GPU economics face pressure as token prices fall, d-Matrix says

The GPU computing economy is encountering structural headwinds as declining token prices undermine the return on investment in AI inference workloads. This shift forces data centers to reassess compute-memory allocation strategies, particularly in edge and cloud-native environments, accelerating a move toward heterogeneous architectures. From a tech stack perspective, NVIDIA and peers face near-term revenue pressure, while domestic alternatives may gain traction, especially in AI training chips. Regulatory tightening could raise compliance costs and supply chain risks, especially in Taiwan, China and Hong Kong, China. In competitive dynamics, major players like NVIDIA and AMD are likely to introduce low-power inference chips to defend market share. Over the next 12-24 months, the AI chip market will see clear divergence—high-performance demand persists, but mid-tier inference segments may contract, reshaping capital and R&D focus.