You're a pizza fanatic. You love it so much. You decide to every single meal of every single day for the rest of your life. Like you are committed to subsisting on 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?

Here's how we're going to fund it. 10% equity. The bank is going to give you 90% of the funding in leverage. And it's dad's restaurant.

Now, Tracy, Joe comes to you for a personal loan to fund his lifestyle, his pizza eating choices. Tracy, trust me, she would lend it to me.

Well, here's the question. Right? Should you?

Tracy, consider the 90% levered pizza restaurant that Joe is eating at for all his meals?

Now, on the one hand, it's not Joe'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 which will die without the restaurant, 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 question. No. There's great. So now who's now that. Let's take it out of pizza.

Who is? So that's whatever. Like, okay, now who is the chips buyer? Whatever. This is essentially what's happening with off balance sheet financing and data

Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal and I'm Tracy Alloway.

I'm Joe Weisenthal and I'm Tracy Alloway. Tracy, there just so many credit related things to talk about right now.

I'll take credit. I love it. I love it. Credit is interesting again.

This might be one of the 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, and randomly I've found someone who knows a little bit something about credit. So I was like, oh, let me do it. Let me.

The stakes are high.

I know I was thinking about that because, you know, how do you, like, pick someone who, like, 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.

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. And so you've seen people like Jamie Dimon using the cockroach analogy, which is now famous.

And at the same time you have the connection with AI, right?

Yes, which we have spoken about a little bit on the podcast with Paul Khodorkovsky, all these complex circular financing structures that are driving a lot of the credit boom or have been driving a lot of the credit boom.

And then at the same time, you also have the impact of AI on credit itself.

Yeah. That's right. Because in theory, right. Like we've talked about this, we did that episode with Joel Wertheimer.

That was in a slightly different context. But we've done these episodes about, you know, just the incredible length of deal text, etc.

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 in is can we break down this, you know, multi hundred page agreement so that we don't have to have, you know, junior associates or junior lawyers or junior bankers up until four in the morning making sure that every comma is in the right place, etc..

In theory, this could be an area in which I could be productively applied.

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

Oh yeah.

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 boom in credit or on credit or violence in public deals.

Yeah.

So it was this idea that you could avoid that by having, you know, this private, close relationship with your borrower where you are higher up in the waterfall.

Yeah. Of payment.

So this is important. It would be really nice if you could upload a credit agreement to you and just say, make sure there's nothing in here that would get me in trouble. Make sure there's nothing in here that would, You know that five years later, I will regret the placement of a server.

Make sure I don't lose money. Make sure 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 Wertman. He's the co-founder of a company called, Noetic AI.

And it does exactly this. It sort of attempts to use AI to understand credit.

So there's a lot of understanding about deals and the text in them. But he also just has a lot of understanding about AI etc..

So can talk about all these things. Dan, thank you so much for coming on the podcast.

Thanks so much for having me. I'm a fan of the show. Love to hear it.

You guys are kind of like celebrities for me. So it's, it's kind of fitting that I'm here because, now, folks, I'm here at least with the folks at Bloomberg, because many people think about us.

I know I, I like the Bloomberg for deal terms.

Okay. Well, let's see let's see if, you actually live up to that.

No. But, so since I said I'm stressed that, this time we're doing a credit episode, and I found the guest.

Can you give us your give us the quick version of, like, your career and what Noetic is?

Yeah. So, start with no idea what we build at. Noetic,

is a AI powered software for benchmarking real time data on what's market and credit, M&A, capital markets, deal terms.

Okay. So another way we help folks like transactional attorneys, credit managers, bankers, we help them figure out whether the terms of their transactional agreements like think, financing agreements, merger agreements, prospectuses and really all other corporate transactions are on or off market by benchmarking them to market comps.

So as far as the genesis, you know, what ago, it was kind of born out of my own experience and 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 developing these new, interesting, innovative, structured products.

And I just learned a ton about the capital markets ecosystem and in particular, just this is a $50 trillion global market.

Yeah.

And it runs on phone calls and relationships and it's unbelievably antiquated.

Then fast forward I went back to get my JD. I joined, walked I lived in where I did corporate transactions.

This was 2017 to 2022. So if you guys remember that time it was heyday of merger activity, right?

So I worked on, you know, T-Mobile's buyout of sprint, the biggest 30 million. Our commitment at the time, Allegan Avenue to see Raytheon.

And I distinctly remember sitting down my desk, I was looking at a transactional group and a multi-billion dollar merger, and I was looking at a term, and I was trying to figure out whether I should help my client except term A or term B in this context.

And I was stuck. So I called a senior partner on the deal. I said, hey, where are 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.

Now, that was, you know, two and a half plus years ago now, I left hotel to start Nautica 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 20 law firms on the street.

We're helping them advise their clients on these deals.

And this year, we're on track to do about $1 trillion of transactions through the platform.

And you get 1% of that. 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.

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, lights. They're kind of the plumbing that goes into the transactions.

Putting in a way that people can understand. Imagine I go sign a lease.

Most people are very familiar with certain things. Right. Like the rent price, the how long the lease is subletting policy.

Exactly.

But if deep in that 20 page lease, the lease says if the weather gets under 30 degrees at any time, you for for 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 markets terms.

To give you a more tangible example.

Yeah.

You know, are you guys fast food people?

Yes, yes.

Okay. So I'm like a McDonald's guy. Yeah.

And whenever I go to McDonald's, I always order the Tempe Chicken McNuggets.

And there's. I've. I've ordered the ten piece hundreds of times. There's exactly three things that happen.

You order Tempe, 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 11 pieces.

Now, if you have nine pieces, you go to the counter and you say, hey, I'm missing a piece. They give you a piece, you get the benefit.

You're working, you get ten. You you enjoy your McNuggets.

If you get 11, what do you do?

You stay quiet.

Exactly. So you hit the jackpot, 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 2020, the exact kind of thing happened in the credit markets. But it ended very differently.

Citibank sent $900 million to lenders in full pre-payment 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 associated with this loan didn't say what happens in that scenario.

Long story short, many funds did not get back that hundreds of millions of dollars and litigation ensued.

But a deal term in credit deals, called erroneous payment deal terms, started popping up in the market.

Nordica's data last clocked that deal term as of last quarter, 90% 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 the alternative is important. These are hundreds of millions of dollars at stake in the context of all these years.

There's something very lawyerly about, like, I have to say, I've never counted the McNuggets.

I usually get it. So just as 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.

Clearly you don't value McNuggets enough.

Evidently. Evidently not.

What are some other deal terms? So that's a great example of that. Okay.

Now, after that incident, which was infamous, language about this started 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 key interesting deal terms that trend over time?

Yeah. So it's really interesting. So there's a whole host of what I would call structural protections, all these deals.

These come in a lot of different flavors.

Many people talk about them as things like anti PetSmart terms, things like J.Crew blockers, things like certain protections.

Let's talk about some of these right.

Yeah. Let's talk about some of these. So you know anti PetSmart terms.

These are protections that prevent guarantor releases when subsidiaries of the credit group become not 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 to 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 back a loan. Bank and foreclose on your house. If you stopped 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 on the house fully.

Well, then what would you do? You would sell a single shingle.

You would stop paying your mortgage, and you get to keep your house and you get the benefit of that.

That's what anti PetSmart terms actually prevent. They prevent the ability for credit groups to actually sell a single equity and actually lose the credits for 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 terms over time.

And so we can actually very precisely tell you the percentages of deals are actually getting a lot of these structural protections and actually gives us this really unique window into the into the anxieties and the optimism 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 on both issuers and powers. I'll explain what I mean.

We're seeing massive increases in lenders getting structural protections in these deals.

Basically, the protections they help make sure their collateral is locked. Things like the anti PetSmart terms. And we'll talk about a bunch a few more in return.

Borrowers are getting the same fortification. In fact they're getting more economic flexibility.

Yeah.

And you could think about it as a way for them to weather the storm is how we're seeing it.

So things like add backs to EBITDA. Yeah.

More ability to send money to shareholders, more ability to make long term investments.

Let's talk about the actual specifics of what we're seeing and more in terms of what I just talked about.

We clocked that at 28% of deals in Q3. That was at 4% in 2023. It was in Q2 is at 25% is the highest we've ever recorded that term.

J.Crew blockers, which prevent issuers from moving material IP outside the credit group. That's at 45% of deals now.

The baseline from 2023 15%. And last quarter it was 38%.

Anti sort of protections which are lean subordination protections. They actually help secure your place in line if and when some sort of distressed activity happens.

That's at 84% of deals. That's the highest jump we've ever seen.

Quarter over quarter it went up from 61% to 23 point jump. And the baseline was 39% in 2023.

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 the ratios under which borrowers need to maintain specific types of leverage.

We saw that at 3.9 times EBITDA, in Q2, and it went down to three and a half times.

But again, that's signaling some sort of anxiety among the lender group that, that we wouldn't normally see.

Now, you may ask, what about getting to this? Again, they're getting more fortification.

One of the ways this is coming up is in EBITDA. Ibex.

You're familiar with EBITDA? Yeah. Okay.

So EBITDA, ibex. Basically, there's a very long and complicated calculation of cash flow. And a lot of these deals

and the add backs to EBITDA, basically allow borrowers and issuers to add back certain things to count them as cash flow to flatter their balance sheet basis.

Correct, correct.

One of the more interesting add backs that we track is what's called a cost savings add back.

So imagine a borrower knows it's going to 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 out back, whether it materializes or not, is added back to to its cash flow.

64% of deals now have cost savings in them. That's the highest we've ever recorded.

20%, for deals with, those add backs being above 20% of EBITDA, that came in at 51%, 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, folks who 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 the loan that was in default.

And that makes them, not exactly aligned with the company that has the debt outstanding.

Term started popping up in the market, which we've tracked, which are called net short lender terms, which allow borrowers to exclude those lenders from voting.

That is now in 13% of deals, which is the highest we've ever tracked.

And it really signals, I think, to us that there's a risk allocation happening, with a lot of these, these anxieties.

So, Joe, first of all, you know, my husband was a corporate lawyer at one point. Yes.

Okay, so one of the things he's most proud of is he came up with some language in a deal shortly after the 2008 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 books. It's like that's his contribution.

I'm the inventor of this deal. So and so so so the invent. 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 there on after. That's how it works.

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. I guess.

One of the things we heard, you know, prior to 2020, and then for some years after it was we had this explosion in covet 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, 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.

Yeah, I, I think about it and what the data supports. That we see on the platform is I think about it less.

So is what they're getting 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 of fortification in part largely due to probably a few things.

Number one being, you know, some of these headline risks that folks have been talking about.

And 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 macro wise. If you think about it, in the credit markets, there was a ton of debt taken out in 2020, 2021, 2020, early part of 2022.

This leads to a lot of maturity walls upcoming. Especially in 2027.

We don't say upcoming on the show. We say looming. Yeah, looming.

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

In 2028, 2029, vintage. And you can think about it as well. That's a macro factor that people are thinking about when they, when they underwrite alone.

Because many of these deals actually have five year tenor, you know, seven year, 10 or 8 year tenure, in some cases 30 year tenor.

And so they're thinking about all these protections in the context of that market.

I also think it's really interesting, aside from the credit context right now, we're seeing a lot of structuring in terms happening in M&A markets.

So, you know, things like regulatory uncertainty, things like tariffs, things like, you know, liability management, as we talked about things like tack ons, tax uncertainty.

Yeah, I'm happy to go into these.

But we're seeing a lot of things in this area.

You know one kind of small example, 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, 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 year, which we tracked in the market.

We had a long term detection on the platform. We sent out a note to all of our clients, and it's called a new outside debt structure term.

Basically, what it does is it allows buyers of of, of, acquirer fees. It allows them to lock in their financing for longer and actually extend their financing. In the case scenario where regulatory review lacks.

And that's just 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 is unsurprising to you as an auto manufacturer deal that made a lot of parts in Mexico.

That's down 5% of M&A deals, for for tariff based M&A carve outs and a lot of respect clauses.

Can we talk a little bit about, you know, you're scanning these documents. You know, Google's Ingram has existed for a long time tracking the the prevalence of a of a term is not novel technology. Yeah.

Control f. Control f. Yeah I do that.

This is sort of like very it's barely even conscious of technology at that point.

What is it that you, you know, when you're talking about the changing prevalence of these terms, what is the actual novel t here that isn't just sort of, you know, document search over time?

Yeah. So Tracy, your your husband, former corporate lawyer, you know, he would cover in corporate lawyer. You're covering for where exactly?

I am myself as well.

Well, one of the things he would tell you is that there's constant innovation in these markets.

These agreements are highly complicated. They're very long.

They have a lot of what's called long range dependencies, which is 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, if you'll find that information, this is why it's also jujitsu 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 point. Exactly.

And these are sophisticated parties paying millions, sometimes hundreds of 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.

I, for the first time, can attribute in particular, new language models can attribute more semantic meaning to phrases and language that was impossible with things like engrams.

And so, when I think it does is it uses a series of language models, including a multi-layered information extraction system, to make sure that it's encoding all the semantic meaning inside all these terms,

so that when you look at a J.Crew back or in the first way, it may be phrased a thousand different, 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.

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.

Is it useful? Is AI useful to go back and look at previous documents in order to shape new ones?

Yeah, exactly. So a new idea. We are ultimately an air power software company, but we actually have the largest knowledge graph of deal terms in existence of traces.

Exactly what you said. It's a database ultimately of precedent, comparable deal terms.

And that database is going to be mind blowing, but has over billion terms in it.

So it's actually the largest in existence. We map that back to deal characteristics.

It's the same in litigation. Right. So in transactional markets, you know, 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 my expertise, but the same concept, which is, you know, when you write a brief, you are constantly citing cases that the judge has, you know, relied on in the past.

And, you know, for lawyers and you know, that sort of lawyers even just deal professionals, generally bankers, credit managers, people are highly reliant on precedent.

What is your tech stack? Did you what do you build and how much is it like?

Oh, you're using ChatGPT API etc.. Like, okay, yes, large language models are good at identifying deal terms or novel tier, etc.

there's semantic meaning of these terms, but what did you actually build and what do you actually employ in your technology?

It's a we we're starting in 2022. So we're what you would call a AI native. Yeah.

We were started in a system that already and language models existed in,

however, we because of the nature of the sensitive documents and terms that we deal with, especially for, you know, major law firms financials. Yeah.

This is like a big issue. Yeah. Right.

That they don't want to just be uploading their stuff to ChatGPT.

Right. Exactly. And so we actually utilize, you know, adopted language models, open source language models that we adopt on our own proprietary data sets and then deploy and secure environments and single tenant 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, you know, are ultimately underpinning a lot of the GPT is in 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 and ideal, but, you may want to know what terms should exist for a JP Morgan deal or for a B value or for, you know, a particular type of counterparty.

And so in those contexts, we actually want to map those deal terms back to deal characteristics.

And we actually utilize a lot of models to extract information and and marry that with third 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 is the user really want.

These are really wants to know how they're going to advise their client on a particular merger and a particular credit deal.

How often does this come up? You always call your attorney and you're trying to figure out all this market. Is it off market?

And that's what our data provides okay.

So structural fortifications in deal terms. What are you seeing right now.

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, you know, 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 have been creeping up recently.

How scared or concerned are people right now?

Well, I, I recently, wrote about this in the Wall Street Journal a little bit.

And then, you know, folks contacted me and, and kind of said, you know, you're causing a stir.

And then I saw Howard Marks came out with his letter, which I think was called cockroaches in the coal mine.

And it had a lot of the same themes. I think folks who have been around credit market for a long time can, can kind of see what's a little bit of what's going on.

To us. Let me just talk about what the data supports to us.

What we see is creditors may be preparing this syst their system for distress.

Okay. And I'll talk about what we're seeing in the data that kind of supports that.

But, you could think about it like the evolution of your house security. Right?

So, you know, first you lock the doors, then you know, you get a bolt lock, you know, then, which gives you better protection,

you know, then you, you know, you had a security system on top of that, an alarm system.

And at the end, what do you do? You kind of count up all your valuables and you insure them.

If people are going to get into the house. And, you know, for the past few years, we've seen lenders really focus on keeping people out.

This is the locks and the deadbolt, and this is what we were talking about with J.Crew blockers.

It says 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.

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, and so we clock that term it 84% of deals in Q3.

Biggest quarterly jump we've ever seen from from from the prior quarter. It's also the highest we've ever clocked that term.

So this begs the question of why. Why are creditors so focused on making sure the place in line is in recovery, and recovery is the same?

Perhaps it's a reaction to the liability management transactions we've talked about.

So perhaps folks are thinking that that will precipitate perhaps into reaction to some of the maturity walls that folks understand.

Or perhaps it's some of what I was saying in the op ed, which is folks are seeing that there may be distress events on the horizon, and they want to make sure that if there is, they have the most negotiating leverage as possible.

So I know it's broad statements, but, you know, when we look at the sort of environment under which companies like First Brands or Tree or some of these other ones that have gone into distress very rapidly, when we look back at when these were birthed, etc., can we say like there was a dus were sloppy times.

These were loose, sloppy times that, people were not thinking much about either just quality due diligence or diligent, terms.

Yeah. So I think with First Brands is a great example or so.

First brands is an automotive replacement company. Right.

So they, they make things like brakes and light wipers and filtration systems.

Beginning in 2019, that company effectively rapidly expanded through 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, you know, they primarily funded these acquisitions with large debt facilities.

Then tariffs hit in April 2025, which obviously changed their business because they actually do a lot of manufacturing

and that kind of magnified problems.

So you can think about 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.

Well, first, brands used is a lot of kind of, you know, receivables financing facilities that weren't 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 $11 billion of total obligations that they had when they actually, started disclosing it in terms of off balance sheet financing.

And, you know, they were disclosing things like 5 to $6 billion of actual, debt obligations.

And so this led one of the creditor lawyers was, say, the $2.3 billion just disappeared.

And so that structure, the ability for first Prince 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 and private credit markets, you could fund, you know, that type of receivables facility to a first brands

and first brands could use that facility to then, you know, make sure they are constantly, continuing to acquire new businesses and, and keep rolling over the the cash.

I have a theory that, receivables financing and factoring is to the private credit market. What, French quants who went to that one really elite school, I like to say, are to, to trading blow ups.

I like that theory. Yeah.

So the other thing we wanted to ask you about, and again, we references 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 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, how 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?

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.

Yeah, he does love pizza.

I had to yesterday twice.

There you go. You're a pizza fanatic.

You decide to eat pizza. You love it so much, you decide. Peter, every single meal of every single day for the rest of your life.

You are committed to subsisting on 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?

That'll be really lucrative.

Yeah, I see where I was customary. We were going with this, but this is actually a very good analogy, right?

Like, you'd have to have a lot of confidence in me to commit to my word if you're going to open a restaurant.

Yeah.

Now, you know, you come to me, you say it's going to be super lucrative.

Yeah.

Here's how we're going to fund it. 10% equity. The bank is going to give you 90% of the funding in leverage.

And it stands Restaurant Joe, you don't own the restaurant, but you're going to eat at it.

I'm the full beneficiary,

full beneficiary of the restaurant, but it's 90% levered okay.

So I open the restaurant. You eat there every single day.

Now, Tracy Jill comes to you for a personal loan to fund his lifestyle, his pizza eating. Tracy.

Trust me, she would lend it to me.

Yeah, well, here's the question. Right?

Should you, Tracy, consider the 90% levered pizza restaurant that Joe is eating at for all his meals?

Now, on the one hand, it's not Joe'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 which will die without the restaurant, which is his.

He's committed to the restaurant and, you know, it kind of makes the restaurant intertwined with Joe's ability to pay your personal loan back.

So I guess that's the question. No. That's great.

So now who's now the let's take it out of pizza.

Who is? So that's whatever. Like, okay, now who is the chips buyer or whatever.

This is essentially what's happening with off balance. You financing and data center deals.

So and includes matters. I I'm sure you saw the Hyperion deal which is met as an infrastructure deal with Blue Owl.

Except I think it's even more intriguing than some of the pizza stuff.

So Meta and Blue Owl basically created a joint venture in a special purpose vehicle. Not that different than the restaurant.

Not that different than the restaurant. And the deal is the joint venture would be owned 20% by meta, 80% by Blue Owl.

So Blue Owl controls it and it would effectively be funded with 90% leverage.

So call it $30 billion of total enterprise value, $3 billion of equity, $27 billion, give or take of debt.

In other words, Blue Owl's effectively owning the restaurant. Meta is effectively eating at the restaurant,

and the banks fund it with 90% leverage.

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 SPV, and that effectively funds the interest expense.

Let's just talk about the debt for a second.

Yeah.

In a normal LBO context, which you guys probably talked about many times here, 90% leverage is pretty exceptionally high.

Most people would consider 50 to 80% leveraged be relatively normal for a stable cash flow business.

So the debt itself is actually quite high on on some of these structures.

The only reason it was possible was because it was given an investment grade 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 cash flow.

However, that guarantee is capped and is only partial, which is why they don't have to take it onto their books.

And why would be a footnote as a contingent debt obligation on their balance sheet.

Now let's talk about the asset that's being underwritten. This isn't pizza.

Pizza actually has a stable price for if we've thousands of views of history on pizza. Right.

And you can track that price over time.

Data center is optimized for GPU performance on training fundamental AI models.

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, you know, 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 to recap, you've got off balance sheet financing, which isn't reflected with whoever is lending money to meta or even buying its equity with 90% 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, these types of financing to run through our platform all the time to make sure you get the terms. Right.

What this structural protections look like in these deals is critical for the fortification of something, that is in the structure.

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 you know, when it AI or die, basically.

And so the spending keeps going. What is your guess on? Like the thing that kind of knocks that cycle or that flywheel, and tear it apart?

I mean, to I'm obviously in the industry where 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 1990s and or the iPhone in 2000 or so for media in the 20 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, and so that I think is very true. But I also think two things can be true.

I think I can 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 than you're used to seeing in a lot of these markets.

But the winners will be bigger than anyone's ever imagined.

All right, so if I don't eat the pizza, someone else is going to pick up the pizza, and they're going to they're going to eat it.

Look what we focus on. I know what I get is in a market moving this fast. Yeah.

We all need to pay attention to the terms actually underpinning a lot of these markets to make sure if there is any bleeding, that bleeding gets stopped as quickly as possible.

You know, just to give you one last example from a recent market deal, you can look at the franc JPM deal as like a really interesting one.

This is, you know, this was a deal where JPMorgan paid $175 million to acquire a company.

This is a very small deal. But to acquire a company called Frank, which is a streamlined Fafsa kind of support service,

I know I remember this.

It turned out, yeah, there was a lot of synthetically, made up types of data in that business.

And the founder is going to prison, right?

Allegedly. There's a lot of there's a lot of made up, stuff in the business.

And I think seven days executive who worked at Frank sent to 68 months. 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 founders litigation for any founders litigation would be paid for by JPM.

Right. They paid her lawyer, they paid $115 million 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.

Dean Wortmann, co-founder of Nautica, thank you so much for coming on.

Let's thank you.

Thanks for having me, Tracy.

I wasn't really sure where he was going with the pizza analogy, but it actually does make a lot of sense.

And it's something I think is a phenomenon in just a lot of financial transactions, which is how much like in certain environments, the lender and the creditor are like both each other's, like they're both leaning on each other.

The both the creditor and lender, they're relying on each. Yeah.

At the same time, much in the way you rely on pizza. You would lend to me to buy it, to eat pizza, right?

I would thank you if it if it was a matter of survival.

Yeah.

It was a matter of survival. Thank you.

I think it's just because you want to eat really expensive pizza, then. No.

You know, the other thing too, is just like from talking to you over these years, you know, how many times I've heard something.

Oh, there's like, there's a lot of light stuff going.

It is interesting to think that, like, you don't often hear that quantified what that means, right?

Things are like covet these days, etc..

And the idea that like, maybe we can get better numbers on some of these things seems like potentially labor saving for lawyers, stuff like that.

The, the specific numbers on specific deal terms were really interesting to me.

And the idea that even today, lawyers, still lawyers and bankers still have trouble, anticipate every single thing that could happen to a 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.

I find that interesting. The tariff example, you know, the problem is, is that I and this is I'm certain we talked about this more.

I 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 you know about 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 detect these phenomena.

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 I probably literally that is what's going to happen.

Yeah. All right. Shall we leave it there?

Let's leave it there.

This has been another episode of the Odd Lots podcast. I'm Tracy Alloway.

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and I'm Joe Weisenthal. You can follow me @thestalwart

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