IBM stock crashes on earnings miss and legacy system revenue decline, signaling enterprise shift away from traditional IT infrastructure.

On the afternoon of July 14, I called Steve Hanke, days after he'd flagged what he called a dual bubble forming in AI markets. This was one day after IBM suffered the worst single-day stock crash in its 115-year history. Though the "money doctor" has spent decades advising governments—including the Treasury Department and the White House—he demurred on IBM's mechanics, saying he doesn't follow the stock closely. But he did recognize it as fitting into a larger macroeconomic theme.

"Did you see the bank earnings?" he asked with astonishment.

I had. JPMorgan posted net income of $21.2 billion—the highest quarterly profit for any bank in U.S. history. Goldman Sachs reported an 84% jump in net earnings attributable to common shareholders, to $6.4 billion, with total revenues hitting $20.34 billion, up 39%. These hit the ticker the same day IBM cratered 25%, erasing roughly $40 billion in market value over a revenue miss that, in any other environment, would have been unremarkable.

That juxtaposition—banks minting money while IBM suffered a 115-year collapse on a 3.7% revenue shortfall—sits at the center of what Hanke, a professor of applied economics at Johns Hopkins, believes markets are dangerously misunderstanding about the AI boom. For two years, investors have debated whether AI stocks are too expensive. Hanke agrees they are, but says that's the wrong question. "We really have two bubbles in markets," he told me. One is a classic valuation bubble—price versus earnings, exemplified by the CAPE Shiller index. But the more dangerous mispricing isn't in valuations at all. It's in the earnings themselves.

IBM's preliminary second-quarter results were unspectacular: revenue of $17.2 billion missed the consensus of roughly $17.9 billion by about 3.7%, and adjusted EPS of $2.93 fell short of the $3.02 expected. Still, IBM was growing, though the preliminary disclosure revealed that revenue had expanded by only 1% instead of the 5% the market had expected. The market's reaction was steeper than Enron's collapse the day the SEC opened its accounting inquiry.

IBM CEO Arvind Krishna, anticipating the fallout, penned an unusually candid letter. Conditions in the market required "our teams to execute perfectly," he wrote, "and this quarter we faltered." His mea culpa offered "not excuses, but … realities."

The New York Times' DealBook wondered if the IBM miss was a "canary in the tech coal mine," while the Financial Times' west coast editor Richard Waters called it a "warning to the IT sector"—a manifestation of the "SaaSpocalypse" that had spooked markets earlier in the year. That scare had been driven by the theoretical potential of AI to displace traditional software, but IBM's profit warning suggested that a secular shift is now underway.

Most bubbles throughout market history have been valuation bubbles: prices race ahead of earnings, leaving P/E ratios that look obviously stretched, as in 2000. An earnings bubble is different and far less common—the profits themselves are inflated or unsustainable, which can make valuations look deceptively reasonable even while the market is dangerously mispriced. This is what IBM seemed to signal to the market: the beginning of an unwinding of the earnings boom.

BCA Research's Peter Berezin has argued for months that today's AI trade is "primarily an earnings bubble rather than a valuation bubble." Such bubbles have historically clustered in boom-bust industries: pre-2008 banks, pandemic-era work-from-home stocks, and cyclicals like natural resources, airlines, and semiconductors—the last of which now sits at the center of the AI capex story.

This rarity carries a detection problem. Analysts typically cut profit estimates only after stocks have already fallen, providing little early warning. When earnings bubbles burst, they tend to leave behind real excess capacity—data centers, chip fabs, server farms—rather than just erasing paper gains. Berezin noted in late May that Wall Street analysts are "not particularly good at predicting when earnings bubbles will burst" because stocks begin falling before profit estimates do.

IBM's earnings reaction bore out that exact detection lag. BofA and UBS both trimmed estimates, but only after the stock had already cratered 25%—reactive moves, not predictive ones. BofA cut its price target to $280 from $330; UBS held its target at $236 while lowering 2026 EPS forecasts. Yet even after the selloff, the Street split sharply on the implications. BofA kept a Buy rating, arguing IBM remained "well positioned" once execution issues cleared, while HSBC downgraded to Reduce and Goldman warned the results would "fully validate the software bear case scenario."

This brings Hanke back to the bank earnings. His point wasn't that JPMorgan's profits are suspicious—it's that they unusually reveal the monetary mechanism most investors misunderstand. It's not the Federal Reserve creating the money fueling what he sees as two bubbles; it's private banks.

I mentioned a famous quote by the midcentury economist John Kenneth Galbraith: "The process by which banks create money is so simple that the mind is repelled." Hanke laughed and recalled meeting Galbraith only once. "Although my orientation is not the same as Galbraith's, I thought he was a great man and had many admirable qualities," he added.

I asked whether record bank profits evidenced credit still flowing freely through the system, simultaneously inflating asset prices and the reported earnings that justify those prices—right up until something snaps. "What you're saying," he responded, repeating a phrase he'd been using frequently, "is that markets are getting mugged by reality."

Even JPMorgan CEO Jamie Dimon seems to concur. He crowed that the earnings were "close to as good as it gets" during Tuesday's analyst call before expressing concern about too much "exuberance" in markets. Like Hanke, Dimon has said for months that markets may be overheating.

If Hanke and Berezin are correct, the market has spent two years watching the wrong gauge. The bull case has rested on the observation that today's AI leaders—Nvidia, Alphabet—generate real cash flow, unlike the profitless dot-com names of 2000, with S&P 500 valuations near 22x forward earnings, below the 25x-plus threshold usually associated with true bubbles. That defense addresses valuations. It says nothing about whether the earnings themselves—swelled by capex cycles, circular AI investment, and easy money from private banks—are sustainable.

IBM's crash may be the first visible crack not in valuations but in the earnings story underneath them. A company whose numbers weren't particularly bad still got punished as if the market suddenly stopped believing the profit growth narrative altogether. Whether that's a single-stock anomaly or a signal that the market has quietly repriced its tolerance for earnings disappointments across the sector is a question the rest of earnings season will answer.

For now, the more dangerous question may have been hiding in plain sight the entire time: not whether AI stocks are too expensive, but whether the earnings behind them were ever as real as they looked.