December 4, 2025 • 44 mins
There have been some wobbles in credit markets lately. It hasn't been too dramatic, but we've had some blowups, leading Jamie Dimon to speculate about the presence of other "cockroaches" lurking in the industry. But what do we actually know about the quality and practices of credit underwriting right now? Dan Wertman is the co-founder and CEO of Noetica, a startup that uses AI to scan deal documents and measure linguistic and term trends over time. Dan talks to us about what he's been seeing in the language of deal documents, and why there are reasons to think that more blowups are lurking around the corner. He also talks to us about how credit agreements are structured in the AI space, and how we should understand some of these huge data center financing deals we've seen lately.
Read more:
Oracle Credit Fear Gauge Hits Highest Since 2009 on AI Bubble Fears
Secretive $3 Trillion Fund Giant Makes Flashy Move Into Private Assets
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of The Odd Lots podcast.
Speaker 3 (00:21):
I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2 (00:24):
Tracy, there are just so many credit related things to
talk about right now, all things credit.
Speaker 3 (00:28):
I love it.
Speaker 1 (00:29):
I love it.
Speaker 3 (00:30):
Credit is interesting again.
Speaker 1 (00:31):
This might be one of the.
Speaker 2 (00:32):
Only credit episodes that we've ever done where like I
found the guest because I feel like when I think
about like a credit all the credit episodes, it's usually
like someone you know. Randomly. I found someone who knows
a little bit something about credit. She's like, oh, let
me do it.
Speaker 3 (00:44):
Let me The stakes are high.
Speaker 2 (00:46):
I know I was thinking about there because you're like, how, Joe,
you like pick someone who doesn't know anything. No, I
don't think that. I think we have a very knowledgeable
credit guest. But I'm a little stressed about this aspect.
Speaker 4 (00:55):
I believe in you, Joe, you do. I trust your judgment.
But to your point, there's a lot going on. So
obviously there are concerns around private credit. We've had some
idiosyncratic defaults and frauds in the market, and each one
is special in their own way. But I think the
worrying aspect is that they keep coming to light. Yeah, right,
(01:15):
And so you've seen people like Jamie Diamond using the
cockroach analogy, which is now famous. And at the same
time you have the connection with AI, right, which we
have spoken about a little bit on the podcast with
Paul Kadrowski. All these complex circular financing structures that are
driving a lot of the credit boom, or have been
(01:35):
driving a lot of the credit boom, and then at
the same time you also have the impact of AI
on credit itself.
Speaker 2 (01:43):
Yeah, that's right because in theory right, like we've talked
about this. We did that episode with Joel Werthheimer that
was in a slightly different context, but we've done these
episodes about you know, just the incredible length of deal text,
et cetera. And perhaps if there's one area where maybe
we could say with some high degree of confidence that
large language models could be useful, it is can we
(02:05):
break down this multi hundred page agreement so that we
don't have to have you know, junior associates or junior
lawyers or junior bankers up till four in the morning
making sure that every comma is in the right place,
et cetera. In theory, this could be an area in
which AI could be productively applied.
Speaker 4 (02:23):
You know, there was an actual case argued over a comma.
I can't remember exactly what it was, but like, you're
absolutely right, the grammar, the specific words clearly matter in
legal language. I would just add one of the things
that's been driving arguably driving private credit is the booming
creditor on credit or violence in public deals. So it
was this idea that you could avoid that by having
(02:45):
you know, this private close relationship with your borrower where
you are higher up.
Speaker 3 (02:50):
In the waterfall of payment. So this is important.
Speaker 2 (02:54):
It would be really nice if you could upload a
credit agreement to chat GPT and just say, make sure
there's nothing in that would get me in trouble. Make
sure there's nothing in here that five years later I
will regret the placement of a.
Speaker 3 (03:05):
Certain Make sure I don't lose money, make sure.
Speaker 2 (03:07):
I don't lose money in some technical way anyway. So
there's just a lot going on. I feel like there's
plenty of episodes to do on this, but we really
do have the perfect guest, someone who literally sort of
sits in the intersection of I think we identified three
distinct trends. Here we are going to be speaking with
Dan Wortman. He is the co founder of a company
called Nohica AI, and it does exactly this. It sort
(03:29):
of attempts to use AI to understand credits. There's a
lot of understanding about deals and the text in them.
He also just has a lot of understanding about AI, etcetera.
So we can talk about all of these things. Dan,
thank you so much for coming on the podcast.
Speaker 1 (03:42):
Thanks so much for having me. I'm a fan of
the show. Love to hear it you guys. It kind
of like celebrities for me. So it's kind of fitting
that I'm here because at least with folks of Bloomberg,
because many people think about us and Oedica like the
Bloomberg for deal terms.
Speaker 2 (03:58):
Okay, well let's see. Let's see if you actually live
up to that.
Speaker 3 (04:01):
No.
Speaker 2 (04:01):
But so since I said I'm stressed that, oh this time,
we're doing a credit episode and I've found the guest
give us the quick version of like your career and
what no Edica is.
Speaker 1 (04:11):
Yeah, so let's start with Oedica. What we build at
no Edica is a power software for benchmarking real time
data on what's market in credit m and a capital
markets deal terms. Okay, so said another way, we help
folks like transactional attorneys, credit managers, bankers. We help them
figure out whether the terms of their transactional agreements like
(04:34):
think financing agreements, murder agreements, perspectuses, and really all other
corporate transactions are on or off market by benchmarking them
to market comps. So as far as the genesis of oedica,
it was kind of born out of my own experience
in my career. So I started my career at Blackrock.
I was on a team responsible for coming up with
new financial products and fixing and markets, and we were
(04:57):
developing these new interesting innovative structure and I just learned
a ton about the capital markets ecosystem, and in particular
just this is a fifty trillion dollar global market and
it runs on phone calls and relationships and it's unbelievably antiquated.
Then fast forward, I went back to get my GD
I joined WALKT toall Lipton, where I did corporate transactions.
(05:19):
This was twenty seventeen to twenty twenty two. So if
if you guys remember that time, it was heyday of
merger activity. So I worked on you know, T Mobiles,
Bioto Sprint, the biggest thirty billion dollar commitment at the time,
Algon ave to see raytheon and I distinctly remember sitting
down at my desk. I was looking at a transactional
grip and a multi billion dollar merger, and I was
(05:40):
looking at a term, and I was trying to figure
out whether I should help my client accept term A
or term B in this context. And I was stuck.
So I called the seenor partner on the deal. I said, hey,
where's the database of information where I could see exactly
how this term should come out and quantify it for
my client? And you know, the answer was that doesn't exist.
(06:01):
Now that was two and a half plus years ago.
Now I left walked out to start no edico with
a fairly simple idea, which is AI enables us to
finally quantify what market agreement terms should look like in
these markets. You know, now we work with almost all
the top twenty law firms on the street, but helping
them advise their clients on these deals. And this here
(06:22):
on track to do about a trillion dollars or of
transactions through the platform.
Speaker 2 (06:25):
And you get one percent of that.
Speaker 4 (06:26):
So that's great, Well, talk to us about what these
financing agreements actually look like and how traditionally they're sort
of judged by both the investors and the lawyers who
are looking at them.
Speaker 1 (06:37):
Yeah, I mean so when I say deal terms, what
I mean is deal terms are really the underpinning of
the entire transactional system, the rules of the road. You
could think about them like speed limits, double yellow lines,
street lights. They're kind of the plumbing that goes into
the transactions, putting in a way that people can understand. Imagine,
(06:57):
I go sign of lease. Most people are very familiar
with certain things, right, like the rent price, the how
long the lease.
Speaker 3 (07:06):
Is, subletting policy exactly.
Speaker 1 (07:08):
But if deep in that twenty page lease the least
says if the weather gets under thirty degrees at any time,
you forfeit your right to the apartment, Well, that's a
deal term, and that affects whether you want to accept
that lease or not. And so it's the same in
capital market terms. To give you a more tangible example, Yeah,
are you guys fast food people? Yes? Yes, Okay, So
(07:31):
I'm like a McDonald's guy. Yea, And whenever I go
to McDonald's, I always ordered the tempiece chicken McNugget. I've
ordered the ten piece hundreds of times. There's exactly three
things that happened to you order a tenpiece, you open
the box, you have nine pieces. You open the box
you have exactly ten pieces. Or you open the box
and you have eleven pieces. Now, if you have nine pieces,
you go to the counter, you say, hey, I'm missing
(07:53):
a piece. They give you a piece. You get the
benefit of your morgan. If you get ten, you enjoy
your McNuggets. If you get eleven, what do you do?
Speaker 3 (08:01):
You stay quiet?
Speaker 1 (08:03):
Exactly so you had the jaguar right now. There's this
kind of unwritten rule in American consumerism, which is that
if a company that's bigger than you gives you something
by accident, then you get the benefit of that as
a consumer. Well, in twenty twenty, the exact kind of
thing happened in the credit markets, but it ended very differently.
(08:25):
City Bank sent nine hundred million dollars to lenders in
full prepayment of a loan for Revlon, and they did
so accidentally. Now, they were supposed to just send an
interest payment. At the time, the terms of the credit
agreement were silent. The governing documentation, especially with this loan,
didn't say what happens in that scenario. Long story short,
many funds did not give back that hundreds of millions
(08:47):
of dollars and litigation ensued. But a deal term in
credit deals called erroneous payment. Deal terms started popping up
in the market. No Whatadka's data last clocked that deal
as a last quarter ninety percent of deals. So if
you don't have that term now in your deal, you're
way off market in terms of the way the market
actually operates. This is why deal terms are important. These
(09:08):
are hundreds of millions of dollars at stake. In the
context of all these deals.
Speaker 2 (09:13):
There's something very loyally but like I have to say,
I've never counted the McNuggets. I really get it, so
just I would this example would have never occurred to
me because I'm not the type of person that opens
a box of McNuggets and start.
Speaker 4 (09:24):
Clearly, you don't value McNuggets.
Speaker 2 (09:26):
Not evidently not. What are some other deal terms? So
that's a great example that. Okay, now, after that incident,
which is infamous, language about this start popping up. What
are some other sort of classic and I'm sure they
get much much more esoteric than that. But what are
some other like interesting deal terms that trend over time.
Speaker 1 (09:45):
Yeah, so it's really interesting. So there's a whole host
of what it would call structural protections in a lot
of these deals. These come in a lot of different vivors.
Many people talk about them as things like anti pet
smart terms, things like j crue blockers, things like sert
of protections. Let's talk about some of the Yeah, let's
talk about some of these, so anti pet smart terms.
These are protections that prevent guarantor releases when subsidiaries of
(10:09):
the credit group become non wholly owned. In other words,
it prevents value from being transferred away from the loan
into some other structure which doesn't provide credits for it.
Let me put this in a way that most people
don't understand. If you were getting a mortgage on your house,
pretty simple framework. You take out the debt, you pay
your mortgage payments, you pay a packfal loan. Bank can
(10:31):
foreclose in your house if you stop paying a mortgage.
But in the mortgage if it said something like well,
if you sell any part of your home front door,
a window, a shingle, the bank loses the ability to
foreclose in the house fully. Well, then what would you do.
You'd sell a single shingle, you would stop paying your
mortgage and you get to keep your house and you
(10:53):
get the benefit of that. That's what anti pest smart
terms actually prevent. They prevent the ability for credit groups
to actually sell a single equity and actually lose the
credit support from that particular equity. So it's kind of
interesting what we're seeing in the market right now. We
have this really unique vantage point from the point of
view of our software where we quantify trends in deal
(11:16):
terms over time, and so we can actually very precisely
tell you the percentages of deals that are actually getting
a lot of these structural protections and actually gives us
this really unique window into the anxieties and the optimisms
that are currently happening in the market. Some people think
about this as kind of an early signal of something
likely to come. So what are we seeing, Well, we're
calling it a flight to fortification, and it's really happening
(11:40):
on both issuers and barbers. And I'll explain what I mean.
We're seeing massive increases in lenders getting structural protections in
these deals. Basically, these are protections that help make sure
they're collateral is locked, things like the anti pet smart terms.
In return, borrowers are getting the same fortification. In fact,
they're getting more economic flexibility. You could think about it
(12:00):
as a way for them to weather the storm. This
is how we're seeing it. So things like add backs
to eve, you know, more ability to send money to shareholders,
more ability to make long term events bins. Let's talk
about the actual specifics of what we're seeing. Antipasmart terms,
the one I just talked about, we clocked out at
twenty eight percent of deals in Q three. That was
at four percent in twenty twenty three, and Q two
(12:23):
is at twenty five percent, is the highest we've ever recorded.
That term J crew blockers, which prevent issuers from moving
material ip out ofside the credit group. That's at forty
five percent of deals now the baseline from twenty twenty
three to fifteen percent, and last quarter it was thirty
eight percent. Anti SERTI protections, which are leansbordination protections. They
actually helped secure your place in line if and when
some sort of distress activity happens. That's at eighty four
(12:45):
percent of deals. That's the highest jump we've ever seen.
Quarter quarter it went up from sixty one percent to
twenty three point jump, and the baseline is thirty nine
percent twenty twenty three. That's pretty significant for a quarterly jump,
and it really signals something about the market. On the
quantitative side, we track a lot of stuff too, including
(13:06):
the ratios under which borrowers need to maintain specific types
of leverage. We saw that at three point nine times
EBITDA in Q two and it went down to three
and a half times, But again that's signaling some sort
of anxiety among the lender group that we wouldn't normally see.
Now you may ask what a borrower is getting for this, Again,
(13:28):
they're getting more fortification. One of the ways this is
coming up is in EBITDA adbacks, so EBADA add backs. Basically,
there's a very long and complicated calculation of cash flow
and a lot of these deals and the adbacks to
EBADA basically allow bars and issuers to add back certain
things to count them as cash flows.
Speaker 3 (13:50):
To flatter their balance sheet basically correct correct.
Speaker 1 (13:54):
One of the more interesting adbacks that we track is
what's called a cost saving satback. So imagine a borrower
knows it's gonna optimize some cost in the future. If
it can reasonably predict that cost, it can add that
back to today's cash flow. That cost savings atback, whether
materializes or not, is added back to today's cashflow. Sixty
(14:17):
four percent of deals now have cost savings atbacks in them.
That's the highest we've ever recorded for deals, with those
adbacks being above twenty percent of EBITDA that came in
fifty one percent, which is also the highest we've ever
tracked on the platform. They're also getting things like excluding
lenders that are short in their debt. So, for instance,
(14:38):
folks may be familiar with what happened with the Windstream
case a few years ago. What happened in that case
is certain hedge funds were actually short the debt the
loan that was in default, and that makes them not
exactly aligned with the company that has the debt outstand.
(15:00):
Terms started popping up in the market which we've tracked,
which are called net short lender terms, which allow bars
to exclude those lenders from voting. That is now in
thirteen percent of deals, which is the highest we've ever tracked.
So you could see the fortification actually on both sides
of the market, and it really signals, I think, to
us that there's a risk allocation happening with a lot
of these anxieties.
Speaker 3 (15:36):
Joe, First of all, you know, my husband was a corporate.
Speaker 2 (15:38):
Lawyer at one point.
Speaker 3 (15:39):
Yeah, okay.
Speaker 4 (15:40):
So one of the things he's most proud of is
he came up with some language in a deal shortly
after the two thousand and eight financial crisis, and it
was he sent it to me just now a significant
dislocation in financial markets. That was him, and that became
like standard language in risk factors, at least in a
bunch of that's a contribution.
Speaker 2 (16:01):
I'm the inventor of this deal, so and so the inventor.
Some people invent great medicine, some people invent some new technology,
and someone invents a new deal term that gets propagated
across that's right documents for years thereon after.
Speaker 3 (16:14):
That's how it works.
Speaker 4 (16:15):
But Dan, I wanted to ask you something. Okay, So
you say there's more fortification in a lot of deal terms,
more protections perhaps for both investors and lenders.
Speaker 3 (16:25):
I guess one of the things we.
Speaker 4 (16:27):
Heard prior to twenty twenty in them for some years
after it was we had this explosion in CoV light deals, right,
fewer protections for investors because everyone was so desperate supposedly
for yield for that particular paper, So the balance of
power shifted to the borrowers they were able to dictate
the terms. How are investors getting better protections now? With
(16:52):
you know, credit spreads still at basically multi decade lows,
which suggests that there's still a lot of demand and
that they don't hold all the power in the market.
Speaker 1 (17:01):
Yeah, I think about it, and what the data supports
that we see on the platform is. I think about
it less so as what they're getting, but more about
what the terms actually reflect in terms of the macro
environment that they're operating in. So, for instance, right now,
we're seeing this flight to fortification in part largely due
to probably a few things. Number one being some of
(17:23):
these headline risks that folks have been talking about, and well,
I'm sure we'll get into some of what's going on
in the private credit market today. So people flooding into
more structural protections because they're worried about their place in
line if there is distress. I think number two is
just mac or wise if you think about it. In
the credit markets, there was a ton of debt taken
out in twenty twenty, twenty twenty one, twenty twenty, early
part of twenty twenty two. This leads to a lot
(17:45):
of maturity walls upcoming, especially in twenty twenty seven twenty.
Speaker 3 (17:49):
We don't say upcoming on the show, we say looming.
Speaker 1 (17:52):
Yeah, exactly. There are a lot of looming maturity walls
in twenty twenty eight, twenty twenty nine vintage. And you
can think about it as well. That's a macro factor
that people are thinking about when they underwrite alone, because
many of these deals actually have five year tenor you know,
seven year ten or eight year tenor in some cases
thirty year tenor, and so they're thinking about all these
protections in the context of that market. I also think
(18:16):
it's really interesting, aside from the credit context, right now,
we're seeing a lot of structuring in terms happening in
M and A markets, So things like regulatory uncertainty, things
like tariffs, things like libildy management, as we talked about,
things like tax uncertainty. I'm happy to go into these,
but we're seeing a lot of things in this area.
(18:37):
One kind of small example of this in situations where
a buyer and a seller have regulatory uncertainty, which you
know a lot of folks think about the administration and
they're not sure exactly how things are going to play out.
You actually see regulatory review in deals get hyper focused
on and it actually precipitated a new deal term this
(18:59):
year which we tracked in the market. We had an
almost term detection of the platform. We sent out a
note to all of our clients and it's called a
new outside date structure term. Basically, what it does is
it allows buyers of acquirees. It allows them to lock
in their financing for longer and actually stand their financing
in the case scenario regulatory review les. And that's just
(19:22):
an example of the kind of innovation that's happening in
the merger markets. In terms of tariffs, we picked up
the first tariff event of default in a credit deal. Ever,
it happened in a Superior industries deal over the summer,
which probably isn't surprising to use an auto manufacturer deal.
I've made a lot of parts in Mexico. That's not
five percent of MNA deals for tariff based m and
A carve outs and railie respect clauses.
Speaker 2 (19:44):
Can we talk a little bit about you know, you're
scanning these documents. Google's ingram has existed for a long time.
Tracking the prevalence of a term is not novel technology
that control EF right control f Yeah, this is sort
of like very barely even councils technology at that point.
What is it that you you know, when you're talking
(20:05):
about the changing prevalence of these terms, what is the
actual novelty here that isn't just sort of yeah, document
search over time.
Speaker 1 (20:15):
Yeah. So, Tracy, your husband's a former corporate lawyer. You know,
he would tell covering corporate lawyer or covering corporate layer exactly.
I am myself as well. One of the things he
would tell you is that there's constant innovation in these markets.
These agreements are highly complicated, there very long. They have
a lot of what's called long range dependencies, which is
(20:35):
that you may be used to seeing something in a
particular area of the document said one way, but in
reality it turns out it's punted to three different causes
deep down, and you actually have to go find that information.
Speaker 4 (20:47):
This is why it's also jiu jitsu between the borrowers
and the lenders, right, because like the borrowers are often
trying to hide something that's favorable to them, or the
lenders are trying to hide something favorable to them. So
the structure and the way it's worded changes a lot
to your.
Speaker 1 (21:01):
Point exactly, And these are sophisticated parties paying millions, sometimes
hundreds and millions of dollars in advisory fees to make
sure that these terms look the way they do. Now
that leads to kind of the technological innovation that I
think has enabled a lot of this AI for the
first time, can attribute in particular, new language models, can
(21:22):
attribute more semantic meaning to phrases and language that was
impossible with things like n grams. And so what America
does is it used as a series of language models,
including a multi layered information extraction system to make sure
that it's encoding all this semantic meaning inside all these terms,
so that when you look at a J. Krublacker in
(21:43):
the first way, it may be phrased a thousand different ways,
but we can track that term over time. That has
enabled the ability to actually quantify for the first time
what a market agreement term looks like in these markets.
And I think that's why it's so interesting to folks
on the platform.
Speaker 4 (22:01):
So I know you're not doing litigation, but I guess
I'm curious how you deal with or if AI is
helpful with in litigation what would be called precedent. But
I'm assuming you're building up a big database of all
these different deal documents.
Speaker 3 (22:16):
Is it useful?
Speaker 4 (22:17):
Is AI useful to go back and look at previous
documents in order to shape new ones?
Speaker 1 (22:23):
Yeah? Exactly, So in New Edica, we are ultimately an
a power software company, but we actually have the largest
knowledge graph of deal terms in ex systems. So Tracy,
exactly what you said. It's a database ultimately of precedent
comparable deal terms, and that database is going to be
mind bowing as over billion terms in it, So its
issue to a large as in existence, we map that
(22:44):
back to deal characteristics. It's the same in litigation, right,
So in transactional markets, folks are innovative, but they also
want to rely on something that has happened before, or
at least in part, they want to rely on something
that has happened before, and so folks are constantly looking
for ways to tie things back to comparable deal terms.
It's the same in litigation. So obviously not expertise, but
(23:07):
the same concept, which is, you know, when you write
a brief, you were constantly citing cases that the judge
has you know, relied on in the past. And you know,
for lawyers and you know outset of lawyers, even just
deal professionals generally bankers, credit managers, people are highly reliant
on present What.
Speaker 2 (23:25):
Is your text acre did you what do you build
and how much is it? Like, oh, you're using chat, epts, API,
et cetera. Like, okay, yes, large language models are good
at identifying deal terms or novelty, et cetera. There's semantic
meaning of these terms, but what did you actually build
and what do you actually employ in your technology?
Speaker 1 (23:45):
So we were starting in twenty twenty two, so we're
what you would call AI native. We were started in
a system that already and language models existed in. However,
we because of the nature of the sensitive documents in
terms that we deal with, especially for you know, major
law firms, financialist yea, this is like.
Speaker 2 (24:03):
A big issue with the right that they don't want
to just be uploading their stuff to chat GPT right exactly.
Speaker 1 (24:08):
And so we actually utilize you know, adapted language models,
open source language models that we adapt on our armed
proprietary data sets and then deploy and secure environments and
single tendon architectures, you know, for individual instances of institutions
that deploy our product. And so you could think about
it as based on the language models that are ultimately
(24:29):
underpinning a lot of the gpds and the clouds. However,
it's fine tuned to this particular data set, which makes
it obviously much better at handling this exact problem, which
is a big problem in the market. Now. We also
layer on top of that information extraction model. So for instance,
you may know that a term exists in what deal,
but you may want to know what terms should exist
(24:50):
for a JP Morgan deal, or for a B of
a deal, or for you know, a particular type of counterparty,
and so in those context we actually want to map
those deal terms back to deal characteristics, and we actually
utilize a lot of models to extract information and marry
that with their party data sets. So that's a little
bit about how the technology works. I think I always
think about it from the user standpoint, What does the
(25:11):
user really want? These really wants to know how they're
going to invite their client on a particular merger on
a particular credit deal. How often does this come up?
You always call your attorney and you're trying to figure out, well,
is this market is it off market? And that's what
our data provides.
Speaker 3 (25:27):
Okay, so structural fortifications in deal terms. What are you
seeing right now?
Speaker 4 (25:33):
Because as we started this conversation, we were talking a
lot about the recent blow ups in the private credit market,
and if you look at some spreads on certain firms,
certain bonds, it does seem like nervousness is creeping back
into the market. I see spreads on you know, it's
not private credit, but spreads on triple C rated debt
(25:53):
have been creeping up recently. How scared or concerned are
people right now?
Speaker 1 (26:00):
Well, I recently wrote about this in the Wall Street
Journal a little bit, and then folks contacted me and
I kind of said, you know, you're causing a stir.
And then I saw Howard Marx came out with his letter,
which I think was called Cockroaches in the coal Mine,
and they had a lot of the same themes. I
think folks who have been around credit market for a
(26:21):
very long time can kind of see what's a little
bit of what's going on.
Speaker 2 (26:24):
To us.
Speaker 1 (26:25):
Let me just talk about what the data supports to us.
What we see is creditors maybe preparing this their system
for distress, and I'll talk about what we're seeing in
the data that kind of supports that. But you can
think about it like the evolution of your house security, right,
So you know, first you lock the doors. Then you know,
(26:49):
you get a bolt lock, which gives you better protection.
You know. Then you you know, you add a security
system on top of that alarm system. And at the end,
what do you do. You kind of up all your
valuables and you ensure them if people are going to
get into the house. And you know, for the past
few years, we've seen lenders really focused on keeping people out.
This is the locks and the dead bolts, and this
(27:13):
is what we were talking about with j crub blockers.
This is making sure you can't structure around me. From
a liability management perspective, But over the last quarter something
kind of changed, which is we started seeing people and
lenders obsessed with lean subordination terms, which is the term
that governs who gets paid first when everything falls apart.
(27:35):
So this isn't really about preventing liability management exercises that much.
It's actually about controlling the recovery when a bankruptcy does happen.
And so we clocked that term at eighty four percent
of deals in Q three, biggest quarterly jump we've ever
seen from the prior quarter. It's also the highest we've
ever clocked that term. So this bes the question of
(27:56):
why Wire Credit is so focused on making sure.
Speaker 3 (28:00):
Their place on line is in recovery.
Speaker 1 (28:02):
In recovery is the same. Perhaps it's a reaction to
the Lieboldy management transactions we talked about, so perhaps folks
are thinking that that will precipitate. Perhaps it's a reaction
to some of the maturity walls that folks understand, or
perhaps it's some of what I was saying in the appad,
which is folks are seeing that there may be distress
(28:23):
events on the horizon and they want to make sure
that if there is, they have the most negotiaing leverage
its possible.
Speaker 2 (28:44):
So I know it's broad statements, but you know, when
we look at these sort of environment under which companies
like First Brands or Tree Color or some of these
other ones that have gone into distress very rapidly, when
we look back at when these were birthed.
Speaker 1 (29:00):
Et cetera.
Speaker 2 (29:00):
Can we say like these were sloppy times, These were loose,
sloppy times that people were not thinking much about either
just quality due diligence or diligent terms.
Speaker 1 (29:12):
Yeah. So I think with First Brands is a great example. Right.
So First Brands is an automotive replacement company, right, so
they make things like breaks and wipers and filtration systems.
Beginning in twenty nineteen, that company effectively rapidly expanded through
(29:32):
debt fueled acquisitions and it dramatically increased its scale. But
I think what First Brands illustrates is something that you know,
we might get into with the private credit markets, which
is that they primarily funded these acquisitions with large debt facilities.
Then tariffs hit in April twenty twenty five, which obviously
(29:53):
changed their business because they actually do a lot of manufacturing,
and that kind of magnified problems. So you can think
about out. One of the main problems with First Brands,
which is also kind of some of what folks are
worried about in the private credit markets today, is what's
called off balance sheet financing. What First Brands used is
a lot of you know, receivables financing facilities that weren't
(30:17):
properly disclosed to a lot of folks that were lending
to the company. In fact, I think in that sense,
just to give you a sense of quantum, this is
over eleven billion dollars of total obligations that they had
when they actually started disclosing it in terms of off balance youe.
Financing and you know, they were disclosing things like five
to six billion dollars of actual debt obligations. And so
(30:39):
this led one of the creditors lawyers to say that
two point three billion dollars just disappeared. And so that structure,
the ability for first brands to get that debt was
made possible by the private credit markets and how deep
the private credit markets have become. Because if you're a
big credit manager in private credit markets, you could fund
(31:03):
you know, that type of receivable facility to a first brand,
and first brands could use that facility to then, you know,
make sure they are constantly continuing to acquire new businesses
and keep rolling over the cash.
Speaker 4 (31:15):
I have a theory that receivables, financing and factoring is
to the private credit market. What French quants who went
to that one elite school are.
Speaker 3 (31:27):
To trading blow ups.
Speaker 1 (31:28):
I like that theory.
Speaker 3 (31:29):
Yeah, thanks.
Speaker 4 (31:30):
So the other thing we wanted to ask you about,
and again we reference this in the intro, is we
are seeing these really complicated deals that I admittedly cannot
keep track of in the AI market, where you know,
one company is going to buy chips from this other company,
and then that company is going to borrow from whoever
and use the chips funding to pay them back, and
(31:50):
then that money somehow goes into the company that is
buying the stuff in the first place. It is all
very circular, all very incestuous in many ways. In my mind,
are you examining those types of deals or just putting
on your credit expertise hat if you see something like that,
what are you thinking?
Speaker 1 (32:07):
Yeah, well, it's probably helpful to kind of talk about
some of the structure of these deals, which I think
again is made possible by how deep the private credit
markets have become. And usually when I do that, I
try to think about, let's try to make this a
little bit more fun. So imagine for a minute, Joe,
you just love pizza. He does love pizza yesterday twice.
(32:29):
There you go, You're a pizza fanatic. You love it
so much that you decide to eat pizza every single
meal of every single day for the rest of your life,
like you are committed to subsisting pizza, committed to the
carbs exactly. So, Joe, you made that decision. You come
to me and you say, Hey, Dan, I'm going to
eat pizza for every meal of my entire life. How
about you open a pizza restaurant for me to eat it.
(32:53):
It'll be really lucrative.
Speaker 2 (32:56):
We're going with this, but this is actually a very
good to note do, right, Like you would have have
a lot of confidence in me to commit to my word.
If you're going to open a restaurant.
Speaker 1 (33:04):
Yeah. Now, now you come to me and say, it's
going to be super lubritive. Here's how we're going to
fund it. Ten percent equity. The bank is going to
give you ninety percent of the funding in leverage. And
it's Dan's restaurant. Joe, you don't on the restaurant, but
you're going to eat at it. I'm the full beneficiary,
full beneficiary of the restaurant, but it's ninety percent. Okay,
So I opened the restaurant. You eat there every single day. Now, Tracy,
(33:25):
Joe comes to you for a personal loan to fund
his lifestyle, his pizza eating.
Speaker 2 (33:32):
Tracy, trust me, she would lend it to me.
Speaker 1 (33:34):
Well, here's the question, right, should you, Tracy? Consider the
ninety pizza restaurant that Joe is eating at for all
his meals. Now, on the one hand, it's not Cho's loan, right,
so he's not on the hook if the pizza restaurant
goes under. On the other hand, it's Joe's only source
of food.
Speaker 3 (33:54):
Which Joe will die without the restaurant.
Speaker 1 (33:56):
Which is his He's committed to the restaurant, and it
kind of makes the restaurant intertwined with Joe's ability to
pay your personal loan back. So, I guess that's a
good question. No, there's great.
Speaker 2 (34:07):
So now let's take it out of pizza. Who is
so that's whatever? Like okay, now who is the chips
buy or whatever?
Speaker 1 (34:14):
This is essentially what's happening with off balance youe. Financing
and data center deals. So, and it includes Metas. I'm
sure you saw the Hyperion deal. It's his Metas infrastructure
deal with Blue Owl. Except I think it's even more
intriguing than some of the pizza stuff. So Meta and
Blue Ol basically created a joint venture in a special
purpose vehicle not that different than the restaurant. And the
(34:34):
deal is the joint venture would be owned twenty percent
by Meta, eighty percent by Blue Owl, so Blue Owl
controls it, and it would effectively be funded with ninety
percent leverage. So call it thirty billion dollars of total
enterprise value, three billion dollars of equity, twenty seven billion
dollars give or take of debt. In other words, Blue
Oul is effectively owning the restaurant. Meta is effetively eating
at the restaurant, and the bank's funded with ninety percent leverage.
(34:57):
So what this does is it keeps the debt off
of Meta's books right while also giving investors credit managers
the ability to put money against a data center asset.
So Meta in this deal will make rent payments associated
with the data center based on its cost of power.
That's the cash flow that's going to the SBV, and
(35:18):
that effectively funds the interest expense. Let's just talk about
the debt for a second. In a normal LBO context,
ninety percent leverage is pretty exceptionally high. Most people would
consider fifty to eighty percent leverage to be relatively normal
for a stable cash flow business. So the debt itself
is actually quite high on some of these structures. The
(35:39):
only reason it was possible was because it was given
an investment great credit rating, and in part because Meta
agreed to a four year operating lease with what's called
a residual value guarantee, which means that Meta is guaranteeing
a capped amount of some of that cashflow. However, that
guarantee is capped and is only partial, which is why
they don't have to take it onto their book and
(36:00):
why would be a footnote as a contingent debt obligation
in their balance sheet. Now let's talk about the asset
that's being underwritten. This isn't pizza. Pizza actually has a
stable price, right. We have thousands of UTI history on pizza, right,
and you can track that price over time. Data center
is optimized for GPU performance on training fundamental AI models.
(36:22):
Not so much of a mature asset, actually, I think
most folks would think about it as a burgeoning asset.
Now I'm in this world. I mean, folks, there's a
high amount of demand for a lot of this compute,
and I definitely think the demand is there, but at
the end of the day, it's an immature asset with
a price that isn't so well defined. So just a recap,
you've got off balance sheet financing which isn't reflected with
(36:45):
whoever is lending money to metal or even buying its
equity with ninety percent leverage on an immature asset, And
I think that's why these deals are so interesting. So
from our point of view, I mean to make sure
you get the terms right, and you know, we will
look at these data center. A lot of these types
of financings run through our platform all the time. To
make sure you get the terms right on what this
(37:06):
structural protections look like in these deals is critical for
the fortification of something that is in the structure.
Speaker 4 (37:12):
So I know we've seen these idiosyncratic blow ups in
the private credit market so far, but just looking at
the AI market in particular and the financing there, it
feels like right now people are still willing to lend money.
And we've talked about this on the show before, but
a lot of the AI competition is couched in this
existential language of you either win it AI or die basically,
(37:38):
and so the spending keeps going. What is your guess
on like the thing that kind of knocks that cycle
or that flywheel.
Speaker 3 (37:48):
And tears it apart.
Speaker 1 (37:49):
So I'm obviously in the AI industry. We're in the
credit industry, so we see both sides of this phenomenon.
I fundamentally believe AI is a paradigm shift. I would
not have left, you know, the deal markets if I
didn't think that. And I think what we're witnessing is
very similar to the Internet in the nineteen nineties, or
(38:10):
the iPhone in two thousands, or social media in the
twenty tens. And I think this paradigm shift is going
to ultimately change a ton of industries, including capital markets
and finance and law and all these amazing industries. And
so that I think is very true. But I also
think two things can be true. I think AI can
(38:31):
be a generation defining category and a technology that's upending
a lot of industries. But I also think that categories
will have winners and losers. And when folks are racing
to define a category, as you know, you often see
with a lot of these transformational types of technology, there
may be more losers in the headlines. Then you're used
(38:53):
to seeing in a lot of these markets, but the
winners will be bigger than anyone's ever all.
Speaker 2 (39:00):
Right, So if I don't need the pizza, someone else
is going to pick up the pizza and they're gonna
they're gonna eat it.
Speaker 1 (39:05):
Look what we focus on in Oadica is in a
market moving this fast. Yeah, we all need to pay
attention to the terms that actually underpinning a lot of
these markets to make sure if there is any bleeding,
that bleeding gets stopped as quickly as possible. Just to
give you one last example from a recent market deal,
you can look at the Frank jpm deal as like
(39:26):
a really interesting one. This is, you know, this was
a deal where JPMorgan paid one hundred and seventy five
million dollars to acquire a company. There's a very small deal,
but to acquire a company called Frank, which is a
streamline fasta kind of support service.
Speaker 2 (39:40):
I remember this.
Speaker 1 (39:41):
It turned out there was a lot of synthetically made
up types of data in that.
Speaker 2 (39:48):
Business, and the founder is going to prison right.
Speaker 1 (39:50):
Allegedly there's a lot of there's a lot of made
up stuff in the business. And I think seven days.
Speaker 2 (39:55):
Executive who worked at Frank sends to sixty eight months.
Speaker 1 (39:58):
Yeah, yeah, yeah, and so. But I think the most
interesting part about this particular transaction to me is JPM
ended up signing a merger agreement that said that the
indemnification for the founder's litigation, for any founder's litigation, would
be paid for by JPM.
Speaker 2 (40:17):
Right, they paid her lawyer.
Speaker 1 (40:18):
They paid one hundred and fifteen million dollars in legal
expenses for her lawyer on her fraud. And so when
you're moving really fast, yeah, right, you can kind of
ignore some of the nuts and bolts. But I think
it's actually even more critical and fast moving markets.
Speaker 2 (40:32):
Dan Workman, co founder of no Edica, thank you so
much for coming on outlook.
Speaker 1 (40:36):
Thank you thanks for having me us.
Speaker 2 (40:51):
Tracy. I wasn't really sure where he was going with
that pizza analogy, but it actually does make a lot
of sense, and it's something I think is a phenomenon
and just a lot of financial transactions, which is how
much like in certain environments, the lender and the creditor
are like both each others, like they're both leaning on
each other. They're both the creditor and lenders, they're relying
on each Yeah, at the same time.
Speaker 3 (41:11):
Much in the way you rely on pizza.
Speaker 2 (41:13):
You would lend to me to buy it to eat pizza.
Speaker 3 (41:15):
Right, I would thank you if it was a matter
of survival, that was.
Speaker 2 (41:20):
A matter of survival, thank you.
Speaker 1 (41:21):
I think it's just.
Speaker 3 (41:21):
Because you want to eat really expensive pizza then no.
Speaker 4 (41:24):
You know.
Speaker 2 (41:24):
The other thing too, is just like from talking to
you over these years, you know how many times I've
heard something there's a lot of cuve light stuff going.
It is interesting to think that, like, you don't often
hear that quantified what that means, right, things are like
cove light these days, et cetera. And the idea that like,
maybe we could get better numbers on some of these
things seems like potentially labor saving for lawyers. Stuff like that.
Speaker 4 (41:47):
The specific numbers on specific deal terms were really interesting
to me. And the idea that even today lawyers and
bankers still have trouble anticipating every single thing that could
happen to particular deal, and so they're having to react
to it and come up with the new terms, the
new deal language, and insert them into the documentation.
Speaker 3 (42:07):
I find that interesting.
Speaker 2 (42:08):
The tariff example, you know, the problem is is that
AI is good and this is I'm certain if we
talked about this more, AI will be used to come
up with new deal terms and the cat and mouse
game will continue forever. So I suspect that we are
not going to have lawyers will always find new work
to do, and they'll just get work. They'll just get
more creative about outsmarting the systems that are designed to
(42:29):
detect these phenomena.
Speaker 4 (42:30):
We will end up with thousands and thousands of pages
of term sheets that, like humans are just physically incapable
of reading, it has to be read by AI.
Speaker 2 (42:39):
I probably literally, that is what's going to happen.
Speaker 3 (42:42):
Yeah, all right, shall we leave it there.
Speaker 2 (42:43):
Let's leave it there, all right.
Speaker 4 (42:44):
This has been another episode of the Odd Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2 (42:50):
And I'm Jill Wisenthal. You can follow me at the Stalwart.
Follow our producers Carmen Rodriguez at Carmen Arman, dash Ol
Bennett at Dashbot and kill Brooks at kill Brooks. For
more Odd Laws content, go to Bloomberg dot com slash
odd Lots, where we have a daily newsletter and all
of our episodes and you can chat about all of
these topics twenty four to seven in our discord discord
dot gg, slash.
Speaker 4 (43:09):
Od loots and if you enjoy odd Lots, if you
like it when we dive deep into deal documentation, then
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And remember, if you are a Bloomberg subscriber, you can
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Apple Podcasts and follow the instructions there. Thanks for listening.
Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of The Odd Lots podcast.
Speaker 3 (00:21):
I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2 (00:24):
Tracy, there are just so many credit related things to
talk about right now, all things credit.
Speaker 3 (00:28):
I love it.
Speaker 1 (00:29):
I love it.
Speaker 3 (00:30):
Credit is interesting again.
Speaker 1 (00:31):
This might be one of the.
Speaker 2 (00:32):
Only credit episodes that we've ever done where like I
found the guest because I feel like when I think
about like a credit all the credit episodes, it's usually
like someone you know. Randomly. I found someone who knows
a little bit something about credit. She's like, oh, let
me do it.
Speaker 3 (00:44):
Let me The stakes are high.
Speaker 2 (00:46):
I know I was thinking about there because you're like, how, Joe,
you like pick someone who doesn't know anything. No, I
don't think that. I think we have a very knowledgeable
credit guest. But I'm a little stressed about this aspect.
Speaker 4 (00:55):
I believe in you, Joe, you do. I trust your judgment.
But to your point, there's a lot going on. So
obviously there are concerns around private credit. We've had some
idiosyncratic defaults and frauds in the market, and each one
is special in their own way. But I think the
worrying aspect is that they keep coming to light. Yeah, right,
(01:15):
And so you've seen people like Jamie Diamond using the
cockroach analogy, which is now famous. And at the same
time you have the connection with AI, right, which we
have spoken about a little bit on the podcast with
Paul Kadrowski. All these complex circular financing structures that are
driving a lot of the credit boom, or have been
(01:35):
driving a lot of the credit boom, and then at
the same time you also have the impact of AI
on credit itself.
Speaker 2 (01:43):
Yeah, that's right because in theory right, like we've talked
about this. We did that episode with Joel Werthheimer that
was in a slightly different context, but we've done these
episodes about you know, just the incredible length of deal text,
et cetera. And perhaps if there's one area where maybe
we could say with some high degree of confidence that
large language models could be useful, it is can we
(02:05):
break down this multi hundred page agreement so that we
don't have to have you know, junior associates or junior
lawyers or junior bankers up till four in the morning
making sure that every comma is in the right place,
et cetera. In theory, this could be an area in
which AI could be productively applied.
Speaker 4 (02:23):
You know, there was an actual case argued over a comma.
I can't remember exactly what it was, but like, you're
absolutely right, the grammar, the specific words clearly matter in
legal language. I would just add one of the things
that's been driving arguably driving private credit is the booming
creditor on credit or violence in public deals. So it
was this idea that you could avoid that by having
(02:45):
you know, this private close relationship with your borrower where
you are higher up.
Speaker 3 (02:50):
In the waterfall of payment. So this is important.
Speaker 2 (02:54):
It would be really nice if you could upload a
credit agreement to chat GPT and just say, make sure
there's nothing in that would get me in trouble. Make
sure there's nothing in here that five years later I
will regret the placement of a.
Speaker 3 (03:05):
Certain Make sure I don't lose money, make sure.
Speaker 2 (03:07):
I don't lose money in some technical way anyway. So
there's just a lot going on. I feel like there's
plenty of episodes to do on this, but we really
do have the perfect guest, someone who literally sort of
sits in the intersection of I think we identified three
distinct trends. Here we are going to be speaking with
Dan Wortman. He is the co founder of a company
called Nohica AI, and it does exactly this. It sort
(03:29):
of attempts to use AI to understand credits. There's a
lot of understanding about deals and the text in them.
He also just has a lot of understanding about AI, etcetera.
So we can talk about all of these things. Dan,
thank you so much for coming on the podcast.
Speaker 1 (03:42):
Thanks so much for having me. I'm a fan of
the show. Love to hear it you guys. It kind
of like celebrities for me. So it's kind of fitting
that I'm here because at least with folks of Bloomberg,
because many people think about us and Oedica like the
Bloomberg for deal terms.
Speaker 2 (03:58):
Okay, well let's see. Let's see if you actually live
up to that.
Speaker 3 (04:01):
No.
Speaker 2 (04:01):
But so since I said I'm stressed that, oh this time,
we're doing a credit episode and I've found the guest
give us the quick version of like your career and
what no Edica is.
Speaker 1 (04:11):
Yeah, so let's start with Oedica. What we build at
no Edica is a power software for benchmarking real time
data on what's market in credit m and a capital
markets deal terms. Okay, so said another way, we help
folks like transactional attorneys, credit managers, bankers. We help them
figure out whether the terms of their transactional agreements like
(04:34):
think financing agreements, murder agreements, perspectuses, and really all other
corporate transactions are on or off market by benchmarking them
to market comps. So as far as the genesis of oedica,
it was kind of born out of my own experience
in my career. So I started my career at Blackrock.
I was on a team responsible for coming up with
new financial products and fixing and markets, and we were
(04:57):
developing these new interesting innovative structure and I just learned
a ton about the capital markets ecosystem, and in particular
just this is a fifty trillion dollar global market and
it runs on phone calls and relationships and it's unbelievably antiquated.
Then fast forward, I went back to get my GD
I joined WALKT toall Lipton, where I did corporate transactions.
(05:19):
This was twenty seventeen to twenty twenty two. So if
if you guys remember that time, it was heyday of
merger activity. So I worked on you know, T Mobiles,
Bioto Sprint, the biggest thirty billion dollar commitment at the time,
Algon ave to see raytheon and I distinctly remember sitting
down at my desk. I was looking at a transactional
grip and a multi billion dollar merger, and I was
(05:40):
looking at a term, and I was trying to figure
out whether I should help my client accept term A
or term B in this context. And I was stuck.
So I called the seenor partner on the deal. I said, hey,
where's the database of information where I could see exactly
how this term should come out and quantify it for
my client? And you know, the answer was that doesn't exist.
(06:01):
Now that was two and a half plus years ago.
Now I left walked out to start no edico with
a fairly simple idea, which is AI enables us to
finally quantify what market agreement terms should look like in
these markets. You know, now we work with almost all
the top twenty law firms on the street, but helping
them advise their clients on these deals. And this here
(06:22):
on track to do about a trillion dollars or of
transactions through the platform.
Speaker 2 (06:25):
And you get one percent of that.
Speaker 4 (06:26):
So that's great, Well, talk to us about what these
financing agreements actually look like and how traditionally they're sort
of judged by both the investors and the lawyers who
are looking at them.
Speaker 1 (06:37):
Yeah, I mean so when I say deal terms, what
I mean is deal terms are really the underpinning of
the entire transactional system, the rules of the road. You
could think about them like speed limits, double yellow lines,
street lights. They're kind of the plumbing that goes into
the transactions, putting in a way that people can understand. Imagine,
(06:57):
I go sign of lease. Most people are very familiar
with certain things, right, like the rent price, the how
long the lease.
Speaker 3 (07:06):
Is, subletting policy exactly.
Speaker 1 (07:08):
But if deep in that twenty page lease the least
says if the weather gets under thirty degrees at any time,
you forfeit your right to the apartment, Well, that's a
deal term, and that affects whether you want to accept
that lease or not. And so it's the same in
capital market terms. To give you a more tangible example, Yeah,
are you guys fast food people? Yes? Yes, Okay, So
(07:31):
I'm like a McDonald's guy. Yea, And whenever I go
to McDonald's, I always ordered the tempiece chicken McNugget. I've
ordered the ten piece hundreds of times. There's exactly three
things that happened to you order a tenpiece, you open
the box, you have nine pieces. You open the box
you have exactly ten pieces. Or you open the box
and you have eleven pieces. Now, if you have nine pieces,
you go to the counter, you say, hey, I'm missing
(07:53):
a piece. They give you a piece. You get the
benefit of your morgan. If you get ten, you enjoy
your McNuggets. If you get eleven, what do you do?
Speaker 3 (08:01):
You stay quiet?
Speaker 1 (08:03):
Exactly so you had the jaguar right now. There's this
kind of unwritten rule in American consumerism, which is that
if a company that's bigger than you gives you something
by accident, then you get the benefit of that as
a consumer. Well, in twenty twenty, the exact kind of
thing happened in the credit markets, but it ended very differently.
(08:25):
City Bank sent nine hundred million dollars to lenders in
full prepayment of a loan for Revlon, and they did
so accidentally. Now, they were supposed to just send an
interest payment. At the time, the terms of the credit
agreement were silent. The governing documentation, especially with this loan,
didn't say what happens in that scenario. Long story short,
many funds did not give back that hundreds of millions
(08:47):
of dollars and litigation ensued. But a deal term in
credit deals called erroneous payment. Deal terms started popping up
in the market. No Whatadka's data last clocked that deal
as a last quarter ninety percent of deals. So if
you don't have that term now in your deal, you're
way off market in terms of the way the market
actually operates. This is why deal terms are important. These
(09:08):
are hundreds of millions of dollars at stake. In the
context of all these deals.
Speaker 2 (09:13):
There's something very loyally but like I have to say,
I've never counted the McNuggets. I really get it, so
just I would this example would have never occurred to
me because I'm not the type of person that opens
a box of McNuggets and start.
Speaker 4 (09:24):
Clearly, you don't value McNuggets.
Speaker 2 (09:26):
Not evidently not. What are some other deal terms? So
that's a great example that. Okay, now, after that incident,
which is infamous, language about this start popping up. What
are some other sort of classic and I'm sure they
get much much more esoteric than that. But what are
some other like interesting deal terms that trend over time.
Speaker 1 (09:45):
Yeah, so it's really interesting. So there's a whole host
of what it would call structural protections in a lot
of these deals. These come in a lot of different vivors.
Many people talk about them as things like anti pet
smart terms, things like j crue blockers, things like sert
of protections. Let's talk about some of the Yeah, let's
talk about some of these, so anti pet smart terms.
These are protections that prevent guarantor releases when subsidiaries of
(10:09):
the credit group become non wholly owned. In other words,
it prevents value from being transferred away from the loan
into some other structure which doesn't provide credits for it.
Let me put this in a way that most people
don't understand. If you were getting a mortgage on your house,
pretty simple framework. You take out the debt, you pay
your mortgage payments, you pay a packfal loan. Bank can
(10:31):
foreclose in your house if you stop paying a mortgage.
But in the mortgage if it said something like well,
if you sell any part of your home front door,
a window, a shingle, the bank loses the ability to
foreclose in the house fully. Well, then what would you do.
You'd sell a single shingle, you would stop paying your
mortgage and you get to keep your house and you
(10:53):
get the benefit of that. That's what anti pest smart
terms actually prevent. They prevent the ability for credit groups
to actually sell a single equity and actually lose the
credit support from that particular equity. So it's kind of
interesting what we're seeing in the market right now. We
have this really unique vantage point from the point of
view of our software where we quantify trends in deal
(11:16):
terms over time, and so we can actually very precisely
tell you the percentages of deals that are actually getting
a lot of these structural protections and actually gives us
this really unique window into the anxieties and the optimisms
that are currently happening in the market. Some people think
about this as kind of an early signal of something
likely to come. So what are we seeing, Well, we're
calling it a flight to fortification, and it's really happening
(11:40):
on both issuers and barbers. And I'll explain what I mean.
We're seeing massive increases in lenders getting structural protections in
these deals. Basically, these are protections that help make sure
they're collateral is locked, things like the anti pet smart terms.
In return, borrowers are getting the same fortification. In fact,
they're getting more economic flexibility. You could think about it
(12:00):
as a way for them to weather the storm. This
is how we're seeing it. So things like add backs
to eve, you know, more ability to send money to shareholders,
more ability to make long term events bins. Let's talk
about the actual specifics of what we're seeing. Antipasmart terms,
the one I just talked about, we clocked out at
twenty eight percent of deals in Q three. That was
at four percent in twenty twenty three, and Q two
(12:23):
is at twenty five percent, is the highest we've ever recorded.
That term J crew blockers, which prevent issuers from moving
material ip out ofside the credit group. That's at forty
five percent of deals now the baseline from twenty twenty
three to fifteen percent, and last quarter it was thirty
eight percent. Anti SERTI protections, which are leansbordination protections. They
actually helped secure your place in line if and when
some sort of distress activity happens. That's at eighty four
(12:45):
percent of deals. That's the highest jump we've ever seen.
Quarter quarter it went up from sixty one percent to
twenty three point jump, and the baseline is thirty nine
percent twenty twenty three. That's pretty significant for a quarterly jump,
and it really signals something about the market. On the
quantitative side, we track a lot of stuff too, including
(13:06):
the ratios under which borrowers need to maintain specific types
of leverage. We saw that at three point nine times
EBITDA in Q two and it went down to three
and a half times, But again that's signaling some sort
of anxiety among the lender group that we wouldn't normally see.
Now you may ask what a borrower is getting for this, Again,
(13:28):
they're getting more fortification. One of the ways this is
coming up is in EBITDA adbacks, so EBADA add backs. Basically,
there's a very long and complicated calculation of cash flow
and a lot of these deals and the adbacks to
EBADA basically allow bars and issuers to add back certain
things to count them as cash flows.
Speaker 3 (13:50):
To flatter their balance sheet basically correct correct.
Speaker 1 (13:54):
One of the more interesting adbacks that we track is
what's called a cost saving satback. So imagine a borrower
knows it's gonna optimize some cost in the future. If
it can reasonably predict that cost, it can add that
back to today's cash flow. That cost savings atback, whether
materializes or not, is added back to today's cashflow. Sixty
(14:17):
four percent of deals now have cost savings atbacks in them.
That's the highest we've ever recorded for deals, with those
adbacks being above twenty percent of EBITDA that came in
fifty one percent, which is also the highest we've ever
tracked on the platform. They're also getting things like excluding
lenders that are short in their debt. So, for instance,
(14:38):
folks may be familiar with what happened with the Windstream
case a few years ago. What happened in that case
is certain hedge funds were actually short the debt the
loan that was in default, and that makes them not
exactly aligned with the company that has the debt outstand.
(15:00):
Terms started popping up in the market which we've tracked,
which are called net short lender terms, which allow bars
to exclude those lenders from voting. That is now in
thirteen percent of deals, which is the highest we've ever tracked.
So you could see the fortification actually on both sides
of the market, and it really signals, I think, to
us that there's a risk allocation happening with a lot
of these anxieties.
Speaker 3 (15:36):
Joe, First of all, you know, my husband was a corporate.
Speaker 2 (15:38):
Lawyer at one point.
Speaker 3 (15:39):
Yeah, okay.
Speaker 4 (15:40):
So one of the things he's most proud of is
he came up with some language in a deal shortly
after the two thousand and eight financial crisis, and it
was he sent it to me just now a significant
dislocation in financial markets. That was him, and that became
like standard language in risk factors, at least in a
bunch of that's a contribution.
Speaker 2 (16:01):
I'm the inventor of this deal, so and so the inventor.
Some people invent great medicine, some people invent some new technology,
and someone invents a new deal term that gets propagated
across that's right documents for years thereon after.
Speaker 3 (16:14):
That's how it works.
Speaker 4 (16:15):
But Dan, I wanted to ask you something. Okay, So
you say there's more fortification in a lot of deal terms,
more protections perhaps for both investors and lenders.
Speaker 3 (16:25):
I guess one of the things we.
Speaker 4 (16:27):
Heard prior to twenty twenty in them for some years
after it was we had this explosion in CoV light deals, right,
fewer protections for investors because everyone was so desperate supposedly
for yield for that particular paper, So the balance of
power shifted to the borrowers they were able to dictate
the terms. How are investors getting better protections now? With
(16:52):
you know, credit spreads still at basically multi decade lows,
which suggests that there's still a lot of demand and
that they don't hold all the power in the market.
Speaker 1 (17:01):
Yeah, I think about it, and what the data supports
that we see on the platform is. I think about
it less so as what they're getting, but more about
what the terms actually reflect in terms of the macro
environment that they're operating in. So, for instance, right now,
we're seeing this flight to fortification in part largely due
to probably a few things. Number one being some of
(17:23):
these headline risks that folks have been talking about, and well,
I'm sure we'll get into some of what's going on
in the private credit market today. So people flooding into
more structural protections because they're worried about their place in
line if there is distress. I think number two is
just mac or wise if you think about it. In
the credit markets, there was a ton of debt taken
out in twenty twenty, twenty twenty one, twenty twenty, early
part of twenty twenty two. This leads to a lot
(17:45):
of maturity walls upcoming, especially in twenty twenty seven twenty.
Speaker 3 (17:49):
We don't say upcoming on the show, we say looming.
Speaker 1 (17:52):
Yeah, exactly. There are a lot of looming maturity walls
in twenty twenty eight, twenty twenty nine vintage. And you
can think about it as well. That's a macro factor
that people are thinking about when they underwrite alone, because
many of these deals actually have five year tenor you know,
seven year ten or eight year tenor in some cases
thirty year tenor, and so they're thinking about all these
protections in the context of that market. I also think
(18:16):
it's really interesting, aside from the credit context, right now,
we're seeing a lot of structuring in terms happening in
M and A markets, So things like regulatory uncertainty, things
like tariffs, things like libildy management, as we talked about,
things like tax uncertainty. I'm happy to go into these,
but we're seeing a lot of things in this area.
(18:37):
One kind of small example of this in situations where
a buyer and a seller have regulatory uncertainty, which you
know a lot of folks think about the administration and
they're not sure exactly how things are going to play out.
You actually see regulatory review in deals get hyper focused
on and it actually precipitated a new deal term this
(18:59):
year which we tracked in the market. We had an
almost term detection of the platform. We sent out a
note to all of our clients and it's called a
new outside date structure term. Basically, what it does is
it allows buyers of acquirees. It allows them to lock
in their financing for longer and actually stand their financing
in the case scenario regulatory review les. And that's just
(19:22):
an example of the kind of innovation that's happening in
the merger markets. In terms of tariffs, we picked up
the first tariff event of default in a credit deal. Ever,
it happened in a Superior industries deal over the summer,
which probably isn't surprising to use an auto manufacturer deal.
I've made a lot of parts in Mexico. That's not
five percent of MNA deals for tariff based m and
A carve outs and railie respect clauses.
Speaker 2 (19:44):
Can we talk a little bit about you know, you're
scanning these documents. Google's ingram has existed for a long time.
Tracking the prevalence of a term is not novel technology
that control EF right control f Yeah, this is sort
of like very barely even councils technology at that point.
What is it that you you know, when you're talking
(20:05):
about the changing prevalence of these terms, what is the
actual novelty here that isn't just sort of yeah, document
search over time.
Speaker 1 (20:15):
Yeah. So, Tracy, your husband's a former corporate lawyer. You know,
he would tell covering corporate lawyer or covering corporate layer exactly.
I am myself as well. One of the things he
would tell you is that there's constant innovation in these markets.
These agreements are highly complicated, there very long. They have
a lot of what's called long range dependencies, which is
(20:35):
that you may be used to seeing something in a
particular area of the document said one way, but in
reality it turns out it's punted to three different causes
deep down, and you actually have to go find that information.
Speaker 4 (20:47):
This is why it's also jiu jitsu between the borrowers
and the lenders, right, because like the borrowers are often
trying to hide something that's favorable to them, or the
lenders are trying to hide something favorable to them. So
the structure and the way it's worded changes a lot
to your.
Speaker 1 (21:01):
Point exactly, And these are sophisticated parties paying millions, sometimes
hundreds and millions of dollars in advisory fees to make
sure that these terms look the way they do. Now
that leads to kind of the technological innovation that I
think has enabled a lot of this AI for the
first time, can attribute in particular, new language models, can
(21:22):
attribute more semantic meaning to phrases and language that was
impossible with things like n grams. And so what America
does is it used as a series of language models,
including a multi layered information extraction system to make sure
that it's encoding all this semantic meaning inside all these terms,
so that when you look at a J. Krublacker in
(21:43):
the first way, it may be phrased a thousand different ways,
but we can track that term over time. That has
enabled the ability to actually quantify for the first time
what a market agreement term looks like in these markets.
And I think that's why it's so interesting to folks
on the platform.
Speaker 4 (22:01):
So I know you're not doing litigation, but I guess
I'm curious how you deal with or if AI is
helpful with in litigation what would be called precedent. But
I'm assuming you're building up a big database of all
these different deal documents.
Speaker 3 (22:16):
Is it useful?
Speaker 4 (22:17):
Is AI useful to go back and look at previous
documents in order to shape new ones?
Speaker 1 (22:23):
Yeah? Exactly, So in New Edica, we are ultimately an
a power software company, but we actually have the largest
knowledge graph of deal terms in ex systems. So Tracy,
exactly what you said. It's a database ultimately of precedent
comparable deal terms, and that database is going to be
mind bowing as over billion terms in it, So its
issue to a large as in existence, we map that
(22:44):
back to deal characteristics. It's the same in litigation, right,
So in transactional markets, folks are innovative, but they also
want to rely on something that has happened before, or
at least in part, they want to rely on something
that has happened before, and so folks are constantly looking
for ways to tie things back to comparable deal terms.
It's the same in litigation. So obviously not expertise, but
(23:07):
the same concept, which is, you know, when you write
a brief, you were constantly citing cases that the judge
has you know, relied on in the past. And you know,
for lawyers and you know outset of lawyers, even just
deal professionals generally bankers, credit managers, people are highly reliant
on present What.
Speaker 2 (23:25):
Is your text acre did you what do you build
and how much is it? Like, oh, you're using chat, epts, API,
et cetera. Like, okay, yes, large language models are good
at identifying deal terms or novelty, et cetera. There's semantic
meaning of these terms, but what did you actually build
and what do you actually employ in your technology?
Speaker 1 (23:45):
So we were starting in twenty twenty two, so we're
what you would call AI native. We were started in
a system that already and language models existed in. However,
we because of the nature of the sensitive documents in
terms that we deal with, especially for you know, major
law firms, financialist yea, this is like.
Speaker 2 (24:03):
A big issue with the right that they don't want
to just be uploading their stuff to chat GPT right exactly.
Speaker 1 (24:08):
And so we actually utilize you know, adapted language models,
open source language models that we adapt on our armed
proprietary data sets and then deploy and secure environments and
single tendon architectures, you know, for individual instances of institutions
that deploy our product. And so you could think about
it as based on the language models that are ultimately
(24:29):
underpinning a lot of the gpds and the clouds. However,
it's fine tuned to this particular data set, which makes
it obviously much better at handling this exact problem, which
is a big problem in the market. Now. We also
layer on top of that information extraction model. So for instance,
you may know that a term exists in what deal,
but you may want to know what terms should exist
(24:50):
for a JP Morgan deal, or for a B of
a deal, or for you know, a particular type of counterparty,
and so in those context we actually want to map
those deal terms back to deal characteristics, and we actually
utilize a lot of models to extract information and marry
that with their party data sets. So that's a little
bit about how the technology works. I think I always
think about it from the user standpoint, What does the
(25:11):
user really want? These really wants to know how they're
going to invite their client on a particular merger on
a particular credit deal. How often does this come up?
You always call your attorney and you're trying to figure out, well,
is this market is it off market? And that's what
our data provides.
Speaker 3 (25:27):
Okay, so structural fortifications in deal terms. What are you
seeing right now?
Speaker 4 (25:33):
Because as we started this conversation, we were talking a
lot about the recent blow ups in the private credit market,
and if you look at some spreads on certain firms,
certain bonds, it does seem like nervousness is creeping back
into the market. I see spreads on you know, it's
not private credit, but spreads on triple C rated debt
(25:53):
have been creeping up recently. How scared or concerned are
people right now?
Speaker 1 (26:00):
Well, I recently wrote about this in the Wall Street
Journal a little bit, and then folks contacted me and
I kind of said, you know, you're causing a stir.
And then I saw Howard Marx came out with his letter,
which I think was called Cockroaches in the coal Mine,
and they had a lot of the same themes. I
think folks who have been around credit market for a
(26:21):
very long time can kind of see what's a little
bit of what's going on.
Speaker 2 (26:24):
To us.
Speaker 1 (26:25):
Let me just talk about what the data supports to us.
What we see is creditors maybe preparing this their system
for distress, and I'll talk about what we're seeing in
the data that kind of supports that. But you can
think about it like the evolution of your house security, right,
So you know, first you lock the doors. Then you know,
(26:49):
you get a bolt lock, which gives you better protection.
You know. Then you you know, you add a security
system on top of that alarm system. And at the end,
what do you do. You kind of up all your
valuables and you ensure them if people are going to
get into the house. And you know, for the past
few years, we've seen lenders really focused on keeping people out.
This is the locks and the dead bolts, and this
(27:13):
is what we were talking about with j crub blockers.
This is making sure you can't structure around me. From
a liability management perspective, But over the last quarter something
kind of changed, which is we started seeing people and
lenders obsessed with lean subordination terms, which is the term
that governs who gets paid first when everything falls apart.
(27:35):
So this isn't really about preventing liability management exercises that much.
It's actually about controlling the recovery when a bankruptcy does happen.
And so we clocked that term at eighty four percent
of deals in Q three, biggest quarterly jump we've ever
seen from the prior quarter. It's also the highest we've
ever clocked that term. So this bes the question of
(27:56):
why Wire Credit is so focused on making sure.
Speaker 3 (28:00):
Their place on line is in recovery.
Speaker 1 (28:02):
In recovery is the same. Perhaps it's a reaction to
the Lieboldy management transactions we talked about, so perhaps folks
are thinking that that will precipitate. Perhaps it's a reaction
to some of the maturity walls that folks understand, or
perhaps it's some of what I was saying in the appad,
which is folks are seeing that there may be distress
(28:23):
events on the horizon and they want to make sure
that if there is, they have the most negotiaing leverage
its possible.
Speaker 2 (28:44):
So I know it's broad statements, but you know, when
we look at these sort of environment under which companies
like First Brands or Tree Color or some of these
other ones that have gone into distress very rapidly, when
we look back at when these were birthed.
Speaker 1 (29:00):
Et cetera.
Speaker 2 (29:00):
Can we say like these were sloppy times, These were loose,
sloppy times that people were not thinking much about either
just quality due diligence or diligent terms.
Speaker 1 (29:12):
Yeah. So I think with First Brands is a great example. Right.
So First Brands is an automotive replacement company, right, so
they make things like breaks and wipers and filtration systems.
Beginning in twenty nineteen, that company effectively rapidly expanded through
(29:32):
debt fueled acquisitions and it dramatically increased its scale. But
I think what First Brands illustrates is something that you know,
we might get into with the private credit markets, which
is that they primarily funded these acquisitions with large debt facilities.
Then tariffs hit in April twenty twenty five, which obviously
(29:53):
changed their business because they actually do a lot of manufacturing,
and that kind of magnified problems. So you can think
about out. One of the main problems with First Brands,
which is also kind of some of what folks are
worried about in the private credit markets today, is what's
called off balance sheet financing. What First Brands used is
a lot of you know, receivables financing facilities that weren't
(30:17):
properly disclosed to a lot of folks that were lending
to the company. In fact, I think in that sense,
just to give you a sense of quantum, this is
over eleven billion dollars of total obligations that they had
when they actually started disclosing it in terms of off balance youe.
Financing and you know, they were disclosing things like five
to six billion dollars of actual debt obligations. And so
(30:39):
this led one of the creditors lawyers to say that
two point three billion dollars just disappeared. And so that structure,
the ability for first brands to get that debt was
made possible by the private credit markets and how deep
the private credit markets have become. Because if you're a
big credit manager in private credit markets, you could fund
(31:03):
you know, that type of receivable facility to a first brand,
and first brands could use that facility to then, you know,
make sure they are constantly continuing to acquire new businesses
and keep rolling over the cash.
Speaker 4 (31:15):
I have a theory that receivables, financing and factoring is
to the private credit market. What French quants who went
to that one elite school are.
Speaker 3 (31:27):
To trading blow ups.
Speaker 1 (31:28):
I like that theory.
Speaker 3 (31:29):
Yeah, thanks.
Speaker 4 (31:30):
So the other thing we wanted to ask you about,
and again we reference this in the intro, is we
are seeing these really complicated deals that I admittedly cannot
keep track of in the AI market, where you know,
one company is going to buy chips from this other company,
and then that company is going to borrow from whoever
and use the chips funding to pay them back, and
(31:50):
then that money somehow goes into the company that is
buying the stuff in the first place. It is all
very circular, all very incestuous in many ways. In my mind,
are you examining those types of deals or just putting
on your credit expertise hat if you see something like that,
what are you thinking?
Speaker 1 (32:07):
Yeah, well, it's probably helpful to kind of talk about
some of the structure of these deals, which I think
again is made possible by how deep the private credit
markets have become. And usually when I do that, I
try to think about, let's try to make this a
little bit more fun. So imagine for a minute, Joe,
you just love pizza. He does love pizza yesterday twice.
(32:29):
There you go, You're a pizza fanatic. You love it
so much that you decide to eat pizza every single
meal of every single day for the rest of your life,
like you are committed to subsisting pizza, committed to the
carbs exactly. So, Joe, you made that decision. You come
to me and you say, Hey, Dan, I'm going to
eat pizza for every meal of my entire life. How
about you open a pizza restaurant for me to eat it.
(32:53):
It'll be really lucrative.
Speaker 2 (32:56):
We're going with this, but this is actually a very
good to note do, right, Like you would have have
a lot of confidence in me to commit to my word.
If you're going to open a restaurant.
Speaker 1 (33:04):
Yeah. Now, now you come to me and say, it's
going to be super lubritive. Here's how we're going to
fund it. Ten percent equity. The bank is going to
give you ninety percent of the funding in leverage. And
it's Dan's restaurant. Joe, you don't on the restaurant, but
you're going to eat at it. I'm the full beneficiary,
full beneficiary of the restaurant, but it's ninety percent. Okay,
So I opened the restaurant. You eat there every single day. Now, Tracy,
(33:25):
Joe comes to you for a personal loan to fund
his lifestyle, his pizza eating.
Speaker 2 (33:32):
Tracy, trust me, she would lend it to me.
Speaker 1 (33:34):
Well, here's the question, right, should you, Tracy? Consider the
ninety pizza restaurant that Joe is eating at for all
his meals. Now, on the one hand, it's not Cho's loan, right,
so he's not on the hook if the pizza restaurant
goes under. On the other hand, it's Joe's only source
of food.
Speaker 3 (33:54):
Which Joe will die without the restaurant.
Speaker 1 (33:56):
Which is his He's committed to the restaurant, and it
kind of makes the restaurant intertwined with Joe's ability to
pay your personal loan back. So, I guess that's a
good question. No, there's great.
Speaker 2 (34:07):
So now let's take it out of pizza. Who is
so that's whatever? Like okay, now who is the chips
buy or whatever?
Speaker 1 (34:14):
This is essentially what's happening with off balance youe. Financing
and data center deals. So, and it includes Metas. I'm
sure you saw the Hyperion deal. It's his Metas infrastructure
deal with Blue Owl. Except I think it's even more
intriguing than some of the pizza stuff. So Meta and
Blue Ol basically created a joint venture in a special
purpose vehicle not that different than the restaurant. And the
(34:34):
deal is the joint venture would be owned twenty percent
by Meta, eighty percent by Blue Owl, so Blue Owl
controls it, and it would effectively be funded with ninety
percent leverage. So call it thirty billion dollars of total
enterprise value, three billion dollars of equity, twenty seven billion
dollars give or take of debt. In other words, Blue
Oul is effectively owning the restaurant. Meta is effetively eating
at the restaurant, and the bank's funded with ninety percent leverage.
(34:57):
So what this does is it keeps the debt off
of Meta's books right while also giving investors credit managers
the ability to put money against a data center asset.
So Meta in this deal will make rent payments associated
with the data center based on its cost of power.
That's the cash flow that's going to the SBV, and
(35:18):
that effectively funds the interest expense. Let's just talk about
the debt for a second. In a normal LBO context,
ninety percent leverage is pretty exceptionally high. Most people would
consider fifty to eighty percent leverage to be relatively normal
for a stable cash flow business. So the debt itself
is actually quite high on some of these structures. The
(35:39):
only reason it was possible was because it was given
an investment great credit rating, and in part because Meta
agreed to a four year operating lease with what's called
a residual value guarantee, which means that Meta is guaranteeing
a capped amount of some of that cashflow. However, that
guarantee is capped and is only partial, which is why
they don't have to take it onto their book and
(36:00):
why would be a footnote as a contingent debt obligation
in their balance sheet. Now let's talk about the asset
that's being underwritten. This isn't pizza. Pizza actually has a
stable price, right. We have thousands of UTI history on pizza, right,
and you can track that price over time. Data center
is optimized for GPU performance on training fundamental AI models.
(36:22):
Not so much of a mature asset, actually, I think
most folks would think about it as a burgeoning asset.
Now I'm in this world. I mean, folks, there's a
high amount of demand for a lot of this compute,
and I definitely think the demand is there, but at
the end of the day, it's an immature asset with
a price that isn't so well defined. So just a recap,
you've got off balance sheet financing which isn't reflected with
(36:45):
whoever is lending money to metal or even buying its
equity with ninety percent leverage on an immature asset, And
I think that's why these deals are so interesting. So
from our point of view, I mean to make sure
you get the terms right, and you know, we will
look at these data center. A lot of these types
of financings run through our platform all the time. To
make sure you get the terms right on what this
(37:06):
structural protections look like in these deals is critical for
the fortification of something that is in the structure.
Speaker 4 (37:12):
So I know we've seen these idiosyncratic blow ups in
the private credit market so far, but just looking at
the AI market in particular and the financing there, it
feels like right now people are still willing to lend money.
And we've talked about this on the show before, but
a lot of the AI competition is couched in this
existential language of you either win it AI or die basically,
(37:38):
and so the spending keeps going. What is your guess
on like the thing that kind of knocks that cycle
or that flywheel.
Speaker 3 (37:48):
And tears it apart.
Speaker 1 (37:49):
So I'm obviously in the AI industry. We're in the
credit industry, so we see both sides of this phenomenon.
I fundamentally believe AI is a paradigm shift. I would
not have left, you know, the deal markets if I
didn't think that. And I think what we're witnessing is
very similar to the Internet in the nineteen nineties, or
(38:10):
the iPhone in two thousands, or social media in the
twenty tens. And I think this paradigm shift is going
to ultimately change a ton of industries, including capital markets
and finance and law and all these amazing industries. And
so that I think is very true. But I also
think two things can be true. I think AI can
(38:31):
be a generation defining category and a technology that's upending
a lot of industries. But I also think that categories
will have winners and losers. And when folks are racing
to define a category, as you know, you often see
with a lot of these transformational types of technology, there
may be more losers in the headlines. Then you're used
(38:53):
to seeing in a lot of these markets, but the
winners will be bigger than anyone's ever all.
Speaker 2 (39:00):
Right, So if I don't need the pizza, someone else
is going to pick up the pizza and they're gonna
they're gonna eat it.
Speaker 1 (39:05):
Look what we focus on in Oadica is in a
market moving this fast. Yeah, we all need to pay
attention to the terms that actually underpinning a lot of
these markets to make sure if there is any bleeding,
that bleeding gets stopped as quickly as possible. Just to
give you one last example from a recent market deal,
you can look at the Frank jpm deal as like
(39:26):
a really interesting one. This is, you know, this was
a deal where JPMorgan paid one hundred and seventy five
million dollars to acquire a company. There's a very small deal,
but to acquire a company called Frank, which is a
streamline fasta kind of support service.
Speaker 2 (39:40):
I remember this.
Speaker 1 (39:41):
It turned out there was a lot of synthetically made
up types of data in that.
Speaker 2 (39:48):
Business, and the founder is going to prison right.
Speaker 1 (39:50):
Allegedly there's a lot of there's a lot of made
up stuff in the business. And I think seven days.
Speaker 2 (39:55):
Executive who worked at Frank sends to sixty eight months.
Speaker 1 (39:58):
Yeah, yeah, yeah, and so. But I think the most
interesting part about this particular transaction to me is JPM
ended up signing a merger agreement that said that the
indemnification for the founder's litigation, for any founder's litigation, would
be paid for by JPM.
Speaker 2 (40:17):
Right, they paid her lawyer.
Speaker 1 (40:18):
They paid one hundred and fifteen million dollars in legal
expenses for her lawyer on her fraud. And so when
you're moving really fast, yeah, right, you can kind of
ignore some of the nuts and bolts. But I think
it's actually even more critical and fast moving markets.
Speaker 2 (40:32):
Dan Workman, co founder of no Edica, thank you so
much for coming on outlook.
Speaker 1 (40:36):
Thank you thanks for having me us.
Speaker 2 (40:51):
Tracy. I wasn't really sure where he was going with
that pizza analogy, but it actually does make a lot
of sense, and it's something I think is a phenomenon
and just a lot of financial transactions, which is how
much like in certain environments, the lender and the creditor
are like both each others, like they're both leaning on
each other. They're both the creditor and lenders, they're relying
on each Yeah, at the same time.
Speaker 3 (41:11):
Much in the way you rely on pizza.
Speaker 2 (41:13):
You would lend to me to buy it to eat pizza.
Speaker 3 (41:15):
Right, I would thank you if it was a matter
of survival, that was.
Speaker 2 (41:20):
A matter of survival, thank you.
Speaker 1 (41:21):
I think it's just.
Speaker 3 (41:21):
Because you want to eat really expensive pizza then no.
Speaker 4 (41:24):
You know.
Speaker 2 (41:24):
The other thing too, is just like from talking to
you over these years, you know how many times I've
heard something there's a lot of cuve light stuff going.
It is interesting to think that, like, you don't often
hear that quantified what that means, right, things are like
cove light these days, et cetera. And the idea that like,
maybe we could get better numbers on some of these
things seems like potentially labor saving for lawyers. Stuff like that.
Speaker 4 (41:47):
The specific numbers on specific deal terms were really interesting
to me. And the idea that even today lawyers and
bankers still have trouble anticipating every single thing that could
happen to particular deal, and so they're having to react
to it and come up with the new terms, the
new deal language, and insert them into the documentation.
Speaker 3 (42:07):
I find that interesting.
Speaker 2 (42:08):
The tariff example, you know, the problem is is that
AI is good and this is I'm certain if we
talked about this more, AI will be used to come
up with new deal terms and the cat and mouse
game will continue forever. So I suspect that we are
not going to have lawyers will always find new work
to do, and they'll just get work. They'll just get
more creative about outsmarting the systems that are designed to
(42:29):
detect these phenomena.
Speaker 4 (42:30):
We will end up with thousands and thousands of pages
of term sheets that, like humans are just physically incapable
of reading, it has to be read by AI.
Speaker 2 (42:39):
I probably literally, that is what's going to happen.
Speaker 3 (42:42):
Yeah, all right, shall we leave it there.
Speaker 2 (42:43):
Let's leave it there, all right.
Speaker 4 (42:44):
This has been another episode of the Odd Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2 (42:50):
And I'm Jill Wisenthal. You can follow me at the Stalwart.
Follow our producers Carmen Rodriguez at Carmen Arman, dash Ol
Bennett at Dashbot and kill Brooks at kill Brooks. For
more Odd Laws content, go to Bloomberg dot com slash
odd Lots, where we have a daily newsletter and all
of our episodes and you can chat about all of
these topics twenty four to seven in our discord discord
dot gg, slash.
Speaker 4 (43:09):
Od loots and if you enjoy odd Lots, if you
like it when we dive deep into deal documentation, then
please leave us a positive review on your favorite podcast platform.
And remember, if you are a Bloomberg subscriber, you can
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Apple Podcasts and follow the instructions there. Thanks for listening.
Hosts And Creators
Joe Weisenthal
Tracy Alloway
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