Last month, we introduced TaskNet with researchers from Mercor, Stanford, O*NET Resource Center, and the National Bureau of Economic Research. TaskNet builds on the structure of O*NET, the US Department of Labor's widely used map of occupational tasks.

From TaskNet and our conversations with workers, we learned that AI is changing some jobs more than others. The missing piece was asking these workers how they felt about AI progress and how they would shape it going forward. Following work by WORKBank (Shao et al., 2025), Bick, Blandin, Deming and Schumacher and others, we surveyed participants from our original TaskNet occupations about how AI has changed their jobs, which tasks they see as core to their occupation and how they view its future.

Running the survey

Each worker received a survey listing their occupation's TaskNet tasks. Drawing on the WORKBank survey (Shao et al., 2025), respondents rated every task they do on several scales, including how much they would want an AI system to do the task for them if it could do it completely (not at all to entirely), how much they enjoy the task, setting salary aside; and how much human-AI collaboration the task would need if AI assisted (none needed to essential).

Workers could then pick the tasks they would most like AI to take over and the ones they most want to keep doing themselves, with a sentence on why. Finally, they answered three questions about the job as a whole: how much AI has changed how they do their job over the past year, what they would most likely do with the time freed up if AI took over the tasks they would like automated, and how they feel about AI’s effect on their occupation over the next five years.

What workers want to hand off and keep

Across occupations, workers most wanted to keep tasks that are based in human interaction and tasks that are high-stakes. For software developers, that means mentoring junior developers or reviewing technical designs; for physicians, leading resuscitations; and for lawyers, interviewing clients and advising them on risks and options.

The tasks workers most wanted to hand to AI were often clerical in nature, like keeping medical records up to date. Elementary school teachers showed a particularly clear contrast: most want to hand data analysis and creating teaching materials to AI but keep teaching daily lessons and helping kids with emotional skills.

We also asked workers for each task, assuming AI was going to help, how much human-AI collaboration would be needed. Of roughly 9,000 task ratings, 14% were "none needed" and 27% "essential," with most somewhere in between.

"I would like AI to listen to my patient encounter and show me a list of tests to approve based on the discussion. I do not want it to autonomously order the tests without my approval." - ER physician

Lawyers gave "essential" ratings most often, for 38% of their aggregate ratings, compared with only 19% for software developers.

What workers would do with the time saved by AI

Asked what they would do with time saved by AI, 38% of respondents said they'd do more of the tasks they enjoy; 22%, the same work at higher volume; 18%, move into new kinds of work; and 15%, work fewer hours.

Elementary school teachers were the most drawn to do more of the work they already enjoy (51%); as one of them put it, AI "would allow me to be more present in my classroom and do a better job directly teaching my students." ER physicians were the most inclined to do the same work at higher volume (36%), and dishwashers to move into new kinds of work (43%).

Exposure scores track reported change

Exposure scores help us estimate how much of an occupation's required skills and tasks overlap with what AI could eventually do. In TaskNet give each task a score from 0 to 100 for how feasible it would be to build a training environment where AI can practice the task and learn from feedback, following the methodology set out in What Jobs Can AI Learn? Measuring Exposure by Reinforcement Learning. The idea here is that understanding how easy it is to train AI to do a certain job should give us insight as to how much AI will affect that job in the short term.

We found that people in more exposed occupations were indeed much more likely to say AI has changed their job a lot in the past year, and more likely to worry about AI's effect on their occupation's future. ER physicians were a notable exception: they fell below the trend for both reported change and worry. This may be partly because the occupation includes many high-stakes tasks involving human interaction, the kinds of tasks workers most wanted to keep doing themselves. These trends hold when we weight each task's exposure score by its importance to the job or by the time workers spend on it and are robust to different exposure measures, such as that found in Eloundou et al.

Looking across occupations, people who rated their jobs as having changed more were much more likely to be worried than people who rated their job as having not changed much at all. The share who were optimistic was relatively similar across groups, while the share who were neutral was smaller among those reporting more change.

Extending the survey to Mercor applicants

In mid-October, we began offering everyone who completes a Mercor application an optional module on AI and the future of work. Responses came from over 2,000 workers across the world, spanning many more occupations.

The link between exposure scores and how much people say AI has affected their jobs holds across this broader set of occupations, with medicine again noticeably below where the trend line would suggest. We note that people on our platform are significantly more likely to be interested in and engaging with AI, so this sample is unlikely to be representative of workers in general.

Conclusion

The people who are helping to train frontier models have valuable perspectives on how those frontier models should be developed and used. Mercor has long focused on organizing intelligence, but there is more to intelligence than the ability to solve a certain predefined task. Our expert talent network allows us to ask people at scale about their perspective on AI, with the hopes of helping to answer some of these big questions. Through TaskNet and follow-up work like this one, we hope to better understand what tasks are and are not essential to the identity of an occupation, as well as what workers do and don’t want for the future of this technology.

Appendix: Exposure correlation under Eloundou-β