Until now, the AI buildout has largely been self-funded. Our
Chief Fixed Income Strategist Vishy Tirupattur and our Head of
U.S. Credit Strategy Vishwas Patkar explain the role of credit
markets to fund a potential financing gap of $1.5 trillion as
spending on data centers and hardware keeps ramping up.
Read more insights from Morgan Stanley.
----- Transcript -----
Vishy Tirupattur: Welcome to Thoughts on
the Market. I am Vishy Tirupattur, Morgan Stanley's Chief Fixed
Income Strategist.
Vishwas Patkar: And I'm Vishwas Patkar,
Head of U.S. Credit Strategy at Morgan Stanley.
Vishy Tirupattur: Today we want to talk
about the opportunities and challenges in the credit markets, in
the context of AI and data center financing.
It's Wednesday, August 6th at 3pm in New York.
Vishy Tirupattur: So, Vishwas spending on
AI and data centers is really not new. It's been going on for a
while. How has this CapEx been financed so far predominantly?
What has changed now? And why do we need greater involvement of
credit markets of different stripes?
Vishwas Patkar: You're right, Vishy. So,
CapEx on AI is certainly not new. So last year the hyperscalers
alone spent more than $200 billion on AI related CapEx. What
changes from here on, to your question, is the numbers just ramp
up sharply. So, if you look at Morgan Stanley's estimates
leveraging work done by our colleague Stephen Byrd over the next
four years, there's about [$]2.9 trillion of CapEx that needs to
be spent across hardware and data center bills.
So what changes is, while CapEx so far has been largely
self-funded by hyperscalers, we think that will not be the case
going forward. So, when we leverage the work that has been done
by our equity research colleagues around how much the
hyperscalers can spend, we've identified a [$]1.5 trillion
financing gap that has to be met by external capital. And we
think credit would play a big role in that.
Vishy Tirupattur: A financing gap of [$]1.5
trillion. Wow. That's a big number, by any measure. You talked
about multiple credit channels that would need to be involved.
Can you talk about rough sizing of these channels?
Vishwas Patkar: Yep. So, we looked at four
broad channels in the report that went out a few weeks ago. So,
that [$]1.5 trillion gap breaks out into roughly [$]800 billion
across private credit, which we think will be led by asset-based
finance. Another [$]200 billion we think will come from
Investment Grade rated bond issuance from the large tech names.
Another [$]150 billion comes through securitized credit issuance
via data center ABS and CMBS. And then finally there is a [$]350
billion plug that we've used. It's a catchall term for all other
forms of financing that can cover sovereign spend, PE (private
equity), VC among others,
Vishy Tirupattur: The technology sector is
fairly small within the context of corporate grade markets. You
are estimating something like [$]200 billion of financing to come
from this channel. Why not more?
Vishwas Patkar: So, I think it comes down
to really willingness versus ability. And, you know, you raise a
good point. Tech names certainly have a lot of capacity to issue
debt. And when I look at some of the work done by my colleague
Lindsay Tyler in this report, the big four hyperscalers alone
could issue over [$]600 billion of incremental debt without
hurting their credit ratings.
That said, our assumption is that early in the CapEx cycle,
companies will be a little hesitant to do significantly debt
funded investments as that might be seen as a suboptimal outcome
for shareholder returns. And that's why we have reduced the
magnitude of how much debt issuance could be vis-a-vis the actual
capacity some of these companies have.
So, Vishy, I talked about private credit meeting about half of
the investment gap that we've identified and within that
asset-based finance being a very important channel. So, what is
ABF and why do you expect it to play such a big role in financing
AI and data centers?
Vishy Tirupattur: So, ABF is a very broad
term for financing arrangements within the context of private
credit. These are financing arrangements that are secured by
loans and contractual cash flows such as leases – either with
hard assets or without hard assets. So, the underlying concept
itself is pretty widely used in securitizations.
So, the difference between ABF structures and ABS structures is
that the ABF structures are highly bespoke. They enable lots of
customization to fit the specific needs of the investors and
issuers in terms of risk tolerance, ratings, returns, duration,
term, et cetera.
So, ABS structures, on the other hand, are pretty standardized
structures, you know, driven mainly by rating agencies – often
requiring fairly stabilized cash flows with very strict
requirements of lessee characteristics and sometimes residual
value guarantees, in cases where hard assets are actually part of
the collateral package.
So, ABF opens up a wider range of possible structures and
financing options to include assets that are on different stages
of development. Remember, this is a very nascent industry. So,
there are data centers that are fully stabilized cash flows, and
there are data centers that are in very early stages of building
with just land, or land and power access just being established.
So, ABF structures can really do it in the form of a single asset
or single facility financing or could include a portfolio of
multiple assets and facilities that are in different stages of
development.
So, put all these things together, the nascent nature and the
bespoke needs of data center financing call for a solution like
ABF.
Vishwas Patkar: And then taking a step
back. So, as you said, the [$]1.5 trillion financing gap; I mean,
that's a big number. That's larger than the size of the high
yield market and the leveraged loan market.
So, the question is, who are the investors in these structures,
and where do you think the money ultimately comes from?
Vishy Tirupattur: So, there is really a
favorable alignment here of significant and substantial dry
powder across different credit markets. And they're looking for
attractive yields with appeal to a sticky investor base. This end
investor base consists of investors such as insurance companies,
sovereign wealth funds, pension funds, endowments, and high net
worth retail individuals.
Vishy Tirupattur: These are looking for
scalable high quality asset exposures that can provide
diversification benefits. And what we are talking about in terms
of AI and data center financing precisely fall into that kind of
investment. And we think this alignment of the need for capital
and need for investments, that bridges this gap for [$]1.5
trillion that we're talking about here.
So, my final question to you, Vishwas, is this. Where could we be
wrong in our assessment of the financing through the various
credit market channels?
Vishwas Patkar: With the caveat that there
are a lot of assumptions and moving parts in the framework that
we build, I would flag really two risks. One macro, one micro.
The macro one I would talk about in the context of credit market
capacity. A lot of the favorable dynamics that you talked about
come from where the level of rates are. So, if the economy slows
and yields were to drop sharply, then I think the demand that
credit markets are seeing could come into question, could see a
slowdown over the coming years.
The more micro risks, I think really come from how quickly or how
slowly AI gets monetized by the big tech names. So, while we are
quite optimistic about revenue generation a few years out, if in
reality revenues are stronger than expected, then you could see
more reliance on the public markets.
So, for instance, the 200 billion of corporate bond issuance is
likely going to be skewed higher in a more optimistic scenario.
On the flip side, if there is mmuch ore uncertainty around the
path to revenue generation, and if you see hyperscalers pulling
back a bit on CapEx – then at the margin that could push more
financing to the way of credit markets. In which case the overall
[$]1.5 trillion number could also be biased higher.
So those are the two big risks in my view.
Vishy Tirupattur: So, Vishwas, any way you
look at it, these numbers are big. And whether you are involved
in AI or whether you're thinking about credit markets, these are
numbers and developments that you cannot ignore.
So, Vishwas, thanks so much for joining.
Vishwas Patkar: Thank you for having me on
Vishy.
Vishy Tirupattur: And thanks for listening.
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