Anthropic Research 3 min

Working at the frontier shapes expectations for the future

🔗 https://www.anthropic.com/research/expectations-for-the-future

📌 Anthropic 揭露:內部 AI agent 工時已超越員工

TL;DR:Anthropic兩份報告顯示內部僱用結構劇變,員工與大眾對AI未來的預期差距懸殊。

當一間公司的 AI agent 工作時數開始超過真人員工,團隊的樣貌會變成什麼樣子?Anthropic 自己就是這個問題的第一手案例。

🤔 兩份報告,兩個切角

Anthropic 這次分享的第一份報告,檢視過去一年 AI 如何改變公司內部的僱用與工作方式;第二份報告則比較 Anthropic 員工與美國一般大眾,對 AI 未來如何影響工作與經濟的預期落差。

📊 Agent 數量六倍於員工,瓶頸轉向人類注意力

報告指出,過去一年 Anthropic 招聘的初階職位佔比下降,技術職務員工佔比也下降。與此同時,AI agent 的工作時數快速攀升:到 2026 年 3 月,agent 工作時數已經超過員工;到 8 月,公司內 agent 數量是員工的六倍,生產力也隨之提升。具體訊號包括每人產出的程式碼量增加、每位銷售代表成交的業務量提升、處理的合約數量增加。研究團隊追蹤同一位員工的時間序列資料發現,員工對 AI 工具的採用程度提升時,其產出也隨之提升。隨著這種加速,報告指出瓶頸正在轉向「人類注意力」,員工因此花更多時間建立機制來監督 agent 的工作成果。

在對未來預期的調查中,Anthropic 將內部員工意見與 Morning Consult 在 8 月針對近 11,000 名美國成年人(經加權使其具代表性)的調查做比較:

問題美國大眾Anthropic 員工
預期 AI 能在 2030 年前做出諾貝爾等級的發現39%91%
預期 AI 到 2030 年能完成目前工作 80% 以上5%過半數

報告也指出,兩個群體中各有約兩成的人,預期與對方群體的典型看法相近;而不論在哪個群體內,只要一個人預期 AI 會做更多自己的工作,他對未來失業率上升的預期幅度也會類似。

💡 不是理論不同,是親身經歷不同

報告的核心論點是:Anthropic 員工與美國大眾對「AI 如何影響勞動市場」的經濟模型其實相近,真正造成預期落差的是兩群人對「AI 現在能做到什麼程度」的親身經驗不同。經驗差異,才是預期差異的根源。

⚠️ Anthropic 是特例,不是常態

報告也自我提醒,身為 AI frontier 公司,Anthropic 採用 AI 的方式與傳統企業不同,其內部動態未必能直接套用到其他產業,不過報告認為這些觀察對理解「AI 原生公司」的未來組織形態仍具參考價值。

🎯 實務啟示

對工程團隊管理者而言,「人類注意力成為新瓶頸」這個訊號值得留意:當 agent 工時超越員工工時,團隊接下來要投資的重點,可能不是再堆更多 agent,而是建立更好的監督與驗證機制。

🔗 來源

#Anthropic #AIEconomics #FutureOfWork #AIAgents #LaborMarket #AIAdoption #WorkplaceAI #AIResearch #Automation #EconomicImpact

原始資料 Anthropic Research · 收集於 2026-10-17
來源原標題
Working at the frontier shapes expectations for the future
機構
Anthropic
原始標籤
Research
原始連結
https://www.anthropic.com/research/expectations-for-the-future

摘要原文

Economics Working at the frontier shapes expectations for the future Oct 16, 2026 We’re sharing two reports: the first covers how AI is changing employment inside Anthropic, and the second looks at the gap between how the public and Anthropic employees expect AI to affect work and the economy. Anthropic staff and US workers don’t seem to have different theories about how AI affects jobs. Instead, they have different experiences of what AI capabilities are. Those different experiences seem closely related to perceptions about what will happen to work in the future. Anthropic is a company at the frontier of AI adoption, and that adoption has changed the way we hire. We find that over the past year, we're hiring for a smaller share of junior positions, and a smaller share of our employees work in technical roles. At the same time, the rate of hours worked by AI agents has shot up. By March 2026, agents worked more hours than employees. By August, there were six times more agents than employees at Anthropic, and productivity increased. That’s led to Anthropic producing more code per person, but that’s a notoriously imperfect measure. We see signs of increased productivity elsewhere: we’ve closed more sales per sales representative, and processed more contracts. When our researchers follow the same employee over time, they find the employee’s output rises as their adoption of AI tools increases. All this acceleration means that, increasingly, the bottleneck is human attention. As a result, employees spend more time building ways to better monitor work their agents are doing. Anthropic could be a leading indicator of how AI will shift work organization, hiring, and labor demand across the economy. An AI lab is different from more traditional businesses in how it adopts AI into its work, but research findings about our own workplace may still be instructive about broader transformations, including dynamics for future AI-native companies. Because Anthropic employees experience AI’s effect on work so differently, it stands to reason that they might have different predictions about AI’s effect on the future of work. That’s what our second report investigates. We surveyed Anthropic employees, and compared them to an August survey of almost 11,000 US adults, fielded by Morning Consult and weighted to be representative of the adult population. The public expects AI capabilities to advance rapidly in the coming years; Anthropic staff tend to expect even faster progress. There are a number of questions with big gaps between staff and the public. For example, 39% of US adults—and 91% of staff—expect AI will be able to make a Nobel-level discovery by 2030. More than half of Anthropic staff expect AI to be able to do at least 80% of their current work by 2030; only 5% of Americans expect AI to be that capable. That said, there’s considerable variation in both groups: two out of 10 Anthropic staff have expectations similar to those of the typical US adult. And Anthropic staff and the public also agree on some key questions. In each bucket, people who expect AI to do comparably more of their own work anticipate similar increases in unemployment. In other words, both the public and Anthropic staff have similar economic models of how AI affects the labor market, but ultimately their personal experiences with AI lead them to different expectations for the economy of the future.

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