Up the Stack: How AI’s Escape From the Commodity Trap Risks Enterprise Lock-in

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Up the Stack: How AI’s Escape From the Commodity Trap Risks Enterprise Lock-in<br>Critics and boosters are both looking in the wrong place

Arvind Narayanan<br>Jul 09, 2026

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By Arvind Narayanan and Akash Kapur<br>Our goal in this essay is to move beyond the debate over whether AI is a bubble. We do so in two ways: clearly separating current financials from the question of who captures value in the long run, and recognizing that the labs are not confined to be model providers. They can migrate up the stack and are already aggressively doing so. This will likely allow them to escape the commodity trap but raises new concerns — customer lock-in and reduced competition.<br>Akash Kapur is a visiting fellow at Princeton and a senior fellow at New America. He is no relation to Sayash Kapoor.

As leading AI companies continue to invest massively in capacity and race toward blockbuster IPOs, serious questions linger about their business models. How will these companies — along with the vast ecosystem of chipmakers, hyperscalers, and infrastructure partners that depends on them — recoup the estimated $4–8 trillion projected to be invested in AI infrastructure by the early 2030s?<br>The current conversation splits between critics and boosters. Critics point to mounting losses, the gap between capex and revenue, and reports about the leading labs’ massive cash burn. Boosters cite accelerating rapid revenue growth, enterprise adoption, and milestones like Anthropic’s first profitable quarter. Each camp has a valid point. But both are looking in the wrong place — the same quarterly statements, the same short-term view of an industry that remains in flux.<br>In recent months, we have been thinking about the nature and sustainability of the AI business, and we’ve landed in a different place than most of the existing commentary. AI companies today earn much of their revenue by charging for inference, but the conditions of frontier inference make this an unusually difficult business to maintain. Models are largely undifferentiated, the leading labs operate with similar capital structures, switching costs are low, and prices can be adjusted freely. All of this appears to set up the conditions for a commodity trap that would pose real challenges to the task of building high-margin or even profitable businesses.<br>At the same time, we believe that the industry remains in a transitional stage, and that its structure will look very different when it matures. Drawing on both historical evidence and economic theory, we argue that competition in this equilibrium is likely to push the price of model inference toward the marginal cost of producing tokens, leaving little room for durable profits at the model layer. This does not mean, however, that the business of AI is inherently unviable. The same analysis suggests a path forward for AI labs, and leads to the central argument of our paper:<br>The labs’ most likely path to durable profitability runs not through the foundation layers (chips, datacenters, models) that have thus far accounted for the bulk of investments, but higher up the stack, through a mix of vertical integration, embedded enterprise deployments, and the deliberate construction of switching costs and other “moats”.<br>The labs’ strategies to capture value by moving up the stack, many borrowed from the playbook of enterprise software, have already begun. Beyond the sustainability of the current AI ecosystem, they raise questions of broader societal concern — about competition, innovation, and the overall distribution of economic and political power. The public discussion over AI so far has been marked by a somewhat paradoxical dichotomy: anxiety about monopolistic concentration and runaway market power, yet a reality of low switching costs and relatively interchangeable models that seems to belie those fears. But if we are right that the labs will increasingly move higher up the stack, then concerns about concentration and competition are worth taking seriously now, rather than after the effects of lock-in start to materialize. We return to these broader issues in the conclusion — and in a forthcoming paper that dives deeper into many of the topics covered in this post.<br>Historical analysis: the infrastructure layer overwhelmingly fails to capture the value that it creates

The AI as Normal Technology framework is committed to drawing lessons, when applicable, from past transformative technologies. We think AI is subject to many of the same dynamics related to investment, competition, and value capture that have shaped previous waves of technological innovation. As part of our research, we therefore examined the puzzle of AI value capture, and the labs’ likely response to it, through a broader historical lens.<br>We looked at six historical instances of capital-intensive infrastructure industries (railroads, electricity, telecom and fiber,...