春晚殺瘋後，誰能再造大模型時代的超級國民應用？

• Zhipu GLM-5 employs DeepSeek's sparse attention mechanism (DSA) to reduce computational costs while enhancing model efficiency[2] • GLM-5 trained entirely on Huawei Ascend chips, achieving independence from US-manufactured semiconductor hardware[2] • Alibaba Qwen 3.5 supports 200 languages with multimodal understanding (text, images, videos)[2] • Qwen 3.5 agents deploy 5x faster than OpenAI ChatGPT and Anthropic Claude latest models for multi-step workflows[2] • Chinese companies increasingly optimize hardware for inference rather than training, with chip companies like Biren focusing on deployment-oriented infrastructure[3] • Chinese firms deploy end-to-end solutions with heavy fine-tuning of open-source models rather than relying solely on frontier models[3]

The race for a 'super national LLM app' reflects a fundamental divergence between US and Chinese AI strategies. While US companies focus on frontier model capabilities, Chinese tech giants are prioritizing rapid commercialization and economic deployment. The 67% vs 34% manufacturing AI adoption gap suggests Chinese companies will likely capture significant productivity gains in the near term. However, regulatory intervention (SAMR warnings against destructive subsidy practices) indicates the government seeks sustainable competition rather than winner-take-all dynamics. The shift toward agentic systems and end-to-end solutions means the next dominant consumer app will likely integrate deeply into workflows rather than function as a standalone chatbot. Chinese companies' emphasis on inference optimization and semiconductor independence positions them to weather potential US export restrictions, while their pragmatic deployment approach may yield faster real-world ROI than US competitors' frontier model focus. The success of any 'super national app' will depend on solving the binding question: what does it cost to deliver useful work reliably, and who will pay?[1][3]