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Richard Waters writes:
Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole.
At one extreme, AI coding assistants have revolutionized the work of software developers. Mark Zuckerberg recently predicted that half of Meta’s code would be written by AI within a year. At the other extreme, most companies are seeing little if any benefit from their initial investments. A widely cited study from MIT found that so far, 95% of gen AI projects produce zero return.
That has provided fuel for the skeptics who maintain that—by its very nature as a probabilistic technology prone to hallucinating—generative AI will never have a deep impact on business.
To many students of tech history, though, the lack of immediate impact is just the normal lag associated with transformative new technologies. Erik Brynjolfsson, then an assistant professor at MIT, first described what he called the “productivity paradox of IT” in the early 1990s. Despite plenty of anecdotal evidence that technology was changing the way people worked, it wasn’t showing up in the aggregate data in the form of higher productivity growth. Brynjolfsson’s conclusion was that it just took time for businesses to adapt.
Big investments in IT finally showed through with a notable rebound in US productivity growth starting in the mid-1990s. But that tailed off a decade later and was followed by a second lull.
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In the case of AI, companies need to build new infrastructure (particularly data platforms), redesign core business processes, and retrain workers before they can expect to see results. If a lag effect explains the slow results, there may at least be reasons for optimism: Much of the cloud computing infrastructure needed to bring generative AI to a wider business audience is already in place.