 5: Sometimes that's wrongful removals of kids and let's say child

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Speaker 5: services for a shaken baby that was a false accusation,

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Speaker 5: that sort of thing, And we try to get people

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Speaker 5: compensation for the wrongs of their constitutional rights. That's about

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Speaker 5: eighty percent of what we do.

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Speaker 3: Question. Are civil slash criminal lawyers? Are they happier than

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Speaker 3: corporate lawyers?

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Speaker 5: I would say on average, sure, but the distribution is

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Speaker 5: probably pretty wide for each you know. Look, I do

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Speaker 5: think on the nice thing on the plaintiff side, and

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Speaker 5: it's like I'm my own boss. I get to, you know,

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Speaker 5: determine how much I work, and each extra case it's

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Speaker 5: just more money or less money if I if I

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Speaker 5: decide not to take it, and so I can sort

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Speaker 5: of set my own work life balance when I do that,

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Speaker 5: you know, and it's it's constantly a decision of sort

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Speaker 5: of what's the expected value of a case and is

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Speaker 5: it worth it to me in terms of the hours

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Speaker 5: I have to put in.

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Speaker 2: So let's talk about when you were at a big

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Speaker 2: law and you know, I joked that you know people

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Speaker 2: they're up at one am and they're looking at commas.

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Speaker 2: I know that's a little bit of an exaggeration, but no,

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Speaker 2: it's not, and it really isn't.

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Speaker 1: Yeah, I guess it's not.

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Speaker 2: And I know that lawyers are really obsessed with billbile

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Speaker 2: hours and I think they even like measure things in

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Speaker 2: ten minute increments, like so six minutes, six minute increments.

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Speaker 2: If you're a junior associated or enter a law firm, whatever,

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Speaker 2: what is the modal use of your time? What are

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Speaker 2: you mostly doing when you're clocking those six minute increments?

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Speaker 4: Right?

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Speaker 5: So it's gonna I can only really speak to litigation

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Speaker 5: because of corporate you're I think we're just reviewing contracts

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Speaker 5: and deal points constantly. For litigator, A lot of your

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Speaker 5: your time is in discovery, and that's true small law,

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Speaker 5: big law, but you are reviewing documents, uh, synthesizing what

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Speaker 5: they say. Maybe you're trying to do a privileged check

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Speaker 5: and say, okay, this document we can't turn over. You know,

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Speaker 5: that's changed some actually with machine learning, the predictive coding

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Speaker 5: has shrunk the amount of time that you know, junior

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Speaker 5: associates are spent doing that legal research, fights with opposing counsel.

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Speaker 5: You need to give us these documents you're saying they're privileged,

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Speaker 5: they're not privileged, that sort of thing. And then there's

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Speaker 5: the smaller section on taking depositions, you know, briefing things

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Speaker 5: to court. So that's very broad overview of those six

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Speaker 5: minute increments, and yes, editing and reviewing, looking for hanging commas,

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Speaker 5: you know, hanging headers. That was always the ban of

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Speaker 5: my existence, was forgetting that there was a hanging header.

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Speaker 4: Yeah, and getting ye all that for it.

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Speaker 2: That sounds bad, you know, the economics of a big

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Speaker 2: law firm. It feels like this very big pyramid structure,

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Speaker 2: which I think is replicated in many industries around the world,

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Speaker 2: probably in finance to some extent, and then I think

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Speaker 2: even academia to some extent. But talk to us a

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Speaker 2: little bit about like how that value accruise to the

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Speaker 2: entire firm and the partners and the distribution of the

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Speaker 2: fees that the clients are paying.

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Speaker 5: Right, so you have what they call leverage in a

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Speaker 5: law firm, So you have equity partners. Some firms have

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Speaker 5: equity partners and income partners who don't actually own a

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Speaker 5: chair of the firm, but the partners are trying to

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Speaker 5: get leverage on the associates. So one partner to say

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Speaker 5: four associate ratio, all of the big law firms basically

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Speaker 5: pay the exact same amount to their associates.

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Speaker 4: This is called the Cravat scale.

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Speaker 5: You can look it up and it'll say a third

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Speaker 5: year associate makes two hundred and fifty thousand dollars and

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Speaker 5: gets this bonus each year.

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Speaker 4: Let's just say for.

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Speaker 5: The average associate bills seven hundred dollars an hour and

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Speaker 5: is expected to bill about two thousand hours a year.

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Speaker 5: So they're generating one point four million for the firm

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Speaker 5: in revenue in terms of build out, and they're getting

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Speaker 5: paid i'd say with taxes and everything, five hundred thousand.

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Speaker 5: So the partners are making nine hundred thousand dollars for

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Speaker 5: every extra associate. So if you have a four to

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Speaker 5: one leverage, nine hundred thousand profits per partner three point

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Speaker 5: six million. And that's how they're trying to think of it,

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Speaker 5: and the big partners, the rain makers are the ones

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Speaker 5: who can generate enough work to support those associates. And

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Speaker 5: if you can't support those associates, your leverage goes down,

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Speaker 5: your profits go down.

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Speaker 4: You might have to let people off that sort of thing.

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Speaker 3: It's nice to be a partner at big law. But

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Speaker 3: on that note, so my understanding, at least in corporate

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Speaker 3: law is that it is harder to make partner nowadays

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Speaker 3: than it used to be. And this is part of

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Speaker 3: the reason why it's a miserable experience, because it used

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Speaker 3: to be in the past. You know, you work really

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Speaker 3: really hard, basically give up your life for like two

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Speaker 3: decades or whatever, and then you get rewarded with a

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Speaker 3: huge salary and you don't have to do as much

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Speaker 3: of the grunt work. Your job is basically sourcing deals

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Speaker 3: and making the intros and stuff like that. Do you

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Speaker 3: get that impression as well on your side?

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Speaker 5: I definitely see from the outside that it seems harder

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Speaker 5: to make partner. I think that's led to people more

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Speaker 5: people leaving earlier to do their own firms. I think

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Speaker 5: it's easier than ever to start.

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Speaker 4: Your own firm.

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Speaker 3: And why is that.

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Speaker 5: I mean, for one, I mean for me, for example,

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Speaker 5: I didn't need an office when I started. I started

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Speaker 5: in January of twenty twenty one. I had a desk

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Speaker 5: at a you know, at another law firm that collected mail.

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Speaker 5: I think the number of services are just easier to access,

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Speaker 5: even things like you know now if you really wanted to,

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Speaker 5: like cut, you could start with a cheaper legal research

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Speaker 5: service than West Law. Things like that, I think, and

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Speaker 5: you've seen it in the in the business creation numbers.

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Speaker 5: I think you see a lot of professional services, you know,

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Speaker 5: splitting off from firms. But I definitely think it's harder

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Speaker 5: to make partner.

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Speaker 4: They have had that.

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Speaker 5: They've added this income partner level, so you make partner,

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Speaker 5: but you're not an equity member of the firm. I

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Speaker 5: think consolation prize, that's right, and you get to shop

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Speaker 5: the title.

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Speaker 4: I think it's the other reason. You know.

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Speaker 5: So let's say you want to go in house somewhere.

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Speaker 5: Now you're a partner at this firm and you can

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Speaker 5: say you're a partner at the firm even if you

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Speaker 5: haven't bought in.

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Speaker 4: But I think that structure has been around longer.

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Speaker 5: Honestly, it feels like the same thing it's in academy

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Speaker 5: it's harder to get ten here now, it's just the

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Speaker 5: latter's getting pulled up.

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Speaker 4: I guess in the sort of.

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Speaker 2: Industry, would anyone go into a big law firm if

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Speaker 2: they knew in advance that they wouldn't make partner? Like

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Speaker 2: there's some like the angel of the future comes to

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Speaker 2: them in a dream and says, you know what, you're

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Speaker 2: not going to make partner? Would that still be worthwhile?

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Speaker 2: Or is the prospect even if it's declining of making

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Speaker 2: partner like? Is that crucial towards that decision? If you

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Speaker 2: knew in advance you weren't going to make it? Would

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Speaker 2: anyone make that decision?

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Speaker 5: So they do have staff attorneys at big law firms

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Speaker 5: that are people who are not on partner track.

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Speaker 1: Okay, and those are fine careers.

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Speaker 5: And they're fine careers I think people you know, but

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Speaker 5: they also have less expectation of how much they're going

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Speaker 5: to work.

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Speaker 4: You typically wouldn't do it.

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Speaker 5: And the reason they have this up or out sort

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Speaker 5: of system at the big law firms is because the

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Speaker 5: prospect of being partner drives you to sell you twenty

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Speaker 5: twenty five hundred hours, how many it is you're showing.

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Speaker 3: How well the light at the end of the tunnel, right.

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Speaker 1: That's right?

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Speaker 5: So you know, I think, Look, the training is really good,

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Speaker 5: the cases are really interesting, Like I actually quite enjoyed

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Speaker 5: some of it, But that pressure to bill and work is.

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Speaker 4: You probably wouldn't I want that.

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Speaker 1: When you say training, like, you know, I don't, like

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Speaker 1: what's the deal? Like in law school you don't even

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Speaker 1: learn about any of the things you actually do as

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Speaker 1: a lawyer.

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Speaker 5: You certainly learn, you know how to think about the law,

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Speaker 5: but you're not learning in the city and you have

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Speaker 5: a leader.

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Speaker 1: Because I think this is.

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Speaker 2: Actually important for the AI component of like, Okay, what

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Speaker 2: is how do you actually learn to be a lawyer

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Speaker 2: if you don't really do it in law school? And

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Speaker 2: then you know AI is going to take over more

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Speaker 2: and more of these functions like the prospect where do

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Speaker 2: you learn?

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Speaker 3: Right?

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Speaker 5: So what is So here's an example. You don't learn

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Speaker 5: how to have a dispute about discovery in law school. Okay,

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Speaker 5: you know I sent out discovery demands you owe me documents.

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Speaker 5: Right under in the American system of law, somebody you get,

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Speaker 5: you end up in a lawsuit, and each side is

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Speaker 5: obligated to exchange documents with each other. And you you

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Speaker 5: send out demands and you ask for a certain category

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Speaker 5: of documents and they come back and say we're not

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Speaker 5: going to give them to that request was overly burdensome

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Speaker 5: or they're not really or they say they lost them,

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Speaker 5: or they say they lost them, and you have to

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Speaker 5: have a fight about getting the documents. What keywords did

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Speaker 5: you search, who did you collect documents from? All of

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Speaker 5: those sorts of things, and how to have that dispute,

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Speaker 5: how to make the request so you don't get the

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Speaker 5: denial those sorts of things, and what the writing looks

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Speaker 5: like and feels like you just don't get. In law school,

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Speaker 5: you just the and even writing a brief, and what's

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Speaker 5: a persuasive brief? The edits those sorts of things. You

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Speaker 5: learn how to do legal writing, but it's just you

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Speaker 5: can't do it until you're doing it.

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Speaker 1: Got it okay?

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Speaker 3: So on that note, I mean, AI seems possibly most

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Speaker 3: at home replicating the work done by say a mildly competent,

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Speaker 3: maybe not too tired, junior associate. And historically you do

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Speaker 3: that work for many years and you build foundational skills

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Speaker 3: of being a lawyer, like attention to detail and issue

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Speaker 3: spot and I guess, risk triage, and that sort of thing.

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Speaker 3: If those tasks, those formational tasks are now outsourced to AI,

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Speaker 3: what do you see as the impact on junior lawyers.

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Speaker 5: It's a great question. I think it's going to be

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Speaker 5: hard for junior lawyers at big law firms to get

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Speaker 5: their sort of that training. On the other hand, it

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Speaker 5: may just be that they are doing more work and

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Speaker 5: so on the same things, but more efficiently. Maybe they're

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Speaker 5: now going to have more fights about documents and less

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Speaker 5: time spending reviewing the documents. Maybe they can increase the

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Speaker 5: volume of things they demand, that sort of thing. I

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Speaker 5: certainly think that the leverage model and the build our

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Speaker 5: model is going to run into some difficulty. It'll also

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Speaker 5: make their lives easier, just in terms of you know,

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Speaker 5: as an example, you.

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Speaker 4: Get discovery requests.

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Speaker 5: I talk about discovery a lot because it's truly like

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Speaker 5: a lot of the work of being a lawyer. But

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Speaker 5: they come, you know, the other side will send you

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Speaker 5: a PDF with a bunch of demands, and then you've

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Speaker 5: got to respond to the demands, and even just the

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Speaker 5: moment where you have to convert the PDF into a

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Speaker 5: word document and start editing the work word document it's

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Speaker 5: like the true bane of my existence. And I've actually

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Speaker 5: started using this AI program which it's called brief Point AI,

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Speaker 5: and and they are it's also very templated, right, so

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Speaker 5: you're constantly trying to find templates. Hey, other associated in

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Speaker 5: this law firm, can you send me, you know, a

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Speaker 5: previous document?

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Speaker 4: Now I can?

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Speaker 5: They have you know, eighty different objections that you're used

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Speaker 5: to making, and they convert them and it's a lot

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Speaker 5: more pointing click And of course you have to know

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Speaker 5: what you're doing when you're you know that these objections

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Speaker 5: are valid. But just the document formatting and getting it through,

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Speaker 5: you know, and it looked to look good and and

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Speaker 5: not have to fight with Microsoft Word for three hours

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Speaker 5: is really valuable.

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Speaker 3: I realized I should have asked this earlier. But when

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Speaker 3: we say AI, what does that actually entail in the

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Speaker 3: legal field, Because, as you point out, I mean template

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Speaker 3: standard forms, those sort of things have been around forever

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Speaker 3: and a lot of law firms already use template software

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Speaker 3: to quickly duplicate ork or make document packages or whatever.

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Speaker 3: So when we talk about AI in law, what exactly

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Speaker 3: would that be?

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Speaker 4: I think it can be a lot of things.

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Speaker 5: One, it can be making that template process a lot faster,

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Speaker 5: and uh, you know that will free up labor time

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Speaker 5: for sure. The you know Chatgypts three pro is doing

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Speaker 5: legal research now that is quite excellent.

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Speaker 2: This is really important. People who think that this that

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Speaker 2: three is dumb or is a hallucination machine are not

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Speaker 2: or that chagpts have not used oh three.

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Speaker 4: That's that's my view.

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Speaker 5: And you know, there's this professor at Georgetown I think

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Speaker 5: its name is Danny Wels Townsend who's just sort of

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Speaker 5: every time a new model has been coming out, has

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Speaker 5: been running sort of exam questions that he gives to

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Speaker 5: his students. I think it's in his Federal Courts class

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Speaker 5: about you know, how they do on each of these questions.

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Speaker 5: And when O three came out, not even O three PRO.

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Speaker 5: When O three came out, it was it answered them all,

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Speaker 5: some of them excellently, and even got what he was

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Speaker 5: called an issue spotter question. So it wasn't on the

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Speaker 5: face of the question it was an appellate jurisdiction question.

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Speaker 5: If you really were thinking hard about it as a student,

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Speaker 5: you would have noticed this issue that he wasn't asking

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Speaker 5: about explicitly. But there might not have been jurisdiction to

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Speaker 5: even bring the appeal that he prompted you about. And

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Speaker 5: O three caught that. And when I've used it, you know,

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Speaker 5: you hear a lot of these cases lawyers getting sanctioned

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Speaker 5: because they're, you know, submitting things with hallucinated cases.

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Speaker 3: And there's some funny examples of that.

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Speaker 4: They're they're amazing.

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Speaker 5: And the thing I think to myself is you would

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Speaker 5: never admit as a as a partner at a law firm,

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Speaker 5: you'd never submit whatever the junior associate just turned over

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Speaker 5: to you. Sometimes they didn't understand the case exactly right.

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Speaker 5: And so that's the job is checking the cases. Shepherdizing

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Speaker 5: is the I think the Lexus term for it, and

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Speaker 5: making sure the cases stand for what they say, that

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Speaker 5: they're still valid precedent.

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Speaker 4: That's the job of being a lawyer.

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Speaker 5: And now I can rely on three to start some

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Speaker 5: of the research for me, point me even just to

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Speaker 5: point me to the cases and exciting to the cases.

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Speaker 5: And this is without access to Bloomberg Law or Thompson

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Speaker 5: Reuters or anything like that. I assume it's coming and

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Speaker 5: once it's going to be integrated with those databases of cases.

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Speaker 5: Right now, it's just trawling the Internet. I think it's

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Speaker 5: going to do a fantastic job. And you spend so

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Speaker 5: much time as a lawyer, as a litigator thinking of

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Speaker 5: the Boollyan search terms for lexus, the and or these

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Speaker 5: sorts of things, and you don't know the exact term

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Speaker 5: that's going to come up in case, you know what

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Speaker 5: the all of the precedent use this term, and not

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Speaker 5: having to think about that, I can run it in

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Speaker 5: the background as I'm doing another task and for twenty

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Speaker 5: minutes and something comes back from three It's got the

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Speaker 5: five cases that I need to to dig in and

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Speaker 5: find the right you know, language to then go on

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Speaker 5: Lexus and then to chase it down.

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Speaker 2: I've been using three more for just sort of my

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Speaker 2: own like intellectual questions from time lawsuits. No, I haven't

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Speaker 2: done any I haven't started any lawsuits yet. But this

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Speaker 2: is really the way that I've been using it, which

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Speaker 2: is that it's a fantastic tool for just like discovering

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Speaker 2: the existing research that's out there. I was looking at

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Speaker 2: something I was curious about, like trade barriers within China

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Speaker 2: between provinces, you know, something like that. There is a

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Speaker 2: lot of research that's been done on the question of

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Speaker 2: like Okay, if each of these provinces has some motivation

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Speaker 2: to promote their champions, is there any tension?

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Speaker 1: There is something?

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Speaker 2: I would have it found all of this all. It

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Speaker 2: had the links to the academic research that's been about it.

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Speaker 2: It had summaries of them. The summaries may have been wrong,

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Speaker 2: but I'm not in the I'm not going to like

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Speaker 2: copy and past summaries and present it as my work.

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Speaker 2: I could click on it most of the time, it is,

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Speaker 2: they are pretty correct. It's so what you say in

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Speaker 2: terms of like just even like being able to find

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Speaker 2: the relevant case law, et cetera, seems extremely powerful and

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Speaker 2: time saving.

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Speaker 5: It's truly been life changing for me. Well, and I

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Speaker 5: like doing legal research. I like getting into the cases,

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Speaker 5: but just you know, if there's a new issue and

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Speaker 5: you just don't know where to start, you know, that's

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Speaker 5: that It was always I think for a juniors, where

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Speaker 5: do I start? And I think it will make their

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Speaker 5: life easier in that regard, even if it might also

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Speaker 5: make their.

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Speaker 1: Lives you know hard.

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Speaker 3: Okay, time saving making associates lives easier, that sounds great

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Speaker 3: from a personal perspective, but what does that actually mean

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Speaker 3: for billable hours? Because of course, the whole business model

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Speaker 3: seems to rely on eking out as much work as

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Speaker 3: many billable hours as possible, right, and that includes grunt work,

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Speaker 3: which theoretically could be done by AI. Now, and I'm

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Speaker 3: not sure Big law or any law is going to

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Speaker 3: be necessarily incentivized to use AI if it means a

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Speaker 3: reduction in billable hours. I suppose they could make it

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Speaker 3: up in volume. Maybe that's that's the idea they can

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Speaker 3: take on more cases. But is that the expectation they

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Speaker 3: might be able to make it up in volume. I

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Speaker 3: do think things like they call them alternate fee arrangements

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Speaker 3: are going to become more popular. We get a flat

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Speaker 3: fee of let's say five million dollars to do this

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Speaker 3: deal for you or something like.

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Speaker 1: That, then you're incentivized to do it in the fewest

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Speaker 1: hours possible. That's right, keep going.

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Speaker 5: So that's one possibility that the model just changes the

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Speaker 5: billable hour, it stops being the model. The other thing is,

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Speaker 5: you know the way it works right now is it's

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Speaker 5: been a while for me, so you know, the big partners,

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Speaker 5: let's say they're building out at two thousand dollars an

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Speaker 5: hour or fifteen hundred dollars an hour and the associates

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Speaker 5: are seven hundred dollars an hour. I think in a

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Speaker 5: lot of ways, what the client is paying for is

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Speaker 5: the is really like five thousand dollars an hour for

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Speaker 5: the partner and four hundred or three hundred for the associate.

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Speaker 5: But it just gets built out sept differently. And I

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Speaker 5: think one thing is that partners might just accrue a

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Speaker 5: lot more of the value with fewer associates. They still

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Speaker 5: have the billd hours, they're just a lot more efficient.

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Speaker 5: And you know, in particular, I think for general counsels

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Speaker 5: who are hiring at the big law firms, they're hiring

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Speaker 5: for safety. You know, nobody ever got fired for hiring Cravat.

439
00:21:48,600 --> 00:21:50,800
Speaker 5: It's sort of like a way to think about this

440
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Speaker 5: or a big law firm, if something goes wrong, you

441
00:21:54,280 --> 00:21:57,359
Speaker 5: want to have hired the best lawyer you could and

442
00:21:57,440 --> 00:22:01,200
Speaker 5: say to your board or your CEO, look I hired

443
00:22:01,200 --> 00:22:04,240
Speaker 5: a great law firm, and they just they just missed it.

444
00:22:04,280 --> 00:22:06,320
Speaker 5: But look this is everybody agrees this is a great

445
00:22:06,400 --> 00:22:09,320
Speaker 5: law firm. Which is all to just say that. I

446
00:22:09,359 --> 00:22:12,359
Speaker 5: think one possibility here is the bill hour remains, but

447
00:22:12,440 --> 00:22:15,879
Speaker 5: all the value accruise to the top top lawyer.

448
00:22:15,960 --> 00:22:18,720
Speaker 2: Yeah, I mean that seems like it'll have implications. We

449
00:22:18,760 --> 00:22:22,000
Speaker 2: should talk about that further. Something I have done is

450
00:22:22,119 --> 00:22:28,240
Speaker 2: I'll take episodes transcripts of odd lats, and afterwards I'll

451
00:22:28,280 --> 00:22:31,159
Speaker 2: do a little self criticism session. Well, I'll like say, like,

452
00:22:31,160 --> 00:22:34,520
Speaker 2: where could we have pressed this? Where were the weaknesses

453
00:22:34,720 --> 00:22:35,480
Speaker 2: in the guests?

454
00:22:35,720 --> 00:22:36,120
Speaker 1: Answer?

455
00:22:36,480 --> 00:22:39,480
Speaker 2: Where should we have pressed further and harder? What were

456
00:22:39,480 --> 00:22:42,560
Speaker 2: some of the logical flaws? What would you have done differently? Etcetera?

457
00:22:42,600 --> 00:22:44,840
Speaker 2: And sometimes I've found that to be useful. Have you

458
00:22:44,880 --> 00:22:48,840
Speaker 2: been able to use AI for essentially like building better

459
00:22:49,200 --> 00:22:52,639
Speaker 2: arguments or finding weaknesses in an argument or anything like

460
00:22:52,680 --> 00:22:53,320
Speaker 2: that yet.

461
00:22:53,200 --> 00:22:55,840
Speaker 5: I haven't done that yet. I'm sure I will. I

462
00:22:55,920 --> 00:22:58,680
Speaker 5: have used it for taking audio and making it into

463
00:22:58,800 --> 00:23:01,720
Speaker 5: oh transcripts. I mean that's just like a foundational thing

464
00:23:01,760 --> 00:23:06,119
Speaker 5: for me. Prison will produce an audio of a hearing

465
00:23:06,200 --> 00:23:08,359
Speaker 5: or something like that, and I need the transcript and

466
00:23:08,400 --> 00:23:10,439
Speaker 5: I need to like just read it instead of listening

467
00:23:10,480 --> 00:23:11,520
Speaker 5: to it for forty minutes.

468
00:23:11,920 --> 00:23:15,080
Speaker 2: But self criticism, Yeah, but it also it does sound

469
00:23:15,160 --> 00:23:18,480
Speaker 2: like you mentioned the issue spotter question, like actually being

470
00:23:18,520 --> 00:23:22,200
Speaker 2: able to identify real legal questions.

471
00:23:22,040 --> 00:23:23,840
Speaker 3: Not just an identify precedent.

472
00:23:23,960 --> 00:23:27,399
Speaker 2: Yeah, not just identify president, but actually logical sequences or

473
00:23:27,480 --> 00:23:30,080
Speaker 2: where like it sounds like if you're getting.

474
00:23:29,800 --> 00:23:31,040
Speaker 4: That, I'm sure it is coming.

475
00:23:31,080 --> 00:23:35,639
Speaker 5: And I think as I, you know, get as the

476
00:23:35,680 --> 00:23:38,000
Speaker 5: models get better, and I have to produce a brief

477
00:23:38,040 --> 00:23:40,960
Speaker 5: and maybe I can start running it through. Of course,

478
00:23:41,040 --> 00:23:45,920
Speaker 5: lawyers all have huge confidentiality concerns so everything they do,

479
00:23:46,280 --> 00:23:50,320
Speaker 5: so I'm very careful about what I'm putting into GPT. Yeah,

480
00:23:50,880 --> 00:23:52,959
Speaker 5: you know, and I can run your own deep sea instance.

481
00:23:53,200 --> 00:23:55,040
Speaker 5: I think that that's right. And I think the big

482
00:23:55,119 --> 00:23:57,760
Speaker 5: law firms are having sort of servers on site with

483
00:23:57,800 --> 00:24:00,880
Speaker 5: their own models, but those models aren't as good as

484
00:24:01,560 --> 00:24:05,480
Speaker 5: three yet, and so I think as you can start

485
00:24:05,520 --> 00:24:09,040
Speaker 5: to have great models in house on your own servers,

486
00:24:09,160 --> 00:24:12,479
Speaker 5: that's going to really be the thing that launches it.

487
00:24:12,560 --> 00:24:14,480
Speaker 5: I can't do that because it's just me and I'm

488
00:24:14,520 --> 00:24:16,679
Speaker 5: not going to host a server farm, but I do know,

489
00:24:16,840 --> 00:24:19,280
Speaker 5: right the new Apple chips on these very nice the

490
00:24:19,320 --> 00:24:22,960
Speaker 5: news they can run you know, things like Lama very well,

491
00:24:23,160 --> 00:24:26,600
Speaker 5: and so it's coming. But I think for the top models,

492
00:24:26,640 --> 00:24:29,359
Speaker 5: I'm sort of reluctant to put everything up there that

493
00:24:29,400 --> 00:24:29,920
Speaker 5: makes sense.

494
00:24:30,359 --> 00:24:32,919
Speaker 3: This is exactly what I was going to ask next,

495
00:24:33,040 --> 00:24:36,159
Speaker 3: which is how do clients feel about AI in the

496
00:24:36,240 --> 00:24:39,520
Speaker 3: legal industry, because I imagine, you know, nobody ever got

497
00:24:39,560 --> 00:24:42,800
Speaker 3: fired for hiring Kravath as you mentioned, or some other

498
00:24:42,960 --> 00:24:46,639
Speaker 3: magic circle law firm. But on the other hand, if

499
00:24:46,720 --> 00:24:50,480
Speaker 3: Kravath is now using AI, I imagine on the client side,

500
00:24:50,600 --> 00:24:56,000
Speaker 3: maybe there's some concern over having their material contracts or

501
00:24:56,080 --> 00:25:00,800
Speaker 3: employee data or financing documents or whatever getting uploads to

502
00:25:00,960 --> 00:25:03,880
Speaker 3: off site servers. And then there's also the question of

503
00:25:04,000 --> 00:25:06,879
Speaker 3: what if AI just makes a mistake, although, as you

504
00:25:06,920 --> 00:25:11,040
Speaker 3: pointed out, no partner would ever submit something done by

505
00:25:11,040 --> 00:25:14,240
Speaker 3: a junior associate without actually reviewing it first, so maybe

506
00:25:14,240 --> 00:25:16,400
Speaker 3: that's not as much of an issue. But are there

507
00:25:16,480 --> 00:25:20,359
Speaker 3: any qualms I guess coming from the client side that

508
00:25:20,400 --> 00:25:21,320
Speaker 3: you see.

509
00:25:21,480 --> 00:25:25,800
Speaker 5: So I can't speak to the big law as much

510
00:25:25,880 --> 00:25:29,760
Speaker 5: any sure, And it's very different I think on my side,

511
00:25:29,800 --> 00:25:33,280
Speaker 5: and I think, you know, my clients are their qualms

512
00:25:33,280 --> 00:25:35,800
Speaker 5: are much sort of bigger in terms of privacy and

513
00:25:36,640 --> 00:25:39,400
Speaker 5: much more you know, focused on the government and are

514
00:25:39,400 --> 00:25:42,399
Speaker 5: wanting to get just the best representation they can. So

515
00:25:42,960 --> 00:25:46,840
Speaker 5: but for example, I'm getting medical records from my clients

516
00:25:46,960 --> 00:25:48,639
Speaker 5: a lot. That's a lot of the job of a

517
00:25:48,640 --> 00:25:52,840
Speaker 5: plaintiff side lawyer is documenting injuries things like that, and

518
00:25:52,880 --> 00:25:56,399
Speaker 5: they're certainly concerned about their medical privacy, and so I can't,

519
00:25:56,440 --> 00:26:00,240
Speaker 5: you know, use AI yet to take the medical recD

520
00:26:00,359 --> 00:26:04,679
Speaker 5: and condense them into you know, the quick summaries. But

521
00:26:05,119 --> 00:26:07,919
Speaker 5: those programs I'm sure are coming where you know you

522
00:26:07,960 --> 00:26:11,399
Speaker 5: have there's there's privacy and security and all of the things.

523
00:26:11,600 --> 00:26:13,960
Speaker 5: They probably exist already. I'm just not using them yet

524
00:26:14,280 --> 00:26:18,240
Speaker 5: to make that part feasible. I am sure that you know,

525
00:26:18,280 --> 00:26:20,560
Speaker 5: if you are Goldman Sachs and you hire a big

526
00:26:20,600 --> 00:26:23,639
Speaker 5: law firm and they've been doing your your deals or

527
00:26:23,760 --> 00:26:27,119
Speaker 5: your IPOs such as they exist right now, and you

528
00:26:27,160 --> 00:26:30,439
Speaker 5: don't want the law firm training on your documents to

529
00:26:30,480 --> 00:26:33,520
Speaker 5: go use them in other deals. I'm sure this is

530
00:26:33,760 --> 00:26:36,920
Speaker 5: an issue. On the other hand, you know it might

531
00:26:37,160 --> 00:26:39,440
Speaker 5: reduce your legal costs by by half, and you can

532
00:26:39,440 --> 00:26:42,080
Speaker 5: fight about that, and so you know what they're willing

533
00:26:42,119 --> 00:26:42,359
Speaker 5: to do.

534
00:26:42,480 --> 00:26:43,399
Speaker 4: I don't. I don't know.

535
00:26:43,920 --> 00:26:48,359
Speaker 2: Let's extend forth some of these economic questions, because allowing

536
00:26:48,520 --> 00:26:52,520
Speaker 2: the senior partners to accrue more value by not needing

537
00:26:52,680 --> 00:26:55,760
Speaker 2: to allocate as many hours to the junior lawyers, that

538
00:26:55,840 --> 00:27:00,480
Speaker 2: makes total sense for this specific generation, right, because Okay,

539
00:27:00,560 --> 00:27:03,200
Speaker 2: some people are lucky enough to have made partner before AI,

540
00:27:03,440 --> 00:27:04,840
Speaker 2: and then other people are going to be in this

541
00:27:04,920 --> 00:27:08,320
Speaker 2: situation which their value may be could be done more

542
00:27:08,359 --> 00:27:10,920
Speaker 2: cheaply or for a few hours, but ten years from

543
00:27:10,960 --> 00:27:13,440
Speaker 2: now whatever, a lot of those partners are gonna be retired, etc.

544
00:27:13,840 --> 00:27:17,840
Speaker 2: Like what is the downstream consequence of your view in

545
00:27:17,880 --> 00:27:20,719
Speaker 2: your view of some of these shifts that happen right now,

546
00:27:20,760 --> 00:27:23,480
Speaker 2: because you know, eventually all the partners you know, ventually

547
00:27:23,480 --> 00:27:25,400
Speaker 2: Evere's get die and there's gonna be new, Like where

548
00:27:25,400 --> 00:27:25,879
Speaker 2: does this go?

549
00:27:26,040 --> 00:27:28,359
Speaker 1: Like what's the next? What are the consequences?

550
00:27:28,520 --> 00:27:32,480
Speaker 5: So I think that the skills that will be valued

551
00:27:32,560 --> 00:27:35,080
Speaker 5: in lawyers are just going to change a lot. The

552
00:27:35,440 --> 00:27:40,560
Speaker 5: interpersonal skills of client management, walking your client through the

553
00:27:40,720 --> 00:27:43,479
Speaker 5: risks that they have, their nervous they're getting sued. These

554
00:27:43,480 --> 00:27:47,320
Speaker 5: are bet the company litigations. The lawyers who can make

555
00:27:47,359 --> 00:27:51,359
Speaker 5: them feel safe and let them understand what's happening to

556
00:27:51,440 --> 00:27:54,359
Speaker 5: them and what their odds are. Those sorts of things.

557
00:27:54,720 --> 00:27:57,560
Speaker 5: The lawyers who are great at taking depositions and the

558
00:27:57,640 --> 00:28:00,560
Speaker 5: lawyers who are great at oral argument, those skills are

559
00:28:00,600 --> 00:28:03,280
Speaker 5: going to go up and up. I think in skill

560
00:28:03,720 --> 00:28:07,800
Speaker 5: in value and the skill of legal research or just

561
00:28:07,880 --> 00:28:10,600
Speaker 5: being great at writing a brief, I do think that

562
00:28:10,600 --> 00:28:13,240
Speaker 5: that's going to get maybe not the ninety ninth percentile

563
00:28:13,280 --> 00:28:15,240
Speaker 5: brief writing the people who are going to the Supreme

564
00:28:15,280 --> 00:28:17,320
Speaker 5: Court and who are just truly excellent.

565
00:28:17,359 --> 00:28:18,320
Speaker 4: Maybe that will take time.

566
00:28:18,600 --> 00:28:22,600
Speaker 5: But the sort of gruntwork, you know, solid junior associate,

567
00:28:22,720 --> 00:28:25,720
Speaker 5: mid level associate, senior associate who are writing the brief,

568
00:28:26,880 --> 00:28:30,600
Speaker 5: that I question whether that skill will have the same value.

569
00:28:30,640 --> 00:28:34,359
Speaker 5: But there's a lot of interpersonal skill that matters a

570
00:28:34,400 --> 00:28:36,439
Speaker 5: lot for being a lawyer, and that's going to go

571
00:28:36,520 --> 00:28:37,040
Speaker 5: up in value.

572
00:28:37,200 --> 00:28:42,200
Speaker 2: Now. Question with any new technology is does it because

573
00:28:42,240 --> 00:28:44,840
Speaker 2: one argument is clearly, okay, there's an x amount of

574
00:28:45,280 --> 00:28:48,720
Speaker 2: demand for legal services and there's an x amount of

575
00:28:48,720 --> 00:28:52,040
Speaker 2: people who supply them, and maybe you save some money.

576
00:28:52,400 --> 00:28:56,240
Speaker 2: Could the market for legal services grow such that if

577
00:28:56,440 --> 00:28:58,880
Speaker 2: capacity to do legal research and some of this other

578
00:28:58,880 --> 00:29:02,120
Speaker 2: stuff gets cheaper, that there are more areas it's like, oh,

579
00:29:02,200 --> 00:29:04,360
Speaker 2: you know what, maybe this is worth filing the laws.

580
00:29:05,360 --> 00:29:08,240
Speaker 3: This was actually going to be my question as well,

581
00:29:08,280 --> 00:29:13,520
Speaker 3: but particularly for poorer plaintiffs who normally can't afford a lawyer,

582
00:29:13,680 --> 00:29:14,680
Speaker 3: what did they call them.

583
00:29:14,520 --> 00:29:17,600
Speaker 5: In indigent and they sometimes will file pro se and

584
00:29:17,680 --> 00:29:18,760
Speaker 5: their on behalf.

585
00:29:18,800 --> 00:29:19,320
Speaker 1: Or maybe more.

586
00:29:19,400 --> 00:29:22,720
Speaker 2: Lawyers like you take on cases, take other cases that

587
00:29:23,600 --> 00:29:26,160
Speaker 2: five years ago the economics wouldn't have made sense, but

588
00:29:26,240 --> 00:29:27,680
Speaker 2: today the economics made sense.

589
00:29:27,680 --> 00:29:30,080
Speaker 1: Like could it just be you know, Jevin's paradox?

590
00:29:30,160 --> 00:29:33,160
Speaker 5: But for law I think that's absolutely going to happen.

591
00:29:33,240 --> 00:29:34,560
Speaker 5: You know, I can just sort of walk through the

592
00:29:34,600 --> 00:29:37,240
Speaker 5: economics in one of my cases. You know, somebody comes

593
00:29:37,280 --> 00:29:39,280
Speaker 5: in and they say they had a false arrest case

594
00:29:39,320 --> 00:29:43,240
Speaker 5: against the NYPD and they spent let's say five hours

595
00:29:43,240 --> 00:29:46,800
Speaker 5: in custody. We sort of know what the expected value

596
00:29:46,800 --> 00:29:49,200
Speaker 5: of that case is. You know, you're doing what chance

597
00:29:49,200 --> 00:29:51,080
Speaker 5: are you do you have to win? And what are

598
00:29:51,120 --> 00:29:53,120
Speaker 5: the damages you are going to get when you win?

599
00:29:53,240 --> 00:29:55,200
Speaker 5: And I can sort of and then I I a

600
00:29:55,240 --> 00:29:59,240
Speaker 5: contingency fee, typically a third in New York, and you

601
00:29:59,280 --> 00:30:01,280
Speaker 5: can sort of do that math of the expected value

602
00:30:01,320 --> 00:30:02,920
Speaker 5: of the case and then think about how many hours

603
00:30:02,960 --> 00:30:05,760
Speaker 5: it's going to take to win that case or get

604
00:30:05,840 --> 00:30:10,520
Speaker 5: to a settlement. And if I'm using AI to make

605
00:30:10,560 --> 00:30:13,719
Speaker 5: myself more productive in my research, the number of hours

606
00:30:13,760 --> 00:30:15,880
Speaker 5: that I'm expecting to put into that case goes down.

607
00:30:16,200 --> 00:30:19,560
Speaker 5: Suddenly my hurdle rate for you know, what it takes

608
00:30:19,600 --> 00:30:21,720
Speaker 5: to take on that case has gone down, and I

609
00:30:21,760 --> 00:30:24,400
Speaker 5: can take on more cases for the same number of hours.

610
00:30:24,880 --> 00:30:27,360
Speaker 5: And so that I definitely think is coming. It's also

611
00:30:27,400 --> 00:30:30,200
Speaker 5: the case that right and because of that, because lawyers

612
00:30:30,240 --> 00:30:32,000
Speaker 5: have to think in that term of you know, how

613
00:30:32,040 --> 00:30:34,040
Speaker 5: much is it going to cost me to do this

614
00:30:34,120 --> 00:30:36,480
Speaker 5: case in terms of labor, there are a lot of

615
00:30:36,600 --> 00:30:40,040
Speaker 5: cases brought pro sae in the court system right now.

616
00:30:40,080 --> 00:30:43,040
Speaker 5: I think it's like twenty twenty five percent of cases

617
00:30:43,040 --> 00:30:45,760
Speaker 5: are from pro se litigants. These are other people represent

618
00:30:45,800 --> 00:30:48,160
Speaker 5: the who represent themselves because they can't afford a lawyer

619
00:30:48,280 --> 00:30:50,520
Speaker 5: or you know, my cases are contingency. People aren't paying

620
00:30:50,520 --> 00:30:53,480
Speaker 5: out of pocket, but so I can't afford to take

621
00:30:53,520 --> 00:30:56,280
Speaker 5: on the case because the damages aren't potentially high enough.

622
00:30:56,320 --> 00:30:57,320
Speaker 4: That's how I have to sort.

623
00:30:57,160 --> 00:30:59,600
Speaker 5: Of think about it. And so I think there's going

624
00:30:59,640 --> 00:31:02,640
Speaker 5: to be a lot more representation.

625
00:31:02,240 --> 00:31:02,840
Speaker 4: Of those people.

626
00:31:02,880 --> 00:31:05,320
Speaker 5: It's also the case probably that pro say it lawyers

627
00:31:05,360 --> 00:31:08,880
Speaker 5: are going to start getting better that you know, you

628
00:31:08,960 --> 00:31:11,240
Speaker 5: said you were doing research on your own, you know,

629
00:31:11,280 --> 00:31:15,000
Speaker 5: with three it's possible that you know, if it was

630
00:31:15,080 --> 00:31:18,120
Speaker 5: just a little dispute with somebody over some amount of money.

631
00:31:18,160 --> 00:31:20,479
Speaker 5: You might think I can do this myself rather than

632
00:31:20,520 --> 00:31:23,440
Speaker 5: trying to go find a lawyer. And so, you know,

633
00:31:23,480 --> 00:31:26,120
Speaker 5: but for legal aid, for people who are criminal defense

634
00:31:26,120 --> 00:31:28,280
Speaker 5: attorneys at legal aid, those attorneys that I think are

635
00:31:28,280 --> 00:31:30,840
Speaker 5: about to go on because of how overworked they are

636
00:31:30,880 --> 00:31:33,920
Speaker 5: and underpaid they are, they they might get a lot

637
00:31:33,920 --> 00:31:36,240
Speaker 5: more efficient and be able to represent a lot more people.

638
00:31:36,360 --> 00:31:40,000
Speaker 2: Tracy, I'm imagining a lot of very annoyed judges by

639
00:31:40,120 --> 00:31:40,640
Speaker 2: like pro.

640
00:31:40,640 --> 00:31:42,080
Speaker 1: Saying frivolous lawsuits.

641
00:31:42,160 --> 00:31:44,080
Speaker 2: No I mean no, I mean like someone who did

642
00:31:44,120 --> 00:31:46,600
Speaker 2: their own research, because nothing's more annoying than someone who

643
00:31:46,600 --> 00:31:48,840
Speaker 2: does their own research. And then they're like they're like

644
00:31:49,080 --> 00:31:51,960
Speaker 2: citing all these cases that they learned about yesterday and

645
00:31:51,960 --> 00:31:54,280
Speaker 2: stuff like that. Like it just seems like you have

646
00:31:54,320 --> 00:31:58,280
Speaker 2: a high potential for obnoxious, obnoxious people who think.

647
00:31:58,160 --> 00:32:01,160
Speaker 3: They know No, no, you're democratized in case. Yeah, that's

648
00:32:01,160 --> 00:32:01,640
Speaker 3: what I say.

649
00:32:01,720 --> 00:32:03,720
Speaker 4: I want to be clear, they're already getting that.

650
00:32:04,000 --> 00:32:06,560
Speaker 5: The judges are getting a lot of it, especially pro

651
00:32:06,600 --> 00:32:08,600
Speaker 5: SA prisoners who get to spend a lot of time

652
00:32:09,160 --> 00:32:11,560
Speaker 5: in the legal librarian or filing cases. And some of

653
00:32:11,560 --> 00:32:15,320
Speaker 5: them are excellent, but sometimes they're not so good. I

654
00:32:15,440 --> 00:32:17,600
Speaker 5: clerk for a federal judge, and we saw a lot

655
00:32:17,640 --> 00:32:20,600
Speaker 5: of this litigation, a lot of it pro se. I

656
00:32:20,640 --> 00:32:23,719
Speaker 5: don't know that they're you know, providing AI access in

657
00:32:23,760 --> 00:32:28,160
Speaker 5: prisons unfortunately in terms of litigation. But I think, I

658
00:32:28,200 --> 00:32:29,840
Speaker 5: actually think there are going to be a lot of

659
00:32:29,880 --> 00:32:32,640
Speaker 5: annoyed judges because there's going this is going to happen.

660
00:32:32,680 --> 00:32:35,200
Speaker 5: It also might be the case that it raises the

661
00:32:35,240 --> 00:32:38,360
Speaker 5: floor of You see a lot of bad legal writing,

662
00:32:38,400 --> 00:32:41,400
Speaker 5: even from lawyers, and I could see it getting it

663
00:32:41,440 --> 00:32:43,960
Speaker 5: actually getting a little bit easier to be a judge and.

664
00:32:43,880 --> 00:32:48,320
Speaker 3: A clerk if everything becomes more volume oriented or the

665
00:32:48,320 --> 00:32:51,440
Speaker 3: business model becomes more about the volume rather than the

666
00:32:51,440 --> 00:32:55,600
Speaker 3: billable hours. Do you see a situation where I guess

667
00:32:56,400 --> 00:33:00,640
Speaker 3: the emphasis on sourcing deals in corporate law or sourcing

668
00:33:00,720 --> 00:33:05,400
Speaker 3: cases in plaintiff, Does that become more important, Like that's

669
00:33:05,440 --> 00:33:07,840
Speaker 3: the skill set that you actually need, that sort of

670
00:33:07,880 --> 00:33:09,280
Speaker 3: like matchmaker process.

671
00:33:09,600 --> 00:33:11,480
Speaker 4: I think that there's yes.

672
00:33:11,600 --> 00:33:16,320
Speaker 5: I think finding the deals, making the economics of the

673
00:33:16,520 --> 00:33:20,240
Speaker 5: of the relationship work is going to go up a

674
00:33:20,280 --> 00:33:23,880
Speaker 5: lot in value. You know, I think it's already the case.

675
00:33:23,720 --> 00:33:28,360
Speaker 5: You all see legal advertisements for personal injury lawyers everywhere

676
00:33:28,440 --> 00:33:32,760
Speaker 5: all the time. So I mean legal search terms on

677
00:33:33,320 --> 00:33:37,719
Speaker 5: Google are the most expensive cost per click. I mean

678
00:33:37,800 --> 00:33:41,240
Speaker 5: these thousands of dollars for cost per click. Case acquisition.

679
00:33:41,400 --> 00:33:44,640
Speaker 5: I think is might go up even more because if

680
00:33:44,640 --> 00:33:47,240
Speaker 5: you can just get the case in and you're still

681
00:33:47,360 --> 00:33:50,200
Speaker 5: you're working less on the case, but you're still getting

682
00:33:50,200 --> 00:33:53,200
Speaker 5: your third contingency fee, you might be willing to pay

683
00:33:53,240 --> 00:33:55,720
Speaker 5: even more and more to just generate this volume. I

684
00:33:55,720 --> 00:33:59,360
Speaker 5: mean already mills exist, they call them, you know, plaintiff's mills.

685
00:33:59,440 --> 00:34:02,320
Speaker 5: People who are doing you know, six hundred cases per

686
00:34:02,600 --> 00:34:04,640
Speaker 5: you know, every couple of attorneys or something like that.

687
00:34:04,840 --> 00:34:07,440
Speaker 5: The economics of that and just acquiring the case are

688
00:34:07,480 --> 00:34:10,080
Speaker 5: going to go I think, way way up until maybe

689
00:34:10,080 --> 00:34:12,040
Speaker 5: they start competing on price because they can do so

690
00:34:12,080 --> 00:34:12,760
Speaker 5: much more volume.

691
00:34:12,840 --> 00:34:14,080
Speaker 1: I want to talk about advertising.

692
00:34:14,239 --> 00:34:16,560
Speaker 2: Have you, Tracy Orgel, have either of you heard the

693
00:34:16,640 --> 00:34:18,319
Speaker 2: radio ads for top Dog Law?

694
00:34:18,880 --> 00:34:19,080
Speaker 4: Yes?

695
00:34:19,440 --> 00:34:20,360
Speaker 3: I have not you.

696
00:34:20,560 --> 00:34:24,479
Speaker 2: Every listener go to YouTube and just type top Dog

697
00:34:24,640 --> 00:34:27,680
Speaker 2: Law ad. They're the most incredible, aren't they, Joel, Like

698
00:34:27,719 --> 00:34:28,680
Speaker 2: they're really incredible.

699
00:34:29,239 --> 00:34:29,399
Speaker 3: Yes.

700
00:34:30,080 --> 00:34:32,200
Speaker 2: Just go search right now or when the episode and

701
00:34:32,239 --> 00:34:35,400
Speaker 2: listen to some of these and do you buy ads

702
00:34:36,080 --> 00:34:36,640
Speaker 2: on Google?

703
00:34:36,960 --> 00:34:40,960
Speaker 5: I've done some Google ad campaigns. I haven't really there's

704
00:34:41,000 --> 00:34:44,080
Speaker 5: actually another topic we're interested in. But a lot of

705
00:34:44,360 --> 00:34:47,240
Speaker 5: I mean a lot of plaintiff side civil rights lawyers

706
00:34:47,280 --> 00:34:50,560
Speaker 5: are buying. It's just not how I've chosen to compete.

707
00:34:50,600 --> 00:34:53,480
Speaker 5: I do more referrals from defense lawyers that sort of thing.

708
00:34:53,880 --> 00:34:58,160
Speaker 5: But I mean I actually have started to think about, Right,

709
00:34:58,440 --> 00:35:01,640
Speaker 5: we know this stuff about young people are starting to

710
00:35:01,800 --> 00:35:05,960
Speaker 5: use chat, GPT and other services as they're sort of

711
00:35:06,040 --> 00:35:08,759
Speaker 5: portal to the Internet. They're using it for search and

712
00:35:08,840 --> 00:35:10,919
Speaker 5: so right, all of these law firms spend a lot

713
00:35:10,960 --> 00:35:14,960
Speaker 5: on SEO, but what is SEO in a generative AI

714
00:35:15,239 --> 00:35:18,120
Speaker 5: world is sort of an interesting question? What are people

715
00:35:18,400 --> 00:35:21,719
Speaker 5: what are the engines turning up in terms of search terms?

716
00:35:21,760 --> 00:35:23,520
Speaker 5: What do you have to have on your website in

717
00:35:23,600 --> 00:35:27,439
Speaker 5: order to get people to find you through GPT rather

718
00:35:27,480 --> 00:35:28,000
Speaker 5: than Google?

719
00:35:28,320 --> 00:35:30,440
Speaker 3: Is Lexus and Nexus doing anything what they are?

720
00:35:30,840 --> 00:35:31,239
Speaker 4: They are?

721
00:35:31,680 --> 00:35:34,920
Speaker 5: I have not really used their AI products Lexus and

722
00:35:34,960 --> 00:35:36,439
Speaker 5: I know West Law is doing the same.

723
00:35:36,840 --> 00:35:37,640
Speaker 4: I use Lexus.

724
00:35:37,760 --> 00:35:41,160
Speaker 5: I assume Bloomberg Law, which is also is doing something similar.

725
00:35:41,360 --> 00:35:46,600
Speaker 5: But right now the models that they're using just aren't

726
00:35:46,719 --> 00:35:47,560
Speaker 5: as good as three.

727
00:35:47,719 --> 00:35:49,479
Speaker 4: It's basically and so if.

728
00:35:49,400 --> 00:35:53,880
Speaker 5: You're still calling to a four era GPT or similar

729
00:35:54,200 --> 00:35:57,640
Speaker 5: from claud or whatever, that sort of quality, I just

730
00:35:57,640 --> 00:36:00,640
Speaker 5: don't think that they're getting good enough recent I think

731
00:36:00,719 --> 00:36:03,920
Speaker 5: it really was whatever that step change was to O

732
00:36:04,040 --> 00:36:08,239
Speaker 5: three's level, I think has made the research so much

733
00:36:08,480 --> 00:36:10,560
Speaker 5: better that I just use it, and then I'll go

734
00:36:10,880 --> 00:36:12,520
Speaker 5: look at all the cases on lexus.

735
00:36:12,560 --> 00:36:16,359
Speaker 2: The difference in what you can actually learn from four

736
00:36:16,760 --> 00:36:19,080
Speaker 2: h or whatever for zero, I don't the name it.

737
00:36:19,120 --> 00:36:21,840
Speaker 2: Convention first all three it really is a very big deal.

738
00:36:22,040 --> 00:36:24,359
Speaker 2: And I think like a lot of people formed impressions

739
00:36:25,000 --> 00:36:28,719
Speaker 2: pre three impressions. What other tools are out there that

740
00:36:28,760 --> 00:36:32,359
Speaker 2: are interesting or that come across your world, whether it's

741
00:36:32,480 --> 00:36:34,279
Speaker 2: like I mean, you know, we've been talking a lot

742
00:36:34,280 --> 00:36:36,600
Speaker 2: about three, and you mentioned that other one brief something.

743
00:36:36,680 --> 00:36:39,440
Speaker 2: Is there anything else out there that's exciting or interesting

744
00:36:39,520 --> 00:36:40,160
Speaker 2: or promising.

745
00:36:40,280 --> 00:36:43,080
Speaker 5: There's one I looked at that look promising. Uh, it's

746
00:36:43,120 --> 00:36:47,520
Speaker 5: like Ecutis or ike iq Idis something like that, and

747
00:36:47,560 --> 00:36:50,280
Speaker 5: they looked pretty interesting in terms of sort of being

748
00:36:50,600 --> 00:36:53,600
Speaker 5: full swite and wrap around. There's Harvey AI, but they're

749
00:36:53,640 --> 00:36:56,040
Speaker 5: really focused on big law firms. But I know that

750
00:36:56,080 --> 00:36:59,000
Speaker 5: they're working with a lot of big firms. But you know,

751
00:36:59,040 --> 00:37:02,520
Speaker 5: the technology you know, for me that looks promising would

752
00:37:02,560 --> 00:37:05,440
Speaker 5: be people where I can safely upload medical records and

753
00:37:05,520 --> 00:37:08,360
Speaker 5: get instead of trawling through two hundred three hundred pages

754
00:37:08,440 --> 00:37:10,359
Speaker 5: just like what was each progress note?

755
00:37:10,400 --> 00:37:11,760
Speaker 4: What did it say? What's the injury?

756
00:37:11,840 --> 00:37:14,319
Speaker 5: That sort of thing, And I think that's coming there

757
00:37:14,400 --> 00:37:17,640
Speaker 5: are I think it's maybe something from case text or

758
00:37:17,800 --> 00:37:23,160
Speaker 5: that summarizes depositions. And of course that's another thing where

759
00:37:23,880 --> 00:37:25,560
Speaker 5: I've tried to use it in the past and it

760
00:37:25,600 --> 00:37:28,160
Speaker 5: wasn't very good. I think just the context windows that

761
00:37:28,200 --> 00:37:31,840
Speaker 5: they were able to use haven't been sufficiently large. But

762
00:37:31,960 --> 00:37:36,800
Speaker 5: I think with RAG and with more and with better models,

763
00:37:36,840 --> 00:37:39,160
Speaker 5: I think that will get better and better, you know,

764
00:37:39,280 --> 00:37:41,719
Speaker 5: like this is the Ben Thompson line of this is

765
00:37:41,760 --> 00:37:44,080
Speaker 5: the worst there ever going to be? Yeah, is something

766
00:37:44,120 --> 00:37:47,120
Speaker 5: that I'm constantly trying to think about because I've used

767
00:37:47,120 --> 00:37:49,600
Speaker 5: these these products in the past or tested them and

768
00:37:49,640 --> 00:37:52,440
Speaker 5: they haven't been very impressive. But I also used four

769
00:37:52,480 --> 00:37:54,600
Speaker 5: oh and wasn't very impressed at it's legal research and

770
00:37:54,640 --> 00:37:57,840
Speaker 5: have seen that jump. And so I assume in terms

771
00:37:57,880 --> 00:38:01,920
Speaker 5: of taking depositions and and getting concise summaries, of the

772
00:38:02,000 --> 00:38:03,920
Speaker 5: most important facts for your case. That's going to get

773
00:38:03,920 --> 00:38:06,919
Speaker 5: a lot better. I just think the products aren't there yet.

774
00:38:06,920 --> 00:38:09,799
Speaker 5: And maybe because the API calls for the lms just

775
00:38:09,960 --> 00:38:12,480
Speaker 5: aren't to their newest models yet, or it's too expensive.

776
00:38:12,920 --> 00:38:15,959
Speaker 5: But I assume that's getting cheaper every you know, more's

777
00:38:16,000 --> 00:38:16,760
Speaker 5: loss still applies.

778
00:38:17,320 --> 00:38:19,120
Speaker 3: If you had to bet, if you had to put

779
00:38:19,200 --> 00:38:22,600
Speaker 3: actual money on this question, would you say there will

780
00:38:22,640 --> 00:38:28,480
Speaker 3: be more or less lawyers, say ten years from now as.

781
00:38:28,320 --> 00:38:29,640
Speaker 1: A percent the population.

782
00:38:29,880 --> 00:38:31,920
Speaker 4: Yeah, that's a great question.

783
00:38:32,080 --> 00:38:33,120
Speaker 3: Thank you for the caveat check.

784
00:38:33,239 --> 00:38:35,680
Speaker 4: It's important and I hadn't I hadn't thought about this one.

785
00:38:35,880 --> 00:38:36,880
Speaker 4: I would.

786
00:38:37,960 --> 00:38:41,880
Speaker 5: I would bet on fewer, but not that much. I

787
00:38:41,880 --> 00:38:45,960
Speaker 5: would say, I don't think this is, you know, going

788
00:38:46,040 --> 00:38:48,960
Speaker 5: to wipe out lawyers. I think fewer and what they

789
00:38:49,000 --> 00:38:50,160
Speaker 5: do will change a lot.

790
00:38:50,960 --> 00:38:55,759
Speaker 2: Could you imagine, you know, tech people are obsessed with

791
00:38:55,880 --> 00:38:58,839
Speaker 2: the mythical who will be the first person to have

792
00:38:58,880 --> 00:39:03,200
Speaker 2: a billion dollars startup with no employees? Could that be like, okay,

793
00:39:03,239 --> 00:39:06,320
Speaker 2: you have you hung a shingle out. I think sometimes

794
00:39:06,320 --> 00:39:12,600
Speaker 2: people use Could you imagine a megafirm with one employee?

795
00:39:12,920 --> 00:39:16,560
Speaker 5: I could imagine mega lawyer with you know, so right,

796
00:39:16,640 --> 00:39:20,080
Speaker 5: you think of se Selena and Barnes, those sorts of big,

797
00:39:20,120 --> 00:39:23,840
Speaker 5: big plaintiffs law firms. I think they that I actually

798
00:39:23,840 --> 00:39:26,360
Speaker 5: don't know, but it's a lot, and it's all across

799
00:39:26,400 --> 00:39:30,360
Speaker 5: the country. But so much of what they do is

800
00:39:31,040 --> 00:39:34,120
Speaker 5: make the discovery demand, respond to the demands, do the

801
00:39:34,440 --> 00:39:37,560
Speaker 5: get the medical records, send out a settlement demand letter,

802
00:39:38,120 --> 00:39:42,760
Speaker 5: Negotiate with the opposing council or the carrier from the insurer.

803
00:39:43,000 --> 00:39:46,319
Speaker 5: You know, my client has a torn rotator cuff and

804
00:39:46,360 --> 00:39:49,360
Speaker 5: a torn meniscus or something like that from this car accident,

805
00:39:49,719 --> 00:39:52,719
Speaker 5: and they have an exact expected value of the case,

806
00:39:52,800 --> 00:39:55,439
Speaker 5: and it's just negotiating that. And I think they could

807
00:39:55,440 --> 00:39:58,719
Speaker 5: go from you know, let's say one hundred cases per

808
00:39:58,719 --> 00:40:01,319
Speaker 5: attorney to a thousand or two two thousand because they

809
00:40:01,320 --> 00:40:05,399
Speaker 5: could set up an automated workflow a lot easier, I think,

810
00:40:05,440 --> 00:40:08,840
Speaker 5: And so you know, that person could could start making

811
00:40:09,120 --> 00:40:12,160
Speaker 5: fifty million or one hundred million a year per one lawyer.

812
00:40:12,239 --> 00:40:15,040
Speaker 5: Then the you know already five million that they're making

813
00:40:15,120 --> 00:40:15,879
Speaker 5: or something like that.

814
00:40:16,000 --> 00:40:16,880
Speaker 4: These top firms.

815
00:40:16,960 --> 00:40:19,640
Speaker 2: One last little question I mentioned your a poker player.

816
00:40:19,800 --> 00:40:22,080
Speaker 2: In poker, you know, big thing has been the emergence

817
00:40:22,080 --> 00:40:24,319
Speaker 2: of solvers and you go back and you like, look

818
00:40:24,320 --> 00:40:26,600
Speaker 2: at that, did you play it very well? But it's

819
00:40:26,640 --> 00:40:28,960
Speaker 2: all about what you kind of what you described of,

820
00:40:29,040 --> 00:40:32,160
Speaker 2: like your calculator EV right and.

821
00:40:32,120 --> 00:40:33,680
Speaker 1: Did you play it right? And you don't always win.

822
00:40:33,719 --> 00:40:36,200
Speaker 2: And you may have calculated ev right and you took

823
00:40:36,200 --> 00:40:38,560
Speaker 2: on the case, but you don't get the outcome. How

824
00:40:39,160 --> 00:40:43,000
Speaker 2: scientific is that the calculating evy on a given case

825
00:40:43,040 --> 00:40:45,759
Speaker 2: and has it is there a technology that's working on that?

826
00:40:46,000 --> 00:40:49,040
Speaker 5: So it's not very scientific at all. You know, it's

827
00:40:49,120 --> 00:40:53,680
Speaker 5: a you can assess liability, I think pretty reasonably. Is

828
00:40:53,719 --> 00:40:56,120
Speaker 5: this going to be something where we're definitely going to win?

829
00:40:56,160 --> 00:40:59,600
Speaker 5: You know, the personal injury car accident lawyers to say,

830
00:41:00,080 --> 00:41:02,200
Speaker 5: you know, the police report says this person made an

831
00:41:02,200 --> 00:41:05,240
Speaker 5: a legal left turn. Perfect liability. I'm going to win instantly,

832
00:41:05,239 --> 00:41:07,960
Speaker 5: and then you're just evaluating the damages. One thing I've

833
00:41:07,960 --> 00:41:13,800
Speaker 5: actually explored is is looking into whether lms can estimate

834
00:41:14,080 --> 00:41:17,880
Speaker 5: the damages based on the injuries that you describe pretty well.

835
00:41:17,960 --> 00:41:20,880
Speaker 5: Go through the case law where there's cases this is

836
00:41:20,880 --> 00:41:24,439
Speaker 5: called remitted, or cases where you might hear like, oh,

837
00:41:24,520 --> 00:41:27,560
Speaker 5: this person got one hundred million dollars for their you know,

838
00:41:27,800 --> 00:41:30,000
Speaker 5: hot coffee or whatever. You know, these sort of classic cases,

839
00:41:30,040 --> 00:41:31,880
Speaker 5: and a judge will knock it down. They'll say, this

840
00:41:32,000 --> 00:41:35,440
Speaker 5: case with this injury is only worth this much. But

841
00:41:35,560 --> 00:41:38,120
Speaker 5: you could take all that language and start to estimate,

842
00:41:38,360 --> 00:41:42,400
Speaker 5: you know, quantify because words or numbers. With these programs,

843
00:41:42,400 --> 00:41:45,840
Speaker 5: you could start to quantify what are the injuries worth

844
00:41:46,000 --> 00:41:48,160
Speaker 5: in the in the case law and then multiply that

845
00:41:48,239 --> 00:41:50,120
Speaker 5: by So I think that sort of thing is going

846
00:41:50,160 --> 00:41:53,600
Speaker 5: to get a lot more scientific, and they're going to

847
00:41:53,719 --> 00:41:56,120
Speaker 5: use it on the carrier side. So Geico is going

848
00:41:56,200 --> 00:41:59,040
Speaker 5: to be able to say, you know, you've described these injuries.

849
00:41:59,040 --> 00:42:01,759
Speaker 5: We have a database of a thousand cases just like this,

850
00:42:01,880 --> 00:42:03,520
Speaker 5: and this was what we you know, and then you

851
00:42:03,520 --> 00:42:05,440
Speaker 5: could haggle, oh, well this is a great plaintiff or

852
00:42:05,560 --> 00:42:09,360
Speaker 5: something like that, and they're super sympathetic and you know,

853
00:42:09,440 --> 00:42:13,440
Speaker 5: have that negotiation. But I think that process of evaluating

854
00:42:13,480 --> 00:42:16,839
Speaker 5: the exact value of an injury or of a case

855
00:42:16,960 --> 00:42:21,000
Speaker 5: is going to get a lot more scientific and facilitate

856
00:42:21,080 --> 00:42:22,840
Speaker 5: settlements to happen quicker.

857
00:42:23,239 --> 00:42:26,680
Speaker 3: I imagine AI might be especially useful in class action

858
00:42:26,800 --> 00:42:30,920
Speaker 3: lawsuits as well, where you're dealing with like finding thousands

859
00:42:30,920 --> 00:42:33,680
Speaker 3: of people potentially to attach to a case and then

860
00:42:33,719 --> 00:42:36,319
Speaker 3: actually contacting them and getting information from them.

861
00:42:36,800 --> 00:42:38,840
Speaker 4: That seems reasonable to me.

862
00:42:38,920 --> 00:42:40,920
Speaker 5: Also, in the class action cases, you just got a

863
00:42:41,160 --> 00:42:44,200
Speaker 5: volume of data that you have to do discovery on.

864
00:42:44,640 --> 00:42:46,759
Speaker 5: But it hadn't occurred to me. But you know, I

865
00:42:46,800 --> 00:42:49,680
Speaker 5: do know that sometimes class actions start because somebody finds

866
00:42:49,680 --> 00:42:52,200
Speaker 5: a Reddit thread and they see on Reddit, you know,

867
00:42:52,520 --> 00:42:54,880
Speaker 5: all the people are all complaining about this same issue

868
00:42:54,880 --> 00:42:58,760
Speaker 5: with this product or something like that, and the lawyers,

869
00:42:59,000 --> 00:43:01,759
Speaker 5: you know, reach out to them, and so it is

870
00:43:02,040 --> 00:43:02,920
Speaker 5: entirely possible.

871
00:43:03,080 --> 00:43:04,080
Speaker 4: I could I could see that.

872
00:43:04,320 --> 00:43:07,040
Speaker 3: That's another thing where you could have AI just trawling

873
00:43:07,200 --> 00:43:09,360
Speaker 3: the internet for possible cases.

874
00:43:09,680 --> 00:43:10,640
Speaker 4: I think so.

875
00:43:10,840 --> 00:43:13,760
Speaker 5: Or you know, what are people posting about on Twitter?

876
00:43:13,800 --> 00:43:15,160
Speaker 5: What are they all getting harmed by?

877
00:43:15,200 --> 00:43:18,600
Speaker 4: I don't know. So it's it seems entirely plausible.

878
00:43:18,960 --> 00:43:20,040
Speaker 1: Since you mentioned it.

879
00:43:20,120 --> 00:43:22,759
Speaker 2: I just want to say you mentioned illegal lefts and

880
00:43:22,880 --> 00:43:27,959
Speaker 2: tracy and listeners. I had a issue last year whereight

881
00:43:29,120 --> 00:43:31,120
Speaker 2: I'm not going to get into it, but it involved

882
00:43:31,120 --> 00:43:32,320
Speaker 2: taking an illegal left.

883
00:43:32,200 --> 00:43:33,760
Speaker 3: Turn a criminal.

884
00:43:34,640 --> 00:43:35,080
Speaker 1: I'm not.

885
00:43:36,480 --> 00:43:37,480
Speaker 3: Where you found innocent.

886
00:43:38,320 --> 00:43:39,920
Speaker 1: I am not a criminal.

887
00:43:40,120 --> 00:43:44,720
Speaker 2: Thanks to a referral that our guest here, Joel helped

888
00:43:44,840 --> 00:43:46,120
Speaker 2: make for me. I'm not going to say it. We're

889
00:43:46,120 --> 00:43:47,640
Speaker 2: not going to talk anymore about the case. It was

890
00:43:47,719 --> 00:43:50,680
Speaker 2: super annoying. I'm not gonna say anything more, but I

891
00:43:50,800 --> 00:43:53,640
Speaker 2: just want to. I'm it's my disclosure for this episode.

892
00:43:53,840 --> 00:43:56,279
Speaker 2: So Joel Wertheimer, thank you so much for coming on

893
00:43:56,320 --> 00:43:56,799
Speaker 2: odd last.

894
00:43:56,840 --> 00:43:57,600
Speaker 1: That was fantastic.

895
00:43:57,640 --> 00:43:58,479
Speaker 4: Thank you for having me.

896
00:44:12,800 --> 00:44:13,200
Speaker 1: Tracy.

897
00:44:13,239 --> 00:44:17,040
Speaker 2: Before we talk about conclusions, I must press upon listeners

898
00:44:17,080 --> 00:44:20,200
Speaker 2: again to search for top dog law firm ads. In fact,

899
00:44:20,239 --> 00:44:22,960
Speaker 2: after we do this episode, we walk back to our desk, Tracy,

900
00:44:23,280 --> 00:44:25,240
Speaker 2: I'm going to insist that you watch.

901
00:44:25,000 --> 00:44:25,520
Speaker 1: A few of them.

902
00:44:25,680 --> 00:44:27,920
Speaker 3: I love that your priority for this episode. The one

903
00:44:27,960 --> 00:44:30,160
Speaker 3: thing you must get out of this episode is listening

904
00:44:30,160 --> 00:44:31,000
Speaker 3: to top dog ads.

905
00:44:31,040 --> 00:44:34,759
Speaker 2: No, I swear I was an uber home several months

906
00:44:34,840 --> 00:44:37,040
Speaker 2: ago and this ad came on the radio and just

907
00:44:37,040 --> 00:44:40,240
Speaker 2: like nothing I had ever heard my entire life. Nothing

908
00:44:40,280 --> 00:44:43,320
Speaker 2: will prepare you for this, this law firm's ads. Anyway,

909
00:44:43,760 --> 00:44:45,920
Speaker 2: I am very intrigued. Yeah, all right, well that was

910
00:44:45,920 --> 00:44:47,239
Speaker 2: a fantastic conversation.

911
00:44:47,440 --> 00:44:50,160
Speaker 3: Yeah. One thing I am wondering is, you know, in

912
00:44:50,200 --> 00:44:53,640
Speaker 3: the US, there's a lot of talk about an overly

913
00:44:54,239 --> 00:44:57,200
Speaker 3: litigious society, and I kind of wonder if AI is

914
00:44:57,280 --> 00:44:58,880
Speaker 3: potentially just going to make it worse.

915
00:44:59,200 --> 00:45:03,040
Speaker 2: It sounds very to me that it will make it

916
00:45:03,080 --> 00:45:05,640
Speaker 2: worse or better or worse. But I think, like, you know,

917
00:45:05,719 --> 00:45:07,799
Speaker 2: it does. My guess is that there's going to be

918
00:45:07,840 --> 00:45:11,040
Speaker 2: some sort of you know, Jeffen's paradox thing where like

919
00:45:11,200 --> 00:45:16,000
Speaker 2: some existing labor will get really cheap or maybe completely unnecessary,

920
00:45:16,520 --> 00:45:20,279
Speaker 2: and then other you know, the optimistic version is that

921
00:45:20,640 --> 00:45:23,279
Speaker 2: defendants or people without a lot of money get better

922
00:45:23,360 --> 00:45:26,799
Speaker 2: quality representation. I get, you know, it feels like it's

923
00:45:26,840 --> 00:45:29,880
Speaker 2: not gonna just it's gonna probably have very big distributional

924
00:45:29,960 --> 00:45:31,800
Speaker 2: and qualitative shifts on the industry.

925
00:45:32,239 --> 00:45:34,680
Speaker 3: Yeah, and this is the thing I'm also wondering. So, Okay,

926
00:45:34,880 --> 00:45:39,160
Speaker 3: junior associates, maybe they they're doing less work, maybe they're

927
00:45:39,200 --> 00:45:43,080
Speaker 3: slightly happier. But I guess the idea that more of

928
00:45:43,120 --> 00:45:45,799
Speaker 3: the value just accrues to the partners at a time

929
00:45:45,880 --> 00:45:49,719
Speaker 3: when it's harder to become partner. Uh, that seems to

930
00:45:49,719 --> 00:45:51,960
Speaker 3: be the downside if you're going into the legal profession.

931
00:45:52,080 --> 00:45:53,440
Speaker 1: Yeah, no, totally.

932
00:45:53,480 --> 00:45:56,759
Speaker 2: Like it feels like we're at this moment and this

933
00:45:56,920 --> 00:46:00,480
Speaker 2: applies to a lot of actually AI economics quest and

934
00:46:00,520 --> 00:46:04,719
Speaker 2: we should definitely do more on them where people imagine

935
00:46:04,960 --> 00:46:07,840
Speaker 2: or people maybe even rightly surmised that you know, there

936
00:46:07,840 --> 00:46:11,319
Speaker 2: will be a lot of benefits for you know, existing

937
00:46:11,960 --> 00:46:16,080
Speaker 2: holders of wealth or companies can do layoffs, right, and

938
00:46:16,360 --> 00:46:18,520
Speaker 2: I don't know the degree to that which is really happening,

939
00:46:18,560 --> 00:46:20,400
Speaker 2: but at least in theory, and there's going to be

940
00:46:20,560 --> 00:46:23,799
Speaker 2: firing of junior workers, et cetera. But that's like a

941
00:46:23,840 --> 00:46:28,480
Speaker 2: temporary condition because you know, first of all, like, Okay,

942
00:46:28,520 --> 00:46:31,360
Speaker 2: a company saves a bunch of money by firing what

943
00:46:31,440 --> 00:46:34,560
Speaker 2: eventually becomes characterized as gruntwork. Well, what do they spend

944
00:46:34,560 --> 00:46:36,960
Speaker 2: that money on, right, because that's savings and so does

945
00:46:37,000 --> 00:46:39,640
Speaker 2: that do they invest in? Does that turn into consumption

946
00:46:39,920 --> 00:46:41,920
Speaker 2: for the partners? Like there are a lot of you know,

947
00:46:41,920 --> 00:46:43,799
Speaker 2: people are like, oh, it's gonna be mass layoffs. But

948
00:46:43,840 --> 00:46:46,319
Speaker 2: I never find these questions joy satisfying because the money

949
00:46:46,360 --> 00:46:51,080
Speaker 2: doesn't disappear. It's someone's savings, and someone's savings either become

950
00:46:51,280 --> 00:46:55,000
Speaker 2: someone's consumption or someone's investment, and its anyone's consumption or

951
00:46:55,040 --> 00:46:59,600
Speaker 2: investment becomes someone else's income. So therefore, you know, thinking

952
00:46:59,680 --> 00:47:02,360
Speaker 2: through some of these next level things I think is

953
00:47:02,400 --> 00:47:05,520
Speaker 2: really important. And AI clearly seemed just like ground zero,

954
00:47:05,880 --> 00:47:08,920
Speaker 2: and I do think O three is really good and

955
00:47:09,000 --> 00:47:11,520
Speaker 2: everyone no, seriously, everyone needs to check it out and

956
00:47:11,719 --> 00:47:13,920
Speaker 2: update their views if they haven't yet on the quality

957
00:47:13,960 --> 00:47:15,000
Speaker 2: of the research you can do.

958
00:47:15,680 --> 00:47:18,840
Speaker 3: It does sound like it's maybe solved. The hallucination problem

959
00:47:19,120 --> 00:47:21,080
Speaker 3: was that everyone was making fun of earlier.

960
00:47:21,200 --> 00:47:23,719
Speaker 2: Yeah, I mean like they're still there for sure. But

961
00:47:23,880 --> 00:47:26,800
Speaker 2: because you can actually find links to documents and stuff

962
00:47:26,800 --> 00:47:28,680
Speaker 2: like that, the speed with which you can get up

963
00:47:28,719 --> 00:47:31,400
Speaker 2: to speed on anything is very impressive.

964
00:47:31,520 --> 00:47:33,319
Speaker 3: All right, I'm gonna go test it. Shall we leave

965
00:47:33,320 --> 00:47:33,520
Speaker 3: it there?

966
00:47:33,600 --> 00:47:34,319
Speaker 1: Let's leave it there.

967
00:47:34,520 --> 00:47:37,120
Speaker 3: This has been another episode of the Oudlots podcast. I'm

968
00:47:37,160 --> 00:47:39,760
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway.

969
00:47:39,920 --> 00:47:42,680
Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart.

970
00:47:42,840 --> 00:47:46,719
Speaker 2: Follow our guest on Blue Sky Jill Wertheimer. He's at Worthwhile.

971
00:47:47,120 --> 00:47:50,239
Speaker 2: Follow our producers Carmen Rodriguez at Carman armand dash Ol

972
00:47:50,239 --> 00:47:53,200
Speaker 2: Bennett at Dashbod and Keil Brooks at Kilbrooks. From our

973
00:47:53,200 --> 00:47:55,879
Speaker 2: Oddlots content, go to bloomberg dot com slash odd Lots,

974
00:47:55,880 --> 00:47:57,719
Speaker 2: where we have a daily newsletter and all of our

975
00:47:57,760 --> 00:48:00,320
Speaker 2: episodes and you can chat about all of these topics.

976
00:48:00,360 --> 00:48:02,480
Speaker 2: Twenty four to seven in our discord. We even have

977
00:48:02,480 --> 00:48:03,120
Speaker 2: an AI.

978
00:48:02,960 --> 00:48:05,799
Speaker 3: Channel in there, and if you enjoy Odd Lots, if

979
00:48:05,800 --> 00:48:07,759
Speaker 3: you like it when we talk about the impact of

980
00:48:07,800 --> 00:48:11,200
Speaker 3: AI on various professions, then please leave us a positive

981
00:48:11,200 --> 00:48:14,520
Speaker 3: review on your favorite podcast platform. And remember, if you

982
00:48:14,560 --> 00:48:17,239
Speaker 3: are a Bloomberg subscriber, you can listen to all of

983
00:48:17,280 --> 00:48:20,480
Speaker 3: our episodes absolutely ad free. All you need to do

984
00:48:20,520 --> 00:48:23,360
Speaker 3: is find the Bloomberg channel on Apple Podcasts and follow

985
00:48:23,400 --> 00:48:25,600
Speaker 3: the instructions there. Thanks for listening.