The competitive threat from custom ASICs is being neutralized as NVIDIA evolves from a GPU company to an "AI factory" provider. It is now building its own specialized chips (e.g., CPX) for niche workloads, turning the ASIC concept into a feature of its own disaggregated platform rather than an external threat.

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Jensen Huang's core strategy is to be a market creator, not a competitor. He actively avoids "red ocean" battles for existing market share, focusing instead on developing entirely new technologies and applications, like parallel processing for gaming and then AI, which established entirely new industries.

While known for its GPUs, NVIDIA's true competitive moat is CUDA, a free software platform that made its hardware accessible for diverse applications like research and AI. This created a powerful network effect and stickiness that competitors struggled to replicate, making NVIDIA more of a software company than observers realize.

Seemingly strange deals, like NVIDIA investing in companies that then buy its GPUs, serve a deep strategic purpose. It's not just financial engineering; it's a way to forge co-dependent alliances, secure its central role in the ecosystem, and effectively anoint winners in the AI arms race.

NVIDIA's complex Blackwell chip transition requires rapid, large-scale deployment to work out bugs. XAI, known for building data centers faster than anyone, serves this role for NVIDIA. This symbiotic relationship helps NVIDIA stabilize its new platform while giving XAI first access to next-generation models.

The real long-term threat to NVIDIA's dominance may not be a known competitor but a black swan: Huawei. Leveraging non-public lithography and massive state investment, Huawei could surprise the market within 2-3 years by producing high-volume, low-cost, specialized AI chips, fundamentally altering the competitive landscape.

Instead of competing for market share, Jensen Huang focuses on creating entirely new markets where there are initially "no customers." This "zero-billion-dollar market" strategy ensures there are also no competitors, allowing NVIDIA to build a dominant position from scratch.

In a power-constrained world, total cost of ownership is dominated by the revenue a data center can generate per watt. A superior NVIDIA system producing multiples more revenue makes the hardware cost irrelevant. A competitor's chip would be rejected even if free due to the high opportunity cost.

NVIDIA's annual product cadence serves as a powerful competitive moat. By providing a multi-year roadmap, it forces the supply chain (HBM, CoWoS) to commit capacity far in advance, effectively locking out smaller rivals and ensuring supply for its largest customers' massive build-outs.

NVIDIA's primary business risk isn't competition, but extreme customer concentration. Its top 4-5 customers represent ~80% of revenue. Each has a multi-billion dollar incentive to develop their own chips to reclaim NVIDIA's high gross margins, a threat most businesses don't face.

While competitors like OpenAI must buy GPUs from NVIDIA, Google trains its frontier AI models (like Gemini) on its own custom Tensor Processing Units (TPUs). This vertical integration gives Google a significant, often overlooked, strategic advantage in cost, efficiency, and long-term innovation in the AI race.

NVIDIA Is Defusing the ASIC Threat by Building Its Own Specialized Co-Processors | RiffOn