英国《金融时报》集团旗下唯一的中文商业财经网站，旨在为中国商业菁英和决策者们提供每日不可或 缺的商业财经资讯、深度分析以及评论。 以下文章来源于FT中文网 ，作者余绍清 日本经济新闻社与金融时报2015年11月合并为同一家媒体集团。同样于19世纪创刊的日本和英国的两家 报社形成的同盟正以"高品质、最强大的经济新闻学"为旗帜，推进共同特辑等广泛领域的协作。此次， 作为其中的一环，两家报社的中文网之间实现文章互换。 编者荐语： FT中文网 . 过去两年，大模型竞赛在硅谷愈演愈烈。表面上看，这是AI公司之间的"军备竞赛"，GPT-4o 对决Gemini Ultra，Claude 3对标Suno，但若只盯住模型本身，或许低估了这场竞赛的真正 本质。 今天美国之所以能够在AI浪潮中跑在最前，不是因为某一项具体技术高明，而是各家产品 在"赛马机制"下角逐的结果。 在中国，互联网行业也曾依靠"赛马机制"在过去十多年力，完成了对用户、流量与生态的统 治式建构…… 阅读更多内容请点击下方" 阅读原文 " （本文由FT中文网提供） 资料图（reuters） 余绍清：今天美国之所以能够在AI浪潮中跑在最前，不是因为某一项具体技术高 ...

This is from the complaint that just dropped of Reddit's lawsuit against Anthropic. Anthropic is a late blooming artificial intelligence company that builds itself as the white knight of the AI industry. It is anything but.Just a day ago, the CEO of Windsurf dropped this news. With less than 5 days of notice, Anthropic decided to cut off nearly all of our first party capacity to claude 3. x models.On the one hand, I understand why Anthropic did this. Windsurf is probably one of the biggest inference consume ...

Core Insights - The article highlights the rapid growth and potential of AI as a new wealth lever, exemplified by the acquisition of AI Coding product Base44 by Wix for $80 million just six months after its founding [1] - Surge AI has emerged as a hidden champion in the AI training data sector, achieving a $1 billion ARR without external funding and surpassing the revenue of competitors like Scale AI [3][13] Company Overview - Surge AI was founded by Edwin Chen, who has a unique background in mathematics and linguistics from MIT, which has contributed to the company's success in the AI field [3] - The company has a team of around 100 people and has been profitable since its inception, focusing on high-quality data annotation services [3][5] Market Opportunity - Edwin Chen identified a significant gap in the availability of high-quality annotated data, even among tech giants like Google and Facebook, which struggle with data annotation challenges [4] - Surge AI was established during the pandemic, leveraging the availability of skilled individuals to build a high-quality annotation workforce [5] Technological Advantages - Surge AI has developed proprietary quality control technologies to ensure high-quality data for training AI models, addressing the sensitivity of large language models to low-quality data [6] - The company employs domain expert annotation teams across various fields, providing the necessary depth and breadth for training advanced language models [7] - Surge AI offers a rapid experimentation interface, allowing clients to quickly design and launch new tasks without lengthy guidelines [9] - The company also conducts red team testing to identify and address security vulnerabilities in AI models [10] Strategic Partnerships - A key breakthrough for Surge AI was its collaboration with Anthropic, which has validated its technical capabilities and established its authority in AI safety and alignment [11] Competitive Positioning - Unlike competitors such as Scale AI, Surge AI positions itself as a high-end data annotation service, focusing on the most complex AI training tasks [13] - Surge AI achieved a tenfold growth within six months of its founding, with an ARR of $1 billion, surpassing Scale AI's revenue of $870 million during the same period [13]

Core Insights - Mary Meeker's latest report highlights the rapid growth of ChatGPT's search volume, surpassing traditional Google search in just three years, marking a significant shift in internet usage [2][3] - The report emphasizes the unprecedented speed of technological change, particularly in AI, and its global impact, contrasting it with the slower adoption rates of previous technological revolutions [4][6] AI Growth Metrics - Since 2010, the annual growth rate of AI training model data has reached 260%, while the required computational resources have grown at 360% [2] - ChatGPT's user base, subscription numbers, and revenue growth indicate its widespread adoption among internet users [3] Developer Engagement - The number of developers in the Google ecosystem has increased from 1.4 million to 7 million, a fivefold increase since last year [5] - Companies are leveraging AI developments to enhance user interactions, with a shift towards AI management roles in customer support [5] Adoption Speed Comparison - AI adoption has occurred in approximately three years, significantly faster than personal computers (20 years), desktop internet (12 years), and mobile internet (6 years) [6] Business Investment Trends - A Morgan Stanley survey indicates that 75% of global CMOs are experimenting with AI, with significant capital expenditures in AI projects, including a 21% increase in related capital spending and a 28% rise in data spending [6][7] Cost Dynamics - The report notes a "cost deflation" phenomenon, with the purchasing power for AI inference increasing tenfold annually [7] Future AI Landscape - New users will engage with AI in a native environment, free from traditional internet constraints, suggesting a transformative impact on daily life [8] Global Usage Statistics - ChatGPT usage rates are reported at 13.5% in India, 9% in the U.S., and 5% in Indonesia and Brazil [9] U.S.-China AI Competition - The report highlights China's leading position in large language model performance, with implications for national strategy and technological innovation [10] Next-Generation AI Interfaces - The transition from text to voice interfaces, and eventually to humanoid robots, is anticipated as a significant development in AI interaction [10]

OpenAI的发言人透露，其截至6月末止的年化营收已飙升至100亿美元，较2024年12月末的55亿美元增长近一倍，主要来自 ChatGPT业务和应用程序编程接口（API），而不包括微软（MSFT.US）和大型一次性交易的影响。 据CNBC报道，OpenAI还定下了到2029年收入达至1,250亿美元的目标。 今年第1季，OpenAI进行有史以来最大笔的融资——由软银牵头，为其融资400亿美元，远超2023年微软所提供的100亿美 元融资，资金可能在年底全部到位。 这轮融资让OpenAI的估值达到3,000亿美元，相当于其2024年末止年化收入55亿美元的54倍。若按其最新的百亿收入计算， OpenAI或许可要求更高的估值。 独角兽估值排名已变 独角兽估值排名已悄然生变，一直独占鳌头的字节跳动被拉下第一王位。在资本的推动下，AI与太空概念成为新的资本宠 儿。 风投项目有望挽回投资者信心？ 从头部独角兽排名的变更可以看出，AI初创公司依然是风投基金的最爱，也因此在今年内频频获得融资加持和估值提升。 此外，太空概念也依然是投资者青睐的标的，除了SpaceX之外，航天军工企业Voyager Technologies ...

随着人工智能（AI）融入日常生活的方方面面，科学家们正争分夺秒地研究其在心理、社会和认知层面的深远影响。从诊断心理健康问题到塑造政治观 念，人工智能工具，尤其是像ChatGPT这样的大型语言模型，正深刻影响着我们的思维方式、工作模式，以及与科技和他人互动的方式。 一系列全新的研究已然展开，揭示这一切对我们的思维、行为以及社会所具有的意义。 在本文中，我们将一同探寻七项前沿研究成果。这些发现揭示了AI正以超乎想象的方式，悄然重塑着人类的思维模式、行为习惯与文化生态。 01.好奇的AI黑客：LLM红队成员的世界 发表在《公共科学图书馆：综合》上的一项研究揭示了 "大型语言模型红队" 新兴文化。在这种文化中，人们将大型语言模型推向极限。其目的并非造成 危害，而是为通过探索与实验，深度解析模型运作逻辑。 研究团队访谈了28位来自软件工程师、艺术家等不同领域的从业者，发现驱动他们投身测试的是强烈的好奇心、道德责任感，以及挖掘人工智能系统隐藏 漏洞的使命感。他们运用充满创意与即兴的策略，试图激发出模型的意外或受限回应。 参与者们用 "炼金术" 和 "占卜" 这样的隐喻来描述他们的活动，这反映出大型语言模型行为的神秘本质。 ...

重新寻找 Scaling Laws。 文丨 贺乾明 编辑丨黄俊杰 像一个人的学习成长一样，每个全新的大模型，都需要从大量的文本中学习 "知识"，才有能力去解 决一个个问题。 Google 训练 70 亿参数的 Gemma 开源模型，让它 "看过" 6 万亿 Token（6 万亿个词）的文本。微软 投资的 Mistral 训练 73 亿参数模型，"看过" 8 万亿个 Token 的文本。 用如此大规模的数据训练参数不到 100 亿的模型，已经是行业中比较重的方法。按照 DeepMind 研 究人员提出的策略，如果考虑性价比，这么大的模型，看 2000 亿 Token 的文本就够了。不少中国 一线创业公司的同等规模大模型只用了 1 万亿～2 万亿个 Token 的文本。 Meta 的 CEO 马克·扎克伯格（Mark Zuckerberg）不满足于此，他直接把下一代开源大模型送进了 "县中"，用更多习题拔高能力。Meta 昨夜推出的 Llama 3 系列大模型，80 亿参数模型用了 15 万亿 Token 的训练数据，比 Google 的多学了一倍还不止，是很多小公司产品的十倍。 根据 Meta 公布的数据，在 ...