What Does the Humbling of Leopold Aschenbrenner Mean for the A.I. Bubble?

Unlike Long-Term Capital Management, Amaranth, and other big funds that collapsed after suffering big losses, Situational Awareness managed to survive that financial crisis, and Aschenbrenner vowed to “fight another day.” Apparently, his fund retains some very valuable investments in private A.I. companies that haven’t yet issued public stock, including Anthropic. But the market tremors that preceded Aschenbrenner’s humbling highlighted some larger questions about the tech-driven stock boom, one of which is the spectre of heightened competition in the A.I. sector.

The A.I.-to-the-sky narrative was built on the supposition that OpenAI, Anthropic, and other U.S. tech companies would dominate the industry, and their customers would happily pay high prices for access to proprietary, state-of-the-art models. Early last year, the Chinese A.I. company DeepSeek challenged this narrative by releasing a cheap open-source model with impressive capabilities. That was only the beginning. In the past few weeks, two more Chinese firms, Alibaba and Moonshot, have put out models that, based on some metrics, can match the latest and most powerful offerings from Anthropic and OpenAI. DeepSeek also launched a new model, V4 Flash, which, according to Bloomberg, can execute certain tasks for a cost of three cents, compared with $3.15 when using Anthropic’s Claude Fable 5 model. “When China asks for cents where American companies charge dollars, the contest between the two nations for A.I. customers . . . starts to look materially different,” Bloomberg reported.

Other potential sources of vulnerability include the vast sums of money that A.I. companies are spending on infrastructure, such as chips and data centers. A new study from Goldman Sachs predicts that global investment in A.I. will total more than a trillion dollars this year, with outlays in the U.S. making up nearly sixty per cent of the total. From one perspective, the financial system’s ability to fund this scale of spending is impressive, but so is the amount of debt and other liabilities that A.I. companies are taking on, both directly and indirectly, to finance their investments.

The dot-com bubble of the late nineteen-nineties and the real-estate bubble of the early two-thousands both saw the proliferation of financial-engineering techniques such as circular financing and offloading debts from corporate balance sheets to special-purpose vehicles. Both have been resurrected in the A.I. boom. The chipmaker Nvidia is famous for investing in companies that buy its chips, and there are many other interconnections. Microsoft owns twenty-seven per cent of OpenAI, which, according to Bloomberg, “accounted for more than half, and likely about 7src%, of Microsoft’s actual AI sales during its most recent fiscal year.” Should anything bad happen to OpenAI, Microsoft would feel the pain. As the sums expended on A.I. investments get larger, the financing that underpins them is getting more complicated and opaque. According to a recent report, Alphabet, Amazon, Meta, Microsoft, and Oracle have a combined total of $1.65 trillion in debt and other obligations that don’t appear on their balance sheets.

To justify all these investments and financing schemes, A.I. will have to create a great deal of economic value. A couple of weeks ago, investors were reassured after Alphabet, Amazon, and Microsoft reported that their respective cloud divisions enjoyed strong revenue growth in the latest quarter. But if A.I. is really going to pay off, its financial benefits will have to show up on the books of its end users—not just A.I. labs, hyper-scalers, and equipment suppliers. A year ago, I incurred the wrath of some people in Silicon Valley by discussing a survey from M.I.T. which found that ninety-five per cent of companies using A.I. tools hadn’t seen any increase in their profits. Perhaps management failures and the novelty of the A.I. models were holding some companies back from finding profitable ways to adopt them, I suggested. But a year later the picture doesn’t seem to have changed dramatically. More recent surveys, including one from the research firm Gartner, still show a surprising dearth of profits from A.I. investments.

What does all this add up to for the stock market? For the moment, A.I. optimism, FOMO, and trend-following are still in ascendancy. It’s hard to say what might change this, but one candidate is a tightening of monetary policy. The market crashes of 1987, 2srcsrcsrc, and 2srcsrc7 were all preceded by periods in which the Federal Reserve raised rates. Until Friday’s disappointing jobs report, which showed employers shedding twenty-three thousand jobs in July, many Wall Street analysts were expecting a rate hike next month because inflation is running above the Fed’s target. Now all bets are off.