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The AI buildout has spent two years running into shortages of chips, memory and power. Now two of the industry’s most prominent executives say the next bottleneck may be money.
Capital Could Matter More Than Chips
Broadcom CEO Hock Tan said at the DBS Asia Future Forward Conference in Singapore on Monday that financing could become a bigger obstacle to AI expansion than semiconductor, memory or electricity shortages. “The biggest constraint is capital,” he said.
Intel CEO Lip-Bu Tan agreed. He pointed to the rising cost of capital as the main near-term challenge and said that over the longer run, investors will want to see a return on investment.
Why Borrowing Costs Matter
The industry has poured huge sums into data centers, advanced chips and power infrastructure in anticipation of surging demand. Many of those projects take years to pay off. Higher bond yields make that funding more expensive, which can squeeze the economics of data center construction and chip purchases.
In other words, a project that looks profitable at one interest rate can look shaky at another. That is the pressure both CEOs are describing.
Supply Bottlenecks Are Expected to Ease
Hock Tan took a relaxed view of the physical shortages. Over the past two years, he said, bottlenecks have kept appearing and the industry has kept overcoming them. The memory shortage today, he noted, might not look the same a year from now.
He suggested wafer shortages could ease by 2028 or 2029, and that power availability should improve as new infrastructure comes online.
Demand Still Outruns Supply
Neither executive is calling a top. Hock Tan said there is more demand for computing infrastructure than the industry can supply, and that the gap could last through 2029 and possibly into 2030. Lip-Bu Tan said he doesn’t expect a significant slowdown over the next two to three years, pointing to growing adoption of AI applications and demand for computing power.
The Conditions for Staying Strong
Both executives tied the longer-term outlook to results. Hock Tan cautioned that demand could cool if adoption of advanced AI models disappoints or if improvements in their capabilities start to plateau. Lip-Bu Tan said companies will increasingly need to show tangible returns, through efficiency gains and new business models.
The message is both bullish and cautious. Demand looks strong for the next few years, and supply constraints look solvable. The open question is whether the money keeps flowing at a price that makes the economics work, and whether AI delivers enough value to justify it. Investors tracking AI infrastructure may want to watch bond yields and corporate returns as closely as chip supply.
Disclaimer: The information in this article is for general purposes only and does not constitute financial advice. The author’s views are personal and may not reflect the views of Chain Affairs. Before making any investment decisions, you should always conduct your own research. Chain Affairs is not responsible for any financial losses.
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