Layoffs might be an unfortunate way of attempting to prove artificial intelligence productivity gains when there are few other ways to quantify the benefits in the near term.
When important new technologies are introduced, there is almost always a lag between adoption and quantifiable productivity gains. In fact, it often happens that productivity drops as the new technology is adopted.
Employees must take time away from current tasks to learn how to use the technology; test its results and so forth.
There is no reason to expect the J curve of technology adoption will fail to be seen for, either.
Economic historians such as Erik Brynjolfsson and Paul David have documented that transformative, general-purpose technologies tend to follow the J-curve pattern.
Initial deployment generates negative or flat productivity returns relative to investment, often for a surprisingly long time.
David's famous 1990 paper on the "dynamo paradox" showed that electrification of US industry began in earnest in the 1880s but didn't produce measurable aggregate productivity gains until the 1920s.
The reasons are structural: firms must reorganize workflows, retrain workers, build complementary infrastructure, and abandon legacy processes before the technology's benefits materialize.
The productivity gains, when they finally arrive, are real and large, but they accrue after enormous sunk costs and a long gestation period.
Amara's Law also suggests we will overestimate the immediate impact of artificial intelligence but also underestimate the long-term impact. But, again, that suggests it will be hard to quantify AI productivity results in the near term.
All that is going to be a problem for financial analysts and observers who demand an immediate boost in observable firm earnings or revenue, as well as the firms deploying AI that will strive to demonstrate the benefit.
But layoffs are quite quantifiable, even if we might argue it is still too early to measure AI productivity impact.
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