Intel, Broadcom CEOs Warn Capital Crunch Could Be AI's Biggest Bottleneck

Broadcom CEO Hock Tan said financing could become a bigger obstacle to AI expansion than shortages of semiconductors, memory chips or electricity. “What I think might be the biggest constraint is capital,” he said at the conference in Singapore.

Intel CEO Lip-Bu Tan echoed concerns about financing, pointing to rising capital costs and questions over whether companies investing heavily in AI will eventually generate sufficient returns. “If there's any challenge in the front, it's capital and the cost of capital, and then the other part is in the longer run, people want to see the ROI,” he said.

Their remarks point to a challenge for an industry that has poured enormous sums into data centers, advanced chips and power infrastructure in anticipation of surging demand for computing capacity. Rising borrowing costs threaten to complicate financing for technology companies investing heavily in AI infrastructure. Higher bond yields could make it more expensive to fund data center construction and chip purchases, potentially pressuring the economics of projects that may take years to generate returns.

Hock Tan said the industry has repeatedly encountered supply bottlenecks over the past two years but expects some constraints to ease as production capacity expands. “Last two years we have seen a series of bottlenecks that keep coming out, but then we overcome it,” he said. “Memory shortage today might not be that way a year from now.”

He suggested wafer shortages could ease by 2028 or 2029, while power availability could also improve as new infrastructure comes online.

Despite the financing concerns, both executives remained bullish on near-term demand for AI computing infrastructure. “There is more demand for compute infrastructure than we can supply,” Hock Tan said, adding that the imbalance could persist through 2029 and potentially into 2030.

Intel's Lip-Bu Tan similarly said the industry was unlikely to experience a significant slowdown over the next two to three years, citing growing adoption of AI applications and demand for computing power. But sustaining that growth beyond the next few years will depend on how quickly businesses adopt AI and whether the technology delivers meaningful economic benefits, the executives said.

Hock Tan cautioned that demand for computing infrastructure could slow if adoption of advanced AI models disappoints or if improvements in their capabilities begin to plateau. Lip-Bu Tan emphasized that companies would increasingly need to demonstrate tangible returns from AI investments through improved efficiency and new business models.