Aruna Ranganathan, who teaches management at UC-Berkeley and has a Ph.D. According to a case study highlighted in some of Xingqi’s “progressive research” by Maggie Ye. student who is part of Ranganathan’s Berkeley program, AI “intensifies” work, and certainly doesn’t make people’s days easier.
In other words, it feels like hell on earth.
If that, paradoxically, is what you want In your workday, you probably work in Silicon Valley, or even a place like OpenAI, where CEO Sam Altman describes AI’s ability to speed up your work in a way that makes him sound oddly surprised and humbled (even as he expresses little or no regret about his ambition to eliminate knowledge worker jobs). “I don’t think I can come up with ideas that fast anymore,” he said in an interview last October. He added, “I think it will mean that things happen faster and you can do that… that you can try a lot more things, and come up with better ideas faster.”
Altman’s experience may coincide with that of the workers mentioned in the article about Ranganathan and Yeh’s research for Harvard Business Review. They describe an eight-month study on the effects of generative AI on working life at a company with about 200 employees. Employees “worked at a faster pace,” covering a “wider scope of tasks” and found themselves working “longer hours of the day, often without being asked to do so,” the authors write.
Ranganathan and Yeh point out that this was a workplace in which the use of AI was not mandated. It has just made enterprise AI tools available. It doesn’t feel like a 200-person workplace where widgets were being glued together. Instead, many of the roles described in the article involve engineering, writing code, and communicating in Slack, so it’s safe to say these were knowledge workers and software engineers, likely using tools like Cloud Code.
It seems that because of AI, many of Ranganathan and Ye’s subjects have begun expanding the scope of their jobs, usurping each other’s roles, and taking on the roles of training others on coding, or correcting their Vibe-coded work. Hiring new employees may have been postponed or avoided altogether, as employees have “absorbed work that previously could have justified additional help or headcount.”
It appears that they secretly fed tasks into their AI tools while employees were in meetings, and submitted prompts during breaks, while waiting for things to load, or when they were about to eat lunch.
How you interpret this case study will vary. If your workplace is a startup in “founder mode” and everyone in your office is working long hours in exchange for equity in a company that everyone expects will be a unicorn, I’m guessing you’ll probably like the sound of this – especially if you’re a CEO/founder and you plan on becoming a billionaire.
However, this is far from a universal experience.
According to a 2024 Pew survey, about half of American workers reported that they were either somewhat satisfied or “very much/not at all satisfied” and the other half said they were “extremely/very satisfied”. When the respondent’s income is lower the “extremely/very satisfied” group decreases from 50% to 42%.
That survey also found that the most satisfying aspect of the job according to respondents is other humans, with 64 percent of them reporting being “extremely/very satisfied” with their relationships with their coworkers. Meanwhile, skills development ranked lower, with 37 percent reporting being “extremely/very satisfied” with that aspect of a given job.
So I don’t think that learning to work more, with fewer people, and working intermittently will help most people’s job satisfaction, but perhaps I lack a certain kind of vision.
In other words, if instead of building an app, you’re someone who works as a hospital receptionist or school administrator, you’re probably not excited by the fantasy where hiring is postponed, you have to do other people’s jobs, you’ll work on your breaks, and instead of getting new, useful software, you’re getting enterprise AI tools so you can build your own software.
But let’s not assume that all tech workers love this kind of productivity theater, or that the feeling of greater productivity in Ranganathan and Yeh’s case study is necessarily anything other than an illusion. An anonymous employee at the cybersecurity firm CrowdStrike wrote in the newsletter Blood in the Machine last year, saying that employees at that company “have been encouraged to handle the additional workload per person simply by working harder and sometimes working longer hours without any additional compensation,” and that “although our machine learning systems continue to perform with excellence, I am not yet convinced that our use of GenAI in proofreading, troubleshooting, and “has been productive in terms of general child care.”
According to this person, “the net result is not the lightening of the burden that has often been promised,” and “morale is at an all-time low.”
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