By acquiring Hugging Face, Nvidia is positioning itself around a strategic gateway where developers discover, evaluate and deploy A.I. models. Photo by I-Hwa Cheng / AFP via Getty Images

Nvidia’s $12.9 billion agreement to acquire Hugging Face looks like another step in the A.I. industry’s march toward vertical integration. The dominant supplier of A.I. compute is moving further into software, models and developer tools. But the more interesting strategic move is not simply that Nvidia is adding another layer to its stack. It is positioning itself around another potential control point in the emerging A.I. ecosystem.

A control point is a position in an ecosystem that gives a company disproportionate influence over how others access customers, technologies or resources, and therefore where value gets created and captured. Apple’s App Store is a familiar example. Apple does not need to develop every successful app. By controlling an important gateway between developers and hundreds of millions of users, it can influence distribution, standards and economics across the ecosystem.

But a control point is not necessarily a bottleneck. A bottleneck limits activity because a scarce resource constrains the system. A control point can remain powerful even when capacity is abundant because it shapes choices, access and interactions.

Nvidia already controls one of the most important control points in A.I.: compute. Its GPUs and CUDA software became the infrastructure on which much of the generative A.I. boom was built. Hugging Face potentially gives it another.

More than 18 million developers, researchers and creators use Hugging Face, which hosts more than three million models, 500,000 datasets and one million applications. More than 200,000 companies use the platform to discover, evaluate, customize and deploy A.I. Nvidia is therefore acquiring not simply software, but a place where developers decide which models to use, what tools to build with and increasingly how those models are deployed.

From controlling compute to influencing choice

Nvidia’s position has been extraordinary because almost every major A.I. player needed what it sold. But that success also creates a strategic vulnerability: its largest customers have strong incentives to reduce their dependence on Nvidia.

Google has spent years developing its own TPUs and now operates across virtually the entire stack, from custom silicon and cloud infrastructure to Gemini and applications reaching billions of users. OpenAI is moving in a similar direction. It increasingly describes its strategy as an integrated system stretching from data centers and chips through frontier models, developer platforms and consumer and enterprise applications. Anthropic is pursuing a different model, deliberately spreading compute across Amazon Trainium, Google TPUs and Nvidia GPUs rather than betting everything on one infrastructure provider.

All three approaches put pressure on Nvidia’s historical control point. Hugging Face provides a hedge against that future. Even if compute becomes more competitive, Nvidia can remain central to the ecosystem by owning one of the places where developers discover, compare, customize and deploy models. This is why Nvidia’s promise to keep Hugging Face open is so important. Developers are expected to remain free to use competing models, frameworks, clouds and compute platforms.

That openness may appear counterintuitive. Why spend $12.9 billion on a platform and then continue allowing customers to use competitors? Because its neutrality is part of what makes Hugging Face strategically valuable.

If Hugging Face became simply another channel for selling Nvidia hardware, developers would have stronger reasons to go elsewhere. Its power comes from being a meeting place for the wider ecosystem.

Microsoft faced a similar logic after acquiring GitHub, keeping it open across competing clouds, tools and technologies rather than turning it into an Azure-only platform. That helped preserve GitHub’s developer trust while strengthening Microsoft’s relationships with developers and, ultimately, its broader cloud ecosystem.

Nvidia, therefore, faces a delicate challenge: capture more value from Hugging Face without destroying the neutrality that created that value.

The next A.I. battle is between ecosystems

The first phase of the generative A.I. race was defined by scarcity. GPUs were scarce. Frontier models were scarce. Large-scale compute was scarce. The next phase looks different.

Models are multiplying, infrastructure alternatives are emerging and enterprises increasingly want flexibility rather than dependence on a single provider. Competition is therefore shifting from dominance of individual layers toward control of the connections between them. This is where control points matter. The winners of an ecosystem do not necessarily need to own every layer. They need to occupy positions through which disproportionate amounts of value, information or choice flow.

Google’s advantage is integration. OpenAI is building toward greater control across the stack. Anthropic is betting on model strength combined with infrastructure optionality. Nvidia appears to be pursuing another model: become the infrastructure and orchestration layer beneath a much broader A.I. ecosystem.

What enterprises should learn from the deal

For enterprises, the bigger lesson is that A.I. strategy should be about position, not just adoption. Most companies are focused on which models to use and which workflows to automate. The more strategic question is whether A.I. allows them to strengthen or create control points of their own, whether in customer relationships, proprietary data, distribution or industry platforms. At the same time, they need to understand which control points they are handing to others as A.I. becomes embedded in core processes.

The challenge is therefore two-sided: preserve optionality where others control the ecosystem, while building defensible control points where your company has an advantage.

That is ultimately what Nvidia’s move illustrates. The next phase of A.I. competition will not just be about who has the smartest model or fastest chip, but who occupies the positions where value and choice increasingly flow.