How employment is changing at Anthropic
https://www.anthropic.com/research/employment-at-anthropic📌 Anthropic 揭露內部數據:AI Agent 工時已是員工 6 倍,初階技術職缺正在消失
TL;DR:Anthropic 公開內部勞動數據,顯示 AI agent 工時遠超員工,初階技術招募大幅萎縮。
當一間公司的 AI agent 工作時數是員工的六倍,「新人要去哪裡學?」這個問題就不再是假設,而是正在發生的組織重組。Anthropic 近日發布一份以自身內部資料為基礎的勞動經濟報告,試探性地回答了這個問題。
🤔 一間刻意用 AI 重塑工作流程的公司
這份報告由 Anthropic 的 Nathan Wilmers、Regina Catipon、Maxim Massenkoff、Peter McCrory 共同撰寫,資料來源包括內部員工聘用紀錄、AI 使用數據、產出指標、員工問卷、內部溝通模式、行事曆資料,並輔以 17 位員工的訪談。報告指出,Anthropic 雖持續擴張,但以每一美元營收計算的招募強度,低於 Meta、Google 在同等成長階段(五年左右)的水準,該公司目前每位員工創造的營收是 Meta、Google 當年巔峰期的 2 到 4 倍。
🧩 Agent 工時六倍於員工,工程師只好「自己把自己做掉」
報告描述,自 2026 年 1 月起,核心技術團隊內部開始出現「初階技術人力需求下降」的討論,因為重度使用 Claude 的團隊發現,寫程式與抓 bug 的工作愈來愈能交給 AI。隨著模型能力提升,這個轉變在短時間內變得明顯:一位受訪者提到,performance engineer 的產出提升「幾乎是一夜之間」改變了團隊的招募標準。到了 8 月,互動式 AI agent 的工時已達員工工時的六倍。隨著 agent 在公司內擴散,Anthropic 觀察到每人程式碼產出提高、每位業務代表成交數上升、合約處理量增加;追蹤同一位員工的數據也顯示,當他們導入新的 AI 工具後,個人產出隨之躍升。
📊 初階技術職缺在一年內大幅縮減
| 指標 | 2025 年初 | 最新 |
|---|---|---|
| 技術部門「初階」員工佔比 | 21% | 14% |
| 技術部門「初階」新聘佔比 | 27% | 9% |
| 公司整體「技術部門」員工佔比 | 61% | 56% |
報告指出,初階職缺的招聘廣告自 2025 年 12 月起下滑特別明顯。與此同時,技術部門佔全公司員工比例雖然下降,但技術人力的「絕對數字」仍持續成長,只是銷售、財務、招募等業務部門的招募強度更快,部分原因是公司規模擴大帶動的業務部門自然成長,部分則是程式碼撰寫工具讓非技術員工也能自行開發工具。文中舉例,一位業務代表自行打造客製化儀表板與資料管線,串接各種工具與 MCP,僅花了三到四小時反覆調整完成。非技術員工(定義為原本沒有程式碼提交記錄、且非新進員工)使用 AI 寫程式的 session 數,從 2025 年 8 月每月 2 次暴增至 2026 年 5 月每月 90 次。
💡 人類注意力,成為新的瓶頸
報告強調一個反直覺的現象:即使在非技術職位,員工光是要「讀完」自己 Claude agent 產出的所有內容,就要花掉半週的工作時間。這促使員工投入所謂的「meta-work」,也就是建立可視化工具、監控 agent 工作管線,而非直接產出內容本身。技術部門內「沒有在寫程式」的員工(過去 30 天無程式碼提交者)比例在一年內(至 2026 年 4 月)幾乎減半;工程師與產品經理的人數比約為 17 比 1,且持續上升;主管層級的程式碼提交數也翻倍。一位工程師形容自己的工作狀態是面對「一支 Claude 大軍,每一個都在跟我要注意力」。
連行政職能也出現類似轉變。一位財務主管打造了採購審核 agent,向同事解釋這是「找出那些佔用大量工時的事,讓大家可以專心做其他事」;一位執行助理建立了排程與信箱分類用的個人助理 agent 後表示,「我們已經超越了 Claude 只是輔助最基層行政工作的階段」,轉而投入更高階的「chief of staff」型工作。另一個產品設計團隊原本負責撰寫 Claude.ai 上的問候語文案,後來建立了一套 context graph 引導 agent 自行產出產品文案,團隊主管形容這「把團隊規模縮小了」,團隊隨後轉型去做 agent prompting、撰寫模型評測與提示詞,正如文中所說:「原本用語言驅動人類行為的文案寫手,現在用語言驅動 Claude 的行為。」報告也提到,在職滿一年的員工,2026 年跨部門轉調的機率比 2025 年高出 50%,顯示公司傾向以內部重新部署應對職務變化,並建立了追蹤「高風險職務」的早期預警機制。
🎯 實務啟示
對工程師而言,這份報告傳遞的訊號相當明確:單純的程式碼實作能力正在被 agent 稀釋,招募端開始更看重「自主性與判斷力」而非特定技術領域知識。與其專注在能被 agent 取代的執行工作,不如主動往「監督、驗證、協調 agent 產出」的角色移動,這可能才是當前組織轉型下真正稀缺的能力。
🔗 來源
- 標題:How employment is changing at Anthropic
- 作者/機構:Nathan Wilmers, Regina Catipon, Maxim Massenkoff, Peter McCrory(Anthropic)
- 連結:https://www.anthropic.com/research/employment-at-anthropic
#AI #Anthropic #FutureOfWork #LaborMarket #AIAgents #Automation #TechHiring #Claude #WorkplaceAI #EconomicsOfAI
原始資料 Anthropic Research · 收集於 2026-10-17
摘要原文
Economics How employment is changing at Anthropic Oct 16, 2026 Read in PDF Nathan Wilmers, Regina Catipon, Maxim Massenkoff, Peter McCrory 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. Key findings 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’ six to one. 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. 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. 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. Introduction 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. 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. 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 judgment 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 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. Labor demand at the frontier Anthropic 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 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.” 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. 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. Figure 1: Decline of entry-level technical hiring at Anthropic. 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. 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. 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.” 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. 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.
由 claude-code/sonnet 自動生成