Short read: A Cold Shower for AI Mania

“The supply chain starts with producers and designers of AI infrastructure: firms like TSMC and Samsung, which fabricate chips; Nvidia, which designs them; and Cisco, which provides connectivity.

Then come hyperscalers like Amazon, Google and Microsoft that are building data centres both for the use of their own AI models and in order to sell compute (processing power) to others. There are also more specialized companies like Equinix (data centres), and, of course, Anthropic and OpenAI, developers of foundational LLMs.

Finally, there are the individual and corporate users of AI services. Individual use is growing fast, and corporate use in some areas (software development and customer support) is exploding.”

He goes on to list the risks at each level:

“For instance, if graphics processing units, CPUs and memory chips become faster and more energy efficient, the equipment in existing data centres could depreciate rapidly, making it harder for them to amortize their costs.

And LLMs, which have become extraordinarily capable based on what is essentially next-word prediction, could plateau until some new technique emerges…

… Projections into next year already suggest that hardware makers and data centres will be unable to supply enough US compute. And as shortages of compute mount, end users will have more reasons to delay implementation. You cannot reorganize all your operations around AI if you have reason to worry about the reliability of access or reasonable pricing in the future.”

He then highlights potential political risks:

“Since data centres consume huge amounts of power—driving up the power price for everyone—state and local governments will be under increased political pressure to limit their construction. In Indiana, for example, multiple counties recently proclaimed a moratorium on data-centre construction.

…It is not hard to imagine disaster scenarios—such as a deadly cyber incident, gross data misuse by AI agents or poorly trained AI models advising children to commit acts of violence against themselves or others (something that has already happened).

The chorus demanding regulation and more liability for AI models will only grow louder. The risks posed by rogue AI could even prompt a sorely needed dialogue among major powers, perhaps leading to some kind of AI Geneva Convention.

Perhaps the most important trigger for political intervention would be massive AI-related job losses. Fearful of the political or social backlash, even firms that are inclined to adopt AI may be hesitant to shed redundant employees outside of a recession, reducing any gains from AI deployment and diffusion.”

In conclusion, he says “The good news is that a more limited, careful AI rollout could give firms more time to find labour-augmenting (as opposed to displacing) uses, and governments and workers more time to adjust.

The bad news is that euphoric visions of quick exceptional profits could be unfounded, a particular problem for AI firms that must make unforgiving debt payments.

AI advances will likely pay off eventually. But not every provider will profit, or even survive.”