Wall Street Quants See 1970s Echoes in the AI Boom and Urge Shorting U.S. Stocks

Most commentary on the artificial-intelligence buildout reaches for the same comparison: the dot-com boom at the end of the 1990s, when enthusiasm for internet companies ran well ahead of their earnings. A quant team at one large Wall Street firm thinks that analogy misses the more relevant precedent. In its view, today's market has more in common with the late 1970s, an era better known for bell-bottom trousers, polyester suits and stubbornly high inflation.

The argument is about the broader market backdrop rather than the technology itself. The decade the team points to was one in which rising prices squeezed corporate margins and left many stock investors with returns that trailed inflation for long stretches. If today's valuations and spending levels rest on an economy that keeps costs elevated, the same pressures could weigh on equities again.

From that comparison, the firm recommends a defensive, contrarian stance: shorting U.S. stocks, which means positioning to profit if prices fall. A call like this reflects one group's models and judgment, not a forecast with certainty behind it. Plenty of strategists still see the AI story as a genuine productivity shift that could support earnings for years, and they argue the dot-com comparison has its own flaws, since many of today's largest AI beneficiaries are profitable companies.

For investors, the useful question is less about fashion history than about mechanics. The bearish case depends on whether inflation stays high enough, and valuations stretched enough, that the AI spending boom fails to lift overall returns. Watching inflation data, corporate margins and how quickly AI investment turns into revenue will show which parallel is closer to the truth.