The artificial intelligence (AI) boom could face a new challenge as the cost of funding its expansion rises, Broadcom CEO Hock Tan and Intel CEO Lip-Bu Tan have warned. Speaking at the DBS Asia Future Forward Conference in Singapore on October 12, the two technology leaders said access to capital and the ability to generate returns could become bigger hurdles for the industry than shortages of chips and memory.
Their comments offer a reality check for a sector attracting billions of dollars in investment. Technology companies are racing to build AI data centres, develop advanced chips and expand computing capacity. However, the growing scale of spending is raising questions about how quickly these investments will translate into sustainable profits.
Hock Tan said Broadcom was working through shortages of wafer capacity and memory, but funding could become the biggest constraint on future growth. His warning comes as AI infrastructure requires enormous investment in semiconductor manufacturing, computing equipment, data centres and supporting networks.
Intel CEO Lip-Bu Tan echoed the concern, highlighting the cost of capital and the need for businesses to demonstrate a return on investment (ROI). Companies may be willing to invest heavily in AI today, but investors will eventually want evidence that the technology can deliver measurable financial benefits.
The comments suggest that the next phase of AI growth may depend not only on technological progress but also on financial discipline. Companies will need to show that their spending can generate revenue, improve productivity or reduce operating costs over time.
Hock Tan also expressed concern about speculative investment in the AI sector, comparing some investors to casino gamblers. He said many were setting extremely high expectations without fully understanding the technology or the businesses they were backing.
His criticism comes at a time when AI companies are attracting substantial investor interest, with OpenAI and Anthropic preparing for potential initial public offerings (IPOs). Their growing influence has helped fuel expectations of a major expansion in the AI economy, but questions remain about the cost of developing and operating advanced models.
Tan’s comments highlighted the difference between generating revenue and achieving profitability. AI companies may attract customers and report rapid growth, but the cost of training models, running computing infrastructure and serving millions of users can remain substantial. Investors will need to assess whether future revenues can cover these expenses and support sustainable earnings.
He also questioned whether businesses are using AI tools effectively. Companies experimenting with AI may spend money on computing resources without achieving meaningful improvements in productivity or business performance. The challenge, therefore, is to move beyond experimentation and identify practical applications that deliver measurable value.
The warning is particularly relevant as companies across industries increase their AI budgets. Businesses are deploying AI for customer service, software development, data analysis and automation. While these applications can improve efficiency, the financial benefits depend on how effectively the technology is integrated into existing operations.
For chipmakers such as Broadcom and Intel, continued investment in AI infrastructure remains a major opportunity. Broadcom supplies networking components and custom AI accelerators used in data centres, while Intel manufactures processors that support a range of computing workloads. Both companies stand to benefit from demand for AI hardware, even as the industry faces questions about the pace and profitability of future investment.
The executives’ comments do not suggest that AI demand is disappearing. Instead, they point to a possible shift in the industry’s main challenge. Hardware shortages and supply constraints have dominated discussions in recent years, but funding costs and the pressure to prove returns could increasingly influence which projects move forward.
The implications extend to financial markets, where AI-related stocks have attracted significant investment on expectations of rapid growth. If investors become more selective, companies with clearer business models, stronger cash flows and practical AI applications could be better placed than those relying mainly on future promises.
The coming years will test whether AI investment can deliver returns that justify the enormous sums being committed to the technology. Businesses will need to balance innovation with cost control, while investors will look for stronger evidence of commercial success.
The message from the two CEOs is clear: AI’s long-term potential remains significant, but access to funding alone will not guarantee success. Sustainable growth will depend on disciplined investment, effective use of technology and a credible path to profitability.