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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, there are just so many credit related things to

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Speaker 2: talk about right now, all things credit.

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Speaker 3: I love it.

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Speaker 1: I love it.

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Speaker 3: Credit is interesting again.

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Speaker 1: This might be one of the.

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Speaker 2: Only credit episodes that we've ever done where like I

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Speaker 2: found the guest because I feel like when I think

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Speaker 2: about like a credit all the credit episodes, it's usually

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Speaker 2: like someone you know. Randomly. I found someone who knows

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Speaker 2: a little bit something about credit. She's like, oh, let

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Speaker 2: me do it.

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Speaker 3: Let me The stakes are high.

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Speaker 2: I know I was thinking about there because you're like, how, Joe,

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Speaker 2: you like pick someone who doesn't know anything. No, I

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Speaker 2: don't think that. I think we have a very knowledgeable

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Speaker 2: credit guest. But I'm a little stressed about this aspect.

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Speaker 4: I believe in you, Joe, you do. I trust your judgment.

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Speaker 4: But to your point, there's a lot going on. So

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Speaker 4: obviously there are concerns around private credit. We've had some

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Speaker 4: idiosyncratic defaults and frauds in the market, and each one

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Speaker 4: is special in their own way. But I think the

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Speaker 4: worrying aspect is that they keep coming to light. Yeah, right,

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Speaker 4: And so you've seen people like Jamie Diamond using the

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Speaker 4: cockroach analogy, which is now famous. And at the same

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Speaker 4: time you have the connection with AI, right, which we

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Speaker 4: have spoken about a little bit on the podcast with

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Speaker 4: Paul Kadrowski. All these complex circular financing structures that are

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Speaker 4: driving a lot of the credit boom, or have been

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Speaker 4: driving a lot of the credit boom, and then at

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Speaker 4: the same time you also have the impact of AI

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Speaker 4: on credit itself.

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Speaker 2: Yeah, that's right because in theory right, like we've talked

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Speaker 2: about this. We did that episode with Joel Werthheimer that

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Speaker 2: was in a slightly different context, but we've done these

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Speaker 2: episodes about you know, just the incredible length of deal text,

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Speaker 2: et cetera. And perhaps if there's one area where maybe

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Speaker 2: we could say with some high degree of confidence that

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Speaker 2: large language models could be useful, it is can we

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Speaker 2: break down this multi hundred page agreement so that we

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Speaker 2: don't have to have you know, junior associates or junior

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Speaker 2: lawyers or junior bankers up till four in the morning

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Speaker 2: making sure that every comma is in the right place,

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Speaker 2: et cetera. In theory, this could be an area in

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Speaker 2: which AI could be productively applied.

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Speaker 4: You know, there was an actual case argued over a comma.

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Speaker 4: I can't remember exactly what it was, but like, you're

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Speaker 4: absolutely right, the grammar, the specific words clearly matter in

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Speaker 4: legal language. I would just add one of the things

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Speaker 4: that's been driving arguably driving private credit is the booming

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Speaker 4: creditor on credit or violence in public deals. So it

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Speaker 4: was this idea that you could avoid that by having

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Speaker 4: you know, this private close relationship with your borrower where

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Speaker 4: you are higher up.

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Speaker 3: In the waterfall of payment. So this is important.

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Speaker 2: It would be really nice if you could upload a

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Speaker 2: credit agreement to chat GPT and just say, make sure

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Speaker 2: there's nothing in that would get me in trouble. Make

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Speaker 2: sure there's nothing in here that five years later I

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Speaker 2: will regret the placement of a.

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Speaker 3: Certain Make sure I don't lose money, make sure.

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Speaker 2: I don't lose money in some technical way anyway. So

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Speaker 2: there's just a lot going on. I feel like there's

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Speaker 2: plenty of episodes to do on this, but we really

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Speaker 2: do have the perfect guest, someone who literally sort of

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Speaker 2: sits in the intersection of I think we identified three

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Speaker 2: distinct trends. Here we are going to be speaking with

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Speaker 2: Dan Wortman. He is the co founder of a company

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Speaker 2: called Nohica AI, and it does exactly this. It sort

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Speaker 2: of attempts to use AI to understand credits. There's a

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Speaker 2: lot of understanding about deals and the text in them.

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Speaker 2: He also just has a lot of understanding about AI, etcetera.

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Speaker 2: So we can talk about all of these things. Dan,

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Speaker 2: thank you so much for coming on the podcast.

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Speaker 1: Thanks so much for having me. I'm a fan of

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Speaker 1: the show. Love to hear it you guys. It kind

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Speaker 1: of like celebrities for me. So it's kind of fitting

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Speaker 1: that I'm here because at least with folks of Bloomberg,

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Speaker 1: because many people think about us and Oedica like the

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Speaker 1: Bloomberg for deal terms.

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Speaker 2: Okay, well let's see. Let's see if you actually live

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Speaker 2: up to that.

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Speaker 3: No.

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Speaker 2: But so since I said I'm stressed that, oh this time,

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Speaker 2: we're doing a credit episode and I've found the guest

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Speaker 2: give us the quick version of like your career and

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Speaker 2: what no Edica is.

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Speaker 1: Yeah, so let's start with Oedica. What we build at

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Speaker 1: no Edica is a power software for benchmarking real time

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Speaker 1: data on what's market in credit m and a capital

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Speaker 1: markets deal terms. Okay, so said another way, we help

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Speaker 1: folks like transactional attorneys, credit managers, bankers. We help them

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Speaker 1: figure out whether the terms of their transactional agreements like

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Speaker 1: think financing agreements, murder agreements, perspectuses, and really all other

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Speaker 1: corporate transactions are on or off market by benchmarking them

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Speaker 1: to market comps. So as far as the genesis of oedica,

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Speaker 1: it was kind of born out of my own experience

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Speaker 1: in my career. So I started my career at Blackrock.

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Speaker 1: I was on a team responsible for coming up with

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Speaker 1: new financial products and fixing and markets, and we were

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Speaker 1: developing these new interesting innovative structure and I just learned

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Speaker 1: a ton about the capital markets ecosystem, and in particular

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Speaker 1: just this is a fifty trillion dollar global market and

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Speaker 1: it runs on phone calls and relationships and it's unbelievably antiquated.

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Speaker 1: Then fast forward, I went back to get my GD

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Speaker 1: I joined WALKT toall Lipton, where I did corporate transactions.

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Speaker 1: This was twenty seventeen to twenty twenty two. So if

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Speaker 1: if you guys remember that time, it was heyday of

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Speaker 1: merger activity. So I worked on you know, T Mobiles,

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Speaker 1: Bioto Sprint, the biggest thirty billion dollar commitment at the time,

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Speaker 1: Algon ave to see raytheon and I distinctly remember sitting

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Speaker 1: down at my desk. I was looking at a transactional

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Speaker 1: grip and a multi billion dollar merger, and I was

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Speaker 1: looking at a term, and I was trying to figure

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Speaker 1: out whether I should help my client accept term A

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Speaker 1: or term B in this context. And I was stuck.

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Speaker 1: So I called the seenor partner on the deal. I said, hey,

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Speaker 1: where's the database of information where I could see exactly

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Speaker 1: how this term should come out and quantify it for

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Speaker 1: my client? And you know, the answer was that doesn't exist.

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Speaker 1: Now that was two and a half plus years ago.

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Speaker 1: Now I left walked out to start no edico with

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Speaker 1: a fairly simple idea, which is AI enables us to

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Speaker 1: finally quantify what market agreement terms should look like in

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Speaker 1: these markets. You know, now we work with almost all

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Speaker 1: the top twenty law firms on the street, but helping

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Speaker 1: them advise their clients on these deals. And this here

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Speaker 1: on track to do about a trillion dollars or of

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Speaker 1: transactions through the platform.

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Speaker 2: And you get one percent of that.

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Speaker 4: So that's great, Well, talk to us about what these

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Speaker 4: financing agreements actually look like and how traditionally they're sort

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Speaker 4: of judged by both the investors and the lawyers who

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Speaker 4: are looking at them.

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Speaker 1: Yeah, I mean so when I say deal terms, what

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Speaker 1: I mean is deal terms are really the underpinning of

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Speaker 1: the entire transactional system, the rules of the road. You

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Speaker 1: could think about them like speed limits, double yellow lines,

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Speaker 1: street lights. They're kind of the plumbing that goes into

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Speaker 1: the transactions, putting in a way that people can understand. Imagine,

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Speaker 1: I go sign of lease. Most people are very familiar

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Speaker 1: with certain things, right, like the rent price, the how

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Speaker 1: long the lease.

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Speaker 3: Is, subletting policy exactly.

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Speaker 1: But if deep in that twenty page lease the least

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Speaker 1: says if the weather gets under thirty degrees at any time,

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Speaker 1: you forfeit your right to the apartment, Well, that's a

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Speaker 1: deal term, and that affects whether you want to accept

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Speaker 1: that lease or not. And so it's the same in

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Speaker 1: capital market terms. To give you a more tangible example, Yeah,

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Speaker 1: are you guys fast food people? Yes? Yes, Okay, So

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Speaker 1: I'm like a McDonald's guy. Yea, And whenever I go

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Speaker 1: to McDonald's, I always ordered the tempiece chicken McNugget. I've

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Speaker 1: ordered the ten piece hundreds of times. There's exactly three

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Speaker 1: things that happened to you order a tenpiece, you open

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Speaker 1: the box, you have nine pieces. You open the box

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Speaker 1: you have exactly ten pieces. Or you open the box

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Speaker 1: and you have eleven pieces. Now, if you have nine pieces,

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Speaker 1: you go to the counter, you say, hey, I'm missing

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Speaker 1: a piece. They give you a piece. You get the

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Speaker 1: benefit of your morgan. If you get ten, you enjoy

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Speaker 1: your McNuggets. If you get eleven, what do you do?

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Speaker 3: You stay quiet?

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Speaker 1: Exactly so you had the jaguar right now. There's this

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Speaker 1: kind of unwritten rule in American consumerism, which is that

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Speaker 1: if a company that's bigger than you gives you something

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Speaker 1: by accident, then you get the benefit of that as

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Speaker 1: a consumer. Well, in twenty twenty, the exact kind of

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Speaker 1: thing happened in the credit markets, but it ended very differently.

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Speaker 1: City Bank sent nine hundred million dollars to lenders in

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Speaker 1: full prepayment of a loan for Revlon, and they did

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Speaker 1: so accidentally. Now, they were supposed to just send an

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Speaker 1: interest payment. At the time, the terms of the credit

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Speaker 1: agreement were silent. The governing documentation, especially with this loan,

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Speaker 1: didn't say what happens in that scenario. Long story short,

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Speaker 1: many funds did not give back that hundreds of millions

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Speaker 1: of dollars and litigation ensued. But a deal term in

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Speaker 1: credit deals called erroneous payment. Deal terms started popping up

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Speaker 1: in the market. No Whatadka's data last clocked that deal

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Speaker 1: as a last quarter ninety percent of deals. So if

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Speaker 1: you don't have that term now in your deal, you're

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Speaker 1: way off market in terms of the way the market

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Speaker 1: actually operates. This is why deal terms are important. These

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Speaker 1: are hundreds of millions of dollars at stake. In the

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Speaker 1: context of all these deals.

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Speaker 2: There's something very loyally but like I have to say,

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Speaker 2: I've never counted the McNuggets. I really get it, so

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Speaker 2: just I would this example would have never occurred to

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Speaker 2: me because I'm not the type of person that opens

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Speaker 2: a box of McNuggets and start.

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Speaker 4: Clearly, you don't value McNuggets.

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Speaker 2: Not evidently not. What are some other deal terms? So

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Speaker 2: that's a great example that. Okay, now, after that incident,

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Speaker 2: which is infamous, language about this start popping up. What

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Speaker 2: are some other sort of classic and I'm sure they

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Speaker 2: get much much more esoteric than that. But what are

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Speaker 2: some other like interesting deal terms that trend over time.

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Speaker 1: Yeah, so it's really interesting. So there's a whole host

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Speaker 1: of what it would call structural protections in a lot

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Speaker 1: of these deals. These come in a lot of different vivors.

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Speaker 1: Many people talk about them as things like anti pet

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Speaker 1: smart terms, things like j crue blockers, things like sert

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Speaker 1: of protections. Let's talk about some of the Yeah, let's

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Speaker 1: talk about some of these, so anti pet smart terms.

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Speaker 1: These are protections that prevent guarantor releases when subsidiaries of

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Speaker 1: the credit group become non wholly owned. In other words,

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Speaker 1: it prevents value from being transferred away from the loan

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Speaker 1: into some other structure which doesn't provide credits for it.

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Speaker 1: Let me put this in a way that most people

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Speaker 1: don't understand. If you were getting a mortgage on your house,

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Speaker 1: pretty simple framework. You take out the debt, you pay

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Speaker 1: your mortgage payments, you pay a packfal loan. Bank can

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Speaker 1: foreclose in your house if you stop paying a mortgage.

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Speaker 1: But in the mortgage if it said something like well,

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Speaker 1: if you sell any part of your home front door,

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Speaker 1: a window, a shingle, the bank loses the ability to

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Speaker 1: foreclose in the house fully. Well, then what would you do.

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Speaker 1: You'd sell a single shingle, you would stop paying your

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Speaker 1: mortgage and you get to keep your house and you

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Speaker 1: get the benefit of that. That's what anti pest smart

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Speaker 1: terms actually prevent. They prevent the ability for credit groups

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Speaker 1: to actually sell a single equity and actually lose the

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Speaker 1: credit support from that particular equity. So it's kind of

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Speaker 1: interesting what we're seeing in the market right now. We

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Speaker 1: have this really unique vantage point from the point of

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Speaker 1: view of our software where we quantify trends in deal

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Speaker 1: terms over time, and so we can actually very precisely

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Speaker 1: tell you the percentages of deals that are actually getting

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Speaker 1: a lot of these structural protections and actually gives us

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Speaker 1: this really unique window into the anxieties and the optimisms

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Speaker 1: that are currently happening in the market. Some people think

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Speaker 1: about this as kind of an early signal of something

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Speaker 1: likely to come. So what are we seeing, Well, we're

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Speaker 1: calling it a flight to fortification, and it's really happening

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Speaker 1: on both issuers and barbers. And I'll explain what I mean.

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Speaker 1: We're seeing massive increases in lenders getting structural protections in

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Speaker 1: these deals. Basically, these are protections that help make sure

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Speaker 1: they're collateral is locked, things like the anti pet smart terms.

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Speaker 1: In return, borrowers are getting the same fortification. In fact,

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Speaker 1: they're getting more economic flexibility. You could think about it

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Speaker 1: as a way for them to weather the storm. This

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Speaker 1: is how we're seeing it. So things like add backs

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Speaker 1: to eve, you know, more ability to send money to shareholders,

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Speaker 1: more ability to make long term events bins. Let's talk

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Speaker 1: about the actual specifics of what we're seeing. Antipasmart terms,

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Speaker 1: the one I just talked about, we clocked out at

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Speaker 1: twenty eight percent of deals in Q three. That was

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Speaker 1: at four percent in twenty twenty three, and Q two

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Speaker 1: is at twenty five percent, is the highest we've ever recorded.

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Speaker 1: That term J crew blockers, which prevent issuers from moving

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Speaker 1: material ip out ofside the credit group. That's at forty

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Speaker 1: five percent of deals now the baseline from twenty twenty

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Speaker 1: three to fifteen percent, and last quarter it was thirty

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Speaker 1: eight percent. Anti SERTI protections, which are leansbordination protections. They

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Speaker 1: actually helped secure your place in line if and when

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Speaker 1: some sort of distress activity happens. That's at eighty four

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Speaker 1: percent of deals. That's the highest jump we've ever seen.

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Speaker 1: Quarter quarter it went up from sixty one percent to

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Speaker 1: twenty three point jump, and the baseline is thirty nine

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Speaker 1: percent twenty twenty three. That's pretty significant for a quarterly jump,

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Speaker 1: and it really signals something about the market. On the

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Speaker 1: quantitative side, we track a lot of stuff too, including

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Speaker 1: the ratios under which borrowers need to maintain specific types

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Speaker 1: of leverage. We saw that at three point nine times

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Speaker 1: EBITDA in Q two and it went down to three

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Speaker 1: and a half times, But again that's signaling some sort

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Speaker 1: of anxiety among the lender group that we wouldn't normally see.

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Speaker 1: Now you may ask what a borrower is getting for this, Again,

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Speaker 1: they're getting more fortification. One of the ways this is

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Speaker 1: coming up is in EBITDA adbacks, so EBADA add backs. Basically,

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Speaker 1: there's a very long and complicated calculation of cash flow

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Speaker 1: and a lot of these deals and the adbacks to

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Speaker 1: EBADA basically allow bars and issuers to add back certain

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Speaker 1: things to count them as cash flows.

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Speaker 3: To flatter their balance sheet basically correct correct.

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Speaker 1: One of the more interesting adbacks that we track is

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Speaker 1: what's called a cost saving satback. So imagine a borrower

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Speaker 1: knows it's gonna optimize some cost in the future. If

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Speaker 1: it can reasonably predict that cost, it can add that

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Speaker 1: back to today's cash flow. That cost savings atback, whether

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Speaker 1: materializes or not, is added back to today's cashflow. Sixty

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Speaker 1: four percent of deals now have cost savings atbacks in them.

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Speaker 1: That's the highest we've ever recorded for deals, with those

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Speaker 1: adbacks being above twenty percent of EBITDA that came in

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Speaker 1: fifty one percent, which is also the highest we've ever

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Speaker 1: tracked on the platform. They're also getting things like excluding

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Speaker 1: lenders that are short in their debt. So, for instance,

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Speaker 1: folks may be familiar with what happened with the Windstream

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Speaker 1: case a few years ago. What happened in that case

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Speaker 1: is certain hedge funds were actually short the debt the

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Speaker 1: loan that was in default, and that makes them not

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Speaker 1: exactly aligned with the company that has the debt outstand.

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Speaker 1: Terms started popping up in the market which we've tracked,

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Speaker 1: which are called net short lender terms, which allow bars

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Speaker 1: to exclude those lenders from voting. That is now in

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Speaker 1: thirteen percent of deals, which is the highest we've ever tracked.

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Speaker 1: So you could see the fortification actually on both sides

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Speaker 1: of the market, and it really signals, I think, to

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Speaker 1: us that there's a risk allocation happening with a lot

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Speaker 1: of these anxieties.

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Speaker 3: Joe, First of all, you know, my husband was a corporate.

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Speaker 2: Lawyer at one point.

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Speaker 3: Yeah, okay.

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Speaker 4: So one of the things he's most proud of is

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Speaker 4: he came up with some language in a deal shortly

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Speaker 4: after the two thousand and eight financial crisis, and it

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Speaker 4: was he sent it to me just now a significant

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Speaker 4: dislocation in financial markets. That was him, and that became

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Speaker 4: like standard language in risk factors, at least in a

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Speaker 4: bunch of that's a contribution.

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Speaker 2: I'm the inventor of this deal, so and so the inventor.

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Speaker 2: Some people invent great medicine, some people invent some new technology,

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Speaker 2: and someone invents a new deal term that gets propagated

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Speaker 2: across that's right documents for years thereon after.

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Speaker 3: That's how it works.

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Speaker 4: But Dan, I wanted to ask you something. Okay, So

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Speaker 4: you say there's more fortification in a lot of deal terms,

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Speaker 4: more protections perhaps for both investors and lenders.

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Speaker 3: I guess one of the things we.

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Speaker 4: Heard prior to twenty twenty in them for some years

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Speaker 4: after it was we had this explosion in CoV light deals, right,

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Speaker 4: fewer protections for investors because everyone was so desperate supposedly

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Speaker 4: for yield for that particular paper, So the balance of

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Speaker 4: power shifted to the borrowers they were able to dictate

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Speaker 4: the terms. How are investors getting better protections now? With

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Speaker 4: you know, credit spreads still at basically multi decade lows,

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Speaker 4: which suggests that there's still a lot of demand and

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Speaker 4: that they don't hold all the power in the market.

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Speaker 1: Yeah, I think about it, and what the data supports

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Speaker 1: that we see on the platform is. I think about

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Speaker 1: it less so as what they're getting, but more about

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Speaker 1: what the terms actually reflect in terms of the macro

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Speaker 1: environment that they're operating in. So, for instance, right now,

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Speaker 1: we're seeing this flight to fortification in part largely due

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Speaker 1: to probably a few things. Number one being some of

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Speaker 1: these headline risks that folks have been talking about, and well,

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Speaker 1: I'm sure we'll get into some of what's going on

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Speaker 1: in the private credit market today. So people flooding into

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Speaker 1: more structural protections because they're worried about their place in

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Speaker 1: line if there is distress. I think number two is

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Speaker 1: just mac or wise if you think about it. In

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Speaker 1: the credit markets, there was a ton of debt taken

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Speaker 1: out in twenty twenty, twenty twenty one, twenty twenty, early

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Speaker 1: part of twenty twenty two. This leads to a lot

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Speaker 1: of maturity walls upcoming, especially in twenty twenty seven twenty.

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Speaker 3: We don't say upcoming on the show, we say looming.

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Speaker 1: Yeah, exactly. There are a lot of looming maturity walls

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Speaker 1: in twenty twenty eight, twenty twenty nine vintage. And you

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Speaker 1: can think about it as well. That's a macro factor

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Speaker 1: that people are thinking about when they underwrite alone, because

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Speaker 1: many of these deals actually have five year tenor you know,

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Speaker 1: seven year ten or eight year tenor in some cases

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Speaker 1: thirty year tenor, and so they're thinking about all these

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Speaker 1: protections in the context of that market. I also think

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Speaker 1: it's really interesting, aside from the credit context, right now,

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Speaker 1: we're seeing a lot of structuring in terms happening in

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Speaker 1: M and A markets, So things like regulatory uncertainty, things

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Speaker 1: like tariffs, things like libildy management, as we talked about,

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Speaker 1: things like tax uncertainty. I'm happy to go into these,

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Speaker 1: but we're seeing a lot of things in this area.

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Speaker 1: One kind of small example of this in situations where

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Speaker 1: a buyer and a seller have regulatory uncertainty, which you

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Speaker 1: know a lot of folks think about the administration and

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Speaker 1: they're not sure exactly how things are going to play out.

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Speaker 1: You actually see regulatory review in deals get hyper focused

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Speaker 1: on and it actually precipitated a new deal term this

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Speaker 1: year which we tracked in the market. We had an

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Speaker 1: almost term detection of the platform. We sent out a

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Speaker 1: note to all of our clients and it's called a

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Speaker 1: new outside date structure term. Basically, what it does is

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Speaker 1: it allows buyers of acquirees. It allows them to lock

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Speaker 1: in their financing for longer and actually stand their financing

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Speaker 1: in the case scenario regulatory review les. And that's just

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Speaker 1: an example of the kind of innovation that's happening in

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Speaker 1: the merger markets. In terms of tariffs, we picked up

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Speaker 1: the first tariff event of default in a credit deal. Ever,

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Speaker 1: it happened in a Superior industries deal over the summer,

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Speaker 1: which probably isn't surprising to use an auto manufacturer deal.

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Speaker 1: I've made a lot of parts in Mexico. That's not

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Speaker 1: five percent of MNA deals for tariff based m and

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Speaker 1: A carve outs and railie respect clauses.

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Speaker 2: Can we talk a little bit about you know, you're

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Speaker 2: scanning these documents. Google's ingram has existed for a long time.

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Speaker 2: Tracking the prevalence of a term is not novel technology

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Speaker 2: that control EF right control f Yeah, this is sort

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Speaker 2: of like very barely even councils technology at that point.

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Speaker 2: What is it that you you know, when you're talking

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Speaker 2: about the changing prevalence of these terms, what is the

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Speaker 2: actual novelty here that isn't just sort of yeah, document

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Speaker 2: search over time.

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Speaker 1: Yeah. So, Tracy, your husband's a former corporate lawyer. You know,

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Speaker 1: he would tell covering corporate lawyer or covering corporate layer exactly.

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Speaker 1: I am myself as well. One of the things he

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Speaker 1: would tell you is that there's constant innovation in these markets.

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Speaker 1: These agreements are highly complicated, there very long. They have

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Speaker 1: a lot of what's called long range dependencies, which is

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Speaker 1: that you may be used to seeing something in a

401
00:20:38,400 --> 00:20:42,080
Speaker 1: particular area of the document said one way, but in

402
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Speaker 1: reality it turns out it's punted to three different causes

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Speaker 1: deep down, and you actually have to go find that information.

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Speaker 4: This is why it's also jiu jitsu between the borrowers

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Speaker 4: and the lenders, right, because like the borrowers are often

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Speaker 4: trying to hide something that's favorable to them, or the

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Speaker 4: lenders are trying to hide something favorable to them. So

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Speaker 4: the structure and the way it's worded changes a lot

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Speaker 4: to your.

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Speaker 1: Point exactly, And these are sophisticated parties paying millions, sometimes

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Speaker 1: hundreds and millions of dollars in advisory fees to make

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Speaker 1: sure that these terms look the way they do. Now

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Speaker 1: that leads to kind of the technological innovation that I

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Speaker 1: think has enabled a lot of this AI for the

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Speaker 1: first time, can attribute in particular, new language models, can

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Speaker 1: attribute more semantic meaning to phrases and language that was

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Speaker 1: impossible with things like n grams. And so what America

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Speaker 1: does is it used as a series of language models,

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Speaker 1: including a multi layered information extraction system to make sure

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Speaker 1: that it's encoding all this semantic meaning inside all these terms,

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Speaker 1: so that when you look at a J. Krublacker in

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Speaker 1: the first way, it may be phrased a thousand different ways,

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Speaker 1: but we can track that term over time. That has

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Speaker 1: enabled the ability to actually quantify for the first time

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Speaker 1: what a market agreement term looks like in these markets.

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Speaker 1: And I think that's why it's so interesting to folks

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Speaker 1: on the platform.

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Speaker 4: So I know you're not doing litigation, but I guess

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Speaker 4: I'm curious how you deal with or if AI is

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Speaker 4: helpful with in litigation what would be called precedent. But

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Speaker 4: I'm assuming you're building up a big database of all

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Speaker 4: these different deal documents.

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Speaker 3: Is it useful?

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Speaker 4: Is AI useful to go back and look at previous

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Speaker 4: documents in order to shape new ones?

436
00:22:23,640 --> 00:22:27,199
Speaker 1: Yeah? Exactly, So in New Edica, we are ultimately an

437
00:22:27,200 --> 00:22:30,160
Speaker 1: a power software company, but we actually have the largest

438
00:22:30,440 --> 00:22:32,600
Speaker 1: knowledge graph of deal terms in ex systems. So Tracy,

439
00:22:32,640 --> 00:22:36,440
Speaker 1: exactly what you said. It's a database ultimately of precedent

440
00:22:36,640 --> 00:22:40,399
Speaker 1: comparable deal terms, and that database is going to be

441
00:22:40,440 --> 00:22:43,280
Speaker 1: mind bowing as over billion terms in it, So its

442
00:22:43,320 --> 00:22:44,879
Speaker 1: issue to a large as in existence, we map that

443
00:22:44,880 --> 00:22:47,320
Speaker 1: back to deal characteristics. It's the same in litigation, right,

444
00:22:47,359 --> 00:22:52,119
Speaker 1: So in transactional markets, folks are innovative, but they also

445
00:22:52,200 --> 00:22:54,720
Speaker 1: want to rely on something that has happened before, or

446
00:22:54,760 --> 00:22:56,480
Speaker 1: at least in part, they want to rely on something

447
00:22:56,520 --> 00:23:00,399
Speaker 1: that has happened before, and so folks are constantly looking

448
00:23:00,440 --> 00:23:03,679
Speaker 1: for ways to tie things back to comparable deal terms.

449
00:23:03,800 --> 00:23:06,720
Speaker 1: It's the same in litigation. So obviously not expertise, but

450
00:23:07,080 --> 00:23:09,560
Speaker 1: the same concept, which is, you know, when you write

451
00:23:09,600 --> 00:23:13,479
Speaker 1: a brief, you were constantly citing cases that the judge

452
00:23:13,560 --> 00:23:17,320
Speaker 1: has you know, relied on in the past. And you know,

453
00:23:17,400 --> 00:23:19,960
Speaker 1: for lawyers and you know outset of lawyers, even just

454
00:23:20,040 --> 00:23:24,280
Speaker 1: deal professionals generally bankers, credit managers, people are highly reliant

455
00:23:24,320 --> 00:23:25,320
Speaker 1: on present What.

456
00:23:25,760 --> 00:23:28,560
Speaker 2: Is your text acre did you what do you build

457
00:23:28,600 --> 00:23:32,280
Speaker 2: and how much is it? Like, oh, you're using chat, epts, API,

458
00:23:32,520 --> 00:23:36,560
Speaker 2: et cetera. Like, okay, yes, large language models are good

459
00:23:36,600 --> 00:23:40,440
Speaker 2: at identifying deal terms or novelty, et cetera. There's semantic

460
00:23:40,480 --> 00:23:42,680
Speaker 2: meaning of these terms, but what did you actually build

461
00:23:42,720 --> 00:23:45,320
Speaker 2: and what do you actually employ in your technology?

462
00:23:45,800 --> 00:23:48,760
Speaker 1: So we were starting in twenty twenty two, so we're

463
00:23:48,800 --> 00:23:51,800
Speaker 1: what you would call AI native. We were started in

464
00:23:51,840 --> 00:23:55,840
Speaker 1: a system that already and language models existed in. However,

465
00:23:56,160 --> 00:23:59,200
Speaker 1: we because of the nature of the sensitive documents in

466
00:23:59,280 --> 00:24:02,120
Speaker 1: terms that we deal with, especially for you know, major

467
00:24:02,200 --> 00:24:03,720
Speaker 1: law firms, financialist yea, this is like.

468
00:24:03,680 --> 00:24:05,520
Speaker 2: A big issue with the right that they don't want

469
00:24:05,560 --> 00:24:08,760
Speaker 2: to just be uploading their stuff to chat GPT right exactly.

470
00:24:08,840 --> 00:24:12,840
Speaker 1: And so we actually utilize you know, adapted language models,

471
00:24:12,880 --> 00:24:15,080
Speaker 1: open source language models that we adapt on our armed

472
00:24:15,080 --> 00:24:17,919
Speaker 1: proprietary data sets and then deploy and secure environments and

473
00:24:17,920 --> 00:24:22,520
Speaker 1: single tendon architectures, you know, for individual instances of institutions

474
00:24:22,520 --> 00:24:25,400
Speaker 1: that deploy our product. And so you could think about

475
00:24:25,400 --> 00:24:29,080
Speaker 1: it as based on the language models that are ultimately

476
00:24:29,160 --> 00:24:32,400
Speaker 1: underpinning a lot of the gpds and the clouds. However,

477
00:24:32,640 --> 00:24:35,680
Speaker 1: it's fine tuned to this particular data set, which makes

478
00:24:35,680 --> 00:24:39,280
Speaker 1: it obviously much better at handling this exact problem, which

479
00:24:39,320 --> 00:24:41,840
Speaker 1: is a big problem in the market. Now. We also

480
00:24:42,000 --> 00:24:44,920
Speaker 1: layer on top of that information extraction model. So for instance,

481
00:24:45,480 --> 00:24:47,640
Speaker 1: you may know that a term exists in what deal,

482
00:24:47,720 --> 00:24:50,399
Speaker 1: but you may want to know what terms should exist

483
00:24:50,480 --> 00:24:52,560
Speaker 1: for a JP Morgan deal, or for a B of

484
00:24:52,600 --> 00:24:56,240
Speaker 1: a deal, or for you know, a particular type of counterparty,

485
00:24:56,280 --> 00:24:58,880
Speaker 1: and so in those context we actually want to map

486
00:24:58,920 --> 00:25:01,359
Speaker 1: those deal terms back to deal characteristics, and we actually

487
00:25:01,480 --> 00:25:04,600
Speaker 1: utilize a lot of models to extract information and marry

488
00:25:04,600 --> 00:25:07,320
Speaker 1: that with their party data sets. So that's a little

489
00:25:07,320 --> 00:25:09,760
Speaker 1: bit about how the technology works. I think I always

490
00:25:09,800 --> 00:25:11,560
Speaker 1: think about it from the user standpoint, What does the

491
00:25:11,600 --> 00:25:14,240
Speaker 1: user really want? These really wants to know how they're

492
00:25:14,240 --> 00:25:16,800
Speaker 1: going to invite their client on a particular merger on

493
00:25:16,800 --> 00:25:20,600
Speaker 1: a particular credit deal. How often does this come up?

494
00:25:20,800 --> 00:25:23,840
Speaker 1: You always call your attorney and you're trying to figure out, well,

495
00:25:23,920 --> 00:25:26,679
Speaker 1: is this market is it off market? And that's what

496
00:25:26,720 --> 00:25:27,640
Speaker 1: our data provides.

497
00:25:27,960 --> 00:25:32,879
Speaker 3: Okay, so structural fortifications in deal terms. What are you

498
00:25:32,960 --> 00:25:33,760
Speaker 3: seeing right now?

499
00:25:33,800 --> 00:25:37,119
Speaker 4: Because as we started this conversation, we were talking a

500
00:25:37,119 --> 00:25:40,119
Speaker 4: lot about the recent blow ups in the private credit market,

501
00:25:40,200 --> 00:25:44,480
Speaker 4: and if you look at some spreads on certain firms,

502
00:25:44,520 --> 00:25:47,640
Speaker 4: certain bonds, it does seem like nervousness is creeping back

503
00:25:47,680 --> 00:25:50,480
Speaker 4: into the market. I see spreads on you know, it's

504
00:25:50,480 --> 00:25:53,720
Speaker 4: not private credit, but spreads on triple C rated debt

505
00:25:53,840 --> 00:25:58,480
Speaker 4: have been creeping up recently. How scared or concerned are

506
00:25:58,560 --> 00:25:59,399
Speaker 4: people right now?

507
00:26:00,240 --> 00:26:03,080
Speaker 1: Well, I recently wrote about this in the Wall Street

508
00:26:03,119 --> 00:26:07,280
Speaker 1: Journal a little bit, and then folks contacted me and

509
00:26:07,320 --> 00:26:10,000
Speaker 1: I kind of said, you know, you're causing a stir.

510
00:26:11,280 --> 00:26:14,280
Speaker 1: And then I saw Howard Marx came out with his letter,

511
00:26:14,600 --> 00:26:17,440
Speaker 1: which I think was called Cockroaches in the coal Mine,

512
00:26:17,600 --> 00:26:19,280
Speaker 1: and they had a lot of the same themes. I

513
00:26:19,280 --> 00:26:21,040
Speaker 1: think folks who have been around credit market for a

514
00:26:21,080 --> 00:26:23,320
Speaker 1: very long time can kind of see what's a little

515
00:26:23,359 --> 00:26:24,120
Speaker 1: bit of what's going on.

516
00:26:24,680 --> 00:26:25,200
Speaker 2: To us.

517
00:26:25,640 --> 00:26:28,119
Speaker 1: Let me just talk about what the data supports to us.

518
00:26:28,600 --> 00:26:35,120
Speaker 1: What we see is creditors maybe preparing this their system

519
00:26:35,160 --> 00:26:38,359
Speaker 1: for distress, and I'll talk about what we're seeing in

520
00:26:38,359 --> 00:26:40,840
Speaker 1: the data that kind of supports that. But you can

521
00:26:40,880 --> 00:26:45,240
Speaker 1: think about it like the evolution of your house security, right,

522
00:26:45,320 --> 00:26:49,520
Speaker 1: So you know, first you lock the doors. Then you know,

523
00:26:49,560 --> 00:26:52,680
Speaker 1: you get a bolt lock, which gives you better protection.

524
00:26:53,119 --> 00:26:54,960
Speaker 1: You know. Then you you know, you add a security

525
00:26:54,960 --> 00:26:58,480
Speaker 1: system on top of that alarm system. And at the end,

526
00:26:58,720 --> 00:27:00,439
Speaker 1: what do you do. You kind of up all your

527
00:27:00,520 --> 00:27:03,439
Speaker 1: valuables and you ensure them if people are going to

528
00:27:03,440 --> 00:27:05,960
Speaker 1: get into the house. And you know, for the past

529
00:27:06,000 --> 00:27:09,320
Speaker 1: few years, we've seen lenders really focused on keeping people out.

530
00:27:09,480 --> 00:27:13,040
Speaker 1: This is the locks and the dead bolts, and this

531
00:27:13,080 --> 00:27:15,000
Speaker 1: is what we were talking about with j crub blockers.

532
00:27:15,359 --> 00:27:18,800
Speaker 1: This is making sure you can't structure around me. From

533
00:27:18,800 --> 00:27:22,240
Speaker 1: a liability management perspective, But over the last quarter something

534
00:27:22,320 --> 00:27:25,280
Speaker 1: kind of changed, which is we started seeing people and

535
00:27:25,359 --> 00:27:29,080
Speaker 1: lenders obsessed with lean subordination terms, which is the term

536
00:27:29,240 --> 00:27:34,560
Speaker 1: that governs who gets paid first when everything falls apart.

537
00:27:35,200 --> 00:27:39,760
Speaker 1: So this isn't really about preventing liability management exercises that much.

538
00:27:39,800 --> 00:27:45,359
Speaker 1: It's actually about controlling the recovery when a bankruptcy does happen.

539
00:27:45,960 --> 00:27:48,600
Speaker 1: And so we clocked that term at eighty four percent

540
00:27:48,640 --> 00:27:51,400
Speaker 1: of deals in Q three, biggest quarterly jump we've ever

541
00:27:51,440 --> 00:27:54,359
Speaker 1: seen from the prior quarter. It's also the highest we've

542
00:27:54,400 --> 00:27:56,879
Speaker 1: ever clocked that term. So this bes the question of

543
00:27:56,920 --> 00:28:00,440
Speaker 1: why Wire Credit is so focused on making sure.

544
00:28:00,280 --> 00:28:02,480
Speaker 3: Their place on line is in recovery.

545
00:28:02,560 --> 00:28:06,800
Speaker 1: In recovery is the same. Perhaps it's a reaction to

546
00:28:07,160 --> 00:28:11,679
Speaker 1: the Lieboldy management transactions we talked about, so perhaps folks

547
00:28:11,680 --> 00:28:14,679
Speaker 1: are thinking that that will precipitate. Perhaps it's a reaction

548
00:28:14,720 --> 00:28:17,199
Speaker 1: to some of the maturity walls that folks understand, or

549
00:28:17,240 --> 00:28:19,800
Speaker 1: perhaps it's some of what I was saying in the appad,

550
00:28:19,840 --> 00:28:23,639
Speaker 1: which is folks are seeing that there may be distress

551
00:28:23,680 --> 00:28:25,480
Speaker 1: events on the horizon and they want to make sure

552
00:28:25,520 --> 00:28:28,000
Speaker 1: that if there is, they have the most negotiaing leverage

553
00:28:28,000 --> 00:28:28,439
Speaker 1: its possible.

554
00:28:44,640 --> 00:28:47,600
Speaker 2: So I know it's broad statements, but you know, when

555
00:28:47,600 --> 00:28:51,840
Speaker 2: we look at these sort of environment under which companies

556
00:28:51,880 --> 00:28:54,200
Speaker 2: like First Brands or Tree Color or some of these

557
00:28:54,240 --> 00:28:57,240
Speaker 2: other ones that have gone into distress very rapidly, when

558
00:28:57,240 --> 00:29:00,160
Speaker 2: we look back at when these were birthed.

559
00:29:00,000 --> 00:29:00,520
Speaker 1: Et cetera.

560
00:29:00,880 --> 00:29:04,480
Speaker 2: Can we say like these were sloppy times, These were loose,

561
00:29:04,520 --> 00:29:08,360
Speaker 2: sloppy times that people were not thinking much about either

562
00:29:08,480 --> 00:29:11,800
Speaker 2: just quality due diligence or diligent terms.

563
00:29:12,280 --> 00:29:15,160
Speaker 1: Yeah. So I think with First Brands is a great example. Right.

564
00:29:15,240 --> 00:29:21,239
Speaker 1: So First Brands is an automotive replacement company, right, so

565
00:29:21,280 --> 00:29:25,480
Speaker 1: they make things like breaks and wipers and filtration systems.

566
00:29:26,160 --> 00:29:32,400
Speaker 1: Beginning in twenty nineteen, that company effectively rapidly expanded through

567
00:29:32,560 --> 00:29:37,760
Speaker 1: debt fueled acquisitions and it dramatically increased its scale. But

568
00:29:37,920 --> 00:29:41,400
Speaker 1: I think what First Brands illustrates is something that you know,

569
00:29:41,480 --> 00:29:43,880
Speaker 1: we might get into with the private credit markets, which

570
00:29:43,920 --> 00:29:49,160
Speaker 1: is that they primarily funded these acquisitions with large debt facilities.

571
00:29:49,760 --> 00:29:53,440
Speaker 1: Then tariffs hit in April twenty twenty five, which obviously

572
00:29:53,560 --> 00:29:56,240
Speaker 1: changed their business because they actually do a lot of manufacturing,

573
00:29:56,680 --> 00:29:59,760
Speaker 1: and that kind of magnified problems. So you can think

574
00:29:59,760 --> 00:30:03,000
Speaker 1: about out. One of the main problems with First Brands,

575
00:30:03,120 --> 00:30:05,520
Speaker 1: which is also kind of some of what folks are

576
00:30:05,600 --> 00:30:08,000
Speaker 1: worried about in the private credit markets today, is what's

577
00:30:08,000 --> 00:30:12,560
Speaker 1: called off balance sheet financing. What First Brands used is

578
00:30:13,280 --> 00:30:17,080
Speaker 1: a lot of you know, receivables financing facilities that weren't

579
00:30:17,120 --> 00:30:20,680
Speaker 1: properly disclosed to a lot of folks that were lending

580
00:30:20,680 --> 00:30:23,120
Speaker 1: to the company. In fact, I think in that sense,

581
00:30:23,200 --> 00:30:24,920
Speaker 1: just to give you a sense of quantum, this is

582
00:30:24,960 --> 00:30:28,080
Speaker 1: over eleven billion dollars of total obligations that they had

583
00:30:28,200 --> 00:30:31,760
Speaker 1: when they actually started disclosing it in terms of off balance youe.

584
00:30:31,760 --> 00:30:35,520
Speaker 1: Financing and you know, they were disclosing things like five

585
00:30:35,640 --> 00:30:38,880
Speaker 1: to six billion dollars of actual debt obligations. And so

586
00:30:39,720 --> 00:30:41,400
Speaker 1: this led one of the creditors lawyers to say that

587
00:30:41,400 --> 00:30:46,719
Speaker 1: two point three billion dollars just disappeared. And so that structure,

588
00:30:47,080 --> 00:30:50,600
Speaker 1: the ability for first brands to get that debt was

589
00:30:50,640 --> 00:30:54,800
Speaker 1: made possible by the private credit markets and how deep

590
00:30:54,880 --> 00:30:58,520
Speaker 1: the private credit markets have become. Because if you're a

591
00:30:58,720 --> 00:31:02,640
Speaker 1: big credit manager in private credit markets, you could fund

592
00:31:03,320 --> 00:31:06,080
Speaker 1: you know, that type of receivable facility to a first brand,

593
00:31:06,600 --> 00:31:09,560
Speaker 1: and first brands could use that facility to then, you know,

594
00:31:09,600 --> 00:31:13,360
Speaker 1: make sure they are constantly continuing to acquire new businesses

595
00:31:13,520 --> 00:31:15,760
Speaker 1: and keep rolling over the cash.

596
00:31:15,960 --> 00:31:20,080
Speaker 4: I have a theory that receivables, financing and factoring is

597
00:31:20,120 --> 00:31:23,280
Speaker 4: to the private credit market. What French quants who went

598
00:31:23,320 --> 00:31:26,280
Speaker 4: to that one elite school are.

599
00:31:27,040 --> 00:31:28,120
Speaker 3: To trading blow ups.

600
00:31:28,480 --> 00:31:29,280
Speaker 1: I like that theory.

601
00:31:29,480 --> 00:31:30,160
Speaker 3: Yeah, thanks.

602
00:31:30,520 --> 00:31:32,280
Speaker 4: So the other thing we wanted to ask you about,

603
00:31:32,280 --> 00:31:35,200
Speaker 4: and again we reference this in the intro, is we

604
00:31:35,280 --> 00:31:39,320
Speaker 4: are seeing these really complicated deals that I admittedly cannot

605
00:31:39,400 --> 00:31:42,240
Speaker 4: keep track of in the AI market, where you know,

606
00:31:42,640 --> 00:31:45,960
Speaker 4: one company is going to buy chips from this other company,

607
00:31:46,000 --> 00:31:48,320
Speaker 4: and then that company is going to borrow from whoever

608
00:31:48,480 --> 00:31:50,640
Speaker 4: and use the chips funding to pay them back, and

609
00:31:50,680 --> 00:31:53,440
Speaker 4: then that money somehow goes into the company that is

610
00:31:53,520 --> 00:31:55,960
Speaker 4: buying the stuff in the first place. It is all

611
00:31:56,040 --> 00:31:59,920
Speaker 4: very circular, all very incestuous in many ways. In my mind,

612
00:32:00,560 --> 00:32:03,240
Speaker 4: are you examining those types of deals or just putting

613
00:32:03,280 --> 00:32:06,680
Speaker 4: on your credit expertise hat if you see something like that,

614
00:32:06,720 --> 00:32:07,440
Speaker 4: what are you thinking?

615
00:32:07,920 --> 00:32:10,920
Speaker 1: Yeah, well, it's probably helpful to kind of talk about

616
00:32:11,080 --> 00:32:13,280
Speaker 1: some of the structure of these deals, which I think

617
00:32:13,640 --> 00:32:16,560
Speaker 1: again is made possible by how deep the private credit

618
00:32:16,560 --> 00:32:19,800
Speaker 1: markets have become. And usually when I do that, I

619
00:32:19,840 --> 00:32:21,320
Speaker 1: try to think about, let's try to make this a

620
00:32:21,360 --> 00:32:24,680
Speaker 1: little bit more fun. So imagine for a minute, Joe,

621
00:32:25,600 --> 00:32:29,720
Speaker 1: you just love pizza. He does love pizza yesterday twice.

622
00:32:29,760 --> 00:32:32,280
Speaker 1: There you go, You're a pizza fanatic. You love it

623
00:32:32,280 --> 00:32:34,320
Speaker 1: so much that you decide to eat pizza every single

624
00:32:34,320 --> 00:32:37,320
Speaker 1: meal of every single day for the rest of your life,

625
00:32:37,480 --> 00:32:40,840
Speaker 1: like you are committed to subsisting pizza, committed to the

626
00:32:40,880 --> 00:32:44,520
Speaker 1: carbs exactly. So, Joe, you made that decision. You come

627
00:32:44,560 --> 00:32:46,400
Speaker 1: to me and you say, Hey, Dan, I'm going to

628
00:32:46,440 --> 00:32:49,080
Speaker 1: eat pizza for every meal of my entire life. How

629
00:32:49,080 --> 00:32:53,400
Speaker 1: about you open a pizza restaurant for me to eat it.

630
00:32:53,400 --> 00:32:54,440
Speaker 1: It'll be really lucrative.

631
00:32:56,760 --> 00:32:58,719
Speaker 2: We're going with this, but this is actually a very

632
00:32:58,760 --> 00:33:00,160
Speaker 2: good to note do, right, Like you would have have

633
00:33:00,200 --> 00:33:02,680
Speaker 2: a lot of confidence in me to commit to my word.

634
00:33:02,720 --> 00:33:04,080
Speaker 2: If you're going to open a restaurant.

635
00:33:04,200 --> 00:33:05,880
Speaker 1: Yeah. Now, now you come to me and say, it's

636
00:33:05,880 --> 00:33:07,920
Speaker 1: going to be super lubritive. Here's how we're going to

637
00:33:08,000 --> 00:33:10,880
Speaker 1: fund it. Ten percent equity. The bank is going to

638
00:33:10,920 --> 00:33:13,360
Speaker 1: give you ninety percent of the funding in leverage. And

639
00:33:13,400 --> 00:33:16,160
Speaker 1: it's Dan's restaurant. Joe, you don't on the restaurant, but

640
00:33:16,240 --> 00:33:18,400
Speaker 1: you're going to eat at it. I'm the full beneficiary,

641
00:33:18,480 --> 00:33:22,440
Speaker 1: full beneficiary of the restaurant, but it's ninety percent. Okay,

642
00:33:22,480 --> 00:33:24,840
Speaker 1: So I opened the restaurant. You eat there every single day. Now, Tracy,

643
00:33:25,160 --> 00:33:29,520
Speaker 1: Joe comes to you for a personal loan to fund

644
00:33:29,600 --> 00:33:31,800
Speaker 1: his lifestyle, his pizza eating.

645
00:33:32,520 --> 00:33:34,040
Speaker 2: Tracy, trust me, she would lend it to me.

646
00:33:34,640 --> 00:33:38,000
Speaker 1: Well, here's the question, right, should you, Tracy? Consider the

647
00:33:38,120 --> 00:33:44,160
Speaker 1: ninety pizza restaurant that Joe is eating at for all

648
00:33:44,200 --> 00:33:47,880
Speaker 1: his meals. Now, on the one hand, it's not Cho's loan, right,

649
00:33:47,920 --> 00:33:49,760
Speaker 1: so he's not on the hook if the pizza restaurant

650
00:33:49,800 --> 00:33:52,719
Speaker 1: goes under. On the other hand, it's Joe's only source

651
00:33:53,000 --> 00:33:53,720
Speaker 1: of food.

652
00:33:54,760 --> 00:33:57,280
Speaker 3: Which Joe will die without the restaurant.

653
00:33:56,880 --> 00:34:00,360
Speaker 1: Which is his He's committed to the restaurant, and it

654
00:34:00,440 --> 00:34:04,120
Speaker 1: kind of makes the restaurant intertwined with Joe's ability to

655
00:34:04,120 --> 00:34:06,280
Speaker 1: pay your personal loan back. So, I guess that's a

656
00:34:06,280 --> 00:34:07,560
Speaker 1: good question. No, there's great.

657
00:34:07,600 --> 00:34:10,040
Speaker 2: So now let's take it out of pizza. Who is

658
00:34:10,760 --> 00:34:13,200
Speaker 2: so that's whatever? Like okay, now who is the chips

659
00:34:13,239 --> 00:34:13,840
Speaker 2: buy or whatever?

660
00:34:14,000 --> 00:34:16,800
Speaker 1: This is essentially what's happening with off balance youe. Financing

661
00:34:16,800 --> 00:34:20,160
Speaker 1: and data center deals. So, and it includes Metas. I'm

662
00:34:20,160 --> 00:34:22,920
Speaker 1: sure you saw the Hyperion deal. It's his Metas infrastructure

663
00:34:22,920 --> 00:34:25,440
Speaker 1: deal with Blue Owl. Except I think it's even more

664
00:34:25,480 --> 00:34:27,719
Speaker 1: intriguing than some of the pizza stuff. So Meta and

665
00:34:27,719 --> 00:34:31,000
Speaker 1: Blue Ol basically created a joint venture in a special

666
00:34:31,000 --> 00:34:34,239
Speaker 1: purpose vehicle not that different than the restaurant. And the

667
00:34:34,280 --> 00:34:36,440
Speaker 1: deal is the joint venture would be owned twenty percent

668
00:34:36,480 --> 00:34:38,959
Speaker 1: by Meta, eighty percent by Blue Owl, so Blue Owl

669
00:34:38,960 --> 00:34:42,280
Speaker 1: controls it, and it would effectively be funded with ninety

670
00:34:42,320 --> 00:34:45,040
Speaker 1: percent leverage. So call it thirty billion dollars of total

671
00:34:45,160 --> 00:34:47,719
Speaker 1: enterprise value, three billion dollars of equity, twenty seven billion

672
00:34:47,760 --> 00:34:51,000
Speaker 1: dollars give or take of debt. In other words, Blue

673
00:34:51,040 --> 00:34:54,200
Speaker 1: Oul is effectively owning the restaurant. Meta is effetively eating

674
00:34:54,200 --> 00:34:57,280
Speaker 1: at the restaurant, and the bank's funded with ninety percent leverage.

675
00:34:57,640 --> 00:35:01,480
Speaker 1: So what this does is it keeps the debt off

676
00:35:01,600 --> 00:35:05,880
Speaker 1: of Meta's books right while also giving investors credit managers

677
00:35:06,080 --> 00:35:09,560
Speaker 1: the ability to put money against a data center asset.

678
00:35:09,640 --> 00:35:13,200
Speaker 1: So Meta in this deal will make rent payments associated

679
00:35:13,200 --> 00:35:15,320
Speaker 1: with the data center based on its cost of power.

680
00:35:15,760 --> 00:35:18,799
Speaker 1: That's the cash flow that's going to the SBV, and

681
00:35:18,840 --> 00:35:21,680
Speaker 1: that effectively funds the interest expense. Let's just talk about

682
00:35:21,680 --> 00:35:25,040
Speaker 1: the debt for a second. In a normal LBO context,

683
00:35:25,600 --> 00:35:29,520
Speaker 1: ninety percent leverage is pretty exceptionally high. Most people would

684
00:35:29,600 --> 00:35:33,480
Speaker 1: consider fifty to eighty percent leverage to be relatively normal

685
00:35:33,640 --> 00:35:36,880
Speaker 1: for a stable cash flow business. So the debt itself

686
00:35:37,000 --> 00:35:39,600
Speaker 1: is actually quite high on some of these structures. The

687
00:35:39,640 --> 00:35:41,560
Speaker 1: only reason it was possible was because it was given

688
00:35:41,560 --> 00:35:45,280
Speaker 1: an investment great credit rating, and in part because Meta

689
00:35:45,320 --> 00:35:47,680
Speaker 1: agreed to a four year operating lease with what's called

690
00:35:47,760 --> 00:35:51,600
Speaker 1: a residual value guarantee, which means that Meta is guaranteeing

691
00:35:51,640 --> 00:35:54,920
Speaker 1: a capped amount of some of that cashflow. However, that

692
00:35:54,960 --> 00:35:58,839
Speaker 1: guarantee is capped and is only partial, which is why

693
00:35:58,840 --> 00:36:00,440
Speaker 1: they don't have to take it onto their book and

694
00:36:00,480 --> 00:36:02,800
Speaker 1: why would be a footnote as a contingent debt obligation

695
00:36:02,840 --> 00:36:05,600
Speaker 1: in their balance sheet. Now let's talk about the asset

696
00:36:05,680 --> 00:36:10,040
Speaker 1: that's being underwritten. This isn't pizza. Pizza actually has a

697
00:36:10,080 --> 00:36:14,160
Speaker 1: stable price, right. We have thousands of UTI history on pizza, right,

698
00:36:14,239 --> 00:36:17,080
Speaker 1: and you can track that price over time. Data center

699
00:36:17,120 --> 00:36:20,720
Speaker 1: is optimized for GPU performance on training fundamental AI models.

700
00:36:22,120 --> 00:36:24,879
Speaker 1: Not so much of a mature asset, actually, I think

701
00:36:24,880 --> 00:36:27,040
Speaker 1: most folks would think about it as a burgeoning asset.

702
00:36:27,360 --> 00:36:29,640
Speaker 1: Now I'm in this world. I mean, folks, there's a

703
00:36:29,760 --> 00:36:31,960
Speaker 1: high amount of demand for a lot of this compute,

704
00:36:32,000 --> 00:36:35,160
Speaker 1: and I definitely think the demand is there, but at

705
00:36:35,160 --> 00:36:37,520
Speaker 1: the end of the day, it's an immature asset with

706
00:36:37,640 --> 00:36:40,920
Speaker 1: a price that isn't so well defined. So just a recap,

707
00:36:41,000 --> 00:36:45,120
Speaker 1: you've got off balance sheet financing which isn't reflected with

708
00:36:45,200 --> 00:36:47,480
Speaker 1: whoever is lending money to metal or even buying its

709
00:36:47,480 --> 00:36:53,759
Speaker 1: equity with ninety percent leverage on an immature asset, And

710
00:36:53,800 --> 00:36:56,040
Speaker 1: I think that's why these deals are so interesting. So

711
00:36:56,080 --> 00:36:58,480
Speaker 1: from our point of view, I mean to make sure

712
00:36:58,480 --> 00:37:00,480
Speaker 1: you get the terms right, and you know, we will

713
00:37:00,600 --> 00:37:02,520
Speaker 1: look at these data center. A lot of these types

714
00:37:02,560 --> 00:37:04,400
Speaker 1: of financings run through our platform all the time. To

715
00:37:04,400 --> 00:37:05,960
Speaker 1: make sure you get the terms right on what this

716
00:37:06,239 --> 00:37:09,200
Speaker 1: structural protections look like in these deals is critical for

717
00:37:09,280 --> 00:37:12,200
Speaker 1: the fortification of something that is in the structure.

718
00:37:12,880 --> 00:37:16,319
Speaker 4: So I know we've seen these idiosyncratic blow ups in

719
00:37:16,320 --> 00:37:18,920
Speaker 4: the private credit market so far, but just looking at

720
00:37:18,920 --> 00:37:23,319
Speaker 4: the AI market in particular and the financing there, it

721
00:37:23,360 --> 00:37:26,880
Speaker 4: feels like right now people are still willing to lend money.

722
00:37:27,200 --> 00:37:29,640
Speaker 4: And we've talked about this on the show before, but

723
00:37:29,920 --> 00:37:32,680
Speaker 4: a lot of the AI competition is couched in this

724
00:37:32,800 --> 00:37:38,759
Speaker 4: existential language of you either win it AI or die basically,

725
00:37:38,800 --> 00:37:42,600
Speaker 4: and so the spending keeps going. What is your guess

726
00:37:42,640 --> 00:37:46,240
Speaker 4: on like the thing that kind of knocks that cycle

727
00:37:46,440 --> 00:37:48,480
Speaker 4: or that flywheel.

728
00:37:48,160 --> 00:37:49,399
Speaker 3: And tears it apart.

729
00:37:49,800 --> 00:37:52,880
Speaker 1: So I'm obviously in the AI industry. We're in the

730
00:37:52,920 --> 00:37:56,319
Speaker 1: credit industry, so we see both sides of this phenomenon.

731
00:37:57,080 --> 00:38:01,640
Speaker 1: I fundamentally believe AI is a paradigm shift. I would

732
00:38:01,640 --> 00:38:05,000
Speaker 1: not have left, you know, the deal markets if I

733
00:38:05,000 --> 00:38:07,640
Speaker 1: didn't think that. And I think what we're witnessing is

734
00:38:07,760 --> 00:38:10,040
Speaker 1: very similar to the Internet in the nineteen nineties, or

735
00:38:10,080 --> 00:38:12,360
Speaker 1: the iPhone in two thousands, or social media in the

736
00:38:12,400 --> 00:38:15,719
Speaker 1: twenty tens. And I think this paradigm shift is going

737
00:38:15,760 --> 00:38:20,800
Speaker 1: to ultimately change a ton of industries, including capital markets

738
00:38:20,840 --> 00:38:24,359
Speaker 1: and finance and law and all these amazing industries. And

739
00:38:24,400 --> 00:38:27,480
Speaker 1: so that I think is very true. But I also

740
00:38:27,480 --> 00:38:31,080
Speaker 1: think two things can be true. I think AI can

741
00:38:31,120 --> 00:38:35,480
Speaker 1: be a generation defining category and a technology that's upending

742
00:38:35,520 --> 00:38:39,440
Speaker 1: a lot of industries. But I also think that categories

743
00:38:39,480 --> 00:38:43,880
Speaker 1: will have winners and losers. And when folks are racing

744
00:38:43,920 --> 00:38:47,319
Speaker 1: to define a category, as you know, you often see

745
00:38:47,400 --> 00:38:50,719
Speaker 1: with a lot of these transformational types of technology, there

746
00:38:50,719 --> 00:38:53,640
Speaker 1: may be more losers in the headlines. Then you're used

747
00:38:53,680 --> 00:38:57,080
Speaker 1: to seeing in a lot of these markets, but the

748
00:38:57,120 --> 00:39:01,120
Speaker 1: winners will be bigger than anyone's ever all.

749
00:39:00,680 --> 00:39:02,799
Speaker 2: Right, So if I don't need the pizza, someone else

750
00:39:02,880 --> 00:39:04,800
Speaker 2: is going to pick up the pizza and they're gonna

751
00:39:04,800 --> 00:39:05,600
Speaker 2: they're gonna eat it.

752
00:39:05,840 --> 00:39:08,680
Speaker 1: Look what we focus on in Oadica is in a

753
00:39:08,760 --> 00:39:11,759
Speaker 1: market moving this fast. Yeah, we all need to pay

754
00:39:11,800 --> 00:39:15,080
Speaker 1: attention to the terms that actually underpinning a lot of

755
00:39:15,080 --> 00:39:17,480
Speaker 1: these markets to make sure if there is any bleeding,

756
00:39:17,480 --> 00:39:20,319
Speaker 1: that bleeding gets stopped as quickly as possible. Just to

757
00:39:20,320 --> 00:39:23,839
Speaker 1: give you one last example from a recent market deal,

758
00:39:24,400 --> 00:39:26,719
Speaker 1: you can look at the Frank jpm deal as like

759
00:39:26,800 --> 00:39:29,640
Speaker 1: a really interesting one. This is, you know, this was

760
00:39:29,680 --> 00:39:32,040
Speaker 1: a deal where JPMorgan paid one hundred and seventy five

761
00:39:32,040 --> 00:39:34,680
Speaker 1: million dollars to acquire a company. There's a very small deal,

762
00:39:34,680 --> 00:39:37,000
Speaker 1: but to acquire a company called Frank, which is a

763
00:39:37,000 --> 00:39:39,280
Speaker 1: streamline fasta kind of support service.

764
00:39:40,760 --> 00:39:41,319
Speaker 2: I remember this.

765
00:39:41,440 --> 00:39:46,080
Speaker 1: It turned out there was a lot of synthetically made

766
00:39:46,200 --> 00:39:48,239
Speaker 1: up types of data in that.

767
00:39:48,160 --> 00:39:50,040
Speaker 2: Business, and the founder is going to prison right.

768
00:39:50,560 --> 00:39:53,440
Speaker 1: Allegedly there's a lot of there's a lot of made

769
00:39:53,520 --> 00:39:55,840
Speaker 1: up stuff in the business. And I think seven days.

770
00:39:55,920 --> 00:39:58,719
Speaker 2: Executive who worked at Frank sends to sixty eight months.

771
00:39:58,760 --> 00:40:01,959
Speaker 1: Yeah, yeah, yeah, and so. But I think the most

772
00:40:01,960 --> 00:40:05,719
Speaker 1: interesting part about this particular transaction to me is JPM

773
00:40:06,000 --> 00:40:09,360
Speaker 1: ended up signing a merger agreement that said that the

774
00:40:09,760 --> 00:40:15,879
Speaker 1: indemnification for the founder's litigation, for any founder's litigation, would

775
00:40:15,920 --> 00:40:17,080
Speaker 1: be paid for by JPM.

776
00:40:17,239 --> 00:40:18,240
Speaker 2: Right, they paid her lawyer.

777
00:40:18,400 --> 00:40:20,480
Speaker 1: They paid one hundred and fifteen million dollars in legal

778
00:40:20,520 --> 00:40:24,400
Speaker 1: expenses for her lawyer on her fraud. And so when

779
00:40:24,440 --> 00:40:28,200
Speaker 1: you're moving really fast, yeah, right, you can kind of

780
00:40:28,239 --> 00:40:30,560
Speaker 1: ignore some of the nuts and bolts. But I think

781
00:40:30,560 --> 00:40:32,800
Speaker 1: it's actually even more critical and fast moving markets.

782
00:40:32,920 --> 00:40:35,200
Speaker 2: Dan Workman, co founder of no Edica, thank you so

783
00:40:35,320 --> 00:40:36,520
Speaker 2: much for coming on outlook.

784
00:40:36,960 --> 00:40:38,319
Speaker 1: Thank you thanks for having me us.

785
00:40:51,080 --> 00:40:53,080
Speaker 2: Tracy. I wasn't really sure where he was going with

786
00:40:53,080 --> 00:40:55,359
Speaker 2: that pizza analogy, but it actually does make a lot

787
00:40:55,400 --> 00:40:57,759
Speaker 2: of sense, and it's something I think is a phenomenon

788
00:40:57,800 --> 00:41:00,960
Speaker 2: and just a lot of financial transactions, which is how

789
00:41:01,040 --> 00:41:04,920
Speaker 2: much like in certain environments, the lender and the creditor

790
00:41:05,040 --> 00:41:07,839
Speaker 2: are like both each others, like they're both leaning on

791
00:41:07,880 --> 00:41:10,160
Speaker 2: each other. They're both the creditor and lenders, they're relying

792
00:41:10,200 --> 00:41:11,920
Speaker 2: on each Yeah, at the same time.

793
00:41:11,880 --> 00:41:13,480
Speaker 3: Much in the way you rely on pizza.

794
00:41:13,640 --> 00:41:16,000
Speaker 2: You would lend to me to buy it to eat pizza.

795
00:41:15,760 --> 00:41:18,880
Speaker 3: Right, I would thank you if it was a matter

796
00:41:18,920 --> 00:41:20,040
Speaker 3: of survival, that was.

797
00:41:20,040 --> 00:41:21,160
Speaker 2: A matter of survival, thank you.

798
00:41:21,280 --> 00:41:21,920
Speaker 1: I think it's just.

799
00:41:21,920 --> 00:41:24,200
Speaker 3: Because you want to eat really expensive pizza then no.

800
00:41:24,560 --> 00:41:24,759
Speaker 4: You know.

801
00:41:24,800 --> 00:41:27,600
Speaker 2: The other thing too, is just like from talking to

802
00:41:27,640 --> 00:41:29,680
Speaker 2: you over these years, you know how many times I've

803
00:41:29,680 --> 00:41:31,759
Speaker 2: heard something there's a lot of cuve light stuff going.

804
00:41:32,040 --> 00:41:34,279
Speaker 2: It is interesting to think that, like, you don't often

805
00:41:34,320 --> 00:41:36,960
Speaker 2: hear that quantified what that means, right, things are like

806
00:41:37,000 --> 00:41:39,440
Speaker 2: cove light these days, et cetera. And the idea that like,

807
00:41:39,480 --> 00:41:41,799
Speaker 2: maybe we could get better numbers on some of these

808
00:41:41,840 --> 00:41:46,560
Speaker 2: things seems like potentially labor saving for lawyers. Stuff like that.

809
00:41:47,000 --> 00:41:51,200
Speaker 4: The specific numbers on specific deal terms were really interesting

810
00:41:51,239 --> 00:41:55,440
Speaker 4: to me. And the idea that even today lawyers and

811
00:41:55,480 --> 00:41:59,359
Speaker 4: bankers still have trouble anticipating every single thing that could

812
00:41:59,440 --> 00:42:02,160
Speaker 4: happen to particular deal, and so they're having to react

813
00:42:02,200 --> 00:42:04,120
Speaker 4: to it and come up with the new terms, the

814
00:42:04,160 --> 00:42:06,839
Speaker 4: new deal language, and insert them into the documentation.

815
00:42:07,120 --> 00:42:08,040
Speaker 3: I find that interesting.

816
00:42:08,120 --> 00:42:10,680
Speaker 2: The tariff example, you know, the problem is is that

817
00:42:10,760 --> 00:42:12,799
Speaker 2: AI is good and this is I'm certain if we

818
00:42:12,880 --> 00:42:14,680
Speaker 2: talked about this more, AI will be used to come

819
00:42:14,800 --> 00:42:17,160
Speaker 2: up with new deal terms and the cat and mouse

820
00:42:17,200 --> 00:42:20,200
Speaker 2: game will continue forever. So I suspect that we are

821
00:42:20,239 --> 00:42:23,200
Speaker 2: not going to have lawyers will always find new work

822
00:42:23,239 --> 00:42:25,319
Speaker 2: to do, and they'll just get work. They'll just get

823
00:42:25,360 --> 00:42:29,000
Speaker 2: more creative about outsmarting the systems that are designed to

824
00:42:29,040 --> 00:42:30,280
Speaker 2: detect these phenomena.

825
00:42:30,480 --> 00:42:34,200
Speaker 4: We will end up with thousands and thousands of pages

826
00:42:34,360 --> 00:42:37,879
Speaker 4: of term sheets that, like humans are just physically incapable

827
00:42:37,880 --> 00:42:39,760
Speaker 4: of reading, it has to be read by AI.

828
00:42:39,920 --> 00:42:42,120
Speaker 2: I probably literally, that is what's going to happen.

829
00:42:42,200 --> 00:42:43,799
Speaker 3: Yeah, all right, shall we leave it there.

830
00:42:43,880 --> 00:42:44,759
Speaker 2: Let's leave it there, all right.

831
00:42:44,840 --> 00:42:47,080
Speaker 4: This has been another episode of the Odd Thoughts podcast.

832
00:42:47,160 --> 00:42:50,360
Speaker 4: I'm Tracy Alloway. You can follow me at Tracy Alloway.

833
00:42:50,040 --> 00:42:52,800
Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart.

834
00:42:53,000 --> 00:42:56,279
Speaker 2: Follow our producers Carmen Rodriguez at Carmen Arman, dash Ol

835
00:42:56,280 --> 00:42:59,000
Speaker 2: Bennett at Dashbot and kill Brooks at kill Brooks. For

836
00:42:59,120 --> 00:43:01,560
Speaker 2: more Odd Laws content, go to Bloomberg dot com slash

837
00:43:01,560 --> 00:43:03,600
Speaker 2: odd Lots, where we have a daily newsletter and all

838
00:43:03,640 --> 00:43:05,799
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839
00:43:05,800 --> 00:43:08,840
Speaker 2: these topics twenty four to seven in our discord discord

840
00:43:08,880 --> 00:43:10,160
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841
00:43:09,840 --> 00:43:12,279
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842
00:43:12,400 --> 00:43:15,279
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843
00:43:15,320 --> 00:43:18,680
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844
00:43:18,760 --> 00:43:21,400
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845
00:43:21,440 --> 00:43:24,440
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