AI Chipsets and Accelerators Market: Hardware Demand, Supply Chain, and Edge AI Processing Forecast (2025?2031)

The global market for AI chipsets and accelerators is entering a phase of specialized diversification. As we move through the 2025?2031 forecast period, the industry is shifting away from the initial "compute-at-all-costs" mentality that characterized the early generative AI era. Instead, we are seeing the emergence of a more nuanced hardware ecosystem where performance is measured not just by teraflops, but by efficiency, latency, and proximity to the data source. This report investigates how the massive capital expenditure of hyperscalers is now trickling down into the broader enterprise and consumer electronics sectors, necessitating a new generation of silicon designed for specific workloads. Edge AI processing has emerged as the most significant growth frontier within this decade. As organizations seek to reduce their reliance on expensive cloud inference and address growing data privacy concerns, the demand for on-device AI accelerators is skyrocketing. This transition is particularly evident in the automotive and industrial sectors, where real-time decision-making requires sub-millisecond latency that only localized, specialized silicon can provide. The report details the rise of Neural Processing Units (NPUs) and AI-optimized ASICs that are replacing general-purpose chips in everything from smartphones to autonomous factory robots. The supply chain remains a critical focal point of our analysis. The industry is currently grappling with a paradoxical environment of high demand coupled with extreme geographical concentration in advanced node manufacturing. We explore how geopolitical tensions and export controls are forcing a redesign of global supply strategies, leading to a surge in domestic semiconductor initiatives and a renewed focus on "sovereign AI" hardware. Furthermore, the report highlights the critical role of advanced packaging and High Bandwidth Memory (HBM), which have become the primary bottlenecks in scaling AI performance beyond current architectural limits. Investment and R&D are increasingly being funneled into "beyond-von-Neumann" architectures. Neuromorphic computing and optical AI accelerators are moving from academic curiosities into early commercial prototyping, promising orders-of-magnitude improvements in energy consumption. This report provides stakeholders with a clear framework for understanding which of these emerging technologies are likely to achieve mainstream adoption by 2031. It serves as a comprehensive guide for navigating a market where the boundaries between hardware manufacturers and software providers are increasingly blurred by the rise of custom silicon.