Core Viewpoint - The article discusses the emergence of AI Infrastructure (AI Infra) and its critical role in the effective deployment of AI Agents, emphasizing that without a robust AI Infra, the potential of Agents cannot be fully realized [2][4][5]. Group 1: AI Agents and Market Dynamics - The global market for AI Agents has surpassed $5 billion and is expected to reach $50 billion by 2030, indicating a competitive landscape where companies are rapidly developing their own Agents [2][5]. - Many enterprises face challenges in achieving expected outcomes from their deployed Agents, leading to skepticism about the effectiveness of these technologies [2][6]. - The misconception that Agent platforms can serve as AI Infra has led to underperformance, as the true AI Infra is essential for supporting the underlying data and model optimization processes [3][4][6]. Group 2: Understanding AI Infra - AI Infra encompasses structural capabilities such as distributed computing, data scheduling, model services, and feature processing, which are essential for model training and inference [7][9]. - The core operational logic of AI Infra is a data-driven model optimization cycle, which includes data collection, processing, application, feedback, and optimization [7][9]. - Data is described as the "soul" of AI Infra, and many enterprises fail to leverage their internal data effectively when deploying Agents, resulting in superficial functionalities [9][11]. Group 3: Evolution of Data Infrastructure - The shift from static data assets to dynamic data assets is crucial, as high-quality data must continuously evolve to meet the demands of AI applications [11][17]. - Traditional data infrastructures are inadequate for the current needs, leading to issues such as data silos and inefficiencies in data processing [12][13][14]. - The integration of data and AI is necessary to overcome the challenges faced by enterprises, as a cohesive Data&AI infrastructure is essential for effective AI deployment [17][18]. Group 4: Market Players and Trends - The market for Data&AI infrastructure is still in its early stages, with various players including AI tool vendors, traditional big data platform providers, platform-based comprehensive vendors, and specialized vertical vendors [20][21][22]. - Companies like Databricks are leading the way in developing integrated Data&AI infrastructure solutions, focusing on multi-modal data processing and low-code development capabilities [22][23]. - The emergence of technologies like "AI-in-Lakehouse" represents a significant trend in integrating AI capabilities directly into data architectures, addressing the fragmentation between data and AI [25][26]. Group 5: Case Studies and Future Outlook - Companies such as Sinopec and FAW have successfully implemented Data&AI integrated platforms to enhance operational efficiency and data management [34][35]. - The article concludes that as the Agent market continues to grow, the integration of Data&AI infrastructure will become increasingly vital for enterprises seeking to leverage AI effectively [35][36].

本文嘉宾朱亦博可以说是国内最了解 AI Infra 的人之一，从微软、字节 AI Infra 负责人到谷歌、再到阶 跃联创，他的职业经历几乎和 AI Infra 的发展并行。 以下为本期对谈内容，文章经过删减整理： 曲凯：从你的视角来看，怎么理解 AI Infra？ 亦博：AI Infra 包括硬件和软件两部分。 硬件是指 AI 芯片、GPU、交换机等设备。软件层面我喜欢用云计算来类比，可以分为三层： 最底层类似 IaaS，解决的是最基础的计算、通信和存储问题。 中间一层类似 PaaS，包含资源调度、资源管理等平台。MaaS（Model-as-a-Service）就归属这一层。 最上层近似 SaaS 应用层，但在 AI Infra 领域，我更倾向于把这一层理解为训练及推理框架的优化层。 曲凯：可以说你的职业生涯跟 AI Infra 的发展基本是同步的吗？ 亦博：是，但我其实是第二批 AI Infra 人，第一批是贾扬清、李沐、陈天奇这些有算法背景的人。他们 当时要做先进的算法，需要充分利用 GPU，于是就做了 AI Infra。 曲凯：所以是第一批人从无到有把这件事做了出来？ 亦博：可以这么理解。我们这第二批 ...

Group 1 - The 2025 World Artificial Intelligence Conference highlights the commercialization of AI, with companies like Tesla and Zhiyuan showcasing innovative products [1][3] - Zhiyuan's CMO emphasizes that 2025 is expected to be the commercial year for humanoid robots, with applications in various sectors such as industry, logistics, and education [1][3] - The rapid development of AI products is noted, with increased user acceptance compared to the previous year [1][5] Group 2 - DeepSeek's popularity in early 2025 has sparked a surge in the application of AI large models in China [3] - Companies like SenseTime are launching platforms that integrate perception, visual navigation, and multimodal interaction capabilities for smart devices [3][4] - The demand for robust AI infrastructure is growing, with companies like Xinghuan Technology addressing data challenges in AI deployment [4] Group 3 - Shanghai's AI industry has seen significant growth, with a scale exceeding 118 billion yuan in Q1 2025, marking a 29% year-on-year increase [5] - The application of AI spans various sectors including finance, retail, transportation, and healthcare, indicating a rapidly forming commercialization path [5] - New leasing areas for AI-related companies in Shanghai's industrial parks have surpassed 30% of total new leasing area in the past year [5] Group 4 - Shanghai has established a development pattern for AI innovation, with communities like Mosu Space and Moli Community fostering AI entrepreneurship [7][8] - The first heterogeneous humanoid robot training ground in China has been launched, capable of training over 100 humanoid robots simultaneously [8] - Shanghai is home to nearly 300,000 AI talents, accounting for about one-third of the national total, creating a thriving ecosystem for AI companies [8]

Core Insights - The article highlights the emergence of a new generation of entrepreneurs in the AI industry, particularly those born in the 1990s, who are increasingly leaving established tech giants to start their own ventures [2][3][4] - It emphasizes the significant role that major tech companies play in nurturing young talent, with 90% of AI entrepreneurs having previously worked at leading firms [2][4] - The article discusses the unique characteristics of this new generation, including their innovative mindset and willingness to challenge traditional career paths [12][14] Group 1: Young Entrepreneurs and Their Ventures - A significant number of 95 post-90s entrepreneurs are entering the AI sector, leveraging their unique perspectives to achieve rapid results [3][4] - Notable examples include Shitianhui, who founded Qingcheng Jizhi, and Guo Renjie, who established Lexiang Technology, both of whom have secured substantial funding shortly after their inception [5][10] - The average age of teams in these startups is under 30, reflecting a trend towards younger leadership in the AI space [5][10] Group 2: Investment Landscape - Major tech companies are investing in AI startups, with significant funding flowing into firms like SenseTime and Zhizhu AI, which have nurtured many young entrepreneurs [16][18] - The article notes that the AI industry is experiencing a high failure rate, with 34% of new AI companies facing closure, highlighting the challenges in securing funding and achieving sustainability [21][22] - Successful fundraising is often linked to the technical capabilities of the team, market timing, and the founder's vision and execution [27][28] Group 3: Future Outlook - The article suggests that the influence of 95 post-90s entrepreneurs in the tech industry will continue to grow, with a potential for them to lead major companies in the next decade [28] - The ongoing evolution of AI technology, including advancements in embodied intelligence and multi-modal AI, is expected to create new opportunities for these young innovators [16][20]