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和AI这道正餐相比,前几十年的科技总和只是前菜
Hu Xiu· 2025-09-19 06:12
过去六十年,科技一次次给人类带来新的惊喜。每一代技术都像是一道前菜:精致、美味、甚至足以改变人的食欲,让人期待下一道菜的到来。但它们终 究只是开胃,而非真正的正餐。 今天,当AI走到我们眼前时,我越来越坚信:AI不是前菜,而是真正的正餐。它不再只是某个新工具的出现,而是我们使用工具、理解世界、生产价值 方式的根本性重写。 如果说半导体是第一把锋利的菜刀,个人电脑是人类第一次拥有了属于自己的餐桌,互联网则把全球所有餐桌连在一起,移动互联网更是把餐厅搬进了每 个人的口袋——那么AI就是那场真正的正宴。前面的几十年,我们只是不断准备食材、布置餐桌,而今天,正餐终于要上桌了。 前六十年的前菜 很多人喜欢把科技史讲得抽象,但我更愿意用故事来描绘。 • 1960年代,半导体:摩尔定律第一次被实践出来,晶体管像一个个微小的火苗,把算力的火点亮。硅谷由此诞生,像是一间厨房第一次配备了真正的炉 灶。 • 1980年代,个人电脑:人们第一次把计算能力带进家里。微软的软件生态,就像给人们提供了刀叉和餐具。第一次,普通人有了自己的餐桌。 • 1990年代,互联网:连接了全世界的信息高速公路。Google、Amazon崛起,餐桌之间连 ...
计算机变成“天坑”专业了吗?
虎嗅APP· 2025-08-15 13:56
Core Viewpoint - The article discusses the challenging job market for computer science graduates in the U.S., highlighting a significant increase in unemployment rates and underemployment compared to other fields, exacerbated by the rise of AI technologies that reduce demand for entry-level positions [4][15][21]. Group 1: Employment Statistics - Among graduates aged 22-27, computer science and computer engineering majors face some of the highest unemployment rates at 6.1% and 7.5%, respectively [7]. - In contrast, biology and art history graduates have unemployment rates of only 3%, indicating that computer science graduates are experiencing more than double the unemployment rate of these fields [8]. - The median early career wage for computer science graduates is $80,000, while mid-career wages reach $122,000, but these figures are overshadowed by the high unemployment rates [9]. Group 2: Job Market Challenges - The article highlights individual stories of graduates like Manasi Mishra and Zach Taylor, who faced significant difficulties in securing job offers despite applying to thousands of positions [21][19]. - Taylor applied for 5,762 tech-related jobs, receiving only 13 interview invitations and ultimately no job offers, leading him to seek employment at McDonald's, where he was rejected due to lack of experience [21]. - The proliferation of AI programming tools has led to a decrease in demand for junior software engineers, contributing to the employment challenges faced by computer science graduates [15][21]. Group 3: AI's Impact on Job Applications - Graduates are increasingly using AI tools to enhance their resumes and automate job applications, but this has created a "doom loop" where AI systems also filter out candidates, leading to frustration among job seekers [25]. - Some graduates, like Audrey Roller, have chosen to avoid using AI in their applications, but still face rejection due to algorithmic decision-making processes [25]. - The article notes that the job market is becoming increasingly competitive, with many graduates feeling trapped in a cycle where they must rely on AI to apply for jobs while being rejected by AI systems [25]. Group 4: Comparison with Domestic Job Market - In contrast to the U.S., the job market for computer science graduates in China appears more optimistic, with companies like ByteDance and Tencent ramping up recruitment efforts, particularly in AI-related positions [26][29]. - ByteDance has announced over 5,000 job openings, with a 23% increase in demand for R&D roles, while Tencent's recruitment efforts emphasize AI as a key focus [29][30]. - Despite the positive outlook in China, the competition remains fierce due to the increasing number of graduates in the field [35].
投了 5762 份简历 0 offer,这个名校计算机系毕业生,最后被麦当劳拒绝了
3 6 Ke· 2025-08-15 06:00
计算机也要变成「天坑」专业了吗? 纽约时报最近最近一篇报道,调查了计算机专业毕业生的就业情况,里面提到残酷的就业困境,美国计算机应届毕业生根本找不到工作,甚至去麦当劳打 工也没有人要。 纽约时报文章,Chipotle 是一家美式墨西哥速食连锁店。 22-27 岁的大学毕业生中,计算机科学和计算机工程专业毕业生面临着最高的失业率之一,分别为 6.1%和 7.5%。 而生物学和艺术史毕业生失业率竟然才 3%,计算机是它们的两倍以上。 Labor Market Outcomes of College Graduates by Major 专业毕业生的劳动力市场结果 | MAJOR | | UNEMPLOYMENT | UNDEREMPLOYMENT | MEDIAN WAGE | MEDIAN WAGE | SHARE WITH GRADUATE | | --- | --- | --- | --- | --- | --- | --- | | 专业 | | RATE | RATE | EARLY CAREER | MID-CAREER | DEGREE | | ▲ ਚ | | 失业率 A | 未充分就业率 A | 宫 ...
计算机变成“天坑”专业了吗?
Hu Xiu· 2025-08-15 04:29
Group 1 - The article discusses the alarming employment situation for computer science graduates in the U.S., highlighting that many are struggling to find jobs even in fast-food chains like McDonald's [1][11][12] - Unemployment rates for computer science and computer engineering graduates are notably high, at 6.1% and 7.5% respectively, which is more than double the rates for biology and art history graduates [4][5] - The rise of AI programming tools is contributing to a decrease in demand for entry-level software engineers, exacerbating the job market challenges for computer science graduates [24][25] Group 2 - A significant number of graduates, such as Zach Taylor, have applied for thousands of jobs (e.g., 5,762 applications) but received very few interview invitations, indicating a highly competitive job market [23][21] - The article mentions that graduates are increasingly relying on AI tools to enhance their resumes and applications, but this has led to a cycle where AI systems also filter out candidates, making it harder for them to secure interviews [24][25] - In contrast, the job market for computer science graduates in China appears more optimistic, with many companies actively hiring and emphasizing AI-related skills in their recruitment [28][30][34]
“利润率要么是0,要么为负”!最火的AI应用竟只是“为大模型打工”?
Hua Er Jie Jian Wen· 2025-08-12 03:31
Core Insights - The AI programming assistant market appears prosperous, but many unicorn companies are facing significant losses due to high costs associated with large language model usage [1][5] - Despite soaring revenues, AI programming companies are experiencing negative profit margins, raising concerns about the sustainability of their business models [2][4] Financial Performance - Anysphere's parent company, Cursor, reached $500 million in annual recurring revenue (ARR) in June, marking the fastest achievement of $100 million ARR in SaaS history [2] - Replit's annual revenue surged from $2 million in August last year to $144 million recently, while Lovable grew from $1 million to $100 million in annual revenue within eight months [2] Profitability Challenges - AI programming companies like Windsurf are struggling with operational costs that exceed their revenue, leading to significantly negative gross margins [4][5] - The gross margins for AI programming companies generally range from 20% to 40%, not accounting for costs incurred from serving free users [4] Cost Structure - The high costs of large language model calls are the primary burden on profits, with these expenses increasing as user numbers grow, contrary to traditional software models [5][6] - The variable costs for startups in this sector are estimated to be between 10% and 15%, making it a high-cost business if not involved in model development [5] Strategic Options - AI programming companies are faced with difficult choices, including developing their own models, being acquired, or passing costs onto users [7][8] - Anysphere announced plans for self-developed models, but progress has been slow, and some companies, like Windsurf, have abandoned this route due to high costs [8] Industry Outlook - The profitability crisis in the AI programming sector raises questions about the sustainability of the entire industry [9] - Direct competition from model providers like OpenAI and Anthropic poses additional challenges, as they are both suppliers and competitors [9] - Investor concerns are growing regarding user loyalty, as users may quickly switch to superior tools developed by competitors [9]
夯实数字化底座!国金证券打造“AI友好型组织”
券商中国· 2025-08-11 23:31
Core Viewpoint - The article emphasizes the role of digital finance in empowering the transformation and upgrading of the securities industry, highlighting the importance of technology in enhancing service capabilities and operational efficiency [2][3]. Group 1: Digital Transformation in Securities Industry - The securities industry is undergoing a digital transformation driven by emerging technologies like artificial intelligence (AI), with firms like Guojin Securities leading the way [2]. - Guojin Securities has implemented a strategy focused on leveraging technology to enhance core business areas such as investment banking, institutional services, and wealth management [2][3]. Group 2: Business Development and Efficiency - Guojin Securities has developed an integrated investment banking platform that incorporates intelligent document review and writing functions, significantly improving work efficiency and quality [4]. - In 2023, the platform assisted in reviewing over 6,000 documents and comparing more than 20,000 data streams, enhancing data comprehensiveness and timeliness [4]. Group 3: Research and Institutional Services - The company has invested in digital technologies to improve analysts' efficiency in information acquisition and processing, utilizing tools like big data and intelligent search [5]. - Since the beginning of 2023, Guojin Securities has served over 50,000 online institutional clients and produced more than 1,700 digital reports covering nearly 20 industries [5]. Group 4: Continuous Investment in Technology - Guojin Securities has maintained a high level of investment in technology, with a significant portion of revenue allocated to tech development, positioning itself as a leader in the industry [6]. - The company launched its first AI advisory service in June 2025, focusing on providing accessible and customized investment support to retail investors [6]. Group 5: AI Integration and Organizational Culture - The rise of generative AI technologies has prompted Guojin Securities to explore and apply large model capabilities across various financial scenarios [7][8]. - The company aims to create an "AI-friendly organization" to foster collaboration and innovation, enhancing its competitive edge in the market [8][9].
编程“学废”了?普渡毕业却只获烤肉店面试!美国IT失业创新高:AI面试成最大屈辱,网友怒称宁愿失业!
AI前线· 2025-08-11 05:30
Core Viewpoint - The article discusses the challenges faced by recent computer science graduates in the U.S. job market, highlighting a significant increase in unemployment rates and the impact of AI on job opportunities in the tech industry [6][10][19]. Group 1: Job Market Trends - Since 2025, the U.S. IT job market has been experiencing a downturn, with the Bureau of Labor Statistics (BLS) revising down job growth figures for May and June, indicating a continued decline in job openings [7][10]. - The total number of IT jobs has decreased by 26,500 this year, significantly higher than the 6,200 job losses in the same period last year [7][8]. - The unemployment rate for the IT sector reached 5.5% in June, surpassing the national average of 4.2% [10]. Group 2: Impact of AI on Employment - The proliferation of AI programming tools has led to a reduced demand for entry-level software engineering positions, which are typically sought after by recent graduates [5][12]. - Many tech companies are adopting AI systems to screen resumes and conduct initial interviews, making it more challenging for candidates to stand out [13][19]. - Graduates report feeling trapped in a cycle where they must use AI tools to apply for jobs, while companies use AI to filter out applicants, creating a paradoxical situation [13][18]. Group 3: Graduate Experiences - Recent graduates have shared their frustrations, with some applying to thousands of positions without success, leading to feelings of despair and disillusionment [11][12]. - The job application process has become increasingly difficult, with many candidates facing automated assessments and AI interviews that lack human interaction [11][20]. - Some graduates express a preference for not participating in AI interviews, feeling that it undermines their dignity and the value of human interaction in the hiring process [15][17].
Jinqiu Select | 价格即品牌:AI产品定价如何重塑企业增长逻辑
锦秋集· 2025-07-28 14:38
Core Insights - The article emphasizes that sustainable growth for companies is driven by two engines: market share and wallet share, which must be balanced to avoid stagnation or financial difficulties [1][10] - The rise of AI technology has shifted pricing strategies from user count to actual usage and the value created, making pricing a strategic decision throughout product design and operations [2][3] Pricing Strategies - A growing number of AI companies are adopting hybrid pricing models that combine subscription fees with usage-based billing, though designing these models can be complex [4][5] - Clay's pricing strategy exemplifies hybrid pricing, offering a subscription package with usage credits, which encourages customer retention and avoids revenue erosion from large discounts [5] - The popularity of hybrid pricing is attributed to its ability to smoothly transition from traditional models, provide natural upsell paths, safeguard profits, and maintain predictable costs for customers [6][7] Common Pricing Models - Various pricing models are discussed, including pay-as-you-go, capped usage fees, and platform fees combined with usage fees, each with its own advantages and challenges [8][9] - Companies should adapt their pricing strategies based on their product's value delivery and customer preferences, potentially combining different models as they grow [9] Market Share and Wallet Share Strategy - Companies must focus on both acquiring new customers (market share) and maximizing revenue from existing customers (wallet share) to achieve sustainable growth [10][11] - Early-stage companies should prioritize product development and user growth, while later stages should enhance monetization capabilities, ensuring both engines are operational [11] Pricing Misconceptions - Entrepreneurs often fall into pricing traps by focusing too heavily on one growth engine, leading to missed opportunities or customer loss [13][14] - Common pitfalls include overemphasizing market share at the expense of retention, complicating pricing structures, and misjudging the relationship between price and perceived value [14] Value Attribution and Pricing Models - A 2x2 pricing model framework is proposed, categorizing pricing strategies based on value attribution and autonomy, guiding entrepreneurs in selecting appropriate pricing paths [15][17] - The ultimate goal is to reach a results-based pricing model, where companies charge based on measurable outcomes, significantly increasing their pricing power [18] Core Principles of Pricing Strategy - Key principles include focusing on the most valuable product features, overcoming price anxiety, and attracting the right customers to reduce churn [19] - Companies should ensure that core value is not given away for free and should be willing to adjust pricing based on the value provided [19] Organizational Changes and Challenges - Transitioning to usage-based pricing necessitates significant internal operational changes, requiring a redefinition of roles and processes across departments [20][21] - Establishing clear pricing responsibilities and collaborative processes is crucial to avoid decision-making paralysis as companies scale [21] Strategic Leadership in Pricing - CEOs must lead pricing strategy changes, setting clear timelines and accountability to ensure successful implementation across the organization [22][23] - Pricing should be integrated into product experience and brand strategy, reflecting the company's value proposition and differentiating it from competitors [23][24] AI Market Dynamics - The shift towards usage-based pricing is driven by structural factors, making it essential for companies to adapt their organizational frameworks to support this model [24][25] - Companies that effectively implement usage-based pricing can gain a competitive edge, as customer loyalty becomes harder to disrupt once established [25]
因为微软,OpenAI收购“AI编程独角兽”Windsurf失败,谷歌“黄雀在后”
Hua Er Jie Jian Wen· 2025-07-12 04:08
Core Viewpoint - OpenAI's acquisition talks for AI programming assistant Windsurf, valued at $3 billion, collapsed due to Microsoft's intellectual property concerns, leading to Google acquiring the technology instead [1][2]. Group 1: Acquisition Details - The acquisition negotiations lasted several months and ended recently, with Windsurf's team expressing concerns about how their programming assistant would integrate with OpenAI and Microsoft's existing agreement [1]. - Google will hire Windsurf's CEO Varun Mohan and some employees, obtaining a non-exclusive license for the company's technology, while Windsurf will continue to operate independently with around 250 employees [1][3]. - The failed acquisition highlights the intense competition among major tech companies in the AI programming tools sector and how complex partnerships can impact industry consolidation [1][4]. Group 2: Microsoft Agreement as a Barrier - OpenAI's existing agreement with Microsoft, which grants Microsoft exclusive hosting rights for OpenAI models on its cloud platform and access to OpenAI's intellectual property until 2030, was a significant obstacle to the acquisition [2]. - OpenAI attempted to exempt Windsurf from this agreement but was unsuccessful, and during negotiations, Anthropic restricted Windsurf's access to its models, complicating the situation further [2]. Group 3: Google's Strategic Move - By acquiring Windsurf's technology license and key personnel, Google aims to enhance its competitiveness in the AI programming tools market without the complexities of a stock acquisition [3]. - This "talent + technology licensing" approach has become a popular strategy among major tech companies to acquire AI talent, as seen in previous deals by Google, Microsoft, and Meta [3]. Group 4: Market Competition - The AI programming tools market is becoming a focal point for tech companies, with products that significantly improve software development efficiency [4]. - Cursor, another prominent startup in this space, recently surpassed $500 million in annual recurring revenue and declined acquisition offers, indicating the high valuation and scarcity of quality AI programming tool companies [4][5]. Group 5: Windsurf's Future - Windsurf, previously known as Codeium, is well-regarded in the developer community, and under the new arrangement, its business head Jeff Wang will become the new CEO, leading the team to develop programming tools for large enterprises [5]. - Non-traditional acquisition agreements allow major tech companies to quickly recruit top AI researchers while avoiding lengthy merger review processes, giving them an edge in the talent competition [5].
放心,为什么说AI永远杀不死真正的程序员?
3 6 Ke· 2025-07-02 07:10
Core Insights - The article argues that technology does not replace skills but rather elevates them to a higher dimension, as evidenced by historical trends in the tech industry [1][11] - The narrative surrounding AI programming tools suggests they will replace programmers, but the reality is that they will lead to a transformation of roles rather than elimination [3][12] Group 1: Historical Context of Technology in Programming - Previous technological advancements, such as no-code and low-code tools, were expected to eliminate the need for programmers but instead created new high-paying roles like no-code experts and backend integration engineers [5][6] - The cloud computing revolution did not eliminate system expertise; instead, it transformed roles, leading to the emergence of DevOps, which commands significantly higher salaries [7][8] - Offshore development was initially seen as a cost-saving measure, but it faced challenges related to communication and quality, leading to a realization that effective software development requires deep business understanding and collaboration [9][10] Group 2: The Current AI Programming Assistant Revolution - AI programming assistants promise to automate code writing, but early experiences show that AI-generated code often contains errors, requiring experienced engineers to spend time correcting them [10][12] - The article emphasizes that while AI can optimize specific functions, it struggles with overall system design, which is crucial for maintaining a sustainable codebase [12][14] - The ability to design system architecture remains a critical skill that AI cannot replicate, highlighting the ongoing need for skilled engineers in the industry [4][14]