Cal Newport | Slow Productivity: The Lost Art of Accomplishment without Burnout

Cal Newport: Cal. Welcome back to the Unmistakable Creative.

Srini, it's always a pleasure.

This is how I know I've written another book. 'cause you and I talk.

Srini Rao: I'm always waiting for you to write another book, which now I understand why having read the latest book that it takes so damn long. Every time you write one, I'm just like, ah, man. I'm like, I need another Cal Newport book to read.

And it's funny, 'cause like I, you and I have met one of our friends is like, what's Cal like? I'm like we had three old fashions each over lunch. And I think people find that hard to believe based on how they perceive you. But anyways, you have a new book out called Slow Productivity, all of which we will get into.

And I was thinking about questions that I hadn't asked you before. And of course we'll do our usual tirade on education. But I realized that I actually hadn't asked you about this. And that is where were you born and raised and what impact did where you were born and raised end up having on where, what you've ended up doing with your

Cal Newport: life and your career.

Oh, that's interesting. My answer surprises people often. I dunno if you would guess, but Houston, Texas. Wait, what? I thought you were born on the East Coast. I moved to the East coast right before I turned eight years old. Wow. Yeah. I didn't know you. I had that We have in common. I spent seven years in Texas.

There we go. So I guess I'm a native Texan moved to the East coast though at the Impressional young age of seven or eight. I never had an accent though. This is why people don't realize, because my dad was had been in broadcasting, so he had trained the broadcaster neutral accent.

So even though we live in Texas, he grew up in Texas. He had this sort of the Midwest accent list, American broadcaster pronunciations. And my mom was from the north, from Michigan. I grew up in Texas, but never had a Texas accent. So I think that's why no one realizes it. I don't have any vestiges of the accent hiding under there.

Srini Rao: Did

Cal Newport: you ever say y'all? I didn't

Srini Rao: say y'all.

Cal Newport: I don't think so. We, I did for the longest time and I didn't realize it and how weird it sounded until I got to California.

Yeah, like uhoh, I gotta start saying what do they say in California? It's wicked is Boston. Everything has its colloquialisms, whatever.

But yeah. Y'all, y'all's a giveaway for sure.

Srini Rao: Yeah. Tell me about the transition. You were eight, which is not your teenage years, but you're still old enough to conceptualize that way. This is a pretty drastic change. I guess so. I guess so. I think I was young enough that it was fine.

Cal Newport: It was early elementary school, made a group of friends, made a group of friends real quick. The big thing that changed in my life, and I don't think this was necessarily due to the move, but we got, when we moved a home computer that was just ours. So we had a computer in the house when I was younger, but it was my mom.

She was a computer programmer. Working remotely, and we weren't really supposed to use that computer, but coincidental with us moving to New Jersey is we bought just a computer for the family. And almost right away is when I started teaching myself computer programming. And so my whole connection with computers and eventually computer science that starts when we moved to New Jersey.

I don't know if it's related to moving to New Jersey, but that's what I associate. That's the big change I associate with that part of my life. And you were, what, nine years old? At that time? I was pretty precocious with computers. Yeah. It just I knew because my mom was a computer programmer, I knew you could tell them what to do.

So I knew the concept. And then I went to the library and just got out books. I'm sure at first I was programming basic programming games and basic, and that was really limited because you couldn't really touch graphic drivers or whatever. So then I just started teaching myself c and c plus and Java and Assembler.

It turned out in the graphing calculators. If you were industrious, you could write a similar code games. All the best games for those ti I 80 threes and ti i 80 fives were actually coded in assembler. And I learned how to do this right, because if you programmed in assembler, you could touch the graphic memory directly and actually do high frame rate graphics.

And so I was programming games in assembler for the graphic calculators. That all just came easy to me. I could just speak computer languages like poly polyglots can speak various no spoken languages just came easy to me. So are we talking like the Oregon Trail era of computing?

Srini Rao: Like that? That's what I'm imagining when you're describing this. 'cause I had a TI 82 I think for calculus my senior year in high school.

Cal Newport: Yeah. It was, I mean's the Oregon Trail era, I think of as late eighties, early nineties, apple two. And I often think of these eras, either in terms of processor speed or graphic cards.

So really in the nineties, that's when I was I moved to 1990 and graduated 2000. So that 10 year period was my sort of schooling period. In New Jersey, that's where we had the PC revolution. We went from 2 86 to 3 86 to 4 86 to Pentiums, I dunno if people remember these processor names.

And graphics went from CGA to EGA to VGA to SVGA. So that was the, that revolution. It was this Moore's law period where, you know, every year and a half. There was a new processor speed, a new graphic card capability you would keep going back to the video game store and like bringing back the, in a big shrink rack box, some game you found and plug in all the disk.

So it was definitely an era that started with Oregon Trail and ended with Quake, yeah. All that in that one decade period.

Srini Rao: Yeah, no I remember network games. I remember thinking this is the biggest waste of time. And then I sat down and I played a game of command and conquer in college, and I was just like completely hooked.

Oh, I was there, man. Warcraft two is what it was for me. And back then, what you had this program, not the geek out too much. You would use this program called Cali for those who remember this, because these network games were originally meant to be played. On an actual local area network.

Cal Newport: And so there's this program you could run that would dial into a server and then make it look like you were just on a local area, networks. You could play these network games across the fledgling internet. So I remember playing Warcraft two over the internet with strangers from around the world, and my parents like, what are you doing?

What is this? Yeah. Our, ours was in the dorm and this was like 1996 at Berkeley. So high-speed Internet had just been put into most of the dorms. And you would literally hear guys yelling down the hall. You're like, you motherfucker, you just saying you send in like a commando to take out some huges.

Srini Rao: It was the, it was, I was amazed at how hooked I was on those games.

Cal Newport: Yeah. This was not exactly a great innovation for those of us looking to have girls like us, but it was pretty fun. Yeah. Yeah. That was probably bad timing that, that these games hit during those formative years, totally. So you mentioned that you had this almost sort of instinctive ability to program and understand computer languages. 'cause I'll tell you for me, like the moment I just realized, okay, computer science and I are clearly not meant to be. We went, I remember 'cause at Berkeley, which I'm guessing at MIT, this is the case too.

Srini Rao: I think there are like three or four schools in the country where they still teach lisp, which you're just like, this is the most useless computer language. I had friends who graduated from Berkeley and they couldn't build a website. And when I asked them, they're like, yeah, but I can learn how to do that in three days.

They're like, I can learn anything. 'cause you learn to think like a computer scientist as opposed to learning how to produce something. But for me, the moment that changed everything was. The moment we got to recursion, I was like, what the hell? I don't understand this. And I remember a friend, like a few years back tried to get me to watch a video about it and I was like, okay, this makes sense.

And then the woman started getting into the math and I was like, turn this off, Tim. She was doing recurrence relations and she started talking about Fibonacci sequences and I was like, okay, now

Cal Newport: I'm lost. That's funny. Here's the interesting thing though. I almost got programming. I basically got it outta my system, right?

I'm a kid, I got really into it. I could program all the languages. I was doing a lot of video game programming, right? I really building video game, network application, stuff like this. It was my job my summer job in high school was, I would, instead of going to lifeguard.

Or what people would normally do. I would go to an office park and it was computer programming for a tech consultancy, so I did a lot of programming, took all the courses, took like the ap, a computer science course when I was real young, and eventually they let me go to Princeton to take some of their computer science courses and I was nearby.

And that's where I learned, oh, for computer scientist programming is very pedestrian, right? It's like being good with the microscope as a biologist. And it wasn't respected. It was like, you did that and it put scare quotes around it industry, like that's like a practical school for industry.

And so by the time I went to grad school, I was saying, I'm gonna be straight up theoretician and never programmed a computer again. It's, it was like I got it outta my system. So when I was at MIT which these places do not respect. It's assumed you can program a course in all the languages, but it's like, Hey, I'm good at.

Focusing the microscope when I'm the biologist. It's useful to know how to do, but that's not what's cool. What's cool is the fact that you're figuring out how DNA works, right? So by the time I got to grad school, I switched over and so I can be a full-time theoretician, whiteboards, proofs math. That's it.

Srini Rao: It's funny because I've always known you're a theoretical computer scientist, and I think this will actually make a perfect segue into talking about the book, but what exactly is a theoretical computer scientist what does that involve? There's a couple things they can do, right?

Cal Newport: There's something called computability and Complexity theory, which goes all the way back to Alan Turing before computers actually existed. And that's straight up just studying. What can be solved with computers, what can't be solved with computers, what can be solved efficiently with computers, what can't be solved efficiently with computers?

So there's no attempt here to actually produce something useful. This is all just a theory of what can and can't be done with computers. The other big part of theory is more algorithm analysis, right? So you're coming up with an algorithm and analyzing it that solves some useful problem better than any algorithm had before.

Or, and this was one of my specialties. You're proving fundamental limits on doing that. So you'll say, no algorithm can solve this problem faster than this under these constraints, right? So it was algorithm analysis and then straight up computability, complexity theory, sort of Alan Turing stuff.

So that's all math, right? So when you're, all of this is basically, it's all just done mathematically. But I wrote this. Essay recently. So on, on the week that my, my book came out I wanted to write something for the New Yorker about it 'cause I'm on the contributor staff there. And we were thinking about, Hey, what do we wanna write?

And we're like, let's not just do the standard. I wrote a bunch of stuff like, let's not just do a standard, here's an idea from the book. Let's try to do something different. And my editor pushed me like, why don't you do something personal? And what I ended up doing was I wrote an essay called How I Learned to Concentrate, and it talked about my time in grad school and the theory group at MIT.

And it essentially made the claim that every idea I've followed I've pursued in my writing professionally all has an origin in that four or five year period. It that like what I was exposed to at MIT planted the seeds for everything I'm known for today, including the new book, slow Productivity.

And it was like an interesting exercise in memoir reflection to go back and excavate actually how influential I. Those years as a theoretician were for the stuff I'm doing today that maybe on the surface seems completely unrelated to theoretical computer science.

Srini Rao: Yeah, absolutely. Speaking of the book, let's get into the book.

What was the impetus for this book? Like why was this the sort of natural follow up to a world with that email? There was two different impetuses that were both pointing towards the same question, right? So one was personal and one was my audience cultural, right? So personal, like the important thing that happened leading up to me working on this book is my three kids, I have three boys, they all entered elementary school age.

Cal Newport: And this was like a real phase shift in parenting because for whatever reason, when they all entered elementary school age, it suddenly became clear that they needed as much time with their dad as possible. Like it was just like they were craving time with their dad like oxygen, right? And so this put me into this interesting moment, midlife crisis of.

I'm at the peak of my professional powers. Like I can produce really good stuff and at for really big platforms, et cetera. But also my kids need as much time as possible, right? And so I was like, okay, what do I do about this? How do I, my question was how do I still produce stuff I'm really proud of and I can support my family on?

And it like leaves a legacy, but without letting work, just take over all aspects of my life so I can spend an almost indulgent amount of time still with my kids at this period where they need it. This is happening completely concurrently with the pandemic, right? And coming outta the pandemic. My audience all of a sudden is also starting to ask the same question, right?

That I keep hearing from them. They're having this sort of existential crisis with knowledge work. They're sitting at home, they're on Zoom all day on their laptops. What is this? What is work? I don't like this. This isn't really work. What, how can I do this for another 30 years? What do I really want to do?

So they were having a crisis with work as well. And so they were also having this question of like, how do I fix what's broken with work? And so those two things came together, those two questions and slow productivity was my answer to both.

Srini Rao: Yeah, absolutely. Let's start by talking about education as we always do because I, one of the things I was thinking about is the intersection of the principles and slow productivity and ai, which I noticed you didn't mention at all in the book, and I'm guessing that was like a very intentional choice.

But what are you seeing now? Because I think usually you and I have this conversation every three or four years, and I think that this might be the one I feel like that has more to say about what we're seeing as changes like in student behavior for better and worse. What are you seeing when it comes to the role of AI and how does that tie into slow productivity?

And then we'll get specifically into the ideas in the book.

Cal Newport: Yeah. First of all, you have to keep in mind how new all of this is with ai, right? So it seems like from right now oh, why is there no AI in the book? I was pretty far along, if not done with that book when chat CPT was first long.

That was a year and a half ago. It's the timelines of this stuff is so fast. It's almost impossible. A lot of writers are having difficulties with this right now, because how do you write about AI today? Where, like a month from now, what you're write, what you're writing, what you're writing is out of date?

But I've been following this closely 'cause it's part of my beat at the New Yorker right now. Is I wrote a big piece last year just trying to explain how LLMs work. I just have a recent piece out now that talks about the things planning, the things LLMs can't do and trying to.

Paint a more realistic future of ai, which is gonna be much more about model ensembles and less about just this one type of model getting bigger. So I'm pretty plugged in drawing on my computer science background here to try to follow what's going on. And it's interesting for sure. So what is your take, Trudy?

Are you thinking this, these technologies, you think in education, what's your bullish, what's your like bullish take on how this might change things?

Srini Rao: Okay, so I think that one, it really depends on how people use it. Like you and I were talking about this just before we hit record, how now one of the things I will do is actually have AI ask me questions instead of me asking it questions.

So for example, if I wanted to dive deeper into your book, one of the things I did was I built this AI zettel castin, that instead of basically I'll write my like short note and say, okay, gimme the questions to explore about this. And then in my own words, answer those in questions.

So on, on the plus side, I think that there's an opportunity for Socratic method learning. I think there's no question this is gonna change the way that we teach. Sam Waltman had an interview, I think on CBS, where he said, look, we have the same issue when the calculator came out, and it forced us to change how we taught math.

But I think that there's potential for abuse, which, like my sort of summary of it is, it's not gonna turn idiots into geniuses. It will just make smart people smarter. But the thing is that I, I wrote this lengthy article about the four keys to success in the age of ai, and almost none of them have anything to do with ai.

It's like systems thinking. Critical thinking, creativity, divergent and convergent thinking. And communication skills, all of which are completely timeless skills like that. Because I went to my agent and experienced the exact same thing you're talking about when I self-published this book called The Artificially Intelligent Creative, but it wasn't great.

But she, the point that she made was that by the time this comes out, it'll be outdated. And I, that's when I realized I was okay, if I'm gonna write a book about ai, it would have to be a book that has nothing to do with ai Yep. But focuses on like human skills. Which is why I thought about the network mind and that idea like a book that is basically going to transcend the sort of AI trends and actually still remain relevant.

But from an educational standpoint, I think the thing that I see as the biggest potential benefit is the ability to personalize learning at scale. Cause I told you like just based on my nephew's word list, we built a custom children's book for him. For Christmas, and then using that same word list, we've been doing custom puppet shows.

And the cool thing is every single thing that is in each of these pieces of content are all things that he recognizes. So they're incredibly engaging. And so what started out as nothing but a list of words suddenly turned into an idea for a company. I was like, wow. Like the little genius learning company, which was the idea of personalizing somebody's education from the age of one based on their natural curiosity, which I think is really potentially powerful and transformative.

There are of course like that's a really high level thing that does overlooks probably a lot of nuances. But that's my take on it. Yeah. There's a couple things in there I think I agree with. First of all I'm pretty bullish on AI impact. While being still pretty non-committal about the exact details of it.

Cal Newport: Just because as everyone is noticing now it's unpredictable. Yeah. But a pretty bullish on there being impact, right? So in education, I'm still trying to get my arms around some of these issues. I agree that I think the Socratic interrogation of information is gonna be very powerful, right?

Because think about what happened, how big of a revolution it was in math education when Saul Khan started putting up those videos. Now, this was not even interactive, but it was just because of volume. There was enough of these videos that almost any standard topic in a junior high school curriculum, you might come across, you could get really good instruction.

You could see someone like walking through those examples. It seems like such a simple thing, but access to that information was a hugely revolutionary thing. Like it really allowed people to learn math on their own pace or to fill in their you could do whole math classes without having to have the teacher language model based.

I can bring in interrogation, which makes it even better, which is, wait a second. Why did this not work? Here's my answer. Why is this answer wrong? Can you gimme another example of how this might, another example like this? Can you gimme another problem? I think that sort of back and forth discussion, especially math is closer to my world, is gonna be really useful.

The other big question I've been thinking about recently, it goes back to Sam Altman interview, is when it comes to things like. What AI can do for you. Like it can write, for example, if we think about writing an ai, I think the big question is whether we're gonna end up seeing AI and writing in the pedagogical context as like the calculator?

Yeah. Or more like centar chess. And here's what I mean by that. The calculator model is the way we dealt with that is a calculator can do arithmetic. We basically disguised pedagogically. But it's important that you know how to do arithmetic. So you're not gonna use the calculator at first.

You're gonna learn how to do all of this basic arithmetic until you're pretty good at it. And then when we move on to more advanced stuff that needs to arithmetic, you can outsource it to a calculator. So that model with AI would be writing is important for your development. And so you're gonna do writing and you're not gonna let AI write for you because actually you just trying to write is something that you need as part of your pedagogical development.

Later on you can use AI to write faster. That would be the calculator model, the centar chess model. Which is referring to this style of chess where it's a human paired with a computer playing chess. And the human paired with a computer. These are the highest ranked entities there are in chess.

You can beat the very best artificial chess engines and the very best grand masters with a pretty good player and a pretty good. Chess engine, them working together. That's the alternative model, which is no from the ground up, we wanna teach you to write with ai and your writing's gonna be better than you ever would've got to much quicker.

And I don't know which of those is gonna play out. I think this is one of the key things we're trying to figure out now, looking at classic education, how much of this is, there's some sort of intrinsic pedagogical value to these activities just in terms of your own development as a thinker, whether or not it can be automated or helped, and how much of these are just the valuable activity themselves, and if AI helps you do it better, then we better be teaching you how to do it better with ai, like as soon as possible.

We don't really have answers for everything on this yet. I think this is the scramble within pedagogical circles is to figure out. What is a calculator activity and what's a centar chess activity? It's funny you say that because I think you and I were talking about this, what I called the theory of personal knowledge capital which in my mind I basically distilled it into three pillars, which were your accessible knowledge, which would come from like the knowledge you've acquired, say from listening to podcasts like this, reading books your personal insight, and then your individualized expertise, which are all three pillars together that basically form the basis for what I am basically calling personal knowledge capital, which I think is effectively going to be the greatest asset that any individual will have in the age of ai.

Srini Rao: But all of that requires reading and deep work. And I'll give you an example of how this comes into play. So I built a custom GPT model and I programmed it in such a way that I said, okay, here are all my book notes from Cal Newport's book Deep Work. Here's my transcript from his interview.

First distill all the core ideas. Then I was like, here's all the Stephen Kotler stuff. Distill everything here. Now rewrite your instructions so that when we go through our daily planning and that you incorporate all this, but what I think is the key realization there is that if I hadn't interviewed you, if I hadn't read those books, if I hadn't written about those ideas, I would've never thought to even think that way.

Cal Newport: Can I ask you though, technically, what does that mean? So how do you train your own GPT? So what are we talking about here?

Srini Rao: Okay, so basically now chat, GPT has a feature called Custom gpt, where you can go in and build a custom model, specifically designed for some purpose. So I built one for example, where it's okay, take the principles from Jonah Berger's book with the contagious book contagion.

Yeah. Yeah. And then anytime I put in a piece of content, apply those principles to that piece of content. Now does it work every time? Is it perfect? No, far from it? But what you can do, it basically gives you the ability. So what I realized as I was watching it come up with these instructions, I caught this one day where it outlines something and what basically was the objective, the process and the output.

And I was like, wait a minute. That's the f underlying framework for how all these things work. I was like, stop right there and explain this to me. I wanna know why this is the way that it works. And I was like, okay, this makes sense. So that just became my template going forward. So that basically you're giving it very clear instructions, like the thing I've seen over and over when people struggle.

I was like, you sound like a moron when you're communicating with this thing or the way they write prompts. That's when I figured out how to have it ask me questions instead, because when I wasn't getting the response I wanted, I would be like, okay, wait. Or if I go through multiple iterations to get to an outcome, then at the end of that interaction I will say, okay, this was my first prompt.

I wanted this outcome. What should have this first prompt been initially so that I got here faster? Yeah. But as far as programming the models you can go and you can put in instructions, you can actually add custom actions that integrate with other tools. So for example, in my GPT assistant who helps me plan my days and now they have voice interaction was completely changes the entire dynamic for how you interact with ai when you use it on your phone.

I'll have like hour long daily planning sessions that have led to some really insightful ideas. So this custom GPT knows what all my current goals are, knows what all my current projects are, knows what I'm working on. And based on that list, it generates tasks also. Basically when we, what I did was I took your transcripts, your book notes, everything, dumped them into chat, GBT had it distill the key insights from your books and transcripts, as well as Stephen Koler.

And then I said, now take all of this, rewrite the instructions for our daily planning sessions. When I say let's start the daily review and put it all into the objective process output framework. So it basically focuses on deep work and flow. Does that make sense? I realize it's it's hard to explain this kind of verbally, it's one of those things you almost have to see.

Cal Newport: Yeah. So it's like technically speaking and so I'm wondering I have a guess of where this falls. So just like from a straight technical standpoint, there's three ways, like three levels of working with a large language model, like one of the GPT family models. There's the original training of the model from scratch, which is, obviously a huge computationally expensive operation. Yeah. It takes days, it takes tens of thousands of GPUs and these custom built data centers with innovative new air conditioning systems. No one's ever tried to run 30,000 GPUs at the same time, full out before in the same building. That's training these models from scratch and yeah.

And you do that very infrequently. So this is GPT-3 five famously was like, I don't know anything past when I was trained in 2021. Then there's fine tuning, right? Which is you can come in and you're not. Retraining the whole thing from scratch. You're training just certain, retraining certain layers.

So typically you come in and this is often like reinforcement signal. So this is bad, this is good. And it's not retraining. The whole thing is too big, but you're retraining maybe some layers on the front and the back. It's like some of the fine tuning is probably actually building a layer that sort of helps reed the input in a more useful way.

And then you have what I think like most of the application in interaction with GBTs are, which is the model is just sitting there. You can't change the model. But you have software intermediaries between you and the model that's basically bringing data from other places, writing really good prompts for you.

So like when you ask with a. Gemini or something to search the web. The language model doesn't search the web. There's another program that interprets your request, does a web search, gets relevant information, writes a prompt that has that information in it, sends that prompt to the underlying large language model, gets the answer back, and then pulls out what's for you.

And then that's what you see back or when GPT is interacting with another application. The model's not there's a program that's talking to it, getting in, getting thoughts back from it, and then the program locally on your computers, like touching the applications. So it's and it, most of the energy now is in that last level, right?

Because obviously you can't, retraining these things is too expensive. The fine tuning is difficult, right? This is how they do the, all the AI safety stuff is all being done in fine tuning. It's expensive, but not super expensive. But it's really. Unpredictable and hard to do totally.

And it gets all these unexpected consequences like the issues Gemini has where they, you have one goal, I'm gonna fine tune you to be more representative racially when you show pictures, but there's this side effect of that as you say, show me George Washington. And George Washington's Asian or something like this.

It's Right because it's a really, it's a it's a very unpredictable system, this fine tuning. And so a lot of the energy now is these, this language model is so smart with language and it can explain things. Let's just build a. These ecosystems around it. And I think that's like where all the energy for the next year is gonna be, is probably, yeah.

Building software that, yeah,

Srini Rao: totally. It's customization. So for example, right now we're building A-A-A-G-P-T model to basically write tweets for one of my clients based on how she sounds. And I finally was like, okay, I want to do another layer to this. Like I'm adding layer after layer.

So the first thing we did was I was like, I want you to do I created a GPT model that is designed to do nothing but generate psychographic profiles. And I told her, I was like, I want you to do this via audio 'cause you'll talk more. And so the entire model's purpose is to just engage her in an ongoing dialogue.

And I modeled the interaction after the way I do interviews because I was like I'm like, the actual information is not what's important. What I'm trying to get to is get a really large set of like her lexicon and her language and a sentiment analysis. And I told her, I was like, okay, I need you to do it again.

This time focus exclusively on personal life stuff. But then using that data to train it over and over today, the next thing is, it's okay, I'm gonna have the model generate a hundred tweets on its own, and then basically put them into the spreadsheet where I have her written tweets, and then have it do a comparative analysis and try to bridge the gaps that way.

So it's, you also can upload your own files to the back end of the custom GPT. So for example, I can upload a bio or, so it uses that contextually is what it says. I don't know how, it's never really every answer I've asked it to explain this uploading knowledge thing all, it says I can't directly access this, but it, I use it to inform the answers I give you.

And I'm like. Yeah, I'm not seeing that very clearly still. Yeah. Yeah.

Cal Newport: Oh, it's

Srini Rao: fascinating. This world of building what do they call it? Vertical artificial intelligence. Am I using that right? But building specialized artificial intelligence is based on these recent innovations and language models among others.

Cal Newport: Yeah. But specialized on particular things that a particular business or individual cares about. That has to be the future, yeah. That's literally how, what half my clients come to me for now. Yeah. And all the big consulting firms are doing this too. Everyone is doing this now.

Like the, it's the realizing like, oh, I see chat. GPT is a demo of these models can do awesome things. Yeah. Now let's start building stuff that use these models in very specific ways. Totally. Let's get into the book. I think that when we look at this idea of soil productivity, I remember thinking about all the places where I'm like, okay, like, where are the different conflicts with the way that we work?

Srini Rao: You go into three core principles that you introduce at the beginning of the book, which are doing fewer things, working at a natural pace and obsessing over quality. Let's start there. How did you derive those three principles to come up with this concept of slow productivity? So we start with the problem, right?

Cal Newport: So what was it that was bothering all my listeners and readers about the world of work? What was it that I was struggling with to try to still do useful work without having all my time be taken away? And the problem we're solving with those three principles is what I call pseudo productivity.

And my argument is because I'm a technologist, so I often see things through the lens of techno impact. My, my argument is if you look at the history of knowledge work. This emerges as a major sector in the mid 20th century when it emerges as a major sector, they have a problem, which is the way that we had been measuring productivity doesn't work anymore, right?

So in the factory we can do this quantitatively, model ts per input, labor hour, and the farm fields, we could do it quantitatively. Bushels of wheat per acre of land and knowledge work. We couldn't do this all of a sudden, right? Because I don't just build model T's, I'm doing 10 different things at the same time.

And there are different things than you are doing, and you're right next to me and there are 10 different things. And the person next to us are doing and there's no well-defined system that we're all using anyways that we can tweak and experiment with. It's all much more internal and personalized and ambiguous.

And so there was no ratios to measure anymore. And so what we came up with as a, I see this as a stopgap heuristic, was what I call pseudo productivity, which was saying we will use visible activity as a proxy for useful effort. It's come and do stuff, come to the office where I can see you and do stuff, and if you're at the office longer, you're more useful than someone who's here.

Shorter. It was like a simple heuristic, and my argument is like, that was okay. It's not very accurate, but it was okay until we had the digital office revolution. And once we got mobile computing and the internet and we could work from home on laptops when we got email and then slack, and now we could demonstrate visible activity at a very fine grain detail at an incredibly high frequency anywhere and any time.

Pseudo productivity rapidly began to become intolerable because suddenly we were pitched into constantly having to fight this internal battle. At every moment I could be doing something else to demonstrate visible activity that was really unsustainable psychologically as well as physiologically.

It also didn't produce very good results because it pushes you to really prioritize performing activity as opposed to just doing stuff that's really useful. And so this became the problem that those three principles, which are the three principles of slow productivity are supposed to be an alternative to I I.

So I first half of the book, I was like, here's how we got the pseudo productivity. Here's why it doesn't work. Second half of the book, here's an alternative view of productivity. And it's based on those three ideas. Do fewer things, work at a natural pace and obsess over quality.

Srini Rao: Yeah. Let's start at the beginning of the book in that first part because one of the things that struck me most, and I was trying to think about this from the standpoint of somebody whose business relies on producing media on a consistent basis and how this plays into media companies.

And you say that more recently, the slow movement, slow media movement has emerged to promote more sustainable and higher quality alternatives to digital. Clickbait. And then the term slow Cinema is increasingly used to describe realistically, largely non-narrative movies that ward extended attention with deeper insight into the human condition.

And so I was thinking about this from the standpoint of, okay, like we produce three episodes a week. And, but it's funny because I think we'll come back to seasonality later in the book, which made me rethink about how we even do this. 'cause we could still produce three episodes a week and apply your principles.

But it got me thinking how this, like how do you deal with this when you're talking about even a business like the New Yorker, right? And the New Yorker, I know holds their writers to incredibly high standards. Everybody tells me like, you want to go through an emotional root canal editorially, go write it right For the New Yorker?

Cal Newport: Yeah. I think that's slow media right there, right? The New Yorkers motto is their ethic is essentially we have subscribers. That kind of pays the bill. We have this core base of subscribers. They subscribe because we produce like really good, interesting writing.

And so our whole thing is trying to prioritize really good, interesting writing. Like we don't really care that much about virality. We don't care. Oh, like business Insider. Yeah. We don't care about like, how many clicks did this or that get. And accordingly it's def like the process internally is you work on something till it's ready and then okay, now when are we gonna, what's the next slot to publish this?

So it's not even a deadline driven business. It's like the whole thing is produce really good stuff. It's the antithesis I think of hunting clicks in like an algorithmic digital recommendation environment. Where there, it's like you want to throw as much stuff out as possible because you never know what's gonna hit the right.

Combination of zeitgeisty factors that makes it really explode into a bunch of views. And a few wins every month is like what you need to keep the view counts high. And that's a completely different rhythm of work. But it's also the problem with fastness when it comes to media consumption is you get the cybernetic influence of now I'm seeing, I'm getting the strong feedback signal on everything I do in terms of engagement from the audience.

And that really warps how I write and think. And then you get into this relationship with the sort of internet hive mind that will completely mutate or transform the way you think it'll transform your voice. The styles you write in it flattens out idio secrecy and uniqueness and taste and puts people into these statistical clusters of well performing interconnected topics.

And so you have that other side effect of just trying to serve the algorithmic sort of virality hive mind.

Srini Rao: Yeah, absolutely. Let's get into the first principle, which is to do fewer things. One of the things you say is that you should strive to reduce your obligations to the point where you can easily imagine accomplishing them with time to spare leverage, reduce load, to more fully embrace and advance a small number of projects that matter most.

And I, I think that figuring out what the hell those projects are is half the battle for so many people. We

Cal Newport: could start even easier than that though, right? And let's just make a distinction. So I'm not even gonna make you filter too much at first, between what you say yes to and what you say no to.

Let's just make a distinction first of the things you've said yes to, between the things you're working on right now and the things you're waiting to work on. And as soon as you finish one of the current things you'll pull it into the working on now, right? Yeah. So let's think about that way.

Why would even that make a difference? Here's the argument of that First principle is that everything that you are actively working on brings with an administrative overhead. So once I'm working on this, I have to send emails about this. I have to attend meetings about this. I have to think about this.

There's an overhead to everything I'm actively working on. So one of the biggest problems afflicting knowledge workers right now, and what I believe to be at the core of the growing burnout epidemic is that we have too many things we're actively working on. So now, if I have 10 things on my plate and I'm all actively working on all 10, that's 10 things worth of emails.

That's 10 things worth of meetings. My day now is entirely fractured into all of this back and forth communication, leaving very little time to actually make progress on any of these. And this is really deranging, right? We saw this during the pandemic when collaboration got less sufficient because of remoteness, where people would end up with eight hours in a row of Zoom meetings.

I can't even do any work. I can't even go to the bathroom like this. Can't, this makes no sense. I'm gonna have to like, wake up at five or do the work at 8:00 PM or something like this. I have to try to squeeze it in. It, it becomes deranging almost when you have too many things you're actively working on.

So if you just take those 10 things and before we do anything drastic with rethinking our work and like what our priorities are, just say, here's the three I'm actively working on, these seven are waiting. And as soon as I finish one of these active things, I'm gonna pull one of the seven into the active status.

I'm only sending emails and heavy meetings about the three. And you know what, this list is public. It's a shared document. So if I agreed to do something for you and you wanna bother me, check the list first. And if you see, you can watch it march up the list towards the active. Category. And if it's not there yet, you're like, okay, Cal's not working on it yet.

He's, it's getting there. Now you only have three projects worth of administrative overhead. You're dealing with your day. Comparably speaking is gonna seem open. Your ability to actually make progress is gonna be vastly increased. You're gonna get through these three things at a much higher rate than if you're trying to do 10 at the same time.

So when you zoom out, you're like, oh, Cal is like getting things done. Look at this thing after thing or finishing, but you never have more than a small number of things you're actively working on at a time. So you're not paying all that overhead tax. And so your time is not only more efficient, but you're just experience of work goes from like intolerable to, yeah, this is great.

I'm working on these things and making progress. So like doing fewer things at once. Even if we don't get to, like, how you decide what those are is a huge win. So I was trying to think about how this, would play out in the context of my own work. So let me give you the lay of the land on the things that I'm working on.

Srini Rao: So I have the maximize Your Output YouTube channel where I do mam tutorials every week. I have the Unmistakable Creative podcast, which comprises of the blog, the newsletter. So I did my writing there, and then I have client projects. So if you're thinking about this, do fewer things idea there, considering I'm recording three new podcasts a week, publishing one new video.

The podcasts include reading books like Your horse. Like how would, like if you were to prescribe the fir, the first step of do fewer things to somebody like me, like what would you tell me to do with my workflow based on what I just told you? All right. Our big concern here is gonna be there's too many active things that on an ad hoc fashion, can demand your attention, like in communications or meetings, right?

Cal Newport: So that's what we wanna at tame. So there's like a couple things to do. The obvious thing to do is just limits, right? Okay. Less clients at the same time, right? So there's an obvious thing to do there. A less obvious way that will also help is starting to actually time division multiplex this work.

Okay, this is, these two days is it's video and podcast, and these three days is client work, right? And so now you have separation between those two. So I'm using a nerd term, TDMA time division multiplexing. It's the way that you can put multiple radio signals at the same time. You give them, you take turns, different signals get different time.

They don't overlap at all. Now you can think of this as almost two different jobs. In fact, you can think of it as two different part-time jobs. I have this two day a week job content creating. I have this three day a week job doing client work and consulting. And then within each of those jobs, you can apply the principles okay, so I wanna make sure I, within my, my consulting job that the number of projects I'm working on at the same time actively, it's not too big, so that within those days, I have more than enough time.

Then over here, I wanna make sure when it comes to video and the podcast production that I'm keeping that contained. And a lot of that might be automation. I always do this at this time at this day, and I read you're just trying to make sure that you're getting rid of you're avoiding as much as possible, these sort of just ad hoc piling up of, oh my God, I have to talk to this person, or jump on this call.

So you could treat it as two separate jobs and then apply this principle to each but what you wanna avoid is a large amount of administrative overhead. So if a, a. Sufficient fraction of your day is spent tending to the things that you ultimately have to do, but not actually doing the construction.

So talking to the clients, not actually building the custom GPT, having a meeting about your podcast, not actually recording the podcast. Once that goes past a certain fraction of your time, it begins to become very negative.

Srini Rao: Yeah. Yeah. It's funny because I was saying, I actually literally, I, there's this new tool called Super List, and I basically literally took one of the lists and called it the holding tank.

So that I was, so tell me about how this affects your, like when you were applying this principle, for example, you're writing for the New Yorker, you're publishing content on your blog, you're recording your podcast, as a content creator, how do you apply this?

Cal Newport: So my podcast, which is also the source of video, my, my only video is video of the podcast.

It only gets a half day a week, and that's my rule. So it gets a half day per week. That's when everything related to the podcast and YouTube channel happens. If I wanna do more things, I have to find a way to get more outta that time, which usually means I have to bring on someone else or do something.

So it's really slow. It is slow productivity, right? Because I have to wait for the, I had to wait for the show, for example, to get large enough and make enough money that I could bring someone on to take over enough of the show that I could then work on the next thing I wanted to add to it, right?

Because I wasn't gonna increase the time footprint. I had constraints there. So that really that helps. On the writing front, I write reg, I just write all the time. And my whole thing is things pile up. If like most days you're writing and really trying hard to write, there's a lot of days in the year.

And a lot of writing builds up without it ever having to be unusually busy. And then I lean into a lot of seasonality. So if it's a teaching semester, all the writing is gonna be lighter because I'm like, that's what I'm doing. I'm teaching, I'm talking to students. If it's the summer by contrast I might be in the woods of New England somewhere doing nothing but writing and I disappear for a month or two months at a time.

So there's also a lot of seasonality over time as well in my schedule.

Srini Rao: Yeah. It's funny 'cause I think the idea of stuff piles up is really, now I'm starting to see why the concepts of network thinking and smart notes align so well with productivity because of the fact that smart notes are literally like five notes a day.

And that's I realized some, one of the things I began to think about was like, wow, maybe I shouldn't be so hell bent on having an editorial calendar and let what emerges organically drive what ends up being my articles, which have ended up being much longer and much more in depth. But they're published less frequently.

Cal Newport: Yeah. This is what I found, like I have, geez, up to, oh, we're gonna up to 300 podcast episodes soon, but I've never spent more than a half day a week, and like most people have a half day a week. That's like a running hobby or something. Like that's worth of time, but it adds up over the years and things get better.

Yeah. So you could imagine it. I don't know. I love your podcast, like what would happen? Just thought experimenting though. If it was one instead of three episodes now you could have one day a week where you record your YouTube video and your podcast and like the whole rest of the day is just like thinking about podcast to podcast ideas.

This is like an interesting slow productivity question. Would that I. Creator audiences? Or would it be like, actually most people are still around. Like it would be maybe I don't know, have you thought about those type of questions? I'll tell you what I did think about when it came to seasonality.

Srini Rao: I actually literally had mems AI take the principles from your book, and I was like, you know what? I'm like. Actually this could work where I could do I, because I did actually do this for a little while to a degree where there are times where we're three months ahead most times. And like the reason I started thinking about this, 'cause I was, we were starting to run low on new content and I thought, what if, instead of doing this every week, I basically had a season that was just for recording, which was one month.

I did as many episodes as possible in that one month and the next month I did nothing. That was my solution because when you have ad revenue, like publishing less episodes would be a significant dent. That's why I didn't think about that. But I did think about the seasonality standpoint.

Yeah. Which I was like, oh, okay, you know what? I could actually distribute this. And you just got me thinking, oh, if I just dedicated one day a week to nothing but my YouTube channel, that would solve the problem of ever being inconsistent with it.

Cal Newport: Yeah. Oh, and I love that. I love that extreme. I love extreme seasonality, by the way.

Yeah. Which is where I, not everyone can do it, but for those who can. I think humans are really well wired for extreme seasonalities. Like this idea of I'm all podcasting for the fall or the winter or something like this. And then I don't touch it at all for eight months after that.

That's actually way more natural. Than the way most of us work. I love extreme seasonality because I we're used to this in our species history of the winter, super quiet I can't forage because there's snow everywhere. Or I can't harvest my crops because there's nothing planted.

Like the fall is super busy because all the crops are coming out, or this is when the animals are migrating. We're used to extreme shifts. Our mind likes that, like this feels really different than that. And it's one of the points in the book, by the way we forget, I argue, we forget how unnatural it is to say we should work all out eight hours a day, year round.

And that's so unnatural. That's not really the way humans were wired to function. It could be very exhausting.

Srini Rao: Yeah, absolutely. I, like I said this is one of the reasons we do our best of series at the end of the year, so that way I don't have to do anything in December and we are basically stockpiling all the content for Yep.

January and February. So we end up not just freaking out at the beginning. Yeah. So one other thing I think that was interesting here that you mentioned about this is you said the advantage of doing fewer things, however, is about more than just increasing the raw number of hours dedicated to useful activity.

The quality of those, these hours also increases when you approach a project without the hurry, need to tend to many barely contained fires. You enjoy a more expensive, expansive sense of experimentation and possibility. Tell me more about that. Like how, what are the benefits of that? Because it comes down to this

Cal Newport: overhead.

So if less of my time is dedicated to administrative overhead, that means more of my time is dedicated to completing work and in a less interrupted manner. The work's gonna be better. Yeah. You'll do it faster because you have less switching around, but also you can really just dive into something.

Of course, that's gonna be a higher quality work, right? Like I just was working on this. Today. You can really make progress. It lets your brain really get going and all of the relevant information and instincts and knowledge you have to bring to bear, you can go through and really make this as good as possible.

And it's why when people express concern over that principle, do fewer things, my boss would never let me do that. I was like no. You have this backwards. Do fewer things. Your boss is never gonna let you go back to the way you were doing it before. You're gonna seem like a superhero compared to the, to what was going on before when it, in terms of the quality of what you produce and the rate at which things come back.

And I think that's really important is that it's a a weird externality of pseudo productivity culture. That we believe the activity is somehow valuable and we've so ingrained that. That even when we're faced with the sort of quantitative reality, that doing fewer things is producing better stuff, it's producing more stuff.

It's producing stuff fast. Everything we should logically care about if we're trying to make money as a business, even if we're confronted with that, we have the psychology of Yeah, but the busyness is valuable. That's the pseudo productivity mind virus. That is the visible activity itself that matters.

The saying yes, the sending the emails. You start taking that away. How do I even know I'm useful? We've been so cut off from there, there's a Marxist term for this I talked about in the book a German term that translate as its estrangement. We're so estranged from our actual productive output that we fall into these weird thought patterns where somehow the busyness itself becomes valuable and it's not doing fewer things is a much more valuable way of working.

Yeah. It's funny, I remember I just one morning just wrote reflections on slow productivity, and somehow I came up with this, like my insight was, oh, this is a shift from output to production. Like it's a focus on production. Yep. Not necessarily on output.

Yeah. Or another way of thinking about it I sometimes say is pseudo productivity is activity based and slow productivity is results based.

It's look what I produced over the last two years. Ooh, that's great stuff. Where pseudo productivity is look at my activity right now. Yeah. And those two things can be completely unrelated or have an inverse relationship. The busier you are, the worst off you might be two or three years from now when you look back and say, what did I do that I really care about during this period?

Srini Rao: Yeah I remember I wrote this article, I, this was long before your book came out. It was like, why task completion is a terrible measure of productivity. 'cause I realized, I was like, oh, just knocking off tasks on the list doesn't really mean a whole hell of a lot if those tasks aren't leading you towards anything.

Cal Newport: And this was not there. Go ahead. Just a quick interjection, but this was classic David Allen. Getting things done which led me to go back and reappraise getting things done. Because if you've read that book, and I don't know if your audience is super familiar, but it was the first major productivity, professional productivity book really to come out after the front office it revolution, right?

So like the big books of the early nineties and late eighties, it's like Stephen Covey, it's Brian Tracy. It's very optimistic. It's self-actualization. It's, you're gonna figure out the perfect mix of activities that's gonna fulfill all your dreams. And then we get to 2003, we get getting things done and it's very dark and nihilistic.

It's often. People misidentify it based off its title. We see this all the time in sort of critiques of productivity. People assume based on the title, getting things Done, that this is a book that says what matters is getting things done, and how do we get more things done? It's not about getting more things done, it's about psychological survival in a professional environment of drowning.

Like the whole goal of the getting things done methodology is finding moments of zen-like peace. Amidst the onslaught of work, right? So it tries to just take all emotion outta work, reduce things down to generic next actions. You just crank widgets. The whole thing is like, how can you just go through your day without having to be stressed, yeah. And it's like the opposite of, there's no word in there about how do you produce something great, it's just how do I just get through this onslaught? With having some moments of peace. And I think that book is so important because it is the that is the beacon, that was the warning that work had changed.

The tone of that book and what was different between the world when that book came out and when First Things First and Seven Habits of Highly Effective People came out, what's the difference between those two worlds, computers, laptops, email. That's what had changed in the world between those two books.

And that is the core about how most people are still thinking about it, is I'm trying to get through tasks. I'm trying to crank widgets. I'm trying to automate my tasks to the point where I don't have to think about it. We're trying to convert ourselves in the factory machinery, not because that's desirable in an abstract way, but because we were trying to find peace from how stressful it was to have so much work on our plate.

Slow productivity is, we could do better. We need to stop the onslaught itself, not figure out how to numb ourselves from its side effects. I think that makes a perfect segue into this other part of the book where you broke sort of these timescales down into missions, projects and tasks. Can you explain that and explain how we integrate that into the slow product principles?

So once we understood doing fewer things is better, right? All of the tactics become about how do we do this? And one of the things that I realized thinking about how do we reduce the number of things we're working on is that different scales of commitments matter here. So like work doesn't all just come in at one scale, just here's a buffet of things and you just say yes, no, yes, no, yes, no, yes.

Like actually, especially for more autonomous workers. So for entrepreneurs in particular, this really applies, right? What we really have is a hierarchy. At the top, we have missions, right? These are the big things I'm trying to do with my work. The Mission Spawn Projects. Alright? Here are projects I'm working on to advance this particular mission.

The projects span daily things we have to do, the tasks, the actual things that we're having to do each day. And so if you wanna work on fewer things, you have to reduce all three levels, right? That's my argument there. If you don't, if you have too many missions, that's gonna necessarily create too many projects in your life because you have to have some projects active for a mission, otherwise, it's not something you're trying to do.

But these projects are all now active commitments that are generating the daily the daily tasks, the administrative overhead, and causes the problem. You have to start by limiting the missions that then allows you to limit the projects. Okay? If I only have one mission, now I can say, don't do seven projects about this mission.

Let's do three. And then once you've limit the projects, then you can be careful about limiting what you work on each day and say if I have fewer projects. Lemme just work on maybe one a day, or first half of the day is for one, the second half is for the other. Now you can limit your daily tasks.

You have to consider the whole hierarchy if you're gonna start reducing the number of things you're working on and the variety of things you're working on in any given day.

Srini Rao: Yeah, it just got me thinking. I was like, when you said that, I'm like, oh, okay. So I would basically spend the mornings working on writing and reading for unmistakable, creative and interviews, and then the afternoon will be dedicated to all things related to maximize your output.

I think that's

Cal Newport: a, I think that's a fantastic way of thinking about it. Or yeah, morning is, or you could imagine morning is content, afternoon is clients, yeah. Like there but this requires, reductions up. Because if you also said, let me add three other missions I'm also gonna build some custom software.

And I also want to do, I don't know, whatever people do, I'm gonna have a really big Instagram channel. And I also wanna start a speaking a speaking career. And I wanna, like you, if those missions grew, I. It would be impossible for you to then when you get to the layer of the day to be like, I'm just doing this in the morning and this in the afternoon.

That's impossible when you're servicing 20 projects because those projects are being fed by seven missions. So you gotta reduce all the way from the top down.

Srini Rao: Yeah. Talk to me about two things as it relates to this. What you call the push versus pull workflows and reverse task lists. So the push versus

Cal Newport: pull, this sort of goes back to what I was talking about before about you pull onto your plate what you wanna work on when you're ready to work on something else, right?

That's pull, push is anyone can push a project onto your plate and when they do, now you're working on it. So push leads to overload, right? Because with push people just push stuff at you when it makes their life easier and it's hard to say no. And you end up with lots and lots of stuff on your plate and you get the overload problem with poll.

You're like no, I only have this many slots. I pull things onto it when I finish something, right? It's an idea that comes outta manufacturing that software developers pick this up with Kanban, which they adopted from manufacturing. It's like well known in industrial processes. The advantages of pull versus push.

I'm saying we should use that in knowledge work as well. And one of the ways to do it is like I talked about before, which is when stuff gets pushed at you, you say, you're not allowed to push that onto my active list. Sure, you can push this at me. It's going over here, right? It's going over here on the list of things that I'm waiting to work on.

I'm still only working on three things at a time. And if you want me to work on this first, let's say you have authority over me, you're my boss. Great, here's my list. You tell me what to take off my active list and swap with it. And I'm happy to do it, but these are the three things I work on at once and I'll pull it in when I'm done.

Yeah,

Srini Rao: I felt like there was some overlap here between some of the ideas in world without email in here. Like for example, like we almost, our entire podcast booking process is completely automated. Like I, I try to limit my interaction. The only thing that still drives me crazy is I can't get my book publicists who work for publicity companies to not email me.

And I tried everything possible to get around that. I still haven't figured it out. I have succeeded.

Cal Newport: Let me, lemme tell you my secret, 'cause I, trust me, I succeeded on this and I, I might've benefited in part because I innovated this when working on a world without email. Yeah. So the publicist might have thought okay, wait a second, this book is about email.

We gotta be careful. But what I figured out with publicists, so I'll give you the, my secret is we used a shared document approach where it's this master document. That includes everything that every interview that we've agreed to, all the information is in there, and it's chronological.

So you can go forward to a date and it's oh, Cal's talking to Srini, and here's all the information and here's what you need to know at the top of this document is pending, right? So my, my, my publicist will add stuff. Hey, here's things I think we should do. We need to schedule it.

Here's all the information I can give you about the schedule. Here's five dates that could work. Or they have a scheduling software so you can use this link. And I let that build up for a day or two. And then when I get a chance, I'll sit down for an hour. Go through all the pending stuff and I'll just write in there this date works.

I'll just highlight it. That date works for me. I scheduled this using the link, here's what I did. And I go through there. And then my publicist sees that and updates everything. And every week she emails me like, here's a reminder of all the things you're doing this week, and I can look ahead, but we never send emails back and forth.

Srini Rao: Okay. I'm talking about pitches from publicists, from publicity companies who are pitching me to have guests on the podcast. They're all coming from like hundreds of people. Oh, those

Cal Newport: are the worst.

Srini Rao: I've been killing myself trying to like, it's the one thing that, and the thing is, if I say here's a link to our contact form, half the time they won't fill it out because we've intentionally made our contact form a royal pain in the ass.

Which I partially blame you for that. Yeah. But you did the right thing there. Yeah, no, of course. 'cause it makes people work. Like literally it redirects. If they say they've never listened to an interview, it redirects 'em to a page saying, sorry, we don't accept people who've never listened to our show.

Cal Newport: But here's the thing about those publicists is they don't care, right? Because what are they actually, a big thing they're selling is here's how many people we reached out to. So they just wanna, that's the problem. I get a lot of these pitches too, and I'm like, I don't think they actually, I think they wanna just say we reached out to a thousand people.

They're trying to take contact. I love that you have the complicated contact form still though, by the way. I think that's, yeah. This sounds like a problem, by the way, that how far can we be from AI solving this? We must be pretty

Srini Rao: close, right? I've been thinking about it. I, so like the closest thing I've come to a solution is I have to go through my inbox.

So the productivity officer at Google was here as a guest. Her interview will Air, she has a new book called Uptime and I think you'd actually probably like it. But she had a really interesting way of breaking down lists. So I was trying to figure out what I'm gonna do later is connect slow productivity to that idea where she called the list funnel.

But her email sorting. By far was like, made the price worthwhile. And she said sort email, like you sort laundry. And I was like, that is the most brilliant thing ever. So it's just two inboxes stuff that you reply, you move it into reply, and anything else gets deleted. And then there's revisit and that's it.

That honestly has got it down to 10 minutes a day to deal with things. But ideally, I don't even wanna do it via email, but I can't get them to fill out my form. Like it's, that's the biggest problem. It's it's just annoying. What about,

Cal Newport: and I love brainstorming with you about this.

I love the intersection of productivity of these tools. So hopefully the audience will indulge us. What if you had, because this, you could convince 'em to do, I'm thinking about the same thing. This you could convince 'em to do is this is the email address for pitches, right? Yeah.

Because then that just gets into their list, right? They just keep these things and then there's gotta be some sort of. Zapier script type setup. Oh,

Srini Rao: that, so I've already done that too. You're already, oh, tell me. Preach. I wanna hear. I've tried that. Okay. So this is, so I realized at a certain point I was like, oh, I could, I, if I literally just added a label to every pub publicist pitch, it would just go into my Airtable database and I could review it from there.

The problem was that like I'm not, I wasn't as diligent about it. I could do that. But the thing is then, like you have to, it, you can definitely manage it that way. I can set it up so that it replies to them directly from there. And I don't have to actually do it. It I can just, you're making me think about a way to make it more efficient.

The other problem is I have one email address that has been on these lists for God knows how long. That's the problem. And that is the biggest problem. It's I, but other thought was, okay, you know what? I think I need a filter in Gmail or a label that based on whatever these domains are, every one of them gets put into cause I, at this point, I know who it's gonna be.

Pretty much. It's like the same, probably a hundred or so people. Yeah. This is

Cal Newport: where, okay, then as a final step, you start connecting. Here's what I'm wondering. Can we connect Airtable to a language model? Yeah. And whenever new entry comes in, it basically, you have a really good prompt design for it.

Okay, here's the things we're looking for, blah, blah, blah. Here's a description from a publicist pitch. Hey if this is something that seems would be a good match for this show I just described, please put the the name of the guest and the, gimme the name of the guest in the email, contact for the publicist and the publicist name in this format.

And then the script takes that and makes a different Airtable entry for good guest ideas that came in this week, and it's just like their name and whatever. Now we're probably talking the time to do this is much more than just reading the emails.

Srini Rao: Yeah I can build that pretty quickly.

Like I actually went and outlined an ai, like A-A-G-P-T podcast research assistant who's entire job just to do exactly what you're talking about. I love it. All right. Yeah, this

Cal Newport: is great. This is great. I love, so can I I keep de I, I know I keep detouring you from the book, but it's just 'cause I love talking with you about the tech intersections here.

Because I think there's an inters, there's a connection here to slow productivity. So let's write, I'm gonna write the chapter on the fly here about slow productivity

Srini Rao: in

Cal Newport: ai.

Srini Rao: Yeah. That's I think that was inevitably gonna be a topic. 'cause I was gonna ask you about that anyways.

Like where, yeah. So let's write

Cal Newport: that. Yeah. Like they could have put in the book because it's too long of a cycle, right? It takes too long from when you write a book to when it comes out and everything has changed or whatever. But we can we can write it on. That might be a worthwhile blog post for your blog.

There we go. Yeah. Like a New Yorker article or something. So this is where, okay, so where is AI gonna help slow productivity? And what I wanna focus on, and it's not really where AI is focusing now, but I have some thoughts about how to get it there. Yeah. Is I wanna focus like a laser on this issue of administrative overhead.

That's the villain in the book is the emails and the meetings and the discussions that surround the actual work. Make it really hard to do the actual work cognitively. It's difficult, it's exhausting, it takes longer, whatever. This to me, seems to be a place where AI could have a really huge impact on people's wellbeing and productivity.

We often think about the AI directly doing certain things. Or maybe like speeding up a task that we're doing. But like where the, I think the huge win for slowing down productivity is taking that stuff off your plate. If everyone here's the dream and there's a few technological hurdles that we're far from leaping to get here, but to me the dream is everyone has a digital equivalent of the Leo McGarry character from the West Wing, a chief of staff, like a really good chief of staff.

'cause the president right in the West Wing, the president doesn't have to do overhead related to work or scheduling or anything. They just say, okay, Mr. President, here's like what you need to work on next. We brought you everything you need to read. We need your signature, we need your order.

We'll take that and we'll go act on it. Like, all you need to do is take an information and make really produce really smart stuff, right? That would be, there's a techno utopian dream there. A dreamer you know what's crazy? Touched

Srini Rao: an

Cal Newport: inbox.

Srini Rao: You know what's crazy is I can literally take the text from your transcript and have my executive assistant, my AI assistant outline how to build exactly what you're talking about.

In minutes, but there's an's another layer to Yeah go ahead. Because there's some issues, there's some issues to this that we haven't solved yet. But, alright, go on. Let me hear, lemme hear. There is another layer as far as slow productivity. So I wrote this four keys to success in the Age of ai article, like this mega article, which we turned into a free ebook, which people hear about on our show all the time.

And one of the things that my, one of my friends was a PhD in AI from Berkeley, and he was just watching me and he said, dude, he was like, you have a completely different way of thinking about this. And he said he gave me the language I needed to put around it. And he said, most people are stuck in a better Google paradigm where AI is focused entirely on execution.

And this is where I think it overlaps with slow productivity and deep work is using it not for execution, but for exploration. The ability to go in directions you never would've even thought to before. That to me, was the real magic of this. When I saw that, I was like, wait a minute. Instead of doing better, doing things better or faster, we should actually be focused on doing better things, which actually aligns well with slow productivity.

Interesting.

Cal Newport: So you see the, you think one of the big underappreciated AI intersections with work right now is it can help you do your work better. You could be smarter, you you can literally

be

Srini Rao: smarter. Yeah. Yeah. I think you can do things that you were never able to do before. I created I wrote a custom children's book for God's sakes.

I don't know anything about children's books. And I was able to use knowledge from interviews from different pieces of information. I think it, it can be a really powerful tool for divergent and convergent thinking, which allows for more innovation, more creativity. Then that in my mind is where we're actually missing the boat.

I think execution is like the better Google layer. It's I can do this faster. I can do so yeah, all that stuff helps, but I think it, it's the first layer.

Cal Newport: Okay. So what about first of all, I love that, right? If we go back to our metaphor from early in the show, this is like the sin to chest.

Metaphor versus the calculated metaphor. Exactly. It's not just, Hey, this thing you did before, we could do this for you so you can free you could focus on the calculus and not on the multiplication you have to do as part of doing the calculus. This is, you're saying now I think that let's double barrel this then.

Now imagine a world where the time to work on this type of stuff is more because you're automating the administrative overhead. And then in this time that remains like, now lemme just like work on creating new things. You are brain boosted with AI as well. Yeah. And now, so you, so the AI gives you more time to work on like the high value stuff and then a different AI makes you smarter at doing the high value stuff.

Now you're getting like a double whammy. Totally. In terms of the productivity hit. Now the issue is with my, part of the vision is my test is. AI could clean my inbox for me. Yeah that's a Rubicon, right? Language models can't do that by themselves. And this was the article, my most recent New Yorker article was like looking into why.

And it has to do with planning, right? So language models are static and feed forward. They don't, they can't, for example, simulate possible futures and try to understand, explore possible actions. It, everything is feed forward through very complicated guidelines and pattern recognizers, but it's all hardcoded in advance.

So this is why there's this interesting whole literature of these models are great at everything except for like simple planning tasks that require looking into the future because they can't look into the future. But. That's fine because the direction AI is going is great. The language model's great at language and understanding language.

We have other types of models that are good at simulating the future. Let's just put those two together and then the language model can talk to this other model and say, now you simulate. That's what's gonna be key for things like a generic email answerer, right? Because in order to answer an email, you have to figure out.

The what's being requested, and then you have to explore possible, Hey, if I do this, what's gonna happen? Actually

Srini Rao: that might upset this person. Yeah. You need contextual awareness. That's I, that's another area I've research is how do you build emotionally intelligent ai? Because I, I thought about this in one of my explorations.

I was like we could theoretically, if we can do it with words, we could codify facial expressions with large enough data sets and conceivably build something that appears to be emotional intelligence, but is actually not. Yeah. But the contextual piece of it, I think is where the biggest problems lie in a lot of issues with ai.

Like it, that's, that, that's why to get something that automatically deals with your inbox. Like something that understands exactly what I look for in a podcast guest. I don't think an AI can do that.

Cal Newport: Yeah. Forget the Turing test. What we care about is the inbox zero test. But the thing is what the industry, and the industry knows this, but like the average person doesn't necessarily know this.

They're thinking just being taught by the last year and a half. They're thinking like, oh, the strategy is to take a language model architecture and just keep making it bigger and bigger, like we did going from three five to GPT-4. But this is as you just said, that's basically what you were just talking about there, this idea of taking that architecture and just giving it more and more data to, to allow it, it has complicated enough feed forward rules now that it can capture a lot of common scenarios that it might see in email.

It's like a really inefficient way to do this. Actually, no. The right thing to do is to have probably a much smaller language model. And then you have another model that just simulates humans. And the cool example, so this is what I wrote about on this New Yorker piece. Is Nolan Bloom and his team at Meta AI built a system that could beat human players at the board game diplomacy.

And the board game diplomacy is interesting because it's a mix of a strategy board game is like risk, but the whole key to the game is human interaction. So what you do in diplomacy is before every turn, every player has a private one-on-one conversation, and you make plans and alliances and double crosses.

And I'm telling you like, I'm gonna support you on this attack. But then I tell the other person, I'm gonna pretend to support him. And then when he's weak let's come around from behind and take over. Russia. It's all about interpersonal negotiations. That's why it's called diplomacy. And they built an AI that could win at it, do really well.

They, it was playing on web diplomacy servers where you could play over the internet. The people didn't know they were playing against the machine. It did very well. It won 80% of the time. And the way they did this. Was not trying to build a bigger like a language model that has seen so much diplomacy that it just knows what to do in every scenario.

They took a modest sized language model. We're talking like 10 billion parameters that understood language and they fine tuned it on diplomacy so it like knows how to like, talk about diplomacy. And then they took a planning engine, right? Because no one Brown who led this team was came to fame beating poker.

He built the first bots that actually beat professional players in poker, which is all looking into the future and planning. So they talk to each other. So the LLM takes the messages from the other players. And it can interpret here's what I think their intentions are and it writes it in here's what this means, right?

Here's what this means. And it can write it in a common language that the planning engine understands. Here's what they're asking. And then the engine simulates the people. Okay, so what if this person's being dishonest and I do this and this, and it simulates all these possibilities for the future.

It simulates their minds because Nolan Brown, the way he built a robot to win at poker is you have to understand, you have to simulate what the other players believe. That's how you win at poker. It's not the cards, it's what other people believe about your cards. So it simulates the minds of other players and explores possibilities.

This is our best way forward. And then it tells the language model, here's what I wanna do, say this in the right language. Yeah. And those two things working together. Could win at diplomacy. So that's gonna be what, how we win the inbox zero test is gonna be three or four models working together.

Yeah. Not from one model that we built like

Srini Rao: gargantuan size. It's funny you say that because I remember thinking to myself, I'm like, I have no ability whatsoever to build this, but I'm thinking, I was thinking this was in that like 40,000 word dialogue with Chad GBT. I was like, wait a minute. The biggest issue now is that different ais can't communicate with each other.

Like we need the equivalent of a Zapier or something that connects different ais together, like AI communication layer. Yep. And and I was like, that's a trillion dollar company and I have no idea what is involved

Cal Newport: in building that. But you know what, it wasn't that hard for this particular system, Cicero, because what they leveraged is it's very easy to ask an LLM.

To put things in other formats. So like they put all the hard, LLMs might be the glue that makes this happen so they could make the planning engine that simulated other minds. It only understood like a very specific language, right? Okay, here they had this very specific language Italy wants you to do this or whatever, but it wasn't that hard to teach an LLM say what you just said, using this format and you explain the format.

And so then like the LLM did the hard work of speaking the incredibly narrow language. So I wouldn't be surprised if there's like a control program, but it uses an LLM to speak between things. And like a small LLM, it's just like really trained on, I don't know. It's cool what's coming.

And by the way, open AI just hired Nolan Brown away from meta. To build planning into their models, right? So like they know this is the whole QStar project, this whole secretive QStar project that was claimed to be maybe what got the board so worried when they temporarily fired Sam Altman over safety concerns.

It's not quite true, but this QStar project is almost certainly no one brown. Building planning engines to connect to open ai, LLM. So like really interesting stuff is coming down the pike. And so people know this in the industry, it's the combination of these models. That's where all the action's gonna be.

Srini Rao: Let's see if I can take your transcript and get my own GPT model to help figure out this inbox zero problem at some point. That might be, that's that. You know what's interesting is I even echoing some of what you said here, I've taken entire transcripts and said, I want you to take this transcript, the book notes from this guest, and I want you to create an interactive course that adapts in real time.

I. To whatever the user's problems are. Interesting. And it took five minutes to create that. I was like, okay, wait a minute. We could do this like hundreds of times over. Yes. And it's still not perfect. Like I, I did it with one interview and I was like, wait a minute. This actually could work.

It would really change the way that we consume content. Yes. But let's get back to the book. Let's I think we went off on a pretty large AI tangent here, which is related, but let's get to the next piece, which was to work at a natural pace, which you actually say The great scientists of the past era would've found our urgency to be self-defeating and frantic.

They were interested in what they produced over the course of their lifetimes, not in any particular stretch. And you gave us the example of Lin-Manuel Miranda, which I think is probably the most relatable to our listeners. So talk to me about this principle as it relates to what we can accomplish with it.

Cal Newport: There's really two aspects to it. One aspect to this is this idea of uniformity of effort, right? So we mentioned this before, but this idea that you should just work at full intensity for the whole workday all year round is really unnatural. It's not the way humans work. There are much more, there's much more variations on different timescales, busy parts of the day, less busy parts of the day, busier weeks versus less busier weeks, busier seasons versus less busier seasons, like that's what we're wired for.

But pseudo productivity pushes us towards this industrial metaphor of like, when you get to work, you should just turn on and be working really hard, all hopped up on caffeine until the day's over, and that's just all year round. So the more natural pace has variation. The other piece to a natural pace is just the timescale in which you measure productivity, and that's where the Lin-Manuel Miranda example comes in is pseudo productivity pushes us to have a very narrow timescale for measuring productivity.

What have I done in the last couple of hours? Like the more things I did, the last couple of hours, the more productive I am, where the more natural scale. It's what did I do this year that I'm proud of? If I look back over my thirties, what are the things that I did in my thirties that I think really made a difference?

And when you measure productivity at that scale, this idea like, I need to be busy right now, makes no sense anymore. In fact, like being busy all the time is probably gonna get in the way of that. And so that's what Lin-Manuel Miranda did with his first play in The Heights. He spent seven years working on that.

Like he spent a long time working on that. So in the moment you just zoomed in on like a 24-year-old Linman, Mel Miranda, and you're like, man, what are you doing? What? What if you, you're not busy. You're not busy right now, but you zoom out now to 45-year-old Liel Miranda, you're like a Li Mel Miranda.

Wow, these two plays you wrote were just fantastic. You're like a very productive, respected playwright. So he was working on a much bigger timescale than what most of us do, and we measure productivity.

Srini Rao: Yeah. We also heard about Jack Kerouac and On the Road, right? It seems like there's, we mythologize some of these things in pop culture, but you shattered a lot of those myths.

Cal Newport: Yeah, there's this famous myth that he himself helped spread that he wrote on the road in this one binge of writing. And the a good myth needs a good detail. And his detail was he was using teletype paper, right? So that's it's all connected, right? It's like a big long roll of paper.

So he could keep typing without having to stop the change pages, right? So it gave this impression of no, he didn't slow down. He went after it. He wanted to write and he just wrote, and he wrote until he was done. And there was the book. It turns out like no. He spent six years on that book, like trying to make it work.

This wasn't working. Publishers weren't interested. What if I came at it this way? There was just like some period in there where he like wrote a bunch of words in a two day period. That wasn't the book though. He spent years trying to figure out how to make this thing work and that's why he produced something that was really epic defining at the time.

So he also. His real productivity was measured on a much bigger scale, even though he's often incorrectly used an example of productivity being about intensity in the moment. Yeah. Like I got to thinking about even my first book Unmistakable, like to get to the point where I could write that book was seven years in the making.

Srini Rao: And when I started thinking about it, I remember Simon Sinek once told me he was like, your why is that you're obsessed with people who are good at unusual things? And I was like, that's great Simon. What the hell am I supposed to do with that? Which 10 years later I look back and I'm like, oh, you're right.

Everything I've done is a reflection of that idea. Yep. But like when I think about it over that scale, it makes a lot more sense now that Oh, of course it took seven years to write that book.

Cal Newport: Yeah. Even though I'm sure there was years in there, months and theres or days in there where you're like what am I doing?

And Lin Memo Miranda, by the way, was writing restaurant reviews in there. Yeah. He was touring with his freestyle rap group. Love Supreme. And substitute teaching. So you could zoom in there and be like what are you doing? But what was he doing long term? You're like no, I'm working on this play.

Yeah. And it's gonna take me a while to get this right, because I wrote the first version of it when I was a sophomore at Wesleyan. I don't have the creative maturity yet to make this worthy of Tony's on Broadway, but I'm gonna get there. It's gonna, I'm gonna keep coming back to it and we're gonna keep making this better until it clicks.

And he didn't give up on it, but he also wasn't all out busily working on it till he was done. It was something that

Srini Rao: took years. It's funny 'cause like I, you, I told you, I've been like toying with this idea of a book called The Network Mind. And I'm like, okay, I'm not a neuroscientist, I don't have a PhD.

And literally all I'm doing is just whatever ideas come to me, I'm just writing about them every couple of days. Yeah. So you're starting the work. Yeah. Yeah, exactly. Let's go to principle three, which is the idea of obsessing over quality. You say obsess over quality of what you produce, even if this means missing opportunities in the short term.

Leverage the values of these results to gain more and more freedom in your efforts over the long term. And it like, this got me thinking about one trade off, which was okay, as a blogger or content creator, you could create blog posts or you could write books, but your payout your, the trade off is you might sacrifice traffic in the short run in order to write an amazing book in the long run.

And the other thing that, and I don't know if this is entirely true, maybe it's a rumor, but rumor has it that Mark Manson was something like eight months into the subtle art of not giving a fuck, and basically told his publisher he wasn't gonna make the deadline and start all over. I could believe that.

Cal Newport: I don't know. I was actually just out there. Physically I did Mark's show, so I, yeah, I could have asked him about it. But I know, I don't know for him, but I do know, I won't use his name, but a, an author of a very successful famous book who for sure did this because I remember talking to this author who remain unnamed when they were like coming up to the deadline of their book proposal being due, and this isn't working.

I think I'm gonna do this instead. And the thing instead is like millions of copies sold later. It was just, we're gonna make, we're gonna make this thing work. It's another thing that people often don't realize, but it's true, is that like a lot of writers I know who are like. Writing nonfiction books and are pretty intellectually famous, right.

Columnists for the New York Times, et cetera, they will often, they'll get these book deals and they'll just blow off the deadlines for years. That's actually really common. I didn't realize you can do this, but it's really common among people who are in the elite idea space that like, it's not right yet.

And and publishers are just used to this five years might go by, okay, now I'm finally, now I have the idea write to write this book. That's really common because their standards are so high for quality. They're like, this isn't here yet. The person I think of when I, when you mentioned that is David Brooks, because we had ME at the beginning of the year and I, like David Brooks is one of my favorite writers.

Srini Rao: I think that he takes very deep, very complex ideas and he makes them very accessible. And I'm like, God, the amount of work that has to go into this is probably the obsession over quality.

Cal Newport: Yeah. And he's used to the thing about Brooks, and this is what's great about having a column like he does, it's like he's intellectually weight training every week.

So all he's doing is trying to make complicated ideas that are relevant to people accessible. And he's practicing that every week at the highest stake level, which is like in the pages of the New York Times, which, which is so widely read. And then he brings that over to his books. That's why his books are so good.

So now he has this this is an idea from that final principle, obsess over quality. One of the first ideas about how to do this is forget about work first, let's work first on your taste. Let's improve your understanding of what's good. And that's what David Brooks does, is he's done that column so long that he has this incredibly high standard now of what this type of writing can be.

Yeah. And so by the time when he's ready to produce a book and he says it's ready to go, it's gonna be a lot better than if you or I tried to write that same book because his taste, his standard for what this could be is just a lot higher. That's why that was one of the first propositions in that chapter was the first thing to do is to actually get better.

Understanding what's good in the field that you're performing in.

Srini Rao: Yeah. There are two other people that you mentioned in this chapter. One was Juul, which I didn't even know that, and I actually added her memoir to my list of reads on Amazon of things to buy. And then you also mentioned Steve Jobs and the Return to Apple.

So talk to me about how those align with this third principle of slow productivity. So the,

Cal Newport: when you obsess over quality, there is a two-pronged connection back to avoiding busyness and slowing down. That's why I think this is the foundational principle and those two stories capture both.

So the first connection is the more you care about doing something really well, the more it just becomes natural and instinctual that you need to avoid busyness and simplify. Like that, that become, instead of something that you're trying to convince yourself to do, it becomes something that seems as natural as anything in the world.

So this was Jewel's case as she's coming up, she's living out of her car, but doing something special in these performances. At the interchange coffee house in San Diego, the record executives start flying her out. They put a million dollar signing bonus on the table and she turns it down because she's wait a second.

I need to do music really well. If I'm gonna make back a million dollar signing bonus, I'm not good enough yet to do that. You're gonna drop me. This is not gonna succeed In three months, you're gonna drop me and that's my shot. No, I don't want the million dollars. I need more time to be signed, but I'm gonna cost you a lot less money to learn how to do this much better.

I need to simplify and take longer. Steve Jobs, of course, famously when he returned to Apple, said, okay, if we're gonna do making a computer company successful, we gotta simplify. We have way too many models. We gotta simplify. We have two consumer, two pro models, one desktop, one laptop for each, right?

We have to simplify if we wanna do what we wanna do really well. But then you have the second prong, which is once you start focusing on doing something really well and get better at it, you get more leverage, more control over your ability to simplify things, right? So this was like Juul after her first major international tour said, I don't want to do international tours anymore.

And you know what? I'm really successful now and I have a lot of money. I don't need to do international tours anymore. I can simplify my life because I have control and leverage now. So it's this like flywheel, that's fantastic. You start caring about quality, you begin to care about avoiding busyness as you get better with what you're doing because you care about quality, you get more and more ability to avoid busyness and simplify and slow down.

And that flywheel, I think can be like the main propulsive power source for slow productivity. You have to have this final piece. Really caring about quality. If you wanna sustainably succeed with any of the other ideas.

Srini Rao: Yeah,

Cal Newport: absolutely. I'm wondering, so you mentioned a couple of other things and this was one of the places where I thought to myself, I'm like, okay I agree with this.

Srini Rao: So this was really interesting where you said the general idea that quality tools can increase quality of work. Your work is not unique to my early academic career. And it's funny 'cause you mentioned the aspiring podcaster buying the fancy recording, like the one that I'm using now.

And I remember I told so many podcasters not to do that because I was like, wait, you're basically spending a thousand dollars on something before you even have an audience. But I think I understand what your point is. 'cause you're right, like the thing that this reminded me of was when I take a Ramit Seti course, his courses are like $2,000.

You can damn well believe that I've completed every model module in every one of his courses. I'm like, I'm Indian, of course I wanna make my money back.

Cal Newport: Yep. Yeah. It's true, right? Like if, and there's a balance here. Yeah. So there's a balance. Like my example was buying a $50 notebook when I was like a poor postdoc.

That was a stretch for me. Now, I didn't buy a $4,000 computer. That's too, I, that's too much. That's not, I'm not gonna get my money's back. But the $50 notebook versus the $3 notebook, I took the notebook more seriously and I took better notes and focused my thinking more like for the podcaster, like maybe you walk the difference, the, you don't get the a thousand dollars condenser mic but maybe you upgrade from the USB mic to the $350.

Sure. Run through like a hundred dollars cloud lifter or something like that, right? Yeah, you don't wanna, you don't wanna stretch too far, but you do wanna signal to yourself like the simplest version of this comes from Ginny Blake, is pay for the non-free version of the software.

Yeah. That's the simplest signal to yourself. It's okay, I wanna write screenplays. Just buy the fi version of Final Draft. Don't like use the free preview. I wanna write a novel. Just pay for Scrivener, just get the tool that, like novelists typically use to do this. I'm running a business, I'm gonna pay for the bookkeeping software, not the like free version that I can have some features and it's $20 a month or whatever, but that's probably the most commonplace where you signal to yourself, I'm taking this thing seriously.

So it's like you put your money where your mouth is, but not a lot more money than you actually have.

Srini Rao: Yeah. So the third thing you mentioned is leveraging your social capital. And you say if you announce your work in advance to people you'll have created expectations. If you fail to produce something notable, you'll pay a social cost in terms of embarrassment.

And just revisiting that, like I'm thinking back to Ryan Holiday who basically says he never talks about a book until it's done. But I'm guessing that this is nuanced. And he does though, because he signed the deal to do the four cardinal virtues. And so then it was announced, I'm writing these four books, and by the way, he had, he delayed the most recent one.

Cal Newport: And he is talked about it real frankly and publicly on his show of you know what? I needed more of a breather, so I'm gonna delay this one by another year. One of the examples of this in the book was the Beatles, and they used this when they were recording Sergeant Pepper to defeat perfectionism, right?

So when they were recording Sergeant Pepper, the problem was not procrastination, but perfectionism because this was right after they decided to stop touring. And there was a side effect of that. By deciding we're not gonna tour anymore meant the songs we record in the studio do not have to be replicatable on stage, which meant anything goes.

Now you could do anything in your albums, right? You could bring in any instruments, you could bring in brass instruments and Indian sitars and use tape loop effects. It doesn't matter because it's just gonna be in the album. So you could endlessly sit there in the studio. And perfectly try to make this more and more interesting and weird.

And so how did they avoid doing that? Is they released a single pretty quickly and now like the public was like, Ooh, we're waiting for the album and we're only gonna wait so long. And so they could still really try to make this really good, but now they had some time constraints on themselves like, okay, but we still have to finish because we've put our stake in the ground.

It's we can do this individually. Once you get going on something and you have all the right tools and it's important. It doesn't hurt to tell some people, I'm writing this thing and I'm gonna give you a draft of the manuscript when I see you in December. Like you put some stakes in the ground, so now you actually have to follow through.

And to me, honestly, we worry about procrastination, but for most people, like once you get to a certain level, it's perfectionism that gets you, and this diffuses both. I, I remember when I got the book deal from unmistakable my editor, because I didn't realize at the time writing a thousand words a day and writing a book were two entirely different animals.

Srini Rao: She asked me, she's can you get it done in six months? And then when we started doing the outline, we realized I'm not gonna revise the self-published book. I'm writing a whole new book from scratch. And I thought to myself I've committed to this. I'm gonna deliver. And I did. I actually delivered it.

There are a lot of revisions in the two months that followed, but I remember I showed up at Thanksgiving with a manuscript in hand and she sends me her comments and she shows, my editor showed up, she's can you get it done by next week? And my editor agent was like, that's why she's buying you the steak dinner.

I looked at her and I said, of course not. I'm not gonna get it done by next week. Yes.

Cal Newport: Yeah, exactly right. So it's like walking that line of, yeah okay, I'm committed to this. I'm gonna do it well, but also giving yourself enough time. And this is the tension between that idea of investing social capital and the working at a natural pace. And it's all about finding the balances between these things. Give yourself enough time to really finish this manuscript at a really high level, but don't give yourself any more time than that. And also commit to that. That's the time I can deliver it.

It's like the realistic time assessment, but then also putting stakes on that time assessment and actually delivering that combination is the key. So you're not rushed, you have time to do it well, but you also can't stop working on it or overwork on it. Like you have enough time to do it.

Not perfect, but and like you ship in the end, you end up shipping.

Srini Rao: Yeah. Where, if any have you gotten pushback from people on this? I mean there's been a couple places. Some of it I think really interesting and some of it just bizarre. So like we will, we'll focus on the more interesting pieces.

Cal Newport: I'll, okay. The most common non-useful pushback. I would say is so you wrote this book about an issue facing knowledge work. And knowledge work has these very distinctive features that generated this issue. This sort of the autonomy, the lack of of formalized way to manage workloads.

The sort of ambiguity of it. The remote work the core role of digital. These factors have come together in knowledge work and created this like really specific issue with pseudo productivity that's causing a lot of trouble. How do we fix it? So I wrote this book about this and a lot of people some critics would come back and say, yeah, but those ideas don't apply to industries that aren't knowledge work.

They're like, okay, yes, that's true. It's a book about something that's happening in knowledge work that I wanna, they're like, that's not relevant to people in the service sector. I was like, yeah, they have like really big issues there, but their solutions are gonna look really different than this. Or how does that help factory workers?

It doesn't I wrote a book about running. I'm sorry, that's not gonna help. Your chess game, those are two different things. So there's a lot of that going on. But that's, that is, to me, that's not too interesting. Yeah, that's just I don't know what that is. There's some, so I did something very specific just 'cause I, I thought this was interesting, but it was a risk, and I think some people like it, some people don't.

But what I decided was, 'cause I was inspired by the slow food movement in the slow food movement, one of the big ideas was instead of inventing from scratch utopian solutions to a problem, look back to traditional cultures that have already gone through a lot of cultural evolution and natural experimentation.

Draw from what has been proven to work in the past. Don't just try to invent something new from scratch. So in slow food they're pushing back on the fast ification of cuisine, but their alternatives, they look back to traditional cuisines. Because they think, Hey, this is the way we aid in this country for 300 years.

There must be something about that trial and error. This is what works. So for the source of my advice in this book, instead of just coming up with ideas from scratch or Hey, let's look at this like software company and see what they're doing. I said, what's the equivalent of traditional cuisines for knowledge work?

And I said we've always had what I'll call traditional knowledge workers people who created value using their brain. Now their jobs look nothing like modern office worker jobs. I'm talking like Galileo, Lin Manuel, Miranda, Jane Austin, Mary Curie. These are the type of examples I was talking about.

But I was like, but here's why these people are interesting. They tended to have a lot of freedom to figure out how they worked. So through trial and error, they figured out. What is the best way to try to create valuable things with your brain? Yeah. And my idea is what we should do then is study the principles.

Let's extract the principles from these traditional knowledge workers. Let's forget the details of their day because they're artists from 300 years ago. Let's look at the principles they identified through all these natural experimentation, and then we can adapt that the modern office jobs.

And that's the approach the book takes. It says, let's look at traditional knowledge. What did they figure out about the timing of producing stuff with your brain? Great. Is there any way we could leverage that in an office? Yes. Here's how some people found that. They're like, yeah, but I can't do exactly what Jane Austen did.

Like the these stories are not about people in my exact job. So there was a risk I took, but I really liked that idea of doing it, of these were the people who had the freedom to do trial and error to figure out what's the right way to create value from their mind. Those principles are generalizable.

I. Then we can adapt them to like your computer and you work in a marketing, that's fine. We can make that adaption, but we gotta find the principle somewhere and I wanna find 'em in the past. But there is a common source of pushback that says no. But I only want the stories to be directly relatable and if I can't directly do what I'm seeing in the story.

So it's much more of a, I wanna be able to just directly do what I see in the stories. I like, it's not so simple we're gonna extract principles from those stories and then adapt 'em to what you do. And some people didn't. Some people are saying no that's not relatable. And other people liked it.

So that's probably one of the big veins of pushback.

Srini Rao: Yeah. Like I said I think I could talk to you all day about this. In the interest of time and the attention of our listeners I'll finish my final question, which it's always interesting to see how people answer this. And I think you're officially the most appeared guest on Unmistakable creative.

Now, ah you, Danielle Aport was ahead of you, but I think now, I think you have officially appeared more times than any other guest. So take that, Danielle, what do you think it is that makes somebody or something unmistakable?

Cal Newport: No, I'm trying to remember if I change my answer every time. I don't remember my answer.

So I don't know if this is new or not. I don't remember what it was from before, but what do I think makes someone unmistakable? I this is probably what I've said every time, to be honest. Doing work too good to be ignored. Yeah. And that's the whole ball game. The whole ball game in terms of doing well professionally, the whole ball game and professional meaning the whole ball game and gaining control over your life and how you want things to do the whole ball game and having impact, like if you do something that's too good to be ignored, all the other details will be figured out.

And that's probably why I was my, the first time I talked to you, the first sort of serious hardcover idea book I ever wrote. That was the core idea. That's no coincidence, yeah. That's still probably at the core of everything

Srini Rao: else I'm thinking. Wow. Amazing. As always, I can't thank you enough for taking the time to join us and share your insights and wisdom with our listen.

Where can people find out more about the book on the unlikely chance they've never heard of you, your work and everything else? The book is called

Cal Newport: Slow Productivity, and you can find it wherever you buy books. Also cal newport.com/slow. If you wanna download an excerpt or find out more about the book.

From there, my weekly podcast is called Deep Questions with Cal Newport. So if you like the tactical side of these type of topics, you'll probably wanna check out that podcast as well. As we've talked about on the show before, I otherwise don't use social media I might not be as easy to find as you might expect, but my books are out there as is my podcast.

Srini Rao 

Amazing. And for everybody listening, we will wrap the show with that.