Group 1: GPT-4.5 Failure and Industry Challenges - The failure of GPT-4.5 is attributed to insufficient data and complex infrastructure, leading to scalability issues and inability to provide open access or API [6][7] - The AI industry is facing a global shortage of wafers and memory capacity, with rising memory prices and chip shortages exacerbating the computational bottleneck [7][8] - Cloud data centers are more efficient than local inference due to better resource utilization, flexibility, and scalability, which are crucial for supporting large AI model training and inference [9] Group 2: AI Tools and Organizational Efficiency - AI tools enhance organizational efficiency and create competitive barriers by allowing non-technical personnel to utilize model capabilities through natural language, simplifying tasks and improving productivity [12] - The competitive advantage can be gained by small teams leveraging AI tools in high-cost areas, emphasizing the importance of tool ecosystems, skill libraries, and shared workflows [12] Group 3: Shift in AI Competition - The competition in AI has shifted from focusing solely on models to deployment, influenced by cultural differences between companies like OpenAI and Anthropic, as well as government collaboration and resource allocation [4][5]

Core Viewpoint - The rapid increase in user demand for the newly released GLM-4.7 language model has led to significant computational bottlenecks for Zhipu AI, prompting the company to implement emergency throttling measures to prioritize existing users' experience [1][2]. Company Summary - Zhipu AI announced that starting January 23, it will drastically reduce the daily new subscription limit for its programming assistant service "GLM Coding Plan" to 20% of its previous levels, ensuring that existing users' access is prioritized [1][2]. - The company has experienced frequent throttling errors and significant response time delays during peak hours due to the surge in user numbers, which it attributes to a phase of resource strain caused by rapid growth [1][2]. - The GLM Coding Plan is positioned as a competitor to Claude, and the company is in direct competition with leading firms like OpenAI and Anthropic [1][2]. Industry Summary - The implementation of throttling measures in response to user surges has become a common phenomenon in the rapidly growing AI industry, as seen previously with DeepSeek, which also limited API access due to server resource constraints [3]. - This "throttling" action highlights the temporary mismatch between the explosive growth in AI application demand and the pace of foundational computational infrastructure development [3]. - The computational bottleneck reflects strong end-user demand while revealing the operational challenges AI companies face in transitioning from technological breakthroughs to stable service delivery [3].