I called Steve Hanke on the afternoon of July 14, days after he’d flagged something he called a dual bubble forming in AI markets, and one day after IBM suffered the worst single-day stock crash in its 115-year history. The “money doctor” has been advising governments—including the Treasury Department and the White House—for decades and often writes as a senior contributing columnist for Fortune. He demurred on the mechanics of IBM’s stock, saying he doesn’t follow it closely, but he did say it fit into a large macroeconomic theme.
“Did you see the bank earnings?” he asked me with astonishment.
I had. JPMorgan had just 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 not 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 on 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 miss—is the puzzle at the center of what Hanke, a professor of applied economics at Johns Hopkins, thinks markets are getting dangerously wrong about the AI boom. For two years, investors have been debating whether AI stocks are too expensive. Hanke said that’s true, but it’s the wrong question. “We really have two bubbles in markets,” he told me. One is a classic valuation bubble of price versus earnings, as exemplified by the famous CAPE Shiller index. But the more dangerous mispricing, he argued, isn’t in valuations at all. It’s in the earnings themselves.
