No account of the modern data-and-AI era is complete without reckoning with the company that sits beneath all of it: NVIDIA. It is not just the most valuable semiconductor company in history but arguably the single most powerful force in the entire AI economy, the company whose chips train and run virtually every significant AI model on earth. Its influence reaches into every layer of the stack, and through strategic investments, including one in Databricks, it has become a kingmaker shaping who wins the AI era. Understanding NVIDIA’s position is essential to understanding the world the data platforms now operate in.
How NVIDIA Ended Up at the Center
NVIDIA’s dominance traces back to a happy accident of history. The company made graphics chips, GPUs, originally designed to render video game images by performing many calculations simultaneously. It turned out that the same parallel-processing ability that draws game graphics is exactly what training AI models requires, because neural networks are, at bottom, enormous quantities of parallel math. When the AI boom arrived, NVIDIA found that its chips were the ideal tool for the most important technological task of the moment, and demand exploded.
But hardware alone does not explain the depth of NVIDIA’s grip. Over many years, NVIDIA built a software ecosystem around its chips, most importantly a platform called CUDA that lets developers harness the chips’ power, and the entire AI field grew up learning and building on it. This creates a powerful lock-in: even when competitors offer alternative chips, the vast accumulated weight of software, tools, and expertise built around NVIDIA’s ecosystem makes switching enormously difficult. NVIDIA owns not just the best shovels in the gold rush, but the entire system everyone has learned to dig with.
The Kingmaker Strategy
With its enormous resources and central position, NVIDIA is doing something strategically shrewd: it invests in the companies building the AI future, spreading its influence across the whole ecosystem. Its investment in Databricks is a prime example. NVIDIA does not need to put money into Databricks for financial reasons; the strategic value is in deepening the partnership, ensuring Databricks’ platform is optimized for NVIDIA’s chips, and aligning one of the most important enterprise-AI platforms with NVIDIA’s hardware and roadmap.
This is part of a broader pattern. NVIDIA invests in and partners with AI labs, cloud providers, data platforms, and startups across the stack, and because every one of them depends on NVIDIA’s chips, NVIDIA benefits from the success of all of them. It is, in effect, an investor in the entire AI economy, with a hand in nearly every important player. When the company whose hardware you depend on also invests in you, partners with you, and shapes its roadmap around you, its influence over your strategy is profound. NVIDIA has become a kingmaker, able to confer enormous advantage on the companies it chooses to back.
Extending Up the Stack
NVIDIA is also pushing beyond chips into higher layers of the stack. It has released its own open AI models, the Nemotron family, positioned as the most capable open models built in America. It builds complete hardware systems and developer machines. It offers software platforms and tools for building and deploying AI. Each move extends NVIDIA’s reach further up from raw silicon toward the models and applications on top, raising a question that hangs over the whole industry: how far up the stack will NVIDIA go, and at what point will it begin competing with the very partners it invests in?
This is the classic tension of depending on a dominant supplier that is also expanding its ambitions. The data platforms rely on NVIDIA’s chips and welcome its investment and partnership, but they also have to watch warily as NVIDIA climbs toward the layers where they themselves operate, the same dynamic that plays out with the cloud giants, who are simultaneously partners and competitors to the independent data platforms.
What It Means for the Market
NVIDIA’s position reshapes the strategic calculus of every company in the data-and-AI landscape. For the data platforms, alignment with NVIDIA, through partnership, optimization, and in Databricks’ case direct investment, is a significant advantage, ensuring their platforms run AI workloads as efficiently as possible on the hardware that dominates the industry. But the dependence also represents a concentration of power that makes the entire AI economy reliant on a single company, a structural risk that worries regulators, customers, and competitors alike. The benefit of NVIDIA’s dominance is a relentlessly advancing hardware and software foundation that makes ever-more-powerful AI possible and pushes the whole field forward at remarkable speed. The risk is the fragility and imbalance of an entire transformative industry resting so heavily on one company’s chips, ecosystem, and strategic choices.
For the data platforms specifically, NVIDIA’s kingmaker role reinforces a defining theme of this era: the AI economy is organizing into tightly interlinked alliances, the chipmaker, the cloud providers, the model-builders, and the data platforms, all mutually invested and mutually dependent. Databricks with NVIDIA’s chips beneath it and NVIDIA’s investment behind it, Snowflake with its own cloud and hardware partnerships, every player is finding its place in a web of relationships centered, more than anywhere else, on NVIDIA. The company that makes the shovels has become the company that helps decide who gets to mine, and that makes NVIDIA the most consequential force in the entire data-and-AI landscape.