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

Last January, a little-known Chinese AI company called Deepseek shocked the world by showcasing model capability comparable to the frontier models but built on meagre resources. Since then, the Americans have pulled ahead, most notably with Anthropic’s Claude. Last week though, another Chinese company, Moonshot AI launched its latest model Kimi K3 which outperforms most American models closing the gap with the frontier ones. What’s more, it is open-weight. The quick changes in leadership at the model layer with increasing demand for open-weight amid expensive tokens reinforces the view held by some that models will eventually be commoditised if not already. This blog by two Princeton professors builds on that but stops short of calling it doomsday for the model companies, saying they can still capture much of the value in AI by moving up the stack, into the intelligence layer (a layer where software or applications in general sit in the traditional tech stack).

They begin by establishing the arguments of the two sides of the AI debate: “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.”

They go on to state their reasoning for why AI models resemble a commodity business: “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.”

And then they hypothesise the way forward for them: “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”.

They build on this argument by saying why they are more akin to an infrastructure business model today and how they could aspire towards becoming SaaS like:

“We believe that AI today has many infrastructure-like characteristics: massive capital requirements; low marginal cost; a commodity product that is somewhat decoupled from the applications that ultimately create value. This makes infrastructure a notoriously tough business to be in.

But at the same time, AI is software, and the software business has historically been lucrative, with exceptionally high margins. The industry has software-margin ambitions. Thus, we also surveyed the value-add and lock-in strategies of software-as-a-service, and analyzed whether AI can replicate these. Our thesis is that AI companies’ sustainability and value-capture largely turns on how successfully they can migrate from the first set of infrastructural properties toward the second enterprise-software ones.”

They cite instances of past infrastructure businesses which began with a lot of promise before leaving value to be captured by its users: “First, infrastructure providers rarely capture the value they create. Across railroads, electricity, telecom, and airlines, the firms that built capacity were eventually competed, regulated, or commoditized into thin margins. In many cases, they were destroyed outright. During the telecom and fiber buildout of the late 1990s, capacity exploded 186,000-fold in seven years, prices crashed, and roughly $2 trillion in market capitalization was erased. The value generated by the infrastructure primarily accrued to industries and applications built on top of it. Commercial aviation has destroyed investor capital for eight decades, because typical net margins are 2–4%, often below the cost of capital — even as businesses of all stripes benefited from a globalized economy.

…Second, enterprise software is not subject to this pattern, and it escapes the trap through a specific and reproducible set of mechanisms. Where infrastructure firms have struggled, enterprise software has sustained gross margins of 75% or more for decades. It does so by combining three properties that infrastructure businesses lack: zero marginal cost of reproduction, deep switching costs, and non-ephemeral value that allows fixed buildout costs to be amortized over decades. The AI labs’ lock-in strategies are best understood as attempts to import these structural properties of software into AI.”

Furthermore, should they not make the leap up the stack, under what conditions can they still capture value being in the infrastructure business: “Finally, two partial exceptions reveal what it may take for AI to beat the historical odds. Two infrastructure businesses — cloud computing and chip fabrication — have managed to escape the commodity trap. Cloud acquired software-like properties (managed-services lock-in, egress fees, committed-spend agreements) and TSMC achieved a near-monopoly position in leading-edge fabrication. These cases matter because they show what escape from the commodity trap actually requires. Capital-intensive industries can sustain durable margins, but only by either becoming functionally software or achieving market concentration.”

The rest of the blog expands on how AI model companies can and already are attempting their move up the AI stack. It is well worth the read in its entirety.