Goldman Sachs and Morgan Stanley project up to $5 trillion in AI infrastructure spending, forcing a shift to private credit

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Terrifying sentence from Goldman Sachs: “Hyperscalers will need financing from across markets, structures, and currencies as they potentially bump up against saturation considerations in liquid credit markets …”

At the point the question for me is not whether hyperscaling will collapse but how bad the collateral damage will be.

Rohan Paul@rohanpaul_ai

Goldman Sachs is now saying the AI race has become a $5.3T capital-spending cycle.

with that figure covering expected hyperscaler spending on AI and data centers from 2025 through 2030.

AI infrastructure is starting to strain normal financing channels, because the same few hyperscalers cannot endlessly push debt into public bond markets without investors worrying about issuer concentration.

A data center is not one asset, because it combines land, power access, network links, buildings, cooling, and AI servers, so the financing naturally spills across infrastructure funds, real estate funds, private credit, and corporate bonds.

Goldman signals that AI capex estimates are rising faster than actual data center construction, which means the bottleneck may shift from model demand to financing capacity, power availability, and project execution.

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Gary Marcus@GaryMarcus

There is no way the hyperscalers are going to make back their $5.3 trillion — unless they take it out of the taxpayers, via massive government subsidies.

Which is *exactly* what they are going to try to do.

Rohan Paul@rohanpaul_ai

Goldman Sachs is now saying the AI race has become a $5.3T capital-spending cycle.

with that figure covering expected hyperscaler spending on AI and data centers from 2025 through 2030.

AI infrastructure is starting to strain normal financing channels, because the same few hyperscalers cannot endlessly push debt into public bond markets without investors worrying about issuer concentration.

A data center is not one asset, because it combines land, power access, network links, buildings, cooling, and AI servers, so the financing naturally spills across infrastructure funds, real estate funds, private credit, and corporate bonds.

Goldman signals that AI capex estimates are rising faster than actual data center construction, which means the bottleneck may shift from model demand to financing capacity, power availability, and project execution.

9h9.7K15323

Nobody Special@JG_Nuke

@GaryMarcus @allthingsf74065 This is precisely what I’ve been warning about this whole time. Unsuspecting savers are being used to pay for the biggest financial fraud is history. The longer this bubble takes to burst, the more money the tech billionaires pocket at the expense of the working class.

4h91353

Gary Marcus@GaryMarcus

If Generative AI were a vehicle, it would be a runaway train.

Gary Marcus@GaryMarcus

Terrifying sentence from Goldman Sachs: “Hyperscalers will need financing from across markets, structures, and currencies as they potentially bump up against saturation considerations in liquid credit markets …”

At the point the question for me is not whether hyperscaling will collapse but how bad the collateral damage will be.

3h4.5K293

Rohan Paul@rohanpaul_ai

Morgan Stanley estimates about $2.9 trillion capital expenditure of global data-center construction through 2028.

With funding sources including $1.4T from hyperscaler cash flows, $200B corporate debt, $150B securitized credit, $800B private credit / asset-based finance / JV debt, and $350B other capital.

i.e. AI capex is increasingly credit-funded, so losses could spill beyond shareholders.

Rohan Paul@rohanpaul_ai

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)

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Rohan Paul@rohanpaul_ai

https://www.goldmansachs.com/insights/articles/private-markets-expected-to-have-growing-role-in-data-center-financing

Rohan Paul@rohanpaul_ai

Goldman Sachs is now saying the AI race has become a $5.3T capital-spending cycle.

with that figure covering expected hyperscaler spending on AI and data centers from 2025 through 2030.

AI infrastructure is starting to strain normal financing channels, because the same few hyperscalers cannot endlessly push debt into public bond markets without investors worrying about issuer concentration.

A data center is not one asset, because it combines land, power access, network links, buildings, cooling, and AI servers, so the financing naturally spills across infrastructure funds, real estate funds, private credit, and corporate bonds.

Goldman signals that AI capex estimates are rising faster than actual data center construction, which means the bottleneck may shift from model demand to financing capacity, power availability, and project execution.

17h2.1K63

Roger@rdd147

@GaryMarcus

7h671

A Eye Bubble (parody account)@A_Eye_Bubble

@GaryMarcus Why are we calling them hyperscalers snd not hyperliars or just hyper coke abusers?

6h1211

s77nu@s77nu

@JakeKAllDay @GaryMarcus Moot point, that aggregate compute represents TAM so the compute will all be used regardless of who wins and who loses.

4h52

Roger@rdd147

@GaryMarcus Soon we’ll just get a Data Center Courtesy Fee tax on our receipts

7h1373

david@tumanako1234

@GaryMarcus What do hyperscalers produce after all? Just models and tokens and after everyone to use their tokens?

1h30

Sonny Crockett (Parody)@MiamiEconomist

@GaryMarcus @grok Summary

7h24

Venkat Madala@venky4a

@rohanpaul_ai It’s not a bubble

19h1432

HanMak@hanmak17

@Ferbin08 @GaryMarcus You have zero idea what you are talking about. Nothing is profitable yet, by any measure

7h8

Hunter Gon@gonlenidefi

@GaryMarcus the question is how long before the bill collectors start asking about ROI

9h612

Jake@JakeKAllDay

@s77nu @GaryMarcus 1) youre placing a lot of faith in TAM estimates that are mostly marketing slides for would be investors of high price IPOs and 2) if the dark fiber build out should teach you anything, it’s that just because something eventually gets used doesn’t mean equity returns are good

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