“Is It an AI Bubble?” is the Wrong Question

In brief

• The question isn’t whether AI is a bubble, but rather whether we’re missing the bigger picture.
• The real issue may lie in capital misallocation and physical constraints.
• The AI boom is exposing the consequences of years of underinvestment in the physical economy.

The Wrong Question

If everyone is asking whether artificial intelligence (AI) is a bubble, it’s likely the wrong question. The focus on whether AI is overhyped distracts from the larger, more critical issue: the misallocation of capital and the physical constraints that limit growth.

Interest rates are the mechanism by which societies allocate capital and resources. Every financial decision must hurdle the risk-free rate. When capital is not allocated based on its economic utility, inefficiencies arise, leading to economic and financial market distortions — eventually manifesting as bubbles.

The artificial suppression of capital costs, which began in the early 2010s, accelerated the diversion of capital from the real, physical economy to a “paper economy” (e.g., stock buybacks and financial engineering). While outsourced manufacturing and falling fixed investment generated immense wealth for owners of capital, they had minimal actual economic utility. The resulting cost is clear today: a lack of sufficient US housing stock, as well as a deficit of goods and skilled labor necessary to construct the foundational infrastructure that the AI revolution demands. 

A Tale of Two Economies

The AI buildout is progressing at a staggering pace, yet the broader macroeconomic environment feels recessionary. The Federal Reserve is cutting rates in response to a weak labor market. Credit weakness is evident in credit cards, auto loans and private debt. Oil prices, a common proxy for growth, are down double-digit percentages year-to-date, signaling excess economic capacity.

However, the AI sector tells a different tale, one of acute capacity constraints. Makers of mission-critical components, such as high-bandwidth memory chips, advanced gas turbines, very large electrical transformers and grid interconnection equipment, are sold out, with lead times measured in years, creating construction delays.

Shifting Funding Dynamics and the Path to Profitability

Until recently, AI investment was primarily funded by two sources: 1) massive cash flows from hyperscalers (e.g., Alphabet, Amazon, Microsoft) and 2) private capital, which has backed model providers and neo-clouds. 

However, the funding landscape has clearly shifted, moving towards public debt markets, asset-backed vehicles and vendor financing. The latter, where suppliers essentially act as both seller and lender to fund customer purchases, can sometimes mask financial health and true default risk. 

These changes highlight two critical points about the AI paradigm:

1. The scale of AI investment is larger than anticipated: Even the largest hyperscalers are seeing their free cash flow decline as they fund the immense capital expenditure requirements of AI development. The deliberate shift to debt financing may be a strategic move to slow this decline and alleviate pressure on their stock valuations. 
2. AI adoption is rising, but prices are falling: As observed with other general-purpose technologies of the past, which we highlighted in a recent Strategist’s Corner, AI is following a competitive pattern that mirrors historical trends seen in industries like air travel, automobiles and personal computing.

AI revenue and profits will come as the technology offers increasing, demonstrable value to users, but due to infrastructure bottlenecks, the timeline may be longer than what is implied in valuations. The AI bubble may merely be one of expectations and time, which would be typical. 

Additionally, and perhaps more importantly, a key challenge that doesn’t seem to be recognized by many market participants is that AI models operate with negative economies of scale. Each complex client query triggers expensive compute processes, where initial operational costs can often exceed the revenue generated by that single interaction. This is in stark contrast to the Internet 2.0 giants, which leveraged powerful network effects to achieve monopolistic positions and historic profit margins. AI services, being highly commoditized at the utility layer, may lack these inherent network effects and possess less convex profit profiles than the previous generation of technology giants, requiring more capital, capex and investor trust.

Opportunities Amidst Constraints

Despite these challenges, we believe that the AI boom presents significant opportunities for investors. The bottlenecks in the physical economy may create underappreciated profit potential for suppliers who sell into these constrained areas. We’re excited about the potential opportunity for best-in-class operators in businesses such as electrical equipment, machinery and tools, specialty chemicals, semiconductor capital and network equipment, power management solutions, and so on. 

Conclusion

Considering the rapid advancement and capital-intensive nature of AI, it is essential to examine how physical and financial constraints will present both risks and opportunities for investors.

We believe that the focus should be on companies that enable the physical infrastructure required for AI and also possess durable competitive advantages. And of course, on avoiding owning businesses where product differentiation is low and the risk of rapid obsolescence is high.
 

The views expressed are those of the author(s) and are subject to change at any time. These views are for informational purposes only and should not be relied upon as a recommendation to purchase any security or as a solicitation or investment advice. No forecasts can be guaranteed. Past performance is no guarantee of future results.