Now a Goldman Sachs report has raised questions over the use of generative AI in business. Tech giants and beyond are set to spend over $1 trillion on AI capex in coming years, with so far little to show for it, the report says. It questions if this large spend will ever pay off? In the report, many experts have expressed doubts over any revolutionary impact of AI in the short term. A few other experts are more optimistic about AI’s economic potential and its ability to ultimately generate returns beyond what they call the current “picks and shovels” phase when AI’s “killer application” hasn’t emerged. “But despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst,” says the report.
How productive can Generative AI be?
In an interview with Goldman Sachs, Daron Acemoglu, Institute Professor at MIT, who has written several books, including ‘Why Nations Fail: The Origins of Power, Prosperity, and Poverty’ and his latest, ‘Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity’, argued that the upside to US productivity and growth from generative AI technology over the next decade—and perhaps beyond—will likely be more limited than many expect.
Acemoglu estimates that only a quarter of AI exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks. And he doesn’t take much comfort from history that shows technologies improving and becoming less costly over time, arguing that AI model advances likely won’t occur nearly as quickly — or be nearly as impressive — as many believe.
Acemoglu also questions whether AI adoption will create new tasks and products, saying these impacts are “not a law of nature.” He estimates that total factor productivity effects within the next decade should be no more than 0.66%—and an even lower 0.53% when adjusting for the complexity of hard-to-learn tasks. And that figure roughly translates into a 0.9% GDP impact over the decade.
“Every human invention should be celebrated, and generative AI is a true human invention,” Acemoglu says. “But too much optimism and hype may lead to the premature use of technologies that are not yet ready for prime time. This risk seems particularly high today for using AI to advance automation. Too much automation too soon could create bottlenecks and other problems for firms that no longer have the flexibility and trouble-shooting capabilities that human capital provides.”Return on investment
Jim Covello is Head of Global Equity Research at Goldman Sachs, argues that to earn an adequate return on costly AI technology, AI must solve very complex problems, which it currently isn’t capable of doing, and may never be.”My main concern is that the substantial cost to develop and run AI technology means that AI applications must solve extremely complex and important problems for enterprises to earn an appropriate return on investment (ROI),” he says. “We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low-wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed in my thirty years of closely following the tech industry.”
“Many people attempt to compare AI today to the early days of the internet,” Covello says. “But even in its infancy, the internet was a low-cost technology solution that enabled e-commerce to replace costly incumbent solutions. Amazon could sell books at a lower cost than Barnes & Noble because it didn’t have to maintain costly brick-and-mortar locations. Fast forward three decades, and Web 2.0 is still providing cheaper solutions that are disrupting more expensive solutions, such as Uber displacing limousine services. While the question of whether AI technology will ever deliver on the promise many people are excited about today is certainly debatable, the less debatable point is that AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn’t designed to do.”
Covello doesn’t think that technology costs decline dramatically as technology evolves due to lack of competition as Nvidia is the only company currently capable of producing the GPUs that power AI, and because the starting point for costs is so high that even if costs decline, they would have to do so dramatically to make automating tasks with AI affordable.
Read the full report here.