Ran the first cohort of AI for Credit Professionals on Friday with a good mix of buy-side and sell-side professionals. It was two hours, part presentation and part hands-on training. This isn't generic AI training. It is specifically for investment professionals in credit. Running a second cohort in the next few weeks given the demand. If you are interested, reach out to me at prashant@eigenstrategy.com

Ran the first cohort of AI for Credit Professionals on Friday with a good mix of buy-side and sell-side professionals. It was two hours, part presentation and part hands-on training. This isn't generic AI training. It is specifically for investment professionals in credit. Running a second cohort in the next few weeks given the demand. If you are interested, reach out to me at prashant@eigenstrategy.com

Check out my latest article: Where the Risk actually sits in the AI Buildout linkedin.com/pulse/where-ris… via @LinkedIn

I miss being in an investing seat right now. The volatility makes the opportunity set in credit markets genuinely interesting. The real edge today is how you use AI across credit markets, corporate or structured. It can triage a pipeline of ideas across primary and secondary. It can run more and better screens. It can synthesize information faster and more effectively, which matters when the first bid or offer is what lets you trade ahead of the rest of the market. And it can help separate signal from noise in a space like AI infrastructure right now. Most of the press around AI infrastructure credit is the concern around the neoclouds. The interesting question is how the financing of this sector evolves; whether it stays a mix of public and private near term, or whether, like in the past, issuers are forced to go to the private markets and pay a premium for certainty of execution. I expect more structuring creativity to show up; warrants, equity kickers, and components that bridge a wider cost of capital gap. Also, I would expect more granularity from issuers around contracts with tenants and more differentiation and dispersion based on whether they are take or pay, cancellable etc. And that is where AI becomes particularly valuable in credit: not because it tells you what to buy, but because it lets you evaluate more situations, more variables and more documents fast enough with your lens, to make better investment decisions.

The quiet, unsexy gap in AI for finance isn't orchestration. It's data cleaning, and the work that goes with it. Foundation model labs and AI wrapper companies may be underestimating how much labeling and cleaning credit data actually requires before a model can use it well, especially once you start dealing with non-public securities. If you are working on any solutions related to this or are focused on the credit data space and perhaps more unique data sets, I would love to connect and hear about it.

There's understandably a lot of consternation about AI related debt out there. I read a NYT interview with Peter Orszag, tied to a paper he co-wrote on the new supply side era, where he compares the scale of AI capex debt to the fiber buildout of the 1990s. The scale of capex investment is enormous, but what is going to matter the most is the speed of adoption of AI, particularly at 'traditional' firms that are not VC or tech related. Whether real adoption happens over the next few years or the next decade, changes the return on capital completely. For the debt financing this capex, there is another question: who actually bears the adoption risk? If the contracts are long-term, take-or-pay and backed by strong counterparties, the pace of end-user adoption may matter much less to the lender. If the contracts leave utilization, pricing or renewals exposed to the market, that is a very different credit. Compute pricing is showing a similar pattern. Rent it now and it's expensive, because near term demand is the one thing everyone can see clearly. Go out 6 months, a year or two years and the compute pricing curve inverts, meaningfully, because nobody has real visibility into usage, adoption or which GPUs will be standard by then. Thanks @stanleylee21 for sharing this info. So if you're pricing any AI related financing, the pace of adoption is a key underwriting input. And just as important is determining who has contractually agreed to bear the risk if adoption takes longer than expected.

A number of people I know have been approached recently by consultants offering to buy their anonymized data including their workflows. Meaning, how they actually do their work, anonymized, then sold to companies training AI models on it. And it makes a lot of sense, if you're Anthropic, OpenAI etc. and you are potentially concerned about model capability flatlining based on your current training data sets. For most companies, the real moat is the data, the relationships and how the work actually gets done. That is the part that models can't easily copy because the training data typically doesn't include this information. But if you choose to monetize this way, that can change quickly. So now I'm wondering, if enough of that gets handed over, do these models eventually get good enough to replace the experienced professional who built it? I don't know, but I am genuinely curious and a bit concerned.

A few thoughts on the recent vol in credit markets 1) I think it's too soon to call the CCC weakness purely idiosyncratic. Distress usually starts with the weakest links. Right now it's happening with CCCs, weaker BSLs and pockets of private credit, and that's before we've seen any meaningful remarks from SaaS portfolio companies. 2) People are underestimating what rates do to leveraged credit. Most sponsor owned businesses don't hedge much interest rate risk. That's not a theory, that's what I saw play out in 2022. A real move in rates hits floating rate debt and any fixed rate debt coming up for refinancing feels it just as fast. 3) On the AI side, if the 25-45% ROI from the hyperscalers on AI capex holds up, these issuers can absorb another 50-100bps (or more) of financing cost and still want the deal done. Doesn't make it a bad credit, just less rate sensitive. 4) The issuance we are seeing from the hyperscalers further out the curve will inevitably have an impact too, since that duration has to get hedged somewhere, and that flows into treasury yields.

This is well written and exactly why domain expertise matters so much. Very applicable in finance.

Yannis and I led forward deployed AI engineering at Palantir. Much of today’s boom is not true FDE work. Companies are filling a product gap with engineering headcount. What comes after that?

How are you managing the pipeline of opportunities in credit right now, especially given the volatility? It isn't just a volume problem, it's also a selection problem and volatility makes it worse. Take Project Jupiter, for example: $18bb of loans behind Oracle's New Mexico data center, quoted around 90 cents. Oracle sent a force majeure notice on September 24. People underwrote this at new issue. If you passed then, is it on your list today, or did it leave your universe when you said no? For most desks, that's settled by whether someone called and told you or you read it on Bloomberg, Octus or 9fin. You didn't choose that filter. That's the case for pointing AI at the top of the funnel and triaging your pipeline of primary and secondary ideas. Not that it removes bias. Your filter is bias. But it's one you deliberately wrote down versus one you inherited. And it also allows you to look at ideas that net out the effect of crowding, which is a big issue in credit investing. The scarce input was never information. It's what you chose to look at.

I don't think 'Does AI capex have ROI' is one question. I think its five. And I am skeptical of anyone who claims to already have a single number that answers it. 1) Start with backlog - Hyperscalers report enormous committed compute demand. And a lot of commentary treats that as proof the spend will pay off. The real question is how much of the backlog is take or pay versus cancellable or usage based. And I haven't seen enough disclosure around that split. 2) Cost of compute - This is getting a lot of attention right now for a reason. GPU rental pricing is elevated because memory and packaging are genuinely scarce, booked out years in advance. And the scarcity is a driver of what's happening in semiconductor pricing and equities well beyond the hyperscalers themselves. Some share of today's implied return is a scarcity premium, not a durable structural margin. At least it's not clear to me yet. 3) Adoption - This is a key piece at the center of my company EigenStrategy, and we need a more precise measure than 'companies say they're using AI'. In my opinion, usage inside AI native startups spending venture money is a weak signal, because the spend doesn't have to justify itself the way it does everywhere else. The real test is whether traditional industries, running on their own PnL discipline with no subsidy, are getting enough value to keep paying for it without a mandate from the top. 4) Closed versus Open source - If open weight models keep closing the capability gap, pricing power moves away from the foundation model labs and down towards whoever owns the compute or the application layer. That changes who actually captures the return on all this spending, independent of whether the spending pays off at all in aggregate. 5) Security- Incidents like the ones making news recently are a reminder that none of the return math above prices in a trust shock. It's the factor more likely to show up as a sudden break than a slow curve. And it's the easiest to ignore until it isn't. A hyperscaler funding capex off its own balance sheet, a neocloud financed against leased GPUs and a data center financed against a single customer's credit are exposed to each of these five factors in genuinely different ways. And until the reporting gets granular enough to separate them, a single ROI figure deserves real skepticism.

A few people have asked what the AI training for credit professionals will look like. I am going to keep it to about 2 hours- start with the basics, what the models are and how they work. Then the downsides, where they fail and the guardrails you need around them. Then the credit-specific part, where AI actually helps. I am going to keep it to a small group and its intended to be interactive. If you are already using Claude, ChatGPT etc we can work through real questions together. If you work in credit and want in, email me at prashant@eigenstrategy.com

There's been a lot of discussion around Harvey AI's margins, which went from 50% to -50% in 6 months. Some of it is the result of foundation model token costs. The interesting part is that apparently margins improved after the company built its own model using open-weight models. What's surprising is not many people in the investing world are talking about post-training small or medium sized open-weight models. Yet. And Anthropic still doesn't have one. I ran an experiment for credit underwriting in June using a Gemma 4 26B model that I post-trained and I tested it on two ABL deals it had never seen. It scored 55/60, while unaided Claude scored 44/60. Just another example of the contrast in performance between a model carrying your judgment and one running unaided.

I have been thinking incessantly about how much a judgement core harness can improve the results of an LLM for credit investing. Turns out, a LOT! linkedin.com/pulse/edge-hold…

I spend most of my time between two groups that see AI infrastructure differently. The tech community and the finance community are at odds on what the buildout will actually earn, and there is very little hard data on the question. Based on commentary from the hyperscalers and VCs, the tech community is far more bullish. So I pointed my own AI tooling at the primary filings rather than the coverage. What struck me is how much the word backlog is being asked to carry. SpaceX's IPO filing discloses a cloud services agreement giving Anthropic access to compute capacity that includes roughly 325,000 GPUs, at $1.25 billion a month through May 2029. That is about $45 billion at the headline run rate. But after an initial three month period, either party can terminate on ninety days' notice, and no termination payment is disclosed. The earliest exit is closer to six months than three years. Nebius is a different version of the same problem. Its Meta arrangement has a contract value of up to $27 billion. About $12 billion of that is dedicated capacity for Meta's own use. The rest, up to $15 billion, is capacity Nebius says it intends to sell to third parties, with Meta purchasing whatever remains. That second piece is a backstop. It is worth having and it helps make the buildout financeable. But it should not be read as $15 billion of compute Meta has said it needs. Headline contract value is being read as a demand signal. Some of it is financing architecture, and the mix matters a great deal to anyone underwriting this. If you are closer to these contracts and are willing to share color, let me know if/where I am wrong.

I have put together some AI training for people in credit. If you’re interested, ping me.

At 8:30 tomorrow morning, CPI prints. By 8:31, the information you need to price your credit book will be coming from three markets you don't trade. Rates reprice. Equities reprice. Crude does whatever crude does. And somewhere, a trader has to make a two-sided market in a bond that hasn't traded in four days, on an issuer whose fundamentals didn't change at all in the last sixty seconds. That was my job for seventeen years. You're not really pricing the bond. You're pricing everything that just happened somewhere else. The buy side has the mirror image of the same problem. Not simply, what is this worth? But: is this a price I should be doing something about, given what I already own and what I already think? Both are cross-asset synthesis under a clock. And both are still mostly done by feel, in someone's head, across a screen full of windows that were never designed to talk to each other. That strikes me as one of the most AI-assistable problems on a trading floor. Not replacing the trader or investor. Giving them a better synthesis of everything that just changed, fast enough to actually matter. I don't think most desks have seriously tried yet. If you haven't thought about where AI fits in that, message me. It's the work I do.

The market is worried about (at least) two things. One is AI infrastructure spending. The debt funding it, the returns underneath it, and what happens if those returns don't materialize. The other is the Treasury market. Dollar debasement, inflation that is sticky, and whether anyone still wants to own long-dated U.S. rate risk. If you were underwriting this blind, without being told which leg was the risk-free one, which would you rather own?

Can anyone point me in the direction of someone at anthropic who handles enterprise partnerships with consultants?