CUDA is the x86 of AI
Open-source AI has a landlord.
In the early 1990s, any manufacturer could build an IBM-compatible machine, and dozens did, from Compaq and Dell on down. Microsoft licensed DOS and Windows to all of them, so the software platform was available to the whole industry. But Microsoft owned that software and Intel owned the chip underneath it, and together they spent the next two decades quietly collecting the economics of an ecosystem that described itself as open.
I was at Microsoft while this was playing out for both acts of it. The toll was invisible to the people buying PCs and entirely structural to the people inside the building. While Compaq and Dell fought each other down to razor-thin margins, Microsoft and Intel competed on almost nothing and took their cut regardless. The openness was real at the top of the stack. Underneath, it never was.
I was on the browser team when Microsoft made its move onto the web. Netscape had the market at the time, so Microsoft bundled Internet Explorer free with the OS and struck deals with every major PC maker to ship it by default. Within a few years Netscape was finished, sold to AOL at a fraction of its peak. The web liked to call itself open, but it ran on Windows, and Windows was anything but. Microsoft used the same advantage that had won the desktop to colonize the web, and the two eras ran the same play.
That structure is being reassembled.
Every major open-weight AI model runs on CUDA: not NVIDIA’s chips specifically, but the software platform NVIDIA has built over more than 20 years, with nearly 6 million developers behind it by its own count. Those developers wrote the kernel libraries, the custom optimizations, and the toolchains that make CUDA the path of least resistance for any team training or deploying at scale. PyTorch, which appears in more than half of all published AI research papers in 2026, optimizes for CUDA first, and TensorFlow does the same. So, the ecosystem that trains, fine-tunes, and deploys the open models was built on CUDA from the ground up. I’ve seen this shape before: the layer everyone builds on, owned by one company, invisible until you try to leave.
AMD’s ROCm exists as an alternative, but the switching costs are measured in years rather than dollars. PyTorch only elevated ROCm to first-class status with version 2.7.0, after years of second-tier support, and custom CUDA kernels still have to be ported by hand. Most teams have accumulated enough of them that leaving isn’t so much an engineering project so much as a platform bet with a multi-year payoff horizon. That was the trap at Microsoft too: the cost of leaving never showed up as a line item; it showed up as a year of your roadmap you’d never get back.
The obvious objection is that this time the customers can buy their way out. Google has its own TPUs, Amazon has Trainium, Microsoft has Maia, and OpenAI is reportedly designing silicon of its own. The biggest buyers of NVIDIA hardware are also the biggest investors in escaping it, and they have the budgets to mean it.
They do. But notice who “they” are. Only a handful of hyperscalers with fab-scale budgets can design around CUDA, and they do it for their own internal workloads rather than for the market. The millions of teams that don’t own a chip program still rent the ecosystem, and the ecosystem is CUDA. Even the giants keep buying NVIDIA for frontier training because their custom silicon covers the workloads they’ve already standardized and not the moving edge in front of them. This is the Wintel pattern rather than a break from it, only more concentrated. Wintel split the toll between two companies, the software vendor and the chip vendor. NVIDIA collects both halves itself, owning the CUDA platform and the silicon beneath it, which leaves no internal seam for a buyer to pry at and no second vendor to play against the first. Compaq and Dell could never have built their own x86, and only the largest players ever set their own terms, even then doing it at the margins while the platform held the center. An escape available to five companies isn’t an open market. It’s the same toll as before, with a handful of exemptions.
At Computex on June 1, Jensen Huang introduced RTX Spark: PC chips built on the same Blackwell architecture as NVIDIA’s data center GPUs, running the same CUDA platform. He called it “the first completely re-engineered line of PCs in 40 years,” and the framing was all about PCs, but the structure underneath was about reach. The same CUDA layer now runs continuously from the data center to the workstation to the robot to the car, every tier where AI will eventually live. I watched Microsoft do the same thing with Windows, pushing the platform into every box that could hold it until being everywhere was itself the moat.
AI data centers now generate roughly 90% of NVIDIA’s revenue, $193.7 billion in fiscal 2026, inside an AI-chip market Omdia puts past $200 billion and still climbing. NVIDIA’s share of that hardware is even slipping as the giants build their own silicon, but the platform they all keep building on doesn’t change, and that’s the more durable position the Computex announcement describes: not dominance of the chip, but ownership of the layer AI runs on, positioned to follow it wherever it goes next.
Value in this era doesn’t accrue to whoever builds the intelligence; it accrues to whoever owns the substrate that intelligence runs on. When the model itself costs nothing to license, the platform underneath it captures the economics. The cost of intelligence has already hit zero; the cost of leaving the platform it runs on has not. CUDA is the x86 of AI: not the intelligence, but the thing the intelligence runs on, the thing every working AI team has quietly built its production stack around.
NVIDIA doesn’t need to build the best AI; it needs to be where AI runs, and to keep switching costs high enough that the question of leaving never reaches a serious analysis. The leverage won’t come from preventing alternatives; it’ll come from making them too costly to choose.
I know how the last version of this ended because I watched it. Wintel held for two decades, and one by one the companies underneath it ran out of room. Compaq was absorbed into HP. IBM, which had built the original machine, sold its PC business to Lenovo and walked away from it entirely. Gateway disappeared, and Dell survived only on margins thin enough that it eventually took itself private rather than keep explaining them. Nobody beat the platform on its own ground, and the toll got paid the entire time.
When the escape finally came, it didn’t come from a better x86. It came from a different box entirely: the phone, running on ARM, an architecture Intel didn’t own, in a category where the platform had never been installed in the first place. The incumbents never lost the ground they held; the ground simply moved to where they weren’t standing.
That’s the real bet against CUDA, and it isn’t a small one: a different foundation entirely, somewhere NVIDIA isn’t already standing. Until that day arrives, the open AI ecosystem will keep running on a platform that isn’t open, and the economics will keep belonging to the company that owns it.