Even as competitors develop their own Application-Specific Integrated Circuits (ASICs) or Tensor Processing Units (TPUs), NVIDIA’s GPU architecture has quietly become the industry's go-to solution.

According to a report by tech media outlet Wccftech, during an earnings call on November 20, Jensen Huang addressed the competitive threats posed by self-developed ASIC chips from tech giants like Google and Amazon. He emphasized that true competition is not about chip speed between two companies but rather a contest between teams.

While hardware parameters can be replicated, ecosystems, software, and systems engineering capabilities cannot be easily surpassed.

When asked if NVIDIA’s dominant position faces real challenges as multiple large tech companies launch their self-developed AI chips (ASIC/XPU) for inference and training,

Huang’s response was direct: "First, you're not competing against a company—you're competing against a team. There aren't many teams in the world capable of building these extremely complex systems."

This statement underscores three key aspects.

First, NVIDIA is not just a chip seller; it is renowned for its integrated systems and engineering expertise. From GPUs to high-speed interconnection networks, cabinet-level deployments, software stacks (such as CUDA), framework optimizations, and operational support, NVIDIA controls the entire system. Hardware is merely the entry point. As Huang stated, even if computing power is replicated, the software stack remains a significant competitive advantage.

Second, when customers evaluate large-scale AI clusters (for training or inference), the focus is not solely on "chip speed" but also on the overall cost and risk associated with stable operation, tuning, maintenance, and upgrades post-deployment. Huang pointed out that for cloud service providers, integrating a "random ASIC" into a data center is not the optimal choice.

Third, team capabilities determine iteration speed and ecosystem scale. NVIDIA’s years of accumulated software, algorithms, models, and industry cases, particularly its CUDA ecosystem, have formed a comprehensive network spanning hardware to frameworks, developers to commercial clients. "Others may be able to build chips, but creating a sustainable, scalable, and operationally friendly system is much harder," Huang emphasized, highlighting the importance of "teams" over "companies."

In summary, NVIDIA’s focus extends beyond chips to encompassing the entire AI infrastructure landscape. If one only concentrates on hardware parameters while neglecting ecosystem integration, software support, and data center deployment capabilities, then so-called "NVIDIA-challenging" solutions remain superficial and have not truly entered the competitive ranks in terms of deployment scale, stability, and versatility.

If hardware can be replicated, why is the ecosystem difficult to surpass?

The answer lies in software and developer networks, system-level integration and operations, and commercial ecosystems with scale effects.

NVIDIA’s CUDA, TensorRT, various deep learning libraries, optimization tools, and model service toolchains have been evolving for over a decade, accumulating extensive case studies, deep tuning experience, and broad developer training.

This network effect is challenging to overcome in the short term. Once customers, model developers, and cloud service providers commit to a single platform, switching means rewriting code, re-tuning parameters, re-testing, and even facing stability and compatibility risks.

Huang noted that even if competitors build similar chips, they have not yet achieved this systemic capability at the engineering level.

As AI models grow larger and training nodes reach terascale levels, data center interconnection, storage networks, scheduling systems, and fault tolerance mechanisms become crucial. NVIDIA has demonstrated end-to-end systems engineering capabilities in its "Blackwell" GPU series, NVLink/InfiniBand high-speed interconnections, cabinet-scale deployments, and optimized training models.

With more customers, concentrated models, and comprehensive services, NVIDIA’s commercial ecosystem includes top cloud service providers, AI model companies, and system integrators, all built on NVIDIA’s platform.

Currently, many tech giants have accelerated their development of self-developed AI chips (ASIC/XPU) to reduce costs or gain stronger customization advantages. However, Huang pointed out that even if these plans are initiated, significant engineering and system challenges remain before large-scale deployment can be achieved.

While the ecosystem barrier is extremely strong, challengers can look for breakthroughs in the following areas:

Compatibility tools: Developing tools that enable existing CUDA code to migrate to other hardware platforms at lower cost.

Niche scenario substitution: For smaller-scale, highly specialized, or inference-heavy scenarios, the benefits of self-developed ASICs may be more attractive.

However, from current observations, transitioning from "chips" to "platform-level ecosystems" presents high barriers and long cycles for challengers.

Computing power is not a moat; the ecosystem is. While hardware is important, it is far from everything. Software stacks, systems engineering capabilities, ecosystem networks, and commercial scale are all indispensable advantages.

References:

https://wccftech.com/nvidia-jensen-huang-explains-why-asics-wont-do-much-to-the-firm-ai-dominance/?utm_source=chatgpt.com