Short Takes #23: The Fish Is In The Water
Arthur Miller | Work Is Not Neutral | A Burnout Machine
The fish is in the water, but the water’s also in the fish.
| Arthur Miller
…
There’s a famous line mistakenly attributed to Marshall McLuhan — ‘We shape our tools and thereafter they shape us’ — actually penned by a friend of his, Father John Culkin. It seems that AI is having such an effect on those that use it, and the greatest impact on those that use it the most. And of course, the systems of work are tools (or a tool) as well.
Are we the fish or the water?
Work Is Not Neutral
I have often said that work in interconnected to everything in our society, and cannot be understood as a thing in isolation, but I have not expressed as clearly as Corinne Murray does, here [emphasis mine]:
The way modern work functions—and what it demands of us—is a byproduct of our cultural and moral frameworks. Underneath all of what we see playing out in our daily lives lies deep-seated religious and secular ideologies that define productivity as a virtue, suffering as a necessity, and worth as something that can only be earned through effort and performance.
If a better future were possible without confronting the depths and power of these systems, we would have arrived in it already. Instead, we’ve only redesigned and rebranded the surface features while leaving the underlying beliefs untouched and unexamined–guaranteeing that the same old outcomes will play out under a different name.
Even if we could isolate modern work in the United States from…everything else…the conditions of modern work are far from ideal. Burnout rates climb to new heights annually. Gen Z still can’t find entry-level work. Parents—primarily women—are leaving the workforce because of childcare shortages and return-to-office mandates. The elderly are working retail to supplement fixed incomes. All while the wealth disparity between billionaires and the rest of us exceeds that of the Gilded Age, and the last vestiges of America’s social safety net get swept away for more tax breaks for them. These are not isolated crises. These are features of our reality, not flaws.
| Corinne Murray, Work Has Never Been Neutral
And they point out we are implicated in the workings of these systems:
Most of us participate in these systems because choosing not to comes at great personal cost, and one we’re rarely allowed to acknowledge. Regardless of our belief and endorsement of what is happening, our participation makes us complicit—willingly or not—in exchange for some semblance of stability and comfort.
As Abraham Joshua Heschel tells us,
In a free society, all are involved in what some are doing. Some are guilty; all are responsible.
One of our tasks is to name those who are guilty, but to accept we are implicated as well. Murray ends with this:
Awareness doesn’t change the fact that we still must participate to survive, but it removes the illusion that unquestioned participation is neutral. Continuing to look away from these systems and their implications is an active choice of disengagement and the grave consequences for all of us.
Breaking through these illusions requires confrontation, and I ask that you stay with me as I do that.
I strongly encourage others to follow Murray. Here’s some background.
A Burnout Machine
Connie Loizos reports on recent research published in HBR1 that was based on on-the-job observation at a 200-person tech company. Loizos puts the thesis of AI as
The tools work for you, you work less hard, everybody wins.
But a new study published in Harvard Business Review follows that premise to its actual conclusion, and what it finds there isn’t a productivity revolution. It finds companies are at risk of becoming burnout machines.
The UC Berkeley researchers detailed the time sink that AI become:
In our in-progress research, we discovered that AI tools didn’t reduce work, they consistently intensified it. In an eight-month study of how generative AI changed work habits at a U.S.-based technology company with about 200 employees, we found that employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so. Importantly, the company did not mandate AI use (though it did offer enterprise subscriptions to commercially available AI tools). On their own initiative workers did more because AI made “doing more” feel possible, accessible, and in many cases intrinsically rewarding.
While this may sound like a dream come true for leaders, the changes brought about by enthusiastic AI adoption can be unsustainable, causing problems down the line. Once the excitement of experimenting fades, workers can find that their workload has quietly grown and feel stretched from juggling everything that’s suddenly on their plate. That workload creep can in turn lead to cognitive fatigue, burnout, and weakened decision-making. The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems.
The researchers detailed the slippery slope of work intensification. Because AI tools can cover for a person’s knowledge gaps, workers began taking on responsibilities that formerly others would have done, like product managers starting to write code. This led to those others having to review that AI-augment output, like engineers reviewing code written by product managers. And because it’s easy to start up an AI project, time that might have been used to rest and reflect was swallowed up by AI-centered or -supported work.
As the researchers — Aruna Ranganathan, Xingqi Maggie Ye — stated the momentum of AI as a ‘partner’ becomes a rollercoaster ride
While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.
Over time, this rhythm raised expectations for speed—not necessarily through explicit demands, but through what became visible and normalized in everyday work. Many workers noted that they were doing more at once—and feeling more pressure—than before they used AI, even though the time savings from automation had ostensibly been meant to reduce such pressure.
All of this produced a self-reinforcing cycle. AI accelerated certain tasks, which raised expectations for speed; higher speed made workers more reliant on AI. Increased reliance widened the scope of what workers attempted, and a wider scope further expanded the quantity and density of work. Several participants noted that although they felt more productive, they did not feel less busy, and in some cases felt busier than before. As one engineer summarized, “You had thought that maybe, oh, because you could be more productive with AI, then you save some time, you can work less. But then really, you don’t work less. You just work the same amount or even more.”
The researchers suggest band-aids for management to minimize AI burnout, but the most important takeaway is from Loizos. While AI can augment what workers can do on their own
[The research] confirms it, then shows where all that augmentation actually leads, which is “fatigue, burnout, and a growing sense that work is harder to step away from, especially as organizational expectations for speed and responsiveness rise,” according to the researchers.
