that it represents as well. Nvidia, by the way, hit another record this morning. That is the stock.Also this morning, CNBC reporting that Goldman Sachs is piloting an autonomous coder. This is potentially a major step in AI's evolution at least for Wall Street. You sun broke that story.He joins us now to discuss it. So tell us what this is. First of all, Hugh, and how it's going to be used at the bank.>> Hey, David. Yeah. So I mean, I think the headline here is we're in the middle of this grand experiment i ...

从"AI能写代码"，到"AI就是同事"，华尔街的自动化革命正在悄悄加速。 据媒体周五报道，高盛已开始测试由人工智能公司Cognition Labs开发的自主软件工程师Devin，并准 备在开发部门正式"聘用"它，与这家投行巨头现有的12,000名人类开发人员共事。高盛是首家正式部署 该系统的华尔街大型银行。 Devin是一个具有自主任务执行能力的AI，可以独立完成从开发到测试再到部署的完整流程，几乎无需 人类介入！是真正意义上的"AI同事"，而非传统意义上的"辅助工具"。Cognition Labs官方称，Devin 是"世界首个完全自主的软件工程师"。 高盛首席信息官Marco Argenti表示，Devin将被部署为"开发团队中的一员"，并可能从"数百个"实例逐 步扩展至"数千个"。其最初任务是处理那些被工程师视为"苦工"的任务，例如将旧代码迁移到新语言， 修补基础架构等。 "我们正在打造一种混合劳动力结构——AI不是取代人类，而是和工程师并肩作战。"Argenti 说。 这一变革的意义在于，AI在华尔街已经不再是单纯的"生产力工具"，而是能执行任务、生成成果的自动 化劳动力。这类"Agentic AI" ...

Core Insights - Goldman Sachs is testing an autonomous software engineer named Devin from AI startup Cognition, which is expected to join the firm's 12,000 human developers [1][2] - Devin is designed to operate as a full-stack engineer, completing complex multi-step assignments with minimal human intervention [2][4] - The introduction of agentic AI like Devin represents a significant shift in corporate AI adoption, with the potential for greater productivity and efficiency [3][4] Company Impact - Goldman Sachs plans to initially deploy hundreds of Devins, potentially scaling to thousands based on use cases [3] - The implementation of such advanced AI tools could increase worker productivity by three to four times compared to previous AI technologies [5] - Devin will be supervised by human employees and will take on tasks considered tedious, such as updating internal code to newer programming languages [6] Industry Trends - The rapid adoption of AI in the corporate sector is evident, with firms like JPMorgan Chase and Morgan Stanley already utilizing cognitive assistants based on OpenAI models [3] - Tech giants like Microsoft and Alphabet report that AI is responsible for approximately 30% of the code in some projects, while Salesforce claims AI handles up to 50% of its work [5]

Core Insights - The article emphasizes the importance of treating AI as a junior developer that requires clear guidance rather than a magical tool, highlighting the need for engineers to adapt their management style to effectively utilize programming agents [1][3][9] - Senior engineers are found to be the quickest adopters of these tools, which can save approximately 80% of time on medium to large tasks [1][8][24] Introduction - The article introduces a practical guide based on two years of experience building Devin, an autonomous programming agent, and aims to share valuable insights from customer feedback and internal practices [1][3] Getting Started: Basics and Daily Applications - Key principles for effective communication with agents include providing specific instructions, indicating starting points, anticipating potential errors, and establishing a feedback loop [10][11][13][15] - The guide suggests integrating agents into daily workflows to enhance personal efficiency, such as handling new requests without interrupting deep work and managing urgent issues on the go [17][19][20] Intermediate: Managing Complex Tasks - For complex tasks, the article recommends having agents draft initial versions and collaborating on implementation plans, while also setting checkpoints to ensure alignment with expectations [23][25][26] - It emphasizes the importance of teaching agents how to validate their work and increasing testing coverage in areas frequently modified by AI [28][29] Advanced: Automation and Customization - The article discusses creating automation templates for repetitive tasks and implementing intelligent code reviews using agents [30][33] - It highlights the need for a unified development environment to enhance agent performance and suggests building custom tools to empower agents [35][36] Practical Considerations: Embracing Change - The article outlines the limitations of autonomous agents, such as their debugging capabilities and knowledge cut-off dates, advising users to manage expectations and time effectively [39][42][43] - It concludes by asserting that the value of software engineers will not diminish, as deep technical knowledge and understanding of business codebases remain essential in the evolving landscape of software development [50]

Core Insights - The event highlighted the transformative impact of artificial intelligence (AI) on software development, emphasizing its evolution from a supportive tool to an intelligent collaborator [1][4][7] - AI-driven tools are enhancing productivity, reducing errors, and accelerating innovation across various stages of the software lifecycle [2][4] - The emergence of large language models (LLMs) is enabling more individuals to engage in programming, thus democratizing software development [3][5][6] Group 1: AI's Role in Software Development - AI is fundamentally changing software engineering by improving speed, accessibility, and reliability, making programming more mainstream [4][7] - Large language models, such as those developed by OpenAI, are capable of understanding and generating human language, which is now being applied to code generation and program development [2][3] - Code LLMs can assist developers in writing, debugging, and refactoring code, thereby enhancing the overall development process [3][4] Group 2: Future Trends in Programming - The future of programming is expected to be characterized by higher automation, stronger collaboration, and deeper integration of AI [4][7] - AI programming tools are evolving to become more intuitive, allowing developers to describe tasks in natural language and receive corresponding code outputs [5][6] - Multi-agent systems are anticipated to play a significant role in automating complex tasks and optimizing workflows in software development [6][7] Group 3: Innovations in AI Programming Tools - Cognition AI has introduced Devin, the first AI programmer capable of managing the entire software development lifecycle autonomously, outperforming existing models like GPT-4 in real-world problem-solving [6] - AI-driven integrated development environments (IDEs) like Cursor simplify the coding process by allowing natural language input to generate and modify code [5][6] - The rise of low-code and no-code platforms is enabling non-programmers to participate in software development, further broadening the scope of who can engage in coding [7]

Core Insights - The current era is characterized by rapid advancements in AI technology, allowing a few individuals to create significant value for many [2][22] - The concept of "AI Native" products emphasizes building new relationships between AI capabilities and users, rather than merely creating new tools [7][11] - The AGI Playground serves as a platform for collaboration among innovators in the AI space, fostering connections and future possibilities [3][4] Group 1: New Goals of AI Native Products - The core focus of AI Native products is to establish new relationships between AI capabilities and users, rather than just creating new tools [7][11] - System prompts play a crucial role in defining the relationship between AI and users, indicating a shift towards a more interactive and relational approach [8][10] - Successful AI products define their identity and relationship with users at the outset, moving beyond traditional tool-user dynamics [12][13] Group 2: New Challenges in AI Native Products - Emotional intelligence has become a critical aspect of product design, as AI products now need to manage user relationships effectively [17][19] - Creating a sense of "life" in AI products enhances their relational capabilities, allowing for deeper user engagement [20][21] - The shift towards relationship-focused products introduces new challenges in understanding and managing user interactions [16][18] Group 3: New Opportunities from Relationships - New relationships between AI and users create opportunities for mixed-value delivery, combining functional and emotional benefits [24][25] - The blending of digital and physical experiences is essential for delivering higher value, as seen in products that integrate hardware and software [30][32] - The evolving nature of user relationships may lead to new distribution channels for services, moving away from traditional platform-based models [38][39] Group 4: New Pipeline for AI Native Products - The new pipeline for AI Native products involves broad input and liquid output, focusing on proactive data sensing and flexible delivery [52][63] - Broad input emphasizes the need for diverse data sources to enhance understanding and value delivery [53][55] - Liquid output encourages a collaborative journey with users, allowing for iterative feedback and engagement throughout the process [64][67] Group 5: New Value Models in AI Native Era - The value model for AI Native companies has shifted from a flat, two-dimensional approach to a three-dimensional model that incorporates AI capabilities [77][79] - Successful companies must consider both user needs and AI requirements in their product engineering to maximize value [75][76] - Traditional metrics for measuring value, such as user count and revenue, may no longer suffice in the AI Native landscape [78][80] Group 6: Future Considerations - The evolution of product economics and management practices is necessary to adapt to the changing landscape driven by AI [83][88] - New business models and growth strategies must be explored, including innovative payment structures and value exchange mechanisms [85][86] - The relationship between productivity and organizational structure will continue to evolve, necessitating a rethinking of traditional management principles [88][89]

Core Insights - The article emphasizes treating AI as a junior developer that requires clear guidance rather than a magical tool, highlighting the importance of effective communication with programming agents [1][8][9]. Group 1: Key Methods for Effective Use - Clear Instructions: Specificity in commands is crucial, such as detailing which functionalities to test rather than vague requests [3][16][18]. - Reasonable Expectations: Large tasks cannot be fully automated, but can save approximately 80% of time; checkpoints should be established for planning, implementation, testing, and review [3][27]. - Continuous Validation: Providing a complete CI/testing environment allows agents to discover and correct errors independently [3][19][33]. Group 2: Daily Usage Tips - Instant Delegation: Quickly assign tasks to agents when urgent requests arise [5][21]. - Mobile Handling: Use mobile devices to address urgent bugs while on the go [5][23]. - Parallel Decision-Making: Allow agents to implement multiple architectural solutions simultaneously for better decision-making [5][25]. Group 3: Advanced Applications - Automate Repetitive Tasks: Create templates for recurring tasks to enhance efficiency [5][35]. - Intelligent Code Review: Utilize agents for precise code reviews based on a maintained list of common errors [5][36]. - Event-Driven Responses: Set up agents to automatically respond to specific events, such as alerts [5][37]. Group 4: Practical Considerations - Understanding Limitations: Agents have limited debugging capabilities and should not be expected to resolve complex issues independently [42][43]. - Time Management: Learn to recognize when to stop ineffective attempts and start anew with clearer instructions [46][49]. - Isolated Environments: Agents should operate in isolated testing environments to prevent unintended consequences in production [51][52]. Group 5: Future Outlook - The value of software engineers remains significant despite advancements in programming agents; deep technical knowledge and understanding of codebases are essential [53].

Core Insights - The discussion emphasizes that true technological breakthroughs can achieve organic growth without relying on marketing, as demonstrated by products like DeepSeek and Manus [1][14] - The rapid application and monetization of generative AI technologies are highlighted, with Genspark achieving an annual recurring revenue (ARR) of $36 million within 45 days of launch, showcasing the increasing willingness of users to pay for AI products [2][11] - The conversation reflects on the evolution of AI from a niche topic to a mainstream tool, with ChatGPT being a pivotal product that has crossed the chasm into widespread use, now engaging over 100 million users monthly [4][10] Investment Trends - The company focuses on early-stage investments in AI startups, having backed several influential projects such as Kimi, Manus, and Genspark, indicating a strong belief in the potential of AI technologies [1][5] - The investment landscape is characterized by a shift back to early-stage opportunities, where understanding new technologies and their marginal changes is crucial for entrepreneurs [5][20] Market Dynamics - The article discusses the importance of creating a "magical experience" for users, where the product's inherent value drives organic growth rather than traditional marketing strategies [13][14] - The AI market is witnessing a transformation where the execution capability of entrepreneurs is becoming more accessible, allowing for rapid prototyping and product development [7][8] Future Outlook - The potential for AI applications to create significant value is underscored, with the belief that as models become more powerful, applications will leverage unique contexts to generate additional value [20][25] - The emergence of virtual employment models is suggested, where AI can perform tasks traditionally done by humans, leading to new business paradigms [16][19] User Engagement - The article highlights that user engagement and retention are increasingly tied to the product's ability to evolve and improve rapidly, contrasting with traditional products that may not see significant updates [10][15] - The concept of "virtual hiring" is introduced, where users can employ multiple AI agents to perform tasks, fundamentally changing the dynamics of productivity and task management [19][26]

Core Viewpoint - The era of AI allows a few individuals to create significant value for a vast audience, emphasizing the importance of community and collaboration among innovators [4][6]. Group 1: AI Native New Goals - The core of AI Native products is not merely creating new tools but establishing a new relationship between AI capabilities and humans [12][13]. - The emergence of system prompts signifies a shift in how products define their relationship with users, moving from traditional branding to embedding this relationship in the product's core [15][20]. - Emotional intelligence becomes a critical aspect of product design, as AI products must now manage user interactions with a higher degree of empathy [21][23]. Group 2: New Challenges and Opportunities - AI Native products face new challenges, such as enhancing emotional intelligence and creating a sense of life in products to foster deeper user relationships [24][26]. - The establishment of new relationships presents opportunities for mixed-value delivery, combining digital and physical interactions to enhance user engagement [30][32]. - New relationships can lead to innovative service distribution channels, allowing for continuous value delivery and higher user lifetime value (LTV) [42][46]. Group 3: AI Native New Pipeline - The new pipeline for AI Native products emphasizes broad input and liquid output, focusing on proactive sensing and flexible delivery of user needs [60][72]. - Broad input involves actively gathering diverse data to enhance understanding and value delivery, while liquid output encourages a collaborative journey with users rather than a one-time interaction [62][73]. Group 4: New Value Models - The value model in the AI Native era shifts from a flat, two-dimensional approach to a three-dimensional model that incorporates AI capabilities and user relationships [85][87]. - Successful entrepreneurs in this era recognize the dual responsibility of serving both users and AI, ensuring that product engineering aligns with AI's needs [82][84]. - Traditional product economics and management principles are becoming obsolete, necessitating new frameworks for understanding growth, value creation, and organizational structure [92][99].

Core Insights - The article discusses the rapid development and integration of AI Agents in various sectors, highlighting their potential to transform workflows and user experiences [1][3] - It raises critical questions about the current capabilities and limitations of AI Agents, as well as the evolving human-AI relationship [1][3] User Perspective: Ideal vs. Reality - AI Agents are defined by their ability to use tools, make autonomous decisions, and engage in iterative processes [3][5] - The relationship between humans and AI Agents is characterized as a partnership rather than a contractual one, emphasizing collaboration [5][6] User Experiences with AI Agents - Users categorize AI Agents into three types: coaching, secretarial, and collaborative, each serving different functions in their daily tasks [9][10] - Specific examples of AI tools like CreateWise and Manus demonstrate their capabilities in audio editing and task management, respectively [12][14] User Complaints - Users express concerns about AI Agents' inability to follow instructions accurately and the tendency for AI to overcomplicate tasks [18][20] - The lack of "human-friendly" design in AI products is noted, as they often fail to capture the nuances of human interaction [21][23] Builder Responses: Technical Challenges and Solutions - Developers acknowledge the need for AI Agents to manage user expectations and improve their decision-making capabilities through experience [30][32] - The importance of user feedback in refining AI performance is emphasized, likening AI to inexperienced interns who need guidance [32][33] Technical Innovations and Market Strategies - The article discusses the potential for multi-Agent collaboration to enhance problem-solving capabilities [41][42] - It highlights the necessity for AI products to focus on specific industries to accumulate valuable user data and insights [46][49] Business Perspective: Competitive Landscape - New data generated by AI Agents can disrupt traditional SaaS models, providing startups with a competitive edge [53][55] - The article suggests that startups should focus on niche markets and specific user needs to avoid direct competition with large model companies [67][68] Philosophical and Future Considerations - The widespread adoption of AI Agents is expected to reshape human-machine relationships and societal structures [70]