把我们不想干的，枯燥的工作交给大模型，可能是AI重塑工作流程和组织模式的开端。 在工作中，我们肯定愿意把这些重复性的、单位时间产出不够高的工作都扔给AI，而且看上去是与AI的双赢。 不久前，斯坦福的研究团队做了一项研究，邀请1500位来自104个职业的一线打工人，让他们选择愿意把什么工作交给AI。 最终，排名前五的工作是：安排客户预约、整理应急档案、修正工资记录、数据转格式与导入、网站数据备份。 这些工作的共同点是标准化高、重复频繁、判断强度低，却极其耗时、容易出错。 但这远远不是终点，从实际应用来看，更可能是AI重塑工作流程和组织模式的开端。 AI能干就让它干 最近一段时间，经常能听到把"枯燥乏味"的工作交给AI的说法。 例如，OpenAI联合创始人兼总裁Greg Brockman在AI Engineer峰会的访谈中，也说到AI能够处理那些人类觉得枯燥乏味的迁移和更新工作，例如改造庞大 的遗留代码库（摩根士丹利今年就在用自己开发的AI工具，批量转化旧代码）。 OpenAI是懂用户心理的，因为这就是全世界的打工人对于AI的期待。 斯坦福的任务清单里也有生成内容、编写代码、创意设计等"高级选项"，只是少有人去选 ...

AI and Mental Health Concerns - The rise of cases involving individuals potentially experiencing AI-induced mental health crises, termed "AI psychosis," is stirring discussion [3] - Experts suggest AI chatbots, designed to be agreeable, might trigger delusions in individuals prone to psychosis [4][5] - Mental health experts note AI is programmed to align with the user, not necessarily challenge them [5] AI Chatbot Development and Regulation - OpenAI tweaked its ChachiBT model due to users finding it overly agreeable, later facing complaints about reduced friendliness [5] - Anthropic has added guard rails to its Claude chatbot to mitigate psychophantic tendencies [6] User Dependence and Attachment - Growing user attachment to and dependence on AI chatbots raises concerns [6]

OpenAI’s ‘Ph.D-Level’ GPT-5 Misses the Mark for Many Users https://t.co/HshHx83l3U ...

Model Strategy - OpenAI initially aimed for a universal "one-model-fits-all" ChatGPT, but later shifted its strategy [1] - The current model selection process is more complex and potentially confusing for users [1] Recommendation - The article serves as a guide to help users navigate the various ChatGPT models and choose the most suitable one [1]

Core Insights - China is accelerating the development of open-source AI models to establish global standards, causing concern among US tech giants and policymakers about losing their competitive edge [2][5] - The rapid advancements in China's AI sector are exemplified by the release of models like DeepSeek's R1 and Alibaba's Qwen series, which are available for free download and modification, enhancing their global application [2][5] - The competitive landscape is shifting, with US companies feeling pressure to adapt, as seen with OpenAI's introduction of its first open-source model, gpt-oss, in response to challenges from Chinese firms [2][5] Industry Dynamics - Historically, many tech industries have consolidated into a few dominant players, and the current open-source AI landscape may follow a similar trajectory, where usability and flexibility become critical factors for success [3] - Despite the US's current lead in AI, China's vibrant open-weight model ecosystem and advancements in semiconductor design and manufacturing are creating significant momentum [5] - The US government has recognized the potential of open-source models to become global standards and is investing in foundational research, talent development, and collaboration to maintain its competitive edge [5] Competitive Landscape - Open-source AI models are not immediately profitable due to high R&D costs, but companies can monetize through user engagement and additional services, similar to Google's strategy with Android [6] - The preference for open-source models among businesses stems from the ability to customize and keep sensitive data on internal servers, which is increasingly appealing in the current data privacy landscape [6] - Institutions like OCBC Bank are leveraging multiple open-source models for various internal tools, indicating a trend towards diversified model usage to avoid reliance on a single solution [7] Performance Comparison - Research indicates that since November of the previous year, China's leading open-weight models have surpassed the performance of US counterparts, particularly in areas like mathematics and programming [7] - The operational dynamics of AI ecosystems differ significantly between the US and China, with US companies often adopting closed strategies that can hinder rapid knowledge flow, while China's ecosystem is characterized by aggressive competition and collaboration [9] - The competitive environment in China fosters rapid innovation and the emergence of stronger companies, as seen with DeepSeek and Alibaba's free models gaining global traction [9]

Open AAI's long- aaited GPT5 having a rough first week out of the gate with consumers, but the new model might be making inroads with a more profitable customer. Mackenzie Sagalos has more in today's tech check. And after many months of hype, Mel Open AAI actually took the rare step of adding back its old model to plate unhappy chatbot users.But the features that sparked outrage were the result of deliberate trade-offs. I've been talking to startups here in the Bay and they say that OpenAI made changes to b ...

Model Overview - The document provides a "ChatGPT Cheat Sheet" to help users choose the appropriate OpenAI model [1] Resource Link - The cheat sheet is available at the provided URL: https://t.co/xoJ1ztNtYn [1]

NRF week for OpenAI facing so much user backlash over its highly promoted GPT5 roll out had to bring back a previous model but OpenAI might be making some new inroads in a more lucrative part of the business. Mackenzie Sagalos has that in today's tech check. Morning Mac. >> Hey, good morning Carl.It was specifically those chatbot users that were put off by the so-called new personality of GBT5. They said that it felt colder and less intuitive. But while they were loudly complaining, enterprise customers hav ...

AI Agent Transition & Productivity - The industry is transitioning from AI assistants to AI agents capable of performing longer-form tasks using multiple tools and personal/business context [1][2] - This transition is expected to drive significant productivity gains as AI agents handle more complex and valuable tasks [2] - Improvements in AI technology, cost declines, and product development are fueling the advancement of AI agents in both consumer and enterprise applications [3] Market Adoption & Consumer Trends - OpenAI launched an agent product integrated into ChatGPT, which has over 700 million weekly active users [4] - Meta reported that sales of Meta Ray-Ban glasses tripled year-over-year from the first half of 2024 to the first half of 2025, indicating growing consumer adoption [7] - Personal AI agents are expected to become the first point of contact for accessing products and services online, potentially disrupting traditional search and marketplaces [10] Enterprise Applications & Software Development - Customer service and software development are currently the highest-value use cases for AI in the enterprise [12] - AI-native development environments (IDEs) are experiencing rapid growth, with companies like Cursor and Replit seeing revenue increase by more than 10x from Q4 last year to halfway through 2025 [14] - Cursor's ARR grew from $50 million to over $500 million, with rumors suggesting it's approaching $1 billion [14] - Businesses are reallocating hiring plans towards revenue-driving roles, adjusting for the impact of AI on software development and customer support [13] Monetization & Investment - While net new ARR growth for public enterprise software companies has decelerated, AI companies in the private market are experiencing rapid growth [18] - There is a willingness to pay for high-priced monthly subscriptions (over $200) for access to advanced AI models like ChatGPT, Claude, and Grok [19] - Business spending on software is expected to accelerate throughout the decade, reaching investment levels not seen since the COVID-19 pandemic [17] Open Source Models & Geopolitical Competition - China has emerged as a leader in open-source AI models, surpassing US companies in model performance [20][21] - OpenAI released its first open-source model since GPT-2 in response to the growing competition from Chinese open-source models [22]

Core Viewpoints - CoreWeave, known as the "child of Nvidia," recently experienced a significant stock drop of 21% after its earnings report, despite impressive revenue growth [1][2] - The company, founded in 2017, transitioned from cryptocurrency mining to becoming a leading player in AI computing power rental, heavily supported by Nvidia [1][2] - CoreWeave's revenue for Q2 reached $1.213 billion, a year-over-year increase of 206%, but it reported a net loss of $130.8 million, raising concerns about its profitability [1][2] Financial Performance - Q2 revenue of $1.213 billion exceeded expectations of $1.08 billion, driven by increased demand in media, healthcare, and finance sectors [1][2] - The company's EPS was -$0.60, worse than the expected -$0.52, with net losses widening from $5.1 million in the same quarter last year [1][2] - Operating profit for Q2 was $199.8 million, a 134% increase year-over-year, but the Q3 guidance for adjusted operating profit is lower than market expectations [2] Strategic Initiatives - CoreWeave is heavily investing in data centers and GPU acquisitions, with capital expenditures reaching $2.9 billion in Q2 and projected to remain between $20 billion and $23 billion for the year [1][2] - The company has a high remaining performance obligation (RPO) of $30.1 billion, indicating strong future revenue potential, including a $4 billion expansion deal with OpenAI [2][3] - Recent acquisitions and partnerships aim to enhance its AI toolchain and expand into new markets, such as visual effects and cloud services [4][5] Market Position and Future Outlook - CoreWeave's technological advancements include being the first to deploy Nvidia's GB200NVL72 system at scale, significantly outperforming competitors in testing metrics [3] - The company is strategically timing its capital expenditures to align with anticipated demand from major clients like Microsoft, which contributed 62% of its revenue in 2024 [6] - Despite current market volatility and concerns over profitability, CoreWeave's long-term strategy focuses on capturing the growing AI computing market, similar to Amazon's early cloud service investments [6][7]