CUDA Software MoatWide moat

CUDA is the real fortress, and it is worth understanding why, because it is so easily overlooked by people who fixate on the chips. CUDA is the software layer that lets programmers actually harness Nvidia's hardware, and because researchers have written against it for the better part of two decades¹, an enormous body of code, tooling, and — most importantly — human skill now assumes Nvidia sits at the bottom of the stack; the FY2026 annual report counts more than 7.5 million developers using CUDA and Nvidia's other tools³, up from 4.7 million two years earlier⁴. That is not an advantage you can photograph or put on a spec sheet, which is exactly why it is so underrated and so powerful.

The crucial consequence is that a competitor can match the silicon and still lose the sale. Suppose a rival ships a chip that is just as fast and rather cheaper. To adopt it, a customer must rewrite or re-optimize a great deal of software, re-validate that everything still works, and retrain the very people whose expertise is the customer's scarcest resource. Faced with that cost and that risk, most customers conclude that the cheaper chip is not actually cheaper at all once the switching costs are counted — and they stay put. The moat, in other words, is measured not in copper but in code and in trained human beings.

Every major AI framework in wide use assumes Nvidia beneath it², tuned and optimized for its hardware over years of collective effort. That framework dependence means the default path for anyone starting new work runs straight through Nvidia, without a decision ever being consciously made. The tools simply expect it, the tutorials assume it, and the path of least resistance leads there.

It helps to see why this is a fundamentally different and sturdier kind of advantage than a fast chip. A chip lead is a lead in a thing you sell; a software ecosystem is a lead in a thing your customers build upon and depend on. The first can be answered by a rival who simply builds a better thing. The second can only be answered by persuading the customer to tear down and rebuild years of their own work — which is why hardware leads are so often temporary and ecosystem leads so often prove permanent. Nvidia was shrewd, or lucky, or both, to invest so heavily in the software when the chips alone were the fashionable thing to sell.

And switching costs of this kind compound quietly and relentlessly. Every library painstakingly optimized for CUDA, every graduate student who cut their teeth on it, every production model tuned to run just so on Nvidia hardware is one more brick in a wall that no one deliberately built and no rival can quickly tear down. It is the accumulated residue of twenty years of the whole field's labor, and that is why the deepest part of Nvidia's moat is not something it manufactures each quarter, but something the entire industry has been unwittingly building on its behalf for two decades.