The author, Ashwin Prasad, is a Staff Research Analyst with the Advanced Military Technologies and Outer Space Programme at the Takshashila Institution. He welcomes inquiries and can be reached here.
The author, Satya S Sahu, was a Research Analyst with the High Tech Geopolitics Programme at the Takshashila Institution.
The authors would like to thank Pranay Kothasthane for his invaluable comments and feedback.
Executive Summary
The rise of Machine Learning, Deep Learning, and Natural Language Processing has driven unprecedented demand for specialised AI chips. These systems require substantial computational resources and can be deployed either in cloud data centres for maximum processing power or at the network edge for reduced latency and enhanced privacy.
The AI chip ecosystem comprises three critical components: accelerators (including CPUs, GPUs, FPGAs, and ASICs), memory and storage systems, and networking infrastructure. Each component plays a vital role in handling AI workloads, with different architectures offering varying trade-offs between performance and efficiency. The market for these technologies is heavily concentrated among a few key players: NVIDIA, Intel, AMD, Google, and TSMC.
A particular concern is NVIDIA’s dominance in the GPU market and its proprietary software ecosystem, which creates significant dependencies for organisations and nations seeking to build sovereign AI infrastructure. As AI becomes increasingly critical to techno-national strategies worldwide, policymakers must understand these technological dependencies and support the development of alternative hardware and software solutions to ensure a more diverse and resilient AI chip ecosystem.