Grace Shao on What the World Should Know About Chinese AI

Grace Shao, an independent researcher and author of the AI Prome Substack, walked hosts Joe Wasenthal and Tracy Alloway through the current shape of Chinese AI: an ecosystem that looks collegial and open‑weight on the surface but is driven by pragmatic constraints beneath. Shao explained that open‑sourcing was partly a branding choice to build developer trust and partly a philosophical choice by founders; because many labs lack the capital, compute and talent of U.S. frontier players, they aggressively share research and build on each other’s work. The “DeepSea” moment in early 2025 accelerated outside interest, and DeepSea’s V4 release—delayed several months to re‑engineer inference on Huawei hardware—served as both a practical step and a sovereignty signal for China’s stack.

Shao described how capital and export controls shape strategy: Chinese labs often prioritize post‑training, efficient data curation and inference optimization over massively parallel pretraining. That has produced vertical specialization—Ziya/GLM leaning into coding, MiniMax toward multimodality, Moonshot on agents, and DeepSea on frontier research—and viable business models despite open weights (managed inference, APIs and hosted services). She cited MiniMax’s Hong Kong listing and a market‑scale figure near US$20 billion, plus end‑of‑year revenue projections of about US$1.0–1.2 billion. On data, Shao pushed back on the myth that China is simply swimming in superior, structured training material: enterprise knowledge work there is newer and messier, and vendors sell exclusivity windows that labs buy more cheaply once they lapse. Regulators are active—AI services must register nationally—and recent legal rulings (a court in “Hongjo”) have blocked firms from using AI as a lawful pretext for layoffs. Finally, Shao emphasized China’s manufacturing and energy advantages—top‑down construction of renewables and East→West compute hubs—but warned robotics remain constrained by integration, physical 3D data needs and battery technology. Hosts and guest agreed that many real‑world applications will mix frontier closed models with cheaper open models at the app layer, and that China’s comparative edge may play out in hardware and integrated AI+industry solutions rather than purely in single largest‑scale pretraining runs.