The companies at the centre of the AI boom have been busy
investing billions of dollars in each other.
I'm sure you've seen the spaghetti diagrams in the media
recently showing how companies like Open AI are investing in
their chip suppliers, or how chip manufacturers like NVIDIA
are investing in their customers, enabling them to buy
more chips. I first noticed how strange
these deals were back in March when Core Weave, a company who
buys chips from NVIDIA, puts them in data centres and rents
out compute, filed to go public. It's IPO prospectus revealed
that NVIDIA owned about 5% of the company.
When investor interest seemed tepid after a long IPO drought,
NVIDIA offered to anchor the deal at $40 a share with a $250
million order. Bryce Elder described the deal
in the FT at the time as an Aura Boris, an ancient symbol of a
snake or a dragon eating its own tail.
A similar metaphor might be an extension cord plugged into
itself. If you don't know much about
electricity, that might look like a perpetual energy machine.
But trust me, I've tried it out and no matter how you configure
it, it won't power your home appliances.
You just need outside energy to get things going.
That rough speaking is the current state of AI
infrastructure financing. While the sheer number and size
of these deals have convinced some investors that the AI value
chain is developing rapidly, others are concerned by the
circularity. Two companies sit near the
centre of nearly every diagram, Open AI and NVIDIA.
Each is likely trying to ensure that everyone in the ecosystem,
from suppliers to customers to cloud providers, has a vested
interest in their success. Open AI recently announced a
$300 billion cloud infrastructure agreement with
Oracle, a $10 billion custom chip partnership with Broadcom,
and strategic alliances with major memory suppliers.
According to UBS analysts, Open a is memory commitments alone
account for half of the world's current capacity.
NVIDIA, meanwhile, pledged up to $100 billion in investment to
Open AI, who will in turn buy millions of Nvidia's AI graphics
cards. AMD is also in on the game.
Open AI agreed to buy 10s of billions of dollars worth of AMD
chips, and in return, AMD gave Open AI the right to buy 10% of
its stock for one cent per share, contingent on AMD hitting
certain share price targets and Open AI deploying its chips.
The way Matt Levine explained the deal at the time was that if
Open AI announces a big partnership with a public
company, that company's stock price goes up.
So Open AI could just pay for the chips in cash, receive
stock, and when the deal is announced, the stock would
rocket, effectively reimbursing Open AI for its purchase.
Everyone wins. Amazon has its own version of
the loop. It invested more than $8 billion
in Anthropic, the company behind the Clawed chatbot, and in
return, Anthropic committed to using Amazon as its primary
cloud provider. That means training and running
it's models on Amazon's custom AI chips, renting compute from
AWS, and integrating Clawed into Amazon Bedrock, the company's
enterprise AI platform. In effect, Amazon is funding a
company that will use its chips run on its cloud and help sell
services. And now Google is getting in in
the loop, too. And Tropic just announced a deal
to access up to 1,000,000 of Google's TP US, bringing over a
GW of compute capacity online by 2026.
The arrangement is worth 10s of billions of dollars and
positions Google as both the major investor and
infrastructure provider. And Tropic says it chose
Google's chips for their efficiency and performance.
But the deal also reduces it's reliance on NVIDIA and Amazon.
Google has already invested $3 billion in Anthropic.
Amazon has pledged 8 billion. Both companies now provide cloud
services, custom chips and strategic capital.
Anthropic insists that it's just pursuing a multi cloud strategy,
but it's hard to ignore how deeply entangled it has become
with all three of the largest US cloud providers, each of whom
now has a financial interest in its success.
Then there's Elon Musk, who seems to believe that the best
way to build artificial general intelligence is to have his
companies date each other. His AI startup XAI acquire
Twitter or the Everything app, which supplies real time data to
Grog, his chatbot, sometimes referred to as Mecca Hitler.
Tesla, his electric car company, uses the chatbot in it's cars
and possibly it's robots, which are coming next year.
Musk owns a majority stake in XAI, which recently bought
Twitter from him. He owns a minority stake in
Tesla and now wants Tesla shareholders to invest in XAI.
It's not incestuous, exactly, but we'd have to get Errol Musk
to explain why it's OK. The whole thing is starting to
look less like a tech boom and more like a Mobius strip made of
venture capital and electricity. And the electricity part isn't a
metaphor either. McKinsey forecasts $5.2 trillion
in CapEx for chips, data centres and energy over the next five
years alone. Bain says that we'll need to see
$2 trillion in annual revenue from AI companies just to
justify that spending open. AI has about $13 billion in
revenues today and is essentially a money furnace, and
Tropic is a smaller money furnace.
NVIDIA is very profitable, but not $100 billion profitable.
So the question becomes, who's going to pay for all of this?
The interconnected nature of these deals, the reason we need
spaghetti diagrams to understand them, has raised concerns about
circular financing. Companies are investing in each
other, buying each others products and pushing up each
others stock prices. Investors are now asking whether
these interdependencies could pose risks if AI demand or
monetization falls short of investor expectations.
The AI industry's investment structure is starting to
resemble something we've seen before, just not in Silicon
Valley. In post war Japan, large
industrial groups known as kiritsu were built usually
around banks and trading houses, with companies taking stakes in
each other and coordinating supply chains.
South Korea's Chai Ball System followed a similar pattern, but
with families in control rather than banks.
These models weren't about competition, they were, at least
initially, about survival. In capital constrained
economies. It seemed to make sense to have
tight financial relationships with the businesses you relied
upon so that your supply chains were secure.
The kiritsu and chibal models were often criticized for
obscuring financial risk, misallocating capital and
propping up uncompetitive firms. When Japanese asset bubble burst
in the 1990's, the tangled web of cross holdings made it almost
impossible to unwind bad beds. Today's AI giants are by no
means short on capital, but they are assembling these webs of
mutual dependence. The question is whether today's
AI giants are building a similarly fragile structure,
which looks stable from the outside but depends on a
constant influx of new capital to keep the lights on.
When we look at the numbers, they almost seem made-up.
Open AI Stargate project, announced this January at a
White House event, is a $500 billion plan to build 10
gigawatts of AI data center capacity across the US It's
firstly kind of crazy that we're talking about data centers in
terms of gigawatts. According to the US Department
of Energy website, a typical nuclear power plant produces one
GW of power on average, and that's enough electricity to
power a million typical US households.
The typical US household is 2.6, so 10 gigawatts is enough power
for 26,000,000 average Americans.
Open AI isn't just building Stargate.
The Financial Times pointed out that the six GW deal announced
with AMD is enough energy to power Singapore for a year.
But there are other deals too. All in, Open AI has committed to
building 23 gigawatts of new data centre capacity, which they
say will cost well over a trillion dollars to develop and
it seems would require 23 nuclear power stations to power
up. In Texas, where several Stargate
sites are planned, electricity demand is rising so quickly that
some operators are installing on site gas turbines and exploring
nuclear partnerships to avoid waiting for grid hookups.
The XAI data center in South Memphis is running gas turbines
with no emissions controls and no permits, creating enough
pollution that, according to Politico, the area surrounding
it leads Tennessee in asthma hospitalizations.
There's not just one or two of these firms.
All of the big tech firms in the United States and a bunch of
additional firms in China and elsewhere are building out AI
capability. So as I mentioned earlier,
McKinsey now estimates that $5.2 trillion in CapEx will be needed
by 20-30 just to build the data centers required for the
projected AI workloads. On top of that, data centers
powering traditional IT applications are exected to
require $1.5 trillion in capital expenditures, meaning that we're
talking about almost $7 trillion in projected data center
spending over the next five years.
The firms are not generating sufficient revenues to justify
that spending and don't appear to have a path to profitability
planned out yet. For a technology that was
supposed to make scientific breakthroughs, cure diseases,
and maybe even replace human cognition, A surprising amount
of AI output looks like slop, and sometimes worse.
Open AI Sora can generate realistic video, but the most
viral clips so far have been deepfakes of Taylor Swift and
SpongeBob as a character in Breaking Bad.
Yo Sponge, This stuff looks extra crystalline, like
restaurant quality. Then there's Elon Musk's XAI,
which has built an anime girlfriend chatbot, which many
feel is an improvement over the Hitler one, which should
hopefully keep basement dwellers occupied for the foreseeable
future. There's also a cartoony red
panda version if that's what you're into.
As easy as it is to make fun of this, there are many less widely
discussed breakthroughs. The 2024 Nobel Prize for
Chemistry went to two Google DeepMind researchers for their
pioneering work on AI powered protein folding, which promises
to expedite drug discovery and development and is already being
used to combat cancers and other diseases.
A number of my viewers think of me as being anti tech and anti
AI as I've made fun of many of the more ridiculous claims out
of Silicon Valley. And there are a lot of them to
keep up with, like the Hyperloop, the metaverse, AI
enhanced water bottles, the general usefulness of the
blockchain, and passing off short term office rentals with
free beer. As a tech business, there are
plenty of uses for AI which don't involve generating slop,
but people do seem to love slop. Open AI is not profitable.
It's spending much more money than it brings in in revenue and
is doing so at a pace that would give most CFOs post traumatic
Stress disorder To fund its infrastructure build out.
The company has secured a $4 billion revolving credit line
from a consortium of banks. This is very unusual, unusual.
Historically high grow tech firms raised capital through
equity, especially if they were burning cash, as lenders like to
see predictable earnings. The shift from equity to debt
and from public listing to private investment is happening
across the sector, where data center providers are borrowing
against assets like racks of GPU's, which might quickly
become obsolete. This creates a strange dynamic.
The companies building the infrastructure are borrowing to
serve customers who are also borrowing or being subsidized by
their investors. The whole system appears to be
leveraged on optimism for now. The money is flowing, and for
users, as I argued in my Blitz scaling video from a few years
ago, it probably makes sense to make the most of these expensive
AI tools that we're currently getting for free.
It's not clear how long that can last for though, or who'll be
left holding the bag if AI providers can't flip to
profitability. The GPU rental market is already
showing early signs of stress and the build out is only
getting going. According to FT Alphaville, the
price to rent Nvidia's B200 chip has dropped from $3.20 an hour
to $2.80 per hour in just a few months.
Older chips like the A-100 are now available for as little as
$0.40 per hour. That's below break even for many
operators, even under ideal conditions.
They calculate in the article that a cluster of eight chips,
which would have cost around $200,000 five years ago and has
a five year useful life, would need to have generated about
$4.00 an hour in rental fees just to break even.
Back in 2020, the average rental price for an A-100 was $2.40 an
hour. That's now fallen to around
$1.65 per hour. And to make it worse, the
average is being skewed by high hyperscalers who are continuing
to charge more than $4.00 when their competitors go as low as
$0.40. If demand for all of this
infrastructure doesn't materialize, it could become
stranded. Data centres built for five
years of peak usage might sit half empty.
The FTP suggests that many pandemic era GPU's will end up
in liquidation, never having earned back their cost.
There is a precedent to this. Telecom firms in the early 2000s
built out fiber optic networks that were never used.
Railways in the 19th century similarly laid track to nowhere,
much of which was later removed. The AI industry is now laying
down gigawatts of compute, betting that someone will not
just show up to use it, but actually pay to use it.
If they don't, the fallout won't be limited to a few start-ups.
It'll hit lenders, landlords, and the Public Utilities that
signed up to support the boom without necessarily
understanding the bet that they were making.
Nvidia's stock market valuation is based on the idea that demand
for its chips is massive and will keep rising, not just this
year, but for the foreseeable future.
The question we have to ask is how much of that demand is real
and how much is driven by Nvidia's investments in other
companies. Open AI is buying and renting
billions of dollars worth of NVIDIA chips, Nvidia's investing
in Open AI, Cor Weave rents NVIDIA chips to Open AI, and
NVIDIA owns a stake in Cor Weave.
The same dollars are circulating through the system, possibly
inflating purchase orders and revenue projections.
It's hard to tell where the demand ends and the subsidy
begins. The circularity makes it
difficult to assess the quality of revenues, and that's why
there are so many people asking if we're in an AI bub.
If Nvidia's biggest customers are also it's investment
targets, and those customers are using Nvidia's money to buy
Nvidia's products, then the margins may not be quite what
they seem. There's also the question of how
well this infrastructure is being used.
Open AI claims to have 700 million weekly users, but only
5% are paying customers. Most of the revenue in the
sector comes from enterprise contracts, not individual
subscriptions. And even among business users,
the success rate of AI pilot projects is low.
McKinsey puts it at less than 15%.
We're not seeing the mass AI driven layoffs that many were
predicting a few years ago. Labor data shows no clear
relationship between AI deployment and trends in
employment, other than for freelance graphic designers and
copywriters, who have seen sharp declines since the arrival of
ChatGPT and some junior coding jobs, which have been in
decline. Not long ago, the complaint was
that American companies were no longer investing.
They were hoarding cash or using it to buy back stock and just
avoiding risk. Now, the complaint is that
they're investing too much and possibly in the wrong things.
The circular deals are big, but they're not overwhelmingly so.
The open AI NVIDIA deal, as an example, should account for
around 13% of Nvidia's expected 2026 revenue, according to UPS.
And that's assuming the full GW deployment goes ahead.
That would mean 50 to $60 billion in total capital
investment, with NVIDIA receiving $35 billion of it
back. NVIDIA says that it might
reinvest $10 billion into open AI, but only if monetization
keeps pace. That's a performance based
approach, which is smart and gives plenty of room to back
out. The financial health of the big
players is solid too. The mega cap US tech firms are
expected to generate over $200 billion in free cash flow next
year alone, even after CapEx. That's enough to fund the
infrastructure build out without leaning too hard on debt or
requiring new financing. The balance sheets are strong
and the earnings are real. This isn't the same as the
telecom bubble. Valuations are elevated, but
once again not absurd. In the late 1990s, Internet
stocks traded at 60 times forward earnings.
Today's AI leaders are closer to 35 times, and the ones everyone
is excited about actually have earnings.
The market isn't pricing in infinite growth.
It's pricing in a bet that AI will be big and that the
companies building it will make a lot of money.
Now, that bed might not pay off immediately.
Monetization so far has been slow, Adoption is uneven, and
some parts of the value chain, especially cloud renters and AI
labs, are more exposed than others.
But the fundamentals are better than they were in past cycles,
and the investment strategies are more cautious.
There's one constraint that doesn't show up on balance
sheets. Electricity.
Open AI. Stargate project alone will
require 10 gigawatts of power, which as I mentioned is around
10 nuclear power stations. The full build out just for open
AI, not for the others, is expected to need 23.
For context, the last new nuclear reactor in the United
States took more than a decade to complete and came online in
20 24. There are no new nuclear sites
currently under construction. Permitting for solar and wind
has been tightened and tariffs have raised costs for those
power sources. Even fast tracked projects face
multi year delays. Some developers are installing
gas turbines on site just to avoid waiting for grid
connections. The chips might arrive on
schedule, the electricity probably won't.
High private market valuations like we're seeing for firms like
Open, AIXAI, and Anthropic only makes sense if one of the men's
up dominating the space. That's what tech investors are
expecting as that's what's happened in the past with Google
dominating search, Amazon dominating e-commerce, Meta
dominating the metaverse and those glasses that Zuckerberg.
It works. If AI turns out to be a winner
take all market then paying up for the winner could be a great
investment, but owning the mall might not as a bunch of them
could fail. If you invested in all of the
big search engines in the mid 1990s it wouldn't have worked
out for you as Google arrived late but then dominated search.
The DeepSeek moment earlier this year caused a bit of panic in AI
as it showed that models can possibly be replicated, located
quickly and cheaply. Elon Musk's rapid deployment of
Grok showed the same thing. These systems might require a
lot of really expensive R&D, but they may not be very hard to
copy. And if the models are all
roughly the same, then the market may not reward anyone
player. Instead of a big winner and a
monopoly, we might see a very competitive market for AI tools
where none have any pricing power.
On top of all of that, there's the question of who profits.
It might not be the model builders, it might be the
businesses that use the models. AI could end up boosting
productivity across the economy, while the labs themselves
struggle to monetize. So while this might be a bubble,
the fundamentals of the biggest companies involved are stronger
than in past bubbles. The investment strategies are
also more cautious, where a lot of the big deals that have been
announced leave lots of room for backing out, but the outcome is
still really uncertain. Someone has to pay for all of
this and it's not clear who wins or if anyone does.
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Bye.