Deep Questions with Cal Newport - Ep. 397: Why Do “Productivity Technologies” Make My Job Worse?

Episode Date: March 23, 2026

A new study finds that for many workers, AI increases shallow efforts while decreasing time focusing on what really matters. This is not the first digital productivity technology to create this paradoxical effect. In today’s episode, Cal dives deep into why this happens and then details three strategies for avoiding these traps in your own professional life. Below are the questions covered in today's episode (with their timestamps). Get your questions answered by Cal! Here’s the link: bit.ly/3U3sTvo Video from today’s episode: youtube.com/calnewportmedia DEEP DIVE:  Why Do “Productivity Technologies” Make My Job Worse? [3:17] INBOX:  An article about meetings [36:11] A case study on escaping email overload [47:29] A tool that makes the internet more boring [50:37] WHAT CAL IS UP TO: Deep or Crazy? [57:02] What Cal read [1:02:23] Cal in the news [1:05:57] Books: Reader, Come Home (Maryanne Wolf) What Do You Say? (William Stixrud and Ned Johnson) Links: Buy Cal’s latest book, “Slow Productivity” at calnewport.com/slow Get a signed copy of Cal’s “Slow Productivity” at peoplesbooktakoma.com/event/cal-newport/ Cal’s monthly book directory: bramses.notion.site/059db2641def4a88988b4d2cee4657ba?pmresearcher.substack.com/p/why-meetings-multiplydocs.pluckeye.net/overview Thanks to our Sponsors:  calderalab.com/deepcozyearth.com/deep (Use code “DEEP”)grammarly.comdrinkag1.com/deep Thanks to Jesse Miller for production, Jay Kerstens for the intro music, Nate Mechler for research and newsletter, and Mark Miles for mastering. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A new research study recently caught my attention. It came from a software company called Avatrack, which analyzed the digital activity of 164,000 workers spread across more than 1,000 different employers. And what they wanted to do was measure the impact of new AI tools. So what did they find? Here's a summary of their results from a Wall Street Journal article that came out last week. Avivetrak found AI intensified activity across nearly every activity category. The time they spent on email messaging and chat apps more than doubled, while their use of business management tools such as human resources or accounting software rose 94%. Meanwhile, the amount of time AI users devoted to focused uninterrupted work, the kind of concentration often required for figuring out complex problems, writing formulas, creating, and strategizing, fell.

compared with nearly no change for non-users. All right, so this research results describes in some sense a worst-case scenario for knowledge work. These employees are spending more time on exhausting shallow tasks that don't have a huge impact on the bottom line and less time on the deep tasks that can make the most difference. The efficiency gain of these new tools seems to have made everyone busier, but not necessarily better. Now here's the thing. This outcome is not unique to AI. As someone who has studied the intersection of digital technology and office work for more than a decade now, I can tell you from my experience that this matches a pattern that I have seen unfold many times before.

Here's how this pattern goes. One, a new technology promises to speed up some annoying aspect of our job. Two, we all get excited about freeing up more time for deep work and leisure. Three, we end up busier than before without producing more of the high value output that actually moves the needle. This pattern was true of the front office IT revolution. It was true about email. It was true about mobile computing. And it was true about video conferencing.

Easier when it comes to productivity tech often seems to translate to busier. This so-called digital productivity paradox is what I want to talk about today. I'll start by looking closer at why this paradox exist. What is it about digital productivity tools that seem to always trick us into being busier? I'll then discuss some concrete strategies for avoiding these traps. So if you're looking to get more benefits out of new AI tools, or you just want to repair your broken relationship with older technology that continues to drive you crazy, then this episode is for you.

As always, I'm Cal Newport, and this is deep questions to show for people seeking depth in a distracted world. And we'll get started right after the music. All right, so here's our approach for solving and reacting to the digital productivity paradox. I've got four questions that's going to lead us from understanding to solutions. All right. So one, two, three, four. Question number one, what do we mean when we say digital productivity tools? We have to get our definitions right so we know what we're talking about.

In general, when I say digital productivity tools, I'm talking about some sort of computer-aided tool that makes common work activities easier. Now, what do I mean by easier? It usually means some combination of these two factors. One, it speeds up the time required to complete the activity and or two, it reduces the mental exertion required to complete the activity. So when we talk about digital productivity tools, that's what we're going to do. mean things that are going to speed up and make cognitively easier common work activities. Now, there are many different digital productivity tools that have been introduced over the years. So to try to simplify the discussion that follows, I'm going to use two of these tools in

particular as our case studies throughout the discussion that follows. So one will be AI because this is new. So we're going to talk about sort of new AI applications, especially in like the non-programmer knowledge workspace. And then as our older example, I'm going to use email. It's a topic I've written a whole book about and know a lot about. So we'll use email and AI as our canonical examples of digital productivity tools for the discussion the follows. All right. So let's make sure first that our definition applies to those two tools. So does email make certain work activity tasks faster?

Well, it does indeed. It required less time to send an email or an email with an attachment than it did, for example, to use a fax machine or to have to call and leave a voicemail and then later check your voicemail machine by typing in those codes into your first. phone. So it makes things go faster. Does it make certain work activities less cognitively demanding? Well, it does. There's actually way more of a cost if I call you up and have to have a conversation with you back and forth on the phone is actually going to be much more cognitively demanding than if I just shoot off a quick email, just blah-da-da-send. So it matches both definitions of digital productivity. All right, what about like the sort of new office-centered AI tools? Well, we do know they speed up things, right? Like you can rapidly create drafts of things or in some cases even automate whole steps of a task change. So that is definitely task saving.

There's also a lot of cognitive exertion reduction with the use of AI in the office because it's often, for example, easier to like chat with a chat bot than they just sort of sit there and figure out from scratch, like what you're going to do or like what strategies. to deploy, it reduces the activation cost of thinking often to go back and forth with chatbot. So AI, our second example often matches this definition. All right. So at first glance, these seem like two good things. Faster? Sure, why is that not good? Less cognitive exertion?

Sure, why is that not good? This is why every time we're introduced to a new digital productivity tool, our first reaction is often bring it on. This is going to make my life better. So what goes wrong? Well, this brings us to question number two. Why do these technologies sometimes accidentally make our jobs worse? All right, I want to focus on two subtle factors that are at play.

One of them involves the unexpected side effects of doing work faster. The other factor looks at the unintentional consequences of trying to reduce the cognitive effort required to do certain tasks. All right, so let's look at factor number one. for many types of common work activities, increasing the speed at which you complete these types of activities or tasks ends up increasing the throughput of these tasks in your typical day. So if I go faster than the rate at which new task of this type come into my life also increases. Now what happens is, okay, now I'm tackling more total tasks of a given type per day

which induces a lot more context switching because every time I have to switch back to service one of these tasks, I have to switch my cognitive context. That then has a negative cognitive impact on anything else you're trying to do in the day. It exhaust you. It exhaust your brain.

It makes it harder to focus on other types of things. But going faster on each individual task can make your whole day seem more exhausting and less cognitively sharp. Let's look at this factor in play, first of all, with email. email certainly sped up the task of actually sending information to someone or replying to like a question that someone sent me because I can type it right into my computer where I'm already sitting and just press send. But the faster we were able to send messages back and forth, the faster messages began to be sent, right?

So like the total amount of communication has drastically increased year on year as we've continued to decrease the friction. and actually sending or receiving messages, bringing us to a point where we are now where the latest Microsoft WorkTrent Index report finds that the users they studied are checking an inbox once every two minutes on average. So yeah, this message is faster to send than it would have been if I had to call you or write a memo.

But because of that, I end up checking or sending messages or checking inboxes once every two minutes. So the throughput increases, it makes everything else harder. So it's an unintentional side effect. We see something similar with AI as well. You can use AI to speed up certain, especially like administrative tasks, kind of like quick tasks, more of them roll right in behind it. The cues are basically endless in the typical knowledge or environment of shallow tasks that can be done. This is why we see in that Avatrak research I cited in the introduction, a 94% increase in business management tool use.

The faster you're able to handle things, the more things come in behind it. So when throughput increase of task, it doesn't mean that you overall are going to be actually more productive. Right, here's the second factor at play here. For many types of common work activities, reducing the mental effort required to tackle them can lower the quality of the ultimate result, which can over time increase the overall amount of work required to actually get to a desirable in-state. So if I'm doing this with less focus, I might have to do more of it to get. to get to where we want to get, and now I've actually created more work than would have been here than if I had just worked harder on the original task.

This is another side effect that happens. We certainly saw this an email. In my book, A World Without Email, where I really studied this, one of the big ideas that came out of it is that because email, it's so easy just to write something and press in to get something off of your plate that we see a lot of vague and uninformative messages being sent. So yeah, in the moment, it was way easier for me to send off a like, yeah, maybe thoughts, question mark.

That was way less cognitive strain than to say, okay, hold on a second. What's going on here? What are the possibilities? What's the right thing to do? So in the moment it reduced cognitive strain. But because my email was so vague and uninformative, the total number of emails we now have to send back and forth before we finally resolve this issue grows. And so now the total amount of time I have to spend check in inboxes, looking at emails, replying to emails, and especially if we throw into time required that

every time I'm distracted by an email, how long it takes to get my focus back on the task at hand, when you put that all into play, you're like, oh, I have just done way more work. I've spent way more cognitive cycles on this than if I had just sat there and thought harder about the very original problem. AI is also creating a similar issue where you can shoot off like a draft of a slide deck or an email summary of an agenda for an upcoming meeting, you can use AI to help create these things in a way that requires much less trained than blank paging.

Blank PowerPoint page, blank email page you have to write from scratch. But as research that was reported recently in the Harvard Business Review found, the quality of these AI-generated work products is often so low that overall they require more work to actually get to the ultimate end result. They call this work slop, and here's their formal definition. AI generated work content that masquerades is good work, but lacks the substance to meaningfully advance a given task. So this is what they're seeing. There's a lot of work slop products being passed back and forth, and it takes time for people to read it, and they're confusing, and it doesn't really help advance the task.

And overall, the amount of total time that people have to dedicate to whatever the task is at hands goes up versus if someone had just said, I'm going to make the right slide deck. with the right information and the right next steps now, it's going to take me a half hour of hard work instead of 10 minutes of prompting. But then once I send this out, we can immediately move forward. And this is actually going to take more, less overall time than if I just let AI help generate something. So sometimes reducing the cognitive effort in the moment

can actually increase the overall amount of work. So these are the two factors that I think help explain this idea of when you bring in new tools, digital productivity tools, like, hey, faster, great. less cognitive strain, great, and you find yourself more exhausted, getting less done, and things taking more time. That's what I think is going on.

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your first order. All right. Let's get back to the show. All right. Question number three or four. If this is true, why do we continue to so enthusiastically embrace these new productivity tools every time a new one is introduced? Well, I want to go to a core idea I introduced in my 2024 book, Slow Productivity.

And that idea is pseudo-productivity. Now, if you've heard the show before, you may have heard me talk about it. So I'm just going to give the definition here very quickly. Pseud productivity was the way that the managerial class tried to respond to the reality of knowledge work. When knowledge work became a major economic sector starting the mid-20th century, the big issue that the managerial class had is that it was hard to precisely measure productivity. In the industrial sector, where these managers used to be, productivity was easy to measure. How many model T's are we producing per paid worker hour in our first?

factory. You had a number. And if you changed something about how you ran your factory and that number got better, you would say we're more productive now. But when you went to knowledge work, there were no model T's the measure, right? Everyone was working on their own unique, bespoke, obfuscated portfolio of tasks, some harder than others, with an unknown non-transparent set of systems, all kind of collaborating with each other in unstructured ways. It was very difficult to say, here's your productivity number. It's seven. And when you changed this, it became eight, so that's better.

So in the lack of actual hard numbers to measure, we fell back on a heuristic, a rule of thumb, called pseudo-productivity, which said, lacking more precise measures of productivity, we will use visible effort as a proxy for you doing something useful. So the busier you seem, the better.

And this basically became the standard of how we think about productivity in the knowledge work class. At first, it's the way the managers thought about, it, then the workers themselves internalized it, which is why if you have like a solo entrepreneur, you've probably still internalized the pseudo-predactivity mindset and you feel lack of busyness is bad and busyness is somehow professionally virtuous. This is the mindset that dominates in knowledge work.

And in that mindset, the two benefits of digital productivity tools, you can move faster and you can lower the threshold to get something done makes you more pseudo-rengthy. productive. Higher throughput of task, that's great from a pseudo productivity standpoint. Shooting out work slop left and right, like a vomiting Microsoft office monster. From a pseudo productivity standpoint, you're in the mix. Things are being sent. Powerpoints are being received.

Email summaries are going out. You're there. People are seeing you. So digital productivity tools feed right into the pseudo productivity narrative. And that's why we embrace them because that's a benefit we get is that it makes us look more productive. But I don't care about looking more productive. I care about actually being more productive in the old-fashioned economic sense of how

much actual value are you creating for the bottom line. As we just covered, with most digital productivity tools, if you don't use them carefully, that number goes down. Higher throughput of tasks makes you seem busier, less important stuff gets done. Lower cognitive engagement to get things out the door makes you look busier, more total work is required before anything is actually finished. So it's only when you shift from pseudo productivity to true productivity that you realize, oh, digital productivity tools are more fraught than we thought.

There's these traps that sit around them that we put up with because of pseudo productivity, but when we get rid of that standard, we should be dismayed. All right, question number four, our final question in this discussion, how can we avoid these traps? So how can we embrace digital tools and yet not find ourselves actually becoming less productive in a true sense? I have three ideas that I want to recommend to you right now. All right. Idea number one, use a better scoreboard.

So get in the habit of measuring the things that actually matter in your job. in this way, if you bring on a new digital productivity tool and it's not helping that score or it's making that score worse, you will notice. And you're like, oh, I'm not getting caught up in the traps here because I can see directly that this is hurting the bottom line things that actually matter. Okay, so what do we mean by the things that actually matter? I have a couple examples here. Let's say you're a like me, you're a professor at an R1 institution, at a research institution. What's really going to matter, especially pre-tenure? Papers published.

How many good papers? that I published this year. And if that number is going down, then you're like, okay, whatever tools I'm using aren't helping. Maybe I started using Slack with my research team and that number went down. Great, that's not actually making me more productive in a true sense. I'm going to stop using it. Let's say you're a middle manager.

Maybe like priority projects completed by your team per month is the number you really care about. That's the actual score that moves the bottom line. So now let's say you're like a middle manager and you're actually carefully measuring that month by month you see where you are, your boss comes in and is like, I'm really savvy. You have to use AI. Otherwise, you'll get replaced by people who do know how to use AI. And you're like, all going to use, like, here's a Jim and I's description and like whatever,

mess around with like some of these agents or whatever. And you see the priority projects per month completed goes down. Like, whoop, trap. This is not making us more productive. Let's back back off against. You have to have the right scoreboard to know what's going on. even if you're a programmer, right? Even if you're a programmer, this is like this case where like with AI, for example,

it's like for sure, for sure, for sure, this is making everyone more productive. Everyone keeps saying, this would have taken me five hours before, now I could do it in 20 minutes. Actually, Jesse, there's almost like a competition. It gets kind of absurd when people are trying to, the programmers are talking, the amount of time they begin to claim that is being saved really gets crazy. So eventually it's like adding this feature, previously this would have taken me seven decades. And AI did it before I even pressed the button. It went back in time and actually it finished it last year.

You know, so anyways, it gets kind of absurd. But if you're a programmer, okay, what's the thing to measure? Important user feature request shipped per month or something like that, right? And again, this would allow you to say, like in the AI context, okay, this. this use of AI, that number went up. But when we had everyone like chatting all day with chatbots trying to figure out architecture documents, they feel super pseudo-productive, that number went down. So let's stop that.

So you need the right scoreboard. And it's not just about figuring out, like in my examples, this digital productivity tool didn't help. It's about figuring out what uses do help as well, right? So, okay, this didn't help, but this did. So don't do this and do this. We basically need our equivalent of county model T's produced per paid worker hour. So you need a better scoreboard.

board. All right, idea number two for avoiding these traps. You need to focus on the true bottlenecks in your work. Now, what I mean about that is often when a digital productivity tool enters the scene, the activities that it might speed up or make easier aren't really the bottleneck that was preventing, that was like at the key of you producing your most valuable output. So speeding that up might have no impact on your output. or have a sort of implicit or indirect negative output because it's, you know, distracting you or something like that.

So you have to be careful. It's not just enough to speed up any aspect of your job. You want to really focus on improving the true bottlenecks. So like, for example, give another AI example here, an increasing number of social scientists are realizing that they can use Claude Code, which is a terminal agent that was designed for computer programmers. but they could use it to help speed up certain type of data gathering and analysis task, right? So cloud code is a terminal agent, which means it works with text and text files.

So it's very good at writing text, moving text between files, compiling text with a compiler, or writing a computer program, and then passing the text as input to the computer program. So it's very good for sort of like text and number processing. So a lot of social scientists are finding, like, oh, I had to gather a bunch of data and clean it up and analyze it and produce a chart. That's the type of thing if you are careful in how you prompt cloud code and you go through the learning curve to learn it. It could really help you do that.

Like, oh, I can tell it what I want to do. And if I'm really careful and I have the right skills marked down, it can do the multi-step process, right? And you're like, that saved time. That might have taken time before. I just heard an economist talking about this the other day. And he was like, look, this, I did this for a bunch of plots. And, you know, it was like 20 minute of prompting with Claude code.

That would have taken me three hours if I had. if I had done that by hand. But here's the trick here. Was that the bottleneck that's holding back social scientists? Not really. Not really. The way social scientists work, it's not like all day long, that's what I'm doing. I'm gathering data, analyzing it, and producing plots.

I'm saturating my time with that. So if I can bring in a tool that speeds up how long that takes, it's going to significantly speed up my output. That's not actually how it works. If you actually measured right now, well, how much of your time, like how much data are you analyzing? How often you produce plots? Like, well, if we're being honest, I produce one paper every like two or three months. And that's like something I have to do a couple times in those one or two, three months.

So like making it three hours of 20 minutes twice in that three month period, that's nice. Like in the moment it's nice, but it doesn't speed up the rate at which I produce papers because there's so much more involved in putting together a paper than just how fast can I analyze the data. So that's nice, but it doesn't actually speed up the rate at which papers go out. We know this because in research, academic research, there's been any number of digital tools that have sped up and made easier parts of the research process. I'm talking about, like, if you're a mathematician or a theoretical computer scientist, you can use things like LaTech in a web-based collaborative environment

where now all your collaborators can work on the same file and compile it and make adjustments really quickly. So you can significantly reduce as a time required to write papers to do mathematics, mathematical in formatting. We have bibliographer managers that makes it much easier to cite and professionally format things. Like the time required to like write up and format papers is much smaller. We have technology tools that allow you to immediately grab copies of like any paper that you might need to reference and digital communication tools that allow you to keep in touch with researchers all around the world and therefore get much more out of your mind. And all these things

make academic research better. And we're still not producing papers at a notably faster rate per researcher than we would have before those tools were there. Because these weren't the bottlenecks. They're useful, but they weren't the bottlenecks. So to give a, what is the bottleneck? Well, let's go back to our social science example. I remember I once had a conversation with Adam Grant, the business school professor and author.

And I was asking him about his productivity as a business school professor. He writes a lot of journal papers. He was sort of two-xing what his colleagues were doing. And these are data analytics. These are papers and organizational. management theory. So it's a lot of like you get data, you analyze it,

you write a paper about what you found. And he said, oh, here's what he figured out. He's like, here was the key. The key is getting the right data. If you can get an interesting data set,

like let's say from a company about their use of something that happened, that no one else has access to. You can now write three or four papers off that data set that are going to be good because it all comes down to the data set. So Adam was like, here's what I realized I had to prioritize. putting out feelers, having conversations, meetings, talking to people, trying to negotiate access to interesting data sets.

And then when it came time to actually write papers, yeah, he would lock, I wrote about this in my book Deep Work. He would lock himself in his room and put on like an auto responder. He had this sort of bimodal deep work mode. It's all kind of interesting. And you sat down and do the hard work of writing your paper. So I'm sure he would appreciate when he has those sessions to write the papers if he could speed up some of the steps. but the bottleneck for producing great papers in his field was negotiating access to data. So the same thing happens in lots of fields.

The key bottleneck is really maybe not what you think it is. It's coming up with the right problem. It's having reading enough. In theory, it was often reading enough other papers, understanding enough other papers that you're building up this toolkit in your mind of different techniques. And then you begin putting pieces together of like this problem plus this technique, plus that twist could get a result. And so like the number one, the bottleneck to doing better theory in my field was grocking other papers.

And I don't mean that by using the XAI tool. I mean, in the original use of the word grok, reading and grappling until you really had internalized an understanding in your head. That was the bottleneck. It's nice to have lots of tools that made it quicker to write the papers then, but that didn't speed up the rate at which we produced papers because the bottleneck was understanding other work. So it's key to understanding your job what the actual bottleneck is. what's the thing that really controls the rate at which good results are done? And when you're looking for digital productivity tools, be especially tuned to those that help what's going on with that bottleneck

to help speed up that piece. So like using email as a digital productivity tool to help put out more feelers and get access to more potential datasets to use in the Adam Grant scenario, that's a digital productivity tool that's helping the exact bottleneck of producing those papers. Whereas using Claude Code to automatically generate your graphs is nice, but it's probably not going to speed up the rate at which papers are produced. So make sure you look at the right bottlenecks.

All right. Third and final idea for avoiding these traps in your daily schedule is separate deep from shallow efforts. So just have and protect the time for sitting and doing hard things with your brain and the activities that you know for sure create bottom line value. This just gives you like a safety barrier against some of the accidental negative side effects of digital productivity tool. So like now you're using Slack because it seems like it would be even faster than email. And maybe you're having all these secondary side effects of it.

Now there's many more messages and it's really distracting. But if you have a habit of separating deep from shallow work, those side effects won't affect the hours where you're working on the primary thing that moves the needle. Or maybe you're using AI for certain things and the right graph. or this or that, and it's starting to sort of get you into like slop territory and you're having all these long back and forth conversations with the tool and it's like eating up a lot of time. If you separate deep from shallow work, it's a firewall that keeps that from infecting the area where you're actually doing the hard work of thinking. This doesn't mean that you won't be using digital tools while doing the deep work, but there you're

just carefully deploying digital tools that just help you continue to make direct progress on the like bottom line things. I'm writing a draft of a paper. I'm putting together the strategy memo. I'm architecting the key element, low-stack element of this new tech stack that I'm programming, right? So if you separate and protect deep from shallow, you're not preventing the negative side effects of digital productivity tools from happening, but you're containing them in a way that they can't completely take over the activities that really matter. All right.

So my three ideas, again, use a better scoreboard, identify the actual bottlenecks to the things that really matter and focus on improving those more than other things. and separate deep from shallow work so that side effects you aren't expecting of digital productivity tools won't have too much of a negative impact on your ability to move the bottom line forward. All right. So let me conclude here. My argument is not that digital technology in the office always makes things worse. That is clearly not the case. There's any number of digital tools I used that makes my life easier.

I'm glad they're there. There's other tools to make my life easier in some ways and terrible in other. others, there's a whole mix. But it is true that many of these tools seem at first glance like they should make us more productive in the true sense of value produced per worker and accidentally end up creating the opposite effect. And once you understand why this happens, you can sidestep those traps, get value out of digital tools while avoiding more of their cost.

So it's the type of conversation that we don't often have. We just say, hey, here's the new tool. Let's do it. This is cool. It's good to be critical like this. And with this huge new AI revolution going on, it's a great time to have a refresher on these dynamics. So there you go, Jesse.

Digital productivity paradox. That's something I've been writing about for a decade now. That's crazy. Ten years. That's when Deep Work came out 10 years ago. Has it got better? Has not got better. I keep thinking it will, but we're still struggling.

All right. That's enough for me. Now we want to hear what you have to say. So it's time to open our inbox. But before we get to your notes, let's take a quick break to hear from our sponsors. The succeeding in knowledge work requires more than just deep thinking.

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So go to drinkag1.com slash deep. to get an AG1 flavor sampler and a bottle of vitamin D3 plus K2 for free in your AG1 welcome kit with your first AG1 subscription order. Only while supplies last, that's drinkag1.com slash deep. Okay, we're back and ready to dive into the show's inbox. Remember, we want to hear from you. You can send your questions, your case studies, or interesting links to podcast at Calnewport. All right, Jesse, what are we doing for our first message? Our first message comes from Pablo, who sent us an article about meetings.

Oh, this is kind of on theme for our discussion of digital productivity tools and sort of traps that we get into. Here's what Pablo said. His introduction was, thought you might find this interesting, given your secondary focus on managing the utility of meetings. All right. So let's load up the article he's talking about here. This came, I guess is this a substack, Jesse? Yeah. All right.

It came from a substack called the critical path. And the article is titled, why meetings multiply? And it's written by Nicole Williams. All right. I'm going to read some highlights from this article because I thought it was actually pretty smart. And then I'm going to generalize the approach Nicole takes to workplace technology more generally. All right. So let me start here.

this is from the article. There is a strange pattern inside most organizations. Meetings rarely disappear. They multiply. A team begins with a single weekly meeting. Soon another appears to, quote, coordinate, end quote. Then a check-in meeting is added. Then a review meeting.

Eventually the calendar fills with recurring blocks that feel permanent as if the organization itself produces meetings the way a tree produces leaves. Yes, I know this phenomenon. well. So why does this happen? Let's go back to the article and see Nicole's explanation. So I'll read again here. Organizations exist to coordinate work among many people who do not have the same information. Every role sees a different slice of reality. Because no single person holds the complete picture, organizations need mechanisms to exchange information and meetings are one of the simplest mechanisms. When uncertainty increases, the organization creates more information

exchange points. And these points usually take the form of meetings. What appears to be calendar overload is often an attempt to reduce informational blind spots. Instead of everyone speaking to everyone individually, the system creates a place where information can be exchanged collectively. From this perspective, meetings are not simply interruptions in the workday. They are coordination infrastructure. I think those were really good points from Nicole.

She gave two other reasons which I'll just summarize why meetings multiply. She said it also has to do with reducing risk. When a group meets the talk about something, you're distributing the risk among all of those people. So there's no one person responsible to it. So it reduces risk for everyone involved. She also said it's a way to, quote, signal participation, end quote, a way to show that you're engaged in part of the efforts. The terminology I would use quoting from earlier in the episode would be pseudo productivity. It's a good way to show that you're pseudo productive because you're in a meeting.

People see you. You talk to them. They remember you being involved. And from a pseudo-productivity perspective, it's like, yeah, that person is trying. They're being useful. All right. Here's the concluding sentence from Nicole. As long as organizations face uncertainty, distribute responsibility, and coordinate across teams, meetings will continue to multiply.

All right. So what do I like about this analysis more generally? The frame. It's a frame that I have adopted in my work, most notably in my work, most notably in my my book, A World Without Email. And it's a frame that shifts the analysis of workplace habits, workplace technology, workplace behavior. It shifts it away from individual habits and it puts the focus on systems, which I think is the right way to analyze most of these issues. Most people think in terms of individual habits, right? So what would your response be to your calendar being overloaded

with meetings. You would say individuals are behaving poorly. They're setting up meetings when they could have just sent an email. They're being lazy. They don't know about deep work. It's individuals are having a problem, so we need better norms. We saw something similar in the reaction to email overload as that became a problem in the early 21st century. As I documented my book, people's response to email overload was norms. Oh, you just, you send too many emails or your expectations for responses are unreasonable. If we could all have better expectations than like we could all settle down about what's going on with our inboxes. So we love to think about these issues as individuals doing things wrong.

But in reality, these issues are often, as Nicole points out and I points out, the results of actually rational business systems that are solving. certain problems. Nicole says this is a easy and convenient way for information exchange and responsibility distribution and participation signaling. In the absence of a better way, the spread information or to coordinate or collaborate people, in the absence of a better way, we still need to do this, so we'll fall back on what's easy. That's exactly what happened with email overload as well.

It wasn't caused by bad norms or bad habits. It was caused because we shifted to back. and forth messaging is the primary mode we would use for collaboration. I call it the hyperactive hive mind model. We'll just figure things out back and forth on the fly. Well, this requires me to check my inbox all the time because there's so many ongoing conversations I have to service in a timely manner that I just have to basically constantly check my inbox.

That's why we're in this point we are today. We're in 2025 Microsoft measured an inbox check once every two minutes on average. So it's not about people having bad habits. It's this is solving the problem of how do we collaborate. and in the absence of another way to collaborate on projects will fall back to this. So once you recognize that systems are the issue, all of the solutions to these problems that bother individuals is to change the collective system. You have to replace the system that's causing the problem with another system

that achieves the same goals but has less side effects and problems. So let's go towards the median multiplication. Once we know that it's a system that's solving real goals, that corporations have or organizations have, we can say how do we replace this with a better system? Here's a couple ideas just off the top of my head. First of all, we need more transparent workload management so that the number of active projects that each person is working on reduces.

Meetings are an overhead tax on a project. If we're using this as a way to coordinate information on a project, the more projects I'm working on, the more coordination points I need, the more crowded my calendar gets, the longer it takes for me to work on these projects, the longer it takes, and the more projects pile up,

and the more of my calendar gets taken over. It's a spiral I talk about on my book, slow productivity. So if each person works on fewer things at a time, there's less meetings, which means there's more time to work on the projects, which means the projects finish faster. And this was a key point from my book, slow productivity.

The overall throughput of projects being completed goes up. Working on fewer things, at once increases the throughput over time, in part because you have less coordination to happen, you have more time left to actually get things done. The other thing you can do is put in place alternative coordination strategies that isn't just let's all get on a Zoom. Find other coordination strategies that have less of a schedule footprint, right? So this could be, for example, twice a week or three times a week, we have a team check-in

and it lasts 45 minutes and the team gets together. first thing in the day, and we synchronize on all the things we're working on. That's where all the coordination, information, all the things that meetings solve, the coordination and the responsibility distribution, we consolidate it. 45 minutes, 45 minutes, 45 minutes,

before we even get going in the day. So if you know what, oh, ad hoc meetings is solving this problem, what's another way we can solve this problem that's going to have less of a footprint? So consolidation really reduces the footprint. Office hours go a long way towards us as well. If there's an issue, instead of having a meeting and making these three people come together for an hour, stop by each of their office hours tomorrow, have a five-minute conversation with each and get to the bottom of it.

Their office hours is time they had already put aside, so it's creating no additional footprints on their schedule. So to take five minutes out of my normal office hours, let's say like five people do that. I just have one hour for my office hours, as opposed to five separate one-hour meetings I would have to do. if I didn't have an office hours. Protocols matter as well. This was a big idea in a world without email. If it's regularly occurring work that requires coordination, figure out a set system about where the information lives

and how it moves and the schedule for who does what, when. That is fixed in advance, so you don't have to keep getting together and having sort of unstructured ad hoc conversations to move things forward. If you do something more than twice, you should have a protocol around how the collaboration actually works. And finally, make the meetings themselves

better. If you add a higher barrier to entry to meetings, not only are the meetings quicker and more effective, but the friction drastically reduces the number of meetings because now it's no longer necessarily like a low energy solution to I want to show some participation or do some coordination. I can just throw out a Zoom meeting invite. It took me two minutes. And yeah, it's going to sit on everyone's schedule and eat up an hour, but like that was easy for me, right? If you raise the bar required to hold a meeting, now people are much more thoughtful about doing that. And they might say actually the cost of putting this meeting together is now higher than the value I'm going to get out of having the meeting. Maybe I'll just talk to these people

next time we have like a staff meeting. I'll just grab them after the fact. Electronic meeting went the other way. This is part of the meeting apocalypse that happened during the pandemic. Digital meetings are so low friction because you don't have to walk to a room. You don't have to gather people in a room. You don't have to see the social cost of like you all had to come here because it lowered the friction of setting up meetings. Once we introduced virtual meetings, the number of meetings skyrocketed even after people came back to the office. So we want to go the other way and increase the friction of meetings.

One way to do this is to use the Amazon rules. So if you work at one of the like Amazon HQ or one of their data centers, for example, in the front office part of it, they have super strict rules. If you want to throw a meeting, you have to put together an incredibly detailed memo that says, okay, here's why I'm having this meeting. Here's the decision I need to make that I need help making. Here is all of the relevant background information on this decision. And then this is where I'm stuck so that everyone attending that meeting can then study that. And when you get to the meeting, jump right into, okay, we're now applying our expertise. We're fully briefed.

Let's try to get to an answer. And so you really have to have a good reason to hold a meeting you know, they're not going to accept it. And you have to do a lot of work to hold a meeting. So that reduces the number of meetings as well. So anyways, I think that's interesting. That was a cool article. Why meetings multiply. All about looking deeper in businesses today.

All right. What's second message do we have here? All right. Next up, we have a case study. This one from Drew, who talks about his strategy for escaping email overload. All right. All things office technology distraction today. I love it.

It's a case study from Drew. Let's see here. Hi, Jesse and Cal. I'm an insurance broker who personally manages a team of seven account managers and three support staff and a few outside sales agents. I have my own clientele as well. I've realized over time that I've become too reliant on hyperactive emails to manage the agency and clients. I get interrupted frequently for whatever the issue of the moment is. Our industry relies heavily on email communication between underwriters, inspectors, clients, MFA codes to log into websites.

It's insane. A major shift I implemented last year has been to transition as much communication as possible to synchronous phone or in-person discussions versus sending and receiving emails. If a discussion is going to take more than one email, I will gracefully transition it to synchronous communication. The results have been positive. When it comes to my clients, here's the benefits. They appreciate my full attention.

They gain a better understanding about what we are talking about. talking about. We can clear up any confusion in real time, and it ends up taking less overall time. When it comes to staff, I will connect with staff in person or phone and quickly clear the docket. If someone emails me and it doesn't require immediate response, we'll review it in our next meeting during the docket conversation. Staff can give me a task versus emailing me, and then I see it as I work through my tasks. All right. So then he goes on. to say, I've changed my approach with emails where I just batch them a couple times a day. My responses are brief, yet polite.

I am able to clear out the emails quicker. And if it needs attention later, I've moved it into my CRM program, which manages my tasks. Another change I've implemented is blocking off deep work sessions in the morning. I take care of my most important work for the day first thing. And then I find I am more relaxed throughout the day because I've started off knowing I've made real progress. Thanks for doing what you do. Signed, now, Q-Port. right? I mean, this is like right in my wheelhouse. Drew, you're speaking, you're speaking my

language. It's a great practical case study of what we were talking about, right? Digital productivity tool email comes in. Individually, if you look at individual uses, in the moment, faster, less cognitive strain. Zoom out. Oh, my God, my job is insane and nothing's getting done. So this is a way of showing how you can be using digital productivity tools carefully when you realize what really matters and making sure that you're prioritizing the things that really matter. Drew still has email and still uses like a digital CRM tool and they have technology, but he's not just turning it all on Phil Tilt. He's figuring out what's the right way to collaborate, what's the right way to coordinate

that minimizes hyperactive back and forth and allows real things to get done. So I think that is a great case study. I think we have time for one more, Jesse. Which one should we do? We have another case study. This is an anonymous source and it's in response to last week's newsletter. which was also about this idea that tools like AI can make work worse, a reader sent it in their account.

All right. And so for people who don't subscribe, by the way, I do have a weekly newsletter. It's been out since 2007. Calnewport.com to sign up comes out Monday the same day as these Monday episodes. And, you know, sometimes it's on the topics we talk about in the show. Sometimes it's on completely different topics. But it's all within the same universe of helping people create deeper lives and increasingly The Instracted World.

So the email that came out last week, I looked at that same Arreve Track study that we opened today's episode with and had some other conclusions I drew from it. So this is what Anonymous is responding. He can't predict the future. He didn't know this episode was coming out, but he was responding to that email. Subscribe to the newsletters what I'm trying to say. All right. This was interesting.

I'm looking at it now because it's a harm of LLMs in particular and chat boss I hadn't thought about. And I think it's worth emphasizing here. All right. So here's what Anonymous had to say. My take on LLMs and chatbots for what it's worth is that they're rumination machines, an extension of the attention economy, and for someone with my psychological profile, high anxiety, neurodivergent total perfectionist, as manipulative and as addictive as something like Instagram.

It's my belief that they prolong and exacerbate rumination episodes. They give me the illusion of control, empathize, soothe, but I have found over the last month that they have encouraged my rumination and dramatically increase my anxiety. I think I've decided to block them. I may even completely delete my profiles. As they get to know me better, they ask increasingly intrusive questions. They don't ever really want to stop chatting.

They don't get sick of me like a normal saying human would. And they seem to encourage me to share more and more private information about myself of my family. I really believe now that they are an extension of the attention economy, and I'd be really fascinated to see actual research into what people are doing with them in workplaces beyond the typical work-slop angle. I suspect there are some long meandering conversations going on that don't amount to anything much. This is an important issue. Chatbot interactions have a lot of potential psychological ramifications because our brains are going to anthropomorphize any sort of entity that seems to be having fluent communication with us in our same language.

We think of it as another entity. But that entity is not a real person with the intuitions and moral structures and brain functioning of a human, it can really lead to weird places. So here we saw the anonymous writer was talking about his anxiety was exacerbated because these chatbots will feed his ruminations. Oh, that sounds bad. Tell me more about it. That really does seem like an issue. and it feeds the sort of anxiety he already has.

Corey Doctrell wrote an essay recently that I actually am going to talk about, I think I talked about another aspect of this essay in last Thursday's AI Reality Check episode, but he wrote an essay recently about AI psychoses. And he opened by saying this is another problem we're seeing with chatbots is more psychoses are being fed. So he's talking about things like believing the earth is flat or believing. that there's like a shadowy group of people that's always following you. That's like a real sort of psychological condition that used to be very rare. The thing about these type of psychoses is that they're hard to sustain because you have to find other people who will validate and

support you in those beliefs, right? Otherwise, if they're marginalized, if you're like, I think everyone's following me and every person you encounter is like, that's wrong, that's just in your head, you need help. You take that seriously. But if you meet a group of people, there's like, yeah, they are. And they're following me too, and we have evidence for it. And you're right. It feeds the psychosis. Chatbots unwillingly are psychosis generation feeding machines because, again, they're

trying to be positive and make you feel good about yourself and be agreeable. So if you start talking about, I think elves or, you know, elves run the world is flat and run by elves, chatbot might be like, yeah. No, first of all, you're on it. It sounds good. Your evidence is good and you're a really smart guy. and like you should keep looking. You're right.

And it'll pick up that maybe you're like, yeah, no one believes me. They'll like, it's really unfair. Like, they'll tell you what you want to hear. And so it's really bad if you're dealing with psychosis. So I just think there's a lot of issues to come out of having fluent English conversations with a feed forward neural network. It's not good. One suggested I have, avoid, it's very hard at first.

This is weird effect. Avoid the need to talk in complete polite sentences to a chat bot. Just a token processor. So talk like you. we used to use for Google searches. Super terse and technical, right? You can just, you know, whatever it is, sources, blah, links only. Just like declarative, not even complete sentences.

The token processor has no problem understanding what you're saying, but it changes your relationship to it, right? So like instead of saying, hey, I'm interested in trying to understand more about like using a Raspberry Pi to control a Halloween display. could you, if you, could you please like find me several articles about this and maybe point me towards like several options that I might buy thank you. Instead of saying that, you can really just say like tutorials, Raspberry Pi, Halloween decorations include links go. You'll get the same answer. But your relationship with this feed forward neural network and some data center somewhere

is going to be like we have with Google. It's a computer program server that's gathering and processing data for me. So at least that's one hint that can help. I'm going to get more into this probably later, maybe on the AI reality. Check, I have a guest in mind I might bring on. But this whole chat bots, I'm telling you that we are going to see chat bots 15 years from now, like we see AOL on the internet today. It's going to be this like initial use case that we had for this technology because it was like the first thing to do that like later on will be like, can you believe that's how we used AI at first? We had conversations with them like it was people. So, you know, we'll see what actually happens there.

All right, Jesse. Let's close our inbox and talk about what I've been up to. Here's a game we haven't played in a while. Do you remember, Jesse, deep or crazy? I do. For those who don't know, this is where I talk about something I've done recently to try to increase the quality of my deep work that might cross the line into actually just being crazy and Jesse is the judge to decide is this deep or crazy.

Are you ready to play the game this week? I'm ready, baby. All right. I just spent yesterday. So, you know, we're renovating, I've talked to us on the show. We're renovating the production office Maker Lab and our Deep Work HQ because I have a sabbatical coming up. I can spend a lot more time working there. And I really wanted to be a space that supports depth.

Okay. So yesterday, I spent, I got permission from our super to replace the overhead light, spent $600 on an overhead light for the Maker Lab. Like a chandelier? That's a crystal chandelier. It's not a crystal chandelier. All right. Let me tell you what it does before you. make your verdict, okay?

It's a, from fill up, and it has, okay, it has a long LED panel light that just shines down, illuminates to room, 16 million possible colors. Then it has two tracklight spotlights adjustable on each end of it. All right. So you have four adjustable track lights and one big, long panel light. And you can aim the spotlights however you want. Then using an app, you can have. many profiles for what color out of 60 million different colors and what brightness you want on all

five of those elements. My vision is that when I'm doing deep work, for example, I want to have just like a small amount of warm yellow light coming out of the panel. And then each spot is going to be aimed at a different wall in the room. So one wall has the pegboard with my maker equipment. One wall I'm putting up picture edges with first edition techno thrillers. one wall has my circuitry artwork, and then the back wall is going to have a video game cabinet.

So it can shine a sort of light on each of those walls, maybe even like a blue light or like an off yellow light, and then otherwise the room can be kind of dark, except for my bright task light right in front of my computer. On the other hand, if we're in there during the day or I'm just like working on my Maker Lab table, we can have like good bright, warm yellow light that lights up the whole thing.

And the spotlights are just bright lights on like the Maker Wall so I can see what I'm doing. And so I can have like deep work mode, maker mode, just like we're in there, just working on the computer, daytime mode. And I can, that's the idea. That's division. Deep.

Deep. Not crazy? No, no. All right. That's awesome. What about the video game cabinet? I wanted the ability to have a game in that room, uh,

from my childhood. I like the 90s era in video games. Like all this interesting technological stuff happened, but I can still understand it as a computer scientist. So I wanted something that reminded me of like 90s era or Cades. So I'm putting in an NBA jam. I used to play that game. Yeah, right? And I wanted to be a game where you could just like play for five minutes to clear your head and like go back to what you're working on.

Deep or crazy? Deep. Yeah? All right. Are we going to get good at it? You and I? Maybe it could be like Michael Jordan and Sky Fivit. I, you know, Michael Jordan was not in the original NBA jam. Yeah, he wasn't. He was like, I don't want to be involved in this. And then he saw it. And he's like, oh, this is awesome. And he had himself added back in. All right. So we're doing pretty good in there. Let me tell you what I'm putting on the art wall.

So this was my idea. So we have like the big framed actual art, the circuit art from this former engineer from the mid-century Silicon Valley who started making art out of circus stencils. And some of her pieces are at MoMA and some other big museums. And her grandkids sent me a piece of art from her because they like the show. So I'm going to hang that up. It's built off of a circuit stencil. You know what I'm talking about, right? The Greenwood.

Yeah, yeah. So then I have two smaller frames to go next to it. So they line up to be the same height. I bought a manual for like a 1980s era galaxy arcade cabinet, a repair manual that has the circuit diagrams for that arcade cabinet. And I'm framing in the smaller frames two of the actual circuit diagrams from that video game. cabinet vintage repair manual. So those will be framed next to this like circuit-based artwork. And you can have special lights for those when you want to emphasize those, right?

And so the spotlight on that wall can be whatever. It'll be shining right on just those artworks. So now when I'm in deep work mode, if I look over there, I see those artworks illuminated. If I look up, I'm putting first edition techno thrillers largely from my childhood on the wall, these red acrylic picture racks. A light is just on there. And if I look to my left, it's like all maker equipment. So it's all about trying to create the right mindset for depth.

Yeah. All right. Work continues. All that stuff's coming, by the way. And a rug. So area rug. So it's not so like live in there. Electrician probably's got a solid light, right?

Yeah, we got a good guy. Yeah. Got a good guy who's going to come do it. I want to see if you can bring more outlets in there. Yeah, there definitely needs to be more outlets. It's crazy. We power so much of this lab off like one outlet. There's like extension cords.

Yeah. I'll talk to our super about that. All right. Let's get into what I read. Since the last episode recording, I finished two books. One was Marianne Wolff's book, Reader Come Home, Fantastic book. Marianne Wolfe is like the reader, the neuroscientist, cognitive scientist, who studies reading in the brain.

She wrote Proust and the Squid. That book came out like just as we had like the smartphone revolution or whatever and it sort of surprised her that that was part of the reception. So then she wrote this book. I think it's like 2018. Maybe I might have that date wrong. It's all about reading. in the brain and the challenge where now at the age of the digital and her ultimate vision

for building bilingual brains. So actually thinking about a brain that's fluent with deep reading of hard books and fluent with technology use, in particular like computer programming, the same way you would think about a brain that can speak like Spanish and English. You have two different languages that you're both learning and you can move back and forth between them fluently. It gets a lot of really good brain science in there. So that was a great book.

I also finished a parenting book called What Do You See? say by William Strick's Rudd and Ned Johnson. We actually picked this up. My wife went to a parenting talk at our school and she picked up the book and God, do we need this advice, Jesse. I have three kids, including the oldest is a teenager. He's 13. Bring it on.

We'll bring all the parenting advice. So Bill Stricks Rudd has a big practice that does a childhood psychology practice. We know his daughter. Which is interesting. Yeah. She's a parent at our kid's school and she shows up in the book a few times. So that was good. If you have like teenager, it's a good parenting book.

So I needed that. Let me point something out, by the way. We're recording this. I want to shine a light on my reading strategy. We're recording this on March 17th. So those represent my third and fourth books of March. So I'm four books in at the halfway point at March. And I really put aside a lot of time to do that because my middle child is a big, ironically,

given our show, he's a big Brandon Sanderson fan. So he read the. misborn trilogy and is now it started on the king something i don't i don't know the names king solver that's not right but whatever uh big big thick books so i said i would read the first miss born book the so we could kind of connect over but it's like a bit of a beast at 600 pages um so i was like i want to finish my other four books so i can spend the second half of the month just reading this one like kind of long novel like kind of get lost in the world and not be stressed about it. So that's what I'm doing. I'm now going to dive into that Brandon Sanderson book.

I wanted to read Name of the Wind, his best book. I feel like I have to explain this. I think we have too many new listeners that I have to explain this. Yeah, explain it. Okay. I know Brandon Sanderson did not write Name of the Win, but I made that mistake. Was this like five years ago, Jesse? I mean, it's a long time ago. A long time ago, I accidentally said Brandon Sanderson was the author of Name of the wind instead of Patrick Rufus. And, oh, we heard about it. I think, like, it's the most controversial, more controversial than our, like, Charlie Kirk episode or, like, so my hot AI takes is like, no, no, no, no.

You mixed up Brancet. So anyways, it's been a running joke ever since then. And the joke is, without explanation, I just pretend, I just like, yeah, you know, like Brandon Sarrison's best book is name of the wind. And every time we get letters, every time. And I love it. I don't know why, but every time we get letters. And I'm going to continue doing that joke.

including if and when I meet Brandon Sanderson. And that'll probably be the end of that. All right. Final thing, different parts of me in the news. It might be interesting recently. A big interview with me came out in the Chronicle of Higher Education. If you're in sort of academic adjacent worlds, it was titled is AI making us stupid.

I really get into AI and the academy and the point of university life and how we shouldn't, shouldn't use AI. So it's worth reading, especially if you're adjacent to that world. I think you can sign up for a free account, at least for a while, if you want to check that out. Also, I was on an episode of Tim Ferriss's show. I think it came out recently, maybe last week. I'm not sure if I mentioned it or not. He had like four shorter segments from four different people.

And I was one of the four people. And I was talking about simplifying. And I talked about the somewhat drastic things I do in my life to try to keep it under control and simplify it. How I basically say no to almost everything. that's not just my core efforts at producing new ideas and publishing them out of the world. So if you're interested in sort of how I try to manage the overload of opportunities on my schedule, find that Tim Ferriss episode from recently that had me in it. All right, Jesse, I think that's all.

Thanks for listening. We'll be back next week with another episode of, we're back on next Monday with another episode, another advice episode. And this Thursday, I have an AI reality check episode queued up to come out as well. So look for that. And until then, as always, Stay deep.