On the Shifting Global Compute Landscape

Summary

The status quo of AI chip usage, that was once almost entirely U.S.-based, is changing. China’s immense progress in open-weight AI development is now being met with rapid domestic AI chip development. In the past few months, highly performant open-weight AI models’ inference in China has started to be powered by chips such as Huawei’s Ascend and Cambricon, with some models starting to be trained using domestic chips. 

There are two large implications for policymakers and AI researchers and developers respectively: U.S. export controls correlates with expedited Chinese chip production, and chip scarcity in China likely incentivized many of the innovations that are open-sourced and shaping global AI development.

China’s chip development correlates highly with stronger export controls from the U.S. Under uncertainty of chip access, Chinese companies have innovated with both chip production and algorithmic advances for compute efficiency in models. Out of necessity, decreased reliance on NVIDIA has led to domestic full stack AI deployments, as seen with Alibaba. 

Compute limitations likely incentivized advancements architecturally, infrastructurally, and in training. Innovations in compute efficiency from open-weight leaders include DeepSeek’s introduction of Multi-head Latent Attention (MLA) and Group Relative Policy Optimization (GRPO). A culture of openness encouraged knowledge sharing and improvements in compute efficiency contributed to lower inference costs, evolving the AI economy. 

Domestic silicon’s proven sufficiency has sparked demand and models are beginning to be optimized for domestic chips. In parallel, software platforms are shifting as alternatives to NVIDIA’s CUDA emerge and challenge NVIDIA at every layer; synergy between AI developers and chip vendors are creating a new, fast-evolving software ecosystem.

The shifting global compute landscape will continue to shape open source, training, deployment, and the overall AI ecosystem.