As generative AI enters a new phase of abundant foundation‑model supply, access to general‑purpose models and compute resources has become widely accessible for enterprises and government bodies. Real‑world experience nonetheless demonstrates that model access does not automatically translate into tangible productivity gains. Identical foundation models can yield vastly different outcomes when deployed by different organizations. The key to success lies in an organization’s “AI absorptive capacity”. What exactly constitutes “AI absorptive capacity”? What institutional, talent and data gaps need to be addressed for general-purpose models to deliver real-world business value? Does sovereign AI boil down to domestically-trained foundation models? How can developing countries break free from the pitfall of “prioritizing model procurement over systemic capacity-building”? Endowed with rich real-world industrial scenarios, how can China translate its scenario advantages into sustainable AI competitiveness? In this episode of SinoAI Insights, we are joined by Dr. Ning Ning, Vice President of International Business & AI Consulting at 01.AI. Centered on the core thesis that “access is not absorption”, we explore how AI can align with an organization’s real-world business contexts, refine supporting rules and authority and governance mechanisms, build business systems with swappable underlying foundation models architectures, and foster human-in-the-loop capabilities. The conversation also focuses on talent development and organizational transformation, mapping out actionable pathways for enterprise-level, industrial scale and sovereign-AI implementation.

·XIAO Qian, Deputy Director, Center for International Security and Strategy, Tsinghua University

·Ning Ning, Vice President, International Business & AI Consulting, 01.AI

Executive Producer: XIAO Qian

Producers: LIU Yuan, WANG Yexu

Podcast Editor: ZHONG Junwen

Content Assistant: GAO Meilin

Graphics Designer: GAO Meilin