What if AI just makes us work harder?

In a column in January about the paradox of work, I recalled the immortal Douglas Adams joke about working conditions: the hours are good, but “most of the actual minutes are pretty lousy”. The joke is back already — and generative AI has flipped the script.

Academics at UC Berkeley’s Haas School of Business have been doing ethnographic research into how technology workers are using generative AI. Some will tell you that ethnographic business research is both the worst kind of business research and the worst kind of ethnography, but I admit to a soft spot for this stuff. What the researchers found was the opposite of Adams’ morose Vogon guard: the minutes are amazing but the hours are terrible.

“In micro moments of prompting, iterating and experimenting, people talked about momentum and a sense of expanded capability,” researcher Xingqi Maggie Ye explained. “But when they stepped back and reflected on their broader work experience, a different tone sometimes emerged. They described feeling busier, more stretched, or less able to fully disconnect.”

These tech workers felt that generative AI was making them dramatically more productive and capable — but they were also trying to do more, voluntarily working longer hours, and hurtling towards burnout.

Are these ethnographic observations a glimpse of the future for the rest of us?

No doubt we shall find out, but while we wait, both economic theory and the history of technology have some things to teach us.

Theory first. Consider a freelance programmer, paid by results, who used to work 10 hours a day and suddenly finds that they can achieve the same results in two. Common sense might suggest that the coder will start to enjoy the pleasures of a two-hour workday, but economic theory is more ambiguous: the “income effect” suggests that the worker should work fewer hours because they can achieve so much by working so little. The “substitution effect” says that workers should work longer hours, as each extra hour yields bountiful rewards.

Then there is the question of what the new equilibrium will be once everyone masters the technology. As an analogy, imagine that a few alchemists discover how to turn lead into gold, but their method is rapidly being copied. They should make and sell as much gold as possible, as fast as possible, before the collapse in the gold market. Coders armed with brilliant AI agents may be in the same position: code as much as you can while you can still charge money to do so, because code may soon become as cheap as dust.

There is also a corporate dynamic to consider. It may be that nine out of 10 in-house programmers are about to be sacked, leaving a handful in charge to manage the coding agents. If so, the imperative is clear: to keep your job, demonstrate that you can out-code everyone else in the building. Winner-take-all dynamics are not a recipe for long lunch breaks and long weekends.

That’s the theory, but history has a few lessons for us, too. Visual aids were once produced by graphic designers and used on special occasions only; the invention of PowerPoint meant that highly paid and skilled professionals started wasting time making their own slides, badly. Email is vastly quicker and cheaper than a letter, but that simply means a profusion of low-quality, low-value messages bleeding into the evenings and weekends. The library photocopier allowed a generation of students to copy academic articles at a speed their parents could hardly have imagined — but it did not make reading, thinking or learning any faster.

In each case there was an astonishing increase in a narrow measure of productivity, but the overall effect was to distract from the real task at hand, to create a bloated pile of busywork, and to intensify the sense of productivity debt, with the list of tasks people felt guilty about not doing getting longer, not shorter.

What the UC Berkeley ethnographers found is strangely familiar. “Workers increasingly stepped into responsibilities that previously belonged to others,” they wrote. That’s the bad slide problem all over again.

“Because AI made beginning a task so easy . . . workers slipped small amounts of work into moments that had previously been breaks.” Everybody who lived through the rise of smartphones will nod in recognition.

“More multitasking. AI introduced a new rhythm in which workers managed several active threads at once . . . This created cognitive load and a sense of always juggling.” Well, yes — how many browser tabs do you have open right now?

I don’t mean to suggest that AI is useless or trivial, but there is a long history of time-saving digital technologies that at best make us more productive yet overwhelmed — and at worst, just make us feel overwhelmed.

Digital tools don’t have to work this way. The Nobel laureate economist Claudia Goldin coined the phrase “greedy jobs” to describe roles such as those in corporate law or investment banking where disproportionate rewards are paid to those willing and able to work long hours and be on call whenever required. She contrasts these with well-paid positions in pharmacy, paediatrics, primary care and veterinary medicine, where the jobs and the IT systems that support them have been designed to allow highly qualified practitioners to work limited hours and then hand over to an equally qualified colleague.

It’s not impossible to imagine AI agents being used to facilitate this handover process, but the discourse at the moment is of brilliant, idiosyncratic human conductors overseeing a frenetic orchestra of AI agents. Handover protocols sound less fun but may be a lot more useful.

And what are the rest of us to do while we wait for the wizards of Silicon Valley to stoop to building such prosaic tools? Todd Brown, a performance consultant and managing partner at Next Action Associates, has long espoused keeping an “Agenda” list for colleagues and important clients — working through the list face-to-face rather than firing off emails whenever something pops up. Now he does the same for ChatGPT, “with ideas for prompts”.

It may sound like an odd practice, given that generative AI — unlike a colleague — is always available. But it makes sense. Just because you can turn to AI at a moment’s notice doesn’t mean you should. There is something to be said for planning ahead before interacting with the AI, and for blocking out time without it — leaving space for the human in the loop to stop, to reflect and to breathe.

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