Quantifying the AI boom crowding-out effect
When investment on the scale of the current AI boom occurs, it inevitably has to come at the expense of something. All the resources devoted to building data centers and developing AI models would otherwise go to something else. The big picture: This crowding out is smaller than you might expect, Goldman Sachs economists find in a new note. But it does exist, they say, and takes the form of…
The rapid expansion of artificial intelligence (AI) investments is causing a "crowding-out" effect that diverts resources away from other sectors of the economy, according to a new analysis by Goldman Sachs economists Jessica Rindels and David Mericle. While investment in AI has soared to an estimated $600 billion this year, representing 2% of GDP, this increased spending does not significantly impact overall economic growth, the economists argue.
The crowding-out occurs in several ways. First, AI spending at major hyperscalers and companies that pay for AI services may lead corporate IT budgets to cut back on other software and tech expenses, though this impact is limited as it is essentially a reallocation of funds. Second, the construction of data centers to support AI growth is displacing resources from other building projects, as data center construction yields higher gross margins compared to non-tech projects.
This has led to data centers competing with other sectors for labor and equipment. Additionally, the hyperscalers' insatiable demand for capital has driven up AI-related debt issuance, resulting in higher borrowing costs for the broader corporate sector. While these higher borrowing costs have reduced corporate investment in non-AI areas by approximately $10 billion, the overall effect on corporate borrowing costs has been modest, increasing them by only 0.05 percentage point.
Goldman's economists caution that both the magnitude of AI's contribution to U.S. GDP growth and its ability to significantly crowd out other activities have been overstated in recent media reports and market commentary.
Written by urgent.news from Axios's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
