Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of The Odd Lots podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, there are just so many credit related things to
Speaker 2: talk about right now, all things credit.
Speaker 3: I love it.
Speaker 1: I love it.
Speaker 3: Credit is interesting again.
Speaker 1: This might be one of the.
Speaker 2: Only credit episodes that we've ever done where like I
Speaker 2: found the guest because I feel like when I think
Speaker 2: about like a credit all the credit episodes, it's usually
Speaker 2: like someone you know. Randomly. I found someone who knows
Speaker 2: a little bit something about credit. She's like, oh, let
Speaker 2: me do it.
Speaker 3: Let me The stakes are high.
Speaker 2: I know I was thinking about there because you're like, how, Joe,
Speaker 2: you like pick someone who doesn't know anything. No, I
Speaker 2: don't think that. I think we have a very knowledgeable
Speaker 2: credit guest. But I'm a little stressed about this aspect.
Speaker 4: I believe in you, Joe, you do. I trust your judgment.
Speaker 4: But to your point, there's a lot going on. So
Speaker 4: obviously there are concerns around private credit. We've had some
Speaker 4: idiosyncratic defaults and frauds in the market, and each one
Speaker 4: is special in their own way. But I think the
Speaker 4: worrying aspect is that they keep coming to light. Yeah, right,
Speaker 4: And so you've seen people like Jamie Diamond using the
Speaker 4: cockroach analogy, which is now famous. And at the same
Speaker 4: time you have the connection with AI, right, which we
Speaker 4: have spoken about a little bit on the podcast with
Speaker 4: Paul Kadrowski. All these complex circular financing structures that are
Speaker 4: driving a lot of the credit boom, or have been
Speaker 4: driving a lot of the credit boom, and then at
Speaker 4: the same time you also have the impact of AI
Speaker 4: on credit itself.
Speaker 2: Yeah, that's right because in theory right, like we've talked
Speaker 2: about this. We did that episode with Joel Werthheimer that
Speaker 2: was in a slightly different context, but we've done these
Speaker 2: episodes about you know, just the incredible length of deal text,
Speaker 2: et cetera. And perhaps if there's one area where maybe
Speaker 2: we could say with some high degree of confidence that
Speaker 2: large language models could be useful, it is can we
Speaker 2: break down this multi hundred page agreement so that we
Speaker 2: don't have to have you know, junior associates or junior
Speaker 2: lawyers or junior bankers up till four in the morning
Speaker 2: making sure that every comma is in the right place,
Speaker 2: et cetera. In theory, this could be an area in
Speaker 2: which AI could be productively applied.
Speaker 4: You know, there was an actual case argued over a comma.
Speaker 4: I can't remember exactly what it was, but like, you're
Speaker 4: absolutely right, the grammar, the specific words clearly matter in
Speaker 4: legal language. I would just add one of the things
Speaker 4: that's been driving arguably driving private credit is the booming
Speaker 4: creditor on credit or violence in public deals. So it
Speaker 4: was this idea that you could avoid that by having
Speaker 4: you know, this private close relationship with your borrower where
Speaker 4: you are higher up.
Speaker 3: In the waterfall of payment. So this is important.
Speaker 2: It would be really nice if you could upload a
Speaker 2: credit agreement to chat GPT and just say, make sure
Speaker 2: there's nothing in that would get me in trouble. Make
Speaker 2: sure there's nothing in here that five years later I
Speaker 2: will regret the placement of a.
Speaker 3: Certain Make sure I don't lose money, make sure.
Speaker 2: I don't lose money in some technical way anyway. So
Speaker 2: there's just a lot going on. I feel like there's
Speaker 2: plenty of episodes to do on this, but we really
Speaker 2: do have the perfect guest, someone who literally sort of
Speaker 2: sits in the intersection of I think we identified three
Speaker 2: distinct trends. Here we are going to be speaking with
Speaker 2: Dan Wortman. He is the co founder of a company
Speaker 2: called Nohica AI, and it does exactly this. It sort
Speaker 2: of attempts to use AI to understand credits. There's a
Speaker 2: lot of understanding about deals and the text in them.
Speaker 2: He also just has a lot of understanding about AI, etcetera.
Speaker 2: So we can talk about all of these things. Dan,
Speaker 2: thank you so much for coming on the podcast.
Speaker 1: Thanks so much for having me. I'm a fan of
Speaker 1: the show. Love to hear it you guys. It kind
Speaker 1: of like celebrities for me. So it's kind of fitting
Speaker 1: that I'm here because at least with folks of Bloomberg,
Speaker 1: because many people think about us and Oedica like the
Speaker 1: Bloomberg for deal terms.
Speaker 2: Okay, well let's see. Let's see if you actually live
Speaker 2: up to that.
Speaker 3: No.
Speaker 2: But so since I said I'm stressed that, oh this time,
Speaker 2: we're doing a credit episode and I've found the guest
Speaker 2: give us the quick version of like your career and
Speaker 2: what no Edica is.
Speaker 1: Yeah, so let's start with Oedica. What we build at
Speaker 1: no Edica is a power software for benchmarking real time
Speaker 1: data on what's market in credit m and a capital
Speaker 1: markets deal terms. Okay, so said another way, we help
Speaker 1: folks like transactional attorneys, credit managers, bankers. We help them
Speaker 1: figure out whether the terms of their transactional agreements like
Speaker 1: think financing agreements, murder agreements, perspectuses, and really all other
Speaker 1: corporate transactions are on or off market by benchmarking them
Speaker 1: to market comps. So as far as the genesis of oedica,
Speaker 1: it was kind of born out of my own experience
Speaker 1: in my career. So I started my career at Blackrock.
Speaker 1: I was on a team responsible for coming up with
Speaker 1: new financial products and fixing and markets, and we were
Speaker 1: developing these new interesting innovative structure and I just learned
Speaker 1: a ton about the capital markets ecosystem, and in particular
Speaker 1: just this is a fifty trillion dollar global market and
Speaker 1: it runs on phone calls and relationships and it's unbelievably antiquated.
Speaker 1: Then fast forward, I went back to get my GD
Speaker 1: I joined WALKT toall Lipton, where I did corporate transactions.
Speaker 1: This was twenty seventeen to twenty twenty two. So if
Speaker 1: if you guys remember that time, it was heyday of
Speaker 1: merger activity. So I worked on you know, T Mobiles,
Speaker 1: Bioto Sprint, the biggest thirty billion dollar commitment at the time,
Speaker 1: Algon ave to see raytheon and I distinctly remember sitting
Speaker 1: down at my desk. I was looking at a transactional
Speaker 1: grip and a multi billion dollar merger, and I was
Speaker 1: looking at a term, and I was trying to figure
Speaker 1: out whether I should help my client accept term A
Speaker 1: or term B in this context. And I was stuck.
Speaker 1: So I called the seenor partner on the deal. I said, hey,
Speaker 1: where's the database of information where I could see exactly
Speaker 1: how this term should come out and quantify it for
Speaker 1: my client? And you know, the answer was that doesn't exist.
Speaker 1: Now that was two and a half plus years ago.
Speaker 1: Now I left walked out to start no edico with
Speaker 1: a fairly simple idea, which is AI enables us to
Speaker 1: finally quantify what market agreement terms should look like in
Speaker 1: these markets. You know, now we work with almost all
Speaker 1: the top twenty law firms on the street, but helping
Speaker 1: them advise their clients on these deals. And this here
Speaker 1: on track to do about a trillion dollars or of
Speaker 1: transactions through the platform.
Speaker 2: And you get one percent of that.
Speaker 4: So that's great, Well, talk to us about what these
Speaker 4: financing agreements actually look like and how traditionally they're sort
Speaker 4: of judged by both the investors and the lawyers who
Speaker 4: are looking at them.
Speaker 1: Yeah, I mean so when I say deal terms, what
Speaker 1: I mean is deal terms are really the underpinning of
Speaker 1: the entire transactional system, the rules of the road. You
Speaker 1: could think about them like speed limits, double yellow lines,
Speaker 1: street lights. They're kind of the plumbing that goes into
Speaker 1: the transactions, putting in a way that people can understand. Imagine,
Speaker 1: I go sign of lease. Most people are very familiar
Speaker 1: with certain things, right, like the rent price, the how
Speaker 1: long the lease.
Speaker 3: Is, subletting policy exactly.
Speaker 1: But if deep in that twenty page lease the least
Speaker 1: says if the weather gets under thirty degrees at any time,
Speaker 1: you forfeit your right to the apartment, Well, that's a
Speaker 1: deal term, and that affects whether you want to accept
Speaker 1: that lease or not. And so it's the same in
Speaker 1: capital market terms. To give you a more tangible example, Yeah,
Speaker 1: are you guys fast food people? Yes? Yes, Okay, So
Speaker 1: I'm like a McDonald's guy. Yea, And whenever I go
Speaker 1: to McDonald's, I always ordered the tempiece chicken McNugget. I've
Speaker 1: ordered the ten piece hundreds of times. There's exactly three
Speaker 1: things that happened to you order a tenpiece, you open
Speaker 1: the box, you have nine pieces. You open the box
Speaker 1: you have exactly ten pieces. Or you open the box
Speaker 1: and you have eleven pieces. Now, if you have nine pieces,
Speaker 1: you go to the counter, you say, hey, I'm missing
Speaker 1: a piece. They give you a piece. You get the
Speaker 1: benefit of your morgan. If you get ten, you enjoy
Speaker 1: your McNuggets. If you get eleven, what do you do?
Speaker 3: You stay quiet?
Speaker 1: Exactly so you had the jaguar right now. There's this
Speaker 1: kind of unwritten rule in American consumerism, which is that
Speaker 1: if a company that's bigger than you gives you something
Speaker 1: by accident, then you get the benefit of that as
Speaker 1: a consumer. Well, in twenty twenty, the exact kind of
Speaker 1: thing happened in the credit markets, but it ended very differently.
Speaker 1: City Bank sent nine hundred million dollars to lenders in
Speaker 1: full prepayment of a loan for Revlon, and they did
Speaker 1: so accidentally. Now, they were supposed to just send an
Speaker 1: interest payment. At the time, the terms of the credit
Speaker 1: agreement were silent. The governing documentation, especially with this loan,
Speaker 1: didn't say what happens in that scenario. Long story short,
Speaker 1: many funds did not give back that hundreds of millions
Speaker 1: of dollars and litigation ensued. But a deal term in
Speaker 1: credit deals called erroneous payment. Deal terms started popping up
Speaker 1: in the market. No Whatadka's data last clocked that deal
Speaker 1: as a last quarter ninety percent of deals. So if
Speaker 1: you don't have that term now in your deal, you're
Speaker 1: way off market in terms of the way the market
Speaker 1: actually operates. This is why deal terms are important. These
Speaker 1: are hundreds of millions of dollars at stake. In the
Speaker 1: context of all these deals.
Speaker 2: There's something very loyally but like I have to say,
Speaker 2: I've never counted the McNuggets. I really get it, so
Speaker 2: just I would this example would have never occurred to
Speaker 2: me because I'm not the type of person that opens
Speaker 2: a box of McNuggets and start.
Speaker 4: Clearly, you don't value McNuggets.
Speaker 2: Not evidently not. What are some other deal terms? So
Speaker 2: that's a great example that. Okay, now, after that incident,
Speaker 2: which is infamous, language about this start popping up. What
Speaker 2: are some other sort of classic and I'm sure they
Speaker 2: get much much more esoteric than that. But what are
Speaker 2: some other like interesting deal terms that trend over time.
Speaker 1: Yeah, so it's really interesting. So there's a whole host
Speaker 1: of what it would call structural protections in a lot
Speaker 1: of these deals. These come in a lot of different vivors.
Speaker 1: Many people talk about them as things like anti pet
Speaker 1: smart terms, things like j crue blockers, things like sert
Speaker 1: of protections. Let's talk about some of the Yeah, let's
Speaker 1: talk about some of these, so anti pet smart terms.
Speaker 1: These are protections that prevent guarantor releases when subsidiaries of
Speaker 1: the credit group become non wholly owned. In other words,
Speaker 1: it prevents value from being transferred away from the loan
Speaker 1: into some other structure which doesn't provide credits for it.
Speaker 1: Let me put this in a way that most people
Speaker 1: don't understand. If you were getting a mortgage on your house,
Speaker 1: pretty simple framework. You take out the debt, you pay
Speaker 1: your mortgage payments, you pay a packfal loan. Bank can
Speaker 1: foreclose in your house if you stop paying a mortgage.
Speaker 1: But in the mortgage if it said something like well,
Speaker 1: if you sell any part of your home front door,
Speaker 1: a window, a shingle, the bank loses the ability to
Speaker 1: foreclose in the house fully. Well, then what would you do.
Speaker 1: You'd sell a single shingle, you would stop paying your
Speaker 1: mortgage and you get to keep your house and you
Speaker 1: get the benefit of that. That's what anti pest smart
Speaker 1: terms actually prevent. They prevent the ability for credit groups
Speaker 1: to actually sell a single equity and actually lose the
Speaker 1: credit support from that particular equity. So it's kind of
Speaker 1: interesting what we're seeing in the market right now. We
Speaker 1: have this really unique vantage point from the point of
Speaker 1: view of our software where we quantify trends in deal
Speaker 1: terms over time, and so we can actually very precisely
Speaker 1: tell you the percentages of deals that are actually getting
Speaker 1: a lot of these structural protections and actually gives us
Speaker 1: this really unique window into the anxieties and the optimisms
Speaker 1: that are currently happening in the market. Some people think
Speaker 1: about this as kind of an early signal of something
Speaker 1: likely to come. So what are we seeing, Well, we're
Speaker 1: calling it a flight to fortification, and it's really happening
Speaker 1: on both issuers and barbers. And I'll explain what I mean.
Speaker 1: We're seeing massive increases in lenders getting structural protections in
Speaker 1: these deals. Basically, these are protections that help make sure
Speaker 1: they're collateral is locked, things like the anti pet smart terms.
Speaker 1: In return, borrowers are getting the same fortification. In fact,
Speaker 1: they're getting more economic flexibility. You could think about it
Speaker 1: as a way for them to weather the storm. This
Speaker 1: is how we're seeing it. So things like add backs
Speaker 1: to eve, you know, more ability to send money to shareholders,
Speaker 1: more ability to make long term events bins. Let's talk
Speaker 1: about the actual specifics of what we're seeing. Antipasmart terms,
Speaker 1: the one I just talked about, we clocked out at
Speaker 1: twenty eight percent of deals in Q three. That was
Speaker 1: at four percent in twenty twenty three, and Q two
Speaker 1: is at twenty five percent, is the highest we've ever recorded.
Speaker 1: That term J crew blockers, which prevent issuers from moving
Speaker 1: material ip out ofside the credit group. That's at forty
Speaker 1: five percent of deals now the baseline from twenty twenty
Speaker 1: three to fifteen percent, and last quarter it was thirty
Speaker 1: eight percent. Anti SERTI protections, which are leansbordination protections. They
Speaker 1: actually helped secure your place in line if and when
Speaker 1: some sort of distress activity happens. That's at eighty four
Speaker 1: percent of deals. That's the highest jump we've ever seen.
Speaker 1: Quarter quarter it went up from sixty one percent to
Speaker 1: twenty three point jump, and the baseline is thirty nine
Speaker 1: percent twenty twenty three. That's pretty significant for a quarterly jump,
Speaker 1: and it really signals something about the market. On the
Speaker 1: quantitative side, we track a lot of stuff too, including
Speaker 1: the ratios under which borrowers need to maintain specific types
Speaker 1: of leverage. We saw that at three point nine times
Speaker 1: EBITDA in Q two and it went down to three
Speaker 1: and a half times, But again that's signaling some sort
Speaker 1: of anxiety among the lender group that we wouldn't normally see.
Speaker 1: Now you may ask what a borrower is getting for this, Again,
Speaker 1: they're getting more fortification. One of the ways this is
Speaker 1: coming up is in EBITDA adbacks, so EBADA add backs. Basically,
Speaker 1: there's a very long and complicated calculation of cash flow
Speaker 1: and a lot of these deals and the adbacks to
Speaker 1: EBADA basically allow bars and issuers to add back certain
Speaker 1: things to count them as cash flows.
Speaker 3: To flatter their balance sheet basically correct correct.
Speaker 1: One of the more interesting adbacks that we track is
Speaker 1: what's called a cost saving satback. So imagine a borrower
Speaker 1: knows it's gonna optimize some cost in the future. If
Speaker 1: it can reasonably predict that cost, it can add that
Speaker 1: back to today's cash flow. That cost savings atback, whether
Speaker 1: materializes or not, is added back to today's cashflow. Sixty
Speaker 1: four percent of deals now have cost savings atbacks in them.
Speaker 1: That's the highest we've ever recorded for deals, with those
Speaker 1: adbacks being above twenty percent of EBITDA that came in
Speaker 1: fifty one percent, which is also the highest we've ever
Speaker 1: tracked on the platform. They're also getting things like excluding
Speaker 1: lenders that are short in their debt. So, for instance,
Speaker 1: folks may be familiar with what happened with the Windstream
Speaker 1: case a few years ago. What happened in that case
Speaker 1: is certain hedge funds were actually short the debt the
Speaker 1: loan that was in default, and that makes them not
Speaker 1: exactly aligned with the company that has the debt outstand.
Speaker 1: Terms started popping up in the market which we've tracked,
Speaker 1: which are called net short lender terms, which allow bars
Speaker 1: to exclude those lenders from voting. That is now in
Speaker 1: thirteen percent of deals, which is the highest we've ever tracked.
Speaker 1: So you could see the fortification actually on both sides
Speaker 1: of the market, and it really signals, I think, to
Speaker 1: us that there's a risk allocation happening with a lot
Speaker 1: of these anxieties.
Speaker 3: Joe, First of all, you know, my husband was a corporate.
Speaker 2: Lawyer at one point.
Speaker 3: Yeah, okay.
Speaker 4: So one of the things he's most proud of is
Speaker 4: he came up with some language in a deal shortly
Speaker 4: after the two thousand and eight financial crisis, and it
Speaker 4: was he sent it to me just now a significant
Speaker 4: dislocation in financial markets. That was him, and that became
Speaker 4: like standard language in risk factors, at least in a
Speaker 4: bunch of that's a contribution.
Speaker 2: I'm the inventor of this deal, so and so the inventor.
Speaker 2: Some people invent great medicine, some people invent some new technology,
Speaker 2: and someone invents a new deal term that gets propagated
Speaker 2: across that's right documents for years thereon after.
Speaker 3: That's how it works.
Speaker 4: But Dan, I wanted to ask you something. Okay, So
Speaker 4: you say there's more fortification in a lot of deal terms,
Speaker 4: more protections perhaps for both investors and lenders.
Speaker 3: I guess one of the things we.
Speaker 4: Heard prior to twenty twenty in them for some years
Speaker 4: after it was we had this explosion in CoV light deals, right,
Speaker 4: fewer protections for investors because everyone was so desperate supposedly
Speaker 4: for yield for that particular paper, So the balance of
Speaker 4: power shifted to the borrowers they were able to dictate
Speaker 4: the terms. How are investors getting better protections now? With
Speaker 4: you know, credit spreads still at basically multi decade lows,
Speaker 4: which suggests that there's still a lot of demand and
Speaker 4: that they don't hold all the power in the market.
Speaker 1: Yeah, I think about it, and what the data supports
Speaker 1: that we see on the platform is. I think about
Speaker 1: it less so as what they're getting, but more about
Speaker 1: what the terms actually reflect in terms of the macro
Speaker 1: environment that they're operating in. So, for instance, right now,
Speaker 1: we're seeing this flight to fortification in part largely due
Speaker 1: to probably a few things. Number one being some of
Speaker 1: these headline risks that folks have been talking about, and well,
Speaker 1: I'm sure we'll get into some of what's going on
Speaker 1: in the private credit market today. So people flooding into
Speaker 1: more structural protections because they're worried about their place in
Speaker 1: line if there is distress. I think number two is
Speaker 1: just mac or wise if you think about it. In
Speaker 1: the credit markets, there was a ton of debt taken
Speaker 1: out in twenty twenty, twenty twenty one, twenty twenty, early
Speaker 1: part of twenty twenty two. This leads to a lot
Speaker 1: of maturity walls upcoming, especially in twenty twenty seven twenty.
Speaker 3: We don't say upcoming on the show, we say looming.
Speaker 1: Yeah, exactly. There are a lot of looming maturity walls
Speaker 1: in twenty twenty eight, twenty twenty nine vintage. And you
Speaker 1: can think about it as well. That's a macro factor
Speaker 1: that people are thinking about when they underwrite alone, because
Speaker 1: many of these deals actually have five year tenor you know,
Speaker 1: seven year ten or eight year tenor in some cases
Speaker 1: thirty year tenor, and so they're thinking about all these
Speaker 1: protections in the context of that market. I also think
Speaker 1: it's really interesting, aside from the credit context, right now,
Speaker 1: we're seeing a lot of structuring in terms happening in
Speaker 1: M and A markets, So things like regulatory uncertainty, things
Speaker 1: like tariffs, things like libildy management, as we talked about,
Speaker 1: things like tax uncertainty. I'm happy to go into these,
Speaker 1: but we're seeing a lot of things in this area.
Speaker 1: One kind of small example of this in situations where
Speaker 1: a buyer and a seller have regulatory uncertainty, which you
Speaker 1: know a lot of folks think about the administration and
Speaker 1: they're not sure exactly how things are going to play out.
Speaker 1: You actually see regulatory review in deals get hyper focused
Speaker 1: on and it actually precipitated a new deal term this
Speaker 1: year which we tracked in the market. We had an
Speaker 1: almost term detection of the platform. We sent out a
Speaker 1: note to all of our clients and it's called a
Speaker 1: new outside date structure term. Basically, what it does is
Speaker 1: it allows buyers of acquirees. It allows them to lock
Speaker 1: in their financing for longer and actually stand their financing
Speaker 1: in the case scenario regulatory review les. And that's just
Speaker 1: an example of the kind of innovation that's happening in
Speaker 1: the merger markets. In terms of tariffs, we picked up
Speaker 1: the first tariff event of default in a credit deal. Ever,
Speaker 1: it happened in a Superior industries deal over the summer,
Speaker 1: which probably isn't surprising to use an auto manufacturer deal.
Speaker 1: I've made a lot of parts in Mexico. That's not
Speaker 1: five percent of MNA deals for tariff based m and
Speaker 1: A carve outs and railie respect clauses.
Speaker 2: Can we talk a little bit about you know, you're
Speaker 2: scanning these documents. Google's ingram has existed for a long time.
Speaker 2: Tracking the prevalence of a term is not novel technology
Speaker 2: that control EF right control f Yeah, this is sort
Speaker 2: of like very barely even councils technology at that point.
Speaker 2: What is it that you you know, when you're talking
Speaker 2: about the changing prevalence of these terms, what is the
Speaker 2: actual novelty here that isn't just sort of yeah, document
Speaker 2: search over time.
Speaker 1: Yeah. So, Tracy, your husband's a former corporate lawyer. You know,
Speaker 1: he would tell covering corporate lawyer or covering corporate layer exactly.
Speaker 1: I am myself as well. One of the things he
Speaker 1: would tell you is that there's constant innovation in these markets.
Speaker 1: These agreements are highly complicated, there very long. They have
Speaker 1: a lot of what's called long range dependencies, which is
Speaker 1: that you may be used to seeing something in a
Speaker 1: particular area of the document said one way, but in
Speaker 1: reality it turns out it's punted to three different causes
Speaker 1: deep down, and you actually have to go find that information.
Speaker 4: This is why it's also jiu jitsu between the borrowers
Speaker 4: and the lenders, right, because like the borrowers are often
Speaker 4: trying to hide something that's favorable to them, or the
Speaker 4: lenders are trying to hide something favorable to them. So
Speaker 4: the structure and the way it's worded changes a lot
Speaker 4: to your.
Speaker 1: Point exactly, And these are sophisticated parties paying millions, sometimes
Speaker 1: hundreds and millions of dollars in advisory fees to make
Speaker 1: sure that these terms look the way they do. Now
Speaker 1: that leads to kind of the technological innovation that I
Speaker 1: think has enabled a lot of this AI for the
Speaker 1: first time, can attribute in particular, new language models, can
Speaker 1: attribute more semantic meaning to phrases and language that was
Speaker 1: impossible with things like n grams. And so what America
Speaker 1: does is it used as a series of language models,
Speaker 1: including a multi layered information extraction system to make sure
Speaker 1: that it's encoding all this semantic meaning inside all these terms,
Speaker 1: so that when you look at a J. Krublacker in
Speaker 1: the first way, it may be phrased a thousand different ways,
Speaker 1: but we can track that term over time. That has
Speaker 1: enabled the ability to actually quantify for the first time
Speaker 1: what a market agreement term looks like in these markets.
Speaker 1: And I think that's why it's so interesting to folks
Speaker 1: on the platform.
Speaker 4: So I know you're not doing litigation, but I guess
Speaker 4: I'm curious how you deal with or if AI is
Speaker 4: helpful with in litigation what would be called precedent. But
Speaker 4: I'm assuming you're building up a big database of all
Speaker 4: these different deal documents.
Speaker 3: Is it useful?
Speaker 4: Is AI useful to go back and look at previous
Speaker 4: documents in order to shape new ones?
Speaker 1: Yeah? Exactly, So in New Edica, we are ultimately an
Speaker 1: a power software company, but we actually have the largest
Speaker 1: knowledge graph of deal terms in ex systems. So Tracy,
Speaker 1: exactly what you said. It's a database ultimately of precedent
Speaker 1: comparable deal terms, and that database is going to be
Speaker 1: mind bowing as over billion terms in it, So its
Speaker 1: issue to a large as in existence, we map that
Speaker 1: back to deal characteristics. It's the same in litigation, right,
Speaker 1: So in transactional markets, folks are innovative, but they also
Speaker 1: want to rely on something that has happened before, or
Speaker 1: at least in part, they want to rely on something
Speaker 1: that has happened before, and so folks are constantly looking
Speaker 1: for ways to tie things back to comparable deal terms.
Speaker 1: It's the same in litigation. So obviously not expertise, but
Speaker 1: the same concept, which is, you know, when you write
Speaker 1: a brief, you were constantly citing cases that the judge
Speaker 1: has you know, relied on in the past. And you know,
Speaker 1: for lawyers and you know outset of lawyers, even just
Speaker 1: deal professionals generally bankers, credit managers, people are highly reliant
Speaker 1: on present What.
Speaker 2: Is your text acre did you what do you build
Speaker 2: and how much is it? Like, oh, you're using chat, epts, API,
Speaker 2: et cetera. Like, okay, yes, large language models are good
Speaker 2: at identifying deal terms or novelty, et cetera. There's semantic
Speaker 2: meaning of these terms, but what did you actually build
Speaker 2: and what do you actually employ in your technology?
Speaker 1: So we were starting in twenty twenty two, so we're
Speaker 1: what you would call AI native. We were started in
Speaker 1: a system that already and language models existed in. However,
Speaker 1: we because of the nature of the sensitive documents in
Speaker 1: terms that we deal with, especially for you know, major
Speaker 1: law firms, financialist yea, this is like.
Speaker 2: A big issue with the right that they don't want
Speaker 2: to just be uploading their stuff to chat GPT right exactly.
Speaker 1: And so we actually utilize you know, adapted language models,
Speaker 1: open source language models that we adapt on our armed
Speaker 1: proprietary data sets and then deploy and secure environments and
Speaker 1: single tendon architectures, you know, for individual instances of institutions
Speaker 1: that deploy our product. And so you could think about
Speaker 1: it as based on the language models that are ultimately
Speaker 1: underpinning a lot of the gpds and the clouds. However,
Speaker 1: it's fine tuned to this particular data set, which makes
Speaker 1: it obviously much better at handling this exact problem, which
Speaker 1: is a big problem in the market. Now. We also
Speaker 1: layer on top of that information extraction model. So for instance,
Speaker 1: you may know that a term exists in what deal,
Speaker 1: but you may want to know what terms should exist
Speaker 1: for a JP Morgan deal, or for a B of
Speaker 1: a deal, or for you know, a particular type of counterparty,
Speaker 1: and so in those context we actually want to map
Speaker 1: those deal terms back to deal characteristics, and we actually
Speaker 1: utilize a lot of models to extract information and marry
Speaker 1: that with their party data sets. So that's a little
Speaker 1: bit about how the technology works. I think I always
Speaker 1: think about it from the user standpoint, What does the
Speaker 1: user really want? These really wants to know how they're
Speaker 1: going to invite their client on a particular merger on
Speaker 1: a particular credit deal. How often does this come up?
Speaker 1: You always call your attorney and you're trying to figure out, well,
Speaker 1: is this market is it off market? And that's what
Speaker 1: our data provides.
Speaker 3: Okay, so structural fortifications in deal terms. What are you
Speaker 3: seeing right now?
Speaker 4: Because as we started this conversation, we were talking a
Speaker 4: lot about the recent blow ups in the private credit market,
Speaker 4: and if you look at some spreads on certain firms,
Speaker 4: certain bonds, it does seem like nervousness is creeping back
Speaker 4: into the market. I see spreads on you know, it's
Speaker 4: not private credit, but spreads on triple C rated debt
Speaker 4: have been creeping up recently. How scared or concerned are
Speaker 4: people right now?
Speaker 1: Well, I recently wrote about this in the Wall Street
Speaker 1: Journal a little bit, and then folks contacted me and
Speaker 1: I kind of said, you know, you're causing a stir.
Speaker 1: And then I saw Howard Marx came out with his letter,
Speaker 1: which I think was called Cockroaches in the coal Mine,
Speaker 1: and they had a lot of the same themes. I
Speaker 1: think folks who have been around credit market for a
Speaker 1: very long time can kind of see what's a little
Speaker 1: bit of what's going on.
Speaker 2: To us.
Speaker 1: Let me just talk about what the data supports to us.
Speaker 1: What we see is creditors maybe preparing this their system
Speaker 1: for distress, and I'll talk about what we're seeing in
Speaker 1: the data that kind of supports that. But you can
Speaker 1: think about it like the evolution of your house security, right,
Speaker 1: So you know, first you lock the doors. Then you know,
Speaker 1: you get a bolt lock, which gives you better protection.
Speaker 1: You know. Then you you know, you add a security
Speaker 1: system on top of that alarm system. And at the end,
Speaker 1: what do you do. You kind of up all your
Speaker 1: valuables and you ensure them if people are going to
Speaker 1: get into the house. And you know, for the past
Speaker 1: few years, we've seen lenders really focused on keeping people out.
Speaker 1: This is the locks and the dead bolts, and this
Speaker 1: is what we were talking about with j crub blockers.
Speaker 1: This is making sure you can't structure around me. From
Speaker 1: a liability management perspective, But over the last quarter something
Speaker 1: kind of changed, which is we started seeing people and
Speaker 1: lenders obsessed with lean subordination terms, which is the term
Speaker 1: that governs who gets paid first when everything falls apart.
Speaker 1: So this isn't really about preventing liability management exercises that much.
Speaker 1: It's actually about controlling the recovery when a bankruptcy does happen.
Speaker 1: And so we clocked that term at eighty four percent
Speaker 1: of deals in Q three, biggest quarterly jump we've ever
Speaker 1: seen from the prior quarter. It's also the highest we've
Speaker 1: ever clocked that term. So this bes the question of
Speaker 1: why Wire Credit is so focused on making sure.
Speaker 3: Their place on line is in recovery.
Speaker 1: In recovery is the same. Perhaps it's a reaction to
Speaker 1: the Lieboldy management transactions we talked about, so perhaps folks
Speaker 1: are thinking that that will precipitate. Perhaps it's a reaction
Speaker 1: to some of the maturity walls that folks understand, or
Speaker 1: perhaps it's some of what I was saying in the appad,
Speaker 1: which is folks are seeing that there may be distress
Speaker 1: events on the horizon and they want to make sure
Speaker 1: that if there is, they have the most negotiaing leverage
Speaker 1: its possible.
Speaker 2: So I know it's broad statements, but you know, when
Speaker 2: we look at these sort of environment under which companies
Speaker 2: like First Brands or Tree Color or some of these
Speaker 2: other ones that have gone into distress very rapidly, when
Speaker 2: we look back at when these were birthed.
Speaker 1: Et cetera.
Speaker 2: Can we say like these were sloppy times, These were loose,
Speaker 2: sloppy times that people were not thinking much about either
Speaker 2: just quality due diligence or diligent terms.
Speaker 1: Yeah. So I think with First Brands is a great example. Right.
Speaker 1: So First Brands is an automotive replacement company, right, so
Speaker 1: they make things like breaks and wipers and filtration systems.
Speaker 1: Beginning in twenty nineteen, that company effectively rapidly expanded through
Speaker 1: debt fueled acquisitions and it dramatically increased its scale. But
Speaker 1: I think what First Brands illustrates is something that you know,
Speaker 1: we might get into with the private credit markets, which
Speaker 1: is that they primarily funded these acquisitions with large debt facilities.
Speaker 1: Then tariffs hit in April twenty twenty five, which obviously
Speaker 1: changed their business because they actually do a lot of manufacturing,
Speaker 1: and that kind of magnified problems. So you can think
Speaker 1: about out. One of the main problems with First Brands,
Speaker 1: which is also kind of some of what folks are
Speaker 1: worried about in the private credit markets today, is what's
Speaker 1: called off balance sheet financing. What First Brands used is
Speaker 1: a lot of you know, receivables financing facilities that weren't
Speaker 1: properly disclosed to a lot of folks that were lending
Speaker 1: to the company. In fact, I think in that sense,
Speaker 1: just to give you a sense of quantum, this is
Speaker 1: over eleven billion dollars of total obligations that they had
Speaker 1: when they actually started disclosing it in terms of off balance youe.
Speaker 1: Financing and you know, they were disclosing things like five
Speaker 1: to six billion dollars of actual debt obligations. And so
Speaker 1: this led one of the creditors lawyers to say that
Speaker 1: two point three billion dollars just disappeared. And so that structure,
Speaker 1: the ability for first brands to get that debt was
Speaker 1: made possible by the private credit markets and how deep
Speaker 1: the private credit markets have become. Because if you're a
Speaker 1: big credit manager in private credit markets, you could fund
Speaker 1: you know, that type of receivable facility to a first brand,
Speaker 1: and first brands could use that facility to then, you know,
Speaker 1: make sure they are constantly continuing to acquire new businesses
Speaker 1: and keep rolling over the cash.
Speaker 4: I have a theory that receivables, financing and factoring is
Speaker 4: to the private credit market. What French quants who went
Speaker 4: to that one elite school are.
Speaker 3: To trading blow ups.
Speaker 1: I like that theory.
Speaker 3: Yeah, thanks.
Speaker 4: So the other thing we wanted to ask you about,
Speaker 4: and again we reference this in the intro, is we
Speaker 4: are seeing these really complicated deals that I admittedly cannot
Speaker 4: keep track of in the AI market, where you know,
Speaker 4: one company is going to buy chips from this other company,
Speaker 4: and then that company is going to borrow from whoever
Speaker 4: and use the chips funding to pay them back, and
Speaker 4: then that money somehow goes into the company that is
Speaker 4: buying the stuff in the first place. It is all
Speaker 4: very circular, all very incestuous in many ways. In my mind,
Speaker 4: are you examining those types of deals or just putting
Speaker 4: on your credit expertise hat if you see something like that,
Speaker 4: what are you thinking?
Speaker 1: Yeah, well, it's probably helpful to kind of talk about
Speaker 1: some of the structure of these deals, which I think
Speaker 1: again is made possible by how deep the private credit
Speaker 1: markets have become. And usually when I do that, I
Speaker 1: try to think about, let's try to make this a
Speaker 1: little bit more fun. So imagine for a minute, Joe,
Speaker 1: you just love pizza. He does love pizza yesterday twice.
Speaker 1: There you go, You're a pizza fanatic. You love it
Speaker 1: so much that you decide to eat pizza every single
Speaker 1: meal of every single day for the rest of your life,
Speaker 1: like you are committed to subsisting pizza, committed to the
Speaker 1: carbs exactly. So, Joe, you made that decision. You come
Speaker 1: to me and you say, Hey, Dan, I'm going to
Speaker 1: eat pizza for every meal of my entire life. How
Speaker 1: about you open a pizza restaurant for me to eat it.
Speaker 1: It'll be really lucrative.
Speaker 2: We're going with this, but this is actually a very
Speaker 2: good to note do, right, Like you would have have
Speaker 2: a lot of confidence in me to commit to my word.
Speaker 2: If you're going to open a restaurant.
Speaker 1: Yeah. Now, now you come to me and say, it's
Speaker 1: going to be super lubritive. Here's how we're going to
Speaker 1: fund it. Ten percent equity. The bank is going to
Speaker 1: give you ninety percent of the funding in leverage. And
Speaker 1: it's Dan's restaurant. Joe, you don't on the restaurant, but
Speaker 1: you're going to eat at it. I'm the full beneficiary,
Speaker 1: full beneficiary of the restaurant, but it's ninety percent. Okay,
Speaker 1: So I opened the restaurant. You eat there every single day. Now, Tracy,
Speaker 1: Joe comes to you for a personal loan to fund
Speaker 1: his lifestyle, his pizza eating.
Speaker 2: Tracy, trust me, she would lend it to me.
Speaker 1: Well, here's the question, right, should you, Tracy? Consider the
Speaker 1: ninety pizza restaurant that Joe is eating at for all
Speaker 1: his meals. Now, on the one hand, it's not Cho's loan, right,
Speaker 1: so he's not on the hook if the pizza restaurant
Speaker 1: goes under. On the other hand, it's Joe's only source
Speaker 1: of food.
Speaker 3: Which Joe will die without the restaurant.
Speaker 1: Which is his He's committed to the restaurant, and it
Speaker 1: kind of makes the restaurant intertwined with Joe's ability to
Speaker 1: pay your personal loan back. So, I guess that's a
Speaker 1: good question. No, there's great.
Speaker 2: So now let's take it out of pizza. Who is
Speaker 2: so that's whatever? Like okay, now who is the chips
Speaker 2: buy or whatever?
Speaker 1: This is essentially what's happening with off balance youe. Financing
Speaker 1: and data center deals. So, and it includes Metas. I'm
Speaker 1: sure you saw the Hyperion deal. It's his Metas infrastructure
Speaker 1: deal with Blue Owl. Except I think it's even more
Speaker 1: intriguing than some of the pizza stuff. So Meta and
Speaker 1: Blue Ol basically created a joint venture in a special
Speaker 1: purpose vehicle not that different than the restaurant. And the
Speaker 1: deal is the joint venture would be owned twenty percent
Speaker 1: by Meta, eighty percent by Blue Owl, so Blue Owl
Speaker 1: controls it, and it would effectively be funded with ninety
Speaker 1: percent leverage. So call it thirty billion dollars of total
Speaker 1: enterprise value, three billion dollars of equity, twenty seven billion
Speaker 1: dollars give or take of debt. In other words, Blue
Speaker 1: Oul is effectively owning the restaurant. Meta is effetively eating
Speaker 1: at the restaurant, and the bank's funded with ninety percent leverage.
Speaker 1: So what this does is it keeps the debt off
Speaker 1: of Meta's books right while also giving investors credit managers
Speaker 1: the ability to put money against a data center asset.
Speaker 1: So Meta in this deal will make rent payments associated
Speaker 1: with the data center based on its cost of power.
Speaker 1: That's the cash flow that's going to the SBV, and
Speaker 1: that effectively funds the interest expense. Let's just talk about
Speaker 1: the debt for a second. In a normal LBO context,
Speaker 1: ninety percent leverage is pretty exceptionally high. Most people would
Speaker 1: consider fifty to eighty percent leverage to be relatively normal
Speaker 1: for a stable cash flow business. So the debt itself
Speaker 1: is actually quite high on some of these structures. The
Speaker 1: only reason it was possible was because it was given
Speaker 1: an investment great credit rating, and in part because Meta
Speaker 1: agreed to a four year operating lease with what's called
Speaker 1: a residual value guarantee, which means that Meta is guaranteeing
Speaker 1: a capped amount of some of that cashflow. However, that
Speaker 1: guarantee is capped and is only partial, which is why
Speaker 1: they don't have to take it onto their book and
Speaker 1: why would be a footnote as a contingent debt obligation
Speaker 1: in their balance sheet. Now let's talk about the asset
Speaker 1: that's being underwritten. This isn't pizza. Pizza actually has a
Speaker 1: stable price, right. We have thousands of UTI history on pizza, right,
Speaker 1: and you can track that price over time. Data center
Speaker 1: is optimized for GPU performance on training fundamental AI models.
Speaker 1: Not so much of a mature asset, actually, I think
Speaker 1: most folks would think about it as a burgeoning asset.
Speaker 1: Now I'm in this world. I mean, folks, there's a
Speaker 1: high amount of demand for a lot of this compute,
Speaker 1: and I definitely think the demand is there, but at
Speaker 1: the end of the day, it's an immature asset with
Speaker 1: a price that isn't so well defined. So just a recap,
Speaker 1: you've got off balance sheet financing which isn't reflected with
Speaker 1: whoever is lending money to metal or even buying its
Speaker 1: equity with ninety percent leverage on an immature asset, And
Speaker 1: I think that's why these deals are so interesting. So
Speaker 1: from our point of view, I mean to make sure
Speaker 1: you get the terms right, and you know, we will
Speaker 1: look at these data center. A lot of these types
Speaker 1: of financings run through our platform all the time. To
Speaker 1: make sure you get the terms right on what this
Speaker 1: structural protections look like in these deals is critical for
Speaker 1: the fortification of something that is in the structure.
Speaker 4: So I know we've seen these idiosyncratic blow ups in
Speaker 4: the private credit market so far, but just looking at
Speaker 4: the AI market in particular and the financing there, it
Speaker 4: feels like right now people are still willing to lend money.
Speaker 4: And we've talked about this on the show before, but
Speaker 4: a lot of the AI competition is couched in this
Speaker 4: existential language of you either win it AI or die basically,
Speaker 4: and so the spending keeps going. What is your guess
Speaker 4: on like the thing that kind of knocks that cycle
Speaker 4: or that flywheel.
Speaker 3: And tears it apart.
Speaker 1: So I'm obviously in the AI industry. We're in the
Speaker 1: credit industry, so we see both sides of this phenomenon.
Speaker 1: I fundamentally believe AI is a paradigm shift. I would
Speaker 1: not have left, you know, the deal markets if I
Speaker 1: didn't think that. And I think what we're witnessing is
Speaker 1: very similar to the Internet in the nineteen nineties, or
Speaker 1: the iPhone in two thousands, or social media in the
Speaker 1: twenty tens. And I think this paradigm shift is going
Speaker 1: to ultimately change a ton of industries, including capital markets
Speaker 1: and finance and law and all these amazing industries. And
Speaker 1: so that I think is very true. But I also
Speaker 1: think two things can be true. I think AI can
Speaker 1: be a generation defining category and a technology that's upending
Speaker 1: a lot of industries. But I also think that categories
Speaker 1: will have winners and losers. And when folks are racing
Speaker 1: to define a category, as you know, you often see
Speaker 1: with a lot of these transformational types of technology, there
Speaker 1: may be more losers in the headlines. Then you're used
Speaker 1: to seeing in a lot of these markets, but the
Speaker 1: winners will be bigger than anyone's ever all.
Speaker 2: Right, So if I don't need the pizza, someone else
Speaker 2: is going to pick up the pizza and they're gonna
Speaker 2: they're gonna eat it.
Speaker 1: Look what we focus on in Oadica is in a
Speaker 1: market moving this fast. Yeah, we all need to pay
Speaker 1: attention to the terms that actually underpinning a lot of
Speaker 1: these markets to make sure if there is any bleeding,
Speaker 1: that bleeding gets stopped as quickly as possible. Just to
Speaker 1: give you one last example from a recent market deal,
Speaker 1: you can look at the Frank jpm deal as like
Speaker 1: a really interesting one. This is, you know, this was
Speaker 1: a deal where JPMorgan paid one hundred and seventy five
Speaker 1: million dollars to acquire a company. There's a very small deal,
Speaker 1: but to acquire a company called Frank, which is a
Speaker 1: streamline fasta kind of support service.
Speaker 2: I remember this.
Speaker 1: It turned out there was a lot of synthetically made
Speaker 1: up types of data in that.
Speaker 2: Business, and the founder is going to prison right.
Speaker 1: Allegedly there's a lot of there's a lot of made
Speaker 1: up stuff in the business. And I think seven days.
Speaker 2: Executive who worked at Frank sends to sixty eight months.
Speaker 1: Yeah, yeah, yeah, and so. But I think the most
Speaker 1: interesting part about this particular transaction to me is JPM
Speaker 1: ended up signing a merger agreement that said that the
Speaker 1: indemnification for the founder's litigation, for any founder's litigation, would
Speaker 1: be paid for by JPM.
Speaker 2: Right, they paid her lawyer.
Speaker 1: They paid one hundred and fifteen million dollars in legal
Speaker 1: expenses for her lawyer on her fraud. And so when
Speaker 1: you're moving really fast, yeah, right, you can kind of
Speaker 1: ignore some of the nuts and bolts. But I think
Speaker 1: it's actually even more critical and fast moving markets.
Speaker 2: Dan Workman, co founder of no Edica, thank you so
Speaker 2: much for coming on outlook.
Speaker 1: Thank you thanks for having me us.
Speaker 2: Tracy. I wasn't really sure where he was going with
Speaker 2: that pizza analogy, but it actually does make a lot
Speaker 2: of sense, and it's something I think is a phenomenon
Speaker 2: and just a lot of financial transactions, which is how
Speaker 2: much like in certain environments, the lender and the creditor
Speaker 2: are like both each others, like they're both leaning on
Speaker 2: each other. They're both the creditor and lenders, they're relying
Speaker 2: on each Yeah, at the same time.
Speaker 3: Much in the way you rely on pizza.
Speaker 2: You would lend to me to buy it to eat pizza.
Speaker 3: Right, I would thank you if it was a matter
Speaker 3: of survival, that was.
Speaker 2: A matter of survival, thank you.
Speaker 1: I think it's just.
Speaker 3: Because you want to eat really expensive pizza then no.
Speaker 4: You know.
Speaker 2: The other thing too, is just like from talking to
Speaker 2: you over these years, you know how many times I've
Speaker 2: heard something there's a lot of cuve light stuff going.
Speaker 2: It is interesting to think that, like, you don't often
Speaker 2: hear that quantified what that means, right, things are like
Speaker 2: cove light these days, et cetera. And the idea that like,
Speaker 2: maybe we could get better numbers on some of these
Speaker 2: things seems like potentially labor saving for lawyers. Stuff like that.
Speaker 4: The specific numbers on specific deal terms were really interesting
Speaker 4: to me. And the idea that even today lawyers and
Speaker 4: bankers still have trouble anticipating every single thing that could
Speaker 4: happen to particular deal, and so they're having to react
Speaker 4: to it and come up with the new terms, the
Speaker 4: new deal language, and insert them into the documentation.
Speaker 3: I find that interesting.
Speaker 2: The tariff example, you know, the problem is is that
Speaker 2: AI is good and this is I'm certain if we
Speaker 2: talked about this more, AI will be used to come
Speaker 2: up with new deal terms and the cat and mouse
Speaker 2: game will continue forever. So I suspect that we are
Speaker 2: not going to have lawyers will always find new work
Speaker 2: to do, and they'll just get work. They'll just get
Speaker 2: more creative about outsmarting the systems that are designed to
Speaker 2: detect these phenomena.
Speaker 4: We will end up with thousands and thousands of pages
Speaker 4: of term sheets that, like humans are just physically incapable
Speaker 4: of reading, it has to be read by AI.
Speaker 2: I probably literally, that is what's going to happen.
Speaker 3: Yeah, all right, shall we leave it there.
Speaker 2: Let's leave it there, all right.
Speaker 4: This has been another episode of the Odd Thoughts podcast.
Speaker 4: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart.
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