Comprehensive up-to-date resources on the Chinese Communist Party's AI strategy, etc?

While far from what I hoped for, this is the closest to what I hoped for that I managed to find so far: https://www.chinatalk.media/p/is-china-agi-pilled 

Overall, the Skeptic makes the stronger case — especially when it comes to China’s government policy. There’s no clear evidence that senior policymakers believe in short AGI timelines. The government certainly treats AI as a major priority, but it is one among many technologies they focus on. When they speak about AI, they also more often than not speak about things like industrial automation as opposed to how Dario would define AGI. There’s no moonshot AGI project, no centralized push. And the funding gaps between leading Chinese AI labs and their American counterparts remain enormous.

The Believer’s strongest argument is that the rise of DeepSeek has changed the conversation. We’ve seen more policy signals, high-level meetings, and new investment commitments. These suggest that momentum is building. But it remains unclear how long this momentum can be maintained–and whether it will really translate into AGI moonshots. While Xi talks about “two bombs one satellite”-style mobilzation in the abstract, he hasn’t channeled that idea into any concerted AGI push and there are no signs on any “whole nation” 举国 effort to centralize resources. Rather, the DeepSeek frenzy again is translating into application-focused development, with every product from WeChat to air conditioning now offering DeepSeek integrations.

This debate also exposes a flaw in the question itself: “Is China racing to AGI?” assumes a monolith where none exists. China’s ecosystem is a patchwork — startup founders like Liang Wenfeng and Yang Zhilin dream of AGI while policymakers prioritize practical wins. Investors, meanwhile, waver between skepticism and cautious optimism. The U.S. has its own fractures on how soon AGI is achievable (Altman vs. LeCun), but its private sector’s sheer financial and computational muscle gives the race narrative more bite. In China, the pieces don’t yet align.

There are new Huawei Ascend 910C CloudMatrix 384 systems that form scale-up worlds comparable to GB200 NVL72, which is key to being able to run long reasoning inference [LW(p) · GW(p)] for large models much faster and cheaper than possible using systems with significantly smaller world sizes like the current H100/H200 NVL8 (and also makes it easier to run training, though not as essential unless RL training really does scale to the moon).

Apparently TSMC produced ~2.1M compute dies for these systems in 2024-2025, which is 1.1M chips, and an Ascend 910C chip is 0.8e15 dense BF16 FLOP/s (compared to 2.5e15 for a GB200 chip). So the compute is about the same as that of ~350K GB200 chips (not dies or superchips), which is close to 400K-500K GB200 chips [LW(p) · GW(p)] that will be installed at the Abilene site of Crusoe/Stargate/OpenAI in 2026. There also seems to be potential to produce millions more without TSMC.

These systems are 2.3x less power-efficient per FLOP/s than GB200 NVL72. They are using 7nm process instead of 4nm process of Blackwell, the scale-up network is using optical transceivers instead of copper, and the same compute needs more chips to produce it, so they are probably significantly more expensive per FLOP/s. But if there is enough funding and the 2.1M compute dies from TSMC are used to build a single training/inference system (about 2.5 GW), there is in principle some potential for parity between US and China at the level of a single frontier AI company for late 2026 compute (with no direct implications for 2027+ compute, in particular Nvidia Rubin buildout will begin around that time).