On the Shifting Global Compute Landscape

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.

Utility of and demand for advanced AI chips has followed an upward trajectory and is predicted to continue to increase. Over the past few years all NVIDIA chips maintained dominance. Recently, new players are garnering attention. China has had long-term plans for domestic production, with plans for self-sufficiency and large monetary and infrastructural investments. Now, the next generation of Chinese open-weight AI models are starting to be powered by Chinese chips.

Broader trends worldwide are intensifying, with both the U.S. and China citing national security in chip and rare earth resource restrictions. As U.S. export controls tightened, the rollout of Chinese-produced chips seemingly accelerated. The rise of China’s domestic chip industry is fundamentally changing norms and expectations for global AI training and deployment, with more models being optimized for Chinese hardware and compute-efficient open-weight models picking up in adoption. In the last few months, Chinese-produced chips have already started to power inference for popular models and are beginning to power training runs.

The changes can affect everything from techniques used in training, to optimizing for both compute efficiency and specific hardware, to lower inference costs, to the recent open source boom. This could shift both U.S. trade policy and China's approach to global deployment, leading to a future of AI Advancements from an American-focused global ecosystem to one where China is at the center.

China’s domestic chip production has been in progress for years before the modern AI boom. One of the most notable advanced chips, Huawei’s Ascend, initially launched in 2018 but expanded in deployment starting in 2024 and increasingly throughout 2025. Other notable chips include Cambricon Technologies and Baidu’s Kunlun.

In 2022, the Biden administration established export controls on advanced AI chips, a move targeting China's access to high-end GPUs. The strategy was intended to curb the supply of high-end NVIDIA GPUs, stalling China’s AI progress. Yet, what began as a blockade has paradoxically become a catalyst. The intent to build a wall instead laid the foundation for a burgeoning industry.

Chinese AI labs, initially spurred by a fear of being cut off, have responded with a surge of innovation, producing both world-class open-weight models like Qwen, DeepSeek, GLM, and Kimi, and domestic chips that are increasingly powering both training and inference for those models. There is a growing relationship between chip makers and open source, as the ability to locally run open-weight models also leads to mutually beneficial feedback. This is leading to e.g. more Ascend-optimized models.

China’s advancements in both open source and compute are shifting the global landscape. Martin Casado, partner at a16z, noted that a significant portion of U.S. startups are now building on open-weight Chinese models, and a recent analysis shows Chinese open-weight models leading in popularity on LMArena.

The vacuum created by the restrictions has ignited a full-stack domestic effort in China**, transforming once-sidelined local chipmakers into critical national assets and fostering intense collaboration between chipmakers and researchers to build a viable non-NVIDIA ecosystem.** This is no longer a hypothetical scenario; with giants like Baidu and Ant Group successfully training foundation models on domestic hardware, a parallel AI infrastructure is rapidly materializing, directly challenging NVIDIA’s greatest advantage: its developer-centric software ecosystem.

The 2022 ban, coinciding with the global shockwave of ChatGPT, triggered a panic across China's tech landscape. The safe default of abundant NVIDIA compute was gone. Claims of smuggling NVIDIA chips arose. Still, the ban had destroyed the trust from the research community, who, faced with the prospect of being left permanently behind, started to innovate out of necessity. What emerged was a new, pragmatic philosophy where a “non-NVIDIA first” approach became rational, not merely ideological.

Chinese labs took a different path, focusing on architectural efficiency and open collaboration. Open source, once a niche interest, became the new norm, a pragmatic choice for rapidly accelerating progress through shared knowledge. This paradigm allows organizations to leverage existing, high-quality pre-trained models as a foundation for specialised applications through post-training, dramatically reducing the compute burden. A primary example is the DeepSeek R1 model, which required less than $300,000 for post-training on its V3 architecture, thereby lowering the barrier for companies to develop sophisticated models. While not the full base model, the cost reduction for the reasoning model is substantial. Algorithmic advances that improve memory such as Multi-head Latent Attention (MLA) with DeepSeek’s V3 model**, likely incentivized by compute limitations, are a large part of January 2025’s “DeepSeek moment”.**

That moment also catalyzed a larger movement for Chinese companies, including those that were closed-source, to upend strategies and invest in compute-efficient open-weight models. These models’ lower costs could result from many variables and also are influenced by efficiency; as Chinese companies lowered compute and inference costs, they passed those lower costs to users, further evolving the overall AI economy.

DeepSeek's (Open) Weight: In addition to high performance and low cost that created waves in early 2025, DeepSeek’s pioneering as an openly compute-efficient frontier lab is a large part of what has made the company and its models mainstays. These advances can likely be attributed to innovating in a compute-scarce environment.