Do We Need More AI Investment?
Investors are questioning whether massive AI infrastructure spending could create overcapacity if demand slows—leaving costly data centers and GPUs underutilized and unable to generate meaningful returns, akin to the 2000s telecom bubble.
There is some concern in the market over the amount of capital being poured into infrastructure supporting artificial intelligence (AI) services. Is there overcapacity building like there was in the early 2000s, or are we in a fundamentally different position in the current technological revolution?
- When capacity is scarce and demand is growing rapidly, it is rational to invest in order to build additional capacity. However, if supply is outpacing demand growth and there is excess capacity, capital investment should slow. In the early 2000s, telecom companies installed thousands of miles of fiber optic cable expecting a surge in internet use. However, most of it went unused because last-mile connections were limited to slow 1G and 2G mobile networks and dial-up service (e.g., loading a webpage would take minutes). While internet data traffic continued to grow steadily, there was an oversupply of fiber as shown above by the very low capacity utilization in fiber networks.
- We believe today’s AI infrastructure deployment is fundamentally different. Instead of excess supply, the challenge is insufficient compute capacity (e.g., data centers and GPUs), with data center utilization exceeding 98%. Leading cloud service providers (hyperscalers) cannot keep pace with surging AI demand. Microsoft recently said, “our customer demand continues to exceed our supply” in reference to compute capacity constraints.1 Nvidia noted, “The clouds are sold out and our GPU installed base is fully utilized.”2 These statements indicate a shortage, not a glut, making continued investments in AI infrastructure both rational and necessary, in our opinion.
- Taken together, we believe these dynamics suggest we are in the early stages of a major capital investment cycle, driven not only by artificial intelligence but also by supportive fiscal and trade policies (see An American Business Spending Boom). Today’s AI infrastructure spending is anchored in real, immediate demand and structural tailwinds that we believe reinforce its durability.
1Microsoft fiscal 2Q26 earnings conference call, January 28, 2026.
2Nvidia fiscal 3Q26 earnings press release, November 19, 2025.
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Companies involved in, or exposed to, AI-related businesses may have limited product lines, markets, financial resources or personnel as they face intense competition and potentially rapid product obsolescence, and many depend significantly on retaining and growing their consumer base. These companies may be substantially exposed to the market and business risks of other industries or sectors, and may be adversely affected by negative developments impacting those companies, industries or sectors, as well as by loss or impairment of intellectual property rights or misappropriation of their technology. Companies that utilize AI could face reputational harm, competitive harm, and legal liability, and/or an adverse effect on business operations as content, analyses, or recommendations that AI applications produce may be deficient, inaccurate, biased, misleading or incomplete, may lead to errors, and may be used in negligent or criminal ways. AI technology could face increasing regulatory scrutiny in the future, which may limit the development of this technology and impede the future growth. AI companies, especially smaller companies, tend to be more volatile than companies that do not rely heavily on technology.
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