August 4, 2026
The Artificial Intelligence bubble in the United States is still growing. It is consuming a large share of the available financial resources. But its output, in form of real products, is so far rather meager.
There are some code generation tools which are, at times, helpful, but, unless subsidized, very expensive. There is ChatGTP and other squawk boxes which in the end are just new forms of inherently unreliable web search tools.
What is missing are useful mass applications billions of people are willing to pay for.
Still – a large number of ‘very important people’ believe that the Large Language Models, which are at the core of OpenAI’s and Anthropic products, will one day reach the capabilities of sentient beings. That is, in my view, utter bullshit, but who am I to tell you.
The NY Times has a long write up (archived) about Oracle founder Larry Ellison and his bet of nearly everything he owns on the A.I. bubble. It states:
The story of A.I. has been as much a financial story as a technological one, a question of how to structure the mind-boggling investments required to train and run the models. Few people doubt that this technology is going to change everything. What’s less clear is when the profits are going to start rolling in and how big they are going to be. “To me, it’s a math problem,” says Asad Ramzanali, the director of A.I. at a policy center at Vanderbilt University. “We are making trillions of dollars in investments on the back of tens of billions of dollars in revenues.”
That is why Ed Zitron and others will rightly tell you that the huge investments spent on AI are making no sense at all.
Where are the products and the customers that will allow to recuperate the hundreds of billions of shady dollars spent on data centers for AI?
The financial structure of the data center build-out makes it especially vulnerable to a crash. The deals themselves are built on enormously complicated debt and equity schemes that involve circular financing. The hyperscalers are investing heavily in the same companies they are counting on to buy their computing power. It’s what economists call an interlocking liability structure. If their customers struggle to monetize their products, they will be hit extra hard — and so will their investors, which include a lot of everyday Americans. And these are just the U.S. companies. The A.I. boom has been a global phenomenon; an A.I. collapse would be as well.
The NY Times presume that everything the U.S. does is copied throughout the world. If the bubble burst in the U.S. it will, in consequence, also burst in other places – especially in China.
This is a misunderstanding of what the Chinese AI models are, and what Chinese AI companies are doing.
