NBER Study Finds "No Productivity Impact" from AI So Far (And Nobody Should be Surprised)

Maybe we should not be surprised that studies of AI productivity often show few results so far. A recent study published by the National Bureau of Economic Research, for example, found:

• around 70 percent of firms actively use AI

• More than 66 percent of top executives regularly use AI, their average use is only 1.5 hours a week, with one quarter reporting no AI use

• firms report little impact of AI over the last three years, with over 80 percent of firms reporting no impact on either employment or productivity. 

source: NBER 

None of that should come as a surprise. Sure, AI adoption is widespread among survey respondents. across the four countries (U.S.; U.K.; Germany; Australia) studied:

• 69 percent of businesses currently use some AI technology

• “text generation” is a top use case

• “visual content creation” follows

• “data processing” is a top-three use case as well.

source: NBER 

But none of those use cases can easily be tied to bottom-line quantitative results very easily, if at all. They should be time savers, but faster text or image creation or some data manipulation, at modest usage rates, in the context of existing business processes, are probably reasonably described as relatively trivial contributors to measurable productivity. 

Also, there is no reason to expect the J curve of technology adoption will fail to be seen here. 

source 

Amara's Law suggests we will overestimate the immediate impact of artificial intelligence but also underestimate the long-term impact. 

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.

And 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.