On December 23, 2025, Reuters reported that global asset managers are increasing allocations to Chinese AI companies and ETFs as concerns mount over lofty U.S. AI valuations. Funds are rotating into Chinese tech names like Alibaba, Baidu, Tencent and domestic AI chipmakers such as Cambricon, Moore Threads and MetaX, helped by Beijing’s push for AI self‑reliance.
This article aggregates reporting from 4 news sources. The TL;DR is AI-generated from original reporting. Race to AGI's analysis provides editorial context on implications for AGI development.
Capital allocation is often a better indicator of long‑term power in the AI race than any single model benchmark. The shift Reuters describes—global money rotating out of U.S. mega‑cap “Magnificent Seven” names and into Chinese AI platforms and chipmakers—shows that investors increasingly view China not just as a follower, but as a parallel AI ecosystem with its own champions and hardware stack. Funds like KraneShares’ KWEB and Rayliant’s China tech ETF are effectively becoming vehicles for betting on China’s Qwen, DeepSeek and domestic GPU players the way U.S. investors once piled into Nvidia and the cloud trio.([reuters.com](https://www.reuters.com/world/china/global-investors-turn-chinese-ai-wall-street-fears-bubble-2025-12-23/))
From an AGI perspective, this matters because it sustains a multi‑polar compute and talent landscape. Beijing’s fast‑tracked listings for Moore Threads and MetaX—and their eye‑popping first‑day pops—lower the cost of capital for Chinese AI silicon, which in turn underwrites more capacity and model experimentation.([reuters.com](https://www.reuters.com/world/china/global-investors-turn-chinese-ai-wall-street-fears-bubble-2025-12-23/)) While some managers warn valuations are hype‑driven, the overall effect is to keep China’s AI complex well funded even as U.S. regulators tighten controls on frontier chips. That dynamic makes it harder for any single bloc to monopolize frontier‑adjacent capabilities, and it increases the chance we see different design philosophies emerge: U.S. labs leaning into ultra‑large models on bleeding‑edge GPUs, and Chinese groups pushing efficiency, integration with industrial systems, and sovereign AI stacks.