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