Deep Questions with Cal Newport
How Do I Escape the “Busyness Singularity”? | Monday Advice
01 Jun 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is the 'Busyness Singularity' and why is it a concern?
There are a lot of concerns in our current discourse about work and technology. Here, for example, is an Economist article from just a couple weeks ago. The headline reads, Prepare for an AI jobs apocalypse. It is not here yet, but government should lay a safety net. Yikes, right? Now here's a Times headline from just last week. California's governor signs AI order aimed at protecting workers.
Governor Gavin Newsom issued an executive order to explore an overhaul of labor policies to deal with potential mass job displacement from artificial intelligence. Now notice what these examples have in common. They're all about the potential catastrophic loss of jobs that might occur if technology automates existing roles.
But there's another possibility that people haven't been discussing as much, but it's a possibility that I've increasingly come to worry about. What if the real fear with new advancements like AI is not that these technologies are going to take your job. But instead are going to make your job miserable. And if this is true, what specifically can you do to avoid this fate?
Well, it's Monday, meaning it's time for an advice episode of this show, which is the perfect opportunity to go seek out some answers. All right, so here's the plan. I'm going to start by telling you a story, a story about how technology has slowly and somewhat accidentally made knowledge work jobs increasingly worse.
And then I will talk about how the arrival of AI might take this long unfolding process and accelerate it to an absurd degree, creating what I've taken the calling The Busyness Singularity. Now, once I've told you this tale, I'll then share some practical advice about what you can do individually to avoid the gravitational pull of this grim fate.
In more detail, I have five important suggestions to share, and we will go through each of those. All right, so we have a lot to cover today, so let's get started. As always, I'm Cal Newport, and this is Deep Questions, the show for people seeking depth in a distracted world. All right, I want to start by telling you a story about the evolution of knowledge work.
And I should say right off the bat, if you want the full version of this story, you need to read part one of my most recent book, which is called Slow Productivity. This is basically sort of the primary source material for a lot of what I talk about these days about work on this show. So you should have your own copy of it.
All right, so here is the short version of the summary I tell in part one, the story I tell in part one of that book. In the beginning...
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Chapter 2: How does AI potentially make existing jobs more miserable?
We have the notion of knowledge work emerge in the mid-1950s, roughly speaking. That's when the management theorist Peter Drucker actually coins the term knowledge work. His job was to help the business leaders of America in particular understand what knowledge work is and how it differs from the industrial work that had been driving the American economy up to that point.
Now, one of the key concepts of knowledge work that Peter Drucker drilled into the heads of American executives was autonomy. Unlike assembly line workers, knowledge workers are creative and have specific skills. They often know more about what they're doing than the managers who manage them.
So instead of trying to give them an exact step-by-step checklist to follow, like you would do if you're building a Magneto for a Model T Ford, you need to give them autonomy to figure out how they're going to work and what they're going to do with their work. It's going to need to be much more of a hands-off management style.
Well, this created a problem for managers because how do we manage knowledge workers? If we can't just have a pile of widgets we look at and say, how many did you produce today? If they're autonomous, how do we manage them? The implicit answer that arose is what I call pseudo-productivity. It was a heuristic that says, let's use visible activity as a proxy for useful effort.
The more we see you doing, the more useful we'll assume you're being. Now, this is not a very precise way to manage productivity in the knowledge sector, but for decades it worked well enough. It was like, At the water cooler, if the boss comes in, change your conversation from, you know, the latest episode of ALF to instead some work-like topic. This was also like the Mad Men era.
Like, well, we'll just be here long hours so we look busy, but also we'll have like full bars in our offices. Like pseudo productivity wasn't the best way of actually managing knowledge workers, but it was good enough. Then digital technology arrives in the office environment.
And this is where pseudo productivity begins to take a turn for the worst, at least when it comes to the subjective experience of the individual workers. First, we get computers on the desk. This greatly increased the number of different things that each individual worker could work on. Many of these things would be administrative focus. Many of these things are sort of never ending.
There's like endless things you could be doing. Well, that created a new level of busyness that didn't exist before we had the computer. And in a pseudo productive environment where more busyness is better than less, we began to feel like we had to run after all these different things.
And suddenly we were switching our attention back and forth between different, many more tasks than we had been before. Then we get networking technology. We networked up these computers. giving us tools like email. Well, this increased both the granularity and rate at which you could now demonstrate to people that you were doing a visible effort, right?
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Chapter 3: What is pseudo-productivity and how does it affect knowledge work?
It's because technology played poorly with pseudo productivity. I'm going to load up another headline here. This is another natural consequence. This headline from Axios says global survey worker burnout reaches new high. This is going to be the obvious consequence of a work that is now hyper busy. And so this is sort of the story I tell in Slow Productivity.
I think it's one of the defining stories of office work in the 21st century. But all of this happened before generative AI began to make its move into the workplace. So what's the effect of this new technology? When we add this into the mix, what's its effect onto this ongoing story? Well, this is where things get even worse.
People often talk about LLM Power Tools as providing some sort of productivity miracle, right? We hear reports of computer programmers who are handing off the actual writing of computer code to AI. And we imagine this is coming for all of our other main work as well and all these other jobs. And this gets us either terrified or excited about AI's possibility.
But what's really happening with AI in most non-computer programming office jobs right now? It's much more mundane. Here are the most common uses of AI in most non-programming office jobs at the moment.
Writing long emails automatically, summarizing long emails, automatically creating slide decks, transcribing meetings and turning transcriptions into some sort of work product that can then be shared, creating verbose reports and trend analysis. So having to do some research and then write a report. What unifies those examples is
is that they're all more or less pseudo-productive activities, the type of things you do to demonstrate effort, to show that you're busy, but that bring not a ton of actual value to the bottom line. Clients aren't paying for the reports or emails you're sending back and forth. So AI right now is often being used in the office
to support pseudo productive activities as things you do to show that you are being busy. Now, the problem is, is AI has essentially reduced the cost and friction of these existing pseudo productive activities down to zero.
So what happens when you set up a work environment in which visible activity is rewarded, then you give everyone a machine that can automate those efforts, making them essentially free. Well, what's going to happen is work will become a mad performative dash of button mashing. Who can turn out more slop quicker than the next person?
Soon we'll be managing teams of agents that are producing emails and slide decks on our behalf while intercepting, summarizing, and responding to those incoming AI decks that have been generated by other people's agents. It will be a digital blitz of back and forth nothingness. The density of shallow work here will become infinite. It will collapse in on itself.
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Chapter 4: What are the five strategies to avoid the pitfalls of pseudo-productivity?
You will end up with a busyness singularity. It's productivity reducto ad absurdum. All right, so if we step back here, what we're facing is not really an AI problem in the sense of, oh, everything was fine, and then AI came along and created a new problem. Pseudoproductivity was never the right answer for measuring useful effort in the knowledge work.
And with each new decade, a new technology came along that made these shortcomings even more apparent. And it's on this trajectory that when you then throw AI into the mix, we now are collapsing towards a self-destructive conclusion. This busyness singularity to me is going to have a much more widespread negative consequence society-wide than the threats of jobs being fully automated.
Let's take a quick break to hear from some of our sponsors. Now, if you listen to this show, then presumably you're interested in ideas about taking control of your mind to produce deep results in a distracted world. But there could be a difference sometimes between scattered advice, like I'm giving over a bunch of different podcast episodes, and a carefully produced class.
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