Subject: The mother of all Bubbles
Today’s WSJ has an AI story, nominally about CoreWeave, which details the reasons they think the investments in this sector is massively overdone: (Gift link below)

Spending on AI Is at Epic Levels. Will It Ever Pay Off?

Tech companies pour hundreds of billions into data centers, taking on heavy debt, but current revenue is relatively tiny; echoes of dot-com bubble


https://www.wsj.com/tech/ai/ai...

Items:

The artificial-intelligence boom has ushered in one of the costliest building sprees in world history. Over the past three years, leading tech firms have committed more toward AI data centers like the one in Ellendale, plus chips and energy, than it cost to build the interstate highway system over four decades,

Flooded with money from Wall Street and private-equity investors, it has metamorphosed into a computing goliath with a market value bigger than General Motors or Target.

Chips in an AI center have a useful life of between 3-5 years before they are outdated.

[This is my favorite:] This week, consultants at Bain & Co. estimated the wave of AI infrastructure spending will require $2 trillion in annual AI revenue by 2030. By comparison, that is more than the combined 2024 revenue of Amazon, Apple, Alphabet, Microsoft, Meta and Nvidia, and more than five times the size of the entire global subscription software market.

[Much of the story is on the fragility of CoreWeave, which leases data centers for up to 10 years, but whose clients only sign use leases for 2-5 years. If/when there’s weakness, this could be a problem?] = “If the wave of building proves far more than needed, or if tech companies pivot away from third-party providers, the risk is that CoreWeave’s data centers could end up like the dormant fiber optic cables that snaked through the U.S. in the 2000s.”

Other issues:

An MIT report found 95% of organizations surveyed are getting no return on their AI product investments. A University of Chicago economics paper found AI chatbots had “no significant impact on workers’ earnings, recorded hours, or wages” at 7,000 Danish workplaces.

OpenAI’s release of ChatGPT-5 in August was widely viewed as an incremental improvement, not the game-changing moment many expected. Given the high cost of developing it, the release fanned concerns that generative AI models are improving at a slower pace than expected.

Each new AI model—ChatGPT-4, ChatGPT-5—costs significantly more than the last to train and release to the world, often three to five times the cost of the previous, say AI executives. That means the payback has to be even higher to justify the spending.

Another hurdle: The chips in the data centers won’t be useful forever. Unlike the dot-com boom’s fiber cables, the latest AI chips rapidly depreciate in value as technology improves, much like an older model car.

“This is bigger than all the other tech bubbles put together,” said Roger McNamee, co-founder of tech investor Silver Lake Partners, who has been critical of some tech giants.

So the question: how to play it?