By investing in chip designer Marvell, NVIDIA ensures that even when hyperscalers develop custom chips, they must still use NVIDIA's NVLink interconnect. This keeps NVIDIA embedded in the stack, preventing competitors like Broadcom from creating a completely proprietary, NVIDIA-free system.

New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.

While competitors chased cutting-edge physics, AI chip company Groq used a more conservative process technology but loaded its chip with on-die memory (SRAM). This seemingly less advanced but different architectural choice proved perfectly suited for the "decode" phase of AI inference, a critical bottleneck that led to its licensing deal with NVIDIA.

NVIDIA is moving "up the stack" from chips to an AI agent software platform to diversify its business and create a new moat beyond its CUDA system. By courting enterprise partners, NVIDIA aims to maintain infrastructure dominance even if AI labs succeed with their own custom silicon, reducing reliance on NVIDIA GPUs.

Tech giants often initiate custom chip projects not with the primary goal of mass deployment, but to create negotiating power against incumbents like NVIDIA. The threat of a viable alternative is enough to secure better pricing and allocation, making the R&D cost a strategic investment.

NVIDIA possesses a powerful strategic weapon: the ability to release a frontier-level open-source model. This could undermine the business case for customers developing their own custom ASICs by commoditizing the model layer, thus reinforcing NVIDIA's dominance in the hardware ecosystem.

NVIDIA's commitment to CUDA's backward compatibility prevents it from making fundamental changes to its chip architecture. This creates an opportunity for new players like MatX to build chips from a blank slate, optimized purely for modern LLM workloads without being tied to a decade-old programming model.

Large tech companies are actively diversifying their AI chip supply to avoid lock-in with NVIDIA. However, the true challenge isn't just hardware performance. NVIDIA's powerful moat is its extensive software and developer ecosystem, which competitors must also build to truly break free from its market dominance.

Broadcom is solidifying its position as the key alternative to NVIDIA's locked-in ecosystem by becoming the preferred design partner for custom AI chips (ASICs). Its deep partnerships with major players like Anthropic and OpenAI to develop specialized hardware highlight a growing demand for tailored, cost-efficient silicon.