A Wall Street Journal deep dive into artificial intelligence (AI) startup valuations found that they are not necessarily based on revenue or products. This could be seen as evidence of an AI bubble, as investors push and pull on the issue.
The story titled “These Billion-Dollar AI Startups Have No Products, No Revenue, and Eager Investors” reports a “new wave of startups some have dubbed ‘neolabs,’ which give priority to long-term research and developing new AI models over immediate profits.”
What bubble?
Too Big to Ignore
At some point, the bubble should be obvious, particularly when the point of entry has a low bar. These no longer have what business school professors call a moat. It is what Warren Buffett called an economic moat. It is deep enough and wide enough to be a barrier to competition. Imagine what it would cost to build Apple or Bank of America. Buffett owns both stocks.
The neolab concept is built largely on gathering people who are among the world’s top experts in a field. They then create AI companies based on the ability to train AI models at an extremely high level. One challenge is that, if there are enough academics in a single area, several neolabs could be aiming at the same market. There is also the problem of whether the project works, as well as whether commercial demand for the end product exists.
The history of investing is littered, over a long period, with balloons. The most recent, it is commonly agreed, was the dot-com bubble of the year 2000. The neolabs, on paper, can proliferate like internet companies, which also had no products, or ones in the early stages of development. And many of the dot-coms folded before they brought in a dime of revenue.
The existence of well-funded neolabs provides another argument that the bubble is huge.
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