The stock market's high valuations depend on AI delivering broad productivity gains.
Nvidia's market cap needs about 3% annual productivity growth to be justified.
Recent data show productivity averaging only 1.3%, casting doubt on the AI trade.
What happened
A new analysis argues that the stock market's high valuations rest on the assumption that AI will lift productivity across the entire economy, not just at software firms. It uses Nvidia as a test case, with a current market cap of about $5 trillion.
Why it matters
To justify Nvidia's valuation, the analysis says U.S. labor productivity would need to accelerate to about 3% per year, similar to the late 1990s and early 2000s. Recent data show productivity averaging just 1.3% over the last three quarters, and a survey of executives found 89% reported no measurable productivity impact from AI.
What to watch
The outlook is binary: either total U.S. corporate profits in 2036 exceed the CBO's baseline of $5.5 trillion, or AI spending and Nvidia's growth slow more than expected. History suggests booms often end when infrastructure economics stop adding up.
Ask the AI about this article →
The analysis highlights a growing gap between stock market optimism and consumer sentiment, attributing it to the AI-driven economy. While data center construction and tech investment boost growth, the benefits are narrow. The article argues that for the boom to continue, AI must deliver productivity gains not just in software but across hotels, hospitals, and retail, yet evidence so far is lacking.
The framework using Nvidia illustrates the scale of the bet: its current $5 trillion market cap implies a future profit share that would be unprecedented. The analysis suggests that achieving this would require productivity growth of about 3%, a level seen only during the internet boom. Recent trends, however, show productivity averaging just 1.3%, and there are signs of struggles, such as expensive AI tokens and companies rehiring after layoffs.
History shows that transformational technologies often end in busts, not because the technology fails but because the economics of infrastructure build-out stop adding up. The article warns that turning points are hard to identify in real time, and the central question is whether productivity gains will materialize before markets lose patience.
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