Ricky Ho@rickyho_1989
One of the biggest misconceptions surrounding China's open-source AI ecosystem is that every successful Chinese model somehow weakens NVIDIA $NVDA. In reality, at least for now, the opposite is often true.
China's leading open-source models, including DeepSeek, Kimi, Qwen and others, have overwhelmingly been developed, optimized and deployed on the CUDA ecosystem using NVIDIA GPUs. That means the software stack, kernels, inference libraries and optimization techniques are all designed around NVIDIA's architecture. Once millions of developers begin building applications on top of those models, the natural inference platform also becomes NVIDIA.
This is exactly how platform effects work. Developers optimize for the dominant hardware because that is where the tooling, documentation, community support and performance are best. The more successful Chinese open-source models become globally, the more demand they potentially create for NVIDIA-based inference infrastructure. In other words, open-source proliferation does not necessarily reduce NVIDIA's moat. It can actually reinforce it.
This is particularly important for inference, which we believe will ultimately become a much larger market than training. Training a frontier model may occur once every few months, but inference happens every time someone asks a question, generates an image, writes code or deploys an AI agent. As AI adoption scales from millions to billions of daily interactions, inference compute will likely dominate total GPU utilization.
Interestingly, very few organizations are attempting to deploy large Chinese open-source models primarily on Google's $GOOG TPUs or Amazon's $AMZN Trainium. Those custom accelerators are largely optimized for their respective cloud ecosystems rather than the broader open-source AI community. CUDA remains the industry's de facto software standard, and software ecosystems are remarkably difficult to replace once developers have committed to them.
This also explains why NVIDIA values neocloud providers so highly. Companies such as CoreWeave $CRWV, Lambda, Crusoe, Together AI and others are building businesses almost entirely around renting NVIDIA GPU infrastructure for AI workloads. Every successful open-source model expands the addressable inference market for these providers, further strengthening NVIDIA's ecosystem beyond the hyperscalers themselves. A diversified compute ecosystem built around NVIDIA reduces the risk that inference becomes concentrated solely within Microsoft Azure, Google Cloud or AWS.
However, this dynamic changes dramatically if China eventually achieves genuine compute independence.
If Chinese frontier models are no longer trained primarily on NVIDIA GPUs, they will naturally begin optimizing for domestic hardware and software ecosystems instead. CUDA compatibility becomes less important. Frameworks, compilers and inference engines would increasingly evolve around Huawei Ascend, domestic interconnects and indigenous AI software stacks. At that point, NVIDIA would gradually lose one of its most powerful competitive advantages: the network effect created by developers building directly on its hardware platform.
That shift would also strengthen competing inference ecosystems. Google's TPUs, Amazon's Trainium and China's domestic accelerators would each have stronger incentives to optimize for their own software environments rather than NVIDIA's. Instead of one dominant global AI platform, the industry could gradually fragment into multiple regional compute ecosystems.
This is precisely why the AI race extends far beyond semiconductor sales. Export controls are not simply about preventing NVIDIA from shipping another batch of GPUs to China. They are about preserving the technological ecosystem built around CUDA. The real strategic asset is not just the chip itself, but the millions of developers, software libraries, optimization frameworks and inference deployments that have accumulated around NVIDIA over nearly two decades.
Ironically, if China remains dependent on NVIDIA GPUs, NVIDIA continues benefiting from both sides of the AI race. The company supplies infrastructure to Western frontier labs while simultaneously remaining deeply embedded within China's open-source AI ecosystem wherever export controls permit. That is an extraordinarily powerful strategic position.
Our View
The long-term risk to NVIDIA is not that China develops better AI models. The real risk is that China develops a fully independent AI compute stack, including GPUs, networking, compilers, inference frameworks and developer ecosystems that no longer rely on CUDA. Once software begins optimizing for a different hardware platform, network effects gradually shift with it. That is why China's pursuit of semiconductor self-sufficiency deserves far more attention than short-term quarterly GPU shipment numbers. The real battle is not over who sells the next chip. It is over who owns the software ecosystem that developers build upon for the next decade.
