August 21, 2026 • 32 mins
Is the AI boom real… or are we watching a bubble get ready to burst? Billions of dollars are pouring into artificial intelligence, but figuring out where that money comes from, where it goes, and whether the spending can last is surprisingly complicated.
Azeem Azhar, entrepreneur, author, and founder of Exponential View, joins Oz to break down the economics behind the AI boom and the findings from his new report, The State of the AI Economy. Azeem and Oz discuss whether today’s massive investments in chips and data centers are sustainable, why companies can spend years on AI before seeing a return, and what recent volatility in semiconductor stocks tells us about the industry.
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Episode Transcript
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Speaker 1 (00:15):
Welcome to tech Stuff. I'm Os Voloscian. We're living through
a pretty extraordinary moment, a technological turning point that is
or inspiring for some and infuriating for many. And it
seems like no opinion is complete without an assertion about
what AI is doing to the economy. Is it a
boom or a bust? Is there a crash in the odds.
(00:36):
Some people think it's the future and the only economic
driver that matters, and others say it's all a big
bubble sending us hurting towards collapse. But how do the
economics of AI actually work? And what does the enormous
amount of money being poured into this technology mean for
our day to day. Our guest today is Azimazar. He's
an entrepreneur, author and founder of Exponential View, a research
(00:58):
center and substack where he just released a report called
the State of the AI Economy. As he welcome back
to tech Stuff.
Speaker 2 (01:05):
It's great to be back, Ols, Thank you.
Speaker 1 (01:07):
One of the things that occurred to you as preparing
for this conversation today was obviously thinking about situational awareness.
You know what started as a manifesto and became a
hedge fund and adventure fund and then had it struggled
a couple of weeks ago. But you know, there's still
very much a going concern. I mean, have you thought
about the exponential Review Fund or do you like to
sit mainly on the kind of observation analysis side of
(01:29):
the table.
Speaker 2 (01:30):
Well, let's talk about situational awagas. I mean credit to
Leopold Aschenbrener who wrote that essay a few years ago.
And I was a little bit skeptical about the numbers.
I think they were too fast. Yeah, he's generally been
proven to be a very very good predictor of what
would happen. And what happened in July was that his
fund imploded. I think it lost thirty five billion dollars
of value in a month. It's still alive.
Speaker 1 (01:52):
He kept his xanthropic stake.
Speaker 2 (01:53):
Yeah. Well, early investors have still done really well. They've
made like twenty times their money, which speaks a lot
to what he had done. I mean, he had basically
run the problem of some leverage is okay, too much
leverage is like over salting your food, and it's going
to be resulting in a really bad outcome. I think
for what we do, I mean, I do think that
it's very helpful to invest when you are looking at markets,
(02:15):
because once you have skin in the game, you are
much much keener about the things that you say, So
you can't be slap dash and be in the peanut
gallery and just sort of shoot your mouth off, like
where are the steaks? Where are the stakes? In all
of this. But I think it's really important in this
particular market to get in amongst the weeds in as
(02:38):
many ways as you can, because this is not like
looking at a market that we've seen before, so you
really have to equally. I talk to bosses of big
and large companies all over the US and Europe to
hear what they are saying about about AI, because there's
just no rule book for it right now.
Speaker 1 (02:56):
I think more than fifty percent who you surveys so
that they believe their job will de end on successful integration.
But let's talk about this report, the state of the
AI economy. What's it about? What we were reporting on?
What was the problem you're trying to solve?
Speaker 2 (03:10):
Well, the main problem is is it real? Is there
any money in it? How much is being spent? And
it's a very very difficult thing to measure, as you
often find with early markets here were used to looking
at mature markets. So we went out to solve that problem.
How much are customers, consumers and businesses spending on AI
(03:35):
around the world and can we get a real economic
value added number out of that? Now, explain why that's important.
If you're spending twenty dollars a month with open Ai,
they will be spending a certain amount of that with Microsoft,
who runs the servers that serve up chat GPT, so
that twenty dollars may also end up with say ten
(03:58):
game to Microsoft. A naive count will call that a
count that as thirty dollars. We will count that as
twenty and apportion it. Of course we have the total number.
So that's a job we did. And what we want
to understand was how much is really been spent by
businesses at the top of the funnel, which is then
distributed to all the companies that are, you know, as
a little stack serving each other to get you that
(04:21):
response to your chat GPT query.
Speaker 1 (04:23):
And how do you do that? How do you deggregate
or deduplicators you put it where the money goes?
Speaker 2 (04:28):
Well, you know this is where it gets interesting. You
have to use a lot of different strategies to piece
the picture together. So a lot of this is happening
in the private company, so they don't have to disclose,
so we will go back and reconstruct their accounts from
public information and other information that has been leaked to
the press. We scan over one thousand AI startups. We
(04:52):
look at when they disclose their revenue. A lot of
this ends up flowing into what we call the hyperscalers.
Companies like Google and Amazon and Microsoft. They make some
public disclosures and then ultimately all of this runs on chips,
and so we know how many chips have been sold,
and so through all of that you do a process
of triangulation and you say, well, these things generally all
(05:15):
have to agree. And that's the big part of the work.
And it's not one thing, it's many, many, many different
things that come together, these different skill sets, different data
to give us that number that we feel reasonably confident with.
Speaker 1 (05:33):
And for the you know, for the average person who's
interested in these topics, but maybe isn't such an insider
that they're a paying subscriber to Exponential View, Like what
what did you find that matters most? What does what
everyone need to know from this report.
Speaker 2 (05:47):
Well, the first thing is you can download the reports
for free on Intelligence on exponential view dot dot co.
You may not get some of the updates, but it's
sixty five pages long, so there's more than enough to
send you to sleep for several days and maybe keep
you awake if you're so inclined. I think the key
thing here is that there is real spending happening. We've
(06:11):
exceeded the one hundred billion dollar per year revenue number
across the industry. It's actually as of the year to
July twenty twenty six, it's one hundred and twenty six
billion dollars. And that that is a number we've got
to about three times faster than with mobile advertising, or
(06:31):
the cloud or the internet itself. So it really is
the fastest revenue ramp in a in any technology wave,
and probably of any sector in history. And that one
hundred and twenty six billion dollars is still growing very quickly,
roughly three times per year, like two hundred percent additional
(06:53):
per annum, and we were really surprised about that. We
had expected that number to be slowing down from last year.
Speaker 1 (06:59):
It had so that was my next question, where does
this money come from? And how much is it from
the AI firms themselves, how much is it from VC
backed companies who may be spending a head of profits, say,
how much of this is like true demand side spending
versus like supply side stimulated spending.
Speaker 2 (07:16):
Yeah, it's super important to separate those out. So the
AI companies will spend on compute to train their models,
and we capture that spending because those are real dollars
that go into the pockets of Microsoft and Google and
Amazon and so on. And they will also, in the
case of open ai, be spending for those that free
(07:39):
tier of chat GPT that most people are on that
they don't yes yet monetize fully through ad revenues, So
we reckon that. That's around about twenty percent at the
top end of the revenue number, but it's a proportion
that is dropping significantly. So if we've looked at this
year and a half ago, that would have been closer
to sixty percent. Since twenty percent of one hundred and
(08:02):
twenty six billion dollars call it twenty twenty five billion dollars.
And then how much of that is other venture backed startups?
That is, you know these startups who raise their capital
from these fancy Silicon Valley firms. That number of from
the venture backed startups is you know, roughly in the
order of four billion a year. Now, the question is
should you discount that? Because venture back startups often do
(08:25):
very well, and I think it's perfectly reasonable. For example,
if you've got a venture backed company in San Francisco,
that the blue bottle coffee shop opposite their office should
be allowed to count as revenue the coffees that the
company buys for the team all hands on a Friday, Right,
I mean, so American capitalism and the rules of accounting
(08:46):
have worked in a particular way, and we should be
careful about where we draw that line. There is of
course a question about like the sustainability of any of
those companies, but we know in general for Formerllion people
in the US who are employed by companies were backed
by venture capitalists. Right, it's not insignificant. So then you
get to the kind of cluster of relationships that have
(09:09):
emerged from say Nvideo. So in Video is a company
that makes the chips that run about eighty percent of
the workloads. And there's an interesting thing here, of course,
so Nvideo has gone out to ensure that the people
who buy their chips and who serve up the compute
have got the ability to buy those chips and serve
(09:30):
the customer demand. So that is you know, supply side
support of an emerging sector. It's really not uncommon. I mean,
there are very very many sectors, and we often go
back to the start of the car industry where Ford
in general motors extended financing to distributors and you know,
(09:50):
literally car salesmen and also to consumers to buy cars.
When you get into a situation where a core supplier
in new sector has got the strongest balance sheet and
the strongest credit rating and the sector is growing very
very quickly.
Speaker 1 (10:07):
It's really it makes sense they become the bank.
Speaker 2 (10:09):
It makes sense they do that. Now. Now the point
is that that can also go bad in the same way.
But I don't think that you can say, like upfront,
but this is always a bad thing to do. This
is not like you know, pilfering from the till. What
it does do, though, is it does introduce a new
kind of class of risk in the system that doesn't
exist if customers are always paying for everything that they buy.
Speaker 1 (10:32):
So how much is this approaching two hundred billion dollars,
and your run rate of AI revenue comes from like
blue chip corporations, how much of it comes from super
users like you know, perhaps you or medium users like me? Like, well,
what's there? What's the split outside of the AI industry
and the venture industry itself?
Speaker 2 (10:51):
If we break that two hundred down, which is the
annualization of the run rate, you know, you're looking at
eighty percent of that being enterprise rather than consumer. You're
up at one sixty of that one sixty maybe twenty
five to thirty at the top end is AI companies
spending on themselves and venture backed companies, which leaves you
in arranged, let's be conservative, one hundred and twenty billion
(11:14):
dollars from enterprises on a run rate, so that was
effectively like ten billion in the last month. And then
through that it's pretty it's pretty diverse. I mean anthropic
and open AI who the ones that we know that
we all recognize have got hundreds of thousands of commercial
customers and they've got dozens, if not one hundreds, spending
(11:35):
more than a million dollars a year. And these are
companies that we might think of as being sort of
advanced tech companies like you know Uber and coinbase, but
it's also you know, the big banks and the like.
And then within that, of course, it leans towards technology
forward sectors and technology forward companies. So I'll give you
(11:56):
one example that may surprise people. If you look at
companies in America that have been bought by private equity,
so not venture capital that which is all the sort
of rocket fuel, and not cash producing public or family
owned businesses, but once owned by private equity, their levels
of investment in AI seem to be much lower than
(12:17):
the average. And the rationale would be that if you've
been bought by private equity, you need to know every
dollar that takes you off the operating plan is going
to give you two dollars back. And if you don't
know that, the owners won't allow you to do that experimentation.
And I think that speaks a little bit too that
where in general bosses are about how comfortable they are
(12:38):
about the return they get from AI.
Speaker 1 (12:40):
Right now, Yeah, you had a headline for one of
your newsletters recently, the AI adopters success and failure look
identical at first. So I had three questions, obviously for us,
on being why, the second being how much patience does
the market have for that at a time where money
is becoming more expensive? And the third is when does
(13:01):
that change?
Speaker 2 (13:03):
Yes, I mean that second question, by the way, is
a brilliant question. I'm going to give you credit. This
is why you are the host of this podcast.
Speaker 1 (13:12):
And takes you see.
Speaker 2 (13:13):
We're really early in where companies in particular are spending
with their AI and that could be one of two things.
It could be that they're going to continue to have
increasing success and that number is going to rise. Or
it could be that they're still experimenting heavily and everyone's
experimenting and these are experimental revenues that might not continue
(13:37):
at this growth rate once companies wise up. And those
two scenarios I think are still live. I think the
balance of evidence is that it's the former, which is
that this number is going to continue to rise. But
why do they look the same? They look the same
because when you get started with a project, whether you're
going to be successful or unsuccessful, you have to make
(13:58):
the same kind of upfront investment. And if you are successful,
you will spend money on that project and then you
will turn the corner and you'll start to make money
from it. But you won't have got to break even.
And a sensible management team will say we've done this once,
now let's do it three times. So just as the
first one starts to pay back, you've now got three
(14:20):
projects that are in the red. And then they'll say
let's do it ten times. And so the depth of
that red, which you call a JCOB, will will continue. Now,
an indisciplined company that is running into all sorts of
failures might not expand as quickly, but they might let
unsuccessful projects run for longer and longer. And that means
(14:41):
for a couple of years, two two and a half years,
a successful firm that has really disciplined in its scaling,
that runs a portfolio will look quite similar to affirm,
at least from the accounting basis, to a firm that's
being a bit indisciplined and isn't having success.
Speaker 1 (14:57):
And so the second question about the patients for this,
and would you be concerned that the patients for these
two year you know, in the red investments at a
time when money is becoming more expensive me may dry
up and that the revenues from the aim may start
to go down as a result, which could create the
cascading crash that people worry about in terms of all
(15:18):
of the circular deals and stuff you know.
Speaker 2 (15:20):
It might do, But who would struggle with it? I
think the really interesting question here is if you're a
particularly if you're a public company, Wall Street only really
cares about your earnings, your earnings guidance and the fact
that guidance is going up and that you're meeting that guidance.
If you're starting to do a deep investment program, that's
(15:44):
going to have to come out of earnings, and investors
are going to have to believe that in that story
to avoid punishing your stock, which in turn makes all
that capital more expensive. And as we know, interest rates
are also trending upwards. Now there is a group that
is going to make money out of all of this,
which are the people who own infrastructure because they they
have made their investments. I mean, Google has used all
(16:05):
its free cash flow up right to buy data centers,
and those data centers are predicated on the demand from
corporate America, which will start to come in the next
two or three years. So those companies will start to
make really significant profits at that time. And I think
there is a tension there that you've identified, which is
what's the patient's actually going to be. There is a
(16:28):
there's a line in the TV series Silicon Valley from
one of the characters I think is called Russ Hannigan,
and he's sort of saying, you've got to be pre
revenue because in pre revenue, you're sold on a dream.
If you have revenues, you're sold on reality. And I
think we're getting to that stage where there will be
a disjuncture where the hyperscalers are big tech companies will
(16:52):
be showing, as they are eighty two percent revenue growth
in Google Cloud in the last quarter, increasing revenues on
these high margin products against which still be measured, but
Main Street will still be on the jam tomorrow model
where they're investing to show those future returns. And I
think there is a little bit of a tension there
that I haven't quite resolved in my head.
Speaker 1 (17:11):
Right now you're writing the report, the open question is
where the cheapening artificial intelligence can create enough volume and
margin to service the build out. One of the most
interesting things which really caught my eye in the report
was that the kind of proportion of spend in data
centers is rapidly migrating from spend on concrete to spend
on chips. And we had Jasmin Sun on tech Stuff
(17:33):
a couple of weeks ago talking about this kind of
deep doubt in the Ross Belt where these data centers
are popping up that this AI revolution is sustainable and
the concern that these may be left as kind of
artifacts of a corporate boom and bus cycle. But what
do you think and what's your answer to that question?
That does cheapening artificial intelligence create the required volume to
(17:54):
continue financing this incredibly expensive infrastructure buildout.
Speaker 2 (17:59):
I mean, in short, it does. Yeah of you is
that mainstream companies are really early in their adoption. Where
early in the adoption cycle in two ways, one is
how many companies are using it and within it company,
how mature are companies in their use And the shift
(18:23):
is really is going to be palpable. I mean, these
chips are currently running in data centers at extremely high utilization.
To give you an idea, about eight or nine years ago,
Google launched their first and second generation chips for AI
they called TPUs, and since then Google's AI compute capacity
(18:46):
has increased by at least a factor of one hundreds
every one hundred times more compute, and yet they're still
at full utilization for those very very first chips, which
are you know, everyone knows in the computer nine year
old computer is really old. And that speaks to the
volume of demand that they are that they're dealing with
right now. And there's something that Andy Jasse, who is
(19:09):
a CEO over Amazon, said about six months ago about
all of this, because a lot of people are saying
that these guys have lost their heads, they've gone they're
starting to dream things that aren't credible, aren't grounded. And
what he said was, look, as Amazon, as Amazon Web Services,
We've got a lot of data about how we plan
(19:29):
for capacity and how we plan for the future, and
we've been pretty good at it over the last twenty years.
And I think you can see that Amazon has been
pretty good at this. So maybe you should look at
the investments and the commitments. So we're making from the
lens of looking at people and paraphrasing him, by the way,
but from the lens of a company that has been really,
really good at this for two decades, and say, what's
(19:51):
more likely that we lost our marbles or that we're
still really good at this? And I think independent of
the transformer model that underpaid largelanguage models that underpins Claude
and chat GPT, we were already seeing a sharp move
towards the digitization of business in general, what they used
(20:12):
to call big data, what we used to call big data,
what we called spreadsheets, what we called digital supply chains.
I mean, these things were already happening, and that trend
was continuing, and maybe AI has brought it forward a
year or two. So I don't have a question about
whether the economy has got use to pay for this
over the coming years, and we'll use all of those chips.
(20:33):
But I think the interesting thing that Jasmin went off
and did was that she identified and evidenced through her
anthropology a lot of intuitions that I think a lot
of us had about resistance to the data center, which
was that it's really about power politics, distribution, and disconnection.
(20:55):
It's not necessarily a single monoline attack on a all
the data. Said to me, the data set to ends
up being a physical instantiation, something you can point to
that says many things are not working. And here's what
the concrete example in front of us that is being
planned by these people over in Washington State or in
(21:18):
California that encapsulates all of that.
Speaker 1 (21:41):
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Vital Proteins. Why have chipstocks been beaten up in the
last few weeks?
Speaker 2 (22:57):
H yeah, wow, they really have been. So you know,
one of the things that's gone on has been that
there's been a tremendous run on chip stops. They've done
really really well over the last two or three years.
And the Korean market is really important for the semiconductor
industry because it of a company called Eskehinix, and because
of lots of the supplies to the ship industry being there,
(23:20):
and the Korean market is very consumer led and consumers
are allowed to borrow very very heavily, and consumers got
very exuberant. And in fact, when the slightest wabble showed
up in early July and the market dropped, at one point,
the Cosby was down thirty three percent, which would have
been its biggest drop ever.
Speaker 1 (23:40):
The Cosby is a South Korean strug exchange or.
Speaker 2 (23:43):
Yeah sorry, the South Arian soups is what in twenty
five Korean households got what's known as a margin call.
So the margin call is the equivalent of Tony soprano
knocking on your door saying I want my money.
Speaker 1 (23:54):
Now got a situational awareness as well.
Speaker 2 (23:57):
Right, yeah, So that that was a sign of a
lot of overheating. And now today as we record, the
market is down again after a really bad day yesterday.
Or two things right, One is there's a sense of
unease about this particular trade because when you have a
moment where there are a lot of high prices and
cost of memory has gone up very very quickly, high
(24:21):
prices cure themselves because people look for alternatives and there's
more innovation. And in a market that involves a commodity
like memory, if high prices do get cook cured by
an alternative, you're left with an enormous oversupply. And the
story of the semiconductor industry, particularly the memory industry, has
been patterns of their own boom and bus going back
(24:42):
forty years. And there are enough investors who remember that
to feel a little bit skeptical and want to take
them at a distance and take some money off the table.
Speaker 1 (24:54):
What are they scared might happen? There might be a
new round of chip innovation that makes all of these chips.
The evolutionizes how memory works in AI data centers, or
what's what's the fear looming over this?
Speaker 2 (25:05):
Well, I think I think it's that fear, and it's
it's other things. It's do prices get so high that
they somehow reduce the demand of the thing that is
keeping prices high? Is it that prices are so high
and that the economic incentive for software optimizations to be
more efficient with memory use rises, and so that reduces
(25:29):
people's forward forward forecasts. And this is a little bit
like a game of musical chairs in a way, right.
You don't want to be the last person when the
music stops, because then you sit on sit on the floor.
And I think that you know, investors are smart. They
look further out and they say, oh, if I think
that we're going to reach this point where the memory
bottleneck's clear, that other people will realize that too, and
(25:52):
so I should take some risk off the table. Looking
at it a different way, we in our models find
it very difficult when we forecast to see how memory
demand really ever gets met by memory supply in the
next two to three years. It's really just quite quite
difficult to see the manufacturers improving that much. Add see
(26:14):
the algorithms get that much more efficient, So you know,
I'm a bit more never bet against the market, but
if I was going to talk about memory demanding of itself,
I think it's feels and it's going to be pretty resilient.
Speaker 1 (26:29):
Let's talk about some other sort of market news. You
mentioned that the Google's I think cloud computing demand or
revenues are up eighty six percent in the last quarter.
But at the same time, Google shook up its leadership
in a huge way in the AI space right with
Demis has a transition from CEO to chairman of deep Mind,
and Jeff Dean, in another very senior twenty year veteran
(26:53):
of Google Brain stepping down and the market sort of
being a little bit spooked. Evidently what's going on.
Speaker 2 (27:02):
Well, Google has a lot of a classic challenge between
making a lot of money renting its infrastructure through Google
Cloud today and prices are going up, people are paying
more or investing in the kind of exceptional, extraordinary research
that Demis has done. And also Jeff Deine, who is
(27:22):
really the one of the two reasons why all of
us have been using Google Search for the last twenty years.
And you've got to decide, right, is it jam tomorrow
or is it sort of boiled rice today that someone's
willing to pay handsomely for with a significant markup. And
I think that they reasonably made a decision to say
(27:43):
there is enough money to be made out of this
that we will allocate the resources this way. And then
what does that mean? Does that mean?
Speaker 1 (27:50):
Does that mean they've basically decided they want to invest
more in the creating capacity for the operating business than
and leading as research is that?
Speaker 2 (27:58):
That's my take. Look company in the world at pushing
off disruptive innovation, and they had all of these different
ways of doing it, from twenty percent time to acquiring
things like YouTube and double click and working with Android.
I mean they are They're not textbook. They wrote the
new textbook. So for me, it's a really interesting signal
that company with that deep culture would effectively signal through
(28:21):
these allowing these departures that there's just a lot of
money right now to be made off the infrastructure. And
from having that focus now they've kept an option because
with in Jeff Dean's company, they've got a commercial relationship
and an equity relationship, so they have the ability to
bring it back in or benefit from it in some way.
(28:41):
But this is a sign not of diminishing commercial demand
for Google's chips. It's a sign of increasing commercial demand
for Google's chips.
Speaker 1 (28:52):
So if you're right and we're still very early in
the cycle, what does this continuing wave of AI investment,
how does it change how the world is for most people?
Speaker 2 (29:03):
Samulton, I think a few years ago, said back when
GPT three was a thing, he said, well, I think
it's going to feel quite boring to most people because
you'll you'll see something, you'll be impressed, and then within
two weeks you would be bored. And you know, by
the third time it's just what you only even care,
but you'll want it to get better. And I think
for a lot of us that's how we will feel.
I do think that we'll start to notice things that
(29:27):
we have stopped doing. I mean, for example, I think
I mentioned this, Lars I was on your show, like
I'm a headphone addict and now I get chat GPT two,
research my next year's purchases for me, and there's no
amazon ing or Googling at all. So that's a behavior
that has started to change. I also think that for
(29:49):
those of us who have more discretion in how we
spend our workdays. We will have much more flexibility because
these things are actually really good at looking at messy
information and helping us think through them. I'll give you
a beautiful example. Oles My mum wasn't well earlier this week,
and you know, she's in her late eighties, So I
(30:10):
went over to to spend the night at her house
and was chatting to her, and I got her to
talk about the early years of her marriage. And as
she did that, she broke out of English into Punjabi
and was speaking very fast quickly between English, Punjabi and Urdu.
And I recorded this with her permission, and I threw
it into CHATCHIPC and I said, listen, do you speak
of doing Punjabi? And of course it replied yes, of
(30:32):
course I do. Take this turn it into a kind
of coherent history, and it was able to things that
I couldn't follow and understand because it had enough contextual
cues from its knowledge. And I think something like that
is really is really kind of a beautiful experience, certainly
for me, with my mom at the age that she's
at and my Punjabi being very very poor. So I
(30:54):
think that those types of things will start to show
up more and more as choices for people. But let's
be really realistic. You know, at the end of the day,
for all these things that this creativity can allow a
lot of us spend a lot of our time just
scrolling the instagramm reels feed. So let's also recognize that
the fact that the technology can do all of this
(31:16):
doesn't necessarily take you to great outcomes. To answer your
question of how is it going to feel? Well, there
are certain things I feel comfortable about. There are other
things I think we need to to discover. I'm not
sure we're all going to be running around with Nobel
Laureates on our phone and being wiser than Socrates as
a result. I wouldn't sit back and assume that you know,
(31:37):
you'll sit there chat chept. You'll get better and better
in your life miraculously will improve alongside.
Speaker 1 (31:42):
It asine, thank you, my pleasure, the tech stuff I
must well. This episode was produced by Eliza Dennis and
(32:02):
Tyler Hill. It was executive produced by me and Julian
Nutter for Kaleidoscope and Katria Novel for iHeart Podcasts. Jack
Insley mixed this episode and Kyle Murdoch wrote our theme song.
A special thank you to all our listeners. Please rate, review,
and reach out to us at tech Stuff Podcast at
gmail dot com. We love hearing from you.
Welcome to tech Stuff. I'm Os Voloscian. We're living through
a pretty extraordinary moment, a technological turning point that is
or inspiring for some and infuriating for many. And it
seems like no opinion is complete without an assertion about
what AI is doing to the economy. Is it a
boom or a bust? Is there a crash in the odds.
(00:36):
Some people think it's the future and the only economic
driver that matters, and others say it's all a big
bubble sending us hurting towards collapse. But how do the
economics of AI actually work? And what does the enormous
amount of money being poured into this technology mean for
our day to day. Our guest today is Azimazar. He's
an entrepreneur, author and founder of Exponential View, a research
(00:58):
center and substack where he just released a report called
the State of the AI Economy. As he welcome back
to tech Stuff.
Speaker 2 (01:05):
It's great to be back, Ols, Thank you.
Speaker 1 (01:07):
One of the things that occurred to you as preparing
for this conversation today was obviously thinking about situational awareness.
You know what started as a manifesto and became a
hedge fund and adventure fund and then had it struggled
a couple of weeks ago. But you know, there's still
very much a going concern. I mean, have you thought
about the exponential Review Fund or do you like to
sit mainly on the kind of observation analysis side of
(01:29):
the table.
Speaker 2 (01:30):
Well, let's talk about situational awagas. I mean credit to
Leopold Aschenbrener who wrote that essay a few years ago.
And I was a little bit skeptical about the numbers.
I think they were too fast. Yeah, he's generally been
proven to be a very very good predictor of what
would happen. And what happened in July was that his
fund imploded. I think it lost thirty five billion dollars
of value in a month. It's still alive.
Speaker 1 (01:52):
He kept his xanthropic stake.
Speaker 2 (01:53):
Yeah. Well, early investors have still done really well. They've
made like twenty times their money, which speaks a lot
to what he had done. I mean, he had basically
run the problem of some leverage is okay, too much
leverage is like over salting your food, and it's going
to be resulting in a really bad outcome. I think
for what we do, I mean, I do think that
it's very helpful to invest when you are looking at markets,
(02:15):
because once you have skin in the game, you are
much much keener about the things that you say, So
you can't be slap dash and be in the peanut
gallery and just sort of shoot your mouth off, like
where are the steaks? Where are the stakes? In all
of this. But I think it's really important in this
particular market to get in amongst the weeds in as
(02:38):
many ways as you can, because this is not like
looking at a market that we've seen before, so you
really have to equally. I talk to bosses of big
and large companies all over the US and Europe to
hear what they are saying about about AI, because there's
just no rule book for it right now.
Speaker 1 (02:56):
I think more than fifty percent who you surveys so
that they believe their job will de end on successful integration.
But let's talk about this report, the state of the
AI economy. What's it about? What we were reporting on?
What was the problem you're trying to solve?
Speaker 2 (03:10):
Well, the main problem is is it real? Is there
any money in it? How much is being spent? And
it's a very very difficult thing to measure, as you
often find with early markets here were used to looking
at mature markets. So we went out to solve that problem.
How much are customers, consumers and businesses spending on AI
(03:35):
around the world and can we get a real economic
value added number out of that? Now, explain why that's important.
If you're spending twenty dollars a month with open Ai,
they will be spending a certain amount of that with Microsoft,
who runs the servers that serve up chat GPT, so
that twenty dollars may also end up with say ten
(03:58):
game to Microsoft. A naive count will call that a
count that as thirty dollars. We will count that as
twenty and apportion it. Of course we have the total number.
So that's a job we did. And what we want
to understand was how much is really been spent by
businesses at the top of the funnel, which is then
distributed to all the companies that are, you know, as
a little stack serving each other to get you that
(04:21):
response to your chat GPT query.
Speaker 1 (04:23):
And how do you do that? How do you deggregate
or deduplicators you put it where the money goes?
Speaker 2 (04:28):
Well, you know this is where it gets interesting. You
have to use a lot of different strategies to piece
the picture together. So a lot of this is happening
in the private company, so they don't have to disclose,
so we will go back and reconstruct their accounts from
public information and other information that has been leaked to
the press. We scan over one thousand AI startups. We
(04:52):
look at when they disclose their revenue. A lot of
this ends up flowing into what we call the hyperscalers.
Companies like Google and Amazon and Microsoft. They make some
public disclosures and then ultimately all of this runs on chips,
and so we know how many chips have been sold,
and so through all of that you do a process
of triangulation and you say, well, these things generally all
(05:15):
have to agree. And that's the big part of the work.
And it's not one thing, it's many, many, many different
things that come together, these different skill sets, different data
to give us that number that we feel reasonably confident with.
Speaker 1 (05:33):
And for the you know, for the average person who's
interested in these topics, but maybe isn't such an insider
that they're a paying subscriber to Exponential View, Like what
what did you find that matters most? What does what
everyone need to know from this report.
Speaker 2 (05:47):
Well, the first thing is you can download the reports
for free on Intelligence on exponential view dot dot co.
You may not get some of the updates, but it's
sixty five pages long, so there's more than enough to
send you to sleep for several days and maybe keep
you awake if you're so inclined. I think the key
thing here is that there is real spending happening. We've
(06:11):
exceeded the one hundred billion dollar per year revenue number
across the industry. It's actually as of the year to
July twenty twenty six, it's one hundred and twenty six
billion dollars. And that that is a number we've got
to about three times faster than with mobile advertising, or
(06:31):
the cloud or the internet itself. So it really is
the fastest revenue ramp in a in any technology wave,
and probably of any sector in history. And that one
hundred and twenty six billion dollars is still growing very quickly,
roughly three times per year, like two hundred percent additional
(06:53):
per annum, and we were really surprised about that. We
had expected that number to be slowing down from last year.
Speaker 1 (06:59):
It had so that was my next question, where does
this money come from? And how much is it from
the AI firms themselves, how much is it from VC
backed companies who may be spending a head of profits, say,
how much of this is like true demand side spending
versus like supply side stimulated spending.
Speaker 2 (07:16):
Yeah, it's super important to separate those out. So the
AI companies will spend on compute to train their models,
and we capture that spending because those are real dollars
that go into the pockets of Microsoft and Google and
Amazon and so on. And they will also, in the
case of open ai, be spending for those that free
(07:39):
tier of chat GPT that most people are on that
they don't yes yet monetize fully through ad revenues, So
we reckon that. That's around about twenty percent at the
top end of the revenue number, but it's a proportion
that is dropping significantly. So if we've looked at this
year and a half ago, that would have been closer
to sixty percent. Since twenty percent of one hundred and
(08:02):
twenty six billion dollars call it twenty twenty five billion dollars.
And then how much of that is other venture backed startups?
That is, you know these startups who raise their capital
from these fancy Silicon Valley firms. That number of from
the venture backed startups is you know, roughly in the
order of four billion a year. Now, the question is
should you discount that? Because venture back startups often do
(08:25):
very well, and I think it's perfectly reasonable. For example,
if you've got a venture backed company in San Francisco,
that the blue bottle coffee shop opposite their office should
be allowed to count as revenue the coffees that the
company buys for the team all hands on a Friday, Right,
I mean, so American capitalism and the rules of accounting
(08:46):
have worked in a particular way, and we should be
careful about where we draw that line. There is of
course a question about like the sustainability of any of
those companies, but we know in general for Formerllion people
in the US who are employed by companies were backed
by venture capitalists. Right, it's not insignificant. So then you
get to the kind of cluster of relationships that have
(09:09):
emerged from say Nvideo. So in Video is a company
that makes the chips that run about eighty percent of
the workloads. And there's an interesting thing here, of course,
so Nvideo has gone out to ensure that the people
who buy their chips and who serve up the compute
have got the ability to buy those chips and serve
(09:30):
the customer demand. So that is you know, supply side
support of an emerging sector. It's really not uncommon. I mean,
there are very very many sectors, and we often go
back to the start of the car industry where Ford
in general motors extended financing to distributors and you know,
(09:50):
literally car salesmen and also to consumers to buy cars.
When you get into a situation where a core supplier
in new sector has got the strongest balance sheet and
the strongest credit rating and the sector is growing very
very quickly.
Speaker 1 (10:07):
It's really it makes sense they become the bank.
Speaker 2 (10:09):
It makes sense they do that. Now. Now the point
is that that can also go bad in the same way.
But I don't think that you can say, like upfront,
but this is always a bad thing to do. This
is not like you know, pilfering from the till. What
it does do, though, is it does introduce a new
kind of class of risk in the system that doesn't
exist if customers are always paying for everything that they buy.
Speaker 1 (10:32):
So how much is this approaching two hundred billion dollars,
and your run rate of AI revenue comes from like
blue chip corporations, how much of it comes from super
users like you know, perhaps you or medium users like me? Like, well,
what's there? What's the split outside of the AI industry
and the venture industry itself?
Speaker 2 (10:51):
If we break that two hundred down, which is the
annualization of the run rate, you know, you're looking at
eighty percent of that being enterprise rather than consumer. You're
up at one sixty of that one sixty maybe twenty
five to thirty at the top end is AI companies
spending on themselves and venture backed companies, which leaves you
in arranged, let's be conservative, one hundred and twenty billion
(11:14):
dollars from enterprises on a run rate, so that was
effectively like ten billion in the last month. And then
through that it's pretty it's pretty diverse. I mean anthropic
and open AI who the ones that we know that
we all recognize have got hundreds of thousands of commercial
customers and they've got dozens, if not one hundreds, spending
(11:35):
more than a million dollars a year. And these are
companies that we might think of as being sort of
advanced tech companies like you know Uber and coinbase, but
it's also you know, the big banks and the like.
And then within that, of course, it leans towards technology
forward sectors and technology forward companies. So I'll give you
(11:56):
one example that may surprise people. If you look at
companies in America that have been bought by private equity,
so not venture capital that which is all the sort
of rocket fuel, and not cash producing public or family
owned businesses, but once owned by private equity, their levels
of investment in AI seem to be much lower than
(12:17):
the average. And the rationale would be that if you've
been bought by private equity, you need to know every
dollar that takes you off the operating plan is going
to give you two dollars back. And if you don't
know that, the owners won't allow you to do that experimentation.
And I think that speaks a little bit too that
where in general bosses are about how comfortable they are
(12:38):
about the return they get from AI.
Speaker 1 (12:40):
Right now, Yeah, you had a headline for one of
your newsletters recently, the AI adopters success and failure look
identical at first. So I had three questions, obviously for us,
on being why, the second being how much patience does
the market have for that at a time where money
is becoming more expensive? And the third is when does
(13:01):
that change?
Speaker 2 (13:03):
Yes, I mean that second question, by the way, is
a brilliant question. I'm going to give you credit. This
is why you are the host of this podcast.
Speaker 1 (13:12):
And takes you see.
Speaker 2 (13:13):
We're really early in where companies in particular are spending
with their AI and that could be one of two things.
It could be that they're going to continue to have
increasing success and that number is going to rise. Or
it could be that they're still experimenting heavily and everyone's
experimenting and these are experimental revenues that might not continue
(13:37):
at this growth rate once companies wise up. And those
two scenarios I think are still live. I think the
balance of evidence is that it's the former, which is
that this number is going to continue to rise. But
why do they look the same? They look the same
because when you get started with a project, whether you're
going to be successful or unsuccessful, you have to make
(13:58):
the same kind of upfront investment. And if you are successful,
you will spend money on that project and then you
will turn the corner and you'll start to make money
from it. But you won't have got to break even.
And a sensible management team will say we've done this once,
now let's do it three times. So just as the
first one starts to pay back, you've now got three
(14:20):
projects that are in the red. And then they'll say
let's do it ten times. And so the depth of
that red, which you call a JCOB, will will continue. Now,
an indisciplined company that is running into all sorts of
failures might not expand as quickly, but they might let
unsuccessful projects run for longer and longer. And that means
(14:41):
for a couple of years, two two and a half years,
a successful firm that has really disciplined in its scaling,
that runs a portfolio will look quite similar to affirm,
at least from the accounting basis, to a firm that's
being a bit indisciplined and isn't having success.
Speaker 1 (14:57):
And so the second question about the patients for this,
and would you be concerned that the patients for these
two year you know, in the red investments at a
time when money is becoming more expensive me may dry
up and that the revenues from the aim may start
to go down as a result, which could create the
cascading crash that people worry about in terms of all
(15:18):
of the circular deals and stuff you know.
Speaker 2 (15:20):
It might do, But who would struggle with it? I
think the really interesting question here is if you're a
particularly if you're a public company, Wall Street only really
cares about your earnings, your earnings guidance and the fact
that guidance is going up and that you're meeting that guidance.
If you're starting to do a deep investment program, that's
(15:44):
going to have to come out of earnings, and investors
are going to have to believe that in that story
to avoid punishing your stock, which in turn makes all
that capital more expensive. And as we know, interest rates
are also trending upwards. Now there is a group that
is going to make money out of all of this,
which are the people who own infrastructure because they they
have made their investments. I mean, Google has used all
(16:05):
its free cash flow up right to buy data centers,
and those data centers are predicated on the demand from
corporate America, which will start to come in the next
two or three years. So those companies will start to
make really significant profits at that time. And I think
there is a tension there that you've identified, which is
what's the patient's actually going to be. There is a
(16:28):
there's a line in the TV series Silicon Valley from
one of the characters I think is called Russ Hannigan,
and he's sort of saying, you've got to be pre
revenue because in pre revenue, you're sold on a dream.
If you have revenues, you're sold on reality. And I
think we're getting to that stage where there will be
a disjuncture where the hyperscalers are big tech companies will
(16:52):
be showing, as they are eighty two percent revenue growth
in Google Cloud in the last quarter, increasing revenues on
these high margin products against which still be measured, but
Main Street will still be on the jam tomorrow model
where they're investing to show those future returns. And I
think there is a little bit of a tension there
that I haven't quite resolved in my head.
Speaker 1 (17:11):
Right now you're writing the report, the open question is
where the cheapening artificial intelligence can create enough volume and
margin to service the build out. One of the most
interesting things which really caught my eye in the report
was that the kind of proportion of spend in data
centers is rapidly migrating from spend on concrete to spend
on chips. And we had Jasmin Sun on tech Stuff
(17:33):
a couple of weeks ago talking about this kind of
deep doubt in the Ross Belt where these data centers
are popping up that this AI revolution is sustainable and
the concern that these may be left as kind of
artifacts of a corporate boom and bus cycle. But what
do you think and what's your answer to that question?
That does cheapening artificial intelligence create the required volume to
(17:54):
continue financing this incredibly expensive infrastructure buildout.
Speaker 2 (17:59):
I mean, in short, it does. Yeah of you is
that mainstream companies are really early in their adoption. Where
early in the adoption cycle in two ways, one is
how many companies are using it and within it company,
how mature are companies in their use And the shift
(18:23):
is really is going to be palpable. I mean, these
chips are currently running in data centers at extremely high utilization.
To give you an idea, about eight or nine years ago,
Google launched their first and second generation chips for AI
they called TPUs, and since then Google's AI compute capacity
(18:46):
has increased by at least a factor of one hundreds
every one hundred times more compute, and yet they're still
at full utilization for those very very first chips, which
are you know, everyone knows in the computer nine year
old computer is really old. And that speaks to the
volume of demand that they are that they're dealing with
right now. And there's something that Andy Jasse, who is
(19:09):
a CEO over Amazon, said about six months ago about
all of this, because a lot of people are saying
that these guys have lost their heads, they've gone they're
starting to dream things that aren't credible, aren't grounded. And
what he said was, look, as Amazon, as Amazon Web Services,
We've got a lot of data about how we plan
(19:29):
for capacity and how we plan for the future, and
we've been pretty good at it over the last twenty years.
And I think you can see that Amazon has been
pretty good at this. So maybe you should look at
the investments and the commitments. So we're making from the
lens of looking at people and paraphrasing him, by the way,
but from the lens of a company that has been really,
really good at this for two decades, and say, what's
(19:51):
more likely that we lost our marbles or that we're
still really good at this? And I think independent of
the transformer model that underpaid largelanguage models that underpins Claude
and chat GPT, we were already seeing a sharp move
towards the digitization of business in general, what they used
(20:12):
to call big data, what we used to call big data,
what we called spreadsheets, what we called digital supply chains.
I mean, these things were already happening, and that trend
was continuing, and maybe AI has brought it forward a
year or two. So I don't have a question about
whether the economy has got use to pay for this
over the coming years, and we'll use all of those chips.
(20:33):
But I think the interesting thing that Jasmin went off
and did was that she identified and evidenced through her
anthropology a lot of intuitions that I think a lot
of us had about resistance to the data center, which
was that it's really about power politics, distribution, and disconnection.
(20:55):
It's not necessarily a single monoline attack on a all
the data. Said to me, the data set to ends
up being a physical instantiation, something you can point to
that says many things are not working. And here's what
the concrete example in front of us that is being
planned by these people over in Washington State or in
(21:18):
California that encapsulates all of that.
Speaker 1 (21:41):
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Vital Proteins. Why have chipstocks been beaten up in the
last few weeks?
Speaker 2 (22:57):
H yeah, wow, they really have been. So you know,
one of the things that's gone on has been that
there's been a tremendous run on chip stops. They've done
really really well over the last two or three years.
And the Korean market is really important for the semiconductor
industry because it of a company called Eskehinix, and because
of lots of the supplies to the ship industry being there,
(23:20):
and the Korean market is very consumer led and consumers
are allowed to borrow very very heavily, and consumers got
very exuberant. And in fact, when the slightest wabble showed
up in early July and the market dropped, at one point,
the Cosby was down thirty three percent, which would have
been its biggest drop ever.
Speaker 1 (23:40):
The Cosby is a South Korean strug exchange or.
Speaker 2 (23:43):
Yeah sorry, the South Arian soups is what in twenty
five Korean households got what's known as a margin call.
So the margin call is the equivalent of Tony soprano
knocking on your door saying I want my money.
Speaker 1 (23:54):
Now got a situational awareness as well.
Speaker 2 (23:57):
Right, yeah, So that that was a sign of a
lot of overheating. And now today as we record, the
market is down again after a really bad day yesterday.
Or two things right, One is there's a sense of
unease about this particular trade because when you have a
moment where there are a lot of high prices and
cost of memory has gone up very very quickly, high
(24:21):
prices cure themselves because people look for alternatives and there's
more innovation. And in a market that involves a commodity
like memory, if high prices do get cook cured by
an alternative, you're left with an enormous oversupply. And the
story of the semiconductor industry, particularly the memory industry, has
been patterns of their own boom and bus going back
(24:42):
forty years. And there are enough investors who remember that
to feel a little bit skeptical and want to take
them at a distance and take some money off the table.
Speaker 1 (24:54):
What are they scared might happen? There might be a
new round of chip innovation that makes all of these chips.
The evolutionizes how memory works in AI data centers, or
what's what's the fear looming over this?
Speaker 2 (25:05):
Well, I think I think it's that fear, and it's
it's other things. It's do prices get so high that
they somehow reduce the demand of the thing that is
keeping prices high? Is it that prices are so high
and that the economic incentive for software optimizations to be
more efficient with memory use rises, and so that reduces
(25:29):
people's forward forward forecasts. And this is a little bit
like a game of musical chairs in a way, right.
You don't want to be the last person when the
music stops, because then you sit on sit on the floor.
And I think that you know, investors are smart. They
look further out and they say, oh, if I think
that we're going to reach this point where the memory
bottleneck's clear, that other people will realize that too, and
(25:52):
so I should take some risk off the table. Looking
at it a different way, we in our models find
it very difficult when we forecast to see how memory
demand really ever gets met by memory supply in the
next two to three years. It's really just quite quite
difficult to see the manufacturers improving that much. Add see
(26:14):
the algorithms get that much more efficient, So you know,
I'm a bit more never bet against the market, but
if I was going to talk about memory demanding of itself,
I think it's feels and it's going to be pretty resilient.
Speaker 1 (26:29):
Let's talk about some other sort of market news. You
mentioned that the Google's I think cloud computing demand or
revenues are up eighty six percent in the last quarter.
But at the same time, Google shook up its leadership
in a huge way in the AI space right with
Demis has a transition from CEO to chairman of deep Mind,
and Jeff Dean, in another very senior twenty year veteran
(26:53):
of Google Brain stepping down and the market sort of
being a little bit spooked. Evidently what's going on.
Speaker 2 (27:02):
Well, Google has a lot of a classic challenge between
making a lot of money renting its infrastructure through Google
Cloud today and prices are going up, people are paying
more or investing in the kind of exceptional, extraordinary research
that Demis has done. And also Jeff Deine, who is
(27:22):
really the one of the two reasons why all of
us have been using Google Search for the last twenty years.
And you've got to decide, right, is it jam tomorrow
or is it sort of boiled rice today that someone's
willing to pay handsomely for with a significant markup. And
I think that they reasonably made a decision to say
(27:43):
there is enough money to be made out of this
that we will allocate the resources this way. And then
what does that mean? Does that mean?
Speaker 1 (27:50):
Does that mean they've basically decided they want to invest
more in the creating capacity for the operating business than
and leading as research is that?
Speaker 2 (27:58):
That's my take. Look company in the world at pushing
off disruptive innovation, and they had all of these different
ways of doing it, from twenty percent time to acquiring
things like YouTube and double click and working with Android.
I mean they are They're not textbook. They wrote the
new textbook. So for me, it's a really interesting signal
that company with that deep culture would effectively signal through
(28:21):
these allowing these departures that there's just a lot of
money right now to be made off the infrastructure. And
from having that focus now they've kept an option because
with in Jeff Dean's company, they've got a commercial relationship
and an equity relationship, so they have the ability to
bring it back in or benefit from it in some way.
(28:41):
But this is a sign not of diminishing commercial demand
for Google's chips. It's a sign of increasing commercial demand
for Google's chips.
Speaker 1 (28:52):
So if you're right and we're still very early in
the cycle, what does this continuing wave of AI investment,
how does it change how the world is for most people?
Speaker 2 (29:03):
Samulton, I think a few years ago, said back when
GPT three was a thing, he said, well, I think
it's going to feel quite boring to most people because
you'll you'll see something, you'll be impressed, and then within
two weeks you would be bored. And you know, by
the third time it's just what you only even care,
but you'll want it to get better. And I think
for a lot of us that's how we will feel.
I do think that we'll start to notice things that
(29:27):
we have stopped doing. I mean, for example, I think
I mentioned this, Lars I was on your show, like
I'm a headphone addict and now I get chat GPT two,
research my next year's purchases for me, and there's no
amazon ing or Googling at all. So that's a behavior
that has started to change. I also think that for
(29:49):
those of us who have more discretion in how we
spend our workdays. We will have much more flexibility because
these things are actually really good at looking at messy
information and helping us think through them. I'll give you
a beautiful example. Oles My mum wasn't well earlier this week,
and you know, she's in her late eighties, So I
(30:10):
went over to to spend the night at her house
and was chatting to her, and I got her to
talk about the early years of her marriage. And as
she did that, she broke out of English into Punjabi
and was speaking very fast quickly between English, Punjabi and Urdu.
And I recorded this with her permission, and I threw
it into CHATCHIPC and I said, listen, do you speak
of doing Punjabi? And of course it replied yes, of
(30:32):
course I do. Take this turn it into a kind
of coherent history, and it was able to things that
I couldn't follow and understand because it had enough contextual
cues from its knowledge. And I think something like that
is really is really kind of a beautiful experience, certainly
for me, with my mom at the age that she's
at and my Punjabi being very very poor. So I
(30:54):
think that those types of things will start to show
up more and more as choices for people. But let's
be really realistic. You know, at the end of the day,
for all these things that this creativity can allow a
lot of us spend a lot of our time just
scrolling the instagramm reels feed. So let's also recognize that
the fact that the technology can do all of this
(31:16):
doesn't necessarily take you to great outcomes. To answer your
question of how is it going to feel? Well, there
are certain things I feel comfortable about. There are other
things I think we need to to discover. I'm not
sure we're all going to be running around with Nobel
Laureates on our phone and being wiser than Socrates as
a result. I wouldn't sit back and assume that you know,
(31:37):
you'll sit there chat chept. You'll get better and better
in your life miraculously will improve alongside.
Speaker 1 (31:42):
It asine, thank you, my pleasure, the tech stuff I
must well. This episode was produced by Eliza Dennis and
(32:02):
Tyler Hill. It was executive produced by me and Julian
Nutter for Kaleidoscope and Katria Novel for iHeart Podcasts. Jack
Insley mixed this episode and Kyle Murdoch wrote our theme song.
A special thank you to all our listeners. Please rate, review,
and reach out to us at tech Stuff Podcast at
gmail dot com. We love hearing from you.
TechStuff News
Hosts And Creators
Oz Woloshyn
Karah Preiss
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