C (4:25)

So how are the big AI players thinking about these costs at the moment?

B (4:30)

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. And our own analysis suggests that those Hopper chips, those older varieties of chips, have a lifespan 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 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. So 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 Anthropic, 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 health care, 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 their own AI infrastructure and AI class that were 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 pat. 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? So 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. They've spent hyperscalers have spent about $300 billion on infrastructure this year. And the projection be, according to Nvidia, Nvidia sees the hyperscalers spending upwards of about $600 billion next year.

C (7:40)

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 there concern at all about how they're going to pay for this given their revenue streams and how much money they have?

B (7:56)

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 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. 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 you can think about it in different baskets. You have those big ticket blue chip names in one basket and then you have maybe Neo clouds in the other basket. These are the core weaves or the nClouds, companies that lease out data centers 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 OpenAI's 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, 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 them 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, 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 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 become 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.

C (10:06)

And an expensive one at that. Tom MacKenzie, thank you very much for joining us, host of Bloomberg Tech Europe on Bloomberg Television. For more explanations like this from our team of 3,000 journalists and analysts around the world, go to Bloomberg.com 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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