China’s artificial-intelligence industry is entering a more difficult phase, as competition shifts from model benchmarks to computing resource constraints. While Chinese developers have narrowed the performance gap with global leaders through architecture optimization, mixture-of-experts models, quantization, distillation, and efficient training methods, the next stage will not be decided by models alone. As models become more capable and integrated into search, coding, and autonomous-agent workflows, demand for computing resources is expanding faster than efficiency gains. The central contradiction is that model capabilities and user demand are growing rapidly, while the supply of high-end GPUs, AI accelerators, HBM, advanced packaging, high-speed networking, and data-center power cannot expand at the same speed—a system-wide supply-chain constraint. Success of models like Kimi K3 may intensify pressure, as lower-cost models expand adoption but produce more inference demand, with aggregate token consumption rising dramatically. Competition is shifting to who can secure stable computing resources and who can convert constrained hardware into the greatest effective computing output. Separately, the founders behind China’s four leading AI model companies (Deepseek, Kimi, Zhipu, Minimax) began trending on Chinese social media, with discussion noting that a founder’s real value now lies in setting direction and building a system. People have also discussed why Yang Zhilin turned down an offer from Apple after his PhD in the United States to return to China, reflecting personal, professional, and policy considerations.