November 26, 2025 • 13 mins
Companies are betting big on AI – and burning through cash to do it.
They’re pouring hundreds of billions of dollars into building data centres and developing new models – such as ChatGPT. And so far, these projects are yet to see a financial return.
There is one clear winner: Nvidia – the company that makes the chips that power this tech.
But how long can its customers keep spending before they have to either turn a profit or scale back?
Today, Reuters journalist Stephen Nellis on how long this AI boom can last – and what a crash might look like.
If you enjoy 7am, the best way you can support us is by making a contribution at 7ampodcast.com.au/support.
Socials: Stay in touch with us on Instagram
Guest: Reuters journalist Stephen Nellis
Photo: AP Photo/Eric Risberg
See omnystudio.com/listener for privacy information.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
I'm Ruby Jones and you're listening to seven AM. Companies
are betting big on AI and of burning through cash
to do it. They're pouring hundreds of billions of dollars
into building data centers and developing new models such as
chat GPT, and so far these projects are yet to
(00:23):
see a financial return. There is one clear winner, however, Nvidia,
the company that makes the chips that power this tech.
But how long can its customers keep spending before they
have to either turn a profit or scale back. Today
Reuter's journalist Stephen nellis on how long this AI boom
can last and what a crash might look like. It's Thursday,
(00:53):
November twenty seven, So Stephen, Nvidia had its latest earnings call,
and you were listening in on that call, So tell
me as it happened.
Speaker 2 (01:07):
What will you listening out for.
Speaker 3 (01:09):
I think the biggest thing we're listening for is any
sign that the AI bubble is near bursting, wif there
is a bubble at all, So any sign of a
lack of demand, any sign of faltering interest in videos,
chips or an AI as a field. And largely we
didn't hear any of that. What we heard was a
forecast that was better than Wall Street analysts were expecting,
(01:33):
revenue that came out better than Wall Street was expecting,
and a lot of talk about how it's not an
AI bubble, but instead a tipping point toward an era
of AI that in Video believes will be much more durable.
Speaker 4 (01:47):
Social intelligence chip maker in Videos latest earnings report came
in and higher than Wall Street expected.
Speaker 5 (01:53):
The world's most valuable.
Speaker 4 (01:54):
Company reported fifty seven billion dollars in third quarter revenue.
Speaker 2 (01:57):
And so how much is Nvideo worth right now?
Speaker 3 (02:00):
Well, it depends on the day, but they're fairly consistently
over four point five trillion dollars, and at various times
during the year they've been not just the most valuable
publicly traded company currently, but the most valuable publicly traded company.
Speaker 5 (02:15):
In the history of business.
Speaker 3 (02:17):
And that's largely due to the unique place that they
occupy in the AI ecosystem, which is that some eighty
to ninety percent of the market is their chips training
these systems that many people use every day. So whether
that's chat, GPT, whether that's some other app that you
use that has AI working in the background, the overwhelming
(02:40):
odds are that it exists because it was trained on
data that was run through in Video's chips.
Speaker 1 (02:46):
Okay, so in Vidia has the monopoly on these chips.
Who is buying them?
Speaker 3 (02:51):
So the biggest buyers and this was an interesting thing
to come out of the earning support. There are four customers.
We don't know precisely who they are, but they make
up sick nexty one percent of in Video's revenue. And
the most likely candidates for that are some of the
names that you're probably familiar with. Open Ai, which is
the creator of chat, GPT, Meta which owns Instagram, Facebook, WhatsApp,
(03:15):
and other properties. And then other firms like Microsoft, which
is deploying artificial intelligence.
Speaker 5 (03:22):
Across its apps like Office.
Speaker 3 (03:24):
But they're all snapping them up as quickly as possible
and trying to string them together into huge piles of
tens of thousands. Some are even talking about hundreds of
thousands of chips at once, so they are buying in bulk.
Speaker 1 (03:36):
Okay, So you've got those companies spending huge sums on
chips that they're buying from Video. But many of these
AI companies are not actually making a profit yet, So
tell me about this gamble.
Speaker 3 (03:48):
So right now we're in an interesting time where a
lot of people are trying these things. I mean, it'd
be hard to find a listener who hasn't chatted with
some kind of a chatbot, clicked an image generation button somewhere,
or even just had something read over a passage or
a sentence and see if it can improve it. Now,
the question is, when you did that, how much did
you pay for it? And if you didn't pay for
(04:09):
it directly, how many advertisements did you sit through before
you use that product. One of the issues that we
have right now is there's a big question all over
the tech world about how much will people pay for
these things directly based on how much they perceive it's worth. Now,
let's talk about the cost side, though, because that's I
think what's really driving a lot of the lack of
(04:29):
profitability for the time being, and that is that these
chips are very, very expensive. They tend to sell them
in a big server rack that will have something like
seventy two are coming soon more than one hundred of
these chips, and those are many hundreds of thousands of
US dollars, if not into the millions of dollars for
(04:49):
a single server. So they are expensive and the electricity
did to operate them is very expensive. You've probably read
that many of these data centers take as much electricity
as a small city. So what you have is a
really simple situation where it costs a lot of money
to make the thing, and it's unclear exactly how much
people are willing to pay for that thing in the
short term.
Speaker 5 (05:09):
What is the path to profitability? When do you see
the lines cross? If you will?
Speaker 3 (05:13):
I mean, I think it could have happened sooner than
I originally thought, if we wanted it too.
Speaker 2 (05:18):
But it seems to me like the right thing to
do is to just keep investing in computing.
Speaker 3 (05:23):
So when we look at companies like open ai, there
have been some reports that they generated about four point
three billion dollars in revenue for the first half of
the year and on on track to hit their revenue
target of thirteen billion dollars for twenty twenty five. But
that's with a cash burn of about eight and a
half billion, and that doesn't even take into account all
of the other operational costs.
Speaker 4 (05:45):
You know, they have what ninety percent of search queries Okay,
they already have that was never going to get better
than that. They have seventy percent of like search ad revenue. Right,
you can see how this company is deemed to be
a monopoly.
Speaker 3 (05:56):
And then we can contrast that with companies like Microsoft
or Google, which are spending lots and lots of money
their capital lot ways, you know, are are north in
the tens of billions of dollars and cumulatively in the
hundreds of billions of dollars. But in the case of
those broader companies, they're suffusing that AI into other products
that already make money and just charging more for it.
(06:17):
So we have seen companies like Google and Microsoft remain profitable,
and then they also just have very large businesses renting
out their data centers as sort of picks and shovels
to other companies trying to cash in on this AI
gold rush, and that's still a pretty profitable business for
those companies as well. So I think the profitability question
ultimately comes down to are you trying to make money
(06:40):
just generating the technology and monetizing it, or do you
spread it out over other stuff that's already making money.
Speaker 1 (06:48):
Okay, but Nvidia is doing great of fall of this,
as you say, the most valuable listed company in human
history at the moment.
Speaker 2 (06:56):
But will that last?
Speaker 3 (06:57):
That is the four point five billion dollar question, and
I think the answer is a complicated one. So in
Video's chips are still in high, high demand, and what
that means is that in Vidia has a lot of leeway.
Speaker 5 (07:10):
In how they price those things.
Speaker 3 (07:12):
Now, the flip side of this is, you know, you
think about other big semiconductor companies over time. One that
people might have heard of from a previous generation would
be Intel. So back in the nineteen eighties, Intel really
jumped into the personal computer market. They had a very
very durable run for thirty or forty years. Now, is
in Vidia going to last that long? It depends on
(07:33):
whether the kind of computing that we do the most
of in the next few decades is the kind that
in Vidia is good at or some other kind. I
think that question is far from settled. You know, right
now we're in the phase we're building the AI, we're
training the AI. In Vidia has a monopoly on that market.
But in terms of actually deploying the AI, putting AI
in your computer at home, putting AI in some kind
(07:55):
of widget that sits on your countertop, those chips are
not all made by and Video, and that market is
much more competitive in the long.
Speaker 1 (08:03):
Term coming up, could the AI bubble trigger a financial crisis?
Speaker 2 (08:18):
So Stephen, we have these companies.
Speaker 1 (08:19):
Open AI, Microsoft, Google, They obviously see the value in
investing in this technology, even if it isn't making them
a return at this stage. So how are these companies
trying to crack this problem of profitability?
Speaker 3 (08:34):
So the biggest thing is going to things that cost
a lot of money to companies right now. And one
of the things these companies spend the most money on
is engineers who write software code right, And one of
the things that they're trying to make money off of
is going to other companies in the software business and saying, listen,
you can write in the case of Microsoft, as much
as thirty percent of your code using some of these
(08:57):
AI tools and make your team seems much more efficient.
So in the short term, I think a lot of
it is automating white collar work, professional work that companies
currently spend a lot of money paying humans to do.
In the longer term, I think it's a much more
mixed bag where some of the interesting scientific use cases,
(09:20):
so developing new drugs and things like that, I think
we'll have to see for those but the longer horizon
is making new things that we really didn't think we
can make or wouldn't be possible to make without this technology.
And I think most of these companies think that you're
going to have some kind of personal assistant, that we'll
be able to take care of a lot of things
(09:41):
in your behalf, and that eventually, if in video's grandest
visions ever come to pass, that virtual assistant would have
some kind of physical manifestation, a robot that can do
the things around your house that you want to be done. Now,
we should say we're really far off from that right now.
I don't think you should and plan on having your
(10:02):
floor scrubbed or your toilets cleaned by robots anytime soon,
but it is something they're thinking about.
Speaker 1 (10:08):
And there is so much speculation about the growth of
these companies and the future of AI and fears that
this could all be a bubble that is set to bursts.
So what are your thoughts on that and where this
is all headed.
Speaker 3 (10:21):
One thing the markets are very good at is making
a judgment about profitability over some kind of term that's
not that far out. So I would say we probably
have a few years for some of these companies to
really start showing a profit, and if they don't, the
question is whether all the investment and sustaining that will
be worth it. You know, if you sort of look
(10:41):
at two previous global financial catastrophes, the dot com bubble
and then the financial crisis that followed it, the dot
com bubble was bad, but because it was largely limited
to the stock market, the societal fallout wasn't that bad,
at least in the United States. It's a very different
case in the financial crisis, where that really wrapped up
(11:02):
a lot of people's homes and their personal livelihood in
a much more direct way. It's a tricky one to
try to understand what the AI bubble will because, on
the one hand, most of the wild wealth creation we've
seen has been, for better or worse, limited to a
very small upper strata of US investors. On the other hand,
if you look at the economic data in the United
States right now, a lot of what is propping up
(11:25):
the economy is spending on this boom, and that spending
is going to people who work in the trades, who
work in pipe fitting, who are electricians. There's an electrician
shortage right now. If you are an electrician. You should
probably get on a plane and come to the United
States if your codes in your country are similar, because
you will probably find work.
Speaker 1 (11:44):
So the crash, if there is one, could affect ordinary people.
Speaker 3 (11:47):
The one thing that is apparent is that if you're
just your average school teacher, firefighter, police officer who has
a pension fund that is funded broadly by the US
stock market, there is no from this because most of
the gains in the stock market have been coming from
this handful of firms that are really concentrated in the
(12:09):
AI market. But I don't think it's quite like the
nineteen twenties where a lot of people went out and
borrowed money to buy stock in these companies. But the
question is how much of it is paper money versus
you know, people's homes and livelihoods every day, and that's
just much harder to predict.
Speaker 2 (12:27):
Well, Stephen, thank you so much for your time.
Speaker 5 (12:30):
Thank you for having me.
Speaker 2 (12:45):
Also in the.
Speaker 1 (12:45):
News, economists predict a December interest rate cut is looking
increasingly unlikely following the latest inflation data. The Bureau of
Statistics consumer price index rose from three point six percent
in September to three point eight percent in October. The
largest contributors to inflation were housing, food and non alcoholic beverages.
(13:06):
And for the first time ever, Australian voters see Labor
as a better economic manager than the Coalition, according to
the results of the Australian Election Study. The study, led
by the Australian National University and Griffith University, is a
post election survey of voters' attitudes and behavior and found
that voters preferred Labour's policies on nine out of ten
(13:26):
issues looked at in the study. The results also found
that Peter Dutton was the least popular leader to go
to a federal election since the survey began in nineteen
eighty seven. I'm Ruby Jones. This is seven am. Thanks
for listening.
I'm Ruby Jones and you're listening to seven AM. Companies
are betting big on AI and of burning through cash
to do it. They're pouring hundreds of billions of dollars
into building data centers and developing new models such as
chat GPT, and so far these projects are yet to
(00:23):
see a financial return. There is one clear winner, however, Nvidia,
the company that makes the chips that power this tech.
But how long can its customers keep spending before they
have to either turn a profit or scale back. Today
Reuter's journalist Stephen nellis on how long this AI boom
can last and what a crash might look like. It's Thursday,
(00:53):
November twenty seven, So Stephen, Nvidia had its latest earnings call,
and you were listening in on that call, So tell
me as it happened.
Speaker 2 (01:07):
What will you listening out for.
Speaker 3 (01:09):
I think the biggest thing we're listening for is any
sign that the AI bubble is near bursting, wif there
is a bubble at all, So any sign of a
lack of demand, any sign of faltering interest in videos,
chips or an AI as a field. And largely we
didn't hear any of that. What we heard was a
forecast that was better than Wall Street analysts were expecting,
(01:33):
revenue that came out better than Wall Street was expecting,
and a lot of talk about how it's not an
AI bubble, but instead a tipping point toward an era
of AI that in Video believes will be much more durable.
Speaker 4 (01:47):
Social intelligence chip maker in Videos latest earnings report came
in and higher than Wall Street expected.
Speaker 5 (01:53):
The world's most valuable.
Speaker 4 (01:54):
Company reported fifty seven billion dollars in third quarter revenue.
Speaker 2 (01:57):
And so how much is Nvideo worth right now?
Speaker 3 (02:00):
Well, it depends on the day, but they're fairly consistently
over four point five trillion dollars, and at various times
during the year they've been not just the most valuable
publicly traded company currently, but the most valuable publicly traded company.
Speaker 5 (02:15):
In the history of business.
Speaker 3 (02:17):
And that's largely due to the unique place that they
occupy in the AI ecosystem, which is that some eighty
to ninety percent of the market is their chips training
these systems that many people use every day. So whether
that's chat, GPT, whether that's some other app that you
use that has AI working in the background, the overwhelming
(02:40):
odds are that it exists because it was trained on
data that was run through in Video's chips.
Speaker 1 (02:46):
Okay, so in Vidia has the monopoly on these chips.
Who is buying them?
Speaker 3 (02:51):
So the biggest buyers and this was an interesting thing
to come out of the earning support. There are four customers.
We don't know precisely who they are, but they make
up sick nexty one percent of in Video's revenue. And
the most likely candidates for that are some of the
names that you're probably familiar with. Open Ai, which is
the creator of chat, GPT, Meta which owns Instagram, Facebook, WhatsApp,
(03:15):
and other properties. And then other firms like Microsoft, which
is deploying artificial intelligence.
Speaker 5 (03:22):
Across its apps like Office.
Speaker 3 (03:24):
But they're all snapping them up as quickly as possible
and trying to string them together into huge piles of
tens of thousands. Some are even talking about hundreds of
thousands of chips at once, so they are buying in bulk.
Speaker 1 (03:36):
Okay, So you've got those companies spending huge sums on
chips that they're buying from Video. But many of these
AI companies are not actually making a profit yet, So
tell me about this gamble.
Speaker 3 (03:48):
So right now we're in an interesting time where a
lot of people are trying these things. I mean, it'd
be hard to find a listener who hasn't chatted with
some kind of a chatbot, clicked an image generation button somewhere,
or even just had something read over a passage or
a sentence and see if it can improve it. Now,
the question is, when you did that, how much did
you pay for it? And if you didn't pay for
(04:09):
it directly, how many advertisements did you sit through before
you use that product. One of the issues that we
have right now is there's a big question all over
the tech world about how much will people pay for
these things directly based on how much they perceive it's worth. Now,
let's talk about the cost side, though, because that's I
think what's really driving a lot of the lack of
(04:29):
profitability for the time being, and that is that these
chips are very, very expensive. They tend to sell them
in a big server rack that will have something like
seventy two are coming soon more than one hundred of
these chips, and those are many hundreds of thousands of
US dollars, if not into the millions of dollars for
(04:49):
a single server. So they are expensive and the electricity
did to operate them is very expensive. You've probably read
that many of these data centers take as much electricity
as a small city. So what you have is a
really simple situation where it costs a lot of money
to make the thing, and it's unclear exactly how much
people are willing to pay for that thing in the
short term.
Speaker 5 (05:09):
What is the path to profitability? When do you see
the lines cross? If you will?
Speaker 3 (05:13):
I mean, I think it could have happened sooner than
I originally thought, if we wanted it too.
Speaker 2 (05:18):
But it seems to me like the right thing to
do is to just keep investing in computing.
Speaker 3 (05:23):
So when we look at companies like open ai, there
have been some reports that they generated about four point
three billion dollars in revenue for the first half of
the year and on on track to hit their revenue
target of thirteen billion dollars for twenty twenty five. But
that's with a cash burn of about eight and a
half billion, and that doesn't even take into account all
of the other operational costs.
Speaker 4 (05:45):
You know, they have what ninety percent of search queries Okay,
they already have that was never going to get better
than that. They have seventy percent of like search ad revenue. Right,
you can see how this company is deemed to be
a monopoly.
Speaker 3 (05:56):
And then we can contrast that with companies like Microsoft
or Google, which are spending lots and lots of money
their capital lot ways, you know, are are north in
the tens of billions of dollars and cumulatively in the
hundreds of billions of dollars. But in the case of
those broader companies, they're suffusing that AI into other products
that already make money and just charging more for it.
(06:17):
So we have seen companies like Google and Microsoft remain profitable,
and then they also just have very large businesses renting
out their data centers as sort of picks and shovels
to other companies trying to cash in on this AI
gold rush, and that's still a pretty profitable business for
those companies as well. So I think the profitability question
ultimately comes down to are you trying to make money
(06:40):
just generating the technology and monetizing it, or do you
spread it out over other stuff that's already making money.
Speaker 1 (06:48):
Okay, but Nvidia is doing great of fall of this,
as you say, the most valuable listed company in human
history at the moment.
Speaker 2 (06:56):
But will that last?
Speaker 3 (06:57):
That is the four point five billion dollar question, and
I think the answer is a complicated one. So in
Video's chips are still in high, high demand, and what
that means is that in Vidia has a lot of leeway.
Speaker 5 (07:10):
In how they price those things.
Speaker 3 (07:12):
Now, the flip side of this is, you know, you
think about other big semiconductor companies over time. One that
people might have heard of from a previous generation would
be Intel. So back in the nineteen eighties, Intel really
jumped into the personal computer market. They had a very
very durable run for thirty or forty years. Now, is
in Vidia going to last that long? It depends on
(07:33):
whether the kind of computing that we do the most
of in the next few decades is the kind that
in Vidia is good at or some other kind. I
think that question is far from settled. You know, right
now we're in the phase we're building the AI, we're
training the AI. In Vidia has a monopoly on that market.
But in terms of actually deploying the AI, putting AI
in your computer at home, putting AI in some kind
(07:55):
of widget that sits on your countertop, those chips are
not all made by and Video, and that market is
much more competitive in the long.
Speaker 1 (08:03):
Term coming up, could the AI bubble trigger a financial crisis?
Speaker 2 (08:18):
So Stephen, we have these companies.
Speaker 1 (08:19):
Open AI, Microsoft, Google, They obviously see the value in
investing in this technology, even if it isn't making them
a return at this stage. So how are these companies
trying to crack this problem of profitability?
Speaker 3 (08:34):
So the biggest thing is going to things that cost
a lot of money to companies right now. And one
of the things these companies spend the most money on
is engineers who write software code right, And one of
the things that they're trying to make money off of
is going to other companies in the software business and saying, listen,
you can write in the case of Microsoft, as much
as thirty percent of your code using some of these
(08:57):
AI tools and make your team seems much more efficient.
So in the short term, I think a lot of
it is automating white collar work, professional work that companies
currently spend a lot of money paying humans to do.
In the longer term, I think it's a much more
mixed bag where some of the interesting scientific use cases,
(09:20):
so developing new drugs and things like that, I think
we'll have to see for those but the longer horizon
is making new things that we really didn't think we
can make or wouldn't be possible to make without this technology.
And I think most of these companies think that you're
going to have some kind of personal assistant, that we'll
be able to take care of a lot of things
(09:41):
in your behalf, and that eventually, if in video's grandest
visions ever come to pass, that virtual assistant would have
some kind of physical manifestation, a robot that can do
the things around your house that you want to be done. Now,
we should say we're really far off from that right now.
I don't think you should and plan on having your
(10:02):
floor scrubbed or your toilets cleaned by robots anytime soon,
but it is something they're thinking about.
Speaker 1 (10:08):
And there is so much speculation about the growth of
these companies and the future of AI and fears that
this could all be a bubble that is set to bursts.
So what are your thoughts on that and where this
is all headed.
Speaker 3 (10:21):
One thing the markets are very good at is making
a judgment about profitability over some kind of term that's
not that far out. So I would say we probably
have a few years for some of these companies to
really start showing a profit, and if they don't, the
question is whether all the investment and sustaining that will
be worth it. You know, if you sort of look
(10:41):
at two previous global financial catastrophes, the dot com bubble
and then the financial crisis that followed it, the dot
com bubble was bad, but because it was largely limited
to the stock market, the societal fallout wasn't that bad,
at least in the United States. It's a very different
case in the financial crisis, where that really wrapped up
(11:02):
a lot of people's homes and their personal livelihood in
a much more direct way. It's a tricky one to
try to understand what the AI bubble will because, on
the one hand, most of the wild wealth creation we've
seen has been, for better or worse, limited to a
very small upper strata of US investors. On the other hand,
if you look at the economic data in the United
States right now, a lot of what is propping up
(11:25):
the economy is spending on this boom, and that spending
is going to people who work in the trades, who
work in pipe fitting, who are electricians. There's an electrician
shortage right now. If you are an electrician. You should
probably get on a plane and come to the United
States if your codes in your country are similar, because
you will probably find work.
Speaker 1 (11:44):
So the crash, if there is one, could affect ordinary people.
Speaker 3 (11:47):
The one thing that is apparent is that if you're
just your average school teacher, firefighter, police officer who has
a pension fund that is funded broadly by the US
stock market, there is no from this because most of
the gains in the stock market have been coming from
this handful of firms that are really concentrated in the
(12:09):
AI market. But I don't think it's quite like the
nineteen twenties where a lot of people went out and
borrowed money to buy stock in these companies. But the
question is how much of it is paper money versus
you know, people's homes and livelihoods every day, and that's
just much harder to predict.
Speaker 2 (12:27):
Well, Stephen, thank you so much for your time.
Speaker 5 (12:30):
Thank you for having me.
Speaker 2 (12:45):
Also in the.
Speaker 1 (12:45):
News, economists predict a December interest rate cut is looking
increasingly unlikely following the latest inflation data. The Bureau of
Statistics consumer price index rose from three point six percent
in September to three point eight percent in October. The
largest contributors to inflation were housing, food and non alcoholic beverages.
(13:06):
And for the first time ever, Australian voters see Labor
as a better economic manager than the Coalition, according to
the results of the Australian Election Study. The study, led
by the Australian National University and Griffith University, is a
post election survey of voters' attitudes and behavior and found
that voters preferred Labour's policies on nine out of ten
(13:26):
issues looked at in the study. The results also found
that Peter Dutton was the least popular leader to go
to a federal election since the survey began in nineteen
eighty seven. I'm Ruby Jones. This is seven am. Thanks
for listening.
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Ding dong! Join your culture consultants, Matt Rogers and Bowen Yang, on an unforgettable journey into the beating heart of CULTURE. Alongside sizzling special guests, they GET INTO the hottest pop-culture moments of the day and the formative cultural experiences that turned them into Culturistas. Produced by the Big Money Players Network and iHeartRadio.
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.