AI Can Tell Us Something About Credit Market Weakness

24:50Recovering corporate lawyer. Recovering corporate lawyer, 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. 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 clauses deep down. And you actually have to go find that information. This is why. It's also jujitsu between the borrowers and the lenders, right?

25:22Because 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. AI, 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 n-grams.

25:59And so what Noetica does is it uses a series of language models, including a multilayered 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 blocker in 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. 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.

26:41But 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 at Noetica, we are ultimately an AI powered software company, but we actually have the largest knowledge graph of deal terms in existence. So Tracy, exactly what you said, it's a database ultimately of precedent comparable deal terms. And that database, this is going to be mind blowing, has over a billion terms in it. So it's digital largest in existence. We map that back to deal characteristics.

27:16It'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 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, outside of lawyers, even just deal professionals, generally bankers, credit managers, people are highly reliant on precedent.

27:56What is your tech stack? Or did you what do you build and how much is it like, oh, you're using chat GPT's API, et cetera? Like, OK, 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? So we were started in 2022. So we're what you would call AI native. We were started in a system that already 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, financial institutions.

28:33This is like a big issue, right? That they don't want to just be uploading their stuff to chat GPT, 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 in 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 are ultimately underpinning a lot of the GPTs and the clods. 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.

29:11Now, 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 for a JP Morgan deal or for a B of A deal. for 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 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 does the user really want?

29:43The user really wants to know how they're going to advise 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. 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 blowups 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.

30:19I 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 recently wrote about this in the Wall Street Journal a little bit, and then folks contacted me 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 markets for a very long time. We 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.

30:58To us, what we see is creditors may be preparing 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, 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 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.

31:40This is the locks and the deadbolt. And this is what we were talking about with J.Crew 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 lien 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 so we clocked that term at 84 % of deals in Q3, biggest quarterly jump we've ever seen from the prior quarter.

32:24It's also the highest we've ever clocked that term. So this beds the question of why. Why are creditors so focused on making sure their place in line is— In recovery. In recovery is the same. Perhaps it's a reaction to the liability 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 op-ed, which is folks are seeing that there may be distressed events on the horizon, and they want to make sure that if there is, they have the most negotiating leverage as possible.

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35:41Speed slow after 35 gigabytes if network's busy. Taxes and fees extra. See mintmobile.com. So I know it's broad statements, but when we look at the sort of environment under which companies like First Brands or Tricolor or some of these other ones that have gone into distress very rapidly, When we look back at when these were birthed, et cetera, 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? Yeah. So I think with First Brands is a great example, right? So First Brands is an automotive replacement company, right?

36:22So they make things like brakes and 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 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 2025, which obviously change 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.

37:11What first brands used is a lot 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 they were disclosing things like$5 to$6 billion of actual debt obligations. And so this led one of the creditors' lawyers to say that$2.3 billion 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.

37:57because if you're a big credit manager in private credit markets, you could fund that type of receivable facility to a first brands. And first brands could use that facility to then make sure they are constantly continuing to acquire new businesses and keep rolling over 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 are to trading blowups. I like that theory. Yeah, thanks. So the other thing we wanted to ask you about, and again, we referenced this in the intro, is we are seeing these really complicated deals that I admittedly cannot keep track of in the AI market where 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.

38:51And 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? 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.

39:28Yeah. He does love pizza. I do. I had it yesterday twice. 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 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? it'll be really lucrative you have a i see i was wondering where you 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 gonna open a restaurant yeah now you know you come to me you say this is gonna be super lucrative yeah here's how we're gonna fund it 10 equity the bank is going to give you 90 of the funding in leverage and it's dan's restaurant joe you don't own the restaurant but you're going to eat at it i'm the full beneficiary of the restaurant but it's 90 percent levered okay so i open the restaurant you eat there every single day now tracy joe comes to you for a personal loan to fund his lifestyle his pizza eating lifestyle tracy trusted me she would lend it to me well here's the question right should you tracy consider the 90 percent levered pizza restaurant that joe is eating at for all his meals now on the one hand it's not joe's loan Right.

40:49So he's not on the hook if the pizza restaurant goes under. On the other hand, it's Joe's only source of food. Joe 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 the question. No, that's great. So now let's take it out of pizza. Who is whatever? Like, okay, now who is the chips buyer or whatever? This is essentially what's happening with off-balance-sheet financing and data center deals. And it includes Meta's, I'm sure you saw, the Hyperion deal, which is Meta's AI infrastructure deal with Blue Owl.

41:25Except 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. 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 is effectively owning the restaurant. Meta is effectively eating at the restaurant. And the bank's funded with 90 % leverage.

41:58So 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 that effectively funds the interest expense. Let's just talk about the debt for a second. In a normal LBO context, 90 % leverage is pretty exceptionally high. Most people would consider 50 % to 80 % leverage to be relatively normal for a stable cash flow business. So the debt itself is actually quite high on some of these structures.

42:40The 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 it 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 years of history on pizza, right?

43:15And you can track that price over time. Data centers 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 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.

43:54And 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 we will look at these data centers, 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 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 blowups 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.

44:28And 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 at 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 tears it apart? So I'm obviously in the 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 1990s or the iPhone in the 2000s or social media in the 2010s.

45:14And 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 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 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.

45:57But 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 in noetica 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 a really interesting one this is you know this was a deal where jp morgan paid 175 million to acquire a company.

46:34There's 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 one. It turned out 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 made up stuff in the business. There's seven days. Executive who worked at Frank sentenced to 68 months in prison. 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.

47:18Right. 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, you can kind of ignore some of the nuts and bolts. But I think it's actually even more critical in fast-moving markets. Dan Wertman, co-founder of Noetica, thank you so much for coming on Notbox. Thank you. Thanks for having me.

47:52Tracy, 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 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. They're both the creditor and lender. They're relying on each other. Yeah, at the same time. Much in the way you rely on pizza. You would lend to me to buy to eat pizza, right? I would. Thank you. If it was a matter of survival. Yeah, if it was a matter of survival.

48:22Thank you. I appreciate that. If 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. There's a lot of Cove 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. The specific numbers on specific deal terms were really interesting to me.

48:52And the idea that even today, lawyers and bankers still have trouble anticipating 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 AI is, and this is, I'm certain if we talk 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.

49:25They'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 AI. Probably literally that is what's going to happen. Yeah. All right. Shall we leave it there? Let's leave it there. All right. This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Jill Weisenthal. You can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at CarmenArmand, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks.

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