The AI data center value chain encompasses the full ecosystem of companies that design, manufacture, assemble, and operate the computing infrastructure powering artificial intelligence workloads. This includes GPU and AI accelerator designers (NVIDIA, AMD), custom ASIC builders (Google TPU, AWS Trainium), ASIC co-design houses (Broadcom, Marvell), CPU makers (Intel, AMD, ARM-based), server OEMs (Supermicro, Dell, HPE), cable and optical interconnect suppliers (Amphenol, Corning, InnoLight), and hyperscale cloud operators (AWS, Google Cloud, Microsoft Azure). The total addressable market is pro

Hyperscalers like Google, Amazon, Microsoft, and Meta are developing custom AI accelerators primarily for cost efficiency — custom ASICs can deliver 40-65% lower total cost of ownership versus GPUs for specific workloads at scale. Additionally, custom chips reduce dependency on a single vendor (NVIDIA), can be optimized for specific model architectures (e.g., Google's TPU for Transformer workloads), and provide supply chain control during GPU shortages. However, CUDA ecosystem lock-in and NVIDIA's rapid innovation pace keep most training workloads on GPUs.

NVLink Fusion is NVIDIA's interconnect strategy that allows third-party chips — including hyperscaler custom ASICs — to plug into NVIDIA's rack-scale architecture (NVL72 and beyond). This is strategically significant because it ensures NVIDIA remains embedded in data center deployments even when customers use competing compute chips. By controlling the interconnect fabric, NVIDIA shifts its value proposition from supplying the only viable AI chip to providing the system architecture that connects all AI chips.