In recent weeks, there's been a lot of market anxiety about the
sustainability of the AI boom. This was partly driven by the
outrage around Sarah Fryer, Open AI finance chief, floating the
idea that a government backstop for its $1.4 trillion data
center build out might be a good idea.
Fryer quickly walked back her suggestion in the LinkedIn post
later that day, saying that she had meant that the government
needed to play their part in combination with the private
sector to contribute to America's AI growth and that
Open AI was not seeking a government backstop for their
infrastructure commitments. Her statement, while attempting
to calm the outrage, only confused matters even further
about how the not yet profitable start up plans to pay for its
massive AI data centre and chip commitments.
Sam Altman tweeted on the Everything app.
We do not have or want government guarantees for open
AI data centres. We believe that governments
should not pick winners or losers and the taxpayers should
not bail out companies that make bad business decisions or
otherwise lose in the market. Then it turned into a Bill
Aikman tweet at that point where he went on and on for around 20
pages. At first I was thinking, who
would write a tweet that long, and then I realized he he'd
probably just used ChatGPT. He knew that people would only
read the first few lines, but he wanted to seem thoughtful.
So how to churn out an entire novel?
The core problem for Open AI is that they've signed more than
$1.4 trillion in infrastructure commitments over the last few
months with the goal of building out the data centers that it
says are needed to meet soaring demand.
But they are nowhere near having the money required to complete
those deals. FIRE gave the example of having
to hold back Sora 2 for months due to compute constraints.
I just want to be clear what it means when I say we're compute
constrained. It means that for example, we
cannot roll out our new models when they are ready.
So when Sora 2 was ready to when Sora 2 actually launched, there
was probably a good 6-7 months actually gap there.
OK, the agreements that they've signed have raised lots of
questions around how a cash burning company with tiny
revenues relative to their plant spending can possibly make such
huge commitment. This was not the first time Open
AI has looked to Washington for help, either.
Just a month ago, the company sent a detailed letter to the
White House urging the federal government to double down ON
Semiconductor subsidies, asking the tax credits be expanded to
cover the entire AI supply chain, from chip fabrication to
data centres and grid hardware. The company argued that
broadening eligibility for taxpayer funded subsidies would
lower the effective cost of capital de risk early investment
and unlock private capital. Open AI and it's data centre
partners are of course amongst the largest buyers of
semiconductors in the world, so any subsidy would directly
benefit them. AI is being pitched to
governments around the world as being a matter of grave national
security and economic importance, akin to past
industrial mobilizations like the Manhattan Project and the
Space Race. If AI companies can put it on
that level and pitch it as being too important to fail, a
government funded backstop might make sense.
The irony though is that while lobbying for taxpayer support in
the name of geopolitical survival, the same businesses
are pumping billions into building models that generate
weird anime girlfriends, SpongeBob deepfakes, Sam
Altman's Studio Ghibli style profile photo and in Elon Musk's
case, a chatbot that appears to have been hard coded this week
to constantly flatter him in the cringiest manner possible.
Which caused all sorts of hilarity on the everything app
formerly known as Twitter this week.
We'll come back to that in just a minute though.
While all of this was going on, NVIDIA warned for the first time
in a regulatory filing that it's customers ability to secure
capital and energy for AI data centres could potentially slow
it's growth. On top of that, Amazon launched
A complaint with the Public Utility Commission of Oregon
that the electric utility was failing to provide sufficient
power for the four new data centres that had built,
highlighting the strain that rapid data centre expansion is
putting on electric grids. I guess the utility agreed to
hook them up to the grid, but not necessarily to provide them
with the power that they wanted. The question hanging over
Silicon Valley is not so much whether AI will change the
world, but whether the world can afford to build it.
This brings us to Nvidia's earnings report on Wednesday
night. The tech rally that has defined
much of 2025, especially after deliberation day sell off in
April, began to lose momentum in early autumn.
Some analysts traced the inflection point to when Open AI
announced a $300 billion cloud deal with Oracle and NVIDIA
pledged up to 100 billion in reciprocal investments.
Those headlines, which were meant to signal confidence,
instead raised questions about circular financing and the sheer
scale of spending commitments. Private credit blow UPS added to
the unease in markets, reviving concerns about lending standards
and fraud in a market already stretched by aggressive
leverage. Valuations were lofty, and the
spaghetti diagrams of interlocking deals with
hyperscalers funding AI labs that fund chip makers that fund
hyperscalers started looking increasingly fragile.
No surprise, then, that bubble talk intensified.
Nvidia's earnings report on Wednesday temporarily eased
those fears. The world's most valuable
company and the beating heart of the AI trade posted a 62% jump
in revenue for the three months to October, far ahead of
expectations. Data center sales hit $51.2
billion, and the company raised its revenue forecast for the
current quarter to $65 billion. For now, the numbers seem to
just by the hype. As Robert Armstrong put it on
the Unhedged podcast, the worry is not invidious price to
earnings ratio. The worry is that the revenue
it's earning, and the growth rate of that revenue is
ultimately unsustainable. At today's pace, Nvidia's
valuation makes sense. The question is whether the
growth curve can defy gravity indefinitely.
Open AI's finances look even more precarious than most people
realize. Microsoft's September earning
filing revealed that Open AI lost roughly 11 1/2 billion
dollars in a single quarter, it's worst on record.
That pushes year to date losses north of $25 billion against
projected annual revenue of about $20 billion.
The company has raised nearly $58 billion in equity so far and
was valued at $500 billion last month.
The company is talking about an IPO at a $1 trillion valuation
next year, which would float the shares on an exchange and
possibly bring in about $60 billion in cash.
But that's just over 4% of it's $1.4 trillion infrastructure
commitment. To bridge the gap, Open AI has
leaned on creative deal structures.
NVIDIA has pledged up to $100 billion in reciprocal
investments, while AMD granted Open AI warrants to buy 10% of
its stock for a penny per share if deployment milestones are
mad. Sarah Fryer, Open AICFO,
explained the company's financing at the Wall Street
Journal event. While she's from Northern
Ireland, she must have been in Silicon Valley long enough to
know that the first step in raising capital is to use the
magic word. The innovation on the finance
side to pay for it is massive. She then went on to say.
We've raised equity as a private company, very kind of typical
path, but we've raised a lot. We're building a really healthy
business. So free cash flow, CFO's
favorite way to fund anything that is absolutely climbing
quickly. But I think the third area we've
gotten into is really working with our ecosystem to do some
really interesting financing deals.
I'm particularly proud of the AMD warrant structure that we
put in place just a few weeks back because it's very strong
alignment of incentives. This was a really bizarre claim
as Open AI can't fund anything with free cash flow.
When that cash flow is negative, she then digs into explaining
the AMD warrant. What we've seen is when someone
comes out and says we're going to work with Open AI, they admit
immediately are often seeing kind of impact on their stock
price. And so to the extent that that's
going to happen, we would like to have some alignment on that.
And I think Lisa and team did something incredibly creative
with that warrant structure. The warrant deal between Open AI
and AMD is a strategic partnership where Open AI
commits to buying billions of dollars worth of AM Dai chips
and in return, AMD grants Open AI warrants to purchase up to
160 million of its shares, which is about a 10% stake in the
company at a nominal price of 1 cent per share.
When the deal was announced, AMD stock went up at 24%, but the
deal only vests if Open AI buys 6 gigawatts of AMD chips, hits
undisclosed milestones, and AM DS share price triples.
The AMD deal would bring in almost $100 billion worth of AMD
stock if all of the targets were hit, including the tripling of
AMD stock price. But it's tied to six gigawatts
of chip purchases, which she later explains.
So one GW data center build today is about a $50 billion
investment. That's for one gig.
How that really breaks down is about 15 billion is for the land
PowerShell and about 35 billion is for the chips.
So to bring in $100 billion, they have to spend $300 billion.
Nvidia's $100 billion pledge to invest in Open AI is also tied
to reciprocal commitments. If all of these deals worked
out, Open AI could bring in $200 billion.
But that still leaves them $1.2 trillion short.
And they're burning 10s of billions of dollars per year
with no end. Insight The unit economics of
running the current generation of LLM is dire.
As Paul Kedrosky explained it on the Odd Lots podcast.
The incentive seems to be for all players to just grow the top
line as much as possible, even if adding more users just leads
to greater and greater losses. The models have negative unit
economics, which is a fancy way of saying we lose money on every
sale and try to make it up on volume.
In AI, costs rise almost linearly with usage, which is
very different to traditional software.
There's no marginal cost magic going on.
According to Forbes, despite an invitation only roll out, Open
AI may be losing around $15 million a day or $5 billion
annualized on Sora 2. It's AI video generating app.
Tech firms have always been creative about financing, but
Open AI's approach borders on the surreal, where it's become
all about trying to find infinite money glitches.
MicroStrategy, or Strategy as it's now called, is trying a
similar trick with its Bitcoin investments, which I don't
expect to end very well. Behind the headlines is a
financing structure that looks increasingly baroque.
Hyperscalers and AI labs are using special purpose vehicles
so that they can borrow but keep the debt off their balance
sheets. Tech firms have essentially been
reinventing structured finance to build AI models so that they
can generate AI girlfriends. That is just the world that we
live in and I will always love you, Sarah Fryer explained at
the Wall Street Journal Offend that each GW of compute costs
around $50 billion, where 15 billion is the land and
infrastructure and $35 billion is the GPU's.
From a financing perspective, people know how to finance data
centres. They typically all have 2025,
even 30 year lives. Those are easy things.
I would say today to finance chips have not been as easy to
finance because number one, I think we're all still getting
our arms around what is the life of a frontier chip.
What this means is that the more innovation that happens with
chips, the faster they can be expected to depreciate.
And so the $35 billion worth of chips in a $50 billion data
centre are very difficult to finance.
People don't want to own them if they might collapse in value
when a new one comes out, and people really don't want to
accept them as collateral on alone.
That's when she put forth this idea.
And so this is where we're looking for an ecosystem of
banks, private equity, maybe even governmental, the ways
governments can come to bear. Meaning like a federal subsidy
or. Something meaning like just
first of all, the the backstop, the guarantee that allows the
financing to happen, that can really drop the the cost of the
financing, but also increase the the loan to value.
So the amount of debt that you can take on top of an equity
portion? For some federal backstop for
CHIP investment. Exactly.
And I think we're seeing that. I think the US government in
particular has been incredibly forward lean has really
understood that AI has is almost a national strategic asset and
that we really need to be thoughtful when we think about
competitive competition with, for example, China.
Are we doing all the right things to grow our AI ecosystem
as fast as possible? Essentially, the problem is that
they want to lever up their bet on AI, but banks wouldn't want
to lend, and the interest rate on a loan backed by rapidly
depreciating chips would be so high that you would need the
government to back the loan. Now I can tell that this will
make some of my viewers angry, but there's actually no need to
get angry about something like this, as both Sam Altman and
Elon Musk have both explained in the past that AGI will soon make
money obsolete, so who cares now?
Even if the money materializes and then suddenly doesn't matter
anymore, the electrons may not open AI.
Stargate project alone would require 10 gigawatts of power,
which is roughly 10 nuclear power plants.
It's full build out implies 23. And that's just open AI.
Google has a model. Facebook, or whatever they call
themselves has one too. There's Grog, good old Grog, and
Tropic, and lots, lots more. What I'm saying is we're going
to need a lot of power plants. We're going to need a bigger
boat, and we also have to plug in our cars and robots.
Only one new nuclear power station has been built in the
United States in the last 30 years.
It took a decade to complete and was the most expensive power
plant ever built. Bloomberg estimates that AI
driven electricity demand will more than double over the next
10 years. Utilities are already balking.
Amazon has filed A complaint against Pacificorp for failing
to deliver promised power to 4 Oregon data centres.
Pacificorp says that it's protecting other customers from
indirect harms or translation. We can't turn the lights off in
Portland so that Jeff Bezos can train a chatbot behind the
meter. Gas turbines are proliferating,
too, as stop gaps. Some operators are whispering
about nuclear partnerships. These fixes create stranded
asset risk, as a natural gas plant lasts 30 years and a GPU
cluster might be obsolete in 18 months.
Lenders see the mismatch, and they flinch.
Tech firms that promise to dematerialize the economy now
need more concrete, copper, and electricity than the steel mills
did. The cloud, which was supposed to
be weightless, turns out to be very heavy.
So why keep spending? Well, because the game is framed
as being existential. US labs talk about sovereign AI
and competition with China. Once you call something
existential, the limit on spending becomes unlimited.
Polkidoski described it as a meta bubble on the Odd Lots
podcast. Tech hype, Real estate, state
speculation, loose credit and a potential government backstop
all in one. There are some bubbly signs.
I remember in 1999 seeing adverts on CNBC for a company
that manufactured equipment used in the way for fabrication steps
of making semiconductors. I couldn't understand at the
time why they were paying for TV adverts when their customers
would all know who they were and what they sell.
No one watches CNBC and decides to start manufacturing computer
chips in their garage. I later worked out that they
were advertising the stock, not the products.
The stock fell around 80% over the next three years.
Recently, I've seen a tech CEO being interviewed wearing AT
shirt with his company's ticker symbol on it.
Not the company name, the ticker symbol.
I've noticed that every podcast I listened to over the last few
weeks seems to have adverts for an AI military tech company.
And once again, I wonder if they think that their potential
customers might be listening to a Bloomberg podcast or if they
just want to pump the stock. I'll note that the CEO of that
company constantly. He talks about burning short
sellers while dumping his own stock.
Even if there is a bubble, it can be impossible to know when
it'll pop. As I mentioned a few weeks ago,
the big tech firms funding a lot of the AI spending are so
profitable in their core businesses that they can afford
this gamble. So should investors cash out of
the stock market then? Well, probably not, unless they
know how they'll get back in again.
If you're a diversified investor with a long holding period, even
if you invested the day before the 1987 crash, right before the
credit crunch, or right before the COVID sell off, you might
have been uncomfortable for a while.
But if you stayed invested, you weren't good returns over time.
The Economist estimates that should an AI crash occur.
Her it could erase 8% of US household wealth and cut
consumption by $500 billion, or 1.6% of GDP.
They show that at the peakofthe.com bubble, the market
cap of the S&P was 124% of USGDP.
When the bubble burst, tech stocks lost on average 76% of
their value. Since CHAT GP TS launch in 2022,
American stocks are up 71% and the S&P is worth 175% of GDP.
They point out that a crash today would have a bigger effect
on ordinary Americans than it did 25 years ago, as the share
of household wealth in the stock market has climbed from 17% back
then to 21% today. If the stock market fell as much
as it did back then, it would wipe out as much as 8% of US
household wealth. Foreign investors who are
heavily invested in US tech would take a significant hit,
too. The fallout wouldn't stop at
Silicon Valley. Pension funds, REITs, and
private credit vehicles are exposed to AI investment, too.
Utilities that built gas plants for data centers could be left
with standard assets. The last time America overbuilt
infrastructure this aggressively was the telecom boom, and much
of the dark fibre that was laid back then was never lit.
As I said a few weeks ago, the tech boom today is very
different to the.com bubble of the late 90s where unprofitable
start-ups were racing to IPO after a few months in business,
burning cash on vague promises of eyeballs and banner ads.
Today's big tech firms, Microsoft, Amazon, Google, Meta
are highly profitable, well run businesses with entrenched
revenue streams. They may be pouring 10s of
billions into AI, but if these bets fail, their core
businesses, cloud, advertising and e-commerce remain intact and
cash flow positive. The real risk sits with the
private AI labs and their venture backers, not really with
the hyperscalers. If anything resembles the fraud
of 1999, it's crypto, not trillion dollar companies with
fortress balance sheets. For AI users, this frenzy is a
gift. Competition has meant that the
models improve rapidly and their prices start low.
There's no reason not to use these products while they're
free or almost free for AI investors.
The economics are unforgiving. Better chips make models faster
and make yesterday space chips worthless.
Every leap forward accelerates depreciation on the collateral
lenders are asked to finance. That's why banks refuse to lend.
They prefer assets that last longer than a news cycle.
Sam Altman says that Open AI isn't and wasn't pitching for a
government backstop, that he thinks government should build
their own AI infrastructure. That might happen, but it does
nothing to solve Open AI's problem financing $1.4 trillion
of private data centres with non government guaranteed bonds.
For now, the company is betting that the capital markets will
keep playing along. If they don't, the build out
debate FIRE stumbled into will return louder, sharper, and
harder to ignore. I almost forgot to include this
piece, but one of the funnier news stories of the week was
about Grok, Elon Musk's maximum truth seeking chatbot.
It seems that the code must have been tweaked a bit this week and
adjusted such that Grok's output is more in line with Musk's way
of thinking. Grok began claiming that Elon
Musk is more physically fit than LeBron James, a better role
model than Jesus, and that his intellect is in the same bracket
as Isaac Newton's, that he was a better fighter than Mike Tyson,
and that he's funnier than Jerry Seinfeld.
People quickly worked out that Grok would say that Musk was
amazing at everything, which led to some inappropriate questions
and this headline at Four O 4 Media.
Many of the Grok responses were quietly deleted on Friday, and
Musk tweeted that someone had manipulated Grok into saying
absurdly positive things about him.
I'm sure if he ever catches that guy, he'll be in a world of of
trouble. Thanks again for tuning into the
podcast, which is entirely supported by viewers like you on
Patreon. If you'd like to support the
podcast, I'll leave a link in the description.
Have a great day and talk to you again soon.
Bye.