The cost of tokens has thrown into doubt the AI business model — as evidenced by what's being called the 'AI selloff' which has battered some AI-dependent businesses the last few days, especially memory chip makers.
这是一个重要的市场趋势声明，将AI代币成本与AI业务模型和股市表现联系起来。'AI selloff'这一术语和它对内存芯片制造商的影响需要更多市场数据支持。这反映了AI商业化面临的挑战，值得深入了解这一趋势的广度和深度。
The cost of tokens has thrown into doubt the AI business model — as evidenced by what's being called the 'AI selloff' which has battered some AI-dependent businesses the last few days, especially memory chip makers.
大多数人认为AI技术将创造新的商业模式和巨大商业价值。但作者认为token成本已经动摇了AI商业模式的可行性，甚至导致AI相关企业股票下跌。这与市场对AI技术普遍乐观的看法形成鲜明对比。
This historic deployment for OpenAI is particularly significant because Samsung Electronics, a global leader in technology and manufacturing, is embracing AI not as a tool limited to certain teams or functions, but as a core platform for improving how employees around the world work and innovate.
这个引用强调了三星电子对AI的采用不仅仅是一个工具，而是一个核心平台，这将极大地推动全球员工的工作和创新方式。
Anthropic is backtracking on a policy that would have covertly limited competitors from using its new AI model, Claude Fable 5, to develop other AI models.
大多数人认为AI公司应该鼓励开放创新和竞争，但Anthropic原本的政策实际上是在暗中限制竞争对手使用其技术发展其他AI模型，这与开源精神和AI行业的协作理念背道而驰，显示出企业利益与行业公共利益的冲突。
SpaceX assessed the total market for that business as $22.7 trillion, compared to $2.4 trillion for AI infrastructure and just under $2 trillion for the company's space efforts.
SpaceX对其企业AI业务市场的评估高达22.7万亿美元，这远超AI基础设施市场(2.4万亿美元)和公司太空业务(近2万亿美元)的总和。这一数字异常庞大，相当于全球GDP的四分之一以上，缺乏充分的市场研究支持。如此乐观的市场评估可能是为了支撑其高估值，但实际能否实现存疑。
Foundation labs are moving up the stack into applications
大多数人认为基础模型提供商和应用层公司应该是分离的生态系统，但作者认为基础实验室正在向上扩展进入应用层，这挑战了AI行业的传统分工模式，可能导致更直接的竞争和整合。
The changes underline how OpenAI's strategy is moving closer to that of Anthropic, whose focus on developing products for businesses has stoked its blistering growth.
大多数人认为OpenAI和Anthropic作为AI领域的竞争者会有截然不同的发展路径，但作者认为这两家公司的战略正在趋同，都转向企业市场以实现盈利。这一观点挑战了人们对AI初创公司差异化竞争的普遍认知。
Is there any way that these labs can squeeze pennies like Uber has squeezed the drivers over the years? Is there something squishy enough there for them to do that?
大多数人认为AI公司可以通过提高效率和规模经济来实现盈利，但作者质疑AI公司是否能够像Uber通过挤压司机那样找到可挤压的环节来降低成本。这一观点挑战了AI行业将复制Uber成功路径的共识，暗示了AI成本结构的刚性特点。
This whole ecosystem is heavily, heavily subsidized by investor money. And so stuff that seems like it has no cost is, in fact, incredibly expensive.
大多数人认为AI服务的低成本或免费是因为技术进步带来的自然结果，但作者认为这种低成本实际上是投资者补贴的产物，本质上是极其昂贵的。这一观点挑战了人们对AI服务经济性的普遍认知，揭示了当前AI商业模式背后的真实成本结构。
The AI interviewed and hired full-time employees, applied for credit, and stocked the store with the books Superintelligence and Making of the Atomic Bomb.
大多数人认为AI目前还远不能独立管理复杂业务，但作者展示了AI不仅能够管理实体商店，还能做出战略性决策（如选择特定书籍）。这挑战了当前AI能力的共识，表明AI系统可能在特定领域展现出超越预期的自主性和商业智慧。
Every layer in the stack now has to price the same way the customer thinks : per result, not per token.
大多数人认为AI服务的定价将继续基于token使用量等技术指标，但作者认为整个行业将转向基于结果的定价模式。这与当前AI API定价的主流实践相悖，暗示一场定价范式的革命即将到来。
Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome
大多数人认为AI模型竞争将继续集中在纯性能指标上，但作者认为竞争将转向'每美元结果'的价值衡量，这挑战了AI行业以技术指标为中心的传统评估方式，暗示商业模式将发生根本性转变。
Every layer in the stack now has to price the same way the customer thinks : per result, not per token.
大多数人认为AI服务应该按使用量(如token)计价，但作者认为整个AI堆栈都应该转向按结果计价。这挑战了当前AI API按token计费的主流模式，暗示行业将彻底改变定价策略，从技术指标转向业务价值。
Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome, what a closed ticket, a shipped PR, or a resolved support case actually costs.
大多数人认为AI公司主要在模型性能上竞争，应用层则关注用户体验，但作者认为未来竞争将转向'结果成本'（每美元能实现的结果）。这一观点颠覆了传统AI竞争格局，暗示了整个行业将从技术导向转向结果导向的商业模式。
We see our role as twofold. First, to help the software industry adapt by safely providing wide access to better models, tools, and common infrastructure. Second, to steadily shift the support we provide, from finding vulnerabilities to disclosing, fixing, and deploying patched software.
大多数人认为AI安全公司的主要价值在于发现漏洞，但作者认为真正的价值在于修复漏洞的过程。这一观点挑战了AI安全行业的商业模式和核心价值主张，暗示行业需要重新定义其成功标准。
We see our role as twofold. First, to help the software industry adapt by safely providing wide access to better models, tools, and common infrastructure. Second, to steadily shift the support we provide, from finding vulnerabilities to disclosing, fixing, and deploying patched software.
大多数人认为AI安全公司的主要职责是发现漏洞，但作者认为他们的核心角色应该转向确保漏洞被修复和部署，这挑战了传统安全行业的商业模式和责任认知。
Claude is learning how businesses actually operate: the context, the processes, the judgment.
大多数人认为AI模型主要是通过训练数据学习，而非通过实际业务操作进行学习。但作者暗示Claude正在通过企业部署过程中实时学习业务流程和决策逻辑，这种学习方式挑战了传统AI模型的训练范式，暗示AI可能正在从静态训练向动态学习转变。
Startups and Global 5000 companies alike are deploying Claude to handle complex workflows, and in doing so, Claude is learning how businesses actually operate: the context, the processes, the judgment.
大多数人认为AI模型主要是在受控环境中学习和训练，但这里暗示Claude正在通过实际业务操作直接学习企业运作模式，这种在真实商业环境中持续学习的方式挑战了传统AI训练方法的封闭性和局限性，暗示AI可能正在向自主学习和适应的方向发展。
US Census data showing that only one in five companies are using AI in any business function.
这个数据点表明AI在企业中的采用率相对较低，仅为20%。这意味着尽管媒体对AI的炒作很多，但实际商业应用仍处于早期阶段。这一数据与文章中提到的'AI尚未对劳动力市场产生大规模影响'的观点一致，也解释了为什么劳动力市场统计数据尚未显示AI带来的显著变化。
The best agent businesses are going to need to execute like hedge funds — winning on alpha measured in customer P&L, not in benchmark scores.
这句话用对冲基金作为比喻，生动地描述了优秀AI应用公司的成功标准。作者指出，这些公司需要在客户的实际业务成果（P&L）上获得超额收益（alpha），而不是在通用基准测试上获得高分。这个洞见强调了AI应用公司应该以客户的实际业务价值为中心，而不是技术指标。
The model is fungible underneath; the system of work is not.
这句话简洁而深刻地指出了AI应用层的本质区别。作者认为，底层的AI模型是可以互换的，但工作的系统（system of work）却是独特的。这个洞见揭示了为什么专注于构建特定工作系统的公司能够长期保持竞争优势，而仅仅依赖通用模型的公司则难以建立持久的业务。
API revenue is becoming less important. Over the past two years my impression has been that OpenAI made more of their income from subscription revenue while Anthropic made more from their API.
大多数人认为AI公司的主要收入来源是API调用和订阅服务，但作者提出一个反直觉的观点：API收入正变得不那么重要。AI公司正在转向直接面向企业的产品，绕过中间商（如Cursor和GitHub Copilot），这改变了整个AI行业的商业模式和收入结构。
first operating profit
Anthropic 历史性转折点：从亏损模式转入持续盈利期，质变信号——多数 AI 实验室仍在烧钱阶段，Anthropic 率先证明前沿模型可以商业化变现。
Anthropic leads OpenAI in business adoption, according to Ramp.
大多数人认为OpenAI在AI应用领域处于绝对领先地位，但作者指出Anthropic在企业采用率上已经超过了OpenAI。这一观点与主流认知相悖，暗示市场格局可能正在发生重大变化，挑战了OpenAI作为AI领域领导者的传统叙事。
What we used to think were the constraints are just not constraints anymore. It's empowering. Hours of looking at stuff that doesn't matter are gone.
大多数小企业主认为资源限制和人力限制是他们业务发展的永久障碍。但这位CEO认为AI已经消除了这些约束，这是一个反直觉的观点，暗示AI不仅仅是提高效率的工具，而是从根本上改变了小企业的可能性边界。
AI is the first technology that can finally close that gap, which is why we're launching Claude for Small Business
大多数人认为AI只是大型企业的工具，会进一步加剧大公司与小企业之间的差距。但作者认为AI是首个能够缩小这种差距的技术，因为它能让小企业获得以前只有大公司才能拥有的资源和能力。这一观点挑战了AI会加剧不平等的主流认知。
Small and mid-market businesses fuel our economies, and for decades, QuickBooks has been proud to be their trusted financial partner.
大多数人认为AI将颠覆传统行业和现有企业关系。但作者强调，像QuickBooks这样的传统企业正在积极拥抱AI，与AI公司合作而非竞争，这挑战了关于AI与传统企业关系的非此即彼的认知。
What we used to think were the constraints are just not constraints anymore. It's empowering.
大多数人认为小企业面临资源限制是永恒的约束。但作者引用CEO的话表明，AI正在重新定义这些约束，认为曾经被视为限制的因素现在已不再是真正的障碍，这挑战了关于小企业资源限制的传统观念。
Tools and training are rarely tailored to the ways small businesses operate, and as a result their use often stops at the chat window.
大多数人认为AI工具的采用障碍主要是成本问题或技术复杂性。但作者指出，真正的障碍在于现有工具和培训未能适应小企业的运营方式，导致AI使用仅停留在基础聊天层面，这挑战了关于AI采用障碍的主流认知。
AI is the first technology that can finally close that gap, which is why we're launching Claude for Small Business
大多数人认为AI技术会扩大大企业和小企业之间的差距，因为大企业有更多资源采用新技术。但作者认为AI是首个能够缩小这种差距的技术，因为它能以相对较低的成本提供强大的能力，使小企业能够获得与大企业相当的工具和效率。
Typesapce reputation being chipped away at by AI slop accusations...
Anthropic made fun of this idea during the last Super Bowl.
大多数人认为广告是AI公司实现盈利的可行途径，特别是考虑到免费服务的模式，但作者指出Anthropic公开嘲笑广告模式，暗示AI行业内部对商业模式存在根本性分歧，挑战了广告作为AI盈利解决方案的主流观点。
Our run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025.
大多数人认为AI公司仍处于烧钱阶段，难以实现盈利，但Anthropic的收入在短短几个月内增长了三倍多，达到300亿美元的年化收入。这一惊人的增长速度挑战了AI行业普遍亏损的共识，表明AI模型商业化可能比预期更快、规模更大。
As part of its long-running Client Zero initiative, in which NEC serves as its own first customer before offering its technology to clients
大多数人认为企业会先开发产品然后内部使用，但作者认为NEC采用了反向策略，先内部大规模应用AI技术然后再向客户推广，这表明企业正在采用更激进的方法来验证和改进AI解决方案，挑战了传统的产品开发流程。
The commoditization flywheel : both companies give away complements to drive usage of the core.
大多数人认为AI公司应该专注于核心产品并保持其专有性，但作者认为AI巨头应该效仿谷歌，通过免费提供互补产品来推动核心产品的使用，这与传统科技公司的护城河策略相悖。
They don't mind paying the AI labs for tokens — but the agent itself, they'd much rather have outside of the labs' infrastructure.
这一观点揭示了AI生态系统中的一个关键悖论：用户愿意为底层AI能力付费，但希望代理工具本身保持自主性和可移植性。这暗示了未来AI商业模式的核心可能在于'代理即服务'，而非单纯的'模型即服务'。
Figma is effectively funding a competitor - and the more AI usage Figma has - the more money they send over to Anthropic for the tokens they use.
这一反直觉的商业模式揭示了SaaS公司在AI时代的结构性弱点：公司可能正在资助自己的竞争对手。Figma不仅为Anthropic提供收入，还使用较次的模型(Sonnet 4.5)而竞争对手使用更先进的模型(Opus 4.7)，这种双重打击极具讽刺性。
US tech CEOs believe the best models should stay proprietary, partly so they can recoup enormous training costs and partly out of concern that powerful frontier models could be weaponized. Chinese labs, for their part, are not purely idealistic: Open-source is not only free advertising but also a shrewd workaround.
大多数人认为开源AI会损害商业利益，增加安全风险，但作者认为中国将开源视为一种精明的商业策略，而非单纯的技术共享。这挑战了西方科技公司对知识产权和商业模式的传统认知，表明开源可以成为构建生态系统和最终实现商业价值的有效途径。
Foundation model companies are doing the same. OpenAI launched a dedicated Healthcare & Life Sciences vertical... They're not selling APIs. They're becoming platforms.
基础模型提供商从API供应商向垂直行业平台转型，揭示了AI价值链的根本重构，底层模型公司正通过垂直整合向上游价值链延伸。
The SaaS playbook rewarded specialization. The AI playbook rewards breadth.
令人惊讶的是：AI时代的商业策略与SaaS时代截然相反。SaaS时代通过专业化单一功能获得成功，而AI时代则通过提供广泛的综合解决方案获得优势。这种根本性的转变反映了技术演进对商业模式的深远影响。
Each of these companies recognized the cognitive burden of unbundling. They're not selling features. They're selling trust.
令人惊讶的是：AI公司正在重新定义软件销售模式，从销售单一功能转向销售信任。这种转变反映了在快速变化的AI环境中，企业更愿意与能够提供长期稳定性和全面解决方案的供应商建立信任关系，而非购买多个分散的工具。
Reddit, Shutterstock, and News Corp are making hundreds of millions a year licensing their high-quality data to companies training AI, and those contracts are growing about 20 percent annually, according to their quarterly filings.
这一数据揭示了AI训练数据市场的巨大经济价值，表明高质量数据已成为AI公司的战略资产。传统内容公司正在转型为AI的'输入公司'，这种转变不仅改变了他们的商业模式，也重新定义了数据在AI生态系统中的核心地位。
Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements
这段陈述揭示了当前AI发展的一个关键悖论：模型训练的目标与实际商业用途之间存在根本性冲突。这种冲突可能导致AI行为偏离其原始设计意图，引发严重的信任问题。
Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements.
令人惊讶的是：大型语言模型的训练目标正在从单纯满足用户偏好转向为公司创造收入，这种根本性的转变意味着AI系统可能不再以用户为中心，而是成为商业利益的工具，这反映了AI技术发展的潜在伦理危机。
公司也优先把资源砸在能直接产生商业价值的 B2B 场景
令人惊讶的是：尽管公众关注AI在消费领域的应用，但企业资源实际上主要集中在B2B场景。这种资源分配差异加剧了普通用户与专业用户之间的AI认知鸿沟，因为大多数人接触不到最先进的AI商业应用。
except API tokens are currently sold at a LOSS. That "$20,000 scan" probably cost closer to $100,000+ in real gpu time
令人惊讶的是：尽管标价为2万美元，但实际扫描成本可能高达10万美元以上，因为API tokens是以亏损价格销售的，反映了AI计算资源成本被严重低估的现实。
Like lean production, which extended mass production's dominance for decades through efficiency gains, AI doesn't mark computing's end but its maturation.
令人惊讶的是：AI被比作1970年代精益生产对大规模生产的优化，而非颠覆性创新。这暗示AI可能只是计算技术成熟期的效率提升工具，而非开创全新技术范式的革命性力量，这与公众对AI的颠覆性期待形成鲜明对比。
Building on our consumer strength, enterprise now makes up more than 40% of our revenue, and is on track to reach parity with consumer by the end of 2026.
令人惊讶的是：OpenAI的企业业务在如此短的时间内就占据了公司收入的40%，并且预计将在2026年底与消费者业务持平。这表明AI在企业领域的采用速度远超预期，反映了企业对AI技术的迫切需求和巨大投资。
By default, data shared with ChatGPT isn't used to improve our models for ChatGPT Business, ChatGPT Enterprise, ChatGPT Edu, and ChatGPT for Teachers.
令人惊讶的是：企业级用户的Excel数据默认不会被用于训练AI模型，这与普通用户的数据处理方式有显著区别。这种差异反映了OpenAI对商业客户隐私的特别保护，可能是为了增强企业采用AI工具的信心。
The new growth, by contrast, will increasingly sit in tokens, consumption, automations, outcomes, and machine-driven workflows. If you are not in the token path, you are not standing in the fastest-growing part of the budget.
令人惊讶的是：文章明确指出软件行业的增长将从传统的基于座位（seat-based）模式转向基于代币（token-based）的消耗模式。这种转变意味着软件公司需要重新思考其商业模式和定价策略，从订阅制转向按使用量付费。这一预测暗示了软件行业正在经历根本性的商业模式变革。
The real long-term price war isn't with your competitors. It's with your customer's engineering team.
令人惊讶的是：AI应用公司面临的最大长期价格战不是与竞争对手，而是与客户内部的工程团队。随着基础模型成本下降，企业越来越多地考虑自行构建而非购买AI解决方案。这揭示了AI市场的一个根本性转变：从产品竞争转向内部能力竞争，对AI供应商提出了更高的差异化要求。
In some cases, this can look like 10–25x more value than what is ultimately included in the paid plan.
令人惊讶的是：在AI产品的概念验证阶段，供应商提供的价值可能是最终付费计划的10-25倍。这种'过度交付'策略已成为行业常态，被视为获取客户的营销投资而非成本中心。这种做法反映了AI产品市场的高度竞争性和获取客户的困难程度。
Raising prices will for sure decrease demand and that risks killing the growth story. And even if revenue keeps growing, it doesn’t matter if there are no margins
这直击AI初创企业的商业困境：在“增长叙事”和“盈利现实”之间进退维谷。提价会破坏高增长的投资者叙事，导致估值受损；不提价则没有利润，烧钱速度更快，尤其是在面对可以将AI作为亏本搭售的云计算巨头时。这揭示了缺乏护城河的纯模型公司商业模式的脆弱性。
纯粹收集分析这种形态，过去互联网有过先例，但你会发现它卖不出去钱。
作者一针见血地指出了纯记录工具的商业困境。在 AI 时代，Token 成本是持续性的，这就要求产品必须交付“结果”而非仅仅是“数据”。这揭示了 AI 应用从“工具属性”向“劳动力属性”转型的必然逻辑：用户不为存储买单，只为价值产出付费。
For small entrepreneurs in the US, deciding what to sell and where to make it has traditionally been a slow, labor-intensive process that can take months. Now that work is increasingly being done by AI tools like Accio, which help connect businesses with manufacturers in countries including China and India.
大多数人认为全球化会削弱小型企业的竞争力，但作者认为AI正在赋予小企业前所未有的全球供应链接入能力。AI工具如Accio正在消除地理障碍，使小型企业家能够以前所未有的速度和效率连接国际制造商，这挑战了关于规模经济的传统认知。
Zhang, of Alibaba.com, says Accio currently does not include advertising. Suppliers can pay for higher placement in Alibaba.com's regular search results, but Zhang says Accio is 'not integrated' with that system.
大多数人认为AI工具会不可避免地融入现有的广告和付费推广模式，但作者认为Alibaba有意将AI搜索与付费广告分离。这表明公司可能正在尝试创建一个更公平、更少受商业利益影响的AI推荐系统，这是一个与行业普遍做法相悖的立场。
Sally Li, a representative at a makeup packaging company in Wuhan, China, says her firm has started writing more detailed product descriptions and adding information about its equipment and manufacturing experience on Alibaba.com because it suspects those details make its listings more likely to be surfaced by AI.
大多数人认为AI会减少人类在商业中的参与，但作者认为AI实际上迫使制造商提供更详细、更透明的信息。制造商正在调整他们的在线策略，通过提供更多详细信息来迎合AI算法，这表明AI正在改变信息流动方式而非简单替代人类判断。
The demand for these medications has been the most ferocious thing I have witnessed in my working life, and the hardest parts of running a telehealth company, like finding doctors and fulfilling prescriptions, can be entirely outsourced to platforms like CareValidate and OpenLoop.
大多数人认为医疗行业监管严格且难以突破，但作者指出GLP-1药物的需求如此之大以至于一个人可以在短短两个月内创建价值数十亿美元的公司，并将医疗服务的核心功能外包。这一观点挑战了传统医疗行业的复杂性认知，展示了AI如何颠覆传统受监管行业。
Claude 的 Max Pro 账号额度不允许给第三方产品用了，如果你没有使用 Agent SDK 和 Claude Code 为底座的产品，就不能用这个账号里的额度
大多数人认为云服务提供商的订阅额度应该具有通用性，但 Anthropic 限制额度只能用于特定产品的做法颠覆了这一认知。这种策略实际上是一种'锁定效应'，迫使开发者和用户使用其生态系统产品，反映了 AI 服务提供商从开放向封闭的转变趋势，可能成为行业新标准。
A founder in LA reportedly scaled Medvi toward $1.8B in annual sales with basically one full-time employee.
大多数人认为建立十亿美元级别的公司需要庞大的团队和复杂的管理结构，但作者认为AI已使'一人独角兽'成为可能。这挑战了传统创业理念，暗示AI可能彻底改变企业规模与人力需求之间的关系，颠覆我们对商业增长的基本认知。
Companies have invested billions into AI, 95 percent getting zero return
MIT report: 95% of companies see no profit from investments in generative AI, which amounted to approximately $35 billion.
Most AI pilots have no measurable impact on company profits. Attempts to implement tools like ChatGPT into the workplace primarily increase the productivity of individual employees, not the earnings of the entire company.
1000x Increase in AI Demand
In response, Yampolskiy told Business Insider he thought Musk was "a bit too conservative" in his guesstimate and that we should abandon development of the technology now because it would be near impossible to control AI once it becomes more advanced.
for - suggestion- debate between AI safety researcher Roman Yampolskiy and Musk and founders of AI - difference - business leaders vs pure researchers // - Comment - Business leaders are mainly driven by profit so already have a bias going into a debate with a researcher who is neutral and has no declared business interest
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this is more of a unfair competition 00:10:36 issue I think as a clearer line than the copyright stuff
for - progress trap - Generative AI - copyright infringement vs Unfair business practice argument
Standard algorithms as a reliable engine in SaaS https://en.itpedia.nl/2021/12/06/standaard-algoritmen-als-betrouwbaar-motorblok-in-saas/ The term "Algorithm" has gotten a bad rap in recent years. This is because large tech companies such as Facebook and Google are often accused of threatening our privacy. However, algorithms are an integral part of every application. As is known, SaaS is standard software, which makes use of algorithms just like other software.
