On the Shifting Global Compute Landscape

Summary

United States export controls on advanced artificial intelligence hardware have catalyzed rapid expansion across China's domestic silicon and open-weight model ecosystem. Faced with restricted access to high-end NVIDIA GPUs, Chinese developers accelerated deployments on domestic accelerators, including Huawei Ascend, Cambricon, and Baidu Kunlun. Hardware scarcity spurred architectural and algorithmic innovations in compute efficiency, notably DeepSeek's Multi-head Latent Attention and Group Relative Policy Optimization, alongside substantial post-training cost reductions. Organizations such as Baidu and Ant Group now train foundation models directly on domestic hardware, fostering non-CUDA software stacks and lowering inference costs globally. Consequently, the global artificial intelligence infrastructure is shifting from an exclusively American-focused paradigm toward a dual-ecosystem landscape powered by domestic chips and open-weight architectures.

Takeaways

1. 1

   Compute constraints incentivized algorithmic optimizations such as DeepSeek's Multi-head Latent Attention (MLA) and Group Relative Policy Optimization (GRPO), lowering model post-training costs.

2. 2

   Domestic Chinese chips, including Huawei's Ascend, Cambricon, and Baidu's Kunlun, have expanded from running model inference to powering foundation model training runs for companies like Baidu and Ant Group.

3. 3

   U.S. regulatory policy progressed from bandwidth thresholds to Total Processing Performance (TPP) and performance density metrics, eventually prompting licensing rules and revenue-sharing mechanisms for modified chips.