Loans are secured against long-term contracts with hyperscalers.
Wall Street is finding new ways to fund the AI boom.
Morgan Stanley is now pitching data center developers on leveraged loans to fund their projects.
The bank's research analysts project a $15 billion market this year. JPMorgan analysts expect $350 billion to be issued across both high yield bonds and leveraged loans in the next five years.
Read more @theinformation; link in reply.
Users reacted negatively to Morgan Stanley pitching leveraged loans for AI data center financing, fearing it echoes the risky pre-GFC housing bubble.
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@rohanpaul_ai This is basically AI turning into a full blown credit market story, not just a tech story anymore.
AI data center finance is becoming its own serious asset class.
Investment-Grade bonds, project finance, private credit, high yield, now broadly syndicated loans.
The Information just published piece on how Morgan Stanley is now pitching data center developers on the leveraged loan market. Not just project finance, not just bank loans, not just high-yield bonds — now the same loan market that usually funds LBOs (Leveraged Buyout) is being pulled into the AI infrastructure buildout.
Morgan Stanley has estimated AI-related debt issuance could cross $570B in 2026. By end-May 2026, AI-linked borrowing had already reached about $236B, something like 4x the year-ago level.
So this is no longer just hyperscalers issuing investment-grade bonds. The whole debt stack is widening.
Just becasue the funding need is just massive.
So it is more like: contracted AI infrastructure cash flows, packaged into leveraged credit.
CLO (Collateralized Loan Obligation) managers are apparently interested in these data center loans because they get floating-rate exposure, better yield, and in many cases cash flows tied to long-term hyperscaler contracts.
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theinformation. com/articles/morgan-stanley-pitches-clients-new-market-data-center-loans
dot-com bubble vs. a possible AI bubble.
From the famous "Dean of Valuation", Professor Aswath Damodaran, of NYU Stern School of Business,
“And that’s the real big difference between the dot-com boom and bust and the AI boom. We don’t know whether there’ll be a bust. History suggests there will be a bust.
The dot-com boom and bust had no huge capital expenditure in that cycle. In fact, there was very little traditional CapEx, or even R&D, driving it. People started apps. They basically started going on it.
This has been the biggest infrastructure run-up I think I’ve ever seen in business. You can go back and compare it to the automobile business 100 years ago. The amount of money that’s being put into AI CapEx is immense, which means that when the correction comes, the pain will be more intense.
And herein lies the second problem. The dot-com boom and bust was almost entirely equity-funded. You think, so what? Well, when the bust came, those shareholders lost 60%, 70%, 80%, or 90% of their money. You felt sorry for them, but the loss was restricted to the shareholders.
The problem with the AI CapEx boom is that not only is it immense, but a big chunk of it is funded with debt, and the debt is coming from private capital rather than banks. There’s a very real chance that if there’s a correction and companies start having problems, that problem is going to show up as distress and default, and that really doesn’t stay restricted. It spills over into the rest of society.
I’m not saying it’s going to be 2008, but 2008 is an example of what happens when lenders overreach, when they lend money at too low a rate, and the correction comes. The pain spills over.
So that is my concern with this big market illusion: the potential societal cost of having to deal with debt coming due that you’re unable to pay. It’s much more painful than your share price dropping 90% and you feeling the pain."
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From "Excess Returns" YouTube channel, (link in comment)
CLO's bounce is really a runway test.
About $570 in cash/cash equivalents gives management room to chase pipeline opportunities, but cash alone does not create operating leverage.
The new signal is conversion: funded backlog, margin quality, and whether scaling happens without dilution. If those improve, the bounce has a business case behind it.
I would watch whether yield, package throughput, scrap, thermal limits, lead times, and power density improve before volume ramps.
That is where the AI hardware story gets paid. Better chips matter most when the packaging line can ship them without margin leakage.
Read more here: https://www.theinformation.com/articles/morgan-stanley-pitches-clients-new-market-data-center-loans
@rohanpaul_ai Strangely, sounds an echo of the housing market leading up to the Global Financial Crisis….
Hope I’m wrong 🤞
