Group 1: Founder Psychology and Roles - Eric Schmidt emphasizes the difference between founders and professional managers, stating that founders are visionaries while professional managers are "amplifiers" who help scale ideas [4][10] - Schmidt reflects on his experience at Google, noting that he was not a typical entrepreneur but rather a professional manager who contributed during the company's scaling phase [3][4] - He discusses the challenges of attracting talent, highlighting that many talented individuals often choose to start their own companies instead of joining established firms [3][5] Group 2: Market Dynamics and Startup Ecosystem - Schmidt points out that many startups are often acquired for their talent rather than their products, indicating a market structure that can be inefficient [6][7] - He notes that the majority of startups fail, with traditional venture capital experiences suggesting that 4 out of 10 will fail completely, and 5 will become "zombies" with no growth potential [7] - The conversation highlights the importance of competition for startups, suggesting that true leadership is demonstrated when facing challenges from larger companies [11][12] Group 3: AI and Future Trends - Schmidt believes that AI is currently underestimated rather than overhyped, citing the scaling laws that drive AI advancements [33][34] - He discusses the potential of AI to transform business processes and scientific breakthroughs, emphasizing the importance of understanding how humans will coexist with advanced AI systems [35][39] - The conversation touches on the competitive landscape between the U.S. and China in AI development, with China investing heavily in AI as a national strategy [41][42] Group 4: Talent Acquisition and Management - Schmidt stresses the importance of attracting top talent by creating an environment where individuals feel they are solving significant problems [18][20] - He differentiates between "rockstar" employees who drive change and "mediocre" employees who are self-serving, advocating for the retention of the former [21][22] - The discussion includes insights on how to identify and nurture high-potential talent within organizations [24][25] Group 5: Challenges in AI Development - Schmidt highlights the challenges of defining reward functions in reinforcement learning, which is crucial for AI's self-learning capabilities [51] - He warns about the potential pitfalls of over-investing in AI infrastructure without a clear path to profitability, suggesting that many companies may face economic traps [47][48] - The conversation concludes with a call for companies to focus on the most challenging problems in AI, as solving these will yield the most significant rewards [52][53]

朋友们，我先声明一下，这是一期有争议的播客，有的人听完受益良多，也有的人感觉被冒犯，但我认为这是时代交替中不可避免的思想碰撞。 美吉：很多人都觉得我应该是个猎头。但我每次都跟别人说我不是猎头。但如果你非要认为我是猎头，我希望你叫我"高管猎头"。他们说有什么区别？我 会告诉他们，高管猎头和猎头是有重大区别的。大家如果看过胡歌演的《猎场》这部电视剧的话，里面的就是高管猎头，更多的就是出入在五星级酒店、 高尔夫球场等等一些上层的这种环境里面，你所聊的都是CXO级别的，西装革履的这些谈吐非常优雅的这些人。 我自己做高管猎头，做了将近十年的时间。2004年入行，到2012年其实算是从高管猎头转型到一个职业经理人，管了一家高管猎头公司。这20年其实都是 在做跟顶级人才交易的事情。所以我自己从不认为我是个高管猎头，我说我是个"做人的生意"的人。在圈子里，创始人们给我一个外号，就是"创业圈最 强妈妈桑"。就是你要什么样的人，我这儿都有，只要你说的出来，就能给你找得到。 我自己其实最喜欢的是"超级链接者"这样的一个定义。我认为高管猎头和猎头是有绝对差异的。或者这么说，我觉得猎头在中国其实不太是个好词儿。因 为大多数创始人或大多 ...

Core Insights - The rise of edge cloud technology is revolutionizing data processing by shifting capabilities closer to the network edge, enhancing real-time data response and processing, particularly in the context of AI inference [1][5] - The demand for AI inference is significantly higher than for training, with estimates suggesting that inference computing needs could be 10 times greater than training needs [1][3] - Companies are increasingly focusing on the post-training phase and deployment issues, as edge cloud solutions improve the efficiency and security of AI inference [1][5] Group 1: AI Inference Demand - AI inference is expected to account for over 70% of total computing demand for general artificial intelligence, potentially reaching 4.5 times the demand for training [3] - The founder of NVIDIA predicts that the computational requirements for inference will exceed previous estimates by 100 times [3] - The transition from pre-training to inference is becoming evident, with industry predictions indicating that future investments in AI inference will surpass those in training by 10 times [4][6] Group 2: Edge Cloud Advantages - Edge cloud environments provide significant advantages for AI inference due to their proximity to end-users, which enhances response speed and efficiency [5][6] - The geographical distribution of edge cloud nodes reduces data transmission costs and improves user experience by shortening interaction chains [5] - Edge cloud solutions support business continuity and offer additional capabilities such as edge caching and security protection, enhancing the deployment and application of AI models [5][6] Group 3: Cost and Performance Metrics - Future market competition will hinge on cost/performance calculations, including inference costs, latency, and throughput [6] - Running AI applications closer to users improves user experience and operational efficiency, addressing concerns about data sovereignty and high data transmission costs [6] - The shift in investment focus within the AI sector is moving towards inference capabilities rather than solely on training [6]

本期，我们非常荣幸地于4月16日邀请虞晶怡老师，为我们开启一次AI的思想远航。 徐一平 腾讯研究院 高级研究员 王强 腾讯研究院 资深专家 以生成式AI为代表的新技术浪潮日新月异，正带来一场深刻的技术、商业与社会变革，推动人类社会从 信息社会向智能社会转变。全世界热切期待AI到来的同时，也非常关心人工智能将带来哪些新机遇、新 挑战。 为此，我们发起了一项 《AI & Society 百人百问》 研讨，广泛邀请AI技术大咖、AI独角兽创始人、AI 投资人，以及社会学家、心理学家、国际关系专家、科幻作家等，用多元视角，深入研讨人工智能技术 引发的广泛影响，发掘AI时代的共识和非共识，共同推动人工智能始终朝着"助人发展，与人为善"的方 向可持续发展。 虞晶怡，上科大讲席教授、副教务长、信息学院院长。在加入上海科技大学前，他任职美国特拉华大学计算机与信息科学系正教 授。他于2000年获美国加州理工学院应用数学及计算机学士学位， 2005年获美国麻省理工大学计算机与电子工程博士学位。他 长期从事计算机视觉、计算成像、计算机图形学、生物信息学等领域的研究工作。他是IEEE Fellow、OSA Fellow、美国NSF ...

Core Viewpoint - CloudWalk Technology, one of the "AI Four Little Dragons," reported a significant decline in performance for 2024, with revenue dropping to 398 million yuan, a year-on-year decrease of 36.69%, marking the lowest revenue point in seven years [1][2] Financial Performance - The company recorded a net profit of -722 million yuan, with losses expanding compared to the previous year, marking eight consecutive years of losses [1][2] - Revenue projections made during the company's IPO in 2022 were significantly overestimated, with actual revenues from 2022 to 2024 being 526 million yuan, 628 million yuan, and 398 million yuan respectively, resulting in a negative compound annual growth rate [2][3] - The company's gross margin fell by 16.39 percentage points to 35.81% in 2024, while its peers like SenseTime maintained a gross margin of 42.90% [3] Workforce and R&D - CloudWalk underwent significant layoffs in 2024, reducing its workforce from 801 to 453 employees, a decrease of 43%, with R&D personnel decreasing by 51% [5][6] - The company’s R&D expenses fell by 18.27% to 472 million yuan, yet the R&D expense ratio increased to 118.72% due to declining revenue [5][6] - The departure of key technical personnel, including a core technology staff member, raises concerns about the company's ability to maintain its technological edge [5][6] Strategic Challenges - The company is facing challenges in keeping pace with technological advancements, particularly in the development of large models, which has hindered its competitive position against major players like Baidu and Alibaba [3][4] - CloudWalk's cash flow from operating activities has been negative for three consecutive years, totaling nearly 1.2 billion yuan in cash outflows [3][4] Industry Context - The struggles faced by CloudWalk are not isolated, as other members of the "AI Four Little Dragons" are also experiencing similar issues, including prolonged losses and workforce reductions [6]

当人工智能的浪潮席卷全球，北京正以科技创新之姿，成为AI大模型领域的战略高地。从智源研究院的"悟道"大模型问世，到"天使投资人"模式孵化顶尖 学者，再到月之暗面、DeepSeek、智谱等人工智能独角兽崛起，这座城市不仅汇聚了前沿技术，更以开放生态孕育突破性成果。 如今，北京正积极打造"全球开源之都"，一大批研发机构、企业积极拥抱开源，而开源也已深入到汽车、机器人等众多行业。发展AI将是一场科技长征， 新京报AI研究院将深度访谈此次AI浪潮的亲历者与见证人，讲述AI竞争新格局与背后的故事。 开栏语 清程极智联合创始人师天麾。受访者供图 人工智能是年轻的事业，也是年轻人的事业。 清程极智联合创始人师天麾正成为这句话的一个生动的注脚，而他的经历也是当前中国年轻一代AI高端人才的缩影——高中拿下信息学奥林匹克竞赛金 奖保送清华大学，大学确定了系统和高性能计算的研究方向，博士毕业后成为中国科学院计算技术研究所课程讲师、中国信通院万卡智算集群服务能力推 进方阵技术专家。 多个身份标签加持，互联网大厂曾向师天麾抛出高薪的橄榄枝，他最终却选择自己创业，理由也很简单，"做一些不同的事"。在他眼中，大厂"老板安 排"和KPI均是 ...

Core Viewpoint - The multi-modal model industry has not yet reached a "GPT-4 moment," as the lack of an integrated understanding-generating architecture is a significant bottleneck for development [1][3]. Company Overview - The company, founded by CEO Jiang Daxin in 2023, focuses on multi-modal models and has undergone internal restructuring to form a "generation-understanding" team from previously separate groups [1][2]. - The company currently employs over 400 people, with 80% in technical roles, fostering a collaborative and open work environment [2]. Technological Insights - The understanding-generating integrated architecture is deemed crucial for the evolution of multi-modal models, allowing for pre-training with vast amounts of image and video data [1][3]. - The company emphasizes the importance of multi-modal capabilities for achieving Artificial General Intelligence (AGI), asserting that any shortcomings in this area could delay progress [12][31]. Market Position and Competition - The company has completed a Series B funding round of several hundred million dollars and is one of the few in the "AI six tigers" that has not abandoned pre-training [3][36]. - The competitive landscape is intense, with major players like OpenAI, Google, and Meta releasing numerous new models, highlighting the urgency for innovation [3][4]. Future Directions - The company plans to enhance its models by integrating reasoning capabilities and long-chain thinking, which are essential for solving complex problems [13][18]. - Future developments will focus on achieving a scalable understanding-generating architecture in the visual domain, which is currently a significant challenge [26][28]. Application Strategy - The company adopts a dual strategy of "super models plus super applications," aiming to leverage multi-modal capabilities and reasoning skills in its applications [31][32]. - The focus on intelligent terminal agents is seen as a key area for growth, with the potential to enhance user experience and task completion through better contextual understanding [32][34].

民营经济促进法创下多个"第一"：我国第一部专门关于民营经济发展的基础性法律，第一次将坚持"两 个毫不动摇"写入其中的法律，第一次明确了民营经济的法律地位。 根据法律规定，民营经济是社会主义市场经济的重要组成部分，是推进中国式现代化的生力军，是高质 量发展的重要基础，是推动我国全面建成社会主义现代化强国、实现中华民族伟大复兴的重要力量。促 进民营经济持续、健康、高质量发展，是国家长期坚持的重大方针政策。 民营经济促进法共9章78条，涉及企业公平竞争、投融资促进、科技创新、规范经营、服务保障、权益 保护等内容。从首次提出到完成立法，仅用不到18个月时间。 作为一部基础性法律，如此迅速地推出与当前背景有关。王振江表示，民营经济在我国国民经济和社会 发展中的地位、作用持续提升。截至3月底，全国登记在册民营企业超过5700万户，占企业总量的 92.3%。 尽快完成立法，"营造有利于包括民营经济在内的各种所有制经济共同发展的法治环境和社会氛围，坚 定不移办好自己的事，不断巩固中国经济企稳回升、长期向好态势，以高质量发展的确定性应对外部环 境急剧变化的不确定性。"王振江说。 法律的生命在于实施。司法部副部长王振江表示，民营 ...

Core Insights - The departure of Dai Zonghong, co-founder and CTO of Zero One Everything, marks a significant shift as he embarks on a new entrepreneurial venture supported by Innovation Works [2] - Zero One Everything has reported an overall revenue of several hundred million this year, indicating a strong financial performance despite recent challenges [2] - The AI Infra team, once a pride of Zero One Everything, faced setbacks in 2024, leading to a strategic pivot towards consumer and business AI applications [4] Group 1: Company Developments - Dai Zonghong was responsible for the AI Infra team, which focused on optimizing training efficiency and reducing costs, achieving a 40% reduction in training costs for the Yi-34B model [3] - The company has been adjusting its project focus based on market product-market fit (PMF), including encouraging independent financing for commercially viable projects [2] - Following the departure of key personnel, including co-founders and early core members, the company has seen a significant reduction in its technical talent pool [4] Group 2: Market Position and Challenges - Zero One Everything struggled to keep pace with the emerging MoE (Mixture of Experts) architecture, resulting in a delayed launch of its flagship model Yi-Lightning [4] - The competitive landscape has shifted, with companies like ByteDance and DeepSeek dominating the high-performance model market, limiting Yi-Lightning's market impact [4] - The company has shifted its business focus towards consumer AI search applications and business model solutions, indicating a strategic realignment in response to market pressures [4]

今天值得关注的国内外新闻有： 【宏观要闻】 商务部：中方愿同各国一道加强合作 促进跨境电商健康可持续发展 5月2日起美国正式取消对华小额包裹免税政策，一些国家的零售商近期调整甚至暂停了对美业务。对此 商务部新闻发言人表示，跨境电商直接满足了各国消费者个性化的需求，具有效率高、到货快、费用省 等独特的优势，近期美方取消对华小额免税政策，这将损害两国企业和消费者的利益，对此我们坚决反 对。我想强调的是，美方取消对华小额免税政策，改变不了跨境电商快速发展的势头，我们愿意同各国 一道加强合作，共同营造公平可预期的政策环境，促进跨境电商健康可持续发展。(央视新闻） 美联储主席：不打算先发制人地降息，将继续观望 北京时间5月8日凌晨，美联储最新的议息决议将联邦基金利率的目标区间维持在4.25%-4.5%不变，符合 市场预期。此前3月，美联储同样维持上述利率不变，去年，美联储累计降息100个基点。 在随后的新闻发布会上，美联储主席鲍威尔表示，由于当前通胀仍高于目标，美联储不打算先发制人地 降息。在看到更多数据前，美联储并不知道该对这些数据作何反应。鲍威尔表示，现在判断美联储双重 任务—高就业率和稳定物价—中的哪一项更重要还 ...