The Nobel Prize–winning economist Robert Solow said in 1987, “You can see the computer age everywhere but in the productivity statistics.” This observation is the so‑called Solow productivity paradox.
The same can be said today: AI is everywhere except in the incoming macroeconomic data.
Today, you don’t see AI in the employment data, productivity data or inflation data. Similarly, for the S&P 493, there are no signs of AI in profit margins or earnings expectations.
Maybe there is a J‑curve effect for AI, where it takes time for AI to show up in the macro data. Maybe not.
Whether there is a J-curve effect depends on the value creation from AI. There is fierce competition between the builders of large language models (LLMs), which is driving the price of LLMs toward zero for end-users. This is the opposite of what the finance textbook says, namely that an innovator will have monopoly pricing power until others arrive with a similar product. This is what we saw during the computer age in the 1980s.
In other words, from a macro perspective, the value creation is not the product, but how generative AI is used and implemented in different sectors in the economy. The academic literature is still inconclusive about the potential macro effects of AI, see table below.
After three years with ChatGPT and still no signs of AI in the incoming data, it looks like AI will likely be labor enhancing in some sectors rather than labor replacing in all sectors.
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