Why do workers hide productivity gains from AI use?
Despite reporting that AI saves time and improves output quality, most professionals and creatives conceal their AI use from colleagues. This note explores what drives this gap between private benefit and public silence.
Anthropic built an AI-powered interview tool, Anthropic Interviewer, "powered by Claude," and used it to run 1,250 interviews with professionals: the general workforce (N=1,000), scientists (N=125), and creatives (N=125). Among the general workforce, 86% reported that AI "saves them time" and 65% said they were "satisfied with the role AI plays in their work," yet 69% "mentioned the social stigma that can come with using AI tools at work" — one fact-checker told the interviewer, "A colleague recently said they hate AI and I just said nothing. I don't tell anyone my process because I know how a lot of people feel about AI." Creatives reported similar productivity (97% said AI saved them time, 68% said it increased quality) alongside comparable concealment: 70% "mentioned trying to manage peer judgment around AI use," with a map artist saying "I don't want my brand and my business image to be so heavily tied to AI and the stigma that surrounds it."
The source frames this as a split between productivity and workplace identity. General-workforce interviewees "want to preserve tasks that define their professional identity while delegating routine work to AI," envisioning "futures where routine tasks are automated and their role shifts to overseeing AI systems." Alongside stigma, 55% of the workforce sample "expressed anxiety about AI's impact on their future" (versus 41% who felt "secure" and that human skills are "irreplaceable"); of the anxious group, a quarter said they "set boundaries around AI use" and a quarter said they "adapted their workplace roles," taking on additional or more specialized responsibilities. For creatives the same anxiety centers on economic displacement and creative identity — a creative director says plainly, "I fully understand that my gain is another creative's loss" — while all 125 creative participants said they wanted to "remain in control" of their creative outputs even though several admitted, in the source's words, that this boundary "proved unstable in practice," with AI driving a majority of some decisions.
This differs from Does AI assistance erode the skills needed to oversee it? in population and in the kind of self-protective behavior reported — that note's internal engineers describe a skills-erosion worry around delegation, while this external sample of professionals and creatives reports actively hiding or downplaying AI use itself, independent of how much work is delegated. It also echoes the self-report divergence flagged in Can self-ratings replace objective performance scores for AI competence?: the same interview sample described AI's role as 65% augmentative and 35% automative, a split the source itself notes is "much more even" (47%/49%) in Anthropic's own measured Claude usage data — though the excerpt breaks off before giving its explanation for the gap. The workforce/creative split into augmentation versus automation also parallels, without matching, the copilot/workflow-agent modalities used to sort adoption in How are national lab staff actually using generative AI?.
The sample was recruited through crowdworker platforms rather than Claude's general user base, interviewed by an AI tool whose reliability as an interviewer the source does not independently validate, and the stigma and anxiety figures are self-reported perceptions, not observed workplace behavior — so the finding establishes that these professionals say they conceal and worry about AI use, not how concealment actually affects their work, their colleagues' trust, or their career outcomes. If the self-report gap on augmentation versus automation is any guide, the true extent or effects of concealment could diverge further from what interviewees report.
Inquiring lines that read this note 23
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
Does AI-assisted work increase total productivity or just shift time?- Why do most organizations lack reliable data on AI's actual impact on productivity?
- How can we isolate AI's contribution from other sources of output growth?
- Do companies time productivity claims to coincide with public offerings or fundraising?
- Are heavy AI users spending more time on solo work instead of collaboration?
- Why do organizations struggle to retrain workers when AI frees up time?
- Do employees spend freed AI time on better work or just more tasks?
- Can self-reported productivity surveys measure AI's real workplace impact?
- Does AI training reduce the time workers need to spend on output cleanup?
- Why do trained AI users report bigger productivity gains than untrained workers?
- Do workers who hide AI use experience different anxiety about job displacement?
- Why do information-intensive jobs expose workers to AI more than others?
- Why do executives report no AI impact on jobs today?
- What workplace cultures make professionals more willing to disclose AI use openly?
- What makes colleagues willing to share how they actually use GenAI at work?
- How do organizational policies on GenAI affect whether workers hide or reveal their use?
Related concepts in this collection 3
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Does AI assistance erode the skills needed to oversee it?
Anthropic engineers report productivity gains from Claude but worry that heavy delegation may wear down the coding skills required to validate its work. The tension raises questions about whether AI collaboration trades expertise for output.
same company's self-report data, different population, different self-protective worry (skills erosion vs. concealment)
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Can self-ratings replace objective performance scores for AI competence?
Do people's perceptions of their own AI competence match what they can actually do? This matters because assessment systems might rely on the wrong type of measure to evaluate workplace readiness.
both surface a gap between what people report about their AI use and what is independently measured
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How are national lab staff actually using generative AI?
This research explores whether generative AI adoption at a US national lab has moved beyond experimentation into routine work. Understanding real usage patterns helps clarify what AI is genuinely changing about knowledge work.
a parallel attempt to categorize workplace AI use into discrete modes, using a different taxonomy
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Evidence of a social evaluation penalty for using AI
- Introducing Anthropic Interviewer: What 1,250 professionals told us about working with AI
- Beyond Productivity: Measuring the Real Value of AI
- What 81,000 people told us about the economics of AI
- The state of enterprise AI
- Adoption of Generative AI in the Workplace: Increasing and Shifting the Balance of Productivity and Communication Activity
- How AI Impacts Skill Formation
- We are Changing our Developer Productivity Experiment Design
Original note title
Anthropic's Interviewer study finds most professionals and creatives hide AI use at work due to stigma despite reporting large productivity gains