MW This year's Super Bowl ads tell you the AI bubble is about to burst
By Jeff Funk and Gary Smith
Why the artificial-intelligence advertising spree could be the last hurrah - like the dot-coms in 2000
OpenAI and Anthropic are losing enormous amounts of money, yet are given valuations as if they were real companies making real profits.
During Super Bowl XXXIV on Jan. 30, 2000, 14 of the 61 television advertisements were for internet startups. Perhaps they should have heeded the advice offered by E-Trade's "Waste of Money" ad that year, which featured a dancing monkey and ended with the on-screen punchline: "Well, we just wasted $2,000,000. What are you doing with your money?"
That Super Bowl was played just before the dot-com bubble burst. Fast-forward to 2026: This year's Super Bowl ads are heavily promoting AI, which may be the only indicator you need of whether AI is a bubble about to burst.
We have been writing about the limitations of artificial-intelligence systems since 2018:
Artificial intelligence is not at all like the real intelligence that comes from human brains. Computers do not know what words mean because computers do not experience the world the way we do. They do not even know what the real world is. Computers do not have the common sense or wisdom that humans accumulate by living life. Computers cannot formulate persuasive theories. Computers cannot do inductive reasoning or make long-range plans.... In the age of Big Data, the real danger is not that computers are smarter than us, but that we think computers are smarter than us and therefore trust computers to make important decisions for us.
We've have been warning of an AI bubble since February of 2023, suggesting it was fueled by unwarranted hype and fake-it-till-you-make-it "shenanigans" but conceding it has been "tempting to believe the hyperbole and to prioritize revenue over profits":
The fundamental problem is that hopeful investors are too often gullible investors. We should have been more skeptical of the dot-com hype and we should be more skeptical of the AI hype. We should also remember that revenue is a means to the end, not the end. We should look instead at earnings, free cash flow, economic value added, and other measures of profitability.
For a time, the boomlet in warnings of an AI bubble seemed to support the case for these companies. The financial press often overreacts about the bullish or bearish momentum behind popular investment themes. Economist Ed Yardeni dubbed this the "front-cover curse" - when financial magazines publish ill-timed covers that in hindsight mark peak optimism or pessimism.
Yardeni also cited a 2016 Citibank study that looked at covers of the Economist dating back to 1998 and identified 44 covers that were decidedly optimistic or pessimistic about the stock market. These covers were a good contrarian indicator 68% of the time. On the other hand, 68% is closer to a coin flip than 100% certainty.
Recently, the Economist has been publishing many lead articles and guest essays warning of an AI bubble, including:
-- "What if the $3trn AI investment boom goes wrong? Even if the technology achieves its potential, plenty of people will lose their shirts," Sept. 11, 2025
-- "GitaGopinath on the crash that could torch $35trn of wealth," Nov. 5, 2025
-- "How I learned to love financial bubbles," Nov. 19, 2025
-- "Investors expect AI use to soar. That's not happening," Nov. 26, 2025
-- "How to spot a bubble bursting," Dec. 1, 2025
-- "Innovations in energy and finance are further inflating the AI bubble," Jan. 15, 2026
The magazine, though, hasn't published an AI-bubble cover story yet.
But Time magazine's annual person-of-the-year covers have been an even better contrarian indicator. When CEOs or a business trend are selected as person of the year, shares of the associated companies were lower a year later in seven of eight instances.
Time's 2025 person of the year was the collective "Architects of AI."
Greater fools and foolish crowds
The prices of AI-dependent stocks have become untethered from realistic projections.
While we actually don't put much stock in front-cover and Super Bowl-ad indicators, we do acknowledge the outsized effects of human emotions on stock prices. People often buy stocks not because they think the companies will succeed but because they think others will buy the stocks, too.
Such reasoning can become textbook Greater Fool Theory - investors paying foolish prices with the expectation that greater fools than they will pay even higher prices.
That is, indeed, the definition of a bubble - people buying investments not because the cash flow justifies the price but because they expect the price to go up.
In this AI bubble, the prices of AI-dependent stocks have become untethered from realistic projections of future profits. LLM-dependent companies such as OpenAI and Anthropic are losing enormous amounts of money yet are given valuations in the hundreds of billions of dollars as if they were real companies making real profits. Is OpenAI really worth more than American Express $(AXP)$, Bank of America (BAC), Coca-Cola $(KO)$, Chevron $(CVX)$ or Costco $(COST)$?
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The core problem is that the large language models are not sufficiently useful and reliable for customers to justify paying prices that cover their costs. Ironically, the more customers these companies have, the more money they lose.
Generative AI is the fastest-growing technology of all time - if we count users instead of profits.
Wishful thinkers point to Amazon.com (AMZN), which overcame initial losses to succeed magnificently. They conveniently neglect the fact that Amazon's early losses would now be considered rounding errors. Amazon had $3 billion in cumulative losses when it became profitable in its 10th year (2004). OpenAI lost $11.5 billion in a single quarter (the third quarter of 2025), based on analyses of Microsoft's $(MSFT)$ third-quarter earnings in 2025) and itself forecasts $115 billion in cumulative losses by 2029, which is most likely an underestimate.
The more likely way these AI companies will cut their losses is by going out of business. With the commoditization of LLMs and competition from deep-pocketed competitors like Google parent Alphabet $(GOOG)$ $(GOOGL)$, with its Gemini, it is hard to see how OpenAI and other LLM-dependent companies can plausibly generate robust profits.
In the absence of profits, the tech bros increasingly emphasize an old metric that was popular during the dot-com bubble: the number of users, with a new flavor. While the "number of users" once meant "number of paying users," the success of Facebook, Instagram and other social-media sites persuaded delusional investors that the word "paying" could be dropped.
Not surprisingly, generative AI is the fastest-growing technology of all time - if we count users instead of profits. We prefer to count profits.
Gary Smith is the author of more than 100 academic papers and 20 books, including "Standard Deviations: The Truth About Flawed Statistics, AI and Big Data" (Duckworth, 2024) and, with co-author Margaret Smith, "The Power of Modern Value Investing: Beyond Indexing, Algos and Alpha" (PalgraveMacmillan, 2024). Jeffrey Funk, a technology consultant and analyst, is the author of "Unicorns, Hype and Bubbles" (Harriman House, 2024).
More: Here's one question about the AI bubble that even ChatGPT can't answer
-Jeff Funk -Gary Smith
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February 06, 2026 18:40 ET (23:40 GMT)
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