Token吃掉三成工资,硅谷AI账单失控了

marsbitPublished on 2026-07-06Last updated on 2026-07-06

Abstract

硅谷AI应用正呈现两极分化:一方面,研究机构SemiAnalysis通过大模型将内部Token支出控制在员工薪资的30%以内,实现了生产效率的数倍提升,重写了专业服务的成本结构。英伟达CEO黄仁勋更是积极推动AI工具普及,视Token为新时代的生产资料。 另一方面,Uber、微软等巨头却因AI使用量激增而面临预算严重超支、成本失控的困境。有研究表明,在许多场景下,雇人仍然比使用AI更经济,且过度使用可能导致运维事故。 关键在于未来的成本趋势。通过软件优化(如使吞吐量提升14倍)和硬件迭代(如新一代芯片性能可达旧款的数十倍),大模型推理成本正快速下降。Anthropic等公司的毛利率不降反升,预示Token价格将持续走低。 当前全球科技公司一边大幅增加AI资本开支,一边持续裁员,但AI对宏观经济的直接影响尚不明显。这类似于基础设施建设的阵痛期:先投入铺设“管道”,应用和效益才会随之涌现。对于企业而言,选择是尽早利用成本下降的趋势拥抱AI转型,还是等待观望。

每百万Token只花0.99美元。

这是SemiAnalysis——硅谷最硬核的半导体研究机构——自己账单上的真实成本。

但更炸裂的是这个数字:内部大模型Token支出,已经占到员工总薪资的30%。

听起来不少——但反过来算,这笔钱买到的产出,过去得靠好几倍的人力成本才能覆盖。人均每月吞掉近50亿个Token,是Meta人均水平的5倍以上,核心贡献者月消耗更突破1000亿。

原本需要初级分析师花几个小时搞定的Excel模型转换、财报图表制作,如今几分钟内完成,只需要几美元。

SemiAnalysis自己的评价一针见血:这不是10%的效率提升,而是专业服务业的单元经济正在被重写。

研究公司、对冲基金、律所——所有靠人脑吃饭的行业,Token支出占到薪资的两三成,只是时间早晚的问题。

英伟达CEO黄仁勋比谁都急。

今年GTC大会上他直接放话:一个年薪50万美元的工程师,年底Token消费不到25万美元?

「我会彻底抓狂。」

他打算给英伟达每个工程师发相当于半年工资的Token预算,还要让7.5万名员工搭配750万个AI智能体一起干活。

不用AI?老黄说,这跟芯片设计师坚持用纸和铅笔没区别。

Token已经不是工具了,它正在变成新时代的「生产资料」。

但硅谷的另一半,正在为AI账单抓狂

有意思的是,就在SemiAnalysis用Token省下真金白银的同时,硅谷的巨头们正因为AI账单焦头烂额。

Uber是最经典的案例。

去年底公司向5000名工程师推广Claude Code,还搞了排行榜——用得越多,排名越高,内部竞争直接拉满。

结果太成功了:2月工程师使用率32%,3月就飙到84%,到了4月,95%的工程师每月都在用AI,70%的提交代码来自AI生成,而全年预算——已经花完了。

CTO说「要从头重做预算」。后来更狠——Bloomberg曝出,Uber给每位员工设了每月1500美元的Token上限,超了要特批。

但COO Andrew Macdonald在播客里说了句大实话:AI使用量确实在涨,但它跟消费者功能创新之间的联系......目前还看不到。

微软的情况更魔幻。上个月《The Verge》曝出,微软正在取消大部分Claude Code许可证,转向自家的GitHub Copilot CLI。

原因很简单:花钱的速度比产出的速度还快。

英伟达应用深度学习副总裁Bryan Catanzaro在今年4月说得更直接:「对我的团队来说,计算成本远远超过了员工的成本。」

MIT 2024年的研究:在以视觉为主要工作内容的岗位中,只有23%的场景下AI自动化在经济上划算。

剩下77%的情况下,雇人比用AI便宜。

甚至还有工程师吐槽AI智能体在使用中「毁掉了他的数据库和网络」——他称之为「过度使用」的代价。

天价预算、使用失控、翻车不断——硅谷正处在AI经济学最撕裂的阶段。

一边是技术带来前所未有的生产力,一边是账单以同样前所未有的速度膨胀。

成本塌缩才刚刚开始

但SemiAnalysis的核心论点是:别盯着今天的价格看,成本塌缩才刚开始。

先看软件端。

在B300上运行DeepSeek R1,通过wideEP、disagg与MTP三层纯软件优化,单GPU吞吐量能从baseline的1000 tokens/秒飙升至14000 tokens/秒——14倍提升,纯靠代码。

再看硬件端。

最优化配置的GB300 NVL72吞吐量是H100的17倍,切到FP4精度直接拉到32倍。

Opus 4.7的标价是输入5美元/百万、输出25美元/百万,看起来不便宜。

但由于智能体工作负载的输入输出比高达300:1,加上90%以上的缓存命中率,实际混合成本被压到了0.99美元。

连标价的五分之一都不到。

把软件和硬件叠在一起,一个结论很难回避:大模型的毛利率扩张,不是一次性的定价巧合,而是结构性的趋势。

Anthropic今年的ARR从90亿美元冲到了440亿以上,毛利率从38%飙到了70%以上——Token变便宜了,但卖Token的人反而更赚钱了。

Gartner今年3月的报告佐证了这一点:到2030年,万亿参数大模型的推理成本将比2025年下降超过90%

SemiAnalysis的判断很明确:如果你想预估2027年Token价格,答案就一个字——降。

钱花了,然后呢?

这正是当下AI最撕裂的地方:全球科技公司今年AI资本开支已宣布7400亿美元,比去年暴增69%;同一时间,科技业裁员速度已超去年全年。

钱在狂烧,人在被裁,但Goldman Sachs首席经济学家说了句大实话——AI对经济的实际影响,到目前为止基本为零。

这不是AI不行,而是每一轮基础设施革命都要经历的阵痛:先烧钱建管道,再等水流过来。

电网如此,互联网如此,AI也不例外。

差别只在于,这一次管道铺设的速度,和水流过来的速度,都是上一代人没见过的量级。

SemiAnalysis已经站在水流过来的那一边了——30%的薪资换来了数倍的产出杠杆,而成本曲线还在急剧下降。

至于其他公司:是现在蹚水过河,还是等对岸的人已经建好了城再追。

参考资料:

https://x.com/SemiAnalysis_/status/2070915305858007345

本文来自微信公众号“新智元”,作者:ASI启示录,编辑:所罗门

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Related Questions

Q根据文章,SemiAnalysis机构内部大模型的Token支出占员工总薪资的比例是多少?

A30%。

Q文章中提到的Uber公司为员工设定的每月Token使用上限是多少美元?

A1500美元。

Q文章中,英伟达CEO黄仁勋认为一个年薪50万美元的工程师,年底Token消费应该达到多少才算合理?

A至少25万美元。

Q根据Gartner的报告预测,到2030年,万亿参数大模型的推理成本将比2025年下降多少?

A下降超过90%。

Q文章指出,目前AI对经济产生的实际影响程度如何?

A根据高盛首席经济学家的观点,到目前为止基本为零。

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This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

763 Total ViewsPublished 2025.01.14Updated 2025.01.14

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