What Anthropic found when its agents outworked its people. · VANDAM

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# What Anthropic found when its agents outworked its people. · VANDAM

> A close reading of Anthropic's report on its own jobs: agents at 6 to 1, attention as the bottleneck, fewer juniors, and lessons for a 300-person firm.

URL: https://vandam.ai/notes/inside-anthropic  
Contact: hendrik@vibelab.be

# What Anthropic found when its agents outworked its people.

Anthropic measured its own company after agent hours passed human hours six to one, and the findings are about attention, management, hiring and people moving. I read the report as a story and draw out what it means for a company of 300.

Hendrik De Winne 24 October 2026 8 min read

By August 2026, the agents that Anthropic employees ran from their own desks worked six hours for every hour their people worked. That number sits in the first paragraph of [How employment is changing at Anthropic](https://www.anthropic.com/research/employment-at-anthropic), a report four researchers on the company’s economics team published on October 16. They used internal headcount, agent telemetry, calendars, Slack, hiring scorecards and 17 interviews, and turned all of it on their own employer.

It is the first time I have seen a company this far into agents publish what happened to its own jobs, and the findings are mostly about organization. The model capabilities are background. What the report keeps returning to is attention, management, hiring and who moves where. Those are the questions every company I work with will face, a few years later and with less money to absorb the mistakes.

- **6:1**
  Agent hours to human hours for employee-directed sessions at [Anthropic](https://www.anthropic.com/research/employment-at-anthropic), August 2026.
- **80%**
  Share of interactive Claude session time spent idly waiting for human input.
- **9%**
  Share of technical hires at junior level, down from 27%.
- **22%**
  Share of interactive sessions spent on meta-work such as specs, reviews and agent dashboards, up from 10%.

## The hiring changed first

The story starts in January 2026, when engineers inside Anthropic began talking about needing fewer juniors. The teams that leaned hardest on Claude found that writing code and finding bugs could increasingly be handed off. By April, engineering and research had deemphasized hiring for lower band technical roles. The share of junior employees in technical departments slipped from **21%** to **14%** since early 2025, and the share of junior technical hires fell faster, from **27%** to **9%**, according to [the report](https://www.anthropic.com/research/employment-at-anthropic).

The company kept growing, so this is a story about mix. Technical departments went from 61% to 56% of headcount in a year while sales, finance and recruiting hired faster. Part of that is a scaling company adding business functions. Part of it is non-technical people building their own software: coding agent sessions for employees with no code commits at baseline went from 2 a month in August 2025 to **90** in May 2026. A sales rep built a custom dashboard and pipeline in three to four hours. Administrative roles, the ones that usually scale with headcount, barely grew at all.

## Then the bottleneck moved to the people

Once agents were doing most of the hours, the scarce resource became a human paying attention. The report measures it directly: in interactive sessions, Claude spends around **80%** of its time waiting for a person to respond. Even for non-technical teams, reading everything their agents produced would take more than half the work week. One engineer described the job as facing “an army of Claudes, with every one asking for my attention.” Another reached for childcare.

> When you are babysitting, every child brings to you concerns with an equal demand for your attention and the various [Claudes] also do that.
>
> An Anthropic engineer, Anthropic, Interviewed for the October 2026 report, [Source](https://www.anthropic.com/research/employment-at-anthropic)

This is the part most AI business cases miss. They count the hours a model saves and assume the output lands somewhere useful. At Anthropic the output piles up in front of a person, and the person becomes the limit on how much of it turns into anything.

## So people started managing Claudes

The response was a new layer of work about the work. Meta-work, meaning skills, plans, reviews for other agents, dashboards and documentation, grew from **10%** of the average employee’s interactive sessions in fall 2025 to **22%** in spring 2026. Written standing instructions for agents went 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.

Meetings went up too. The median employee spends 20% more time in meetings than a year ago, and technical individual contributors 16% more. The authors read it as the hard calls moving upward: when an agent chases a bug far enough, it hits organizational problems that need people in a room. And because agents only know what is written down, private decisions became a liability. One employee kept getting agent requests for t-shirts and mugs because their name was still on a months-old document about swag.

A non-technical employee summed up where the job went: “that 20% that was strategic becomes 90% of the job.”

## Hiring now looks for judgment

The hiring scorecards show the same shift. Mentions of a candidate’s track record with AI tools tripled for technical roles and quadrupled for non-technical roles between 2024 and 2026. For technical candidates, the biggest increases were in comments on autonomy and judgment, while domain expertise shrank as a share of the assessment. One manager put it plainly: “I expect models to continue to be jagged, so for my team I want them to be fungible across domains.”

That matches what [Boris Cherny](https://x.com/bcherny/status/2071379474277613732), who leads Claude Code at Anthropic, wrote in June. He sees five archetypes on his team: the Prototyper who churns out ideas, the Builder who turns one into production, the Sweeper who simplifies and unships, the Grower who iterates toward product-market fit, and the Maintainer who keeps a mature system secure and fast. A healthy team mixes them depending on the product’s stage, and he notices they cut across job titles, with designers, engineers, PMs and data scientists in every category.

> Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?
>
> Boris Cherny, Head of Claude Code, Anthropic, June 2026, [Source](https://x.com/bcherny/status/2071379474277613732)

## People moved instead of leaving

The last finding is the one I find most useful. Employees with at least a year at the company were **50%** more likely to move across departments each month in 2026 than in 2025. Whole teams moved. A product design team that wrote the greetings on Claude.ai built a context graph that let agents write that content, then rotated into writing prompts and model evals. Their manager’s line: “Writers who used language to drive human behavior are now using it to drive Claude behavior.”

Anthropic is now standing up an early warning system that reads role-level demand and productivity against AI capabilities, so it can move people before their work disappears. The authors argue that committing to redeployment pushes a company to look for new tasks and new products, and helps it resist what economists call “so-so” automation: replacing workers with technology that barely beats them, which costs the displaced people a lot and earns the company almost nothing. They also credit broad stock ownership, and say plainly that many of their practices cannot be copied.

They also name the weak spot in their own plan, the juniors who never got hired.

Employment protections for incumbents will not benefit those not hired to begin with.

Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory, economics team at

[Anthropic](https://www.anthropic.com/research/employment-at-anthropic)

There are caveats worth keeping in view. Anthropic sells the model, grew explosively in the same period, and tripled in size in a year. The productivity comparisons follow 192 engineers who adopted agents against 74 who did not, and the authors note that adoption was not random. Treat it as one company’s evidence, collected by people with every reason to look carefully.

## What I take from it for a 300-person company

Everything below is my reading. Anthropic makes none of these claims about other companies.

**Roles.** Most mid-sized companies still organize around functions, and the job descriptions describe execution. If execution is what agents absorb first, those descriptions will age fast. I would start sorting people by what Cherny’s list describes, how they work on a product or process at a given stage, and staff teams by stage. A plant running a mature ERP needs Maintainers and Sweepers. A new service line needs Prototypers and Builders, whatever their title says today.

**Managing agents.** Somebody has to own the attention budget. In a company of 300, nobody yet asks how many hours of agent output land on each person’s desk every week, who reviews it, and which standing instructions are current. I would treat those instructions like any other operational document, with an owner and a review date, and I would push decisions into writing so the agents work from the same context as the people. The t-shirt story will happen everywhere.

**HR.** The junior question is the hardest one. A company with ten junior analysts today may want three next year, and then has no one to promote into the judgment roles in five. I would decide on purpose how juniors still get time on the object-level work that builds judgment, and I would build internal mobility before the cuts come: a simple list of roles under pressure, read every quarter, and a path out of each one.

**Finance.** I would stop approving business cases that only count headcount removed. That is the so-so automation the report warns about, and the gains tend to stay small. The cases I trust count agent hours, the human review time they consume, and the new work the freed people take on. If a case has no line for the third, it is a cost cut.

The first step costs nothing. Pick one team that already uses agents heavily, measure for two weeks how much agent output each person has to read and approve, and put that number next to the hours the agents saved. That ratio tells you where your bottleneck is.

## More notes

29 October 2026 6 min read

## Who owns what your AI learns.

Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections.

26 October 2026 9 min read

## Delete the step first and give the agent whatever survives.

Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go.

## Your agents need a critic and so does the plan they carry out.

Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.