Special Episode: Here's Why AI Costs Still Worry Investors

0:00Hi, this is Caroline Hyde from Bloomberg Tech. Today, we're sharing something a little bit 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. Host Stephen Carroll is joined by our Bloomberg Tech Europe anchor, Tom McKenzie, to dive into a story that's 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 of Here's Why, you'll find a link to the podcast feed in the show notes. Hope you enjoy. Bloomberg Audio Studios.

0:39Podcasts, radio, news. I'm Stephen Carroll, and this is Here's Why, where we take one news story and explain it in just a few minutes with our experts here at Bloomberg.

0:53It's 10.30 p.m. in this AI party. It started 9 p.m. and that party goes to 4 a.m. 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. You're seeing an exponential growth of adoption and use of AI. The number of applications that are going to be using these AI is also growing. Everyone has an opinion on where the AI frenzy is going next. But while optimism is rampant about the technology's potential, more questions are now being asked about AI's running costs.

1:35We are putting mostly chip silicon into these data centers that have a lifespan of perhaps four years. Those chips, they depreciate very quickly. Even NVIDIA, there's a new chip every 18 months and it's 10 times as powerful as the earlier ones. The thing with the rally this year 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 has a clock. Even with bumper results and bullish revenue forecasts, here's why AI costs still worry investors. Tom McKenzie, who hosts Bloomberg Tech Europe on Bloomberg Television, joins me now for more.

2:16Tom, the investor Michael Burry of Big Short fame is among those who's worried about these future costs of AI and data centres in particular. What's the concern? Yeah, absolutely. Michael Burry putting on famously short positions, so shorting the stocks of NVIDIA and Palantir 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. 90 % of the market share is dominated by NVIDIA. So across the sale of these chips, NVIDIA has that significant market gain versus its rivals.

2:57And the concern is that as you get newer versions of these chips, the older ones essentially become less valuable. And Michael Bari making the argument that companies, the hyperscalers, So the Microsofts and alphabets and metas of the world are not properly accounting for how quickly these assets depreciate. The other part of the concern, and it kind of ties into this, that you hear voice from the sceptics around the AI bubble, is that there are comparisons, they say, with what happened in the late 1990s. 1999, early 2000, the dot-com bubble, when it was the 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.

3:52The 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 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 and to which they accumulated huge losses.

4:25So how are the big AI players thinking about these costs at the moment? So pushback to the depreciation argument would come from NVIDIA. 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 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 consumers 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.

5:09And our own analysis suggests that those hopper chips, those older varieties of chips, have a lifespan of about six years and are fully utilised by most of the companies that own those. So that does address some of that concern. The question going forward would be to what extent these companies are going to be able to find products that match the investments that they are sinking into the AI infrastructure story. Bain Capital came out with a report recently suggesting that by 2030, the hyperscalers and other AI giants would have to be turning around revenues of about$2 trillion 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 about as customers and as enterprises and companies use the end product.

6:02So the go-to-market, the product fit is going to be really, really important. And what the big AI players say, whether that is the hyperscalers, again, the likes of Meta and Alphabet and Amazon say, or the likes of OpenAI and Anthropics is we're going to be in this world of agentic AI. We're going to have AI agents booking our holidays, checking up 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.

6:32And 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 their own AI infrastructure and AI clouds. They will be 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 2026. Our own team at Bloomberg Intelligence say the end of 2026 is going to be a question mark as to whether or not investors continue to have patience. Will they continue to invest in the hyperscalers if they're not seeing real material returns, if that product fit and that custom use isn't there in a really, really significant way?

7:12So I think the patience of investors and to what extent they can continue to lean into the hyperscalers as they spend these huge amounts is going to be a key question mark. And our own team think that that's really going to come to the fore at the end of 2026. They'll need to answer that question. The hyperscalers have spent about$300 billion on our infrastructure this year. And the projection is that they could be, according to NVIDIA, NVIDIA sees the hyperscalers spending upwards of about$600 billion next year. 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.

7:46They have massive cash piles in a lot of cases. Why is there concern at all about how they're going to pay for this, given their revenue streams and how much money they have? 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 businesses that come through with very strong earnings. These are not non-profitable, 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 it to customers. And they've been doing that for decades.

8:21Microsoft, 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 you can think about it in different baskets. You have those big ticket blue chip names in one basket. and then you have maybe NeoClouds in the other basket. These are the CoreWeaves or the NClouds, companies that lease out data centres to some of these hyperscalers 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 OpenAIs and the Anthropics, that are losing money on an annual basis, even as they're seeing a lot of growth and revenues increase year on year.

9:06They'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 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.

9:39Then there's the circularity of the financing. So OpenAI, for example, doing deals with NVIDIA and NVIDIA investing in OpenAI. And in response to that, OpenAI 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 what is a bet on the future and how the future evolves. And an expensive one at that. Tom McKenzie, thank you very much for joining us. Host of Bloomberg Tech Europe on Bloomberg Television.

10:13For more explanations like this from our team of 3 ,000 journalists and analysts around the world, go to bloomberg.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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