Why one Wall Street firm sees parallels to the late 1970s and recommends shorting U.S. stocks

The artificial-intelligence investment cycle is most often compared to the dot-com boom, but one Wall Street quant team argues a more fitting historical parallel may be the late 1970s — an era defined by high inflation, energy shocks, and weak equity returns. The firm has reportedly recommended shorting U.S. stocks on that basis, according to Dow Jones.

The comparison matters because it reframes the debate around AI spending. The dot-com analogy focuses on whether today's valuations are justified by future earnings. The 1970s analogy shifts attention to the macro backdrop: persistent inflation and the policy response it provokes. If inflation proves stickier than expected, the same capital-intensive build-out could coincide with compressing valuations rather than expanding ones.

For observers, the takeaway is not the trade itself but the framework. It is a reminder that technological enthusiasm and macroeconomic conditions are separate variables, and that strong thematic narratives have historically coexisted with poor broad-market returns. What to watch: inflation data, rate expectations, and whether AI capital spending translates into durable earnings.