At the DBS Asia Future Forward Conference in Singapore on Monday, Broadcom chief executive Hock Tan warned that access to financing could become the most significant barrier to the artificial-intelligence surge, surpassing shortages of chips, memory or electricity. He said, “I think might be the biggest constraint is capital,” highlighting capital as a potential choke point for AI expansion.
Intel’s chief executive Lip-Bu Tan echoed the financing worries, pointing to higher borrowing costs and the need for clear returns on AI spending. He remarked, “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.”
The two CEOs underscored that the AI sector has already poured massive capital into data-center construction, advanced chip development and power-grid upgrades to meet soaring compute demand. However, rising bond yields are lifting the cost of debt, making it more expensive to fund new facilities and chip purchases, which could strain the economics of projects that may not generate returns for several years.
Tan noted that the industry has repeatedly faced supply bottlenecks over the past two years but expects many to ease as capacity expands. He suggested that wafer shortages could recede by 2028 or 2029, and that power availability is likely to improve as new infrastructure comes online, reducing one of the current constraints on AI hardware deployment.
Despite financing worries, both leaders remained confident about near-term demand. Hock Tan said the gap between compute infrastructure demand and supply “could persist through 2029 and potentially into 2030,” while Lip-Bu Tan projected that the sector would not see a material slowdown for the next two to three years, driven by expanding AI applications and growing compute needs.
Looking further ahead, the executives warned that sustained growth will hinge on how quickly businesses adopt AI and whether the technology delivers measurable economic benefits. Lip-Bu Tan emphasized that companies must show tangible returns through efficiency gains or new business models, and cautioned that demand could wane if advanced AI models fail to meet expectations or their performance improvements plateau.