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OpenAI掌门人曝GPT-6瓶颈,回答黄仁勋提问,几乎为算力“抵押未来”
3 6 Ke· 2025-08-16 04:04
智东西8月16日消息,近日,OpenAI联合创始人兼总裁Greg Brockman在世界AI工程师大会上,分享了他对AI技术发展瓶颈、科研与工程关系 等AI关键议题的最新看法。作为2015年便入局AI的行业老兵,在回答主持人关于GPT-6发展挑战的问题时,Brockman提出了一项十分重要的 观察: 随着算力和数据规模的快速扩展,基础研究正在回归,算法的重要性再次凸显,成为未来AI技术发展的关键瓶颈。 对Brockman而言,这并非坏事。他觉得整天围绕Attention is All You Need这一经典论文和Transformer做文章已经有些乏味,智力上难免让人觉 得"不过瘾"。目前,强化学习已成为算法研究的新方向之一,但他也意识到,仍存在许多明显的能力缺口。 ▲Greg Brockman(右)与主持人(左) 工程与科研,是驱动AI发展的两大引擎。工程师出身的Brockman认为,工程师的贡献与研究人员不相上下,甚至在某些方面更为重要。如果 没有科研创新,就无事可做;如果没有工程能力,那些想法就无法实现。 OpenAI从一开始就坚持工程与研究同等看待,而两者的思维方式又有所不同。对新加入OpenAI的 ...
最朴实的商战,掏100亿挖前员工
投中网· 2025-08-15 06:10
将投中网设为"星标⭐",第一时间收获最新推送 没挖成。 作者丨 刘燕秋 来源丨 投中网 这段时间,硅谷上演了一出出高价挖人的戏码。Meta首席执行官扎克伯格在被前OpenAI CTO米拉 ·穆拉蒂拒绝后,转而直接向她新创立的Thinking Machines Lab的多名核心员工抛出天价聘用条 款。 其中,对联合创始人兼首席研究员安德鲁·塔洛克的报价,六年内最高可达15亿美元(约108亿元人 民币),包括奖金和高回报股票。还有点值得一提,安德鲁·塔洛克还是Meta的前员工,Thinking Machines的其他员工也收到了从数千万到上亿美元不等的长期薪酬与期权承诺。 2025年Thinking Machines推出后,迅速成为硅谷最受关注的AI初创公司之一。尽管公司尚未推出 任何产品,但最近在Andreessen Horowitz的领投下,成功完成了高达20亿美元的种子轮融资,估 值接近120亿美元。 求而不得,那就退而求其次,这是小札的策略,足见其挖人的目标之坚定。Meta已投资超过10亿美 元来组建"超级智能"研究部门的全明星阵容,目光主要瞄准的是OpenAI及其出走者领导的企业。 作为成果的一部分,M ...
OpenAI联合创始人Greg Brockman:对话黄仁勋、预言GPT-6、我们正处在一个算法瓶颈回归的时代
AI科技大本营· 2025-08-13 09:53
出品 | CSDN(ID:CSDNnews) 投稿或寻求报道 | zhanghy@csdn.net 所有人都仰望星空、谈论着通用人工智能(AGI)何时降临的时代里,我们或许更应关注那些低头铸造火箭的人。 OpenAI 的联合创始人兼前总裁 Greg Brockman 近日在 AI.Enigineer 上进行了一场对话分享,期间还邀请到英伟达 CEO 黄仁勋 和他进行了一段连线 问答。 责编 | 王启隆 对话的主线,并非一个英雄的成长史,亦远不止是 ChatGPT 或 GPT-5 发布瞬间的狂热与混乱,而是一条贯穿 70 年计算机历史的、从个人魔法到工业 革命的演进脉络: 一个因点击排序按钮感受到"魔法"而投身编程的少年黑客,如何成长为驾驭十万 GPU 集群、与黄仁勋商讨下一代 AI 基础设施的工 业巨擘? 这并非一个简单的线性成长故事。在 Greg Brockman 对自己过往经历的叙述中,我们能看到两个世界的重叠与碰撞: 一个是"游牧民族"的世界:信奉第一性原理,蔑视陈规。 为了一个客户,可以在 24 小时内攻克银行需要 9 个月的技术对接。 这是 Stripe 崛起的 秘密,也是硅谷精神的原始图腾——相信 ...
他救了OpenAI、年赚过亿、三家明星CTO,却自曝跟不上AI发展了!硅谷大佬告诫:不是马斯克,就别碰大模型
AI前线· 2025-08-07 10:08
Core Viewpoint - The article discusses the complexities and dynamics within OpenAI, particularly during a crisis involving the board and the return of Sam Altman, highlighting the importance of leadership and decision-making in the tech industry [2][3][4]. Group 1: OpenAI Crisis and Leadership - Bret Taylor, a key figure in OpenAI's board, was initially reluctant to get involved but felt compelled to help after reflecting on the significance of OpenAI's impact on the AI landscape [2][3]. - Taylor emphasized the need for a transparent and fair process to address the crisis, aiming to restore trust among employees and stakeholders [3][4]. - The crisis led to a collective employee response, with a public letter demanding Sam Altman's return, indicating the strong connection between leadership and employee morale [3][4]. Group 2: AI Market Dynamics - The AI market is expected to evolve into three main segments: foundational models, AI tools, and application-based AI, with a particular focus on the potential of AI agents [5][33]. - Foundational models will likely be dominated by a few large companies due to the high capital requirements for training these models, making it a challenging area for startups [34][35]. - The AI tools market presents risks as larger infrastructure providers may introduce competing products, necessitating careful strategic planning for smaller companies [36][37]. Group 3: Application-Based AI and Business Models - The application-based AI market is seen as the most promising, with companies developing AI agents to handle specific business tasks, leading to higher profit margins [37][38]. - The shift towards AI agents represents a significant change in how software is perceived, moving from tools that assist humans to systems that can autonomously complete tasks [41][42]. - The concept of "outcome-based pricing" is gaining traction, where companies charge based on the results delivered by AI agents, aligning business goals with customer satisfaction [44][46].
Claude Opus 4.1被曝即将发布!Anthropic靠两大客户API收入超OpenAI
量子位· 2025-08-05 04:13
明敏 发自 凹非寺 量子位 | 公众号 QbitAI GPT-5又咕咕,但是把Claude新模型诈了出来—— Claude Opus 4.1 ,被曝正在进行内部测试。 文件将这个模型描述为"更具问题解决能力",推测它可能会 重点提高推理和规划能力 。 有网友补充说,最近使用Claude Code时会被询问使用体验,可能是在进行一些A/B测试。 尽管从型号名称判断,4.1会是一次小版本更新,但参考此前从Claude-3到Claude-3.5的"飞升",以及同样惊艳的Claude-3.7……历史重演的 话,0.1版本升级也可能是重大飞跃。 网友们似乎也不担心Claude-4.1的性能不够好,大家更在意的是模型价格太高怎么办。 毕竟,Claude模型的生产力是有目共睹的, 尤其是在编程方面 。 最新数据显示,Anthropic过去7个月的ARR (年度经常性收入) 翻了5倍,涨到50亿美元。 其中API收入主要来自于编程,两个最大客户Cursor和GitHub Copilot,就带来了14亿美元的收入。 Claued-4.1箭在弦上? 被曝光的模型型号为"claude-leopard-v2-02-prod",它在配 ...
主题研究 - 创投视野:人工智能全景图谱Thematics-Venture Vision Artificial Intelligence Landscape
2025-08-05 03:15
Summary of the Conference Call on Artificial Intelligence Landscape Industry Overview - The report focuses on the **Artificial Intelligence (AI) and Machine Learning (ML)** industry, highlighting significant growth trends and investment dynamics in the private capital markets since 2018 [1][3][19]. Key Insights - **Investment Surge**: Private capital raised for AI and ML in the US has increased by over **600%** since 2018, with **$275 billion** raised year-to-date (YTD) in 2025, surpassing totals from each of the previous seven years [1][19]. - **Total Capital Investment**: Since 2018, approximately **$1 trillion** has been invested across around **46,000 deals** in the AI and ML sector in the US [3][16]. - **Market Dynamics**: The report outlines early versus late-stage funding trends in 2025, indicating a robust investment environment with around **4,000 deals** across various venture capital stages [24][25]. Notable Transactions - **Largest Deals**: The largest transactions in 2025 include: - **The Stargate Project**: $100 billion joint venture [14][31]. - **OpenAI**: $40 billion funding round, raising its valuation to **$300 billion** [29][31]. - **Scale AI**: Received $14.3 billion from Meta Platforms, leading to a valuation of approximately **$30 billion** [29][31]. - **Juniper Networks**: Acquired by Hewlett Packard Enterprise for about **$16.23 billion** [28][31]. Company Updates - **Anduril Industries**: Launched Copperhead, a family of Autonomous Underwater Vehicles [20]. - **Anthropic**: Introduced Claude for Financial Services, enhancing data unification for financial applications [20]. - **Figure AI**: Released a new battery with a **94%** increase in energy density [23]. - **Hugging Face**: Made a new Desktop Robot available for pre-order [20]. - **xAI**: Aiming to secure up to **$12 billion** for expansion plans [20]. Market Trends - **Funding Trends**: The report notes a significant increase in funding for AI private markets, with considerable activity in both early and late-stage investments [24][25]. - **Sector Proliferation**: There is a noted increase in AI use cases across public markets, with **412 stocks** increasing their AI exposure, representing a combined market cap of **$8.7 trillion** [12]. Additional Insights - **Economic Impact**: The report discusses the implications of AI on labor markets and the economy, with initiatives like the Anthropic Economic Index aimed at understanding these effects over time [20]. - **Future Outlook**: The upward trend in AI and ML funding is expected to continue, driven by technological advancements and increasing corporate adoption [12][19]. This summary encapsulates the critical points from the conference call regarding the AI landscape, highlighting investment trends, significant transactions, and company developments within the sector.
极狐驭码:私有化AI Coding引擎,让世界500强的研发全流程提效30%
36氪· 2025-07-28 09:48
Core Viewpoint - The article discusses the rapid development and competition in the AI coding sector, highlighting the emergence of various AI coding products and the strategic moves of major companies in this space [3][4][10]. Group 1: Industry Trends - AI coding products like Cursor, Devin, and Windsurf have gained traction, with significant funding and user adoption [3][4]. - Major players such as Google and OpenAI are actively entering the AI coding market, with notable acquisitions and product launches [4]. - The trend of "Vibe Coding," which allows non-programmers to generate code through natural language, is gaining popularity but has limitations in professional environments [5][10]. Group 2: Company Focus - GitLab's Chinese counterpart, 极狐GitLab, aims to provide AI coding solutions tailored to the needs of Chinese enterprises [7][8]. - The company launched its enterprise-level AI coding product, 驭码CodeRider, which integrates AI capabilities into its existing DevOps platform, focusing on private deployment and full-cycle software development [10][18]. - 驭码CodeRider has already secured several clients and is positioned to address the specific needs of Chinese companies regarding AI coding [10][32]. Group 3: Private Deployment and Market Differentiation - Private deployment is a key differentiator for 驭码CodeRider, as many overseas AI coding products do not support this feature, which is crucial for Chinese enterprises concerned about data security [28][30]. - The company emphasizes the importance of understanding the unique requirements of Chinese enterprises to effectively implement AI coding solutions [31][34]. Group 4: Open Source and Commercialization - The trend towards open-source AI coding tools is emerging, with companies like 驭码CodeRider considering open-sourcing parts of their product to gain market trust and facilitate commercial conversion [36][43]. - The company aims to leverage open-source strategies to attract users and encourage upgrades to enterprise versions, thereby enhancing its market presence [44][45]. Group 5: Future Aspirations - 驭码CodeRider aspires to be the first local enterprise application to successfully navigate the AI commercial landscape, focusing on practicality and innovation [46].
一个月重写三次代码库、三个月就换套写法!吴恩达:AI创业拼的是速度,代码不重要
AI前线· 2025-07-25 05:36
Core Insights - The key to the success or failure of startups lies in execution speed, which is more critical than ever before [4][5][6] - The greatest opportunities in the AI industry are found at the application layer, as applications can generate revenue that supports cloud, model, and chip companies [6][8] - Entrepreneurs should focus on specific ideas that can be quickly executed rather than vague concepts [13][15] Group 1: Execution Speed - Execution speed is a crucial factor in determining the future success of a startup, and efficient entrepreneurs are highly respected [5][6] - The new generation of AI technologies significantly enhances startup speed, and best practices are evolving rapidly [5][6] - The trend of Agentic AI is emerging, which emphasizes iterative workflows over linear processes, leading to better outcomes [9][11] Group 2: Specific Ideas - Startups should focus on concrete ideas that engineers can immediately begin coding, as vague ideas hinder execution [13][15] - Successful entrepreneurs often concentrate on a single clear hypothesis due to limited resources, allowing for quick pivots if necessary [17][18] - The "build-feedback" loop is essential, and AI coding assistants have accelerated this process dramatically [18][20] Group 3: AI Coding Tools - The introduction of AI coding assistants has drastically reduced the time and cost of software development, with prototype development becoming significantly faster [18][21] - The evolution of coding tools has made it common for teams to rewrite entire codebases within a month, reflecting lower costs in software engineering [23][24] - Learning to code is increasingly important for all roles within a company, as it enhances overall efficiency [25][26] Group 4: Product Feedback - Rapid product feedback is essential, and traditional methods may become bottlenecks as engineering speeds increase [29][32] - Various feedback methods range from intuitive assessments to A/B testing, with the latter being slower and less effective in early stages [32][33] - The ability to gather user feedback quickly is crucial for aligning product development with market needs [33] Group 5: AI Sensitivity - Understanding AI is vital for enhancing operational speed, as the right technical decisions can significantly impact project timelines [37][38] - Continuous learning about new AI tools and capabilities is essential for leveraging emerging opportunities in the market [38][39] - The combination of various AI capabilities can exponentially increase the potential for innovative product development [39] Group 6: Market Trends and Misconceptions - There is a tendency to overhype AGI, and many companies exaggerate their capabilities for marketing purposes [2][41][42] - The focus should remain on creating products that genuinely meet user needs rather than getting caught up in competitive dynamics [45] - The importance of responsible AI usage is emphasized, as the application of AI technology can have both positive and negative implications [44][48]
OpenAI-以自身节奏奏响颠覆之鼓-OpenAI-Marching to the Beat of Its Own Disruption Drum
2025-07-21 00:32
Summary of OpenAI Research Report Company Overview - **Company**: OpenAI - **Founded**: 2015 in San Francisco, CA - **Employees**: 4,500 (as of January 2025) - **Active Users**: 500 million weekly active users (WAU) as of March 2025 - **Valuation**: $300 billion (as of March 2025) - **Total Capital Raised**: $63.9 billion (as of March 2025) - **Key Investors**: Microsoft, SoftBank - **Monetization Model**: Subscriptions and API usage [9][22][43] Industry Insights - **AI Market Growth**: Over $315 billion has been invested in AI/ML startups since 2023, with 18% allocated to OpenAI [2][16] - **Total Addressable Market (TAM)**: Estimated to exceed $700 billion by 2030, with consumer AI projected at $300 billion and enterprise AI at $400 billion [12][21][22] Core Points and Arguments - **User Engagement**: ChatGPT reached 100 million users within two months of launch, making it the fastest-growing app in history, with current engagement at 500 million WAU [22][48] - **Revenue Growth**: Annual recurring revenue (ARR) increased by approximately 82% in the first half of 2025 to $10 billion, but profitability is not expected until 2029 [4][29] - **Competitive Landscape**: OpenAI's GPT-4 model has seen a decline in competitive edge, now ranking 95th in model performance benchmarks, indicating potential commoditization of AI models [4][23][26] - **Monetization Strategies**: OpenAI is exploring new revenue streams beyond subscriptions, including AI agents and potential advertising models [27][28] Risks and Challenges - **Talent Acquisition**: The competition for AI talent is intensifying, with significant compensation packages being offered, complicating recruitment and retention [38] - **Litigation Risks**: Ongoing legal challenges related to AI training could impact operational strategies and financial liabilities [39] - **Regulatory Environment**: Evolving AI regulations may pose challenges, particularly in the EU, where the AI Act is set to be enforced by August 2026 [39] - **Macroeconomic Factors**: Economic volatility could affect technology spending and raise costs associated with AI infrastructure [40] Strategic Initiatives - **Stargate Project**: A joint venture aiming to invest $500 billion in AI infrastructure by 2029, positioning OpenAI to reduce dependency on hyperscalers [32][48] - **Acquisitions**: The $6.5 billion acquisition of io Products aims to enhance OpenAI's hardware capabilities [22][28] - **Organizational Structure**: Transitioning to a Public Benefit Corporation (PBC) to facilitate capital access while maintaining a focus on societal benefits [33][64] Conclusion OpenAI is positioned as a leader in the AI industry with significant growth potential and a robust user base. However, it faces challenges related to competition, regulatory scrutiny, and the need for sustainable monetization strategies. The company's strategic initiatives, including substantial investments in infrastructure and talent, will be critical in navigating these challenges and capitalizing on market opportunities.
在OpenAI上班有多卷?
虎嗅APP· 2025-07-20 13:18
Core Insights - OpenAI has been under intense media scrutiny, especially following the departure of several key employees, leading to discussions about its internal culture and management style [1] - The article provides firsthand insights from former employee Calvin French-Owen, detailing his experiences and reflections on working at OpenAI [1] Group 1: Company Culture and Communication - OpenAI has experienced rapid growth, expanding from over 1,000 employees to more than 3,000 in just one year, resulting in significant changes in leadership responsibilities [9] - Internal communication primarily relies on Slack, with minimal use of email, leading to a unique work environment where attention management is crucial [10] - The company emphasizes a "bottom-up" culture where promotions are based on actual capabilities rather than political maneuvering, valuing good ideas and execution [11][12] Group 2: Decision-Making and Strategy - OpenAI is characterized by its quick strategic adjustments, allowing for efficient decision-making that is not commonly seen in larger organizations like Google [14] - The company maintains a high level of confidentiality regarding its projects, often leading to media reports on developments before internal announcements [14] - Safety and ethical considerations are paramount, with a focus on addressing real-world risks rather than theoretical concerns [16] Group 3: Engineering and Development - OpenAI employs a large monolithic codebase primarily in Python, with a mix of Rust and Golang services, reflecting a diverse coding style [21] - The engineering team is noted for its rapid action and high mobility, with quick responses to project needs without bureaucratic delays [19] - The Codex project exemplifies OpenAI's "sprint to release" mentality, with the team completing the product from the first line of code to launch in just seven weeks [25][26] Group 4: Product Development and Market Impact - Codex, an AI programming assistant, was developed with a focus on user engagement, generating significant user interest immediately upon release [26][27] - The product's design allows for asynchronous operation, positioning it as a collaborative tool for users [26] - Codex has shown impressive performance metrics, generating 630,000 public pull requests within 53 days of its launch, indicating strong user adoption [27] Group 5: Personal Reflections and Industry Insights - The author reflects on the challenges of transitioning from entrepreneurship to a large organization, highlighting the unique opportunities at OpenAI [28][32] - The competitive landscape for AGI development is noted to be dominated by three main players: OpenAI, Anthropic, and Google, each with distinct approaches [32]