Anthropic 的用工方式正在如何变化

内森·威尔默斯、雷吉娜·卡蒂蓬、马克西姆·马森科夫、彼得·麦克罗里Nathan Wilmers, Regina Catipon, Maxim Massenkoff, Peter McCrory

AI 如何重塑了 Anthropic 的工作与就业？作为一家处于 AI 应用前沿的公司，Anthropic 可能预示着 AI 将如何改变整个经济中的工作组织方式、招聘和劳动力需求。How has AI restructured work and employment at Anthropic? As a company at the frontier of AI adoption, Anthropic could be a leading indicator of how AI will shift work organization, hiring, and labor demand across the economy.

关键发现Key findings

• Anthropic 招聘的初级技术岗位占比有所下降。与此同时，AI 智能体的工作时长相较于员工迅速增长。到 8 月，交互式 AI 智能体的工作时长已是员工的 6 倍。
  Anthropic has been hiring for a smaller share of junior technical positions. This occurred as hours worked by AI agents relative to employees rapidly expanded. By August interactive AI agent hours outnumbered employees’ 6 to 1.
• 随着 AI 智能体在公司内部逐渐普及，Anthropic 的人均代码产出增加，每位销售代表成交的销售额也提高了，处理的合同数量也更多了。追踪同一名员工随时间的表现发现，随着他们开始使用新的 AI 工具，产出也随之提升。
  As AI agents diffused through the company, Anthropic began producing more code per person, closing more sales per rep, and processing more contracts. Following the same employee over time, output rose as they adopted new AI tools.
• 这种加速使人类注意力成为智能体产出的瓶颈。即使是非技术员工，读完 Claude 智能体生成的所有内容，也要占去他们一半的工作时间。为此，员工转而从事“元工作”：构建可视化工具并监控智能体流水线。
  This acceleration makes human attention the bottleneck for agent output. Even among non-technical employees, reading all the output of their Claude agents would take half their work week. In response, employees turn to “meta-work,” of building visibility tools and monitoring agent pipelines.
• 工作组织方式的变化体现在招聘经理优先事项的转变上：与技术领域专长和实施能力相比，他们越来越重视自主性和判断力。
  Changes in work organization are reflected in shifting priorities among hiring managers, who increasingly emphasize autonomy and judgment, relative to technical domain expertise and implementation.

简介Introduction

随着 AI 日益强大，我们可以预期工作和就业会发生怎样的转变？Anthropic 值得研究，因为它已彻底围绕 AI 重塑了工作流程。智能体在 Slack 聊天中穿梭，安排会议。它们自主修复漏洞，并协同攻克大型工程项目。智能体的工作时长是人类的 6 倍。Anthropic 正在发生的变化，为工作和就业可能的未来提供了早期信号。What transformations of work and employment can we expect under increasingly powerful AI? Anthropic is worth studying because it has thoroughly molded its workflows around AI. Agents flit through Slack chats and schedule meetings. They autonomously fix bugs and collectively tackle huge engineering projects. Agent hours worked outnumber human hours 6 to 1. Changes happening at Anthropic now give advanced signals about a possible future of work and employment.

本报告依据 Anthropic 全面的内部数据，涵盖就业、AI 使用、产出指标、员工调查、内部沟通模式、员工日历数据以及招聘决策记录，并结合 17 名员工的访谈对这些量化数据进行背景解读。This report draws on comprehensive internal Anthropic data covering employment, AI use, output metrics, employee surveys, internal communication patterns, employee calendar data, and hiring decision records. It contextualizes these quantitative data with 17 employee interviews.

我们发现，尽管公司规模不断扩大，但相对而言，初级技术岗位的招聘增速已经放缓。工程师和非技术员工的生产力都大幅提升，而在他们采用编程智能体后，生产力更是显著跃升。招聘经理越来越看重应聘者的自主性和判断力，而非特定的技术专长。在一家高度依赖 AI 的组织里工作，一个显著特点是，人们始终承受着压力：只要有可能，就要精简自己的工作，摆脱瓶颈角色。一位工程师形容自己的工作状态是“面对着一支 Claude 大军，每个 Claude 都在等我关注”。Anthropic 的员工仍然至关重要，但他们贡献的性质已经发生了变化。We find that even as the company grows, relative hiring in junior technical positions has slowed. Productivity for both engineers and non-technical employees has leapt up and did so sharply when they adopted coding agents. Hiring managers increasingly emphasize applicants’ autonomy and judgement relative to specific technical expertise. A defining feature of working in an AI-heavy organization is constant pressure to streamline oneself out of this bottleneck position when possible. One engineer described work as facing “an army of Claudes, with every one asking for my attention.” Employees at Anthropic remain critical. But the nature of their contributions has changed.

Anthropic 已通过建立一套预警系统来应对这些变化，以追踪面临被颠覆风险的岗位；随着岗位不断演变，公司也将内部调岗列为优先事项。这些举措发生在人工智能发展和职场变革的前沿，展现了具体的组织承诺如何影响人工智能对就业的冲击。但它们也引发了一个棘手的问题：这些承诺究竟应该是什么？Anthropic has responded to these changes by setting up an early warning system to track jobs at risk of disruption, and as jobs evolve the company has prioritized internal redeployments. These actions, taken at the frontier of both AI development and workplace change, show how specific organizational commitments can shape the impact of AI on employment. But they also raise difficult questions about exactly what those commitments should be.

前沿领域的劳动力需求Labor demand at the frontier

Anthropic 发展迅速，但与上一代大型科技公司相比，其每创造一美元收入所雇用的员工更少。成立五年时，Anthropic 的人均收入是 Meta 和 Google 在各自快速增长达到顶峰时的 2–4 倍。1Anthropic has expanded rapidly. But it has hired less per dollar of revenue than an earlier generation of large tech companies. At five years old, Anthropic takes in between 2–4 times as much revenue per employee as Meta and Google as they crested in their own rapid growth.¹

哪些岗位不见了？在 Anthropic 内部，核心技术部门于 2026 年 1 月开始讨论初级技术员工需求下降的问题。那些在工作中最依赖 Claude 的团队发现，编写代码和查找错误越来越多地可以交由 Claude 处理。能力更强的新模型加深了这种看法：“真正令人侧目的是，性能工程师的效率提升尤其显著，这几乎在一夜之间就改变了我们的招聘需求。”What jobs are missing? Within Anthropic, discussion of a decline in demand for junior technical employees emerged in core technical functions in January 2026. The teams that relied most heavily on Claude in their own work found that writing code and finding bugs could increasingly be offloaded to Claude. Newly capable models accelerated this perception: “the thing that raised eyebrows was the uplift for performance engineers in particular and it basically overnight changed what we were looking for.”

到4月，工程和研究团队已不再那么重视招聘较低职级的技术岗位。同月接受调查时，多数管理者表示，由于人工智能，他们招聘时看重的技能有所不同。约三分之一的机器学习经理和五分之一的软件工程经理表示，人工智能导致他们减少了招聘。By April, engineering and research teams had deemphasized hiring for lower band technical positions. When surveyed in the same month, a majority of managers described hiring for a different set of skills due to AI. Around a third of ML and a fifth of SWE managers reported hiring less due to AI.

就业份额的变化令人瞩目。图 1 显示，自 2025 年初以来，Anthropic 技术部门初级员工的占比从 21% 降至 14%。初级技术岗位新聘员工的比例下降得更快，从 27% 降至 9%。自 2025 年 12 月以来，面向初级人才的招聘岗位数量尤其大幅减少。The shift in employment shares is striking. Figure 1 shows that the share of junior-level employees in technical departments at Anthropic slipped from 21% to 14% since early 2025. The proportion of junior technical hires fell even faster, from 27% to 9%. Postings for juniors have fallen particularly sharply since December 2025.

与此同时，Anthropic 内技术部门的整体员工占比在过去一年中从 61% 降至 56%。随着公司不断扩张，技术岗位招聘人数绝对值仍在持续增长，但销售、财务和招聘岗位的大规模招聘增长得更快。这一转变的部分原因，是公司规模扩大时业务职能自然随之拓展；另一部分原因可能是，编程工具日益让非技术员工也能自行开发软件。例如，一位销售代表设计了一个定制仪表盘和工作流程，专门适配自己的工作：“像这样的开发本来不在我的能力范围内，尤其是界面、连接各种工具和 MCP。但我大概只花了三到四个小时就做出来了，一边做一边迭代。”面向非技术员工的编程类 AI 代理会话（按基准时没有代码提交记录的员工界定，不包括新员工），从 2025 年 8 月的每月 2 次激增至 2026 年 5 月的 90 次。At the same time, the overall employment share of technical departments within Anthropic has fallen, from 61% to 56% in the last year. As the company expands, technical hiring has continued growing in absolute terms, but intensive hiring in sales, finance, and recruiting has outpaced it. Part of this shift is the natural expansion of business functions as the company scales. Part could reflect coding tools increasingly allowing non-technical employees to make software themselves. For example, a sales representative designed a custom dashboard and pipeline tailored to their work: “a build like this was out of my wheelhouse, especially with the UI, connecting all the tools, the MCPs. But I built this probably in three to four hours, just iterating on it.” Coding-focused AI agent sessions for non-technical employees (identified as those with no code commits at baseline, excluding new hires) leapt from 2 per month in August 2025 to 90 in May 2026.

然而，招聘构成的这一变化并不只是自动化编码使其扩散到其他领域专家中的故事。在技术部门内，未主动编写代码的员工占比（定义为过去30天内没有提交任何代码的人）截至2026年4月的一年间下降了近一半。部分原因是非工程师的占比减少：例如，工程师与产品经理的比例约为17比1，而且自2025年以来有所上升。管理人员的代码提交次数也翻了一番。技术团队中的监督与协调岗位，正越来越多地涉及编写并交付代码。Yet this shift in hiring composition is not only a story about automated coding allowing it to diffuse to other domain experts. Within technical departments, the share of employees not actively coding (defined as those making no code commits in the prior 30 days) fell by almost half over the year through April 2026. Part of this is due to a thinning share of non-engineers: the ratio of engineers to product managers, for example, sits at around 17 to 1 and has increased since 2025. The number of commits from managers has also doubled. The supervising and coordinating roles within technical teams increasingly involve building and shipping code.

非技术岗位招聘并未加速的一个领域，是通常会随着公司规模扩大而扩张的行政职能。代理助手、自动化审核和工单服务外包相结合，使 Anthropic 在快速发展的同时，文职人员依然精简。一位财务经理搭建了一个采购申请审核代理，并向同事解释说：“它只是想找出那些特别耗时的事情，这样他们就能专注于其他工作。”One area in which non-technical hiring has not accelerated is in the administrative functions that typically scale with a company’s size. A combination of agent assistants, automated review and ticket service outsourcing has left Anthropic clerical-light even as it has grown rapidly. A financial manager built a purchase request review agent and explained to colleagues “it's just trying to find the things that take a lot of hours so they can focus on the other stuff.”

即便是与面对面互动密切相关的文职工作，增长也很缓慢。一位行政助理为日程安排和邮件筛选打造了个人助理智能体，他解释说：“我认为，我们已经过了 Claude 通过增强人力来取代真正入门级行政岗位的阶段”，现在则把工作重点放在更高阶的“幕僚长”职责上。Even clerical work tightly connected to in-person interaction has grown slowly. An executive assistant, who built a personal assistant agent for scheduling and email triage, explained: “I think we've already moved past the point of Claude augmenting away the really entry-level admin type role,” and instead focused their work on higher-order “chief of staff” tasks.

这些变化已经涉及人员重新调配：2026 年，每月至少在公司工作满一年的员工跨部门调动的可能性，比 2025 年高出 50%。随着团队将工作移交给智能体，整个团队也被重新调配。例如，有一支产品设计团队负责撰写文案，比如 Claude.ai 上的问候语：“夜猫子，你好；”“来杯咖啡，和 Claude 一起工作？”2026 年春天，这支团队开发了一种上下文图谱，能有效引导智能体撰写引人注目的产品内容。该团队的经理解释说，这“让团队的产出规模得以扩展”，但也引出了一个问题：人类写作者还剩下什么工作？随后，团队转向为智能体设计提示，并开始为 Claude 编写模型评测和提示词。“过去用语言影响人类行为的写作者，如今正用语言影响 Claude 的行为。”These changes have already involved reallocation: employees working at the company for at least a year were 50% more likely to move across departments each month in 2026 than they were in 2025. Whole teams have also been reallocated as they handed off their work to agents. Consider one product design team, which wrote content like the greetings on Claude.ai: “Hello night owl;” “Coffee and Claude time?” In spring 2026, the team developed a context graph that effectively guided agents to write compelling product content. This “scaled the team out,” its manager explained, but also raised the question of what was left for human writers. The team then rotated into agent prompting and began writing model evals and prompts for Claude. “Writers who used language to drive human behavior are now using it to drive Claude behavior.”

AI 是否正在推动 Anthropic 提高生产力？Is AI driving productivity increases at Anthropic?

这些变化可以归因于 AI 吗？在 Anthropic，随着 AI 智能体队伍不断壮大，人类只占据了薄薄一层。根据智能体会话遥测数据，2026 年 3 月，自主智能体和交互式智能体的工作时长总和首次超过了人类的工作时长。其中一部分来自成群的智能体以工业化规模开展软件工程工作，完成无数常规测试和检查，维护不断扩大的基础设施代码，并协同推进大规模代码库的迁移工作。但即便只看个别员工部署的智能体（如图 2 所示），到 8 月，这些智能体的工作时长也达到了其人类操作者的 6 倍。Are these shifts attributable to AI? At Anthropic, humans are a thin layer atop a growing contingent of AI agents. Based on agent session telemetry, in March 2026, the sum of autonomous and interactive agent work hours leapt past human work hours for the first time. Part of this is fleets of agents conducting industrial scale software engineering, filling in myriad routine tests and checks, conducting maintenance on a growing mass of infrastructure code, and driving concerted campaigns to translate massive codebases. But even narrowing in on the agents deployed by individual employees, as in Figure 2, by August the agents worked 6 times as many hours as their human handlers.

Anthropic 是一家软件开发商，而这些智能体处理的绝大多数 token 都用于编写软件代码。仪表板会监控公司各处运行的智能体集群。不过，个人助理智能体也会查看员工的日历和消息，安排日程，根据收到的消息整理并发送待办清单，还会自动向员工介绍即将参加的会议中的与会者及会议目的。token 正被用于越来越多的日常工作任务。Anthropic is a software producer, and the vast bulk of tokens flowing through these agents is spent producing software code. Dashboards monitor fleets of agents operating across the company. But personal assistant agents also monitor employee calendars and messages, scheduling, sending to-do lists accumulated from incoming messages, and automatically briefing employees on the people and purpose of their upcoming meetings. Tokens touch a growing range of everyday work tasks.

这些代币最终意味着什么？图 3 展示了多种可衡量产出的岗位职能随时间变化的产出情况。与此前对 Anthropic 工程师进行调查、并描述模型训练和研究自动化情况的报告一致，工程师人均代码提交量增长最快，速度达到 6 个月前的 5 倍。数据科学家也更频繁地查询不断增长的 Anthropic 数据，并让 Claude 智能体将实时指标呈现在仪表盘上，供决策者查看。但图 3 显示，随着编码智能体的普及，即使是非技术岗位的员工，产出也有所提升：律师处理了更多合同；应付账款团队处理了更多发票；销售人员谈成了更多交易。What do all of these tokens amount to? Figure 3 plots output over time for a range of job functions with measurable output. Consistent with prior reports surveying Anthropic engineers and describing automation of model training and research, code commits per engineer have risen the fastest, coming in at 5x their velocity as 6 months ago. Data scientists are also querying the growing mass of Anthropic data more, asking Claude agents to get real-time metrics to dashboards and decisionmakers. But Figure 3 shows that even non-technical employees saw output rise as coding agents diffused: lawyers processed more contracts; accounts payable ushered through invoices; salespeople closed more deals.

但这一切都是由 Claude 的使用带来的吗？在此期间，公司业务也出现了爆炸式增长。销售强大的模型，更容易谈成交易。为了评估 AI 的独立影响，我们比较采用了智能体编程工具的员工与尚未采用这些工具的员工的产出。这样，我们就能比较同一个月在公司工作的员工：他们面对着相似的业务环境，却使用不同的工具。不过需要注意的是，工具的采用并非随机的：例如，如果即将推动某个大型新项目的员工决定为此开始使用工具，就会使我们的估算产生偏差。But is all of this due to Claude usage? During this same period, the company saw explosive business growth. It is easier to close deals when you sell a strong model. To assess the independent effect of AI, we compare the output of employees who adopt agentic coding tools to those who have not yet adopted the tools. This lets us compare employees working in the same month at the company, and therefore exposed to a similar business context, but approaching it with different tools. Note however that tool adoption is not random: if employees on the cusp of driving some big new project decide to tool up for it, for example, this would bias our estimates.

我们长期跟踪了192名采用编码代理的工程师，并将他们与研究期间未采用编码代理的74名工程师进行比较。图4显示，采用编码代理后的几个月里，工程师新增的代码量大幅上升。代码更多并不一定意味着代码质量更好或业务影响更大。为检验是否存在 AI 生成的低质量代码，我们研究了 Anthropic 的代码提交。我们比较同一名员工在同一个月、同一个代码目录中提交的代码，其中一部分由编码代理编写，另一部分由员工亲自编写。代码质量有许多衡量维度，而且 AI 的错误可能比人类的错误更难识别。但我们发现，后来被删除或回滚的代码行比例并无差异。We follow 192 engineers over time as they adopt coding agents, compared to 74 engineers who do not adopt agents during the period studied. Figure 4 shows that in the months following adoption, the amount of code that engineers add rises sharply. More code does not necessarily mean better code or business impact. To test for AI code slop, we study code commits made at Anthropic. We compare code committed by the same employee, in the same month, in the same coding directory, but written by coding agents rather than written directly by the human employee. There are many dimensions of code quality, and AI errors may be harder to identify than human ones. But we find no difference in rates of lines of code subsequently removed or reverted.

图 4 还考察了工程师采用首个完全自主智能体后发生的情况。与未采用者相比，采用者此前的产出就已呈快速上升趋势。这表明，转向使用完全自主智能体的员工，其产出本就在不断提高。Figure 4 also looks at what happens after an engineer adopts their first fully autonomous agent. Those who adopt were already on a steeply upward trajectory compared to non-adopters. This suggests that employees shifting toward fully autonomous agents were already ramping up their output.

这些模式是否超越了编程领域，预示着知识工作更广泛的转型？自去年九月以来，Anthropic 的销售代表逐步开始采用 AI 智能体。图 4 显示，销售代表采用智能体后，能与更多客户达成交易。这种模式部分可能是因为销售机会更多的代表更倾向于采用自动化，但也可能反映了记录工作和日常沟通的自动化。一位客户经理解释说，Claude 自动化功能处理了邮件起草和客户管理软件集成，取代了他们此前约 20% 的工作任务。另一位则指出，类似的自动化让他们得以专注于面对面交流，与比以往多 30% 的客户交谈：“每当你第二次做某件事时，你就会想，好吧，我怎么用 Claude 来处理它，这样第三次就不用再做了？”Do these patterns go beyond coding, into a broader transformation of knowledge work? Anthropic sales representatives steadily adopted AI agents starting last September. Figure 4 shows that after sales representatives adopt agents, they close deals with more clients. Part of this pattern could be driven by representatives with more sales opportunities selecting into automation. But it could also reflect automation of logging and routine communication. One account executive explained that Claude automations handled email drafting and customer management software integrations, replacing around 20% of their prior work tasks. Another noted that similar automations freed them up to focus on face-to-face time, letting them talk to 30% more of their customers than was previously possible: “anytime you do anything a second time, you're kind of like, okay, how can I use Claude to make it so that I don't have to do it a third time?”

除了销售之外，一位 Anthropic 律师还构建了一系列 Claude 自动化流程，用于扫描风险和机会、对法律问题进行分流，并对面向公众的文案进行初步审阅，以供法律审核。一位招聘人员则利用 Claude 协助寻找候选人、跟踪申请，并完成一项原本“可能要花上几周”的分析。这让他得以重新设计过时的面试问题，并与更多申请者会面。Beyond sales, an Anthropic lawyer built a series of Claude automations to scan risks and opportunities, triage legal questions and take first passes through public-facing copy for legal review. A recruiter used Claude to help with candidate sourcing, tracking applications and an analysis that would have taken “maybe a couple of weeks,” freeing him to redesign outdated interview questions and meet with more applicants.

所有这些都表明，随着 AI 能力不断提升，Anthropic 内部众多岗位都会受到影响。从描述性数据来看，在使用 Claude 处理最复杂任务的岗位中，初级员工的就业增长正在放缓（图 5）。All of this suggests that as AI capabilities improve, it impacts a wide range of jobs across Anthropic. Descriptively, we see junior employment slowing in jobs that use Claude for the most complex tasks (Figure 5).

瓶颈与流程：人工智能密集型工作场所中的新旧任务Bottlenecks and process: new and old tasks in an AI-intensive workplace

大规模采用 AI 对就业和生产力的影响还不止于此。它还推动工作流程不断变化，催生新任务，并改变员工对工作场所的看法。一名员工将这种情况描述为“Anthropic 内部永远存在着尽可能快地推进工作、凡事都用 Claude 的压力”；另一名员工则表示，“我们期望一周内完成的事情，换在别处要花三个月。”一项面向内部员工的调查询问，Anthropic 的工作流程是否让受访者能够尽可能高效地工作：2024 年底，44% 的员工表示认同；到 2025 年 11 月，这一比例升至 58%。（2026 年 4 月，75% 的受访者回答“是”，但问题的表述有所弱化，删去了“尽可能”。）截至 8 月，Anthropic 的员工预计，到 2030 年，AI 将能够完成他们 75% 的工作；相比之下，其他美国人对此的预期为 28%。Heavy AI adoption reshapes more than employment and productivity. It also drives continual changes in work processes, generating new tasks and shifting employees’ perceptions of their workplace. One employee described this as the “eternal pressure at Anthropic to move as fast as possible and use Claude for everything” and another that “things we expect in a week are things that would take three months elsewhere.” An internal employee survey asked whether work processes at Anthropic made respondents as productive as possible: 44% of employees agreed in late 2024; 58% by November 2025 (in April 2026, 75% answered yes, but the question weakened by dropping “as possible”). As of August, employees at Anthropic expected that AI will be able to do 75% of their work by 2030, compared to 28% for other Americans.

内部模型发布的节奏将这种不断攀升的生产力预期具体展现了出来。新模型一上线，Anthropic 员工就开始在交互式编码智能体会话中处理更难的问题。我们通过一个自动化的 Claude 评分器来衡量这一点：它根据任务的深度、范围以及涉及的系统数量，对去标识化会话中的任务要求进行评估。难度超过旧模型第 90 百分位的任务占比大幅上升约 30%（从全部任务的 10% 升至 13%），并在模型发布后的几周内维持在较高水平。交给智能体的任务中，简单任务所占比例变化不大。These ratcheting productivity expectations are dramatized by the cadence of internal model releases. As soon as a new model is available, Anthropic employees start tackling harder problems in interactive coding agent sessions. We measure this with an automated Claude grader rating deidentified sessions’ task demands based on depth, scope and number of systems implicated. The share of tasks above the 90th percentile of difficulty for the old model rises sharply by around 30% (from 10% to 13% of all tasks) and stays elevated in the weeks following model releases. The easy end of tasks given to agents changes little.

智能体生产力不断攀升，意味着大量产出会涌向人们的屏幕，争夺他们的注意力。一位工程师将此比作照看孩子：Spiraling agent productivity translates into output pitched across humans’ screens and attention. One engineer compared this to babysitting:

他们会说：“我需要把果汁盒打开，我的果汁盒打不开，这是紧急情况。”而且他们说这话时的担忧程度，和那个割伤手指、正在流血的人一样。[…] 你照看孩子时，每个孩子都会带着同样急切的关注需求来找你，各种 [Claudes] 也会这样。They're like, I need my juice box opened and it's an emergency that my juice box is opened. And that is voiced with the same level of concern as the one who has sliced their finger and is bleeding. [...] When you are babysitting, every child brings to you concerns with an equal demand for your attention and the various [Claudes] also do that.

在2026年4月的调查中，针对人工智能如何影响日常工作的开放式问题，27%的人提到 Claude 工具带来的生产力提升，20%的人表示自己的角色正从亲自完成工作转向管理 Claude。In the April 2026 survey, an open-ended question about how AI has impacted day-to-day work, 27% described productivity gains from Claude tools and 20% described their role shifting from actually doing work to managing Claudes.

在这种由模型驱动的加速进程中，人类注意力始终是一个瓶颈。在交互式多轮会话中，智能体绝大部分会话时间并没有用于实际工作，而是在等待。我们通过用每个交互式会话的总墙钟时间减去智能体的主动工作时间，来计算空闲时间。Claude 会话中，约有 80% 的时间都在空等人类输入。正如一位工程师所说，人们正在“成为智能体的瓶颈”。Amid this model-driven acceleration, human attention is a constant bottleneck. In interactive, multi-turn sessions, the vast majority of agents’ session time is not spent working at all, but spent waiting. We track idle time by subtracting each agent’s active working time from each interactive session’s total wall-clock time. Claude sessions amount to around 80% idly waiting for human input. As one engineer put it, people are “bottlenecking the agents.”

究竟需要多少时间才能读完 Claude 在交互式会话中的所有输出？对于每个交互式会话，我们都会将会话中智能体输出的 token 数换算成词数，并以人类的平均阅读速度为依据。图 6 显示，过去 6 个月里，技术职能部门的编码智能体输出量大幅跃升。但即使对于非技术团队，交互式 Claude 会话产生的内容也足以占去一半以上的工作周时间。如此海量的 Claude 文本亟需整理。How much time exactly would be needed to read all of Claude’s output from interactive sessions? For each interactive session, we convert each session’s agent output token to word count, assuming average human reading speed. Figure 6 shows that technical functions have seen the volume of coding agent output leap up over the last 6 months. But even for non-technical groups, interactive Claude sessions are outputting enough content to take up over half of the work week. The deluge of Claude text needs organizing.

应对这股信息洪流的一种办法，是进一步提高自动化程度。但这需要做另一类工作。给智能体编写的书面常设指令，从 2025 年 7 月的几百条增加到一年后的 8,500 条。一位工程师设立了一个智能体幕僚长，负责分流智能体请求。另一位则为自己的 Claude 团队写了一份绩效评估：“嘿，我们来聊聊我们的沟通方式哪些有效，哪些无效。”招聘人员借助智能体搭建了集成式仪表板，由智能体发现问题并发起行动。一位律师把注意力从审核法律问题，转向了“为法律建议做 SEO；你需要编写一些框架，让[客户的]智能体能够发现它们”。One solution to this deluge is rising automation. But this requires work of a different kind. Written standing instructions for agents grew from a few hundred in July 2025 to 8,500 a year later. One engineer set up an agent chief of staff to triage agent requests. Another wrote a performance review for their Claude team: “Hey, let’s talk about what is and is not working about our communication style.” Recruiters built integrated dashboards with agents surfacing problems and initiating actions. A lawyer shifted their attention from reviewing legal questions to doing “SEO for legal advice, you need to write frameworks that [clients’] agents discover.”

图 7 显示，这类元工作在普通员工与 Claude 的交互会话中所占比例，从 2025 年秋季的 10% 飙升至 2026 年春季的 22%。这一增长很大程度上源于代理管理：为多代理运行编写规范、让一个代理反复审查另一个代理的输出，以及监控流水线。各职能领域中的人类参与，越来越不再聚焦于某段具体代码或某篇具体文稿，而是转向为代理有效完成任务所需的工具。Figure 7 shows that this kind of meta-work leapt from 10% of the average employee’s interactive Claude sessions in fall 2025 to 22% in spring 2026. Much of this rise was due specifically to agent management: writing specs for multi-agent runs, looping one agent to review another’s output, and monitoring pipelines. Across functions, human involvement increasingly abstracted from a particular piece of code or writing to the tooling necessary to get agents to do their tasks effectively.

尽管智能体的可靠性有所提升，人们也在监督它们的工具上投入了大量资源，但人类的信任与问责仍至关重要。一位员工解释说，如今追查漏洞“会揭示出一些组织层面、系统层面的问题，或技术债，而要解决这些问题，实际上需要人们聚在一起讨论、作出决策并进行治理”。在一次又一次的访谈中，员工都表示，面对面的协调不可或缺。根据日历数据，员工会议时间的中位数比一年前增加了20%。这部分反映了公司的发展壮大，以及岗位结构向会议较多的非技术岗位转变。但即使是在技术类个人贡献者岗位，员工的会议时间在过去一年也增加了16%。宏观层面的战略问题涉及众多高杠杆岗位，通常需要展开辩论并与他人协作。人们通常会通过参加会议来暂时放下对智能体的管理工作。Despite increases in agent reliability and these significant investments in tooling to supervise them, human trust and accountability remains critical. One employee explained that, increasingly, chasing down bugs “uncovers these organizational or systemic or tech debt things that actually require some amount of human convening and decision-making and governance to solve.” In interview after interview, employees described face-to-face coordination as indispensable. Based on calendar data, the median employee spends 20% more time in meetings than a year ago. Part of this reflects the growth of the company, and a shift toward meeting-intensive non-technical roles. But even among employees in technical individual contributor roles, meeting time is up 16% over the past year. Big picture strategic questions touch a broad range of leveraged positions, and typically require debate and engagement with others. A break from managing the agents typically looks like jumping into a meeting.

总体而言，技术和非技术岗位中，工作的执行环节都已实现自动化（编写代码、发现 bug、记录进度、起草邮件）。工程师表示，自己逐渐成为智能体的领导者和管理者；非技术员工则表示，自己越来越多地承担起工作中具有战略性的部分：“原本占 20% 的战略性工作，如今成了工作内容的 90%。”但对许多非技术岗位而言，核心执行工作也涉及人际互动：销售电话、面试，以及围绕复杂问题开展跨职能协同。这些工作仍然离不开人。Broadly, both technical and non-technical roles have seen implementation parts of their job automated (writing code, identifying bugs, logging progress, drafting emails). Engineers described becoming leaders and managers of agents; non-technical employees described doing more and more of the strategic parts of their job: “that 20% that was strategic becomes 90% of the job.” But for many non-technical roles, core implementation work is also interpersonal: a sales call, an interview, cross-functional alignment on a complex problem. All of this remains human.

还可以考虑 Slack 频道，它们既是 Anthropic 沟通的公开窗口，也是私下交流的渠道。智能体逐渐遍布 Slack，最终出现在近一半的活跃公共频道中。图 8 聚焦于一个大型部门的通信网络，涵盖该部门成员参与交流的所有公共 Slack 频道。图中根据人类和智能体参与 Slack 讨论串的情况，将双方联系起来。图中展现了智能体数量惊人的增长：橙色圆点不断涌现，最终占据了整个网络。Consider too the Slack channels that provide both the front page and backchannels of Anthropic communication. Agents diffused through Slack and eventually appeared in nearly half of active, public channels. Figure 8 zooms in on the communication network of a single large department, drawing on all public Slack channels that department members interacted through. It links both human and agent participants by whether they participated in a Slack thread. The figure shows the remarkable rise of agents, as orange dots blossom and dominate the network.

智能体更多出现在专门用于 Claude 与人类互动的频道中，而不是在现有的人类交流频道里频繁发言。人类交流的底层结构和密度变化不大。智能体并没有取代人类互动，而是在其基础上增加了互动：该部门的人类讨论串维持了相同数量的人类回复，同时还增加了智能体回复。智能体逐渐成为单个员工的团队，而不是同时与许多员工保持高度连接的节点。Agents appear disproportionately in dedicated channels for Claude-human interactions, rather than messaging a lot in incumbent human channels. The underlying structure and density of human communication changes little. Agents did not replace human interaction, but rather added to it: human threads in this department sustained the same number of human responses, while additionally layering on agent responses. Agents become teams for a single employee, rather than nodes with high connectivity to many employees at once.

随着人类与日益壮大的 AI 智能体队伍共同工作，他们面临着压力，不得不把人类工作转移到智能体能够看到的渠道中。如果某项决定是在面对面会议中做出的，却没有留下笔记或会后汇报，智能体就会缺少更新目标和假设所需的关键信息。一名员工解释说，由于几个月前一份关于 Claude 周边产品的文档上还署着自己的名字，智能体便一直找他们索要 T 恤和马克杯，尽管他们的职责早已改变。在 AI 无处不在的工作环境中，未记录在案的会议和私信中的私人工作不仅不可见、令人困惑，也会受到抑制。一位研究人员将这种默认倾向于开放的做法描述为 Anthropic 全面整合 AI 的关键：“大多数组织并非如此运作，我认为这是一个重大转变，因为领导者必须公开为反馈辩护。” 为 AI 提供上下文的需求，意味着人类的工作和决策必须具备可观察性。As humans work amid a growing fleet of AI agents, they face pressure to shift human work into agent-visible channels. If a decision is made in an in-person meeting, and no notes or report-out appears, agents are missing crucial context for updating their goals and assumptions. An employee explained how having their name on a months-old document about Claude swag meant agents kept bringing requests for t-shirts and mugs, long after their responsibilities had changed. Private work, in unlogged meetings and direct messages, is invisible, confusing and discouraged in an AI-heavy workplace. One researcher described this default toward openness as critical to Anthropic’s thoroughgoing integration of AI: “most organizations do not run that way and I think that's a big shift because leaders have to defend feedback out in the open.” The need to feed AI context requires observability of human work and decisions.

谁会在这样的职场中脱颖而出？Anthropic 工作内容的变化，也体现在招聘经理在招聘决策中优先考虑的技能上。我们使用自动化编码工具处理了自 2024 年以来 Anthropic 聘用的数千名员工的招聘经理面试评分表。这些评论是面试官对候选人表现的定性评价。最明显的变化是，招聘经理越来越重视候选人是否展现出创造性地使用 AI 工具的能力。2024 年，招聘经理在约 5% 的技术岗位评估和 7% 的非技术岗位评估中提到了候选人在 AI 工具方面的过往表现。到 2026 年，这些比例在技术岗位中增长了三倍，在非技术岗位中增长了四倍。招聘经理越来越关注候选人是否能够有效地使用 AI。Who will thrive in a workplace like this? Shifts in the content of work at Anthropic also show up as changes in the skills that hiring managers prioritize in hiring decisions. We used an automated coder to process hiring manager interview score cards for thousands of employees hired at Anthropic since 2024. These comments are qualitative reactions by interviewers assessing candidates’ performance. The clearest change is increased emphasis on whether candidates have demonstrated creative use of AI tools. In 2024, hiring managers noted candidates' track record on AI tooling in around 5% of assessments for technical roles, and 7% for non-technical roles. By 2026, these rates had tripled for technical roles and quadrupled for non-technical roles. Hiring managers increasingly focus on whether candidates effectively use AI.

评估方式也出现了更深层的变化。在技术岗位候选人的面试中，关于候选人自主性和判断力的评价增幅最大。领域专业知识在整体评估中所占的比重则有所下降。正如一位经理所解释的：“我预计模型的能力仍会参差不齐，因此我希望团队成员能够在不同领域之间灵活调配。”同样，对于非技术岗位候选人，面试中对客户导向和使命感的关注增加了，而对执行能力的讨论减少了。如今，招聘经理越来越多地依据候选人的判断力和投入度来评估他们，而非那些可由 AI 工具自动完成的特定领域技能和执行工作。Deeper changes in assessment are also visible. Among interviews for technical candidates, the single biggest increases were in comments on applicants’ autonomy and judgment. Discussion of domain expertise shrank as a share of overall assessment. As one manager explained, “I expect models to continue to be jagged, so for my team I want them to be fungible across domains.” Similarly, for non-technical candidates, customer and mission orientation increased, while discussion of execution skills fell. Increasingly, hiring managers assess applicants based on their judgment and commitment, rather than the domain-specific skills and execution automated by AI tools.

雇主可以做什么What employers can do

Anthropic 对 AI 的深入应用，使其成为一个颇具参考价值的案例。随着 AI 的普及和进步，工作的组织方式可能会像以往几轮技术变革那样发生转变。铁路催生了管理型公司，蒸汽动力让工厂成为可能，个人电脑则带来了初创公司。会使用工具、能够推理的大语言模型将需要怎样的新型组织？而我们在其中又将扮演什么角色？一种可能是单人初创公司：每家公司都由一位人类创始人指挥成群的智能体，从而赚取数十亿美元收入。另一种可能是由天才 AI 首席执行官创建公司，只雇用人类智能体来操作移液器和在国会作证。The deep adoption of AI at Anthropic makes it a useful case. As AI diffuses and improves, the organization of work could shift as it has in prior waves of technological change. The railroads gave us the managerial corporation, steam power allowed factories, and personal computers delivered the startup. What new organizations will tool-using, reasoning LLMs require, and what will be our role in them? One possibility is single-person start-ups, each with a human founder steering swarms of agents to rake in billions in revenue. Another is companies built by AI genius CEOs employing human agents only to wield pipettes and testify before congress.

Anthropic看起来与这两者都不同。它在过去一年里规模扩大了三倍，早已不再是一家小型初创公司。它也没有把高管决策权交给自己的模型。书面 Slack 讨论和面对面会议主导着从公司战略到团队决策的一切，AI 代理很少参与其中。让全公司的人工工作保持可见，能确保自主代理依据最新的背景信息进行推理。在知识需求和工作中的相互依赖关系方面，统筹多个代理团队的个人贡献者看起来更像管理者。在高度依赖 AI 的组织中，公开开展工作和决策至关重要。Anthropic does not look like either of these. It has tripled in size in the last year. It is no longer a small startup. It has also not ceded executive control to its models. Written Slack debates and in-person meetings steer everything from company strategy to team decisions, with little input from AI agents. Keeping human work visible across the company ensures autonomous agents reason from fresh context. Individual contributors orchestrating teams of agents look more like managers in terms of the knowledge they need and the interdependencies in their work. Working and deciding in the open is critical at an AI-intensive organization.

随着员工越来越多地将自己的工作视为管理一支支 Claude 团队，他们花更多时间在各自独特的领域中构建有效完成这项工作的工具。这种尝试意味着，员工不断把自己的任务自动化。在某些岗位上，他们越来越远离那些最初帮助他们培养技能的具体工作。当智能体飞速推进各项工作流程时，人类的注意力和判断力成了系统竭力突破的瓶颈。从销售到法律，员工都被期待承担更多、也不同以往的任务。岗位职责不断拓宽，涵盖产品开发、智能体管理，以及处理仍超出顶尖大语言模型能力范围的战略性和人际交往工作。As employees increasingly see their work as managing teams of Claudes, they spend more time building the tools needed to do this effectively in their own distinctive domains. This experimentation means that employees are continually automating their own tasks away. In some roles, they are increasingly removed from the object-level work that originally built their skills. As agents sprint through workflows, human attention and judgment are a bottleneck that the system strains against. From sales to legal, human employees are expected to do more and different tasks. Roles broaden to include building products, managing agents and tackling the strategic and interpersonal work that remain beyond the scope of the best LLMs.

工作组织方式和内容的这些变化，伴随着初级技术岗位在员工总数、招聘人数和职位发布中的占比不断下降。招聘经理越来越看重应聘者的自主性和判断力，而非执行能力和狭窄的专业知识。总体而言，Anthropic 的营收相对于员工人数的增长速度，已超过上一代科技公司早期生产率快速增长的水平。These changes in the organization and content of work have appeared alongside declining shares of junior technical roles in headcount, hiring, and posting. Hiring managers increasingly focus on applicants’ autonomy and judgment, rather than execution and narrow expertise. Overall, Anthropic’s revenue growth relative to its employment growth has sped past even the rapid early productivity growth of a prior generation of technology companies.

将这些模式向前推演并推广到整个经济领域，表明 AI 的普及可能带来劳动者被替代的风险。此前的研究还发现，AI 原生初创企业和 AI 采用者的组织架构更扁平，雇用的初级员工也更少。我们能从 Anthropic 身上学到什么，才能以有益于在其周围工作的人的方式应对 AI 带来的剧变？Reading these patterns forward and out across the economy suggests that AI diffusion could risk worker displacement. Prior work also finds that AI-native start-ups and AI-adopters are flatter and employ fewer entry-level employees.. What can we learn from Anthropic about how to manage AI upheavals in a way that serves the humans working around them?

在这里，我们希望谨慎行事。Anthropic 的许多用工做法都无法在整个经济体系中照搬。Here we want to proceed carefully. There are many parts of Anthropic’s employment practices that cannot be replicated across the economy.

但有三项特点格外突出。首先，广泛的员工持股让员工对公司的成功有着切身利益。当然，对于一家估值大幅上涨的公司来说，这种做法很容易推行。对许多企业而言，共享所有权可能并不足够，或者并不合适。但很难想象，一种有利于劳动者的 AI 应用模式会完全不让劳动者分享成功推广带来的经济收益。正如受访者所说，如果一线员工被期望去“管理智能体”、“做出副总裁级别的决策”，并“经营自己的业务线”，那么让他们的利益与组织保持一致至关重要。二战后的劳资关系安排于 20 世纪 50 年代成形，其基础正是这种交换：像美国汽车工人联合会和美国钢铁工人联合会这样的工会，允许管理层自主重组工作（甚至加大工作强度），以换取与生产率提升挂钩的工资增长，以及种类丰富的福利。无论受 AI 影响的员工与雇主之间将达成怎样的新协议，建立分享技术应用收益的机制都不可或缺。在 Anthropic，员工要把自己的专业知识编写成机器可用的形式，眼看着自己的工作惯例不断被搅乱和打破，而且工作中很大一部分已经变成了快速浏览 AI 协作者的输出。他们可不是白干这些事的。But three features stand out. First, widespread stock ownership gives employees a strong interest in the company’s success. Of course, this is an easy posture for a company that has seen substantial increases in valuation. For many firms, shared ownership may be insufficient or otherwise undesirable. But it is difficult to imagine a version of pro-worker AI adoption in which workers do not hold a financial stake in successful rollout. If individual contributors are expected to “manage agents,” “make VP level decisions” and be “their own business line,” as interviewees put it, then aligning their interests with the organization is crucial. The post-World War II union-management settlement forged in the 1950s rested on exactly this kind of bargain: labor unions like the United Auto Workers and the United Steel Workers allowed management prerogative over work reorganization (and even intensification) in return for pay increases tied to productivity increases and to a rich array of benefits. Whatever the new deal between AI-affected employees and their employers, a mechanism to share benefits of adoption is essential. At Anthropic, workers codify their expertise for the machine, see their work routines constantly roiled and disrupted, and have seen a large part of their job become speedreading the output of their AI collaborators. They do not do this for free.

其次，Anthropic 正在建立一套持续运行的预警系统，用于监测正面临 AI 驱动型转型的工作岗位。该系统会将一系列岗位层面的需求和生产力指标，与 AI 的能力及使用情况进行对照。这些汇总指标有助于标示工作内容正在发生变化的领域。预警系统使公司能够开始将受影响的员工重新分配到技能更匹配、且近期风险可能较低的岗位。随着整个经济领域的企业加快重组工作的步伐，能够有效指导员工内部调岗的系统将愈发重要。AI 进步得越快，依靠外部劳动力市场来满足 AI 进步催生的每一项新任务，就越会成为瓶颈。在本报告中，我们发现部门之间的人员流动有所增加，工作内容不断变化，员工也在旧任务自动化后转向新任务。对于 AI 密集型组织而言，建立能够灵活在内部重新调配员工的系统至关重要。Second, Anthropic is standing up an ongoing early warning system to monitor jobs facing AI-driven transformation. A series of role-level metrics for demand and productivity are read against AI capabilities and use. These aggregate metrics help flag where work is shifting. An early warning system allows the company to start reallocating affected employees to jobs with a skills fit that may be less immediately at risk. As companies across the economy reorient to an accelerated pace of work reorganization, effective systems for guiding internal employee reallocation will become increasingly valuable. Turning to the external labor market to meet every new task demanded by AI advances will be a stronger bottleneck the faster AI advances. In this report, we found increased mobility between departments, changing work content, and employees shifting to new tasks as old ones were automated. Building systems to flexibly reallocate workers internally will be critical for AI-intensive organizations.

这种内部员工重新配置的承诺会带来重要的权衡。运作良好的劳动力市场应当让劳动者转到最能发挥其生产力的岗位，无论是在当前雇主处还是转投其他雇主。承诺重新配置现有员工，可能会让雇主在冒险招聘新员工时犹豫不决。在正常的经济环境下，尤其是在竞争异常激烈时，这类承诺可能代价高昂。This commitment to internal worker reallocation raises important tradeoffs. A well-functioning labor market should shift workers into their most productive positions, whether that is at their current employer or another one. Commitments to reallocate incumbent workers could make employers hesitant to take a risk on hiring new employees. In normal economic conditions, particularly intensely competitive ones, such commitments could be costly.

但优先考虑重新配置，会促使采用人工智能的企业思考如何利用人工智能为新产品和新市场创造新任务，而不是将人工智能应用一味用于取代人类劳动者以节省劳动力成本。这有助于雇主抵制“差强人意”或“低效”自动化的诱惑——在这类自动化中，人工智能或其他技术仅仅勉强胜过人类劳动者：雇主取代人类劳动者，使失业者收入受损，造成巨大的社会成本，却只能带来微乎其微的生产率提升。But prioritizing reallocation forces AI-adopting companies to consider ways that AI can create new tasks for new products and new markets, rather than funneling AI applications into replacing human workers to save on labor costs. This could help employers resist the temptation of ‘so-so’ or ‘inefficient’ automation in which AI or other technologies just barely outcompete human workers: employers replace human workers, imposing substantial social costs in lost income for displaced workers, while delivering minimal productivity benefits.

从我们在 Anthropic 看到的情况来看，即使 AI 得到广泛应用，新出现的任务和长期仍由人类承担的任务也会并存。未来会有哪些任务尚不确定，因此应保留一支灵活的劳动力队伍。正如 Anthropic 一位高管所说：“一支忧心忡忡的员工队伍，不可能发挥出最佳水平。”From what we see in Anthropic, both novel and persistently human tasks remain alongside intensive AI use. Uncertainty about the future set of these tasks should recommend preserving a flexible workforce. As one Anthropic executive noted, “a workforce that is anxiously worried is not a workforce doing its best work.”

这些举措都无法阻止强大的 AI 冲击就业。事实上，这些举措都没有直接应对本报告所记录的初级岗位招聘放缓。在职员工的就业保障，无法惠及那些一开始就没有被录用的人。雇主采取的私人行动，必须与政府在再培训和收入支持方面的长远政策相结合。此类政策应以清晰了解 AI 可能带来的职场变革规模为依据。None of these actions will prevent powerful AI from disrupting jobs. And indeed none directly address the slowdown in junior hires documented in this report. Employment protections for incumbents will not benefit those not hired to begin with. Private action on the part of employers must come alongside farsighted government policy around retraining and income support. Such policy should be guided by a clear picture of the scale of workplace transformation that AI can bring.

建议引用格式Suggested citation

@techreport{wilmers2026employment,

author = {Nathan Wilmers and Regina Catipon and Maxim Massenkoff and Peter McCrory},

title = {How employment is changing at Anthropic},

institution = {The Anthropic Institute},

type = {Technical Report},

year = {2026},

month = oct,

url = {https://www.anthropic.com/research/employment-at-anthropic}

}

致谢Acknowledgements

We are grateful to the Anthropic employees who gave time for interviews about how their work has changed. For very helpful comments and assistance, we thank Kate Astbury, Shehzad Bashir, Zoe Blumenfeld, Scott Booth, Henry Brill, Yena Cho, Henry de Valence, Adam Farina, Johannes Hermle, Zoe Hitzig, , Hasnain Lakhani, Eva Lyubich, Kerry Persen, Kristina Pham, Sarah Pollack, Hannah Pritchett, Georg Rilinger, Santi Ruiz, Szymon Sacher, Florian Scholz, Meg Tong, Monika Tuchowska, Helen Wang, Kim Withee, Charles Yang, and Jack Clark.

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