Nvidia invested $40B in AI startups. What that means for product builders

When a chipmaker becomes the largest venture investor in the industry, it's not about altruism. Nvidia has already recorded $40 billion in equity deals with AI companies this year. The number is impressive, but if you look at the scheme as an engineer rather than an analyst, you see a simple mechanism: Nvidia sells not just chips, but a whole stack — CUDA, InfiniBand, software, and support. By buying a stake in a startup, it ensures that startup stays on that stack. For Nvidia itself, it's a closed loop: the money comes back as purchases of its own hardware.

Venture capital as a way to lock in customers

Standard venture capital hopes for an exit via IPO or sale. Nvidia, however, gets dividends in the form of platform loyalty. A startup that received $100 million from Nvidia is unlikely to switch to AMD or custom ASICs — that would be politically risky. In our experience, when we were choosing infrastructure for an AI product, vendor lock-in was often a hidden factor: not just price and performance, but also access to early hardware revisions and engineering support. Nvidia turns this into a formal contract through an equity stake.

Where's the catch for clients

For a team building an AI product, these deals are not a reason to celebrate. The more startups are "locked in" to Nvidia, the less freedom of choice the client has. If you're building a RAG system or a chatbot, you don't care what it runs on — but your contractor might already be hostage to the Nvidia ecosystem. We've seen projects where migrating from one GPU platform to another took months due to dependencies on CUDA libraries. $40 billion is a signal that Nvidia intends to maintain control over the market, not lower prices.

Not just hype: a pragmatic view

On the other hand, for startups that are truly doing breakthrough things (e.g., new model architectures or specialized software), partnering with Nvidia gives access to resources that would otherwise be unavailable. But for 90% of companies that simply apply LLMs or fine-tune models, it's just an extra layer of dependency. We'd recommend building architecture that can work with different backends — at least at the inference abstraction level. It's not a silver bullet, but it's cheap insurance in case Nvidia's next round is about dictating terms rather than investing.

In the end, $40 billion is not so much about money as it is about leverage. For Nvidia — a way to secure future demand. For startups — a chance to survive, but at the cost of freedom. For us, as engineers — a reminder that even in the AI industry, the physics of hardware still determines the economics of software.