Uncertainty over the extent and pace of artificial intelligence-driven change is set to make credit underwriting more fraught for lenders in the near term, a senior Goldman Sachs executive said on stage in New York.
Mahesh Saireddy, who is co-head of the Goldman Sachs Capital Solutions Group, told attendees at the Bloomberg Invest conference that the potential for disruption cuts across sectors and will draw heightened attention from lenders evaluating risk.
"It’s not just software, it’s other industries that are getting disrupted that will get a lot more attention," Saireddy said. "For the next six, 12, 24 months, there’s going to be a lot of unknowns. So it is going to be a challenging time to underwrite things."
The Capital Solutions Group was formed last year to finance large deals and lend to corporate clients. Saireddy’s comments underscored the challenge for that unit and for other lenders as they weigh the credit profiles of borrowers operating in sectors facing AI-driven change.
Concerns about how AI could alter business models have already rippled through financial markets, according to the executive’s remarks. Software stocks have been moving lower for months, and shares of asset managers that invested in and extended credit to software companies have also seen declines.
Those market movements illustrate the transmission of AI-related fears from equity markets into credit markets and into the capital-raising process for companies within the sector. Lenders and investors are confronting a period in which near-term outcomes are uncertain and underwriting judgments will need to account for a range of possible scenarios.
The comments did not offer precise predictions about outcomes, but they did emphasize that the coming six to 24 months will contain many unknowns, making risk assessment and pricing more complex for institutions underwriting loans or backing large corporate transactions.
Context limitations: The remarks capture a senior banker’s view on how AI-related uncertainty may affect lending and market behavior. They do not quantify the size of potential losses, specify particular borrowers at risk, or provide a timeline beyond the 6- to 24-month window referenced by the executive.