After the AI Crash | POTs and PANs

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Everything I read about the AI industry leads me to think there will be an AI crash. Consider the following:

Unsustainable Capital Expenses . It’s hard to imagine there can ever be enough revenue to pay for the huge capital investments in data centers and electronics. Several analysts have estimated that it will take $2 trillion a year in revenue to pay for the infrastructure that has already been built, and there are no believable forecasts for generating even half that much revenue. The capital needs of the industry are relentless since expensive AI data center electronics have to be replaced within five years, or less.

Circular Revenues . A small handful of tech firms, chip manufacturers, and AI companies are propping each other up by investing and buying from each other. If one stumbles, they might all fall.

Huge Debt . Much of the industry is being funded through debt, which has to eventually be repaid, instead of through equity.

Public Pushback . Local governments and people are increasingly pushing back hard against the creation of new data centers. Most new technologies have been welcomed by the public with open arms.

Increasing Corporate Skepticism . The news is full of stories of corporations that are throttling the employee use of AI since the costs to use the software are a lot higher than expected. There are many companies having second thoughts about replacing people with AI. The AI industry needs complete corporate buy-in to have any chance of succeeding, and large companies are generally still on the sidelines.

Diseconomies of Scale . Every new technology I can think of thrived, in part, due to economies of scale, where the larger the industry grew, the more efficient it got. AI is going in the opposite direction, where every new AI model consumes more resources than its predecessors. This may turn out to be the fatal flaw – the bigger the industry gets, the more its operating costs increase.

Institutional Warnings . Moody’s recently warned that high AI infrastructure spending threatens the credit of AI companies and their large tech partners. I read recently that the number one question being fielded by investment advisors is people asking how to divest from AI.

I don’t have a crystal ball to foresee the nature of the crash. It could be a total crash like the 2000 tech crash, where four out of five tech startups disappeared practically overnight. I lived in the DC area at the time, and I will never forget the rows of abandoned CLEC headquarters buildings in Northern Virginia. A crash could be milder, where a few firms disappear, with the outlooks for the survivors greatly diminished, and industry expectations are reset to something more realistic.

The reason I wrote the blog is to speculate about what happens after an AI crash. I foresee some of the following consequences of an AI crash.

An article in the Economist said a total crash would wipe out $20 trillion in U.S. wealth. That means wiping out the wealth of the investors in the new technology, along with a huge hit on the stock market.

Data center construction would stop dead, and unfinished projects would collapse. Communities that contributed to the costs of bringing data centers will end up eating those investments.

There will be stranded investments by electric utilities and water companies that built new infrastructure to support data centers. They won’t eat these losses, though, which will all be passed on to ratepayers in the form of higher electric and water rates.

A lot of vendors will be in big trouble. Companies that pivoted to supporting data center electronics, like Micron, might fold. But a lot of other vendors also would take a big hit. For example, Corning announced investments in three new fiber factories just to support data centers.

There have been some huge investments by carriers in middle-mile fiber to support data centers. The companies that made these investments won’t see the expected revenues.

The most interesting thing about a major crash is that it can do as much long-term good as it does short-term harm. I want to again use the analogy from the tech crash. I know of at least a half dozen CLECs that had business plans to capture 30% of the voice and data market in Atlanta. The crash cleaned them all out of the market, but without the crash they would have all failed more slowly. The tech crash brought a sense of reality to the telecom market, which still experienced phenomenal long-term growth after the original tech companies had died.

I don’t think there is any chance of AI failing as a technology. But that doesn’t mean the early developers are the ones who will see the ultimate success. Most, and maybe all of today’s players might be gone. A crash will bring financial constraints, which would mean that AI companies will have to figure out efficiency and economies of scale. If AI is ever going to be a viable technology, it has to...