Deep Questions with Cal Newport - Ep. 348: Manage Your Time in 5 Minutes a Day

Episode Date: April 14, 2025

Cal has been writing about time management for a *long* time. In this episode, he returns to a chapter from a book he wrote twenty years ago that is titled “manage your time in 5 minutes a day.” He revisits his old advice to see what he thinks still works and what needs updating. He then answers listener questions and returns to the tech corner to get back in the weeds on a topic involving AI.  Find out more about Done Daily at DoneDaily.com! 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: Manage Your Time in 5 Minutes a Day [3:11] - How specific should I make my Quarterly Plan? [27:28]- Does reading Mangas count as reading? [29:30]- How do I store information related to tasks? [34:14]- How do I speak better at work in spontaneous conversations? [38:31]- Are paper and pencil calendars suitable to use if my work is in two different time zones? [45:42] CALL: Utilizing coaching [51:38]CALL: Embracing boredom [57:48]TECH CORNER: RL vs. LLM [1:08:04] Links:Buy Cal’s latest book, “Slow Productivity” at calnewport.com/slowGet a signed copy of Cal’s “Slow Productivity” at peoplesbooktakoma.com/event/cal-newport/Cal’s monthly book directory: bramses.notion.site/059db2641def4a88988b4d2cee4657ba?Thanks to our Sponsors: grammarly.com/podcastcozyearth.com/deeporacle.com/deepquestionsexpressvpn.com/deep Thanks to Jesse Miller for production, Jay Kerstens for the intro music, Kieron Rees for the slow productivity music, 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.

I'm Cal Newport, and this is Deep Questions, the show about cultivating a deep life in a distracted world. I'm here on my Deep Work, HQ, joined as always by my producer, Jesse. What did you have going on when it came in today, Jesse? It looked like golf. Is that what you were watching? Yep, golf was on. It's master's time, right? No cell phones allowed at the Masters.

Is that for, like, everyone attending? Yeah. Oh, interesting. Yeah. So if you're going to the Masters, you've got to put it. in a bag or something when you get in. Oh, I love that. That's cool.

So you're just like outside in nature, uh, watching people play golf. So they have like stretchers, I assume for when people pass out from boredom. Just bodies falling. But they got to do it old school where they, you know,

I'll meet you at whole four T-box in two hours. I like it. Just a lot of people yelling really loud. Hey, Jesse. All six buddy. Master's Rory's playing. right, McElroy? McElroy.

McElroy. Well, he's my man. Yeah. He's my man. He has come out a couple times and talked about how some of my books have been influential for him. So I figure I got a route for him. If he wins, he gets a career grand slam.

Oh, yeah? That's all he's missing? Yes. Interesting. Well, so a few years ago at a master's press conference, he talked about digital minimalism is being an important book for him. I saw an article about him this week. He's moved on.

I think he's doing like hypnosis. Like, he's always looking for things. He works a lot. with Bob Rattella. You know what I'm right? Oh, he's like the golf psychologist, sports psychologist from UVA. No, I hear it.

I hear you. Well, we should go sometime. No cell phones. I went to Augusta a couple years ago. That's the nicest city. The golf course is nice. Interesting place. Anyways, though, we got a good show today.

God, this might be a problem. This might be a mistake. Jesse, we're going to have some fun. We're going to go back to something I wrote 20 years ago. I'm holding this off, the cameras off, but we're going back to something I wrote 20 years ago to revisit it. I did not read it in advance. So we're just going to revisit this together. I've got a, excuse me, a bunch of list or questions.

And then back to tech corners. Again, God help us, Jesse, but I'm having fun doing tech corners on AI, like way too technical, way too technical looks at AI. What I'm going to do today is people are mixing up different types of AI systems is all just sort of like AI writ large. And so I'm going to pull apart the difference between language models and reinforcement learning models and how they're different. And what I worry or don't worry about each and maybe, you know, edify us all a little bit more. Nice. Talk about boredom and stretchers. Well, Jesse out if you're on a stretch when I'm done with that.

But I can't help myself. I'm a computer scientist. I got a tech corner. All right. We got a lot to get through. Let's get started with their deep dive. So I've been writing about time management for a long time now. When I was 22 years old, I wrote a book called How to Become a Straight A Student.

Here it is for people who are watching instead of just listening. That was supposed to be a book of no nonsense advice about how to get good grades in college. How to Quiet Start. This book's actually quietly done well in the background. I think it sold like over 300,000 copies quietly since it came out in 2006. Anyways, this book includes a chapter. I've forgotten this, but I saw the other day. This book includes a chapter where I described.

I have a time management system for students. And the title of that chapter is how to manage your time in five minutes a day. This was written back 20 years ago. So I thought what would be interesting would be for me to go back and reread that chapter. Let's go back and revisit my ideas from 20 years ago about how to manage your time. Because what I'm really curious about is to see where was I on to something that remains true today and where is their advice from 20 years ago that no longer holds to today. So what works from before and what doesn't? I decided not to really revisit this until we were live here on air. So this should be, should be interesting. God knows what's in here, Jesse. I'm looking forward to it. Yeah. It's going to be a lot of, let me tell you this. By 2025,

our biggest problem is going to be finding air parking for our flying cars. And let me tell you, I guess I was going to do a whole like Donald Trump thing, but I'll know, I guess he was on TV then. I don't know. All right. Let's get into it. I'm going to open my book here. Page 19. Boom. All right. Manage your times in five minutes a day.

I'm nervous about this. I haven't read this in a while. Here's the intro to this chapter. Real straight-day students like most reasonable students hate time management. After all, college is supposed to be about intellectual curiosity, making new friends, becoming obsessed with needlessly complicated drinking games.

See, that was social proof, Jesse. I was proving to the reader. See, I'm young too. I'm on your page drinking. An overwhelming interest in time management is best left to harry business executives. Oh, man, that's me now. Or perhaps premeds. At the same time, however, you can't abandon all attempts to keep tabs on your schedule.

And so I'm going to give you some time management, blah, blah, blah. All right. So the setup is like we're reluctantly going to do time management. We don't want to spend too much time thinking about this. So I guess I was really worried about students saying like, look, man, this is square. Because that's the way we talked back then, Josie. This is square. Man, I'm looking for tubular advice.

I don't want to hear about time management, so I was being careful about it. All right. I lay out now in the chapter. Here are my criteria for a time management system. Four things. One, requires no more than five to ten minutes of effort in a single 24-hour period. Two, doesn't force an unchangeable minute-by-minute schedule on your day. That's interesting.

That's changed. Three, helps you remember plan and complete important tasks before the very last moment. And four, can quickly be restarted after periods of neglect. interesting thing I'm noticing already, Jesse, is like this tone reminds me of early Tim Ferriss. But this is before, for our work week, is before I knew Tim or I'd read his book. So there's, it must have been in the air back then. That's sort of really declarative, you know, like ambitious, like we're going to do this, we're going to do that. It was a tone that was in the water back then.

All right, what you need. So my system requires a calendar and a list. And I say the list is some piece of writing material that you can update throughout the the day. You do have to carry this with you, so make it something simple like a sheet of paper ripped out of a notebook each morning. So one sheet of paper each day and a more permanent calendar. I said the permanent calendar could be physical or it could be digital. All right. Let's get into the details of this system from 20 years ago. Record all of your to-does and deadlines on your calendar. This becomes your master schedule. The one place that stores everything you need to do.

The key to our system, however, is that you need to deal with your calendar once every 24 hours. Each morning you look at it to figure out what you should try to finish that day. Then throughout the day, whenever you encounter a new to-do or deadline, simply jot it down on your list. The next morning, you can transfer this new stuff from your list onto your calendar where it's safe. We're back where we started. The whole system can be summarized in three easy steps. Jot down new tasks and assignments on your list during the day. Next morning, transfer these new items from your list onto your calendar.

And three, take a couple minutes to plan your day. All right. I then give an example schedule for a day. So I guess you look at your calendar each morning, you write down on your sheet of paper your schedule for the day. And so the example I had in here was on one side a list of appointments. So it was like 10 to 12 econ, 12 to 1 lunch with Rob. One to 1.45 government reading, 2 to 4 government class, 4 to 530, finished government reading 530 to 630 start french essay and then i had a list called things to remember

and i guess these are things that you would jot down during the day uh econ study group moved french quiz laundry start researching summer internship opportunities so this is interesting jesse it looks like the plan you write down includes basically rudimentary uh time blocking oh it was okay here we go. So let me look at this a little bit closer. So when you're making your plan for the day, and jumping ahead here, look at your calendar entry for the current day. It will probably contain a handful appointments and to-does. Your goal is to figure out how much of this work you can realistically accomplish. You might be tempted to simply copy all of these tasks into your today's schedule column and then treat it as a simple to-do list for the day. Don't do this. If you want to avoid

getting overwhelmed by your work, you need to be smarter about your time. Here's what you should do instead. I bolded this. Try to label each of your to-dos for the day with the specific time during which you are to complete it. Be honest. Don't record that you're going to study for three hours starting at three if you know you have a meeting at five and be reasonable about how long things really take. I also say you can batch together little errands and you should try to end your day at an appropriate time. The only other thing that's important here is when you update your calendar each day, you move the stuff that you didn't get done onto other days. So let me look at this example here.

This was the kind of tricky part back then. Blah, blah, blah, blah. Okay. So not everything gets done. The things that doesn't get done have to exist somewhere. And so you move them onto another day when you think you're going to get it done. Interesting. I'm interested by myself.

You would appreciate this, Jesse. See in one of my sample lists here, I have one of the entries from five to seven, quote, get huge. That's been a little bit facetious there. I think that means to go to the gym. All right. Interesting. So what were the features of this? And then what do I think works and what do I think doesn't work?

All right. So back then I was saying, I'm just going to summarize all of this, right? All of your tasks need to have a place to live. And back then I said, why you have them live on your calendar. So obviously, like appointments and stuff exist on the specific days and specific times when happens, but to-dos, you put them on the day you think you're going to do them. And then when you get to a current day, you have some stuff on your calendar that's timed, like classes and meetings, but you also are going to have this list of things,

to do's that happen to be assigned to that day. Then you make roughly a time-block plan. That's what I was talking about way back then, 20 years ago, make a plan for your hours of your day, like when you're going to do what, when your appointments are. See if you can fit the things, the to-dos you had on your list. Can you fit them in there? You might not fit everything or you might try to fit everything and not everything gets done. Well, that's fine because what you do is the next day when you create your new plan,

you're going to take everything from your list from the day before that was added new. This capture stuff and put it places on your calendar. This is also when you'll take the stuff you didn't get done and make sure those show up for a new day. And maybe you move into the current day or you look into the future to find a day that's less crowded. And so in this plan, you make a plan, you do your best, you capture stuff on your sheet of paper throughout the day. the next day, the new stuff and the stuff you didn't get done goes back to your calendar and then you make a new plan for the day ahead. So what I like about this plan, and then I'll get into what I think is missing. I like the full capture methodology.

Now, I can tell you, I had just read getting things done. David Allen's book, not long before this. That book came out, I think, in 2004, maybe 2003. I wrote this book largely in 2005. So I had just read that. And so clearly that full capture idea of everything. thing you need to do needs to be written down somewhere. There's nothing that you only are keeping track of in your head. That, uh, it suffuses this system. And so I like that. I still think that's a very

good idea. Things you're only keeping track of in your head either cause stress or will be forgotten. The other thing I like about this, and this was obviously a big emphasis in my student book, low friction. I was really careful that when you go through your day, you just have like a sheet of paper with you that you're glancing at to be like, what should I do next? And you can't, can just jot things down on. It's a paper in your pocket, right? No systems, no logging into anything, no complicated planners back. It just jot things down on this paper. And the only time you really have to grapple with tools beyond that sheet of papers once every morning where you say, okay, let me move things back into my calendar, let me update my calendar, let me make a plan for the day ahead,

and that was supposed to take only five minutes. So I like that simplicity. I like the planning, the rudimentary planning in this system, too, of tasks go on a day. So you're kind of, you're kind of making a plan. Like, this day looks light. Let me try to do things there. As opposed to just having a bunch of to-dos and saying, let me try to get some of these done today. So it was a much more of an intentional. That's a time block planning philosophy.

Being intentional about your time as opposed to just being reactive or saying, hey, what should I work on next? This plan works really well with something I talked about later in this book. And I still talk about today, which was put your regular work on your calendar as well. this type of planning for a student works really well when regular work like reading assignments, problem sets, labs, stuff you know is due on a regular basis, when you put the time when you're going to work on those things on your calendar and advance for the whole semester. And so then that just becomes part of your day. You're not even thinking about studying is just the same as like going to class.

I have class at 11. I have studying from 1 to 230 on this type of reading. So making that more automatic worked really well with this system. Another thing I added later, I think after this book came out, I talked about this on my blog. And it's a little known fact that, well, maybe somewhat known, Jesse, but my blog, study hacks, which now became my newsletter as well, originally started in the aftermath of this book as a way to discuss ideas that didn't make it into this book that were about this book. It was the missing chapters for the straight-A student book was its original purpose. And there I introduced the idea of, you know what, at the beginning of the semester, do an extra planning. So this is the origin of multi-scale planning where you go and find all your major deadline.

and start working backwards from those deadlines and putting markers on your calendar, like three weeks before midterm, study plan for the midterm, you know, a month before the term paper, make your plan for when you're going to write this midterm and then get that onto your calendar. So it's all about like when you encounter your day, the stuff was there that you need. Okay, so what's missing here? Well, obviously the task volume is very low, which would make sense for a college student in the early 2000s. If you are, let's say, a standard 2026, 2025 knowledge worker, your task volume is going to be much, much higher because of tools like email and Slack. That's not even dealt with in here.

The task are things like do your laundry or pay your cell phone bill. You had a sort of very reasonable amount of tasks, so of course they could live on your calendar. In the modern professional context, you might have many hundreds of different things to do. So to try to like assign everything to what day you think you're going to do it. that does not make sense with modern, modern task volumes that you would see with sort of modern jobs. I'm time blocking, but I'm doing it roughly, whereas by the time I got 10 years after this book was deep work,

I was like, no, no, just time block specifically. Draw the blocks. Block out all your working hours. Here I was kind of roughly doing it. I was like, write down what to do as you want to do and maybe like mark with the time you think you're going to do them. That evolved into you might as well just block your hours of your working hours of your working day.

This is also missing shutdown rituals, which is very important. Have a way of making a transition from I'm working to not working. I'm working this very clear. I think that's particularly important in college context. You'll just work forever, but it's important in other work context as well. But all in all, I think this is actually, I am seeing seeds of lots of my ideas in this system, and I like it simplicity. So where would this make sense?

I guess if you don't have a large task volume, you're not plugged in the email having to send and receive 150 messages a day on 17 committees and working on 19 projects, if you have a pretty controlled task volume, a pretty autonomous schedule, something like this could work. Just make your calendar or your home. That's where your tasks live. You make a plan each day based on the calendar for the day. Maybe throw in some higher scale planning, you know, once a month or once a semester, get some major deadlines on there.

It works well. It really would take only five minutes a day. So there is a lesson here is like not every system needs to be the type of system that a super busy modern business person would need the tame their work. You know, some situations, your workflow is simpler. Simpler systems work as long as you keep the key ideas that show up here that have remained in my thinking about time management organization to this day, which is full capture, some intention over your time as opposed to just working off of a list. And basically that, actually, those are the two things, full capture and intention about your time, and that's there. I guess maybe I'll throw a low friction in there and keeping the friction as low as possible.

So I don't know. I think it holds up. Jesse, what do you think? 20 years later? Yeah, I've read that book and I've gifted it to a bunch of kids that I coach that go off to school. Yeah, school is easier. School is easier. The main thing missing from this book is focus, training your mind.

That's the missing chapter that today I would add if I was rewriting it. You didn't start that until after your freshman year, though, right? Yeah, yeah, this was my, God, what's the timeline? Now, I graduated in 2004. this book, it came out in 2006. No, I'll talk about the process of you doing the work. Oh. Like you studying that way.

Yes, that's right. So that's right. So the backstory of this book, okay, how to become a straighti student, the original draft, and this is true of this, the final draft. Like, most of the ideas are mine, right? Because I did very well in school after my freshman year. So you're absolutely right about that. So my backstory was, I went to Dartmouth, conference. college coming out of just like a public school. I wasn't coming out one of these elite private

schools where they learn how to study. And so my first year was fine, but not great. I think I had the past fail, a course or two here to prevent like a bad, like a really bad grade. And did okay. And I was rowing crew and I was kind of busy. And then I had this change, right? I said, I want to get serious about being a student. I got very systematic about my study habits. Why am I studying this way versus that? How am I managing my time in my schedule? And I experimented to figure out a set of tools that worked well. And that's when my grades got fantastic and got 4-0s every quarter sophomore, junior, and senior, except for my senior spring in which I got one A-minus.

And it was in political philosophy, which is why I hate John Locke to this day. Damn you lock. I actually, it's true, Jesse, when we were doing the promotion for this book, I went back to Dartmouth and got my transcript. And so we would send it. We would send, like, copies of my transcript approved it, like, I actually got all these A's or whatever. Yeah.

So, yeah. So then I figure out, so basically what I figured out is if you're systematic about being a student. By which, like, treat it like a business executive would treat their job, like, really caring about how you do it. School wasn't that hard. Like, you could do, you could do well without as much time as you thought. So basically what I did to pitch this book is I had gone to, I was an early inductee in the Phi Beta Kappa because I had such a high GPA. So, like, the top one percent of students, they would induct in the Phi Beta Kappa early. So I was, I was very, you know, I think like top five of my class or something, right, out of a thousand.

I had a very high GPA. And that was back when it was harder to do that, especially in computer science. And so I went to this thing with like the top 30 students GPA-wise in the class. And I was like, oh, well, like I recognize some of these students. They're not grinds or whatever. And I interviewed them a little bit about like, how do you study? I think I interviewed everyone. I sent them an email interview.

And it was like based off my techniques and their techniques that I came up with most the ideas in the book. But then after I handed in the first draft of the manuscript, my editor was like, Anne Campbell is her name. She was like, I think, you know, we need to hear from other students, not just from Dartmouth and from your class at Dartmouth. So then I went out and surveyed straight A students from a bunch of schools. And then I integrated their quotes kind of into the book. And I changed some of the, I learned some things from them and I used their ideas.

But a lot of the ideas were in place, like before I interviewed them. So there's like, you know, there's a lot of quotes from students all thrown through. Those kind of were added later. So that's a little bit peek behind a curtain. How did you find the straight A student's other school? schools. I would look at things like back then my technique was typically look for press releases or articles from schools that would say things like five beta kappa class announced. And then what I would do, this was my technique for my first book as well, is all you have back then all you had to do was find any student's email address and you could learn the naming convention. So you would find anyone's email address at Harvard and you'd be like, oh, it's first name. Last name. at FAS.harvard.edu, right? Faculty of Arts and Sciences.

And they're like, great. Then I'll see this other student's name on a press release about Phi Beta Kappa, University of whatever, at Harvard. So I'll just do their first name. Last name at FAS. I would learn to format and then I would cold email. And that used to work back then. And that's how I would do it.

And I got the interviews and I threw it in. So those are those ideas. But basically it was like, look, you treat, man, look, these old references. Reality check balanced. You treat school systematically, but you know what the thing is I've learned? I think this is true about a lot of jobs. Forget school.

I think a lot of jobs. Like you have some sort of reasonable system for organizing yourself, especially like in the first 10 years of a job. I think you're at our age. Jobs become more about like leadership and your ability to work with other people and other types of things. But like in the first 10 years of your job, 15 years of your job, you have some sort of system for organizing yourself, keeping track of what has to do, some sort of way you make. plans for what you're going to do. It really seems like a superpower. People are just like, you're amazing.

People are so haphazard that having a plan can get you like the equivalent of straight A's. So I think that that's cool to see. That system, I guess, more or less holds up. But that's the thing that's missing. It's back then I never would have thought about focus. It didn't, we didn't have phones when I started. Most people didn't have laptops. There was no way to avoid.

This goes back to our recent episodes, Jesse. There was no way to avoid getting lots of cognitive exercise. Like studying was boring. You had no distractions. You were at a desk at a carol somewhere. There was nothing there to distract you. I mean, you could walk across a room to wait in line for a public computer terminal that check your email. Like, that was it.

So, like, people just got lots of, you just read and thought and studied and you never, you never thought about focus as mattering. 10 years after this book came out, especially 20 years after this book came out, this is like the number one problem faced by students, is that they're so distracted that their brain is in a perpetual state of fragmented cognitive context, which makes it impossible to focus. Like, that would have to be chapter. But I talk about focus in here as being important, but I don't see it as something like practice and protect. I just say if you focus really hard, you get your work done faster.

But I would have a much bigger chapter about now if I was ever to, I was ever going to rewrite that. Don't really have time for that, though. All right. So there we go. That's our deep dive. We got a lot of good questions. So let's do those. But first, take a brief moment to hear from one of our sponsors.

We'll talk about our friends at Cozy Earth. I love my Cozy Earth bamboo sheet set. So we have, my wife and I have two sets of these sheets. We've talked about this before. We like them so much. We want to make sure that when one set of sheet is being cleaned, there's another one to be in our bed. Jesse, we just bought a third set of Cozy Earth bamboo sheets because here's what we were thinking about. We sometimes have visitors.

And like we were in, my wife and I went to Philadelphia and her parents. parents watch the kids. So they stayed in our room. So we changed the sheets. My wife felt like we need a third set of sheets so that in that scenario, we can change back to a fresh pair of cozy earth sheets. So like our whole- When you get home?

Yeah, when we get home. So our whole system is now like there's no scenario in which we're not sleeping on the cozy earth bamboo sheets. That's really how comfortable they are. Softest, coolest with breathable sheets you'll ever own. Design to restore, refresh and revitalize you every night. If I could just cut a hole in the top of the sheet and wear it like a robe, like a Toga. I would. Jesse won't let me do it, but it's that comfortable. They actually do have

clothing as well. I have the sweatshirt. My wife has the pajamas. So you can get clothing that has the same comfortable fabric in it. But I'm telling you, when you sleep on these sheets, they're so soft, they're cooling. We really have a hard time now sleeping without them. I 100% recommend them. And then once you love those, get the pajamas so that you can kind of have the coozy earth feel, follow you everywhere else you go. If you're on the fence, don't worry. You can try it at risk free for 100 nights. Love it or send it back.

No questions asked. There's a 10-year warranty on all their betting project. You will love it. These sheets are very, very comfortable. I don't know. There's a few things I can endorse more strongly than I endorse these sheets. So make sleep a priority now. Visit cozy earth.com and use my exclusive code deep for up to 40% off.

Cozy Earth's best-selling sheets, towels, pajamas, and more. Oh, we have the towels as well. Hmm. Super soft. That's cozyearth.com code deep. And if you get a post-purched survey, tell them you heard about cozy earth right here on the Deep Questions podcast. I also want to talk about our longtime sponsors at Gramerly. From emails to reports and project proposals, it's more challenging than ever to meet the demands of today's competing priorities without some help.

Gramerly is the essential AI communication assistant that boost productivity. So you can get more of what you need done faster no matter what and where you are writing. I really love Grammlee's product because it works in whatever tool you're using to write. It's going to help that writing be better. It's going to help you produce good writing faster. So Gramerly has been very smart, I think, about how they've been integrating generative AI into their already really good product. So now, wherever you're writing, Gramerly can help you write a draft right away that you can start to work with. It can help you with things like tone detection or rewriting sections to be.

in a different tone or to be more professional, as well as the standard grammarly stuff of making sure that your writing is actually correct and clear and grammatically correct. It just makes your writing better. It allows you to communicate with more confidence. And now with the AI help, it speeds things up. There's a survey here that says 90% of professionals say grammarly save them time writing and editing their work. 93% of professionals now also report that grammarly helps them get more work done.

So if you're in a knowledge of your writing, writing is important. Grammally makes you a better writer. It's just that simple. So let Grammally take the busy work off your plate so you can focus on high-impact work. Download Gramerly for free at Gramerly.com slash podcast. That's Gramley.com slash podcast. All right, Jesse. Let's do some questions.

First questions from Alex. I'm starting to multi-scale plan. How specific should my quarterly plan be? For example, take long Saturday rides starting at 50 miles. increasing five miles each week or bike regularly to prep for the 100-mileer. That's a good question, Alex. I don't think we've had that one before, but it's a critical one. All right.

So when you have a complicated long-term plan, the way I like to think about its interaction with multi-scale planning is that your quarterly plan gives you a pointer towards what you think is important on that time scale. But then you might have completely separate systems and rules and processes and tools that you use for that goal. Like, for example, in this quarter, post my, like, surgery and other injuries, I've prioritized my quarterly plan. I call them semester plans because I'm an academic.

It's pretty clear about getting back in shape in certain ways, right? That has with it, I mean, what I'm doing, I have a pretty complicated training plan and I have a trainer who's helping me and this and that. That's not all of my quarterly plan. My quarterly plan says, we're focusing on getting back in shape, like generally aiming towards these goals. And then, you know, I have like my true coach app and working with my trainer and I have a particular way of a specific scheduling system. I'm using this, like these are the days I'm exercising and I kind of have a schedule worked out and this and that.

That's not all my quarterly plan. My quarterly plan is reminding me what's important. And then I have complicated other tools I'm using the practice. Like in your case, Alex, no, your quarterly plan would say prep for the 100 miler. And then however you track and train, you probably have a training notebook where you're keeping track of like what you're doing on each day. you might have some training plan that you're following and you can keep track of that however you want to keep track of that. So yeah, your quarterly plan doesn't need to have the details of exactly how you're pursuing

big goals. It's just a reminder that when you look at it every week when you're making a weekly plan, oh yeah, I need to make sure that I'm making progress on these big things. And then whatever system you want to use to work on those other things, you can use those systems. All right. What we got next? Next is Andy from Vienna. My son, 10 years old, reads Mangas for hours.

Does do reading Yeah, do reading mangas count as reading? I think it's manga. Mongas. Or is it manga? I think it's manga. Mongas.

This is like Japanese graphic novels. I asked somebody about them. So they're like, yeah, I'm a little concerned on, but I love seeing him reading books, having books in his hands. This is actually like a longstanding debate.

I was looking this up before. Do, does manga, I hope it is manga now that I'm saying it that way. We're going to get a lot of hate mail. Very popular. Does manga count? as reading books in some sort of way. Here's the thing.

It's good. Reading analog things is good, right? You're holding something in your hand that's not electronic. You're engaging in a story. There's a sort of visual literacy that you build up reading manga trying to understand like what's happening graphically. And there's a linguistic proficiency growing as well because, you know, the dialogue and the words and you're seeing different people communicate. Some of the other things you get a novel reading.

is there to some degree. You're simulating the other minds of the characters in your own minds. There's some world building happening in your head. You have the visuals in front of you, but there's still a sort of like 3D version in that world that you're building in your head. There's complicated emotions that you're following. There's logic. Like, it's good.

I think reading analog things is good. It brings you into a world. It takes time. There's no algorithm flipping to random manga panels. You actually have to read it sequentially. So no, it's not a bad thing. Would you be, like, should you be content with that being the, you, only thing your son reads, I would say, no, I would also want to try to introduce more

traditional text-only reading as well. They're both good. Text-only reading is going to do certain things, though, that you're not going to get just in manga, right? I mean, first of all, the processing requires more intense, so it's more of a cognitive workout. You have to do a lot more in your head with text reading. So if you're reading a novel, you have to actually construct a much more detailed model

of what you're reading in your head. There's more, it tends to be a little bit more nuanced, too, because you have descriptions of what's going on that are third person. So you get kind of like a more emotionally real world, and you can do more sort of mental simulations and get more empathy. And pure linguistic processing just requires more focus. So you're practicing, focusing your brain more. So I don't know, if we're going to draw a physical analogy.

manga is like, yeah, there's something that, like, my kid likes to do outside, and it gets a moving and they're outside, and it's better than just being inside. and reading might be like they're actually they're, you know, jogging or running. It's like they're not just outside getting movement but are like actually like training in a systematic way. It's going to get you an even better shape than just being outside moving around. I saw a suggestion somewhere a librarian was saying there's certain like light Japanese novels

that are a good stepping stone for manga. It's sort of similar themes but in purely textual form and that might be a good way. I mean, for kids, I'm all about matching books to interest. What you want is books, find the books that they're really excited to read. And don't worry too much about what that genre is. Find what they're really excited to read because you want that exposure of not just reading, but being very excited about reading and getting pleasure out of it. You don't want it to be like math homework. You have to read your chapters.

This is a good book and you're good because you got through these chapters. I've been a big believer in like you really want to find. And it takes a lot of work. I mean, this is sort of my job in our house is because we have boys. And I spend a lot of time because it really matters, trying to find the book that's going to, like, spark each of the kids and they're going to be excited about reading it. And it's not always obvious. You'll try, this isn't working, this isn't working, and you have to find a thing that really works. So it's not always obvious.

Like my 12-year-old really likes not just Tom Clancy, but the really like the really like Red Storm Rising. So the Tom Clancy books where it's not Jack Ryan going on adventure. but it's we're going to just work through the scenario of like a world war like all the military technology or this or that and so like those sort of books he gets very excited about right it's a very specific genre but fine we'll like find those books and you know that's that's what you're going to read my 10 year old likes uh sci-fi more but only specific and so I'm constantly trying to search and try different books to try to see like what's actually going to work because I just want them to be excited about reading and then just get used to it and then you can read good

stuff. I don't know. You can do that later. I read a lot of Crichton when I was growing up. Crichton, Grisham, all the sort of big 90s, Clancy, all the big, there's like big 90s writers, genre writers. I was sort of raised on those and I came out okay. All right. Who got next? Next is Yolanda. How can I efficiently separate notes and reference information from actionable

tasks in my productivity system without duplicating work? I use Trello for task management but struggle with vague projects and scattered information across multiple tools. What strategies or rules can help streamline this process and keep everything organized? Well, Yolanda, my general approach is if what we're talking about is there's some information you need specifically to complete a task. Like you're summarizing a report and so like what mattered. The task is to summarize a report.

So like the relevant information is that report. Or you're trying to schedule something and the relevant information is like what people people have told you about their availability. I just move that in the Trello onto the card corresponding to those tasks. If I could attach the relevant files to the card for the task that needs those files, I'll do it. If I can copy information from an email to a back of a card and now I have the information there that I need, I'll just do that. Sometimes if there's like a really detailed email thread, right? And so, and I have a task based on that, what I'll do is I'll copy, because I use Gmail, the subject line of that thread into the task card.

I'll say, just search for this. So I'll know what to search for in the archive, the pull up exactly that thread. So it's not being copied in, but I say, here's what to do. I do that with book. Sometimes when I have a book blurb I need to get started on and they've sent me an electronic copy. Sometimes I'll attach the file to the card, or sometimes I'll just put the title of the email in which that file is attached.

And I'll just search and find it and print it or load it on to a. reader, you know, when the time comes. For information, it's more complicated. Like you're working on an article or something, and there's going to be a lot of different sources, right, where you don't want three dozen sources attached to a Trello card. My general rule is when possible, store research information in the tool you're going to use when you need it, right? So I write books and articles using Scrivener.

I move relevant notes and links and resources and files that are relevant to projects, like to a Scrivener project. for that article or for that book chapter. So I just move the information straight in the script. When I'm writing academic article, I use latex, markup language that mathematicians use, right? I'll just move stuff I need right into the latex file in which I'm working on. I have an idea. I'll just start building a latex file, basically like I'm writing a technical article.

And I'll just add citations and notes right into that document and it grows and grows. And then eventually what happens is the actual paper starts getting written at the front of this and the notes get pushed to the back. And then eventually they get moved out and you have the. final paper, but I just put the information straight, like proofs and links and citations and summaries, like right into the same tool I'm going to use to write the final paper. So for complicated information, if you can keep it close to the tool you're going to use, I tend to think that's probably best as well. So with the email search, do you just keep the email on your inbox?

Well, so in Gmail, it archives. So you archive it. It's out of your inbox. Okay, so you archive it. And then you, it's searchable at that point. But Gmail search is not very good. So you really need the exact subject line. So I'll just copy, like I'll actually copy and paste the subject line and just put it right on the card.

Do you ever go into your archive folder and take stuff out or is it just grow up? It's not a folder. So the Gmail paradigm, it's based on search. So the Gmail paradigm is the archive, it's like the internet or something. You never navigate it. It's just something you search into. So you just have this, like that was their big idea. Like we're good at search.

So when you're done with a thing, don't organize it to a file, a folder. don't have like a hierarchy of different folders. Just archive it. And you can just use our smart search define what you need later, the unstructured storage. The problem is their search is not that smart. Like the whole reason why Google searching works well on the web is that it's leveraging all this extra information, in particular link structure. So it knows like this page is probably what you're looking for because it's being linked to by a lot of other important pages. You don't have any of that information with just your own emails.

So it's really just doing like a much more standard search. It's not very good, to be honest. So you really need like a subject line. Like I know exactly what I'm looking for. And if you want to pull up, but again, you always need to have the subject line. Yeah.

Yeah. Yeah. That's the way that works. All right. Who we got? Next question is from Diana. How can I improve my ability to communicate effectively in spontaneous workplace conversations,

especially when I struggle with clarity, organization, and understanding others' needs? I speak well for planned presentations, but not as well in off-the-cuff scenarios. Well, Dan, I think a good rule of thumb and Jesse will agree is you want to break the ice. So the thing is you want to find some sort of characteristic of the person you're talking to, preferably like an immutable characteristic. They have no control over it.

Make fun of them and then say, you burned. That is like, it kind of breaks the ice and then you're not going to be, you're not going to be, you're not going to be so nervous, right? like you so bald you got to talk like that you so bald I'm blinded by light off your head you burned right

and then the boss is like all right well I mean you're clearly fired no it's a good question speaking is a practiced art I think people don't recognize this younger people

supposedly are worse at this because there's actually less in person spontaneous interaction than before because more things are done on phones and more things are done digitally like through text messaging

So there's this idea out there that I think Sherry Turkle wrote about this. Like there's this idea out here that younger people who are coming up with smartphones are particularly bad at in-person conversation. They don't want to sit down and talk to their bosses. They certainly don't want to call someone that's really fraught. When Jesse and I were growing up, that was a big part of our childhood, right, was calling someone and you would say the person's name Mr. or Mrs. You'd be like, you know, hi, Mr. Miller, this is Cal Newport. It's like Jesse there. And you had to learn how to do that and like how to interact.

Interestingly, and maybe just because I'm in like elite institutions, I haven't seen this issue. Like my Georgetown students, they're super articulate. But that might be self-selecting. When I was up at Dartmouth teaching a couple years ago, these kids are super articulate. But that might not be a representative sample. That being said, speaking is hard. Speaking is hard. Like I make a living speaking.

I've been working at it for a really long time. The degree to which right now, for example, speaking on a podcast, that I'm having to rely on essentially cognitive muscle memory training, not to say, um, not to say like, to get the right pauses, to keep things moving,

like that's very practiced, to have an idea that you're pursuing, to pursue it clearly, not to get caught in a cul-de-sac, you know, that takes practice. So what I'm trying to tell you, Diana, is it's not a problem

that you're struggling right now. You haven't practiced it yet. It's no different than you say, look, I picked up a banjo the other day. It's not going well. It doesn't sound good. It's like, yeah, of course, you've got to practice.

But if you practice, you'd get better. And that's the way you want to think about speaking off the cuff. Writing and reading actually helps people who read a lot and write a lot. They're just being exposed so much to structured formal language that you build those structures, those predictive structures in your head. It's like you're a language model and you get better at reproducing it.

So reading will help trying to write very clearly will also help. Practicing is ultimately a thing that's going to help best. You can practice in situations where it's lower stakes. So like with your friends or family, you're having conversations over dinner. say, hey, I'm going to try a little harder in this interaction here. I'm going to be a little bit more formal. Just straight up practice is going to make you better. There are heuristics that can help as well.

So, for example, if you're talking to someone at a business meeting or at a conference or you're standing up to make a point, sometimes it's useful to think about the following structure. Set up point contrast implication. Let me clearly set up what I'm going to say. So here's the context or the stakes. hey, I know that we've been talking, you know, recently about making our meetings more efficient. I have an idea there to share. That's set up. So make it very clear what you're going to talk about.

Point. Deliver clearly. Here's my point. So here's the original information I'm offering. So in this sample, this is where you might say something like, we have too many meetings that could probably be consolidated into fewer. We should think about ways of combining meetings. You've set up what you're going to talk about. you've made your point.

Then you contrast, right? This is the dialectical approach, as opposed to what we have been doing, which is, if anyone has something they want to discuss in a group, they send out a separate invite for it. So you've made your point and what you want to deliver, you contrast it to existing points, and then you give details and implications. So the way we might do this, for example, would be instead to have a standing meeting block every afternoon that can be used for multiple purposes. The implications of that details would be some stuff might have to wait or that media might get rushed, but I think it's probably worth it.

So if you hear someone follow that pattern, you don't recognize that they're following that pattern, but it seems very persuasive. Let me cue you in setup. Let me cue you into what I want to tell you about. Let me give you very clearly. Here's my point. Let me contrast it to other ways you might do this to clarify the contours, give you some details and the implications of it report and I'm out. You talk that way in a business setting. And they're going to say, Diana's sharp, really clear, interesting ideas.

They're really on the ball. So there are heuristics like that you can do. But practice is the best. If you listen to, this is a canard, but it's true. This is like one of these cliches that true. Screenwriters will tell you, Hollywood screenwriters. I know some Hollywood screenwriters. They'll tell you that you want your dialogue to sound quote unquote natural, right?

You don't want people to sound like they're reading a script. But what will happen is most screenwriters early on in their career will go through this point where they're like, let's just listen to real conversation. I can take notes on real conversation so I can make it very natural. And what they realize is real conversation is barely comprehensible. If you just sit and listen over, listen to people casually talking and then you transcribe that and tried to put it into a movie, people are going to think that the point was like that character had a traumatic brain injury recently. Like this is so incoherent. Like what's going on? because the way most people talk when the stakes are low and it's casual and it's with a friend or whatever, it's weird and back and forth and interruptive, very digressive, all sorts of verbal tics and re returns and ums and likes and yeah, I don't know, man, what's going on.

And it's fine in that informal context, but, you know, it's not at all the way people actually, yeah, it's not formal at all. And so in Hollywood naturalistic dialogue writing is its own thing. It sounds naturalistic, but it's actually way more structured and clear. and directed than the normal people actually really talk. And it's really in the delivery of the actor that makes it sound naturalistic, which is all to say, to practice this, you have to think, I'm having a conversation with someone where I could be super casual and doesn't matter. Let me try to be clear.

The other thing people always suggest is Toastmasters. I don't know if these still exist. But yeah, you can also go to a speaking club like Toastmasters where you practice getting up and giving speeches. But practice is the key here and have some heuristics like set up point contrast, details implication. It's a cool skill to the master because they clear your communicate, the smarter people think you are, which is going to be a big boost. All right. What are we got next?

Next question is from Flying Professor. My life and work are divided between two different countries with a seven-hour time difference. Google Calendar just couldn't handle that, so I've gone back to paper and pencil to calendars. Is this okay or have I missed something? That's a good question. Quick follow up on, I was just thinking about this, Jesse, though. Quick follow up on Diana. I was a very good speaker as a kid.

And I got a lot of like unfair advantages out of that. I think because I read a lot. Mm-hmm. But I remember doing an oration at, I guess was that like a VA. And I had to give a speech in front of everyone about flag burning and whether there should be a constitutional amendment. And I remember just ad liby did it. I didn't realize it was supposed to prepare something. Anyways, they selected me.

I got to go to Boy State. Have you heard about Boy State? I think you might have mentioned it before. Yeah. So like that was all oration. So I was considered, I was good at model you in as well. I would do very little work, but I could speak well. And I think it was largely because I read a lot.

There you go. That's my secret power. All right, flying professor, two time zones. Yeah, you need two calendars. You can use electronic calendars. You just need two of them. So you could have an electronic calendar for one time zone and another electronic calendar for the other time zone.

The problem is you can't use the same Google account as far as I know. I think the time zone is going to be consistent across your vision. various calendars. So you have to create, it's fine, just create a different Google account just for this calendar. And then you can share, you know, whatever. Actually, you don't want to share them. You don't want the one time zone calendar to share onto your current time zone calendar because it's going to be the wrong time zone.

Let me think about this for a second. So have one calendar for your time zone here. Have another calendar with another account for the time zone for the place you go. So those are both now local calendars. So when you're in the one place, you look at the local. local calendar for that place correct times. When you're in this place, look at the calendar for that place. You're seeing the correct times for that place.

You can then share between these different time zone calendars if you want to and you'll see where these things fall on each other's time zone. So seven hours, I'm assuming you're talking like Europe and East Coast, let's just say. When you look at your East Coast calendar, you can share your Europe calendar onto your East Coast calendar and it will show it like the East Coast Times, but you probably don't care about that. But it'll show it like, yeah, this thing's happening at two in the morning or whatever. But I guess you wouldn't want to share them. Just have two separate calendars. So you can

use paper calendars. Fine. I like electronic calendars because I want to do email reminders. I want to add information. Like here's my, here's the Zoom link for this particular Zoom meeting that's going on my calendar. I want to move things around easier. I want to do repeats. So I would create two Google calendars if I was you. Completely different accounts, completely different time zones. And you can just switch between them depending on where you are. And you're right. Don't share between the two. That'll confuse you with the time zones. the time zone issue that's the worst Jesse

speaking of Europe is there's a month everyone who's works with the UK knows this and I you know I work close with the UK we do a lot of book sales there there's a month in March where we get out it's it's like we daylight savings times

changes in the US and not there it's different and there's a month where until recently no one from the US meeting with someone in the UK would ever be on time. It's just a crap shoot. Everything was off by this hour and it was different than it was the month before and it changes to month after. Now

it's better because of calendar invites. So if my UK publisher sends me a meeting invite or phone call, Google fixes the time zone. And so now it's okay. But there's a long time where I just assumed that it's going to get

everything wrong. I think when I was doing UK publicity, even as late as like digital minimalism, it was like, we're going to dismiss things. And so finally Google Calendar has made that possible. Everyone gets that wrong. It becomes like a six-hour difference or an eight-hour difference temporarily for just one month. The worst thing. I have a couple of follow-up questions.

What do you do with email reminders? I use them for important appointments and stuff like that. So how does it work? In Google Calendar, you can click on an event and then you can go to notifications. Oh, then you can choose email me about this. and here's how early before the event that email me about it. So I often do that for important things.

So I'll see it first thing. I don't always trust myself. I'm not like an executive where, you know, my day has been scheduled inside and out. And my whole day's run off my calendar. First thing I'm going to do is look at my calendar. I'm not like that.

Like there might be days where I think, yeah, I know I have to go in at 11 to teach. I'm going to try to write this morning. Yeah. And so like I might not look at my calendar until then, but maybe there was something important on there that I forgot about.

Like, some appointment. So I'll have it email me because I usually will, you know, see my, usually I'll email the day before. So when I check my, you know, shut down my email the day before, I see this reminder of like, oh, yeah, yeah, tomorrow morning I got an event. Mainly it's psychological. It just makes me worry less about missing something. If I know I'm also going to get an email about it. And then I have another follow-up question about the archives. So do you only archive items that you have those detailed notes of or sometimes do you archive things and just knowing that you'll never be able to find them because the search won't be good.

That's the Gmail. The Gmail methodologies you never delete. You archive. So in theory, their whole thing is storage is cheap and just keep archiving all your emails. So you never delete in email. So you archive all yours? Yeah. But you only search for like a small percentage.

Yeah. Yeah, that's the Gmail data model is never delete, just archive. Interesting. Yeah. And you can't see the archive really. It's just you can search into it. And then they want you to actually use the data. So they use the data from your email.

They're like train things and the target ads to you. It's a whole, oh, it's a whole world. Surveillance capitalism and it's best. All right. We're going to do two calls today. Do I have that right? Yeah. I love this.

Let's get some calls in. All right. Let's get our first call. Hi, Cal. It's Jessica. I have a question about coaching. So I was listening to one of your podcasts and you mentioned that you discuss something with a coach. I'm a female academic in Europe.

and there's a lot of coaching programs that are free and offered by the sort of university development service, often to women, but also to sort of junior academics at the university. And I struggle a bit to see which ones are sort of worthwhile and useful to go to and which ones are not so worthwhile. And I was wondering if you could expand a little bit on how you integrated coaching, I guess receiving coaching into sort of building a deep life and setting up a schedule. that has sort of appropriate blocks of deep work. Thank you. Well, it's a good question. I think coaching's underrated. You know,

in general, I think there should be more coaching. It's part of it is informational. So you get information you want it otherwise have. A big part of coaching is often backstopping, right? Having someone backstop a decision that you're making so that you have some confidence in it. So it's a whole bunch of confidence boosting, right? So coaching, I think it's really useful.

It can mean a lot of different things. If you're an academic, there's a couple types of coaching that's relevant. The first type is what I might more accurately call mentorship. And this is where you're receiving advice from someone who is in your field an academic, but it's just more senior. But they're really important. Actually, this might be one of the more important things you can do, especially as a junior academic, is go to lunch once a month with a more senior academic in your field. And just pick their brain about what about this? I'm worried about this.

What do you recommend here? what do you think is going well or not. There's a lot of nuances in this world, as in lots of different knowledge work worlds, lots of nuances that aren't given to you in a manual. And it's left of you to figure it out. And you could be going down a wrong end. Georgetown, you know, where I've been my whole academic, my professor career, they formalized this.

You would be assigned as an assistant professor, junior faculty. You'd be assigned or you could choose, but you needed a mentor. And then the university or department would pay for you to go to lunch. And so I'd go to the tombs once a month for a while with, with my mentor. Good place, right? I was just there. I love the Tunes.

Teams is awesome. And phones don't work well down there. Yeah. Yeah. And they have rowing stuff on the wall. Yeah, Tumes is great. But anyways, we go to the Tumes once a month.

And it's just like whatever it's on your mind. And I learned a lot about navigating academia, but navigating the specific institution and like who's who and who's what and what matters and what does it and what you should be worried about. That's very useful. So I think mentorship is very useful. And that's different than coaching because it's not someone coming in from the outside of your world. It's someone in your world who's ahead of you. So definitely try to

find mentorship, make that happen. People, senior professors like to talk to their junior professors. That shouldn't be hard. And coaching is more, I would say, tactical. It's okay, help me, I have this problem I want to solve. I want to, here's my goal. I want to be doing more of X. Help me figure out how to do that. In academia, I think the most effective type of coaching of that type that I've seen really does focus on the issue that you mentioned there, which is making sure that you're finding enough time to get the work done, the work that matters, which is going to be your research production. So having someone in your life who's looking at your schedule and is helping you figure out smart ways of you need more time, you're not getting enough time writing, how can we help you get that time? They're a sounding board.

They're a backstop. They're a confidence booster. They're a source of ideas. So you need to drop that committee. That's okay. You need to protect your mornings. They might see insights you might not have. I mean, I don't know if you remember this, Jesse, but when we had my friend Laura Vandercambe on the podcast, we were.

doing a case study, I think she helped me answer questions, if I remember properly. And we had a question from a professor, and she was having a hard time, finding time for deep work. And Laura had like this really interesting scheduling idea that I hadn't thought of and the caller hadn't thought of. But it made a really big difference. She's like, here's what you really need to do. And I don't remember the details other than that this young professor was trying to fit in her deep work into all these little slivers. And Laura's like, no, no, here's a change you can make. you have a babysitter here and you swap so your husband takes these shifts and you take those shifts,

now you have two long sessions you can count on each week, four plus hours, and now you can actually make progress on papers. And they were relatively minor. They're not hard things to do, but she just wasn't thinking about it. But having someone who's done a ton of time management like Laura was like, hey, I think you're missing this option here and it made a big difference. So I think that type of tactical coaching, and there is a lot of coaches to focus on this in academia. I help, they're often people who left the academic track. Like, I help professors make

sure they get the important things done. I think that is absolutely worth the time and worth the money. The coach I have, shout out to Cheryl, she focuses on something very specific, which is people who are doing creative work and have to deal with, like, the business and logistical challenges of making that happen. So, like, she also, we've mentioned this works with a lot of professional writers, screenwriters, movie directors, people where the core thing you do is creative. And as you do it better, it becomes harder and harder to actually do that thing that you do well, right? Like all these new responsibilities and opportunities and options come up. So it's really focused.

She focuses on people. So it's really focused on my writing life, not my professor life. And how do I keep producing and enjoying what I'm doing? She's really focused on like enjoying what you're doing and producing creative output that I'm really proud of while managing. the business aspects of this in a way that that doesn't just take over. And so it's a very specific type of coaching. So, anyways, I'm a big fan of coaching. It's mentorship and coaching itself.

Do it all. Do it all. It's only going to help and it makes things seem less scary and less lonely. Coaching's a good business. Our friend Brad coaches Stolberg. Yeah. That's like one of his, that's like his job outside of writing. He's in super demand too.

I think you can't get on his, his, it's like a long wait list at this point. All right. Who do we got next? Another call? Hi, Cal, on a recent episode, you talked about scheduling a time in your day to embrace boredom so that the rest of your day you don't have to worry about feeling bad about listening to a podcast, etc, etc. If I were to schedule boredom, how would that look? Could you elaborate? Do you just sit in a room for 20 minutes? or the next time you're in traffic, you just deal with it. Am I allowed to just let my mind wander? Please explain how this works for you.

Thank you. Okay, so embrace boredom. That phrase comes from deep work. So it's a chapter in deep work. And I think what's important about it is boredom is not something that I think has a moral valence. So unlike some commentators, I don't think boredom is somehow good and non-bortem is bad, and therefore by having more boredom,

that somehow like that act itself of being bored is virtuous. I see this way more technically. So when I say embrace boredom, what I'm hoping that you accomplish is that your brain gets used to craving novel stimuli and not getting novel stimuli in response. So it's really just working on the dopamine-mediated

short-term reward circuits. So if your brain learns, every time it feels boredom it gets a treat from a phone, right? Something comes out and it gets a novel stimuli. You build up these reward circuits that says, ooh, as soon as I feel that sort of discomfort of lack of novel stimuli,

which is natural to people and we felt it throughout all of our species history, as soon as I feel that there's a shiny treat reward I'm going to get, it really floods the zone with dopamine and makes it really hard not to look at your phone. Why do I worry about that? It's because sometimes you don't want to look at your phone

when it becomes important that you actually do focus on something, like you're working on a really important paper or a breakthrough or a meaningful conversation or trying to figure out something hard, there's no novel stimuli when you're doing that. But if your brain is learned, I always get a shiny treat when I feel that lack of stimuli that we commonly describe as boredom, you're not going to be able to focus. And you're going to have to look at your phone. So the way you break that is you make sure that you get consistent practice feeling boredom and not getting the reward and things being okay.

and then your brain's the strength of that Pavlovian response weakens. And now when it comes time to do something like deep work on something important, you'll do better at it. So I see embracing boredom, that's why it came in my book Deep Work, is just training your brain to be better at deep work down the line. All right, how do you actually do this? There's two scales at which you need to embrace boredom. Every day, do something short where you're free from input from other minds. So you're welcome to think as much as you want inside your own head. You're welcome to observe the world around you as much as you want and make observations.

to be interested by it. But you're not inputting input that was generated by another mind. You're not reading something or listening to something. Every day, do some sort of short exposure to that, like once or twice. And by short, I mean, you know, you do an errand. You don't take out your phone. You take the AirPods out. Like, I'm going in the pharmacy to grab something.

I'll just go and do that and come back. It takes 10 minutes. But I'm going to do it without my phone. Or I'm doing a short drive, you know, to a friend's house. It's 10 minutes away. I'll just do that drive, nothing on the radio, right? So that's like a short one or two exposures, you know, once a day. Then once a week, if possible, try to have a longer exposure, which really I typically

think of as being a long walk is probably the best. Go for a long walk or a hike or something like this without anything in your ear or anything in your hand and just be alone with your thoughts. And this gives you time to do some more structured thinking and introspection. And this just gets your mind used to this idea of our brain can function just fine without shiny treat stimuli. I've been adding, however, if I was to rewrite that chapter, I would add a third type of boredom training that wasn't as relevant back when I wrote deep work, but I'm caring more about now, I would say be very wary about dopamine stacking. So if you're watching something, don't look at your phone. Have your phone plugged in somewhere else, right?

Don't get in this idea of because that really gets those reward circuits going, that even if I'm watching something that's pretty interesting, I might want to stack on something that's even more interesting. That really is going to short circuit those reward circuits and make it really hard. So be very wary about dopamine stacking. If you're going to do something, look at a screen, only look at one screen at a time. So I would add that in as well. You do those things. You're not bored all the time.

We're not lauding boredom. But you'll get more attraction to thinking and being with your own thoughts. That'll become more appealing as you get practice. But it makes sure your brain does not develop this knee-jerk reaction. When I feel boredom, I have to get stimuli. So that's what I would recommend. What if you have like a laptop that you keep on your coffee table and you want to like look something up when you're watching something? I would keep it somewhere where you have to move.

That's what I would say. Like I, my couch face on my TV, we have like have an open plan thing. So the kitchen's in that same bigger space. I like to plug my phone in in the kitchen. We have like a charging thing. So I can see over there from the TV. But if I want to look something up, I walk over there and look it up on my phone. And then I can come back to the couch.

So that's fine, but you can't dopamine stack, right? It's a little bit different. If it's right there, you're going to start. rabbit holeing. And I'll find myself if I start rabbit holeing. I'll like to turn off the thing I'm doing and then do that. And then when I'm done with that, go back and do the thing, the thing that I'm doing. Oh, I like that.

Yeah. Or press pause or something. Yeah. Yeah, yeah. Or muted or something like that. All right. We got a speaking of, it's not really speaking of, but we got a geeky final segment coming up.

But really what we're saying there had nothing to do with reinforcement learning. That's what I'm going to talk about. But before we get to our final tech corner, let's talk about another sponsor. Talk about our friends at ExpressVPN. Going online without ExpressVPN is like leaving your blinds open at night. Anyone can watch what you're doing. You have no idea who might be out there lurking in the dark. If you let me put my CS hat on, this is why when you communicate over the internet,

you're sending what are known as packets. So the packets have an address of who you want to talk to and then the contents, like what you want to say. Now, those contents are often encrypted, right? if you're using a protocol like HTTPS, those are encrypted but not the address. So what you're saying is encrypted but not the address of who you're talking to, which means if you're on a wireless access point, anyone with a radio antenna in the right software can read those addresses from your packets

and say, oh, I know what websites or services you're talking to. Or if you're at home and you're like, look, I live on a farm and I'm isolated, no one's near me, your internet service provider who is communicating to the internet, your portal to you know, they see who you're talking to. They see the addresses. And they can sell that information. And you know what? A lot of them actually do.

This is where a VPN, like ExpressVPN, enters the scene because what it does is, instead of just talking directly to the website you want to talk to, you take that message, and you encrypt the whole thing, and you send it to the VPN server. And the VPN server opens up that envelope of encryption, takes out the original message, and talks to the site and server on your behalf, encrypts the response, and sends it back to you. So the people who are just watching you and reading the addresses of your packets, all they learn is that you're talking to a VPN server. They have no idea what site and service you're actually talking to, and therefore you get some privacy back. Your internet service provider doesn't know what sites and services you're using.

That creepy guy sitting next to you in the coffee shop with like the satellite dish hat who keeps like staring at you oddly. He doesn't know who you're talking to. All you learn is that you're talking to a VPN server. So you should use a VPN, especially, you know, when you're traveling or around other people. And if you're going to use a VPN, I suggest that you use ExpressVPN. ExpressVPN has servers all around the world. They have a lot of bandwidth, so it's fast connections. There's going to be a server nearby.

It's easy to use. You turn it on, and now all of your internet traffic automatically goes through the server, and you use your sites and services on your devices, just like normal. It works well, and it gives you that privacy back. Not just on your computer. You can use this on your phones. You can use this on your tablets, like whatever devices you use. They even now have an optional dedicated IP service that's engineered with innovative zero-knowledge design.

So now not even ExpressVPN can trace an IP address back to the user. Zero knowledge. I could go on about that for a while. It's a complicated cryptography idea, but let's just put it this way. It gives you an even extra bigger layer of privacy. There's a reason why ExpressVPN has been rated number one by the top tech reviewers like CNET and the verge because it works well. So protect your online privacy today by visiting expressvpn.com slash deep. That's EXPR-E-S-V-S-V-N.com slash deep to find out how you can get up to four extra months free.

That's expressvpn.com slash deep. Also want to talk about our friends at Oracle Cloud Infrastructure. There is a growing expense eating into your company's profits. It's your cloud computing bill. You may have gotten a deal to start, but now the spend to sky high and increasing every year. What if you could cut your cloud bill in half and improve performance at the same time? Well, if you act by May 31st, Oracle Cloud infrastructure can help you do just that. OCI is the next generation cloud designed for every workload where you can run any application,

including any AI projects, faster and more securely for less. In fact, Oracle has a special promotion where you can cut your cloud bill in half when you switch to OCI. The savings are real. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. Join modal Skydance Animation and today's innovative AI tech companies who upgraded to OCI and saved. This offer is only for new U.S. customers with a minimal financial commitment. See if you qualify for half off at oracle.com slash deep questions. That's Oracle.com slash deep questions.

All right, Jesse, with that technical ad read in mind, let's move on. to our final segment. All right, well, because I can't help myself for our final segment, I want to do another tech corner where we can dive into some needlessly technical details about the world of AI. All right, so what I want to talk about today, Jesse, is two different types of AI technologies that are often getting mixed up. And I want to talk about what they have in common and what makes them different and what we have to worry or not worry about each of them.

so we can kind of have a more nuanced understanding of what's going on out there. So let me start with two examples. Here's two things AI has done in recent years that has been deemed impressive. One, AI beat a grand champion for the first time at a board game called Go, which is a, it's a simple to learn game where you take turns placing black or white rocks on a grid pattern. and if you surround, one color surrounds all of the rocks of another color, you flip them over to your color. And you're trying to win on the board.

And this was considered a hard game for a computer to win because there's this sort of astronomical number of possible boards. And so you can't just brute force search. Like if I do this and they do this, and then I do this, you can't brute force search as easily as you can for chess. So it was considered a hard game. And DeepMind, which is now owned by Google, created a system called AlphaGo that beat for the first time

a grand champion, Lisa Dell, and it was a big deal. Another recent accomplishment with AI was the latest models from OpenAI, their chat, GPT, reasoning models, are doing really well on math tests that are international caliber and something that AI had stumbled on before. Like, look, this is indication that the reasoning ability of these models is really good. I think a lot of people just see this all as, like, generally, AI is getting smarter. But those two accomplishments have two very different types of AI systems underlying them, and it's useful to know the distinctions.

The system that's winning at the game AlphaGo, like most other game playing AIs that we see, is using an AI technology called reinforcement learning, whereas the system that is doing really well in that math tests, so these like chat GPT systems, for example, these are large language models that are more data-trained generative models. These are different technologies.

So let me start quickly with the common history of these two technologies, and then I'll talk about where they change. So there is a common revolution that occurred in the early 2010s that paved the way for lots of these different AI innovations that are happening today, even if the technologies in these innovations aren't the same. That revolution was the introduction of what are called deep networks, which you can just think of as a neural network that has a lot of layers, really big neural networks. Up to that point, AI researchers had not really tried to train big neural networks to do things. things. Part of the problem was figuring out, okay, how do we train neural networks that have lots of layers, right? You have to kind of propagate through signals back through all the layers

to adjust the weights when you're training it. We solved that in the 80s. This is when Jeff Hinton and others figured out back propagation, a particular algorithm that was going to, could in theory train these networks. Okay, fine. But there's another problem. They're so big that you would need a lot of compute power to train them and you would need a lot of data. It would require a lot of iterations to train them. Those obstacles fell as we got more compute and we got more data because of the internet. We got more compute in part because of the rise of graphic processing units, which the video game industry pioneered to make 3D graphics faster and PlayStation's. Turns out those same cards, GPUs, are great for training neural networks. So suddenly we got a lot

more compute and the internet gave us a lot more data. And so now we knew how to train these things. We had enough compute power to train these things. And we had enough data to train these things. The final obstacle was just philosophical. Machine learning experts up to that point said, no, no, no, you can't. If you train a really large network, they have so much potential memory, right?

Because they're so big. If you train them on some data set, like I'm going to show you a bunch of pictures of pictures, some have cats, some don't. And I want you to learn what a cat is. I want to train you to recognize pictures with cats. So, like, the problem is that these networks are so big, they could just memorize a feature of every single picture with a cat from your training set.

And they haven't really learned anything about cats. They've just learned about these pictures you've shown them. And then when you give it a novel picture that's never seen before, it'll do terribly because it has quote unquote overfit. All right. We finally got over that obstacle, said, well, let's just try it. This was basically the revolution of modern AI was like, let's just try it. And the original things that were tried were on image recognition. And they trained these big networks and they didn't overfit.

instead they got really good at recognizing novel images and started far outperforming other AI systems because it turns out what happened is you have enough compute and time to train these things instead of just memorizing your training set, they learned and generalized a lot of interesting information about the context in which they were operating. So suddenly we realized really big neural networks

could do stuff that we never thought was possible. That was the origin of sort of all of the AI revolutions. But if we look at this technical sort of phylogenic tree, things began to split. So if we look to the language model split of this tree that led to things like chat GPT, the way you're training these very large neural networks for something like a language model is with a lot of data. A lot of data from the real world. So the original models is a lot of text. And you're giving it real text from the real world.

and you're knocking out a word from that text and saying do your best to replace that word with what makes sense. And if it does a pretty good job, produces a word that's pretty close to the actual word that was there, you sort of reinforce those weights in the network, and if it does a bad job, like, now that's not working too well. And if you do this over enough data, what it does is it learns, it basically estimates

the underlying processes that produce those texts. So the underlying grammatical subject matter processes we use when we produce text, it began to estimate those in these really large neural networks and the large we made the networks to more complicated processes it could estimate and now when you ask it to produce text from scratch it can use it has an estimate of the process we use in our head and it can produce really good text

it's a data-driven data trained so there's a lot of understanding baked into those rules but really the understanding is is trying to estimate an existing process for which it was seen a lot of products the machines that play games like the machine that played Go or the breakthrough that happened around 2014

when Deep Mind said, look, we can win at Atari games. It was sort of like the big breakthrough. Those are using reinforcement learning. And the way they work is different. So again, they have a big neural network underneath it. They're using big neural networks. But now what they're doing

is they're having that neural network interact with a world. It's not just being given a lot of data. Typically, it's going to be an online training. So, like, if you're training a game to play Atari games, they were actually giving as input to the neural network every pixel on the screen of the Atari game. And then the network was outputting an action. Move the controller left, move the controller right, press A, press B, whatever. It was outputting an action. And then the simulation system would update.

Oh, great, let's make that move. And then it would give feedback. Like, did this make our situation better or worse? And if it was worse, it was sort of say, don't, let's change those weights away from this. And if it was good, it was like, let's change our weights towards this. And through these like interactions with a real world, with these reinforcement signals, what the model ends up learning is a policy. It learns a policy that is good at doing well by whatever definition of well you had,

whatever reward function you are using in the training. It comes up with a policy for how do I react to different situations in a way that's going to help me maximize this whatever reward I care about, like a point or whatever it is the reward I care about. So these are two very different ways of training things. And this is how they trade AlphaGo. AlphaGo, they trained that at first with like actual Go games and then they created two models to play Go against each other.

And it was, you know, if it would do well against the other computer model, it would sort of reinforce those. And if it did worse, it would unreinforced it. They played millions of games with each other. And it created policies for how to play the game. these are two different things. So something you're going to get with a data trained language model. It's estimating real processes in the real world, like the way generally that we produce text and the bigger the model, the more sophisticated that estimation is going to be.

That's not going to, first of all, it's just, it's producing text and it's trying to estimate what it's seen. So it's in some sense it's not, it can do kind of novelty things, but really it's trying to, its goal, is to be as close as possible to the things that produce the text it trained on. In reinforcement learning, all it cares about is having a policy that does well with the reward. It's not trying to estimate an existing way

that people play a game. It's not trying to look at a bunch of examples of a game and figure out how do people play games. It's just coming up with something that does really well in the reward function, which allows for a lot more originality. So like the classic case is when DeepMind was training one of these big neural networks to play

the Atari game breakout where you move the paddle and you knock the bricks. It devised a strategy that no human player had known about. Like it figured out that there was space on top of all the bricks and you could tunnel, if you're very precise, you could knock out the bricks out of diagonal,

open up a channel and then send the ball up that channel, it would get on top of the bricks and just bounce down and take all the bricks out, just bouncing around from top. And it took super precise moves, but it just learned like, oh, that's the right thing to do. There's another example of this that's often given by reinforcement learning experts where

they're trying to train it to do well with a boat racing video game where you collect points for getting various rewards as you make your way through the track or whatever. And the policy that came up with, because all I was trying to do is maximize its points, is it figured out, like, forget the racing. There's a place where the rewards recharge pretty quickly. I'm just going to endlessly go in this loop kind of crashing into things and I'm just going to keep getting

these coins that appear again and again and again in this spiral or whatever because it's a policy that did really well at maximizing reward function. Whereas if you went to chat GPT and said, let me explain to you like I'm a situation,

it's a boat and I'm trying to drive the boat and here's what's around me, where should I go? It'll give you like pretty reasonable. Like this is like the type of thing someone would say here. Like avoid the obstacles, you know, go this direction.

direction. So there are two different things that are going on. I think the place, if you want to be worried about things, well, first of all, keep this in mind, these are two different technologies. Let's just start there, right? All they have in common is making big networks bigger tends to lead the more intelligence. But the way they're trained are completely different. A breakthrough in one does not mean a breakthrough in the other.

Either of these could get stuck in different places. And the concerns are completely different. So you can't just assume they're both innovating at the same level. But I'm saying if you want to get a little bit more worried about something, the sci-fi horror stories, the sort of like cautionary tales, you would worry about that reinforcement learning world. It's not trying to estimate how people would talk. It will learn whatever it can to try to get that reward function maximized. And it could do things you don't expect. And if you give a reinforcement trained model actuation, like it can control the real world and just say it, I just trust its policy will work well.

it can go to really weird places because you don't know what that policy is. It's very different than a data-driven. You can have a data-driven model is like it's trying to be as close to a person as possible. It's close to whatever process was producing the text that trained on it wants to be as close to that as possible. There's maybe more to be worried about

in that reinforcement learning world than there is in the chat chagip-tie-t world. But keep in mind, these are different technologies and when you think about things that are learning how to work in the world and walk and locom mode and play games. That's not the same thing that's happening with chat GPT. That's a completely different technology and one that we should keep an eye on.

Anyways, it's an important distinction. These worlds are kind of coming together. There's people who are using language models to help do reinforcement learning. Jan Lacoon doesn't like reinforcement models at all. He thinks we should just build up understanding of the world and have sort of more intentional simulation of the world. I agree with that because I think it's a lot safer. That's where my intentional AI model comes in.

A lot to talk about here, but at least we have this distinction to start with reinforcement learning is different than data-driven, data-driven learning, semi-supervised learning, unsupervised learning, rather. These are different models, different technologies, different capabilities, different concerns. All right, well, there we go, Jesse. There's my lecture for the day. I liked it.

Let's leave it there, but we'll be back next week with another episode. And until then, as always, stay deep. Hi, it's Cal here. One more thing before you go. If you like the Deep Questions podcast, you will love. my email newsletter, which you can sign up for at calnewport.com. Each week, I send out a new essay about the theory or practice of living deeply. I've been writing this newsletter since 2007, and over 70,000 subscribers get it sent to their inboxes each week. So if you are serious

about resisting the forces of distraction and shallowness that afflict our world, you got to sign up for my newsletter at calnewport.com and get some deep wisdom delivered to your inbox each week.