来源 | 新智元 OpenAI是历史上最烧钱的初创公司！ OpenAI没有上市，因此也无需公开其营收。 不过，根据其和微软达成的一项协议，微软分享了OpenAI总收入的20%，同时公布的还有针对Azure服务的模型推理成本。 据此，图1看到展示了，从2024年第一季度至2025年第三季度，OpenAI在微软Azure上的季度推理支出（红色实线）与基于微软20%分成反 推的隐含收入（绿色虚线）。 基于微软数据估计的OpenAI的收入与推理成本 OpenAI的推理支出的增长速度远超收入，两者之间的「成本-收入剪刀差」在2025年后急剧扩大： 2024年Q1支出仅略高于收入。 但到025年Q3，支出已飙升至约36.5亿美元，而同期隐含收入仅为20.6亿美元。 这意味着每赚1美元收入，OpenAI需要花费近1.8美元用于推理计算，亏损幅度持续恶化。 推理成本的增速远高于收入，这揭示了 OpenAI 为何需要持续融资方可维持运营。 在采访中，奥特曼宣称OpenAI的「年收入将远超130亿美元」，但按照上图给出的估计，这是完全不具备可行性的。 从年度对比来看，更能看出问题: 2025前9个月推理支出（86.7亿）已是20 ...

Core Viewpoint - Shanghai has established a demonstration effect in building a large model industry ecosystem, focusing on a development model of "policy guidance + ecological collaboration + scenario-driven" [1][7] Group 1: Definition and Importance of Large Model Industry Ecosystem - The large model industry ecosystem is driven by general large models, comprising various elements such as data, algorithms, and computing power, along with multiple stakeholders including government, enterprises, and users [2] - The formation of the large model industry ecosystem is both necessary and inevitable due to the complexity of large model technology and the need for high-quality data and computing resources [3] Group 2: Development Trends and Challenges - The current large model industry ecosystem in China is rapidly developing, focusing on multi-modal integration, human-machine interaction, lightweight technology iteration, and open-source ecosystem construction [4] - Multi-modal integration is a key development direction, enhancing decision-making capabilities in complex scenarios while increasing data security risks [4] - The open-source ecosystem is a powerful driver for development, lowering barriers to application and attracting developers, but it also poses risks of misuse and dependency on computing resources concentrated in certain regions [5] Group 3: Institutional Innovation and Governance - Institutional innovation is essential for supporting technological innovation in the large model industry, requiring a balanced approach to address key risks [7] - The sharing and flow of critical resources like data and computing power are crucial for the development of artificial intelligence large models [7] - A governance framework involving multiple stakeholders is necessary to address liability issues in human-machine interactions and ensure compliance in generated content [9]