AI Bubble Special Report: Debt, Depreciation, and Losses — With Gil Luria

Channel: Alex Kantrowitz

Published at: 2025-11-17

YouTube video id: DS5IIClBA9E

Source: https://www.youtube.com/watch?v=DS5IIClBA9E

AI bubble fears are growing as Wall
Street tries to do the math. Let's break
it down in the Big Technology Podcast
Friday edition special report on the AI
bubble with DA Davidson, head of
technology research, Gil Laura. That's
coming up, a one topic special report
Friday edition right after this. Welcome
to Big Technology Podcast Friday edition
where we break down the news in our
traditional, coolheaded and nuanced
format. Today we're actually going to
bring a name that you've heard on the
podcast before because we've read his
analysis and bring him to life for you
at least via voice. Uh Gil Laura, who's
the head of technology research at DAD
Davidson is here with us to discuss it
all. Gil, great to see you. Welcome to
the show.
>> Thanks for having me, Alex.
>> So I have really appreciated your
analysis. Um, often times when we see
Wall Street analysts uh weigh in on
trends, it's typically we typically hear
from them about the things they think
are going well, but less about the
things they think uh are not going well.
You're somebody who's uh who's really
called balls and strikes. You've talked
about, you know, the companies that are
doing this the right way, the companies
that are doing it the wrong way. And uh
that analysis is super valuable because
we find ourselves in this moment where
Wall Street and really all of us are
trying to figure out whether the AI
investment curve is going to keep going
the way it's been going or whether it's
a bubble and everything is going to pop.
So let me give you at least to start the
argument that it is not a bubble. And
this is coming from uh Reed Alberg at
Semaphore. He says, "AI is in a market
of opportunity and uncertainty, not a
bubble." He writes, "The market punished
AI stocks like Orweave and Palanteer
this week. It seems like the world is
convinced that the AI bubble is
deflating, and nobody wants to be the
last one out. This isn't just a Wall
Street phenomenon. Every tech dinner
I've been to lately, a good chunk of the
conversation was spent on journalists
peppering bullish tech executives about
how long this really can last." And yet
what the executives are saying makes
sense. They are selling a product that
customers can't get enough of. And the
total addressable market for this
product is virtually every person and
company on the planet. And this week we
saw AI companies touting rosy numbers
from AMD CEO Lisa Sue predicting the AI
compute market would grow to one
trillion to anthropic getting profitable
by 2028. So for all this talk of AI
bubble, too much debt, uh I think Reed
is making a really good point here which
is that there is insatiable demand for
the products and you do have public
company CEOs like Lisu uh who like have
to have some rigor behind the things
they say talking about these major
numbers uh and even companies like
Anthropic which are losing a lot of
money planning to get profitable in a
few years. So what's your read on this
Gil? There's a lot to unpack there. And
the framework that I do it with is to
say both things are true. So AI is the
most revolutionary technology that we've
had in a really long time. Whether it's
back to the internet or back to the
industrial revolution. We'll only know
in retrospect, but clearly the tools are
very powerful and are getting better.
All you need to know to do to know in
order to realize that is just use them.
may ask Chad GPT to do things for you
that are hard that you would ask other
people to do whether it's summarizing,
writing, giving you advice and you see
that not only is it incredibly capable
but it's much better than it was a year
ago and it's much better than the year
before that. So yes, there is insatiable
demand for this product. That is true.
That's there's a lot of healthy behavior
around that capability and the healthy
behavior are reasonable, thoughtful
business leaders like the ones at
Microsoft, Amazon and Google that are
making sound investments in growing the
capacity to deliver AI and the reason
they can make sound investments is that
they have all the customers. They have
all the business customers and by
extension of their relationships with
OpenAI and Anthropic, they have all the
consumer relationships as well. And so
when they make investments, they're
using cash on their balance sheet. They
have tremendous cash flow to back it up.
They understand that it's a risky
investment and they balance it out. So
all of that is true. At the same time,
we are seeing behaviors that are
unhealthy. And that's where those of us
that have lived through financial
bubbles or technology bubbles are
recognizing patterns that we've seen in
the past and are saying, "Hold on, this
is unhealthy behavior." And and there
are companies that are are exercising
unhealthy behavior. And that's what
we're being called we're trying to be to
call out. And uh that's why there is a
good reasonable debate here is what's
healthy and what's unhealthy. And you
mentioned several companies here, so I
want to touch on a few of them because
they represent some of this range.
Halunteer is the best company in the
world right now. I'm not going to even
say the best software company. They're
the best company in the world right now.
Because what they are able to do is go
into a company and ask them, "What's
your biggest need right now? What is it
that you think AI can do for you?" And
then do it soup to nuts. And that's why
you're seeing extraordinary growth rates
there. and they're being incredibly
successful both there and on the
government side where they're doing sim
similar things in just a more
clandestine way. Then there's companies
like Cororeweave which is the poster
child for the bad behavior that I'm
talking about. We're talking about a
startup that is borrowing money to build
data centers for another startup.
They're both losing tremendous amounts
of cash and yet they're somehow being
able to raise this debt capital in order
to fund this uh this buildout again
without having the customers or uh or
the the visibility into those
investments paying off. So there's a
whole range of behaviors between healthy
and unhealthy and we just need to to
sort that out so we don't make the
mistakes of the past. And we we can
delve into why I think debt is an
unhealthy way to uh to invest in data
centers. I think that's a worthwhile
discussion because when we just say,
"Oh, we don't want debt financing."
There's a reason for that and it's not
just our experience from the past.
>> Okay. Actually, let's go right there.
right now. Um the debt is an issue. I'm
thinking about companies like Oracle,
which is taking on Oracle itself is
taking on a tremendous amount of debt to
fund its AI data center buildout. Uh
with this like promise that OpenAI will
eventually pay them some revenue that it
may or may not materialize. seems to me
I mean as someone who's not spent too
much time digging into the finances of
Oracle that they're effectively
leveraging the company on Sam Alman's
ability to deliver revenue and effective
maybe profitability growth for OpenAI.
Then you have Meta um which has had a
lot of cash on the balance sheet and
they're now using debt to fund their AI
data center buildout. Uh maybe with Meta
you could say uh you know companies
typically build this way and even though
they have the cash it just makes more
sense for them from financial standpoint
uh because there are arguments to say
okay fund it with debt who cares you'll
pay it back everything's growing well.
Um but I I actually will will turn it to
you and and hear your perspective on why
debt is such an issue here. We we've
just started to see debt make its way
into this conversation. So how why is it
a problem and how concerned should we
be? So, we have to go back to finance
101, right? There's certain things we we
finance through equity, through
ownership, and there are certain things
we finance through debt, through an an
obligation to pay down interest over
time. And as a society, for the longest
time, we've had those two pieces in
their right place, right? So debt is
when I have a predictable cash flow and
an asset or and or an asset that can
back that loan and then it makes sense
for me to exchange capital now for
future cash flows to the lender. So
again the conditions are an asset that
is longstanding that can back the loan
and or predictable cash flows to support
the loan payments. Right? That's why we
have a mortgage. A mortgage is an
example of both. All right, a mortgage
is wait a second. If I if I stop paying
my mortgage payments, the bank owns the
house. And since they only lend me 80%
of the value of the house, even if the
value of the house goes down a little
bit, they'll be fine. And they have an
access to my income, which is relatively
predictable even on Wall Street. And so
they know that I'll pay my my mortgage
payment. That's that's a loan that
should be there, right? We use equity
for investing in more speculative things
for when we want to grow and we want to
own that growth but we're not sure about
what the cash flow is going to be.
That's that's how a normal economy
functions. When you start confusing the
two, you get yourself in trouble. And
that's what to your point Oracle is
doing. They're saying, "Well, I have
this startup that's promising me $300
billion dollars of revenue at a high
margin over the next five years. So, I'm
gonna go borrow money to build out the
infrastructure in order to deliver
that." And and what Oracle has been
exposed as is, hold on, OpenAI promised
me 300 billion. They also promised
Microsoft 200 billion, Amazon 38
billion, Cororeweave 25 billion. In
total, 1.4 4 trillion. And who's this
company that just promised all that? A
company that at best will have $15
billion of revenue this year and will be
losing more than that. So we'll be
losing probably more than $20 billion
this year. So are they in a position to
for me to borrow money because I have
certainty around those cash flows? No.
That's that's bad behavior. And that's
what we're talking about here is if
you're borrowing money to make a
speculative investment based on a
speculative customer, that's bad
behavior. And and frankly, that's what's
dragged the the market down over the
last few days is the realization that
this bad behavior is happening. And
nobody wants a piece of that. Okay. So,
but what are the consequences then if
you can't I mean, all right, let's say
Oracle, let's keep with this Oracle
example, right?
they can't they can't pay. Uh you know
they're building these data centers. Uh
it's uh one of those things that all
right let's say the music stops and you
know open AI is like all right there's
not going to be any more AI improvement
left or for any number of reasons uh AI
development slows down. They're like
actually we're not going to need those
data centers um or we don't have the
money to pay you.
>> What is is it it seems like that's just
a local issue for Oracle. Is that or an
econ is it that or is it an economywide
problem?
>> The answer to that is it depends on what
magnitude we're talking about. Again,
lessons of previous cycles and
especially the financial cycle. If we
have tens of billions of dollars of debt
into an asset that stops being a
productive asset, then if there's a
problem, it's the people that issued
that debt or own that debt that lose
money. And the people that own stock in
the companies that made those loans,
Oracle, Cororeweave, etc., they'll lose
if it's tens of billions of dollars.
some financial firms will lose and and
that's and and mostly the owners of that
debt and that equity will lose. The
problem starts happening when you get
into hundreds of billions of dollars of
debt, which is where we were headed, at
least as of a couple of weeks ago.
Again, OpenAI, this startup, great
startup, great product, a startup
committed $1.4 $4 trillion to all these
entities. So those entities as well as
OpenAI could go out and raise debt
capital which means they were seeking
they and their customers were seeking
hundreds of billions of dollars of debt.
If we are here 2 years from now and
there's hundreds of billions of dollars
of debt and the demand for AI stabilizes
or we built enough data centers to
support the demand we have at that point
in time and the price for leasing pieces
of AI for renting access to GPUs goes
down.
All of those assets then can't pay
enough to pay the interest expense on
that debt. all of that debt defaults at
once. Now, we're talking about systemic
risk. That's what folks are warning
about right now is it's okay if some
financial investors lose tens of
billions of dollars here and there. If
we have hundreds of billions of dollars
of debt into what is really just one
product with one price and that price
goes down and all those assets become
worthless, now we're going to drag the
entire economy down. And again, we're
all saying this from experience.
Everybody should rewatch The Big Short
and not just to see Christian Bale and
Margot Robbie and Selena Gomez. It's a
great movie that talks about how it's a
little problem until everybody does it
and then it's a big problem that affects
everybody, right? Okay. So, one more
question about this. Who are they
borrowing from? Like who are the
institutions that are giving them or the
the investors or the individuals giving
them this money? And if it I mean Gil,
you lay it out so well. Uh we don't know
this is uh speculative. Shouldn't really
use debt for speculation because again
it could go under and then you could
have a problem. Um who are they
borrowing from and what do you think the
calculations were from the people
lending this money that they who
obviously understood the things that
you're saying and said, you know what,
let's give them the give them the cash
anyway.
>> Well, the short answer is the largest
institutions in the land. US Bank, JP
Morgan, Mitsubishi Bank, those are the
companies lending to to Coreeave. Uh and
and and again, the math they're doing,
we believe, isn't the right math. Let me
dig into that. What I mean by that a
little bit. Again, this is a speculative
asset. Just because we're all using it
and excited about, which we laid out at
the beginning, we are and it is exciting
and we need a lot more compute. It's
still a speculative asset in the sense
that we don't know how much of it we're
really going to need in two to five
years because we don't have experience
doing that. This is brand new. We don't
know how much a GPU is going to rent in
5 years. And so when we get the revenue
projections from Open AI that they're
going to make like, you know, a hundred
billion dollars a year, I guess I'm
being exaggerating a bit.
No, you can't trust you can't trust them
because you just don't How do they know
>> exactly? So, so one,
AI may turn out as well as we we expect,
but it may not. And two, OpenAI is not
in a vacuum. They're competing. Part of
the reason they're overpromising and and
creating this too big to fail and and
fake it till you make it and getting
everybody else to have skin in the game.
Part of the reason they're doing that is
because they know they're competing with
Meta and with Google and with Elon,
people that have a lot more resources
than they do. So for them to say, "Oh,
we're going to have a hundred billion
dollars of revenue by 2027, which Sam
Alman just did," is completely
disingenuous. He has no idea. He's
competing against much bigger, more
powerful companies that have technology
that's at least as good as his. So, so
lending money based on that is is
dangerous because again these GPUs,
you're building a data center, you're
renting out GPUs, and right now maybe
you're renting out a GPU for $4 an hour.
And maybe that way the business makes
sense. But these GPUs keep getting so
much better every year that that same
GPU in just three years may be only
renting out at 40 cents an hour at which
point the data center is literally
worthless because that won't be enough
to cover the expense of operating the
data center. So this is where we get in
trouble when somebody underwriting a JP
Morgan or US Bank or Mitsubishi Bank
ignores that. And to to answer your
question, why would they do that? These
are professionals. It's because they
don't have the downside, right? They
have a mandate to deploy capital into
AI. They get a they got an order from
their boss who got an order from their
boss that says, "We don't have enough AI
in our portfolio. Go find me AI to
invest in." And so somebody comes to
them and says, "Hey, look, I'm building
a data center. Lend me money. I'll pay
you 9%. That's fantastic interest." And
you sign up for it. You get a big bonus
that year based on signing that deal. If
the deal goes sour, if if the data
center is worthless in three years, you
don't care. You're not giving your bonus
back. That's the world we had back in
the financial crisis. That's how we got
in trouble then and that's how we could
get in trouble now if we don't do
something about it.
>> So Gil, it's a great segue because you
brought up the big short and this has
definitely been a week where that is
applicable because Michael Bur basically
the star of that movie uh of that story.
Um he who the guy who effectively
shorted he shorted the housing market
when he saw that um we were engaging in
extremely speculative loan behavior to
people who should not get loans. um he
has started to well not started he has
sounded a real alarm now and it is
interesting because the incentives that
you described are sound exactly similar
to the incentives of the people writing
loans for people for the subprime
mortgages people who shouldn't have have
have gotten that loan for a house they
couldn't afford u but they were going to
get their bonus anyway right it's the it
mirrors that story and this week Bur
made headlines because he'd shut down he
completely shut down his his firm Um and
he he basically um you know described it
to the valuations and the behavior we're
seeing uh with AI and for the reason
that you just outlined which is
depreciation. Here's a tweet from him.
Understanding depreciation by extending
useful life of assets artificially
boosting boosts earnings uh is one of
the more common frauds of the modern
era. Okay. So basically, if you don't if
you don't if you don't accurately
capture depreciation of the GPUs, he's
effectively calling it a fraud.
Massively ramping capex through purchase
of Nvidia chips and servers on a 2 to
threeyear product cycle should not
result in the extension of useful lives
of compute equipment. Yet that is
exactly what all the hyperscalers have
done. By my estimates, they will under
state depreciation by 176 billion from
2026 to 2028. By 2028, Oracle, there's
Oracle again, will overstate earnings by
26.9%,
Meta by 20.8%,
etc. But it gets worse. Um, and so that
so that then Bur basically, you know,
closes up shop. So what he's saying is
that like all these companies say that
these chips will depreciate over five or
6 years, but like you said, if the if
the Nvidia chips get that much better,
that much more quickly, um we could have
a much more accelerated depreciation,
making the data centers that they're
investing billions in today worthless,
as you put it. How do you evaluate Bur's
critique of the situation? Sounds like
you agree with him. He's spot on. He's
spot. By the way, Big Short is a story
of how he was spot on, but he almost
didn't make it. Right. A lot of the
movie is about how long it takes to play
out and you can be right, but if you're
right too early, you you don't make it.
And and the story is about him and the
handful of people that did make it.
There were a lot of people that were
short the market for a long time and
lost everything because they couldn't
wait long enough. He was just in a
position to wait. And he's spot on right
now. And and look, depreciation gets
wonky. So let me just hit it at a high
level because it is really important to
this conversation, right? Depreciation
is based on an accounting standard that
helps companies say well how I have an
asset. How long is it used for? How long
can that asset generate revenue for me?
And if it if it can generate revenue for
me over five years, then I should take
the cost of acquiring that asset and
spread it over five years as an expense
for accounting purposes. Right? That's
that's what accountants are there to do.
And these accountants spent time 3 to 5
years ago with companies like Microsoft
and Amazon and said, you know what,
based on where the technology is now,
we're looking at these chips and it
looks to us like they can generate
revenue for you for about five or six
years. And and that's why we're going to
allow you to depreciate that to extend
your depreciation to five to six years
because then you have less expenses and
you look more profitable. So we're going
to allow you to do that. But what's
happened over the last 3 years is the
technology has taken huge leaps forward.
Jensen Wong has been preparing for this
for decades and here we are and we can
make the most of the the brilliant chips
that he's designed and now he can make
one every year that's 10 times better
than the one he made the year before.
And that's great. That's why we have all
these great tools and that's why they're
getting so much better. But back to
those accountants, if you ask the
accountants today,
how long will this asset generate
meaningful revenue, they would not
answer five or six years, they would
probably answer three years. And to Mr.
Bur's point, if you told Amazon and
Microsoft, and certainly if you told
companies like Oracle and Cororeweave,
no, no, no, no. these chips will only
generate meaningful revenue for three
years. Their profitability would decline
very dramatically. So again, for
Microsoft, Amazon, Google, I don't worry
about that too much. They can handle it.
For companies like Corweave and Oracle,
it means like they'll never be able to
raise any more capital again, which
would which means they would all go
away. So that's why this point is
important. Even though it's a little
wonky, it's in because when these
companies come back and tell you, "Oh,
no, no, no. I have 5-year-old chips that
work just fine." That's not the same
thing. Saying, "I have a 5-year-old chip
that works just fine," doesn't mean that
it can generate the same revenue that it
did 5 years ago, which is the accounting
question. So, that slight of hand by
these companies to tell you that the
chip still works. If it's only
generating 1% of the revenue it
generated five years ago, it's by all
intents and purposes worthless. And so
that's where we have to ask the accounts
the right question and I think we will
be over the next couple of years and
we're going to be correcting this
dislocation which is what Mr. Bur is
betting on.
>> Right. Sat Yadello was on Dwaresh's
podcast this week in an interview with
him uh and Dylan Patel from Semi
analysis and he basically was talking
about this uh a couple what a year ago
two years ago the the H100
uh Nvidia chip was state-of-the-art now
we're already talking about the we
Blackwell is deploying there's another
generation coming out and the generation
after that is is underway via Rubin
which is going to make these H100s which
again just came out a couple years ago.
Um, you know, not I wouldn't say
completely worthless, but um, you were
paying $30,000 per GPU a couple years
ago. Um, it's going to be very hard to
justify uh, you know, having that same
value, which is what you're pointing
out. But let's look at the other side
here, which is uh, semi analysis has the
counterpoint from Jordan Nanos. Okay.
Jordan says there's basically no
precedent to say the chip would wear
would fail out would fail or fail or
wear out in two to three years.
>> Uh the hardware manufacturers have
contracts that are standard for 3 to 5
years and they offer extended warranties
for 6 to 7 years. The proof of Bur's
argument would be uh predicated on
Nvidia releasing chips so dra that that
so drastically outperform the current
generation in two to three years that
all hyperscalers everywhere are
incentivized to go through another
capexile capex cycle. They'd have to all
buy new chips and rip out the existing
ones. That seems like a much farther
leap than saying we might be able to run
these chips for five to six years in the
data centers themselves. What do you
think about that?
>> A couple of things here. So, first of
all, I think uh I like that Nvidia picks
fun names like Vera Rubin and Richard
Fineman to to name their chips. I like
that as a naming convention. He's
clearly a dork, right? In the best way
possible. The other thing to note is
that Dylan uh and and Daresh are
roommates and boy is that a fun
apartment to hang in hang out in, right?
Those those two are some of the smartest
people around and I love hearing them
speak. Um, now that I've said that, I'll
I'll bring you back to the fact that
what they just said is is slight of
hand, right? The fact that the chip
works after 3 years doesn't mean it's
going to generate the same revenue,
right? You could have a working chip. I
could have a a a 10-year-old Mac uh or
or a 10-year-old PC that still turns on.
I I wouldn't want to use it because it
wouldn't be able to to do the things I
need it to do. So just the fact that it
doesn't break after three years doesn't
mean that it can generate the same
revenue that it did three years ago. So
that's that's one level of slight of
hand. The other thing they point to is
oh don't worry about it. We do have
5-year-old chips that we're still
renting out at decent prices. And that's
really just a function of where we are
right now in the expansion cycle. We are
so short on chips to process these open
these AI transaction these AI token
generation inference transactions
that people are renting out anything
they can get their hands on. This is
like used cars during COVID. People
would pay a premium to a new car to to
buy a used car because there were just
no cars around. That doesn't mean that
that used cars was worth more than a new
car. It just means there was so much
scarcity that people overpaid and that's
where we are right now. That's not a a
sustainable situation because everybody
is building out data centers. And again,
even if you took out the bad players
that are lending that are borrowing
money to build data centers, you still
have Amazon, Microsoft, Google, Meta,
Elon using cash flow to build data
centers. So, we're building tremendous
amount of capacity. Once that capacity
even gets close to catching up, then
then the old chips that can't do as many
calculations for you will be worth a
fraction. And this is where the market's
going to end up right here. Markets are
efficient. And and here's where the the
equilibrium is. Here's where we'll get
to to the balance of supply and demand
is on dollar per per flop. Dollar per
flop is to say dollar per calculation,
right? Remember what these AI chips do
is they generate tokens which is what we
call words or numbers or images. And if
a a Richard Fineman chip can generate X
tokens per second and an H100 can
generate 1 1 millionth of a token per
second or take a million seconds to to
generate a token, then the Richard
Fineman chip is worth a million times
more than an H100. Even if the H100's
working, we won't use it because it
can't keep up. it's not worth it because
we'd rather use a chip that can generate
the amount of tokens that we need. And
so this is two different conversations.
Will the chip work? It might, but will
it be worth something? Will it be able
to do enough computation to generate
revenue? That's a completely different
question. And that's where we think in a
three-year time frame, three-year-old
chips will just not be able to do enough
computations to be worth keeping them
on. So either we replace them or we use
them for much less important things that
will generate much less revenue. So
again we have to be careful not
confusing does it work with can it
generate revenue.
>> And just to go back to the bur point
then what he's saying is there because
these companies are writing the chip
depreciation at 6 years or maybe who
knows seven years um but they're not
going to actually be doing anything. um
that uh effectively they're going to be
overstating their their profits uh and
you know smart in smart investors is he
saying that smart investors will catch
on and then bing their valuations
because of it or what's the risk there?
That's exactly it is that all we're one
account conversation away from having
all these companies have to report much
lower profits and you know we use uh we
use profit multiples to value companies.
So if a company has to depreciate most
of its assets over three years instead
of five years that means their
profitability is going to go down
proportionally and and we could have in
in an stylized case the value of a
company decline by 40%
because an accountant said you have to
depreciate this over three years instead
of five years. That's why this is very
real. Sounds wonky but this is very
real. If you reduce a company's
profitability by 40%, their value will
go down by 40%.
>> This is, by the way, this is why I
thought it was important to have this
conversation on the show because it is
like when debt gets involved, uh that's
when uh things start to get and and
Bur's talking about more than debt,
right? He's talking about because these
depreciation costs can even hit the
companies like the Microsofts that
aren't taking on a tremendous amount of
debt to do this. So, that's another
issue. So these these conversations,
debt, depreciation,
um this is where the rubber meets the
road on the AI bubble conversation. It's
one thing to say the valuations are out
of whack. It's another to say here's
actual the actual pressure points and
these are the pressure points.
>> That's right. And and again, I go back
to Microsoft, Amazon, Google, they can
handle it. They have a big business.
It's diversified. This is only one part
of the business. They can they can
literally stop on a dime. Again, they're
deploying cash cuz right now that cash
is sitting on their balance sheet and
generating 4% returns. And so they're
saying, "Well, this AI thing is huge. We
think it can generate 15% returns based
on our math. Let's use the 4% cash and
deploy it here. We think we can get
those returns. But by the way, the
second we think that stops, we could
stop our capex on a dime, go through a
couple years where we don't do any capex
while we absorb the previous
investment." and those companies will be
just fine. It's the companies that
either have the debt or are lending the
money or are or have equity investments
in highly leveraged entities. Those are
going to be the ones that can't handle
that kind of a a transition.
>> Is this does this all is it all sort of
forgiven to use that term if open AI
just delivers what Sam Alman promises?
>> Yeah, maybe it's worth having the the
our mental framework for looking at AI
is that there's three camps, right?
There's we have to take a weighted
probability of three outcomes. There's
the pessimist outcome which is AI is
cute and it's useful but it's cute like
um like the metaverse or it it's cute
like social media in which case it'll be
a useful technology but really we're
we're spending way too much money on it.
Then there's the op and a lot of people
are in that camp right now. I'm not sure
I agree with that. There's the optimist
scenario which is AI is the most
powerful technology in in in in a long
time. It will be so powerful that it
will make us so much more productive
that it will drive an acceleration in
GDP growth that this is where Microsoft,
Amazon, Google are are at from their
perspective. And then there's a
maximalist scenario which is we are
maybe as close as a couple of years away
from super intelligence. A technology
that can do anything a human can do
better than any human. In which case
it's going to replace us on mass. Create
uh untold wealth to whoever owns a piece
of that. And therefore no investment we
could possibly make is enough in order
to get there. Especially if you believe
that only one the one entity that gets
there first will own everything. This is
where Mark Zuckerberg lives. Sam Alman,
Elon Musk, Dario Mod, they live in this
maximalist camp of this is a race that
we can't afford to lose and therefore we
need to build everything we can. Now all
three of these things are possible and
so you have to plan ahead for that. But
let's but most of us are in this
optimist camp which is we should invest
a lot. We just have to be thoughtful and
careful about how we do it. So if we're
wrong and it's the pessimist scenario,
we don't bring everything down with us.
>> Economy doesn't fall apart. You know,
that would be
>> and by the way, let's leave ourselves
room. But maybe the maximalist scenario
is right, too. So let's at least be near
the rim when that happens so we can be
competitive there. That's the healthy
way to see all this and uh and again it
it accounts for the fact that AI is very
good. It will grow a lot. By the way,
OpenAI when we step and talk about Open
AI is is is as an entity and what it's
doing. First of all, I want to I want to
give them credit and put some blame as
well. So, let's give them credit for the
fact that in November of 2022,
Google was sitting on GPT and it left it
in what they call the pantry. They chose
not to introduce GPT because they didn't
know what to do with it or if it was a
good idea to share it with the world.
And OpenAI came out and said, "This is
unbelievable. People are going to love
this." And they came out with a great
consumer product that we now know as
Chad GPT and now has 800 million weekly
active users and is driven the fastest
growth of any startup ever. Give them
credit for that. Then let's talk about
the detriment of their behavior
recently. which is they have extended
their ambition to a point where they've
made all these commitments that they
can't possibly live up to and as that's
been exposed they've been dragging
everybody down with them. So open AAI is
just this unique entity. Now, if you
were to ask me what should OpenAI do,
some people do ask me that question. I
would say um just focus on Chad GPT.
Just focus on having the best frontier
model. Ramp Chad GPT. It's an amazing
product. You have a head start. People
are using Chad GPT as as the verb. Like
they used to say, I Google this. People
are saying I ch GPT this.
>> If they just focused on that and grow it
responsibly, they will be very
successful. if they go down the current
path overcommitting,
deciding that they have to build their
own data centers, they need their own
hardware, they need their own chips,
they won't make it. So hopefully for for
the the benefit of of of their customers
and their shareholders, I hope they
focus on what they're really good at,
which is this model and Chad GPT.
>> Okay, I have more questions uh about
this for you. We do need to take a
break. So, let's uh hop away for a
moment and come back and continue. I
guess we're going to have to call this
the AI bubble special report because
there's so much to discuss. So, we'll
continue talking about that and we will
try to hit some of the news. Uh so,
we'll do that right after this.
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And we're back here on Big Technology
Podcast Friday edition with Gil Laura,
the head of technology research at DAD
Davidson. Gil, we've been talking a lot
about the potential um the potential uh
risks here of the AI buildout and uh we
ended with OpenAI. Let's just uh go
right back to Open AI here. Um is it
possible that already, you know, you you
kind of in the first half separated the
companies that are behaving well with
the companies uh that are not? Um, I
just want to ask you this. Is it
possible that companies are already too
leveraged on Open AI? Here is uh the
Wall Street Journal. Big tech soaring
profits have an ugly underside. Open
AAI's losses. Uh, here's the story.
Quarterly profit soared at Nvidia,
Alphabet, Amazon, and Microsoft. Is AI
revenue related? Poured in. Cash flows
are mostly fine, albeit a lot is now
going into building new data centers.
Some of the money comes from actually
selling AI services to businesses. But
much of the AI related profits come from
being a supplier to or an investor in
the private companies building large
language models behind AI chatbots and
they're losing money as fast as they can
raise it. OpenAI and Anthropic are
sinkholes for AI losses uh that are the
flip sides of the chunks of uh flip
sides of the chunks of the public
company profits. I think this story says
something like 60. Okay, here's here
here it is. Uh, opening eyes loss in the
quarter equates to 65% of the rise in
the underlying earnings uh of Microsoft,
Nvidia, Alphabet, Amazon and Meta
together. And that ignores Anthropic
which Amazon recorded a profit of 9.5
billion from its holding in the
lossmaking company in the quarter. So
all these profits that we're seeing from
these companies, not all, but certainly
the majority is just the money that
these two companies are spending on the
buildout. How does that equate with this
idea that, you know, I mean, maybe
they're doing it responsibly, but
certainly the the all these companies
stock uh share prices have have jumped
dramatically this year. And so again,
going back to our bubble question, isn't
that a problem, too?
>> Yeah, absolutely. But let's let's parse
that out a little bit. Let so first of
all, OpenAI is is a really big part of
Microsoft Azure growth and Microsoft
Azure is the most important business
within Microsoft. Um so let's let's
focus on Microsoft and just say that
this is actually less true about Amazon
and Google. They're a lot less reliant
on on Open AI and even Anthropic. Uh but
let's focus on Microsoft and say that
half of their AI revenue approximately
uh which is again a big piece of the
Azure growth which is the biggest piece
of Microsoft's growth is coming from
Open AI. Um, let's talk about the other
half of the AI growth for Microsoft. The
other half of the eye growth is very
healthy. That's companies because
everybody's a Microsoft customer going
to Microsoft and saying, "I I I built
this AI tool and I really need compute
capacity to be able to use it. Um, I'll
buy it from Azure." And then Microsoft
says, "That's great. we'll sell you the
the GPUs, access to the GPUs, but then,
you know, on top of that, we'll send you
we'll sell you some database products
and data warehouse products and data
fabric products and and and oh, by the
way, your your Microsoft 365 license is
going to go up because you're going to
use C-pilot and this is great for
Microsoft. So, all the other AI stuff is
absolutely great for Microsoft and it's
a big reason why they've done so well.
Then, let's talk about the open AI piece
of this. Absolutely. This is the piece
that's at risk because to your point,
OpenAI is a negative gross margin
business. They claim they're not, but
they are, right? Which is to say it
costs them more to answer your chat GPT
question than they make revenue from
you. And and that's something we need to
be aware of and concerned with, right?
Especially if it's a big part of
Microsoft revenue. It's like, wait a
second, you're getting this for a
company
>> that losing money.
I just saw this week there was uh
someone who tweeted that like OpenAI
Switcher says either they will answer
you immediately with some slop or
they'll go out and spend $10,000 on
compute thinking through like your your
query. Um because you're right, it does
seem like with these especially with
these queries that require a lot of
reasoning, it just takes a lot of
computing processing power. So I didn't
mean to jump in but um sorry.
>> So that this is why it's dangerous,
right? But let's let's think about
here's an analogy that I think is very
useful for understanding why this is
okay from Microsoft perspective and
that's Uber. If you remember when Uber
started the rides were a lot less
expensive than taxi. Let's say it was
like a let's call it a $10 ride anywhere
across town which was so attractive that
everybody started using Uber. And and
what what happened was we were we
started using Uber a lot more than we
used cabs before and it started
replacing driving and we expanded the
market for riding well beyond where it
was because the price was so attractive.
But then what happened is we changed our
behavior and we started using it so much
that Uber can then gradually
ratchet up the price to a point where
today Uber is a very profitable company
because it's a $30 ride and and some
people may may have may not be using it
as much as they did when it was $10, but
most of us are because we've changed our
behavior and we see a lot of benefit in
using it and now we're paying the
appropriate price. The appropriate price
wasn't $10. It was always $30. and now
we're willing to pay $30 because we've
learned over time that that's beneficial
to us. We see value in it. We're willing
to pay for it. The same thing should
happen with these chats. And let's use
chat GPT. It's still the leading one.
Right? Right now, I may be paying $20 a
month. Very few people are paying $200 a
month. But my neighbor Jane, who has her
own law firm, is using Chad GPT so much
in her practice that she could postpone
or even avoid hiring another associate.
So let's say that associate was $100,000
a year. If she's paid $20 a month, even
$200 a month, that creates so much value
for her that in the future, if she
continues to do this and realizes she
never has to hire that associate, she
could just use Chad GPT to summarize
deposition, extract important
information, help her strategize, uh,
and and create documents, and she
doesn't need to hire 100,000. She may be
willing to pay 10, $20,000 a year. So,
as we use Chad GPT a lot more broadly,
we're going to be willing to go from a
$20 a month price point to a much higher
price point. So, at some point, this is
a product that will be profitable. We
just have to expand the usage so much
that the people that are using it in a
very u valuable way will be willing to
pay what it's worth. That's the journey
Uber went through and that's the journey
we're going to go through. chat. What I
would point out though is that unlike
Uber and the ride share market which
lent itself to winner takes most or
winner takes all, chat is entirely not
like that
>> because I could have the same
conversation with Gemini and Meta's
going to give me the tools to do this
and Grock is going to give me the tools
to do this. So it's not a winner take
all market which means that that process
of getting to a price point that is
beneficial enough may take longer and
Google and Meta may decide that they
never want to do that that they're
willing to pay for all this compute to
keep you in YouTube and keep you in
Insta and that's where the risk is to a
company like OpenAI and chat but if
you're Microsoft that's okay because
you'll just use your data center capac
capacity to host Grock or or to host uh
another chat that is worthwhile. And if
not, you'll you'll rent that capacity
out to your business customers that are
using it to produce more value in their
business and therefore willing to pay
that premium and then buy databases and
data warehouses, etc., etc. And that's
why the market freaked out when Meta uh
I guess uh was taking on this debt and
increasing its capex because it's harder
to see that direct line of it's going to
be okay if you're a company like Meta
versus a company like Microsoft that has
the data centers.
>> That's right. That's exactly right. And
so a couple of things happened with Meta
because Meta again is an unusual
situation because they don't actually
have business customers to rent this
capacity to. It really is just Mr.
Zuckerberg wanting a bigger and bigger
toy, right?
magical adventure
>> and and again remember the reason that's
happening is that he's a AI maximalist
he thinks that we may be a couple of
years away from having a tool so
powerful he will get to rule it all
that's why he's willing to spend and
what he told investors last time was I
am managing this unbelievable business I
just grew ads ad revenue by 25% I'm
unbelievably profitable doing that but
instead of being disciplined and
spending $25 % more next year. I'm going
to go well beyond that and spend a lot
more than 25% next year. And that's what
investors said. That's it seems
irresponsible, Mark. That's a lot of
money you're spending this year. Why
don't you just spend 25% more next year?
And when he said, "No, I'm going to go
well beyond that." They they sold the
stock.
>> And the other thing that happened
>> before
>> Yeah. then he's okay with it. He owns
the whole thing. as far as he's
concerned, it's his money and and that's
how he behaves and it's worked out for
him so far. So, I don't know that I want
to challenge him. The other thing that
happened with Meta that was interesting
is that when they went out to borrow,
they didn't borrow the capital. They
created a special purpose vehicle,
right? They went out with Blue Owl and
they said, you know, we'll put a couple
of billion in Blue O. you put a couple
more and then you can borrow 10 times
that and build data center capacity for
us and and the reason that hit a nerve
is I don't know if you remember when we
really started using the term
specialurpose vehicle it's about 25
years ago with Enron right and and and
now special purpose vehicle in itself
not illegal hiding it was illegal and
that's what Enron did and that's why
incredibly we actually got to put
somebody in jail. But a special purpose
vehicle is is meta saying the capital
markets are so irrational now in their
ability to lend money to anybody to do
build AI that we're going to use this as
an infinite money glitch and we're going
to have somebody else borrow the money.
It's not going to go on debt as a b to
our balance sheet. It's not going to go
as capex ppn on our balance sheet. It's
going to go somewhere else. We will have
a line item in our balance sheet that
says operating lease commitments, but
it'll be a lot smaller and people don't
pay attention as much to that line. And
and why wouldn't they? If they can, why
wouldn't they? And this is again one of
those things that got people to say,
"Oh, this is unhealthy. We don't want to
be doing this again. We know how this
ended 25 years ago."
>> Uh Gil, can I keep you here for another
couple minutes? I want to talk to you
about this AI prisoners dilemma before
we head out because
>> because the question is again like we've
talked a lot today I think appropriately
about debt about depreciation about
OpenAI's ability to uh pay back its uh
or to actually meet its commitments. Um,
but then there's this question of how
anything becomes profitable and it's not
so simple. And so I think what what I'm
seeing here from Bloomberg is that there
is a suggestion uh from the OddLots team
that there's some game theory involved
that might keep this unprofitable for a
long time. So they're quoting this one
report. An analyst suggested that
there's a prisoners dilemma of sorts in
inference pricing. Inference of course
is when you actually use the models
versus train them. If every inference
application prices its services based on
quality and charges by usage, the market
might remain stable. But because market
share is more important than margins for
the equity investors and the venture
capital investors supporting these
inference firms, every firm has a
greater incentive to offer flat rate
pricing with unlimited usage triggering
a race to the bottom. And by the way,
that would apply to OpenAI and everyone.
So he they say they say everyone
subsidizes power users. Everyone posts
hockey stick growth charts. Everyone
eventually posts important pricing
updates. Now, here's Bloomberg. AI isn't
normal technology. It's not clear
whether they will be at some point,
whether there will be at some point ever
when someone will be in position to say,
you know what, this is good enough. It's
cliche by now, but people talk about AI
like they're building a new god or they
talk about it like they're building
nuclear bomb. And we have to get there
before any country on Earth does. In
fact, it's because of these huge stakes
that in recent week there's been talk
about how a US government might backs
stop some of these companies uh some of
the companies financing and debt. So,
basically what they're I mean what
they're saying is
you you uh this is not acting like a
rational uh uh technology because a
everybody wants market share and b um
yeah they're they're willing to spend uh
to get there and so what do you think
about that? Is it is this going to be a
persistent issue? How should we view
this? Yes. Yes. Because so here here's
the thing. Who who are the players in
this game theory, right? It's Meta, it's
Google, it's Microsoft, it's Amazon.
It's companies that are used to winner
take all markets. And they think of all
markets as being winner take all market.
Meaning if I don't win this market, I'll
get none of it or at least not enough of
it that will be meaningful to me. So
they are willing to do anything to win.
which to the point of that means they'll
be willing to lose money for a long
period of time so they have a chance to
win. And what happens then is it's only
the biggest most deepest pocket player
that can win because they they can wait
it out or at least communicate to
everybody else that they're willing to
wait it out. So that's that's where a
company like Open Air has no chance
because there they can't make it through
another year or two at this level of
spending. So they certainly won't be
able to outlast Google Meta and
Microsoft in this game, right? So and it
explains a lot about Mr. Zuckerberg's
behavior. Again, he's not just spending
the money. He's telling us he's willing
to spend anything to win. He's signaling
to all the other players is I will not
lose. So, you can keep throwing money at
this. I'll keep throwing money at it
longer. And that is exactly where we're
at, which is why we may have persistent
losses for a while here. Uh because
these companies have very deep pockets.
Again, the smaller ones will either get
absorbed by the big ones or just have to
walk away. But those big ones believe
this is winner takes all. By the way,
you can tell I'm not sure it's winner
takes all. I think we could be using
different chat programs over time and
companies will be using different AI.
So, I'm not sure winners takes all. I do
think that some of these companies can
all succeed together, but I do think the
analysis is correct and that they see it
as winner takes all and they're doing
what they can to not only stay in the
game, but communicate to everybody else,
signal to everybody else that they're
staying in the game. And
>> then the Bloomberg piece brings up this
risk because of that, right? So that
there could be um not a credit crunch
credit crunch but a collateral crunch
right a collateral crunch uh is the
sudden collapse in the value of assets
underpinning all these loans. Um and
then they quote this chief economist
from Raymond James Jeff Sa who made this
uh statement before the financial
crisis. Um the risk is that the
contagion spreads and morphs from this
collateral crunch into a full-blown
credit crunch. And that is exactly what
happened in 2008. So is that is that I
mean it sort of kind of encapsulates
what we've been talking about that um
you could you could see a contagion here
from um you know people being burned on
a handful of of these deals. Maybe it's,
you know, just throwing it out there.
Maybe it's the Oracle deal. Maybe maybe
Coreweave. And then saying, "All right,
I'm just not lending. I'm going to
really tighten up my lending practices
because this went bad.
>> Yeah. And I think that's where we're
headed. So again, we're tens of billions
of in so of dollars of debt into this,
which means if it goes away, then some
people get hurt, but not the whole
system. It's only if we get hundreds of
billions of dollar in that will get
hurt. Well, the more likely scenario is
that what's happening right now in the
market might scare these underwriters
straight. they'll stop making these
irresponsible loans and we'll go back to
funding this out of cash flow by the
companies that have the customers and
have the wherewithal and again have the
deep pockets to ride this out because if
you play this scenario out what you'll
realize is Meta Microsoft and and and uh
Amazon Google fully expect all those
companies that are barring to build data
center to go bankrupt.
This is great for them because that
means that in when when they go
bankrupt, they can buy assets at pennies
on the dollar. So if I'm Microsoft, I
know that in two or three years, I
probably won't have to do any capex
because I'll be able to buy data centers
out of bankruptcy for pennies on the
dollar. So I might as well let this play
out. And if irresponsible lenders want
to make these loans, it's their problem.
Um I'm going to be able to capture those
assets when I need it at pennies on the
dollar. All right, Gil, last one for
you. The one thing we haven't talked
about is the potential bottleneck on
power. Uh Satyandela, for instance, was
talking about this um on what? Oh, on uh
on Brad Gersonner's podcast. Um talking
about how like he has chips he can't
plug in because he can't power this. He
doesn't have warm shelves. He can't
power uh the shelves. Mustafa Sulleman
uh CEO of Microsoft AI was on this show
earlier this week talking about how they
do have capacity for training but
inference is a problem. Here's Zero
Hedge who puts it in uh Zero Hedgeway.
Has anyone done the math on how many
hundreds of new nuclear power plants the
US will need by 2028 for all these AI
daily circles
to be powered?
What do you think about the power
question? To me, it's a it's an
increasing issue that like if anything
is going to put the brakes on this,
maybe it's just that the power will will
run out.
>> So, power is the the bottleneck. But
what happens is in in a in a
market-based economy such as ours is
that when there's enough revenue and
profits at stake, we work our way
through bottlenecks. And that's what's
happening and will happen here. So yes,
the grid may not be able to give us
enough the enough capacity to to turn on
a data center because at peak they can't
let us have us access to electricity.
But with storage solutions, they could
give us some. And then what these
companies are doing is putting power
what they call behind the meter, which
is to say generators and turbines and
diesel trucks because it's so lucrative
that it's worth it for them to park 10
diesel trucks and run them so they can
power those chips because they make so
much money renting out those chips. So
we will find a way. Markets find a way
and we're going to find a way through
this. It's just a matter of being
creative and you know it's very
lucrative if you're an electrician right
now or an HVAC technician. Boy, are you
making bank. You're you're you're
getting flown on private jets to and
making twice as much money so you can
install a data center. So it's a good
time to be electrician or HVAC
technician.
>> This is all gonna make a great movie one
day, Gil.
>> Yes.
>> Hopefully not as devastating as
>> Yes. I have Adrien Brody in playing me
in the movie.
>> Okay, sounds good. Um, this has been an
AI bubble special report. Gil Laura,
thank you so much for joining us. I I
you know, I feel like I needed this. We
needed this. It's uh the deep dive I've
been waiting to do and I'm so glad we
did it. So, thanks for coming on the
show.
>> Appreciate it, Alex. Enjoyed the
conversation.
>> Same here. All right, everybody. Thank
you so much for listening and watching
if you're on Spotify or YouTube. Nick
Kle, the former president of global
affairs at Meta, former deputy prime
minister of the United Kingdom, is
coming on on Wednesday to talk about
whether we could trust Silicon Valley
with super intelligence, and Nick has
some really interesting thoughts about
the economic value of super
intelligence, whether it'll even make
financial sense to own it. So, we hope
that you tune in then. Thank you for
listening and we'll see you next time on
Big Technology Podcast.