No, It's Not The Dotcom Bubble All Over Again
Not all parallels are, well, parallel
Recently, there’s been a deluge of takes in the financial community about how AI is basically over. It makes sense - since Mag 7 stocks are down, people want to read that stuff. However, there is one thesis that specifically irks me that I’d like to address here:
AI buildout in the 2020s = Fiber buildout in the late 1990s
This is a parallel that people love to use because it’s super easy to understand. Oh, there’s NewTech, people get unreasonably excited about NewTech, invest too much money, and then the revenues take much longer to appear than people had expected, so everyone who spends money on the early buildout loses their shirt.
It does, however, miss a few very important differences between the two situations.
1. Who’s funding the buildout?
The fiber buildout was financed by a few companies, for example:
Global Crossing was founded in 1997 and went bankrupt in 2002. It never had a profitable quarter and didn’t have any business other than telecom. Its 1998 revenue was $418M. It spent around $15B on the fiber buildout in total.
Enron was a fraud - with fraudulent activities dating back to at least 1997. It had an actual business, but not an enormous one - 1996 revenues were $13B, profits - $584M. It ranked #141 on the Fortune 500 list that year.
Nortel Networks was a telecom, about the same size as Enron.
Level 3 was a spinoff that went public in 1998. That year, it had revenues of just $392M. By 2001, it had accumulated $6.2B in debt.
There were others - Qwest, Worldcom, 360networks, etc. But the big picture for all of them is the same: none of them had been strong businesses prior to the bubble with large free cash flows available to invest.
By contrast he companies investing the most in AI buildout right now are Microsoft, Meta, Amazon, and Alphabet. All of them are in the top 10 most profitable companies in the world. Each one generates tens of billions of dollars of free cash flow to fund any potential investments.
While companies in the late 1990s needed to tap capital markets for their buildouts, tech companies today can comfortably cover their capital expenditures with money earned with their main business. Alphabet alone had $72B in free cash flow in 2024, and, as a reminder, that FCF is a number you get after you subtract the $52B they actually spent on capex.
Can capital markets force tech companies to spend less on AI? Maybe? Like I’m sure if Google’s stock price falls 99% it would rather repurchase its stock than invest, but short of that, it’d take a lot. The leaders of these companies fully believe that AI is the future. Sergey Brin, for example, is personally back in the Deepmind offices every day working side by side with the AI researchers. If you’re one of the richest people in the world, and you believe in something so much that you go back to the trenches yourself, what’s a Wall St analyst being bearish on AI to you?
Even if you believe that the tech moguls are high on their own supply, don’t know the price of a gallon of milk, and don’t realize that no real people care about AI - you have to admit that they believe in it, and they control how much money gets invested into AI.
2. Useful life
Fiber is a long-term asset. Generally the depreciation schedule for fiber optic cable is 15-25 years, though in good conditions it can last over 30. That means that when you invest in fiber optic cable you are investing based on long term projections. Generally, people can’t see into the future very well, and 20 years is a long time.
By contrast, GPUs are a relatively quickly depreciating asset. The useful life of a GPU is somewhere in the 3-5 year range1.
So, if you see your customers’ orders for 2 years in the future - in the case of fiber, you are seeing ~10% into the useful life of your asset. In the case of GPUs - you are seeing about 50%. That means that the companies making the investments into AI have much more sound projections about what their capex will power than the fiber companies ever did.
3. No revenues
Finance professionals love to mention that “all this AI stuff isn’t generating any revenue, much like all the dotcom startups. It’s just OpenAI with $4B, and beyond that, nobody is making money. That doesn’t nearly begin to cover the tens of billions of investment”.
That argument is largely blind to how AI is being used today. Consumer applications are the most visible ones, but so far AI has been an enterprise story. For example, Klarna currently has 3500 employees, down from 5000 recently - the CEO says that AI is doing the equivalent of work of 700 customer support agents. That’s very valuable for a business, but it won’t show up as a discrete revenue number analysts can point to. The place where enterprise usage is bound to show up is in the cloud segments of the hyperscalers - that’s where the AI jobs actually run. Let’s take a look:
Microsoft: “Already, our AI business has surpassed an annual revenue run rate of $13 billion, up 175% year-over-year.”
Google: “Google Cloud revenues increased 30% to $12.0 billion (quarterly) led by growth in Google Cloud Platform (GCP) across core GCP products, AI Infrastructure, and Generative AI Solutions.”
Meta: Meta is a strange one. It does not make revenue by selling AI products directly, however, it’s very successfully using AI to increase revenue per user. It’s really hard to break out what the impact of it might be.
Amazon: AWS revenues grew 19% YoY last quarter to $29B.
Now, it’s not clear how much of the cloud revenue for Amazon and Google is attributable to AI, but given that Microsoft is #2 in cloud, and their AI business run rate is $13B, it’s not crazy to assume that for the top 3 cloud hyperscalers, the AI business run rate is around $40B combined (and growing rapidly). That’s an order of magnitude above the $4B in OpenAI revenue that people love to bring up.
To sum up: this is not the dotcom bubble. Even if hyperscalers are too optimistic/excited today, they are much more stable financial entities than dotcom telecoms ever were. The hyperscalers are seeing real and meaningful revenue growth from AI, and they have visibility into the revenue the GPUs they are buying today will generate over their useful life. Note that none of these differences rely in any way on AGI, ASI, robots, sharks with freaking lasers on their heads, or any other wild future projections - while I do think that all of those things will happen, you don’t have to believe in any of them to see the differences between the 1990s and today.
This doesn’t mean that tech stocks will go up immediately! Overall, I tend to think that passive flows as described by Michael W. Green are more important to Mag 7 performance than anything the financial analysts think or say - they are affected by the broader economy, the Fed, animal spirits, etc. But the idea that we’re living through a repeat of the late 1990s/early 2000s is intellectually lazy and wrong.
Data centers have longer-term depreciation schedules but GPUs are by far the biggest cost contributor to them.
Nothing to argue, still too rational for the irrational market 🫣