智通财经获悉，高盛(GS.US)技术高管透露，该行正与人工智能(AI)初创企业Anthropic合作开发AI智能 体，逐步实现银行内部多个岗位的自动化转型。 高盛首席信息官Marco Argenti表示，过去六个月，Anthropic的工程师已入驻高盛，双方联合研发自主智 能体，首批落地领域聚焦两大核心：交易核算，以及客户尽职调查与开户流程。 Argenti称，基于Anthropic的Claude模型开发的智能体现已处于研发初期，该类智能体将大幅缩短上述核 心业务的处理时长。他透露智能体即将上线，但未公布具体日期。 尽管高盛的会计与合规部门目前拥有数千名员工，且上述AI智能体即将在该类部门投入使用，但 Argenti强调，现阶段就认为该技术会导致相关岗位裁员，还为时尚早。 去年10月，高盛首席执行官David Solomon曾表示，该行已启动一项多年规划，围绕生成式AI进行全面 业务重组。自2022年末OpenAI旗下ChatGPT问世以来，生成式AI技术便掀起行业巨浪。Solomon同时指 出，尽管高盛等投行的交易与咨询业务收入持续飙升，但在此次业务转型中，该行将着力控制员工规模 增长。 不过他坦言，随着A ...

智通财经APP获悉，高盛(GS.US)技术高管透露，该行正与人工智能(AI)初创企业Anthropic合作开发AI 智能体，逐步实现银行内部多个岗位的自动化转型。 高盛首席信息官Marco Argenti表示，过去六个月，Anthropic的工程师已入驻高盛，双方联合研发自主智 能体，首批落地领域聚焦两大核心：交易核算，以及客户尽职调查与开户流程。 去年10月，高盛首席执行官David Solomon曾表示，该行已启动一项多年规划，围绕生成式AI进行全面 业务重组。自2022年末OpenAI旗下ChatGPT问世以来，生成式AI技术便掀起行业巨浪。Solomon同时指 出，尽管高盛等投行的交易与咨询业务收入持续飙升，但在此次业务转型中，该行将着力控制员工规模 增长。 值得关注的是，就在高盛公布这一合作之际，由OpenAI前高管联合创立的Anthropic完成了模型升级， 这一动态引发软件企业及其信贷机构的股价大幅下挫，投资者正纷纷押注AI赛道的最终赢家与失意 者。 Argenti透露，高盛早在2025年就已开始测试一款名为Devin的自主AI编程工具，目前该工具已向全行工 程师开放使用。而在测试过程中，高盛很 ...

亿万富翁，专指净资产超过10亿美元的富翁。在这几年的硅谷，不断有新的亿万富翁跑出来，他们往往很年轻。 光是"世界上最年轻的亿万富翁"的头衔，在短短的时间里就已经多次易主。 先是28岁的Alexandr Wang ，Scale AI的创始人，现在已经是Meta的AI掌舵人。18个月后，是27岁的Shayne Coplan，他 是Polymarket的CEO。又过了20天，"世界上最年轻的亿万富翁"就变成了AI 数据标注初创公司 Mercor 的三位创始人， 他们只有22岁，甚至打破了马克·扎克伯格当年创下的纪录（23岁）。 如今，硅谷又跑出一位华人亿万富翁——Cognition.AI公司的联合创始人兼CTO Steve Hao。 Cognition刚刚完成了超过4亿美元的融资，投后估值102亿美元，而根据《福布斯》的估算，Steve Hao作为公司持股比 例最高的联创，净资产已经达到了13亿美元左右。 Devin是Cognition.AI公司的明星产品。 这是一个自主编程智能体，被公司直接定义为"AI软件工程师"。 和此前的GitHub Copilot、Claude、Cursor这类"代码助手"不同，Devin ...

鹭羽 发自 凹非寺 量子位 | 公众号 QbitAI 陈丹琦首次转身工业界，第一站就选择Mira初创的理由找到了—— 有个赛友也在这儿，还足足"潜伏"了一年之久。 这人就是和陈丹琦同年拿下IOI金牌的 Neal Wu 。 还不止一届，Neal Wu可是足足拿了三次IOI金牌，是美国队当之无愧的顶梁柱。 他还是全球首个AI程序员、此前炸翻硅谷的 Devin 缔造者之一。 而他的存在，原本一直被Mira视作 顶级机密 来着。 直到这场公司内讧，多名创始人集体"叛逃"回OpenAI，这位传奇程序员的行踪才意外浮出水面。 不过相对于老朋友陈丹琦，Neal Wu则显得更为低调。 其公开资料中从未透露过具体职位，仅隐晦地表示自己正在以联合创始人兼顾问的身份参与一项新计划。 开始时间是一年前，和当初Mira宣布成立新公司的时间线高度重合。 08年同为金牌的 陈丹琦 ，目前是普林斯顿大学计算机系副教授，以及NLP小组的联合负责人，还曾收获斯隆奖。 有趣的是，以前是对手现在成战友。 那么，Neal Wu究竟有什么过人之处，值得Mira如此大费周章地将他 "藏" 起来？ Neal Wu其人 翻开Neal Wu的履历，可谓是天才少 ...

以下文章来源于福布斯 ，作者Forbes 福布斯 . "Forbes福布斯"品牌始终秉承企业家精神和创新精神，坚持专业、公正、创新和进取的价值观，中国地区业务涵盖广告、咨询、调研、策 划、会议及展览服务等。 来源丨福布斯（ID： forbes_china ） 作者丨Richard Niev 翻译丨 Lemin 去年 9 月，风头正劲的 氛围编程 初创公司 Cognition 宣布完成 4 亿美元融资，公司估值一举跃升至 102 亿美元。 伴随公司 估值 上涨 的还有 三位年轻创始人 的身家 ——他们已 成为生成式人工智能热潮中最新的造富赢家。 三人中最年轻的 Yan 曾入选 Neo Scholar 项目 ，该计划由 Facebook 早期投资人阿里 · 帕托维 （ Ali Partovi ） 发起，旨 在挖掘在 校大学生中的 潜力科技人才。与 蒂尔奖学金（ Thiel Fellows ） 计划不同， 参加 Neo 项目 无需退学创业，但 Yan 仍选择从哈佛大学 退学 ，全 身心投入 Cognition 的 创建 。 目前尚无信息披露 Hao 的持股比例为何高于 Yan 和 Wu ，三位创始人未回应《福布斯》 ...

去年9月，风头正劲的氛围编程初创公司Cognition宣布完成4亿美元融资，公司估值一举跃升至102亿美 元。伴随公司估值上涨的还有三位年轻创始人的身家——他们已成为生成式人工智能热潮中最新的造富 赢家。 根据《福布斯》获取的财务文件，这笔融资让公司首席技术官Steven Hao成为亿万富豪。以其在融资时 持有的公司股份计算，Hao的身家约为13亿美元；首席产品官Walden Yan的身家约为8.3亿美元，距十亿 美元门槛仅一步之遥；首席执行官Scott Wu在三位创始人中持股比例最低，身家估计接近6亿美元。 Cognition方面拒绝对创始人的身家置评。 去年10月，人工智能数据标注初创公司Mercor的三位创始人以22岁的年纪成为史上最年轻的白手起家科 技亿万富豪，刷新了马克·扎克伯格创下的纪录——扎克伯格当年跻身亿万富豪之列时比他们还大一 岁。 在这三位年轻人之前，史上最年轻白手起家亿万富豪是27岁的博彩平台Polymarket首席执行官夏恩·科普 兰（Shayne Coplan），不过他只将这一头衔保持了20天。 Wu来自路易斯安那州，两年前，一段他少时在一场对战式数学竞赛中碾压对手的视频走红，成了人 ...

Core Insights - In 2025, the focus of AI venture capital in the U.S. shifted from "spreading wide" to "placing big bets," with 64 deals exceeding $100 million, including 8 companies receiving multiple large investments, leading to rising valuations [1][4] Group 1: Investment Trends - The trend of headlining investments is evident, with 35 transactions exceeding $200 million covering 29 companies in 2025 [3] - Investment is primarily directed towards two main lines: restructuring the physical foundations of AI and targeting core business flows in high-value industries [4][54] - AI infrastructure investments accounted for 7 out of 18 major funding rounds, with significant amounts raised by companies like Cerebras and Unconventional AI [5] Group 2: Notable Companies and Their Innovations - Unconventional AI raised $475 million in seed funding, focusing on bio-inspired computing, aiming for a theoretical efficiency improvement of 1000 times over traditional GPUs [6][7] - Cerebras Systems secured $1.1 billion in G round funding, specializing in wafer-scale AI computing, with a product designed for accelerated training and low-latency inference [8][10] - Celestial AI completed a $250 million C round, developing photonic AI accelerators that significantly enhance efficiency compared to traditional GPUs [12] - Modular raised $250 million, focusing on unified AI computing infrastructure with a product that optimizes across various hardware [13][15] - Fireworks AI, an open-source large model cloud platform, raised $250 million, providing extensive AI infrastructure services [17][18] Group 3: AI Applications in Vertical Industries - Cognition AI raised $400 million, developing an AI engineer capable of independent software development, targeting tech companies and financial institutions [20][21] - Sierra, an AI-driven conversational platform, raised $350 million, focusing on automating customer interactions across various sectors [23][25] - Ambience Healthcare, specializing in clinical documentation automation, raised $243 million, aiming to reduce documentation time for healthcare providers [27][28] - OpenEvidence, an AI clinical decision support company, raised $200 million, providing real-time answers to clinical questions based on authoritative medical literature [30][32] - EliseAI, an automation platform for real estate and healthcare, raised $250 million, focusing on operational efficiencies in both sectors [35][36] Group 4: AI for Science - Lila Sciences, an AI-driven scientific platform, raised $350 million, integrating generative AI and automated laboratories for research [48][49] - Periodic Labs, focusing on materials science, raised $300 million, developing a triad science stack for accelerating material discovery [50] - SandboxAQ, a quantum and AI technology company, raised $450 million, providing solutions for post-quantum cryptography and AI-driven quantum simulations [51][53]

Core Insights - Infosys and Cognition have announced a strategic collaboration to scale the AI software engineer Devin across global enterprises, aiming to enhance software development and engineering productivity [1][2][4] - The integration of Infosys Topaz Fabric with Devin is designed to automate engineering processes, reduce technical debt, and modernize systems, thereby accelerating time-to-market for enterprises [3][4] Company Collaboration - Infosys will integrate Devin into its internal engineering teams and client delivery models, facilitating deployment within customer environments to enhance engineering quality and efficiency [2][3] - The collaboration includes the development of shared engineering frameworks and enablement programs to promote the integrated capabilities of Infosys Topaz Fabric and Devin across various industries [2][3] Technological Advancements - Infosys Topaz Fabric and Devin will work together to automate brownfield engineering and create virtual engineers to tackle complex production and maintenance challenges [3] - The partnership will focus on developing industry-specific solutions and AI-native modernization blueprints, supported by co-innovation labs [3][4] Leadership Statements - Scott Wu, CEO of Cognition, emphasized the collaboration's potential to redefine software engineering and accelerate time-to-market for clients [4] - Salil Parekh, CEO of Infosys, highlighted the synergy between Cognition's AI capabilities and Infosys' industry expertise as a significant advancement in realizing AI value for global enterprises [4]

Core Insights - The article summarizes the key AI trends anticipated for 2025, as outlined by AI expert Andrew Ng, highlighting significant developments in AI capabilities and industry dynamics [1][3]. Group 1: AI Model Capabilities - The ability of models to reason is becoming a standard feature, moving beyond being a unique trait of a few models [5][8]. - The evolution of reasoning capabilities in models can be traced back to the paper "Large Language Models are Zero-Shot Reasoners," which introduced the prompt "let's think step by step" to enhance output quality [9]. - The introduction of models like OpenAI's o1 and DeepSeek-R1 has marked a paradigm shift, embedding multi-step reasoning workflows directly into model architectures [12][13]. Group 2: AI Talent Competition - The AI talent competition, ignited by Meta, has led to salaries for top AI professionals reaching levels comparable to professional sports stars, fundamentally reshaping the tech industry's talent pricing [18][19]. - Meta's establishment of the "Meta Super Intelligence Lab" and aggressive recruitment strategies have intensified the competition for AI talent [20][21]. - This talent war is seen as a strategic necessity for companies aiming to compete in the AGI race, with the potential for salary structures to evolve beyond mere price competition by 2026 [23][24]. Group 3: Data Center Investments - The surge in data center investments signifies the onset of a new industrial era, with AI companies' plans for data center construction rivaling national infrastructure projects [25][26]. - Major investments include OpenAI's $500 billion "Stargate" project, Meta's $72 billion infrastructure investment, and Amazon's projected $125 billion expenditure by 2025 [28]. - The AI industry's capital expenditure has exceeded $300 billion this year, with projections suggesting total investments could reach $5.2 trillion by 2030 to meet AI training and reasoning demands [29][30]. Group 4: Automated Programming - AI-driven automated programming is transforming software development processes, with coding agents achieving completion rates over 80% for similar tasks [34][35]. - These agents have evolved from simple "auto-complete" tools to comprehensive "digital engineers" capable of planning tasks and managing entire codebases [36][37]. - The integration of reasoning capabilities into these agents has significantly reduced overall computational costs by allowing them to think through tasks before execution [37][40]. Group 5: Software Development Learning Tips - Continuous learning is emphasized as essential for entering the AI field, with recommendations to participate in AI courses, build AI systems, and read technical papers [42][45]. - Practical experience is deemed crucial, as theoretical knowledge alone is insufficient for proficiency in software development [49][51]. - Reading research papers, while not mandatory, is encouraged for those seeking to enhance their understanding of AI [52][53].

Core Insights - The article discusses the unconventional methods used by AI startups, particularly the practice of pretending to be AI through human labor, highlighting the blurred lines between innovation and deception in the tech industry [1][4][9]. Group 1: Human Pretending to be AI - Fireflies.ai's founders initially posed as an AI named "Fred" to record meetings, demonstrating a "human intelligence" model that surprisingly succeeded in generating revenue [5][6]. - This practice is not isolated; many startups employ similar tactics, such as hiring workers to manually operate processes that are marketed as automated [6][7]. - The phenomenon reflects a broader survival strategy in the AI boom, characterized by deception, extreme dedication, and brute force [7][9]. Group 2: The Dark Side of "Pretending AI" - The case of Devin, a self-proclaimed AI software engineer, illustrates the risks of overpromising capabilities that are not yet realized, leading to a backlash from the tech community [10][13]. - Pear AI's controversy over copying an open-source project highlights the ethical dilemmas faced by startups in the competitive landscape [14]. - The "Wizard of Oz technique," where human operators simulate AI functions to gather data for future automation, is a legitimate but controversial strategy [15][17]. Group 3: The Culture of Hardship - A culture of extreme work ethics, termed "performative suffering," is prevalent among AI founders, where personal sacrifices are made to signal commitment to investors [20][27]. - Founders often live in substandard conditions, such as cramped sleeping pods, to save costs and maximize work hours [24][26]. - This culture is institutionalized, with some companies explicitly seeking employees willing to work excessively long hours [26][27]. Group 4: The Role of Brute Force - Many founders rely on "brute force" tactics, engaging directly with customers and manually handling tasks to drive initial growth [30][34]. - Historical examples, such as Airbnb's founders selling cereal to raise funds, illustrate the lengths to which entrepreneurs will go to survive [31]. - Fireflies.ai's growth strategy involved the founder personally securing early clients, emphasizing the importance of direct engagement over automated processes [36][38]. Group 5: The Paradox of AI Development - The article concludes that the true drivers of success in AI startups are not just technological innovations but also the human elements of sacrifice, market intuition, and relentless effort [53][54]. - The irony lies in the pursuit of an automated future that heavily relies on the most basic human qualities [55].