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NewsMaterial3 min readDisconfirmAI Editorial

Li Lu, Founder of Himalaya Capital: The Cost Structure of AI Is Fundamentally Different from the Internet

Speaking at Columbia Business School in New York, Li Lu pointed out that AI is always accompanied by incremental costs, which is fundamentally different from the internet model where one-time investments lead to long-term monetization. Therefore, development pacing requires multi-layered considerations.

2026-10-13 09:00 GMT+8

Li Lu, founder and chairman of Seattle-based value investment firm Himalaya Capital, made a judgment on the economic structure of artificial intelligence during a dialogue at Columbia Business School in New York: this technology is always accompanied by incremental costs, which is fundamentally different from the internet and many previous technologies. His exact words were that in the internet era, you could make a one-time investment and then monetize it for a long time through advertising; but AI is always accompanied by incremental costs, so "you have four or five layers of consideration for the pacing and development."

The practical implication of this distinction is that in the previous technology cycle, the core logic of platform companies was heavy asset construction in the early stages (servers, networks, content ecosystems). Once infrastructure was in place, marginal costs approached zero, and revenue could rely repeatedly on advertising or subscriptions. Investors granted high valuations based on the belief that economies of scale would continue to dilute costs. Li Lu believes AI has broken this premise—reasoning and computing are not one-time investments but expenses that grow continuously with usage volume, meaning that scale does not necessarily bring the same marginal cost reductions.

The background for Li Lu's remarks was his overall observation of the divergence between Chinese and US stock markets. He acknowledged the impact of China's property downturn and the ten-year stagnation of its financial markets, but also pointed out that China's manufacturing capabilities, technological self-reliance, and competitiveness are advancing, with some excellent Chinese companies trading at relatively low prices. He believes that the current market divergence—where capital flows excessively into top-tier targets—actually creates a better opportunity set for rational investors, because "a large number of originally excellent enterprises are trading at very low prices."

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However, Li Lu did not elaborate on the specific content of these "four or five layers of consideration." He did not explain what these layers are, how they can be quantified, or how they are reflected in valuation models. This judgment currently remains at the level of attributional opinion rather than a testable financial model or independent research. The SCMP report of this dialogue was an exchange between Li Lu and Columbia University professor Tano Santos, representing personal investment views expressed in a public setting.

The practical significance for readers is this: when evaluating an AI company, if its growth logic is built on the assumption of "building once like an internet company and monetizing long-term," Li Lu's structural reminder is worth incorporating into your considerations. Whether AI's continuous computing power expenditures truly change the shape of economies of scale depends on specific business models, the speed of reasoning cost reductions, and pricing strategies. At the very least, this difference in cost structure is a question that needs to be answered positively, rather than being ignored as background noise.

Sources

  • scmp.comSouth China Morning Post↗
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