AI stocks are finally getting hit and
you can feel the story kind of evolving
in real time as they do. The tech sector
is in correction territory. Big AI names
are selling off. Broadcom can report
record AI revenue and still get punished
because investors wanted more. Alphabet
and Microsoft can keep growing cloud
revenue and still trade down because the
market is suddenly asking the same
question over and over again. Was this
whole AI trade just a bubble? The
spending numbers are frankly absurd.
Google, Microsoft, Amazon, and Meta are
all on pace to spend somewhere around
$700 billion this year on AI
infrastructure. Some of these companies
are raising debt, some are issuing
stock, power is tight, memory is
expensive, data centers are taking
longer to build. And inside most
companies, the clean story that says
this is where we get a return on all of
those bucks, that's still not fully
there. So, if you want to say this is
starting to look like a bubble, I get
it. It's not an insane reaction, but I
do think it is the wrong core question
to ask. A stock correction will tell you
that investors think prices are
stretched. It doesn't automatically tell
you that demand is fake. And that
distinction matters a lot because the
companies closest to demand are not
pulling back. Open AAI went from roughly
$2 billion in annualized revenue in 2023
to more than $20 billion and counting.
In 2025, Thropic grew even faster.
Nvidia's data center business did almost
$194 billion in fiscal 2026. The
hyperscalers are still talking about
capacity constraints, not lack of
demand. So the question, I think, is not
is AI a bubble. The question is which
part of the AI buildout is speculative
financial froth and which part is the
physical supply chain for demand that
already exists? And that's what I want
to separate in this video because the
lazy version of the bubble argument
compresses way too many things into one
word. It treats inflated stock prices
and aggressive private valuations and
overbuilt data centers and weak
enterprise ROI and Nvidia's revenue and
OpenAI's growth and the whole future of
AI as if they're all the same question
and they're really not. You can have a
correction in AI stocks and still have a
tremendous amount of locked up AI demand
that is not met. You can have some
companies overbuild capacity and still
have the world be dramatically
underbuilt for inference. You can have
weak return on investment in a random
corporate pilot and still have massive
demand for coding agents and research
agents and customer support automation
and model APIs and enterprise AI tools
that actually replace hours of work. The
mistake is treating bubble as a verdict
on the whole technology. It is more
useful to treat the bubble concept as an
invitation to map the sector because
there can be bubble dynamics in the
assets around AI. There can be
overvaluation. There can be crowded
trades. There can be data centers
financed on assumptions that don't
survive contact with reality. Some
investors are going to lose money. Some
suppliers are going to get overpaid.
Some companies are going to build too
much of the wrong thing in the wrong
place. All of that can be true. But none
of it suggests that the underlying
demand is imaginary. Start with OpenAI.
Open AAI has said its annualized revenue
went from $2 billion in 2023 to 6
billion 2024 to more than 20 billion
2025 and growing. That's an insane
growth curve and it is the slowest
growth curve of the hyperscalers.
Anthropic has grown even faster from a
smaller base and now is on a higher
revenue run rate than OpenAI reported.
And this is not about consumer
curiosity. Enterprises now roughly 40%
of the business at OpenAI even more at
Anthropic. And companies are just lining
out the door. They literally can't get
on boarded fast enough. And that matters
because enterprise revenue is a lot
different from I tried the chatbot once,
I subscribed and now I regret it. A
company doesn't keep spending real
budget on AI because a demo was fun. It
spends because someone inside the
company is passionate about this and
thinks this tool is critical for code,
for research, for analysis, for customer
work, for compliance and sales and ops
or some workflow where the old process
was slower or more expensive. Now, do
some of those dollars come from
companies that are chasing FOMO and
they're worried about other companies
adopting AI? 100%. Does that mean that
they are irrational to spend on
intelligence inside their business? No.
They may not use those dollars well. And
that's why we see that forward deployed
engineering push from both these
companies, but the demand is there.
Anthropic and OpenAI are both setting
new records for how quickly a private
company can grow revenue. You don't do
that on a whim. It means there are
paying customers in the system. Look at
Nvidia next. Nvidia's fiscal 2026 data
center revenue was about $193.7 billion.
That is a very clear public signal that
we have massive physical side AI demand.
Those are people willing to put down
checks for chips and systems and
networking and memory and racks and
commitments moving through the supply
chain for data centers. And the
important part is not just that Nvidia
is selling a lot. The important part is
what those purchases imply. Nobody buys
this much AI infrastructure because they
are casually experimenting with a
dashboard. The chips are being bought by
very serious boards and CEOs because
training and inference workloads already
exist and because everyone close to the
demand strongly believes those workloads
are spiking fast. If you cannot have a
bubble conversation at the same time as
you have prominent leaders complaining
about the fact that their developers are
burning through their claude credits too
fast. Those things should not coexist.
And yet this rational market, they do.
Now, this is where the bubble argument
gets even more interesting because the
bears are right about one thing. Revenue
and spending don't match as neatly as
they should yet. Right? If the
hyperscalers spend 600, 700, maybe a
trillion dollars on AI infrastructure,
you need a huge amount of future revenue
to justify that investment. That's fair.
You need enterprise adoption to move
from pilots to production. You need
agents to become reliable enough to run
long workflows and easy enough to roll
out that every company can do it. That's
the critical one. You need software
teams and legal teams and finance teams
and support teams, everybody to change
how they work. That is not a guarantee
that's going to happen in fits and
starts. It's going to happen in an
adoption curve. And if you're an
investor paying a huge multiple for
every company that touches the AI supply
chain, that timing matters a lot. But
this is exactly why the word bubble is
too blunt. The question is not whether
AI is real. The question is whether the
cash flows arrive in the right place at
the right time for the companies that
are financing the buildout all the way
through the supply chain. That's a much
more interesting question because the
demand can be real and the investment
can still be poorly timed in certain
parts of the supply chain. The
technology can be absolutely
transformative and some stocks can still
be too pricey. The infrastructure can be
necessary and some of the builders can
still earn bad returns. This has all
happened before. Railroads were real. A
lot of railroad investors still got
absolutely destroyed in the market even
though it was a tremendous buildout and
a huge success for the economy. Fiber
was real. A lot of telecom investors
still got destroyed. Cloud was real. Not
every cloud adjacent company managed to
capture that value. So when people say
this looks like the.com bubble, I think
the honest answer is maybe in some ways,
but not the way you think it means.
The.com bubble was notoriously decades
ahead of demand. That is not what we
should be seeing here. The.com bubble
did not prove the internet was fake. It
proved that markets can overpric the
first order version of a real platform
shift. It's not that nothing is
happening. The risk is that the market
prices every AI exposed asset as if it's
automatically going to magically capture
value. It it won't, right? Some
companies will provide commoditized
input. Some are going to get squeezed.
Some are going to build way too far
ahead of demand. Some will have the
right thesis and the wrong balance
sheet. But the buildout itself, that's
not a hallucination. The reason is
inference. And this is the part of the
AI spending story that feels very
underexplained on Main Street and Wall
Street. To me, training a model is
expensive, but it's episodic. You build
a huge cluster, you run a training job,
you produce a model, and then you move
on to the next generation. Inference is
different. Inference is the model
running every time someone uses it.
Every prompt, every agent step, every
tool call, every retry, every long
context window, every document, every
codebase, every verification pass. When
AI was mostly chat, inference looked
super manageable. And that was not that
long ago. That was like seven, eight
months ago. A person asks a question and
waits and reads and maybe asks another
one. And a lot of the buildout was
around training runs. Agents in the last
six months changed that math
fundamentally forever. An agent does not
ask one question and stop. It goes
right. It loops. It reads files. It
calls tools. It writes code. It checks
the result. It fixes the failure. It
asks another model to review the input.
It searches again. It runs again. It
burns tokens over and over. That's not a
conversation. That's a production job
that runs into millions and billions of
tokens really, really fast. And once you
see AI work that way, you can't see it
any other way. and the infrastructure
buildout starts to make a ton more sense
because any given agent run can be
thousands of times the inference cost of
a chat conversation. Tokens are not
magic. They're manufactured. Behind
every answer, behind every agent tool
call is a physical production system.
Chips and memory and networking and
power and cooling and land and
construction and ops. And that's why the
capex numbers are getting very serious.
AI makes the most valuable software
companies in the world look industrial
today. Microsoft and Google and Amazon
and Meta don't just ship features
anymore. They're building factories for
inference. And factories are expensive.
They require upfront capital and
utilization and supply assurance. They
require power contracts. They require
depreciation schedules and routing and
batching and caching and efficiency
improvements. So expensive compute is
not wasted on cheap work. That is the
real operating question. It's not is AI
a bubble. The operating question is
this. Are expensive tokens being spent
on work valuable enough to justify them?
That's the question of 2026. And that
question separates what's real in this
AI explosion of demand from what's fake
very quickly. A coding agent that saves
an engineering team days of work can
justify that expensive inference. And
these days, they're saving weeks and
months sometimes. A legal review agent
that processes thousands of contracts
can justify expensive inference. a
customer service system that resolves
real tickets and reduces escalation. You
can justify expensive inference that
way. Now, a random enterprise chatbot on
the website that answers shallow
questions from a stale knowledge base
and provides a terrible customer
experience. Not really justifying your
inference there. And that's why the
enterprise ROI data looks like a
complete mess right now. AI is not one
single thing. It's a generalpurpose
technology. It's a thousand different
workflows with different economics. Some
are really useful, some are terrible
ideas. Some save time at the individual
level but never make it into the P&L.
Some will look impressive in a demo and
collapse when you put them inside a real
workflow with permissions and exceptions
and messy data and accountability.
None of this is proof of a bubble. This
is proof that adoption is uneven and
frankly that companies don't necessarily
know what to do with the new general
purpose technology yet. And frankly, it
should be uneven. Most companies are bad
at process change. They were bad at
software implementation before AI. They
were bad at data projects before AI.
They were bad at cloud migration before
AI. And now they're bad at AI
transformation. And we're all acting
surprised. The technology can be real
while companies struggle with change
management. And that's where I think the
better mental model is. It's not bubble
versus no bubble. It's buildout versus
payback. The buildout is real. The
demand signals are real. The constraints
are also real. OpenAI's revenue is real.
So is Anthropics. NVIDIA's data center
revenue is real. Hyperscaler capex is
also real. And capacity constraints are
real. The payback is the open question
circling around all of those facts. Who
gets paid back and when? How fast do
they get paid? At what margin? On which
workloads does it matter that they get
paid back? How much pricing power do the
hyperscalers have when they are setting
prices for tokens and for workflows?
This is where the market ought to be
more thoughtful. Frankly, if you're
buying every AI stock because AI is the
future, you're not really doing due
diligence and analysis. You're buying in
narrative and narratives can flip on a
dime. But if you're dismissing the
entire thing because stocks corrected,
you are also not doing analysis. You are
reacting to price action and pretending
it is insight. The useful middle ground
requires a lot more due diligence and
thoughtfulness, and it's much harder and
rarer. It says AI is a real platform
shift that is so transformative that
there are a bunch of local bubble
dynamics frothing around it. That means
prices can fall and technology can keep
advancing. It means some infrastructure
might be overbuilt while other kinds of
capacity remain profoundly scarce. It
means some companies will spend too much
and still not spend enough in the exact
place that matters. Yes, that can be
true. It means the biggest winners may
not be the companies with the loudest AI
story today. They may be the ones that
control the bottleneck or own the
customer workflow or route inference
more efficiently or turn Asian output
into durable business value. This is the
distinction I would watch. Don't ask if
a company is doing AI if you're trying
to figure out investments here. And none
of this is investment advice. Ask where
the demand is showing up. Is it paid
usage or is it just engagement? Is it
production workloads or is it just a
pilot that got dressed up in a press
release? Is it improving a workflow with
really clear economics or is it creating
more work for humans to review? Is the
company buying capacity because
customers are waiting or because the
board wants an AI strategy? Is the model
being used where expensive reasoning
matters or is it just premium compute
being burned on cheap tasks? Those
questions are a lot less dramatic than
bubble or revolution, but they're a lot
more useful for determining where
investment dollars ought to go. And they
also explain why the stock correction
does not remotely settle the issue.
Markets can correct and then uncorrect
for lots of reasons, right? Valuations
stretch, trades get crowded,
expectations get too high, capital
rotates, financing costs matter. A
company can disappoint investors even
while the underlying business grows. And
that is what makes this moment so
profoundly tricky. It's not that the
correction is meaningless. It's telling
you that investors are having some
feelings in the middle of a global
energy crisis about underwriting
unlimited AI spending without asking
harder questions. That's great. They
should ask harder questions. But the
correction is not proof that the
buildout is fake. It's proof that, you
know, maybe the easy phase of this trade
is over and the next phase is going to
require a little bit more discrimination
from people who are trying to figure out
investment dollars. The market will
start separating companies with real AI
revenue from companies with AI language
in the deck. It's going to separate
infrastructure bottlenecks from
commodity exposure. It'll separate tools
that create measurable workflow value
from tools that create demo value. It'll
separate companies that can finance the
buildout from companies that need the
buildout to be financed by someone else.
And yes, it will separate SAS companies
that have sticky services that are still
valuable in the era of agents from ones
that don't. and that's super healthy and
it's exactly what should happen in a
real platform shift. The first phase is
almost always narrative. Everyone piles
into the obvious names. The second phase
is correction. The market realizes the
story is more expensive. It's slower.
It's messier than the headlines may be
implied. So when someone asks, "Is AI a
bubble?" My answer is parts of it are
yeah, if you can release a press release
and get a 500% pop in your stock, which
I've seen once or twice, those are
examples of a bubble. But that doesn't
mean the whole system is a bubble at all
remotely. Sure, some valuations are
stretched. Some spending will be wasted.
Some private market marks are probably
ridiculous. Some companies are
pretending a thin wrapper is a business.
The seed rounds are getting really
pricey in the valley. Some enterprises
are buying expensive tools without
changing the workflow enough to get the
value. But the broader buildout is not
hype floating above reality. There's
real demand underneath. There's real
revenue underneath. There's real
physical scarcity underneath. And so the
better question, it's not when the
bubble pops. I don't think that's
coming. The better question is who
survives the sorting that is going to
happen because if intelligence becomes a
production system, then the winners are
not just the companies with the best
demos. They're the companies that can
turn demand into reliable, affordable,
high utilization inference. They can
route the right task to the right model.
They can get power. They can get memory.
They can build capacity. They can make
agents useful enough that customers keep
paying after the novelty wears off.
That's a much harder game than the stock
chart made it look last year. But it's
also a much more serious game. A bubble
is hype detached from reality. That's
kind of the definition of a bubble,
right? The famous South Sea bubble was
all about the hype. It cost Isaac Newton
his fortune. This is messier than that.
This is a real buildout with speculative
money piled over the top. And the
correction is the market starting to ask
which layer is which. And that's
fantastic. And that's a good question to
keep in front of you. You should be
asking where's the paid demand? Where's
the bottleneck really? Who captures
value when the tokens get cheaper? Is
this business able to finance itself? Uh
is the work really moving to agents in
association with this particular
company? And that's where the real story
is. So if you are worried about a
particular movement in the stocks, I
want you to keep these questions in
mind. These are questions that are
evergreen questions. You can come back
to them next month and the month after.
We will take a while as a market to go
through this sorting. Remember, AI is a
marathon. It's not a sprint. The
business of putting AI into companies
and installing it is a 10, 20-year
exercise. We're just at the beginning of
that. We are writing the first chapter.
Think about that the next time you look
at your Robin Hood account or think
about that the next time you think about
where stocks are at. That provides a
larger picture and I think it's a
healthier picture because AI is here for
the long term. AI is the most
transformative technology of our lives
and it can still have a ton of froth
around the edges while that remains
true. I hope this has been helpful. You
can follow me for more relatively sober
takes in a world that likes to argue
about big big overcorrections in
binaries. I do not believe the world is
a light switch world, right? I don't
believe it's either bubble or not. And
I'm going to argue against that a lot
because I think it's a lazy question
that underscores how little people
understand how powerful AI actually is.
As well as how little people understand
the complexities of the AI dynamic. Yes,
there are absolutely places in a
buildout this big where capital is
wasted. And yes, that does not mean that
we don't see absolutely massive unmet
demand with AI. Both can be true at
once. Demand more of your investors.
demand more of your analysis, demand
more of the markets in understanding how
these companies actually work. And
frankly, if you're in financial press,
feel free to get in touch with me
because I feel like this is often
incorrectly reported, especially that
inference piece. I don't think people
properly understand that one. All right,
I will see you in the comments. Let me
know what you think. Sound off on where
you think some of these companies are
at. I would love to hear because we can
argue about that. I think that's a
healthy conversation to have and that
would be a way for us to talk as a
community about which companies are
sorting where and why. All right, I'll
see you next time. Cheers.