IBM just suffered its worst single-day collapse in 115 years, shedding 25 percent and roughly $40 billion of market value on a revenue miss of 3.7 percent. The same day, JPMorgan posted net income of $21.2 billion, the highest quarterly profit for any U.S. bank in history, while Goldman Sachs reported an 84 percent jump in earnings attributable to common shareholders to $6.4 billion on revenue of $20.34 billion, up 39 percent. The juxtaposition is the puzzle at the center of what Steve Hanke, a professor of applied economics at Johns Hopkins and longtime adviser to the Treasury and White House, calls a dual bubble in AI markets.
Hanke argues the market is fixated on the wrong distortion. One bubble is the familiar valuation variety, visible in the CAPE Shiller index. The more dangerous mispricing, he says, sits in the earnings themselves. An earnings bubble means profits are inflated or unsustainable, making valuations look deceptively reasonable even as the market is dangerously mispriced. IBM’s preliminary second-quarter numbers were unspectacular on their face: revenue of $17.2 billion missed consensus of roughly $17.9 billion, and adjusted earnings per share of $2.93 came in under the $3.02 expected. Revenue grew 1 percent instead of the 5 percent the market had priced. In any other environment the miss would have been unremarkable. The reaction was steeper than Enron’s collapse the day the SEC opened its accounting inquiry.
BCA Research’s Peter Berezin has been arguing for months that the 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 capital-expenditure story. IBM’s profit warning appeared to confirm that a secular shift is under way. The New York Times’ DealBook asked whether the miss was a canary in the tech coal mine; the Financial Times’ Richard Waters called it a warning to the IT sector and the manifestation of the SaaSpocalypse that spooked markets earlier this year.
CEO Arvind Krishna knew the damage would be severe. In an unusually candid letter he wrote that conditions required teams to execute perfectly and that this quarter they faltered, offering, he said, not excuses but realities. The market’s verdict was that the earnings boom underpinning the AI narrative may be more fragile than the valuation metrics suggest. If Berezin and Hanke are right, the next reckoning will not come from compressed multiples but from profits that fail to materialize.
What to watch now is whether the bank earnings, real, recorded, and historic, prove more durable than the AI revenue trajectories priced into semiconductor and software equities. The earnings bubble thesis implies the crash arrives not when prices look expensive but when the profits themselves roll over. IBM’s 115-year record may be the first data point.