AGIX 诞生于我们对"如何捕获 AGI 时代 beta 和 alphas"这一问题的深度思考。毫无疑问，AGI 代表了未来 20 年最重要的科技范式转换，会像 互联网那样重塑了人类社会的运行方式，我们希望 AGIX 成为衡量这一新科技范式的重要指标，如同 Nasdaq100 之于互联网时代。 "AGIX PM Note"是我们对 AGI 进程的思考记录，希望通过学习 Warren Buffett、Ray Dalio、Howard Marks 等传奇投资者们的分享精神，与所 有 AGIX builders 一同见证并参与这场史无前例的技术革命。 Semianalysis 在 GPT5 发布后讨论了他们对于GPT5 的路由器作为 AI chatbot 商业化引擎的看法：由于大多数科技公司对服务新增用户几乎没有 边际成本，运行搜索引擎有一些固定成本，但额外一次查询的增量成本几乎为零。 人工智能代理和 LLMs 颠覆了这一概念，资金、计算能力与更好答案之间存在某种直接关系。用户在高价值问题上花费更多的计算开销，就有 可能获得更好的答案。所以 GPT5 可以识别用户的高价值问题，并在 AI 助手帮用户完成订票，购物等 ...

证券研究报告：计算机|点评报告 发布时间：2025-08-12 行业投资评级 强于大市 |维持 行业基本情况 | 收盘点位 | | 4993.28 | | --- | --- | --- | | 52 | 周最高 | 5440.49 | | 52 | 周最低 | 2805.53 | 行业相对指数表现（相对值） 2024-08 2024-10 2025-01 2025-03 2025-05 2025-08 -5% 4% 13% 22% 31% 40% 49% 58% 67% 76% 85% 计算机 沪深300 资料来源：聚源，中邮证券研究所 研究所 分析师:孙业亮 SAC 登记编号:S1340522110002 Email:sunyeliang@cnpsec.com 分析师:常雨婷 SAC 登记编号:S1340523080001 Email:changyuting@cnpsec.com l 海外应用侧业绩超预期，反映出 AI 商业化加速 近期研究报告 《稳定币+数字人民币，共同推进人民 币国际化》 - 2025.06.29 海外进展顺利，关注国内 AI 商业化进程 l GPT-5 发布，有望带动 B 端 age ...

Core Insights - The focus of the market has shifted from "what AI can do" to "how to effectively build, deliver, and commercialize AI products" as AI technology moves into deeper industrial applications [1][2] - Companies are no longer debating whether to use AI but are instead considering how to implement it effectively [2][3] Group 1: Building AI Products - Companies are evolving from traditional SaaS models to AI-driven futures, with 31% embedding AI in existing products, 37% developing standalone AI products, and 32% building their core business around AI [4] - AI-native companies are significantly ahead in product development, with 47% in the scaling phase compared to only 13% of AI-enabled companies [6][9] - Nearly 80% of AI-native companies are developing Agentic Workflows, which have become a popular product direction [10] - The focus has shifted from performance to cost, with 57% of companies now prioritizing cost considerations in model selection [18] - Companies are increasingly adopting multi-model strategies, using an average of 2.8 different model providers, while OpenAI maintains a 95% adoption rate [20] Group 2: Market Entry and Compliance - AI-driven features are rapidly becoming central to product strategies, with projections showing that by the end of 2025, AI-driven features will account for 43% of high-growth companies' product roadmaps [31] - The most common pricing model for AI products is a hybrid approach, combining traditional subscription with usage-based billing [35] - Companies are exploring new pricing models linked to ROI, with 37% actively investigating changes [43] - Transparency and explainability in AI products are becoming essential as products mature, with 25% of companies providing detailed model transparency reports at the scaling stage [48] Group 3: Organizational Structure - Establishing dedicated AI leadership roles is a sign of maturity in AI strategy, with 61% of large companies having specialized AI leaders [56] - AI/ML engineers, data scientists, and AI product managers are critical roles, but hiring challenges persist, with an average recruitment cycle of 70 days for AI/ML engineers [60][64] - High-growth companies plan to allocate 37% of their engineering teams to AI projects by 2026, significantly higher than the 28% of other companies [68] Group 4: AI Cost Structure - Companies are allocating 10-20% of their R&D budgets to AI development, with plans to increase this share by 2025 [72] - The cost structure of AI projects shifts from talent costs dominating in the pre-launch phase (57%) to machine costs becoming significant in the scaling phase (nearly 50%) [80] - API usage fees are identified as the most challenging cost to control, with 70% of respondents highlighting this issue [81] Group 5: Internal AI Utilization - Companies are expected to double their internal AI budgets by 2025, with significant investments in productivity-enhancing AI tools [94] - Despite high availability of AI tools, actual usage rates reveal a gap, with only about 50% of employees consistently using them [97] - Coding assistance is the most popular internal AI application, with a 77% adoption rate, leading to productivity improvements of 15-30% [104][108] Group 6: AI Builder Technology Stack - Traditional deep learning frameworks like PyTorch and TensorFlow remain popular among developers, while managed platforms like AWS SageMaker are gaining traction [120] - Monitoring and observability tools are still dominated by traditional solutions, but ML-native platforms are beginning to gain early traction [122] - The market for AI tools is fragmented, with many teams still unaware of the specific tools they are using, indicating a knowledge gap [126]