MW Big Tech's AI dream demands real money - but even more wishing and hoping

By Jeffrey Funk and Gary Smith

Companies claim they are using AI to do things better, faster and cheaper - with no evidence whatsoever

Facts be damned. Everyone loves a good story.

In March of 2000, Gary Smith was one of four speakers at an academic conference on the booming U.S. stock market and the widely publicized prediction that the Dow Jones Industrial Average DJIA - then below 12,000 - would soar to 36,000. James Glassman and Kevin Hassett, two scholars at the American Enterprise Institute, had been getting attention for estimating that "the right price for stocks" was Dow 36,000. It was a provocative assertion and it was taken seriously by serious people, including academics.

The first professor at the conference talked about Moore's Law. The next professor talked about how smart the dot-com whiz kids were. The third professor talked about Alan Greenspan being a wonderful U.S. Federal Reserve chair. Each time, the audience applauded enthusiastically.

Smith was the final speaker and he was the grumpy outsider at this euphoric event. He politely agreed with everything the three professors said, but noted that none of them had said anything about whether stock prices were too high, too low or just about right. Smith analyzed stock prices from a variety of perspectives - including dividends, profits, revenue and economic value added - and concluded that not only was it farfetched to think that the Dow would reach 36,000 anytime soon, but that the current level of stock prices in March 2000 was much too high. Smith told the audience: "This is a bubble, and it will end badly." There was a conspicuous absence of applause.

The conference was on Saturday, March 11, 2000. The Nasdaq Composite COMP finished lower on the following Monday. It fell by 75% over the next three years from its March 10, 2000 peak. The takeaway from this? Many intelligent people avoid looking at what really matters for stock prices.

Back in early 2000, most dot-com companies had no profits, so many dot-com enthusiasts looked at their spending - the more the better, as if expenses were revenue. Some looked at the number of people who visited a company's web page, or the number who stayed for at least three minutes. Even more fanciful was "hits," the number of files a web page loaded from a server. Incredibly, some people thought these were good reason to buy the dot-com stocks.

Investors didn't think very much about actual and projected profits. Dot-com stocks were "story stocks," whose lofty values were due to the magical novelty of the internet and dreams about a "New Economy." One study found that, on average, companies that simply added "dot-com," "dot-net" or "internet" to their names nearly doubled the price of their stock.

Unfounded hopes and wild dreams have always fueled the marketing of small, untested firms to people looking for the next IBM $(IBM)$, Microsoft $(MSFT)$ or Apple $(AAPL)$. These lottery tickets usually turn out to be disappointing. Jay Ritter, a finance professor at the University of Florida, estimates that the average three-year return on IPOs issued since 1975 has been 19.9% worse than the overall U.S. stock market.

It isn't only investors who are blinded by these castles in the air. As demonstrated at that March 2000 conference, academics are easily seduced by alluring stories. The seductive story now is, of course, AI.

In their title-says-it-all 2011 book, "Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy," Stanford University's Erik Brynjolfsson and MIT's Andrew McAfee argue that companies are replacing people with machines at an accelerating rate, with dire consequences.

They were hardly the first to make such claims. In his 1996 book, "The End of Work," the Wharton School's Jeremy Rifkin wrote that, "In the years ahead, more sophisticated software technologies are going to bring civilization ever closer to a near-workerless world."

More recently, a 2021 study by Princeton University and New York University professors concluded that the jobs most at risk because of AI "consist almost entirely of white-collar occupations that require advanced degrees, such as genetic counselors, financial examiners and actuaries." If that conclusion seems backwards, consider their conclusion that surgery and meat slaughtering are similar occupations, but surgeons are more at risk of being replaced by robots because their jobs require more intelligence. What a wonderful example of being beguiled by a narrative.

The University of Toronto's Geoffrey Hinton focused on a single job category in 2016 when he declared: "We should stop training radiologists now. It's just completely obvious that within five years, deep learning is going to do better than radiologists." More than eight years later, there are more employed radiologists than ever.

Overall, except for the Great Recession from late-2007 to mid-2009 and the COVID-19 recession in 2020, employment has been steadily going up, not down, in the United States.

Facts be damned. Everyone loves a good story. As Robert Solow once quipped, "You ask of a parable not if it is literally true but if it is well told."

The dot-com story about the dawn of a new economy was well told, as is the AI story that computers will soon be (or already are) smarter than us and are about to take our jobs, but, as we have written here and here, the story is largely imaginative fiction.

The AI castle in the air achieved liftoff when OpenAI introduced its generative AI service, called ChatGPT, in late 2022. Suddenly, economists and consultants began "demonstrating" its superiority in call centers, management consulting, the design of new materials and programming. Companies began announcing that they were close to or already had achieved artificial general intelligence $(AGI)$ - the ability to do any intellectual task that humans can do. During the dot-com bubble, companies added dot-com to their names. During the current AI bubble, companies claim they are using AI to do things better, faster, and cheaper - with no evidence whatsoever.

The breathless AI hype coming from companies trying to raise cash and sell products is unsurprising. Nor is it surprising that academics have again been caught up in the frenzy, perhaps most spectacularly by Wharton professor Ethan Mollick's assertion that the productivity gains from AI's large language models (LLMs) might be larger than the gains from steam power.

At the Dow 36,000 conference in March 2000, stock-market cheerleaders were fixated by stories, narrative and mostly ignored the question of whether corporate profits justified the lofty stock prices. The same is true today. Enthusiasts talk a lot about computers playing chess and "Jeopardy" and about the magical verbal prowess of LLMs, but very little about profits.

We have discussed the absence of profits for the so-called unicorn startups and we recently showed that AI companies have far less revenue than did dot-com companies. We estimate that the 2024 revenues for AI companies are in the range of $10 billion to $30 billion. In 2000, internet subscribers paid about $850 billion in 2024 dollars, e-commerce generated about $500 billion in 2024 dollars, and 134 million PCs were sold for about $1.2 billion in 2024 dollars. The revenue of AI companies is 50 times smaller.

We are not the only ones to notice the dearth of AI revenue and profits. Sequoia Capital's David Cahn, Goldman Sachs' Jim Covello, and Citadel's Ken Griffen have all argued that AI's meager revenue is evidence of a bubble. Indeed, this is a bubble - and it will end badly.

Jeffrey Funk is a retired professor and winner of the NTT DoCoMo mobile science award. His latest book is "Unicorns, Hype and Bubbles: A guide to spotting, avoiding, and exploiting investment bubbles in tech" (Harriman House, 2024).

Gary Smith is a professor of economics at Pomona College and the author of more than 100 academic papers and 17 books, most recently (co-authored with Margaret Smith): "The Power of Modern Value Investing: Beyond Indexing, Algos, and Alpha," (Palgrave Macmillan, 2024).

More: DeepSeek is giving Big Tech the drubbing it deserves. Will it be a wake-up call?

Plus: Is Intel now a Trump stock? Its shares surge after upbeat comments by J.D. Vance.

-Jeffrey Funk -Gary Smith

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February 12, 2025 07:55 ET (12:55 GMT)

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