November 22, 2025 • 10 mins
Today we’re sharing something a little different in your feed — an episode from our colleagues at Here’s Why, Bloomberg’s weekly show that answers one big question in under 10 minutes, explaining the ideas shaping our world.
This episode looks at a story right at the heart of the tech world: the massive investments in AI data centers, and the hidden costs that come with them.
If you’d like to hear more episodes like this one, you can follow Here’s Why wherever you get your podcasts:
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Bloomberg: https://www.bloomberg.com/podcasts/series/heres-why
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hi, this is Caroline Hyde from Bloomberg Tech. Today we're
sharing something a little different in your feed, an episode
from our colleagues at Here's Why, Bloomberg's weekly show that
answers one big question in under ten minutes. Host Stephen
Carroll is joined by our Bloomberg Tech europanker Tom McKenzie
to dive into a story that's right at the heart
(00:20):
of the tech world, the massive investments in AI data
centers and the hidden costs that come with them. If
you'd like to hear more episodes of Here's Why, you'll
find a link to the podcast feed in the show notes.
Hope you enjoy.
Speaker 2 (00:36):
Bloomberg Audio Studios, podcasts, radio news. I'm Stephen Carol and
this is Here's Why, where we take one new story
and explain it in just a few minutes with our
experts here at Bloomberg.
Speaker 3 (00:53):
It's ten thirty pm in this AI party. It started
nine pm now and that party goes to four am,
and the reality is like, look, this is going to
be a two to three year left in this bull
cycle for tech. The tech sector is very strong because
artificial intelligence is really a qualitative leap in the kind
of technology that we've had over the last several decades.
(01:15):
You're seeing an exponential growth of adoption and use of AI.
Speaker 4 (01:19):
The number of applications that are going to be using
these AI is also growing.
Speaker 2 (01:24):
Everyone has an opinion on where the AI frenzy is
going next. While optimism is rampant about the technologies potential,
more questions are now being asked about AI's running costs.
Speaker 4 (01:36):
We are putting mostly chips silicon into these data centers
that have a lifespan of perhaps four years, So those
chips they appreciate very quickly.
Speaker 3 (01:47):
Even in video, there's a new chip every eight months
and it's ten times as powerful as the earlier ones.
Speaker 4 (01:52):
The thing with the valley this here is that almost
every investor knows it's all going to turn into pumpkins
and mice at midnight. Only, as Buffett would say, no
one in the room as a clock.
Speaker 2 (02:02):
Even with bumper results and bullish revenue forecasts, here's why
AI casts still worry investors. Tom McKenzie, who hosts Bloomberg
Tech You're U bum Bloberg Television, joins me now for more. Tom.
The investor Michael Burry of Big Short fame is among
those who's worried about these future casts of AI and
(02:23):
data centers in particular.
Speaker 4 (02:25):
What's the concern, Yeah, absolutely, Michael Burry putting on famously
short positions, so shortening the stocks of Nvidia and Pallanteer
before he wrapped up his fund. His concern does focus
on the depreciation of some of these assets by assets.
I'm talking about specifically these AI chips, very expensive AI accelerators.
(02:47):
Ninety percent of the market share is dominated by Nvidia,
so across the sale of these chips and video has
that significant market gain versus its rivals. And the concern
is that as you get newer versions of these chips,
the older ones essentially become less valuable. And Michael Barrie
making the argument that companies the hyperscalers, so the Microsofts
(03:09):
and alphabets and metas of the world, are not properly
accounting for how quickly these these assets depreciate. The other
part of the concern and kind of ties into this
that you hear voice from the skeptics around the AI
bubble is that there are comparisons, they say, with what
happened in the late nineteen nineties, nineteen ninety nine, early
two thousand, the dot com bubble, when it was the
(03:31):
telecom equipment makers that leading up to all of the
online expectations around how our digital economy was going to change,
spent huge amounts of money on building the infrastructure to
power the dot com era and ended up losing a
lot of money because the gains didn't come as quickly,
(03:52):
the technology didn't evolve as rapidly as they had expected.
Of course, on the back of that, you did get
some very significant players like Amazon who came through the
dot com bubble and of course now remain one of
the most valuable companies on the planet. But there was
a lot of capital, there was a lot of investment
that was burnt in that process. And so that is
another comparison that people are making. It's the depreciation around
(04:13):
the assets and the chips that they're worried about, but
also comparisons with what happened during the dot com era
and the pain that was felt by those telecom equipment
makers that sunk so much money into which they accumulated
huge losses.
Speaker 2 (04:25):
So how are the big AI players thinking about these
casts at the moment?
Speaker 4 (04:30):
So pushback to the depreciation argument would come from in
video and we've heard that recently from the CEO Jensen Huang,
and he's made the case that in fact, even their
older AI chips, one of their older versions is called Hopper,
has a lifespan of about six years and is very versatile,
so you can use it not just for the training
(04:53):
of these large language models, but for the post training
and for the inference. That's when they're actually being used
by us, by consumer and by enterprise, and so you
can move them around. They have different functions and therefore
they actually have a longer lifespan than some of the
skeptics are suggesting, and our own analysis suggest that those
Hopper chips, those older varieties of chips have a life
(05:13):
span of about six years and are fully utilized by
most of the companies that own those. So that does
address some of that concern. The question going forward to
what extent these companies are going to be able to
find products that match the investments that they are syncing
into the AI infrastructure story. A Bane Capital came out
(05:36):
with a report recently suggesting that by twenty thirty, the
hyper scalers and other AI giants would have to be
turning around revenues of about two trillion dollars and that
right now there's a huge gap, hundreds of billions of
dollars in terms of the gap between the investments into
the AI infrastructure and the actual revenues that are coming
(05:57):
about as customers and as enterprises and companies use the
end product. So the go to market, the product fit
is going to be really, really important. And what the
big AI players say whether or that is the hyperscalers again,
the likes of Meta and in Alphabet and Amazon say,
all the likes of open A and Anthropic because we're
going to be in this world of agentic AI. We're
going to have AI agents booking our holidays, checking up
(06:19):
on our healthcare, finding good schools and universities for our students.
All those kind of things are going to come together.
Enterprises are going to be embedding AI much more than
they already are. We're only in the first opening stages
of that would be the argument. And then there's the
sovereign AI story where different countries, and we're seeing that
in the Middle East but also in Europe as well
and Japan are investing heavily to ensure that they have
(06:40):
their own AI infrastructure AI clouds. That will very early
in that story as well. Those are all the cases
that the big AI players would underscore in terms of
why this is going to be driving momentum going forward
at least through twenty twenty six. Our own team at
Bloomberg Intelligence say the end of twenty twenty six is
going to be a question mark to whether or not
(07:00):
investors continue to have patients. Will they continue to invest
in the hyperscalers if they're not saying real material terms,
if that product fit and that custom use isn't there
in a really really significant way. So I think the
patients of investors and to what extent they can continue
to lean into the Hyperscalers if they spend these huge
amounts is going to be a key question mark, and
(07:22):
our own team think that that's really going to come
to the fore at the end of twenty twenty six.
They'll need to answer that question. They've they've spent. Hyper
Scalers have spent about three hundred billion dollars on air
infrastructure this year, and the projection is that they could
be according to Vidia in the video, sees the Hyperscalar
spending upwards of about six hundred billion dollars next year.
Speaker 2 (07:40):
One of the things that occurs to me in this
as well. As we're talking about some of the world's
most valuable companies. They have massive cash piles in a
lot of cases, Why is their concern at all about
how they're going to pay for this given their revenue
streams and how much money they have.
Speaker 4 (07:56):
You're absolutely right. So when we talk about the hyperscalers,
these are companies with massive balance sheets and huge cash reserves.
These are incredibly profitable with businesses that come through with
very strong earnings. These are not nonprofitable major punts and
risky parts of the market. These are not companies that
no one's heard of. They're making real product, they're selling
(08:18):
it to customers, and they've been doing that for decades. Microsoft, Alphabet, Meta,
and Amazon. They have that balance sheet strength, they have
that cash on hand. The concern then is around other
parts of this ecosystem. So if you can think about
it in different baskets, you have those big ticket blue
chip names in one basket, and then you have maybe
(08:38):
neo clouds in the other basket. These are the core
weaves or the en clouds companies that lease out data
centers to some of these hyper scalers, and some of
the large language models who have business models that are
less proven than the hyperscalers. Then another bucket would be
maybe some of the key large language models themselves, the
open eyes and the anthropics that are money on an
(09:01):
annual basis. Even as they're seeing a lot of growth
and revenues increase year on year, they're still not profitable.
So you can break it down into different categories in
terms of the level of risk. But even amongst the
big publicly listed companies with those strong balance sheets, you
have seen examples of then tapping the public markets and
raising debt on the public markets, and so far that's
(09:22):
been well received by the markets. But how long is
that going to continue? And to what extent is the
leverage that now these companies are starting to tap into
going to be acceptable to investors. And again I think
you have to put a different framework over the different
companies in terms of how you answer that question. Then
there's the circularity of the financing. So open Ai, for example,
(09:42):
doing deals with Nvidia and Nvidia investing in open Ai,
and in response to that, open Ai committing to buying
a certain number of chips from Nvidia. Those circular financing deals,
as they've been described by some have also caused some
concern as all of these companies becoming increasingly enmeshed and
intertwined in terms of their deals and their investments on
(10:03):
what is a bet on the future and how the
future evolves, and.
Speaker 2 (10:06):
An expensive one at that. Tom McKenzie, thank you very
much for joining us, host of Bloomberg Tech Europe on
Bloomberg Television. For more explanations like this from our team
of three thousand journalists and analysts around the world, go
to Bloomberg dot com slash explainers. I'm Stephen Carroll. This
is Here's why. I'll be back next week with more.
Thanks for listening.
Hi, this is Caroline Hyde from Bloomberg Tech. Today we're
sharing something a little different in your feed, an episode
from our colleagues at Here's Why, Bloomberg's weekly show that
answers one big question in under ten minutes. Host Stephen
Carroll is joined by our Bloomberg Tech europanker Tom McKenzie
to dive into a story that's right at the heart
(00:20):
of the tech world, the massive investments in AI data
centers and the hidden costs that come with them. If
you'd like to hear more episodes of Here's Why, you'll
find a link to the podcast feed in the show notes.
Hope you enjoy.
Speaker 2 (00:36):
Bloomberg Audio Studios, podcasts, radio news. I'm Stephen Carol and
this is Here's Why, where we take one new story
and explain it in just a few minutes with our
experts here at Bloomberg.
Speaker 3 (00:53):
It's ten thirty pm in this AI party. It started
nine pm now and that party goes to four am,
and the reality is like, look, this is going to
be a two to three year left in this bull
cycle for tech. The tech sector is very strong because
artificial intelligence is really a qualitative leap in the kind
of technology that we've had over the last several decades.
(01:15):
You're seeing an exponential growth of adoption and use of AI.
Speaker 4 (01:19):
The number of applications that are going to be using
these AI is also growing.
Speaker 2 (01:24):
Everyone has an opinion on where the AI frenzy is
going next. While optimism is rampant about the technologies potential,
more questions are now being asked about AI's running costs.
Speaker 4 (01:36):
We are putting mostly chips silicon into these data centers
that have a lifespan of perhaps four years, So those
chips they appreciate very quickly.
Speaker 3 (01:47):
Even in video, there's a new chip every eight months
and it's ten times as powerful as the earlier ones.
Speaker 4 (01:52):
The thing with the valley this here is that almost
every investor knows it's all going to turn into pumpkins
and mice at midnight. Only, as Buffett would say, no
one in the room as a clock.
Speaker 2 (02:02):
Even with bumper results and bullish revenue forecasts, here's why
AI casts still worry investors. Tom McKenzie, who hosts Bloomberg
Tech You're U bum Bloberg Television, joins me now for more. Tom.
The investor Michael Burry of Big Short fame is among
those who's worried about these future casts of AI and
(02:23):
data centers in particular.
Speaker 4 (02:25):
What's the concern, Yeah, absolutely, Michael Burry putting on famously
short positions, so shortening the stocks of Nvidia and Pallanteer
before he wrapped up his fund. His concern does focus
on the depreciation of some of these assets by assets.
I'm talking about specifically these AI chips, very expensive AI accelerators.
(02:47):
Ninety percent of the market share is dominated by Nvidia,
so across the sale of these chips and video has
that significant market gain versus its rivals. And the concern
is that as you get newer versions of these chips,
the older ones essentially become less valuable. And Michael Barrie
making the argument that companies the hyperscalers, so the Microsofts
(03:09):
and alphabets and metas of the world, are not properly
accounting for how quickly these these assets depreciate. The other
part of the concern and kind of ties into this
that you hear voice from the skeptics around the AI
bubble is that there are comparisons, they say, with what
happened in the late nineteen nineties, nineteen ninety nine, early
two thousand, the dot com bubble, when it was the
(03:31):
telecom equipment makers that leading up to all of the
online expectations around how our digital economy was going to change,
spent huge amounts of money on building the infrastructure to
power the dot com era and ended up losing a
lot of money because the gains didn't come as quickly,
(03:52):
the technology didn't evolve as rapidly as they had expected.
Of course, on the back of that, you did get
some very significant players like Amazon who came through the
dot com bubble and of course now remain one of
the most valuable companies on the planet. But there was
a lot of capital, there was a lot of investment
that was burnt in that process. And so that is
another comparison that people are making. It's the depreciation around
(04:13):
the assets and the chips that they're worried about, but
also comparisons with what happened during the dot com era
and the pain that was felt by those telecom equipment
makers that sunk so much money into which they accumulated
huge losses.
Speaker 2 (04:25):
So how are the big AI players thinking about these
casts at the moment?
Speaker 4 (04:30):
So pushback to the depreciation argument would come from in
video and we've heard that recently from the CEO Jensen Huang,
and he's made the case that in fact, even their
older AI chips, one of their older versions is called Hopper,
has a lifespan of about six years and is very versatile,
so you can use it not just for the training
(04:53):
of these large language models, but for the post training
and for the inference. That's when they're actually being used
by us, by consumer and by enterprise, and so you
can move them around. They have different functions and therefore
they actually have a longer lifespan than some of the
skeptics are suggesting, and our own analysis suggest that those
Hopper chips, those older varieties of chips have a life
(05:13):
span of about six years and are fully utilized by
most of the companies that own those. So that does
address some of that concern. The question going forward to
what extent these companies are going to be able to
find products that match the investments that they are syncing
into the AI infrastructure story. A Bane Capital came out
(05:36):
with a report recently suggesting that by twenty thirty, the
hyper scalers and other AI giants would have to be
turning around revenues of about two trillion dollars and that
right now there's a huge gap, hundreds of billions of
dollars in terms of the gap between the investments into
the AI infrastructure and the actual revenues that are coming
(05:57):
about as customers and as enterprises and companies use the
end product. So the go to market, the product fit
is going to be really, really important. And what the
big AI players say whether or that is the hyperscalers again,
the likes of Meta and in Alphabet and Amazon say,
all the likes of open A and Anthropic because we're
going to be in this world of agentic AI. We're
going to have AI agents booking our holidays, checking up
(06:19):
on our healthcare, finding good schools and universities for our students.
All those kind of things are going to come together.
Enterprises are going to be embedding AI much more than
they already are. We're only in the first opening stages
of that would be the argument. And then there's the
sovereign AI story where different countries, and we're seeing that
in the Middle East but also in Europe as well
and Japan are investing heavily to ensure that they have
(06:40):
their own AI infrastructure AI clouds. That will very early
in that story as well. Those are all the cases
that the big AI players would underscore in terms of
why this is going to be driving momentum going forward
at least through twenty twenty six. Our own team at
Bloomberg Intelligence say the end of twenty twenty six is
going to be a question mark to whether or not
(07:00):
investors continue to have patients. Will they continue to invest
in the hyperscalers if they're not saying real material terms,
if that product fit and that custom use isn't there
in a really really significant way. So I think the
patients of investors and to what extent they can continue
to lean into the Hyperscalers if they spend these huge
amounts is going to be a key question mark, and
(07:22):
our own team think that that's really going to come
to the fore at the end of twenty twenty six.
They'll need to answer that question. They've they've spent. Hyper
Scalers have spent about three hundred billion dollars on air
infrastructure this year, and the projection is that they could
be according to Vidia in the video, sees the Hyperscalar
spending upwards of about six hundred billion dollars next year.
Speaker 2 (07:40):
One of the things that occurs to me in this
as well. As we're talking about some of the world's
most valuable companies. They have massive cash piles in a
lot of cases, Why is their concern at all about
how they're going to pay for this given their revenue
streams and how much money they have.
Speaker 4 (07:56):
You're absolutely right. So when we talk about the hyperscalers,
these are companies with massive balance sheets and huge cash reserves.
These are incredibly profitable with businesses that come through with
very strong earnings. These are not nonprofitable major punts and
risky parts of the market. These are not companies that
no one's heard of. They're making real product, they're selling
(08:18):
it to customers, and they've been doing that for decades. Microsoft, Alphabet, Meta,
and Amazon. They have that balance sheet strength, they have
that cash on hand. The concern then is around other
parts of this ecosystem. So if you can think about
it in different baskets, you have those big ticket blue
chip names in one basket, and then you have maybe
(08:38):
neo clouds in the other basket. These are the core
weaves or the en clouds companies that lease out data
centers to some of these hyper scalers, and some of
the large language models who have business models that are
less proven than the hyperscalers. Then another bucket would be
maybe some of the key large language models themselves, the
open eyes and the anthropics that are money on an
(09:01):
annual basis. Even as they're seeing a lot of growth
and revenues increase year on year, they're still not profitable.
So you can break it down into different categories in
terms of the level of risk. But even amongst the
big publicly listed companies with those strong balance sheets, you
have seen examples of then tapping the public markets and
raising debt on the public markets, and so far that's
(09:22):
been well received by the markets. But how long is
that going to continue? And to what extent is the
leverage that now these companies are starting to tap into
going to be acceptable to investors. And again I think
you have to put a different framework over the different
companies in terms of how you answer that question. Then
there's the circularity of the financing. So open Ai, for example,
(09:42):
doing deals with Nvidia and Nvidia investing in open Ai,
and in response to that, open Ai committing to buying
a certain number of chips from Nvidia. Those circular financing deals,
as they've been described by some have also caused some
concern as all of these companies becoming increasingly enmeshed and
intertwined in terms of their deals and their investments on
(10:03):
what is a bet on the future and how the
future evolves, and.
Speaker 2 (10:06):
An expensive one at that. Tom McKenzie, thank you very
much for joining us, host of Bloomberg Tech Europe on
Bloomberg Television. For more explanations like this from our team
of three thousand journalists and analysts around the world, go
to Bloomberg dot com slash explainers. I'm Stephen Carroll. This
is Here's why. I'll be back next week with more.
Thanks for listening.
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