{"slug": "anthropic-and-the-changing-nature-of-work", "title": "Anthropic and the changing nature of work", "summary": "Anthropic's white paper \"How employment is changing at Anthropic\" by Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory finds that human attention, not agent capacity, is the bottleneck in LLM-agent work inside the company. The paper's Figure 6 estimates that reading a week of agent output would take 12 hours for marketing and communications (30% of a 40-hour week), 26 hours for legal, policy and trust (65%), 43 hours for finance and accounting (108%), and 424 hours for engineering and research — more than the 168 hours in a week. The analysis, read through the lens of cognitive systems engineering, argues the agents are poor \"team players\" because they lack a model of the humans' goals and state and cannot judge when to compete for an engineer's attention.", "body_md": "The research field known as [cognitive systems engineering](https://github.com/lorin/cognitive-systems-engineering) (CSE) emerged in the early 1980s as a subfield of safety science. That time period saw a dramatic rise in the use of computer-based automation for controlling safety-critical systems. One of the big ideas of CSE, I would even call it *the* big idea of the field, is that ***introducing new automation changes the nature of the work that humans do.*** It’s not that automation means that humans now have less to do: instead, it means that humans face a different kind of work, some of it being genuinely novel. \n\nIt’s in the spirit of CSE that I read with interest the recent Anthropic white paper from Nathan Wilmers, Regina Catipon, Maxim Massenkoff, and Peter McCrory titled [How employment is changing at Anthropic](https://www.anthropic.com/research/employment-at-anthropic). It’s a study of how work is changing inside of Anthropic with the embrace of LLM agents. Here are some thoughts I had reading this.\n\n## Attention is all used up\n\nKeeping the agents busy is a challenge: these agents spend more time waiting for human input than they do getting work done. Notably, there’s no discussion in the paper about whether agent utilization is genuinely important to the business or not. But what’s clear from the paper is that humans are the bottleneck. More specifically, they point out that *human attention* is the bottleneck.\n\nFigure 6 in the paper has a fun graph about how much time it would take a human to simply read all of the output generated by their agents, given the average number of tokens outputted per session, assuming 0.75 words per token, and a reading speed of 250 words per minute. For marketing and comms, it would take 12 hours for a person to read the output generated by a week of agent usage, which is 30% of a 40-hour work week (assuming 40 hour work weeks is naive, I know, but let’s go with it). For legal, policy & trust, it’s 26 hours, which is 65% of a 40-hour work week. For finance and accounting, it’s 43 hours of reading, or 108% of a work week. And for engineering and research, it would take 424 hours(!). I’m not going to compute the work week percentage for this one, but the discriminating reader will note that there are only 168 hours in an entire week. What this means is that there is literally not enough time in a week for engineers to read all of the Claude output, even if they work 24×7.\n\nThey’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.\n\nThe quote above is taken from an engineer pointing out that not all agent requests for human attention are created equal, but that the agents themselves do a poor job at coordinating with the humans to effectively communicate the level of urgency involved in a request. Here I want to reference a famous CSE paper, now over twenty years old, called [Ten challenges for making automation a “team player” in joint human-agent activity](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1363742), written by Gary Klein, David Woods, Jeffrey Bradshaw, Robert Hoffman, and Paul Feltovich. Two of the challenges mentioned in the paper are:\n\n- An agent must have a model of the behavior and beliefs of the other people involved in the joint activity\n- An agent must be able to observe the behavior of the humans and use that information to identify their current states and intentions\n\nThese are about how agents need to have a model of the humans they are interacting with, in order to effectively coordinate with them In CSE terms, these agents are not good team players. Because the agents don’t have a good model of the engineer’s current goals and state, they aren’t able to make decisions about when it’s appropriate to vie for the engineer’s attention.\n\n## From engineers to managers (but still engineers)\n\nOne solution to this deluge is rising automation. But this requires work of a different kind.\n\nThe paper describes how the work at Anthropic is shifting to *meta-work*, more commonly referred to as management. More and more of their engineers’ time is spent on coordinating work in dealing with the agents. The paper has some fun examples of this, including attempts to automate it: an agent that triages requests from other agents, doing performance reviews of agents, writing specs for multi-agent runs, looping one agent to review the output of another agent. \n\nThe paper even tries to estimate the amount of time spent on meta-work, as shown in figure 7, which has the fantastic title “Becoming managers of Claudes”. The group with the highest meta-work overhead is infrastructure, at 29%.\n\n## Judgment and autonomy: the decline of the junior hire\n\n“the thing that raised eyebrows was the uplift for performance engineers in particular and it basically overnight changed what we were looking for.”\n\nOne of the main themes in the paper is a hiring trend: they are hiring fewer junior engineers. However, this drop in junior hiring isn’t related to the fact that seniors have deeper domain expertise than juniors. In fact, they specifically call out that domain expertise is becoming less of an issue for hiring managers. Instead, they note that hiring managers are looking for candidates who exhibit good judgment and are capable of working autonomously. Junior engineers are typically responsible for executing on well-defined tasks, and it sounds like they see have internalized the idea that LLM agents can replace juniors at doing this sort of work. But they aren’t yet able to use LLM agents to replace the judgment of senior engineers, and they don’t yet trust their LLM agents to work at the level of autonomy that a senior engineer works at.\n\nThis is both unsurprising and depressing. Senior software engineers don’t spring fully formed from Zeus’s forehead; if the industry stops hiring junior engineers, eventually there won’t be senior engineers around anymore. I’d had a sense that it was getting harder for junior devs to find positions, and this is a data point in support of that theory.\n\nI also wonder if Anthropic will tailor their interviews to focus on assessing the quality of judgment of their senior candidates, as well as their ability to work autonomously. I bet if you asked them today, they’d say “we already do assess that”. But I also bet they don’t just have an explicit “judgment” interview and an explicit “autonomy” interview, that they try to gather this info from the existing interview tropes, like coding and system design. Would be very interesting if they transitioned entirely to focusing on experience for these senior hiring interview rounds, although there’s no evidence in the paper about this happening.\n\n## Grab bag of trends\n\nA break from managing the agents typically looks like jumping into a meeting.\n\nTime spent in face-to-face meetings is trending upwards. The authors hypothesize that engineers spend more time on strategic work, and resolving questions of strategy requires more conversation among the humans.\n\nThe percentage of non-engineers is dropping at Anthropic. They note that the ratio of engineers to product managers is increasing over time: currently the ratio is seventeen engineers to one product manager. This surprised me: I would have assumed that product managers would be endlessly busy coming up with new product ideas, and so the demand for product managers would be going up relative to engineers rather than going down. Then again, I don’t really know what product managers do at Anthropic.\n\nThere is organizational pressure to have work discussions happen in places where the agents can observe. This means having them in public Slack channels, as opposed to private DMs or in unrecorded meetings. They want to ensure that the work is visible so that it is legible to the agents.\n\n## Outro\n\nIt was fun to see Anthropic make an attempt to observe itself like this. I hope more companies look at how AI is changing the work that their employees do, and what new challenges their employees face. I also hope people discover CSE research and how relevant it is to the age of LLM-based automation.", "url": "https://wpnews.pro/news/anthropic-and-the-changing-nature-of-work", "canonical_source": "https://surfingcomplexity.blog/2026/10/18/anthropic-and-the-changing-nature-of-work/", "published_at": "2026-10-19 04:52:53+00:00", "updated_at": "2026-10-19 05:20:48.828748+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "ai-research"], "entities": ["Anthropic", "Nathan Wilmers", "Regina Catipon", "Maxim Massenkoff", "Peter McCrory", "Claude", "Gary Klein", "David Woods"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/anthropic-and-the-changing-nature-of-work", "markdown": "https://wpnews.pro/news/anthropic-and-the-changing-nature-of-work.md", "text": "https://wpnews.pro/news/anthropic-and-the-changing-nature-of-work.txt", "jsonld": "https://wpnews.pro/news/anthropic-and-the-changing-nature-of-work.jsonld"}}