This week Sam Altman, perhaps AIS biggest influencer, just sat
down with a journalist and answered a really interesting
question whether or not we're in a bubble.
And he said, and I quote my opinion is yes, in the same week
that Chachi BT had a mass rebellion over users desperately
wanting the drug that is Chachi BT4-O and other loved models and
Sam Altman announcing billions of dollars in spending on data
centres and infrastructure over the next 10 years.
Today we're going to examine this contradiction and try and
answer the question of whether we think AI is really in a
bubble. This is in the leap with Jack
Horton. I hope you enjoy the show.
Let's start right at the top with what he actually said now
with the journalist and The Verge.
He actually said he thinks we're in a bubble.
And what he said was actually quite interesting and quite
clever because when he was asked are we in a phase where
investors as a whole are over excited about AI?
And he said his opinion is yes. And he called it insane that AI
startups would say 3 people and an idea are getting massive
valuations. And he warns that someone's
going to get burned here and that person who loses out are
going to lose in a phenomenal amount of money.
Now Almond's point didn't come out of nowhere.
It was just obviously days after church.
GBTS work probably could be only described as a disappointing
launch, or at least the reception of that launch has
been disappointing. And actually you could argue
that it's just damage control after maybe that big flagship
product of the year didn't really go down as well as maybe
they hoped. Now, his point in particular was
that when bubbles happen, smart people start to get overexcited
about, let's say, a kernel of truth, as he puts it.
And if you look at most bubbles in history, most of the time,
like the tap bubble, there was a it was a real thing.
And that's that kernel of truth. We know this thing is going to
reshape the world. And he thinks AI might be doing
the same to people. Everyone knows it's going to
reshape the world. And that's that kernel of truth
that people obsess over. And therefore that's so big, but
that's so badly. But by comparing it to the.com
bubble, actually what he's doing is implicitly arguing that for
all of the failed companies and the companies that I guess
popped during that bubble, there are the Amazons.
And those Amazons are the ones that will take the lion's share
of the profits. And obviously he's implying that
Open AI is going to be one of those Amazon companies.
Now, I think a lot of this narrative, and it's been a real
narrative that's emerged online in the last few days can be
boiled down to this, I guess, summary and tweet by Antonio
Garcia and Nick Bauman. And the reason it's such a good
explanation of, I guess this narrative around the bubble is
because of the historical analysis it makes.
What basically they say is that during the Web 2 revolution, so
when the Internet first emerged and you have the Amazons,
essentially what they say is that venture capital money for
very wealthy individuals were basically going towards paid ads
to pump the number of monthly active users on the platforms.
And then crypto essentially use a very similar mechanism, but
raising money from retail investors and people who are
desperate to make some money. And what they were doing was
spending money on user rewards to then try and get more people
using the platform, again, inflating usage with artificial
access to cash and AI. What these people are claiming
is actually using venture capital money like Web 2 did.
And what they're arguing is that in the AI industry right now,
what's happening is this venture capital money being used again
to subsidize compute costs. So costs to pay API providers
such as open AI or data centers and infrastructure, which as a
result inflates the number of people using it for the amount
they're paying. And what they say is that
eventually this is just going to blow up at some point because
the venture capitalists, whether they stop putting money in,
which means that nobody can afford to get usage, and because
they never delivered real value, the entire industry just kind of
collapses like a souffle. So in other words, every
technology boom basically is being driven by external
capital, by investors that subsidizes much of the costs,
which helps create artificial demand that then evaporates when
eventually the money runs out. And he thinks that following
this playbook, that AI is just therefore in a bubble, and
eventually it's going to follow the exact same path.
Now, if you've been following this podcast, you might guess
that I don't agree with the analogies and the comparisons
they're making. But I guess before I give my
personal opinion, I'll go through some of the arguments
and the data supporting this opinion.
So TechCrunch did an investigation into the cost of
these, for example, AI coding assistance is just one example,
and the cost of compute, so paying for API services and all
the infrastructure that sits behind everything.
And so they did an investigation and they actually found that
margins on many of these products are either neutral or
negative. So that means they're losing
money on almost every single user interaction.
And if the user always wants the best model, because why would
you not, then each competing company is forced to always have
the best model or the newest thing, which just cost more and
more money. And when the gross margins are
sometimes negative, then that screams like a big problem.
And The Information reported that Replits are another big AI
coding platform. Its revenue and gross margins
fluctuated between or its gross margins, shall I say, fluctuated
between 36% and -14% which really showcases that pain and
relying on 3rd party AI models. And for context, as a SAS
companies are technology providers selling software as a
service for a subscription or a license fee.
You're looking for eighty 8590% gross margins.
So this data makes it I guess easy to maybe assume or think
that we must be in a bubble because clearly venture capital
money is just subsidizing these big companies and they're all
going to go bust eventually. Well, personally, I don't buy
that. There are many, many reasons why
and I'm going to go through some of them.
And one of the first ones is that this data, what it didn't
reveal is actually the speed in which these companies are trying
to solve these problems. Because naturally clever
successful companies just adapt. They make something work.
They've got a very good incentive to fix this problem.
A great example of this is Replit.
So one of the companies that I talked about just a minute ago,
they actually replaced their previous pricing model to a
usage based pricing model and that increased the gross margins
to 23%. So from -14 to 23%.
Again, still very small numbers, but big and clever companies
have the incentive to figure it out.
Another reason I don't buy this historical analysis is that
there's just this huge infrastructure build out right
now, which is extremely long term.
Because the historical comparison to say, Web 2
suggests that these external subsidies of venture capital
money, for example, props up completely unsustainable usage,
which completely vanishes when funding stops coming.
But I don't really see that. For example, infrastructure
investments look nothing like what you see in a big bubble
because the scale is just unprecedented and it's long term
focused. A great example of this is
Equinox who has just secured a GW of nuclear energy.
These are 20 year power purchase agreements for factories that
are still in development. To me, this just shows that AI
is reaching such an industrious scale it's now being treated
like aluminium or steel mills in power planning.
And Bloomberg just announced that U.S. data center
construction is actually on pace to now take over office building
in America. So that is now more data centers
being built out than there are general office buildings being
built. In a world still adjusting to
remote work, this slack in office construction is now being
filled by server farms to basically just fuel AI.
Another really big thing to me is that adoption is through the
roof. So when adoption metrics are
disconnected from real value creation.
So Web 2's monthly active users or cryptos total value on the
chain, then I'd worry about a bubble.
But AI presents a different dynamic because each interaction
with an AI system is representing usually some form
of work being performed. So usage data just as of this
month. So in August 2025.
It's staggering in both scale and what it shows us about
value. We're literally a point where
ChatGPT weekly users have hit 700 million, which is up from
400 million in just February, and ChatGPT is on track to have
more conversations per day than all human beings combined.
And even more telling is token processing, because Google
announced that it's now processing 980 trillion tokens
monthly, which is double the level of just two months
earlier, which represents 104% growth in usage just in two
months alone. Unlike previous bubbles, each
token represents a specific number of words and is actually
a request for AI to perform actual work or something, often
quite helpful, whether that's writing code or analyzing data
or drafting documents. And the ChatGPT 5 backlash, to
me, validates real usage and value.
Users weren't suddenly abandoning ChatGPT, and AI tools
because they weren't happy with GBT 5.
They were actually demanding online in a movement that they
bring back the old models that people used to live.
There are literal Reddit posts online about how people feel
like they've just lost a friend because the GPT 4-O model has
been killed. This is the behavior of people
who have integrated AI so deeply into their lives that they
couldn't imagine not having this thing in their life anymore.
So this bubble thesis also assumes that the subsidized
costs remain super, super high, making there for a eventual
pricing correction almost inevitable.
But I don't agree with that either because what we're seeing
is pricing improvements happening faster than ever.
So for example, in the past year open air has GPT 4 with an 8000
context window had cost $30 per million input tokens and then
$60.00 per million output tokens.
Whereas GPT 4 turbo just a year later costs 50% less and then
67% less than that. And ChatGPT 5 is coming in even
cheaper than that. So this pricing model validates
underlying demand. When Replit shifted to compute
time billing so usage based Millet bidding margins
immediately increased. When cursor added usage charges
to their product, customers started paying more money rather
than churning. I'm not saying that this startup
world is going to be easy for many of these companies in the
space, but there's evidence of real value creation, not
artificial demand propped up by extremely low prices.
And finally, to me, this ignores a very important discussion
because many of these metrics are talking about consumer
dynamics, whereas most sustainable businesses are going
to be built on enterprise integration.
So this survey found the integration complexity was the
number one barrier to enterprise adoption, ranking even above
cost. It's expensive, it's time
consuming, it's really specific to individual businesses, but
it's where the recurring revenue gets built.
So let's come back to where I think this could be heading.
So scenario 1 is that we have, I guess, small pop of the bubble
within a very large build out of infrastructure.
So we might see some correction of the markets over this next
period. So maybe there's going to be a
drop in the value people are placing on equity by maybe 30 to
50%. So you're not going to get these
hugely inflated valuations really quickly, which may be
results in many start-ups being bought by other companies and
then resulting in less venture funding going into the space
potentially. But infrastructure spending will
just continue because major tech companies see this as absolutely
essential no matter what the short term sentiment is.
And to track this, it'd be quite simple.
We track GPU utilization rates, maybe data center construction,
build outs over the world, despite the fact that stock
markets might go up and down. So let's go to scenario 2 of
what could happen, which is I guess a very soft landing and
massive scale up. So eventually markets slowly
adjust what they expect to see. Revenue growth from many of
these companies starts to catch up from the amount they've spent
and gross margins for all these companies again start to go up,
not down. In this scenario, enterprise
adoption will just accelerate as integration challenges get
solved. Both scenarios assume again that
infrastructure build out continues.
And I guess the question really to me is just how bumpy the
financial ride becomes. So I guess to conclude, this
discussion definitely captures something real about the current
AI market because we are seeing subsidized usage, we're seeing
massive valuations and really unsustainable unit economics and
gross margins for many of these companies.
So the application layer absolutely shows bubble
characteristics. But at the same time, there's a
lot of evidence saying the absolute opposite, the immediate
utility that's driving massive adoption, the infrastructure
commitment spanning decades, the rapidly reducing costs of all
these incredible services. And most importantly for the
success of these products, the deep psychological integration
that ChatGPT really showcased. And this, to me, points to
something that's very durable, more durable than a typical
bubble. So the next six months are going
to test these narratives. And I might turn out to be
completely wrong, but if infrastructure spending
maintains its current trajectory despite these corrections that
we'll probably experience, and if enterprise adoption continues
to grow, and if users keep demonstrating a love and in some
very bad cases, a dependency on these AI tools, we'll know that
we're witnessing a build out disguised as a bubble.
Anyway, I hope you enjoyed today's episode and I'll see you
next week.