IBM experienced its most significant single-day stock decline in 115 years recently, shedding approximately $40 billion in market value. This sharp downturn, following a revenue miss of merely 3.7%, stands in stark contrast to the record-breaking profits simultaneously announced by major financial institutions like JPMorgan and Goldman Sachs. JPMorgan reported a net income of $21.2 billion, the highest quarterly profit for any U.S. bank, while Goldman Sachs saw an 84% jump in earnings, reaching $6.4 billion. This juxtaposition has prompted some economists to suggest that the market is grappling with not one, but two distinct bubbles, with the implications potentially extending far beyond technology stocks.
The consensus among many investors for the past two years has revolved around whether artificial intelligence stocks are overvalued. However, some analysts contend that this focus on valuation, typically measured by metrics like the CAPE Shiller index, misses a more fundamental concern. The argument posits that the true danger lies not in inflated prices relative to earnings, but in the sustainability and legitimacy of the earnings themselves. This concept of an “earnings bubble” suggests that profits may be artificially inflated or unsustainable, making valuations appear reasonable even when the underlying market conditions are precarious.
IBM’s preliminary second-quarter results provided an unexpected jolt to the market. The company reported revenue of $17.2 billion, falling short of the $17.9 billion consensus estimate. Adjusted earnings per share came in at $2.93, below the anticipated $3.02. While these numbers indicated growth, it was a modest 1% rather than the 5% the market had expected. The severity of the market’s reaction, steeper than even Enron’s collapse after its accounting inquiry began, suggested a deeper unease among investors. IBM CEO Arvind Krishna acknowledged the underperformance in an uncharacteristically candid letter, attributing it to execution challenges rather than market conditions.
This event has led some to question if IBM’s experience serves as a bellwether for the broader tech sector, echoing concerns about a “SaaSpocalypse” that had previously been theoretical. The idea that AI could disrupt traditional software models seemed to gain tangible confirmation with IBM’s profit warning. Historically, most market bubbles have been valuation bubbles, where prices outpace earnings, creating visibly stretched price-to-earnings ratios. An earnings bubble, however, presents a different challenge. The profits themselves are questionable, making it difficult for analysts to detect impending issues until after stocks have already begun to fall.
Peter Berezin of BCA Research has been a proponent of the earnings bubble theory, pointing to historical parallels in boom-bust industries such as pre-2008 banks, pandemic-era work-from-home companies, and cyclical sectors like natural resources and semiconductors. The rarity of earnings bubbles also means they carry a significant detection problem. Analysts often only revise profit estimates downwards after a stock has already experienced a decline, offering little in the way of an early warning. When these bubbles burst, they can leave behind substantial excess capacity, such as underutilized data centers or chip fabrication plants, rather than simply erasing paper gains.
The market’s reaction to IBM’s figures underscored this detection lag. While Bank of America and UBS eventually trimmed their estimates and price targets, these adjustments occurred only after IBM’s stock had already plummeted by 25%. Even then, analysts remained divided, with some maintaining a “Buy” rating while others issued downgrades. This divergence highlights the ongoing uncertainty surrounding the true state of earnings across the sector. The extraordinary bank earnings, some argue, are not suspicious in themselves, but rather reveal a critical monetary mechanism: the role of private banks, not the Federal Reserve, in fueling what some see as these dual market bubbles through credit creation. This perspective suggests that record bank profits could indicate a free flow of credit, simultaneously inflating asset prices and the reported earnings that underpin them, a situation that persists until an inevitable disruption.
JPMorgan CEO Jamie Dimon himself has expressed caution, despite his bank’s record profits, warning against excessive “exuberance” in the markets. If these assessments hold true, the market may have been focusing on the wrong indicators for the last two years. The prevailing bull case for AI stocks often cited strong cash flow from companies like Nvidia and Alphabet, contrasting them with the unprofitable dot-com ventures of 2000. This argument, however, primarily addresses valuation and does not fully account for whether the underlying earnings—potentially inflated by capital expenditure cycles, circular AI investments, and readily available credit from private banks—are truly sustainable. IBM’s recent collapse may therefore represent the first visible crack, not in inflated valuations, but in the very foundation of the earnings growth narrative that has propelled much of the market. The question now becomes whether this was an isolated incident or a signal that the market’s tolerance for earnings disappointments has fundamentally shifted.
