Too Much AI, Too Soon

I decided to share some thoughts about the current state of the market regarding AI.

I have become very cautious due to recent financing developments, the projected amount of capital to be raised, and the general valuation levels of many of these companies. As a result, some of you already know, I have trimmed or sold many of my tech/AI positions lately.

I want to start off and say that my conviction in AI in the long term has not changed one bit. I continue to think it will be the biggest transformation in history for society and the economy. That being said, I believe the stock market expectations in the short term have gotten ahead of the reality that we face. The issues I see can be segmented into the following categories:

1. We are running out of organic capital and entering the phase of »creative« deals

2. GPUs are a faster depreciating asset than what is thought

3. Valuations are factoring in a very small chance of things slowing down

We are running out of organic capital, so »creativity « has taken front stage

When funding unicorn startups, the classical setup used to be: big VC firms at $1B-$10B; at $10-$30B, someone like Softbank, and then you do an IPO. But AI labs like OpenAI and Anthropic don’t want the IPO route for now, as going that way means your business model and economics get dissected, and analysts dive deeper into what makes sense and what doesn’t. Even if they did an IPO, it wouldn’t raise nearly enough capital, as now we are entering a stage where AI labs need over $100 billion of new investments on an annual basis. OpenAI, with its deal with Nvidia and AMD on top of their Stargate datacenter, plans to build a total of 26GW of data centers in the next few years. And this is just a current number that we know so far. One GW of an AI data center costs around $60B, so we are talking about raising more than $ 1.5 trillion. To put that number in perspective, the most profitable business models from the big tech companies, Amazon, Google, Meta, Microsoft, and Apple, in the last 5 years produced a total of $1.4T in Free Cash Flow. And this was in a pandemic environment where usage and profits soared from the increased demand. So now we are talking about a company needing to raise more than the combined 5-year Free Cash Flow of Big Tech.

OpenAI is on track to make around $15-$20B in revenue this year. Even if that number doubles or triples next year, it is not even remotely enough to justify the investment size, so OpenAI will, of course, have to continue raising capital and possibly debt. On top of that revenue, they are expected to lose around $9B, with losses continuing to rise to $47B in 2028. Bloomberg also reported that xAI, another AI lab, is losing around $1B per month.

Financing trillions of CapEx via their own FCF will be very hard to do, so it’s clear that OAI will need to raise capital and debt, but who is big enough to put down over $ 100 billion?

Nvidia decided to potentially invest $100B in OAI, structured as $10B for each GW of power that OAI brings. To me, this deal is concerning, especially as we are now entering a phase where Nvidia is the only possible financier for these types of deals, as the FCF of everyone else is already depleted from investing in heavy CapEx to build datacenters and buy Nvidia chips. In my view, the main reason Nvidia did this deal is that, as we will discuss later in this article, OpenAI is key to the entire AI sector right now. Dylan Patel from Semianalysis recently said in a podcast that OpenAI and Anthropic are the end buyers of 1/3 of all Nvidia GPUs right now.

The problem is not just the circular type of deal itself; the problem is that, at a size of over $100B, the only possible investor is a company like Nvidia or perhaps Apple. Even the hyperscalers have all of their cash already committed to their own CapEx, which is hitting the $70-$100B annual range. Even the hyperscalers are on their limits when it comes to spend as the CapEx is rising much faster than the revenues and FCFs. On top of it, even with Nvidia’s $100B OAI, it still needs $1.4T? Who will finance that?

Another concerning sign for me is that debt financing has started to roll in in these deals. We just got information on $20B financing for xAI, where the $20B is provided by an SPV. Of that $20B, $12.5B is debt and $7.5B is capital, with Nvidia contributing $2B of the $7.5B. xAI will then rent those chips from the SPV for 5 years, where the GPUs act as collateral. Meta has also raised $29B for a data center recently, with $26B of that $29B being debt, and that data center is expected to be the collateral. Oracle has also completed a $38B debt raise. Hyperscalers like Microsoft and others are going to the neoclouds. Nebius has signed a $17.4B (potentially expandable to $19.4B) deal with Microsoft. Looking at the deal, debt is in play again:

»Nebius expects to finance the capital expenditure associated with the contract through a combination of cash flow coming from the deal and the issuance of debt secured against the contract in the near term, at terms enhanced by the credit quality of the counterparty. «

So we have entered a market stage where debt and a company like Nvidia, with its own motivations, act as the lender of last resort.

The creative deals where chips are collateral are also a big problem, as I will explain further here. I expect to see more of these GPU collateral deals until the market finally figures out the problems with those.

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GPUs are a faster depreciating asset than what is thought

The life of the current generation of GPUs is shorter than most think, and what many companies are projecting in their amortization plans. We are entering the inference phase of the AI cycle, where we are running out of data centers and energy. The most important metric has become tokens per watt. Nvidia has also moved to a 1-year upgrade cycle, which means that each year you will get a much more capable and energy-efficient accelerator than the previous generation. And this is not at the scale of anything we had in history with Moore’s law and chips. Jensen said it himself: between Hopper and Blackwell, they are driving the cost of tokens down 10 to 20x. Moore’s law would have achieved that by just 20%, so this is much faster, and the amortization of these GPUs should be much, much faster than what the neoclouds and hyperscalers are modeling. On a recent podcast, Jonathan Ross, the CEO of Groq and one of the founders of Google TPUs, said that at Groq, they are using a 1-year cycle in terms of amortization, as the people who are using 3-5 year amortization cycles are wrong. With chips, you don’t just have an upfront investment in CapEx; you also incur the OpEx of running that chip, along with the electricity and water costs that come with it. Not to forget, electricity costs are going up because these AI factories require a lot of electricity. Looking at the statements and financials of Neoclouds and hyperscalers, you can see that their numbers differ. The hyperscalers follow a 3-4 year amortization cycle for GPUs, whereas Corewave and some neoclouds follow a 6-year depreciation of Nvidia GPUs, as stated by their leadership. The losses on these neoclouds would have been much, much bigger if the amortization cycle were 1-2 years instead of 6, which is another concerning pressure point in the whole ecosystem.

But some might say, well, you still see people renting Nvidia H100, which are chips that Nvidia started selling 3 years ago. Yes, but there are two factors to that. The first one is that you have two clients pushing demands sky high, as they are subsidizing the end users, as the computing to do the services that they offer is much more expensive than the price that they are charging the end users. This works out only to the point where investors are willing to give you the money to continue doing that. And the second, even more important point is that the H100 is still useful despite being 3 years old, because NVDA switched to a 1-year product cycle between H100 and Blackwell, so this is in late 2024. Before that, the cycle was 18-24 months. So, in terms of cycle times, the chip isn’t that old from a generation perspective compared to looking at it in years. However, with Nvidia now on a one-year product cycle, this change affects things significantly. In my view, the real amortization of these chips should be in 1-2 years.

For the sake of math, let’s take Coreweave’s amortization of 6 years. This means that when Nvidia Vera Rubin comes out in late 2026, people will still want to rent Ampere A100, which started shipping in late 2020. That is crazy and not going to happen. Even the hyperscalers ammorization rates of 3-4 years are a stretch in my view, especially as we go to a world where we don’t have any »free« AI data centers waiting around, we have to build new ones and get new power to them, which takes time, so for all the comapanies that will want to scale they will have to switch up their old GPUs at the data centers they already have running for new GPUs to get more tokens per watt as their watt usage is limited.

The problem with extending your amortization cycle is that it shows higher profits today than they really are. So here is another concern of mine, as the profits of all the hyperscalers in the cloud space are going to come under pressure as the true amortization rate shows up in the coming years. It becomes a broader industry problem when investors start to focus more on this and see that the neoclouds are losing even more money than they state. Again, for the AI circle to continue, investors need to pour money into these neoclouds as well…

Nvidia is well aware of this problem, so this comment from an NVDA employee doesn’t come as a surprise:

»…taking out the old ones and put in the new ones, and those old ones we’ll actually buy back. If a customer has A100s and they want to go to H100s, we’ll buy back the servers and the chips and then resell them overseas.

Source: AlphaSense

My speculation is that overseas means China, but now that they can’t sell to the Chinese market, the question is, who will buy these old chips? At the end, someone has to take those useless chips on their books. And Nvidia is already committed to taking on some of these potential problems if they arise. As recently reported in a CoreWeave deal, Nvidia is obligated to pay the company up to $6.3 billion through 2032 if the cloud provider has unsold capacity. The agreement was actually signed in 2023, but was only publicly revealed in an SEC filing this month. So Nvidia is already acting as a backstop to some extent, although Coreweave’s debt by itself is much larger than the $6.3B.

Why do you think Microsoft is doing deals with Neoclouds? Because they are seeing a surge in demand for compute from their clients. Microsoft wants to maintain the client relationship and keep the client happy, but they are not confident enough in the CapEx growing even further, so they would rather offload some of the risk to someone else. The client doesn’t know or care that Microsoft doesn’t own the physical infrastructure, and when the hype fades, Microsoft doesn’t have to write those old chips off as a loss, since the neoclouds have taken over that risk. It’s a win for Microsoft as they keep the client, and if the demand turns out to be durable in the long term, they have more than enough time to build out their own data center and switch back to their own infrastructure. In the meantime, in the frenzy cycle we are in right now, they can offload the risk of chips becoming obsolete faster than expected. One of the main reasons Microsoft wants to work with neoclouds is that they are uncertain about CapEx and prefer to take OpEx.

On top of everything already stated about these creative deals, we are now even doing deals where GPUs are in SPVs that serve as collateral. As already stated before, if the real GPU depreciation rate is 1-2 years, which I believe is correct, then the collateral on many of these deals will be a problem.

Valuations are factoring in a very small chance of things slowing down

Current valuations of many of the technology companies are factoring in very little risk. First is the customer concentration risk. Groq CEO said that 35-36 companies are currently responsible for 99% of token spending in AI right now. And even among those 35 companies, 2 are by far the most significant spenders: OpenAI and Anthropic. We already mentioned the stat from Patel that 1/3 of Nvidia GPUs end up going to OpenAI or Anthropic. The demand from these two companies is reflected not only in Nvidia but also throughout the semiconductor chain and in the revenues of hyperscalers and neoclouds. This means that a big chunk of the market is dependent on the success and progress of these two companies.

Both OAI and Anthropic need to continue growing at a very high clip, in terms of users, user engagement, and model performance. In addition, both of these companies (OAI & Anthropic) have to continue to raise enormous amounts of new capital at +$500B valuations, which we already talked about, and it is going to be very challenging, to say the least. We haven’t even mentioned the rate of progress of these models. I am not an AI tech skeptic, but I believe that, as with anything, there is a risk of things not working out. Right now, the market is pricing in a perfect execution of future roadmaps. It is also telling that Microsoft, which had complete access to OAI (even their IP), chose not to fulfill OAI’s future compute demand needs at the rate OAI wanted. Keep in mind, Microsoft has rights of first refusal, meaning they could have the Oracle cloud purchase order if they wanted it, but they didn’t. One has to at least think about why that is. Microsoft’s Satya has, over the years, proven to us that he is one of the best CEOs out there.

Also, I don’t see a future where 5 companies are spending on $100B training runs for the next frontier AI. I believe that it will become much more narrow in the future with 3 or even fewer players forming the market, which means that a lot of the current compute spend for training is being wasted as they create similar functioning models, and in terms of the model performance layer, the moat doesn’t seem to be sticky or long-lasting.

The market is also discounting the risk of disruption for many public technology companies, in my view. When it comes to disruption, everyone thinks only of Google Search, but this potential disruption has now expanded further. The business models of companies like OAI, Anthropic, and xAI are expanding into areas such as social media, e-commerce distribution, productivity tools, and even cloud infrastructure. Information retrieval (the Google Search alternative) is only the first step.

If we just look at the cloud market, most of us, including myself, thought that we would have an oligopoly of Amazon, Microsoft, and Google just a few years ago, as it was unimaginable to expect that anyone would raise enough funds to invest +$100B to build out an AI cloud infrastructure. Well, today, if companies like OAI actually achieve at least half of what they have in plan, they will have the same, if not even more, capacity than some of those hyperscalers. The direct deals they are doing with Nvidia, AMD, and SK Hynix also mean they are skipping cloud providers. A current employee at Nvidia even said that xAI’s goal is to actually become a compute provider:

NVDA employee: »xAi Elon’s company. They’re building up a tremendous salesforce. They’ve probably called me like 10 times in the last 6 months, and they’re building out there. They want to make a massive disruption…

Analyst: They want to become Oracle?

NVDA employee: Bingo.«

source: AlphaSense

We also have companies like Oracle, which are willing to take big risks, with debt and OAI orders to build out capacity. We have the neoclouds. So, for the three hyperscalers, if the market doesn’t soon cool down in terms of funding for these neoclouds and AI labs, they could face serious competitors down the line.

The flip side is that when the market cools down, hyperscalers with positive FCF will have opportunities to buy some of these competitors today, as they might become distressed assets. Nonetheless, the disruption risk with this technological shift is significant, affecting the entire technology industry, and the valuations currently do not reflect that, in my view.

What also doesn’t get enough attention is that much of the spending by current tech leaders is not tied to new revenue streams but actually to defend the moats and business models they already have. They are in a race that has gotten out of hand, but as Meta’s Zuckerberg has recently stated, the risk of overspending a few hundred billion on infrastructure is smaller than the risk of being left out. I agree with Mark on this point and understand why all of these companies have to be in this race. However, the capital market’s job would be to properly reflect that risk in valuation multiples, and right now, they are not.

To be clear, I am not calling for a 2000s-like bubble drawdown of +50%, but I do believe that we are reaching financial limits that will cause the market to reevaluate some of the multiples it has given to companies today, and that we are about to enter a consolidation phase. In this phase, it will also become much clearer who has a sustainable moat and what the new business opportunities are.

For AI to reach its economic potential, we need better and more efficient hardware and more efficient software for inference and training these models. I believe we will get that, but right now the market is in a race with itself, and short-term expectations have gone far too high, especially as we consider that most tech companies are going to go through heavy CapEx cycles, and profits and FCFs will shrink. On top of that, you have moats being shaken all across the industry, and many will even question not only the moats but the capex-light business models, as everyone needs AI infrastructure. The trigger point for stopping this AI race is in the hands of the capital markets. Once they decide we will no longer fund this at this pace, it will signal to both private and public companies that the normalization phase has begun, and I believe we are very close to that point.

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