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In late July, I warned you about how Big Tech companies were getting on board the “AI crazy train.”
More recently, I introduced the Enron documentary to my daughter, and I was shocked all over again by how much that company wooed Wall Street while pursuing wild, expensive infrastructure projects.
These two threads just came together in the form of a fascinating interview I just spotted: The Institute for New Economic Thinking scored an interview with famed short seller Jim Chanos recently.
Dr. Lynn Parramore, a senior research analyst at INET interviewed Chanos on a number of topics, but his comments about AI really stood out for me.
Chanos is a famous short seller. He ran a hedge fund firm called Kynikos Associates that most focused on betting against stocks. This is incredibly hard to do, and he did it pretty well.
His most famous trade was shorting Enron stock way before the energy trading behemoth collapsed in a massive bankruptcy in 2002.
Yes, Enron was an energy company, but it also tried to trade internet bandwidth, which intertwined its fate with telecom and broadband giants such as MCI WorldCom, which also went bust in the Dotcom collapse of the early 2000s.
Chanos shut his hedge fund firm and converted his operation to a family office a few years ago. However, he’s worth listening to, so check out the excerpts from his recent interview with INET below.
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(Via INET, I asked Chanos if he was shorting any of companies involved in this AI spending frenzy. Chanos said he didn’t talk about any companies by name in the interview, so there was nothing to disclose specifically. Although, he did disclose that he’s currently long Bitcoin and short certain Bitcoin treasury companies).
AI’s potential to destroy old business models:
“I have no idea where AI is going to go and take us. It’s getting better by the by the month, by the week, by the year. We’ll just have to see. I’m as curious as anyone — will it replace certain jobs? Will it bring forth new jobs? Will it render industries obsolete?
The Internet was an amazing development — it basically took things that were analog and digitized them and enabled you to transfer them at almost no cost and store them at no cost. It brought forth all kinds of new businesses. It also destroyed lots of old businesses: any business that had an analog product that became digital was in trouble, like Eastman Kodak or whatever.
I suspect we’re going to see the same thing with AI and we’ll just have to see how that plays out.”
On the weird inability of new tech to boost productivity:
“I keep pointing out, though, that one of the more interesting takeaways from the Dotcom era and the networking boom — when we all started communicating with each other — is to look at US real GDP in the 10 years before Netscape, from 1987 to 1997. It was almost exactly the same as U.S. real GDP in the 10 years after Netscape, from 1997 to 2007. And that’s not even counting the global financial crisis.
So there was no increase in economic growth. Productivity increased somewhat, but total economic growth didn’t rise much, partly because population growth was lower. Overall, the boom that you would have expected to see in the world’s most advanced economy was not noticeable because of the Internet. And it hasn’t been since. It’ll be interesting to see if AI changes that.”
How accounting drives huge AI spending
“What these things do — and they certainly did with fracking, but more ominously with the telecom and dotcom era — is drive capital spending and earnings for a very interesting reason. It’s not just that animal spirits are stirred; there’s also an accounting angle that everyone forgets. When you get a major capex boom focused on physical tech assets, like the fiber and Internet buildout in the late ’90s, it significantly boosts reported earnings, even if the underlying returns aren’t there.
What we’re seeing now with AI chips is similar to what happened during the telecom boom. The big tech companies — the so-called Mag 7 — are buying chips as fast as they can. The companies selling the chips book that spending as revenue and profit, just like Lucent and Nortel did back then. But the companies doing the spending — like MCI or AT&T in the past — are the ones taking on the cost. They capitalize those expenses and spread the write-off over several years. So, a chip that might become obsolete in just two years is being depreciated by some of the big hyperscalers over five, six, or even seven years. That creates a huge boost to corporate earnings during tech buildout booms like the one we’re seeing now. That’s number one.”
What happens if the AI spending boom stops?
“Number two, the spending — and thus the earnings — can collapse. Everyone kind of remembers the global financial crisis, but they kind of forget that from 2000 to 2002, corporate earnings dropped 40%. The same as the global financial crisis. Because capex spending stopped in 2001, and literally there was no lag time. Earnings just completely collapsed when the spending did.
I’m starting to worry there’s so much spending right now on the AI physical boom — the buildout of data centers, chips, and so on — that if anyone decides to pause and ask, ‘What’s our real economic return here?’ it could be a big problem. It’s one thing when we’re spending $50 billion a year; it’s another when that changes. As a group, we’re spending $500 billion a year, and if there’s no real return, can we really spend a trillion dollars a year on this without seeing results? It’ll be interesting to watch. Right now, spending is growing faster than operating income or revenues.”
Uncomfortable questions coming soon
“So we’re getting to the point now where within a year or two, some of these large companies are going to start having to make some very uncomfortable decisions as to when and how they will monetize AI, and what the returns will ultimately be on this massive spending, because the chips basically depreciate over two years.”
Sign up for BI’s Tech Memo newsletter here. Reach out to me via email at abarr@businessinsider.com.
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