American Affairs, Spring 2026. Vol. X, Issue 1. Understanding the LLM Bubble [ungated PDF] Hubert Horan.

The current debate on the existence of an “AI bubble” centers on a single question: is the current high level of investment into AI data centers a massive misallocation of capital or the key to future economic growth? The companies tightly linked to generative artificial intelligence (GenAI) drove 80 percent of the stock market’s growth across much of 2025. The equity value of these seven companies increased by $16 trillion in the last three years and now accounts for a third of the equity value of all listed U.S. companies. Even before potential trillion-dollar capital spending increases begin, data center investment accounts for about half of current U.S. GDP growth and is now a larger portion of GDP than all consumer spending; it may have also prevented the United States from falling into a recession.

The tools at the center of the investment boom are Large Language Models (LLMs) that evolved from decades of language modeling based on the probabilistic pattern-matching of neural networks. Arguments that LLM investments are the key to future economic growth are based on the belief that growing LLM capabilities will lead to the rapid automation of a broad range of jobs, driving major improvements in business productivity. More importantly, advocates claim that in a few short years, today’s LLM apps will achieve artificial general intelligence (AGI), which could be one of the most important technological breakthroughs ever, as it will supposedly allow humanity to solve a wide variety of critical problems.

This essay aims to disprove and overturn that view: it is divided into three sections, each with a main counterargument to advance. The first is that the current LLM industry is not economically viable. Equity appreciation, repayment of the investors currently funding data center expansion, and the belief that the industry could drive huge national productivity growth all depend on the claim that LLM software will achieve superintelligence by (roughly) 2028. This section will explain why inherent, structural problems with current LLM software preclude the medium-term ability to achieve superintelligence; they also preclude the significant near-term improvements to their problem-solving capabilities needed to reduce the industry’s huge current cash flow drain. The question is not whether LLMs have value—it is whether they create enough value to justify the orientation of today’s industry, which is entirely focused on extremely large general-purpose LLMs that require trillions in investment. If the industry cannot provide end users with huge increases in productivity-enhancing capabilities, those end users will not provide the revenue the industry is forecasting, and the equity and investment boom will inevitably collapse…