Deep Questions with Cal Newport - Why Do Digital Detoxes Fail? What Works Better? | Monday Advice

Episode Date: July 27, 2026

Why do digital detoxes fail to create lasting change? What works better? To answer these questions, Cal draws from the book “A Brief History of Intelligence” to reveal the relevant neuroscience at play, and then use this understanding to figure out a better method for improving your relationship with your devices. Below are the questions covered in today's episode (with their timestamps). Get your questions answered by Cal! Here’s the link: https://bit.ly/3U3sTvo Video from today’s episode:  youtube.com/calnewportmedia (0:00) Why do digital detoxes fail? (28:28) An article on the phone algorithm addiction (35:13) Follow up on the Brad Stulberg interview (40:21) A policy article on digital addiction (52:34) Deep work in the age of AI (57:46) What Cal is up to Links: Buy Cal’s latest book, “Slow Productivity” at www.calnewport.com/slow  Get a signed copy of Cal’s “Slow Productivity” at https://peoplesbooktakoma.com/event/cal-newport/  Cal’s monthly book directory: bramses.notion.site/059db2641def4a88988b4d2cee4657ba? https://www.nytimes.com/2018/02/04/us/politics/online-addictions-cut-back-screen-time.html https://www.nytimes.com/2026/07/14/opinion/culture/phone-algorithm-addiction.html https://www.splunk.com/en_us/blog/learn/goodharts-law.html https://www.healthaffairs.org/content/forefront/digital-addiction-public-health-problem-public-health-law-solution Thanks to our Sponsors:  https://www.masterclass.com/deep https://www.gusto.com/deep https://www.expressvpn.com/deep https://www.vanta.com/deepquestions Thanks to Jesse Miller for production and mastering, Jay Kerstens for the intro music, and Nate Mechler for research and newsletter. Learn more about your ad choices. Visit podcastchoices.com/adchoices

So here's a mystery that has long interested me. Back when I was working on my 2019 book, Digital Minimalism, I ran an experiment where I recruited around 1,600 people and I had them do what I call a digital declutter. So the idea was they would spend 30 days abstaining from the use of what I called optional digital tools. And then at the end of the 30 days, they would reflect on which tools they really missed and which ones they actually wanted to add back into their lives. I was inspired by Mary Condo when I did this.

You know, she said, if you want to clean out a closet, take everything out, and then only add back in the stuff that you really need. And I figured we should try to do this with our digital lives as well. So this was a fun experiment. It even ended up being reported on in the New York Times. But here's the mysterious thing. There was a real division in the outcomes of the people who participated. Some people had great success in moderating and controlling their digital behavior

going forward while other people really failed and fell back almost immediately into their old habits. So what was the difference between these two groups? Well, here's one of the big things that caught my attention. The group that failed tended to treat the experiment like a digital detox. They hoped that a sufficiently long break from their devices would reduce the allure of devices, but this largely didn't happen. The group that succeeded by contrasts tended to to fill the declutter period with lots of activity and action and experimentation. We're talking about working on new hobbies or being much more aggressive about socializing or reading or walking or exercising more, doing more self-reflection, journaling, all these type of things.

So they treated this experiment like it was a chance for them to do some analog reprogramming. That's why I called it at the time. So this is the mystery then. Why did this analog reprogramming approach work so much better than simply trying to do a digital detox? Well, it's Monday, which means it's time for an advice episode of this show, which is the perfect opportunity to look closer at this question. Now, there's a specific reason why I'm tackling this issue right now is because at the moment, as you might see, if you're watching, I'm not in the studio, but I'm on vacation. I'm up in the upper valley of Vermont and New Hampshire. And while I've been up here on vacation, I've been reading this book, this is Max Bennett's book,

A Brief History of Intelligence, which is a really fascinating history of the evolution of the brain from the very first multisolar life through modern humans. And when I was reading this book, which gets really in the weeds of neuroscience, I came across an answer to our mystery. So here then is our plan. I'm going to use the neuroscience that I learned from Bennett's book to answer three relevant sub questions. Number one, what's going on in our brain that makes our phone so appealing?

Number two, why did digital detoxes not really work so well in making the phone less appealing? And three, why is analog reprogramming like I saw among those people who succeeded in my experiment, something that works much better with our brain wiring? We'll then use this wisdom to isolate some concrete advice that you can use to hack your own brain to spend less time on your phone and more time doing things that matter. All right, we have a lot of sort of brain science geeking out to do here. So let's get started. As always, I'm Cal Newport, and this is Deep Questions, the show for people seeking depth in a distracted world. All right, so let's start with our first sub-question here. From a neuroscience perspective,

why are you addicted to looking at your phone? All right, so here's what I've learned. from Max Bennett's book. I've talked about this before at a sort of higher level, but I'm really honing in now on what is actually going on in the brain. I think it's important to get precise so that our advice can get precise. So if we really want to know what in your brain is responsible for you picking up that phone more than you want to, it is a truly ancient neural structure called the basal ganglia. When I say ancient, I really do mean ancient. The circuitry of your basal ganglia is basically the same as the basal ganglia. that you will find in a lamprey fish, even though our last shared ancestors with the lamp rays

are the original vertebraes from 500 million years ago. That's how old this particular part of our brain actually is. Now, what does it do? Well, in his book, Max Bennett calls it the puppeteer of the animal, right? So the basal ganglia, it takes an input from all sorts of different parts of your brain so it can monitor your actions in the external environment. And then its output is connected to the motor circuits in your brain stem. And so most of these motor circuits are inhibited all the time. The basal ganglia can turn off that gate on particular circuits and actually cause you to do specific actual physical actions, right? So it's like it's the puppeteer that controls your physical actions based on the input that it's getting. Now, what's critical is that

once you get past the very simplest animals, what makes basal ganglia so important is that they're connected to dopamine neurons that generate dopamine when exposed to things that generate a reward. And rewards are typically, we have these other ancient structures like the hypothalamus that recognize if something is rewarding or not. The dopamine neurons will also withhold dopamine if the activity is non-rewarding or harmful. So the basal ganglia is actually one of the main things it wants to do is repeat actions that maximize dopamine release. So if it has sort of learned to a particular physical action creates dopamine release, it will be highly motivated, if I can sort of anthropomorphize the brain, which is sort of meta, to repeat that action.

All right, here I'm going to read a quote here. Here's how Bennett actually describes this decision-making process happening within the Basil Gingula. So as Bennett writes, the basal ganglia accumulates votes for competing choices with different populations of neurons representing each competing action, ramping up an excitement until it passes a choice threshold, at which point an action is selected. All right? So we have the puppeteer, the basal ganglia that ultimately decides the actions we take,

and it learns through exposure to pass rewards that certain actions, if it's going to generate a reward, it's going to be much more likely to actually take that action. There's actually even a competition happening within the basal ganglia of potential next actions where whichever relevant dopamine neurons get more excited, then that's where it's going to go. So it wants to go with what it thinks is going to give us the biggest reward. So why do we pick up our phones? Well, attention economy apps on our phone are very good at consistently producing reward.

So the input pattern neurons that are connected to seeing the phone are going to activate dopamine neurons often. So the basal ganglia learns when I see that pattern of a phone, is nearby or that's one of my possible future actions, I've learned that I'm going to get a reward if I do that because consistently when I pick up the phone, I'm getting a little bit of reward, a little bit of dopamine. So I've really strengthened those circuits. So the vote for picking up the phone gets very strong. You can actually even think about the machine learning algorithms behind something like TikTok's curation, which decides what video to show you. What it's really doing

is building a model of this system in your brain and trying to figure out the reward. it's trying to get is you actually continuing to watch. So it's figuring out what can it show you that most consistently will generate dopamine so that it can get the strength in the action it wants. So it's literally sort of hacking the way this system works. All right. So we've heard things like this before. I've talked about this before. But it's good to actually have some more neuroscience expertise behind this and recognize this is the basal ganglia with has its own dopamine neuron inputs. And this is what's leading us to pick up our phone. Okay. With that in mind, subquestion number two, why do digital detoxes fail? So why did the people in my declutter experiment who were just like, I need to get

away from my phone for 30 days so I can lose that addictive appeal? Why did they go back to using their phone just like they did before? Well, when we understand the basal ganglia as our puppeteer, we realize not being around a stimuli for a little while doesn't change much. Because think about the role of this, right? Like this is, it implements reinforcement learning within our brain. It's how we learn what patterns generate awards and what patterns we should avoid because they generate harm. From an evolutionary perspective, we don't want to forget those patterns quickly.

Right. So if I am an early vertebrae and my basal ganglia has learned that when I see a certain type of plant, that there's often like food behind it that's like useful to me, I don't want to forget that. So look, okay, if I don't see that plant for a month, but then I come across a part of my sort of Cambrian sea and I see it again, I want to remember like, yeah, that's good. That's where the food is so that I can take advantage of that. So we don't lose, right? it's not as if these memories of rewards will quickly fade if we don't get exposed to those rewards again and again. So if I do a detox, I spend a week without my phone, I might feel better in that week in the sense that I'm not numbing my brain on these apps. But as soon as I see that phone

again after this detox is over, my basal ganglia is like, boom, reward, that wins to vote, puppeteer, actions, pick up the phone. Same thing with like a digital Shabob. each week. I take one day off from the phone. I mean, all this might have immediate benefits, but it's not going to make your phone less appealing. If you really wanted to directly hack the reward centers in the Basel Ganglia, what you would actually have to do is you would need to have a direct harm programmed into picking up the phone. So you would need to set something up where there was, you know, electroids on your groin or something. And every time you touch the phone, it gave you a shock.

That would rewire those, that would rewire those reward neurons very quickly. And pretty soon you'd be like, oh, I'm definitely not going to pick up the phone anymore. But simply not being around your phone, does it make that phone any less appealing next time you actually see it? So this was the issue that was going on with the participants of my experiment

who just treated it as like a white knuckle experience. Is it that Basil Ganglia was like, all right, we're not seeing any phones right now. But when it sees one again, it's like, oh, I remember that. There's food behind that plant. And it's just as appealing as it was three weeks earlier. Hey, let's take a quick break to hear from some of the sponsors that makes this show possible.

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One more time, Gusto, G-U-S-D-O-com slash deep. All right, let's get back to the show. All right, so sub-question three. Now we're going to have to get to a new level of geekdom in our neuroscience that I don't think we have yet reached on this show. Our sub-question number three is why does analog reprogramming, work when digital detoxing does it. So remember,

analog reprogramming is my term for aggressively exposing yourselves to other value-producing activities, that the people in my experiment who ended up with long-term success spent the declutter period aggressively reinventing themselves through all sorts of activity

and experimentation and self-reflection. All right, so to understand why that actually does work, we're going to have to dive even deeper into the brain functioning, they get into the core of the new things I learned from the Max Bennett book. All right. So let's take this step by step. So in our brain, we have a cortex. This is old, this evolved relatively early. It's what recognizes things in our world, right? So it's what allows us to do pretty sophisticated pattern recognition so that we can understand where we are and what's happening. and then we can connect at the things we've learned previously about rewards or harms.

When you get to the first mammals, however, you get a new part of the brain becoming much more prominent and it's what we would call the neocortex, which is actually like a layer that's on top of the brain. It covers the old cortex. So the neocortex, this is where things start to get more interesting. If we look closer at it, we'll see a big part of the neocortex is what's known as, the sensory neocortex. This actually, as far as we know, is running simulations of the real world. It uses these cortical columns to run simulations of all parts of the real world. And it's constantly sort of simulating. One of the things we think happens is it's constantly doing these short-term

simulations and making sure that what it thinks is going to happen matches up with what does. So it does a simulation of what it thinks it's going to happen as you step your foot forward. And if that matches what happens, you move on happily. But if it's a thing, it doesn't because like the there's a loose rock or something it's immediately that discrepancy is like okay problem what's happening the real world doesn't match what we thought and we have to put more resources to bear to figure out what's going on so we have the sensory cortex is like a world simulator but then we have the frontal neocortex which has three main subregions but the ones the primary subregion that it's going to matter for our discussion here is what's known as the

a granular prefrontal cortex or the apcc now this is something to evolve with the first mammals. Actually, for the very first mammals, their frontal neocortex only had an APFC, and then these other regions evolved as mammals got more sophisticated and as we got the primates. Okay, so now we're getting a little bit more complicated. The a granular prefrontal cortex, the APFC, we think what this is used for in part is to figure out what to simulate. So again, we have the sensory neocortex that can run all these pretty detailed simulations. And for the most part is just simulating everything it thinks is about to happen just to make sure that the world matches our understanding. But we can use these sensory neocortex to run all simulations of stuff that's not about to happen.

And there's other parts of the brain we can also involve in these simulations, including other parts of the prefront, the frontal neocortex. That allows to do pretty advanced simulations of the future. And what if this happened? Or what if I went over here? What will happen? How will that make me feel? and we think that the APFC, the agranular prefrontal cortex, is like the coordinator of these simulations.

It's the part of your brain that says, okay, I want to explore this possibility. And let's see what happens. It's actually pretty cool that mammals can do any sorts of simulations at all. They can actually measure this in rats. They can do, they call it visceral trial and error, or vicarious, rather, trial and error. You can actually see when the rat pauses

and they're simulating the different possibilities. of the different places they could go in the maze before they then start moving again. So we can kind of pause everything and run these simulations. And the APFC is we think in charge of that. All right. So what is the role of these simulations that the APFC initiates? Well, this is the way Bennett explains the simulations connecting to our behavior. So if the APFC initiates a simulation of something that we could do,

it basically is feeding those simulations into the basal gangula, which doesn't know if it's seen the results of a simulation of the real world. It's way too simple and primitive. It doesn't know that. It's just being shown stuff through its input. If the simulation leads to a rewarding output, then the steps of that simulation are reinforced. So I want to be careful about this, Because this is, you know, temporal difference reinforcement learning is a little bit complicated. But essentially, when you get to a reward state in the type of reinforcement learning that happens in our brain, that gets reinforced backwards through the steps that led to that reward state, even if they go back relatively far.

So even like the initial step that led towards a eventual reward ends up getting reinforced. right? So this is a model of learning that is attributed originally to Richard Sutton figuring it out, and then we realize like, oh, this is really happening probably in the brain of a lot of different animals, including humans. And this is actually how the basal gangula, when we say it learns about rewards, it's doing this type of reinforcement learning where the rewards propagate back, right? So what's happening is actually the APFC starts a simulation of something. It shows it to the basal gangula, who thinks it's actually happening. If it leads to a reward,

then it reinforces the steps along the way. Then the APFC turns off simulation mode. Now we're back in the real world. Like we're getting in real input about what's actually happening in the real world. And the basal gangula says, so if we just simulated one possibility, it's like, oh, I just saw that.

And if we take this first step here, that's going to lead us, you know, that's just been reinforced because that's leading us down a path to a reward. So it's a little bit complicated. I had to kind of read this a couple times. essentially by showing the basal gangula simulation that leads to a reward about something you could do right now, when you go back to the real world mode, it thinks it's back at the beginning

again. You just reinforce those steps. So it's probably going to actually then take those real actions if that reward was really strong. So the basal gangula still is the puppeteer that makes all the decisions. So it's like the APFC is like, I'm going to show you these movies of like, if we went and did this, it's going to lead somewhere good so that you'll start. reinforcing those type of actions so that when I then say, okay, now we're back in the real world, you're going to follow those actions. So you have to influence the basal ganglia. You have to convince it that a certain set of actions you're going to start heading down leads you somewhere well. And you do it by just like simulating life. And it learns, oh, this led somewhere good. So I'm going to do that

again if I see it again. So let's put this back now. Let's take this all. Let's try to connect this back to our phone behavior. All right. So your phone is here and you don't want to pick it up. The Basil Gangula knows there's TikTok on the phone and picking up the phone is, you know, it's reinforced because it's going to give us that little hit and that hit will have some sort of rewards. And so that's what it wants to do. Your APFC at this point can say, I'm going to simulate an alternative, right? So I'm going to simulate going and picking up my running shoes, putting them on, and trying to log training miles, which I'm going to put in my log and see if I'm making progress towards like getting in better shape, right? In the absence of this simulation,

the basal gangula would just say, like, picking up my shoes doesn't seem very rewarding, but picking up the phone does, you know, pick up the phone. But the simulation is going to run through this whole simulation that ends in a very rewarding in-state where you finish the run and you have the endorphins and you feel a sense of accomplishment from having made progress in your training. And that's really rewarding. And the basal gangular thinks this just happened. It doesn't know the simulation is fake. And so it starts reinforcing in its circuitry all of the steps that led to that reward state all the way back to the initial step of picking up your shoes. Now you switch back to like, I'm in the real world. You see your shoes. You see your phone.

Well, that picking up your shoes just got a bunch of reinforcement back from that in-state in the simulation. And if it's strong enough, if that reward state was strong enough that you experience at the end of the simulation, picking up the shoes now outvotes the phone. And you pick that up and you go and you get the run and you get the reward in the end. So basically, we have to expose ourselves to rewards. If there's a possible reward that is not only more compelling than picking up the phone, but it's compelling enough that when those rewards back propagate all the way to the very beginning of the whatever steps lead there, it's still really strong, then we can, uh, sitting down the path to that deep reward can outfo, picking up the phone.

So it's a little bit complicated what's happening, but it's interesting to think about. So what this tells us, and this, I think this explains the mystery is that the more you expose yourself to deep rewards from non-phone activities, the more you make it possible, to have the simulations of those activities win over the short-term desire to pick up the phone. But the key point from Bennett is you actually have to have experienced these. The simulations have to be compelling, which means you have to experience these rewards before for that simulation to be compelling enough that you're going to head down that path instead of the shallower path of picking up the phone.

So detoxing doesn't work. What you really need to do is to prepare your basal gangula so that your APFC simulations will be sufficiently compelling. And this means exposing your basal gangula as much as possible to real rewards that came from more value-driven, longer-term analog activities. It's a bootstrapping process. The more you do this up front, the easier it will be to keep doing this going forward, and the easier it will be to actually subvert the attraction of the phone.

Because really these reward signals you get from looking at something like TikTok are like fine. They're consistent. So they're very pure, but they're not massive, right? It's like it's the alleviation of boredom and the exposure to novelty. That's what you get, right? I mean, like TikTok and X is like, it's almost, you know, it's rococo and its abstraction of just like stuff that's like, ooh, that's kind of weird. does just kind of like interesting and novel, right? So that's a consistent but not super strong reward signal.

So real value-driven analog activities that give you like deeper rewards or help your sense of self, etc. Or big sense accomplishment. These can weigh outweigh the phone. But you have to have been exposed to them enough time that your basal gangula knows about them and then therefore it will rate the simulations of that possibility high enough that you'll take the right first step instead of picking up your phone. Right? So it's not about trying to separate yourself from your phone, but instead about trying to

repeatedly and repetitively expose yourself to things that are better. That's, I think, why in my experiment, the people who are very aggressive about activities, the better is that they're basically training their basal gangula to recognize a bunch of these rewards for the valuable activities as being very strong, so that later, when the APFC triggers a simulation of going a non-phone route, those simulations are very compelling. All right, so this leads us to what's the practical advice if you feel like you're using your phone too often. You need repeated direct exposure to what we can call deep rewards. You need the ability to take steps towards these rewards to be both ubiquitous and accessible.

So you really want to sort of surround yourself with opportunities to make steps towards reaping a deep reward. You need those steps to be possible and nearby in order for that the possibly win out. The first step towards whatever you're doing that's not the phone has to win out against pick up your phones. They need to be ubiquitous and nearby, right? This is why if you get a lot of reward out of art, building a really nice art studio in your backyard is important because it's right there. You could literally just take a few steps and you can be there working on the art. Whereas if you have to drive, you know, across town to an art studio, that first step is not proximate. it's not really very easily able to compete with the phone that is right next to you. It's why

having notebooks, we talked about this in a recent episode, having notebooks handy to take notes on some sort of bigger design project or writing project you're working on matters because that's a step you can take right away that's leaning towards a deep reward or reading meaningful books. You have those books with you at all places or training. You have the ability to do physical training or exercise. Like the stuff you need is at least right there to get started on it. you need to make the paths to these deep rewards accessible and ubiquitous if they are going to compete with your phone. You also need sufficient pathways to deep rewards in your life that you have a sufficient density of options. You probably need three to six different deep reward producing activities that you sort of keep juggling or in the hopper.

They're there as possibilities. I mean, again, I saw this with the digital declutter results. It's the people that did a lot during that period without their phone that had the best success. after that period ended because if there's just one thing you do there's a lot of situations where that's not something you can really reasonably make progress on and then there's nothing to compete against the phone all right so if I pull these threads together I just thought this was really interesting to learn about what's really really going on and it's all about simulations of possibilities and if the simulation ends with a really big reward going down that path can

went out. But the only way that the thing at the end of that simulation is going to get received in your brain in the basal gangula as a real reward is if you've actually experienced it before. And that's why I say it's like a bootstapping process. Like you have this initial process of like, I'm forcing myself to do a lot of things to generate deep rewards that I want normally do at this level or density. But the more you do it, the more easy it will be to keep doing it. And then eventually you fall into this rhythm. where you're much more interested in pursuing deep rewards than, you know, seeing someone get hit by a bowl on X.

So it bootstraps on each other. So this is why the digitally clutter was successful is because it's not because it got people away from their phones. It's because it got people analog reprogramming. It bootstrapped the process of helping the simulations win over the consistent but moderate value signals of picking up the phone. And so stop thinking so much about how do I get away from my phone. and think much more about how do I get towards the things that are better.

It'll be hard at first. You'll have to force yourself and take some days off, go so however you want to do it, but it will get easier if you keep pursuing those rewards. All right. So there you go. Jesse, reporting from my studio in D.C.,

how do you rate my neuroscience lecture? I kind of like it. Have you finished the book yet? No, I have it here. I am on page. No, I'm on page 261. Is this part of the thinking research, or is it just a random book? It is, yeah.

So, you know, I'm working on this book about thinking. And so I'm reading a lot about thinking and its role, like the history of thinking. So if I want to know the history of thinking, I realize I need to know the history of the human brain. And that is why, yeah, so that's why I'm reading this book. It's like my fourth or fifth book. My plan is to read like maybe 10 books this summer that are just about thinking, the impact of thinking. I'm just trying to understand thinking as well as possible before I move forward in that new book project I'm thinking about.

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slash deep questions. That's va nta.com slash deep questions. All right, let's get back to the show. But anyways, that's probably enough from me. Let's hear from you like we like to do on Monday episodes as we want to open up our inbox and hear what questions, observations, or interesting things you have to share. Remember, you can always reach us with your own contribution at podcast at calnewport.com. All right, Jesse, what do we got to get going today? Our first note comes from Paul, who was passing along an article.

All right, so Paul says, in case you haven't seen it, I was sure you'd find this essay interesting. Well, Paul, I'll be the judge of that. Maybe I won't, but let's see what Paul had to send along. I'm going to bring this up on the screen for people who are watching.

All right, so here's the article. It's from the New York Times. The title is The Lofi Way I broke my addiction to the algorithm. There's a sort of claymation picture here. I did read this. So, Paul, I have seen this before, but let me try to get to the core of it. All right. So the author of this article says, it's why I've been trying to live a summer of Ludd, a term I'm cribbing from a group that has been papering flyers all over New York City this past few weeks. in an effort to get people off their phones. Dot, dot, dot. For me, less and less has been... Okay, I've been staging an experiment for the past few months. I've removed all my social media apps, Instagram, Twitter, TikTok, and moved them onto an old iPhone I had lying around my house.

I dubbed this phone my scroll phone. I designated a single spot in my home where I could use it, a stripy armchair in my living room, which I have now anointed the scrolling chair. All right. So this is this op-ed about this idea of all my social media is on one phone that I only use while sitting in a particular chair. And this author said, like, okay, that's been really successful. Jesse, what's your, what's your, what's your, I have a complicated thinking of this.

What's your guess, though? What's your guess on where I'm going to come down on this suggestion? Oh, that's a good question. Pro or con or in between. I guess it depends on how long she sits in the chair. Yeah, she goes on to say she sits in the chair. chair 17 hours a day. Khan.

She just sits there scrolling constantly. Just, ah, put it my veins. Actually, it's a hard question to ask you because I have sort of a mixed reactions. The thing I think that is good about this advice, if we're going to put on our neuroscience hat, is that the, what you're tuning down is to pattern recognition from the cortex, the old cortex of the phone and the phone being nearby, right? So like those apps are not on her. normal phone. They're only over by a chair. She's sort of convinced herself that's the only place

you use the phone. So if she's not in a context where she has time or is near the chair, it's just not coming up as an option as much. So that does probably help, right? Is that your brain then is like not really voting for using your phone unless you're like in the room where the chair is and have time to go sit in the chair. That also puts a little bit of friction on it. So that's, that is probably somewhat useful. On the other hand, you know, what you really need to do eventually is like what we just talked about, which is analog reprogramming, right? So like what you really need to do is to, instead of just having elaborate ways to try to put

gates around this otherwise highly appealing activity, is to make more valuable activities more appealing. They get to a place where even if your pattern recognizers are sparking about your phone, you're not drawn to pick it up, right? So I think analog reprogramming is going to be much more effective in the long term than simply trying to add more friction or gates around phone usage. My bigger issue is that this reminds me a little bit. I mean, I don't know. This has vibes of the alcoholic that has like the super complicated rules around like when

and how they drink. And I, you know, it's only on these days if like I'm, I've seen, I'm around this person and only this much. And I keep it over here. I keep the alcohol. And in the end, you're like, man, you're going through a lot of effort to make it seem like you're you're doing something about your drinking issue but making sure that like the alcohol stays in your life like it does kind of have that feel a little bit that sort of like the

elaborate rules that drunks end up having that that they put around their they're drinking so if you're having to have a scrolly chair and all these other times in an old phone or this or that i mean at some point you're like maybe i just shouldn't use social media so i'll throw that out there as well but anyways i like the idea i like that it's successful uh we know from our brain science some reasons why that that we would expect that to be successful. But I don't think by itself it's a full solution. Even before, like when I was just a fan of the show and you had all your advice about social media, I just basically followed it.

So now I never go on it. But if people like text me a link or something, I'll open it because I still have the apps. I just never go on it. So I can see like a link if somebody sends me a link or something like that. So that's all I do. Well, that's probably where you want to get, right? is in some sense you want to be in a place where you don't have to have elaborate rules and gates to try to keep you away from it.

It's just not as appealing to you. I mean, it's all bootstrapping. You use it less. Other things build more rewards. However, I did draw a line a couple days ago. Somebody sent me a TikTok link and I was like, I can't open it. So the next time I saw it was like, you have to show me on your phone because I'm not going to download the TikTok app. Good for you.

Good for you. I know. It's funny. Like I have most of these apps. somewhere on my phone from various articles I've written in the past where I have to use them or this or that. But like, I don't know. I have TikTok. I have TikTok on here, I think from the article I wrote for the New Yorker last year where I use TikTok. And I have zero interest in, like, I'm clicking on it now. Does this even work anymore? All right. Clicking on TikTok, update your app. So there we go. It's like I can't. Is that Brad? No. Sounds like Brad.

All right. There's a video of someone burying an airplane. I mean, that's awesome. I take it back. Can I tell you what I just saw, Jesse?

Is this real? All right. So I just saw a guy. This whole video is only like two minutes long. He buried an airplane in his backyard. Like a passenger jet, it looked like. Covered it over.

Put grass on. top of it, cut off the front, put like Hobbit doors on it so you can go down this like passage in his backyard to be inside an airplane. And it's probably a deep work studio. I take it back. We all should be watching TikTok. So look, this is what I have to contend with. Reading my book about the history of brain structures or watching a video of a guy bearing

a plane in his backyard. Oh, man. Okay. What else do we got here? Danny has a follow-up to last week's interview with Brad Stolberg. Right. Last week we had Brad on, we were talking about optimization and about how over-optimization doesn't necessarily lead you.

It can make things worse. All right. So Danny says, this isn't necessarily a new article or link, but I was listening to your episode with Brad Stolberg about the optimization paradox. And a lot of the discussion revolved around problems that are basically explained by good heart's law. This is the idea that once an indicator becomes a metric that is targeted, it loses its power as a useful indicator. All right, I feel like I should load this up here.

Good Hearts Law. So I found the summary of it. I'd heard it, but people mention this a lot in the context of AI. So I have a summary here. What is Good Hearts Law? There's three key takeaways. Let's just look at these. Takeaway number one, Good Hearts Law warns of distorted metric.

when tied to goals. It states when a measure becomes a target, it ceases to be a good measure, emphasizing how metrics can lose their effectiveness when manipulated to meet specific objectives. The second takeaway, the law highlights the risks of overfocusing on targets. Prioritizing specific metrics can lead to unintended consequences such as gaming the system, neglecting broader goals, or sacrificing quality. And three, the takeaway is balancing metrics with context is key to avoid the pitfalls of Goodheart's law, organizations to treat metrics as tools for guidance rather than rigid targets, ensuring they align with long-term objectives and core values. I don't know if that needs to be a law, but I do think that is common sense that if you have a

single metric that you're like, this is what I'm pursuing, that doesn't necessarily optimize the actual result that you care about if those two things are different. Okay, I completely believe that because this, to me, is the core problem of knowledge work right now. So in my book, Slow Productivity, I talk about this idea of pseudo-productivity, that the primary way that we try to organize our efforts in the knowledge-work context is around the belief that visible effort is a proxy for useful efforts. So the more busy you seem, the more useful we assume you're being, right? So there we have a metric, busyness, visible activity that a lot of people in knowledge work are

trying to maximize. But as we know, if you listen to my podcast or read my books, busyness is often far disconnected from actually producing things of value. So we've seen this real clearly with AI and computer programming. There was this period that already has come to an end. But there was this brief period this year after the coding harnesses got successful in which we had, but before the prices had actually been raised to their actual real prices when everything was still heavily discounted, we had a bunch of companies that would say, The metric we care about for you as a programmer is how many AI tokens you burn using our coding harnesses.

So the more AI tokens you're burning, we will assume the more useful code you are producing. We're going to have leaderboards at many of these companies to see who's burning the most tokens. This turned out to be a terrible metric to optimize for because it's very easy to get these AI models that burn endless tokens and produce code that it checks to code and this code goes to that that code and here's 30 versions of the code. It doesn't mean that you're producing good code and it doesn't speed up the rate at which features actually get added or products actually get shipped. Now pretty quickly the token leaderboards went away because again, as I mentioned, to try to get people to use these harnesses on top of these models, the Anthropic and Open AI were

selling tokens at a real discount. They basically had these unlimited accounts. You pay $200 a month and burn as many tokens as you wanted. It was costing a lot more to actually do this compute because it's expensive. And so when they adjusted to say, here's the real price, all those leaderboards went away

because you had people who were spending tens of thousands of dollars a month to get to the top of that leaderboard. And again, if it was leading to massive increases in the amount of value being shipped from the companies,

then maybe it was worth it, but it doesn't. And it's kind of the paradox of AI programming is that AI tools help you produce a lot more code but they don't necessarily massively speed up the rate at which features get added or products get shipped. The real place you see the major productivity gains is if you're building proof of concepts,

if you're trying to hack together prototypes, or if you don't really care about the quality of the code, it seems magical. If you're working on a mature code base, things are much more complicated. So Goodhart's Law, I think that is useful. Again, I think we got this with, if we rewind the clock with pseudiproductivity, we get like email response. times, amount of times you're on Slack, the number of meetings you're jumping in and out of all metrics you can maximize that aren't directly connected to actually producing value. So I agree, Danny, I think Goodhart's law, I think that is, that's useful.

That's useful terminology for us to throw into the mix here. All right, Jesse, what do we got for question three? Our next note is from Nina, who is sharing a policy article she thought you might find interesting. Interesting, yeah. So Nina said I want to share a screen time policy piece as someone reached out to me about. Well, hey, you know, nothing gets me more interested than screen time policy pieces. This is like catnip for me. Let's load this up here on the screen for those who are watching.

All right. So this article is showing up in health affairs. The title is, digital addiction is a public health problem? Is public health law the solution? And we see a group of authors here, lead authors. Sophia Palmyeri. All right. The subhead says engagement, maximizing architecture such as infinite scroll,

autoplay, and emotionally targeted notifications remain broadly permissible. That gap, however, is now being challenged on multiple fronts. So I'm going to read a core paragraph here. Let's see, where did I find this? Okay. I read this earlier, so I'm just going to hone in on this paragraph, which I think kind of gets to the kind of the big. idea here. Public health law has long addressed harms arising from commercially engineered products by

deploying a familiar regulatory toolkit. Product design standards, warning and disclosure requirements, advertising restrictions, age-based access controls, surveillance obligations, and funding mechanisms for treatment and prevention. These tools align with different public health objectives. Regulators might prioritize reduced consumption, controlled actions, controlled actions, Access restricts availability to ensure that only patients with appropriate clinical indications could obtain prescriptions or safer engagement through mandatory design standards. All right. So what they're saying here, and let me find there's one other paragraph I want to define here.

Okay, so up here it says contemporary digital platforms are explicitly designed to maximize engagement by leveraging well-characterized reinforcement mechanisms, what scholars studied gambling of termed addiction by design, including variable reward schedules, emotionally salient feedback, and frictionless continuation, and social media, algorithmic feeds, and AI chat system, the risk is further amplified by hyper-personalization, modern recommendation systems continuously infer user preferences, emotional states and vulnerabilities, tailoring content or conversations real-time to maximize using engagement, intensifying exposure to emotionally salient or validating stimuli, and reinforcing compulsive use through variable and individualized reward structures.

So what they're arguing is, like, hey, this sounds... familiar to other things that we have regulated. And if we look at the way those other regulations have happened, we might see a possibility for how we would regulate screen time. If you read the article more, and I read it in some detail earlier, again, they have these models of like tobacco use, gambling, and other age opioids, right? And they said we have overlaps between all three of those with screen time

and each of those has particular solutions. So like with tobacco use, we had age gating, right? It was kids shouldn't use tobacco because their brains can't handle it, and we have education. We're going to educate about the harms and addictive nation of tobacco. With opioid, it's controlled access. Actually, the state is going to control who gets access to it and under what circumstances.

And then when it comes to gambling, there is design, restrictions, right? So, like, there are restrictions on what you can and can't do when you're doing gambling games, right? Like, what is allowed, what's not, what you can do with the variable reward schedules or not. There's a lot of restrictions, for example, around slot machines, like how slot machines are allowed to behave or not behave, etc. So they said we have, like, responses for all three of these things that overlap screen times. And as I read closer, they said the gambling response is probably the most relevant. So in the same way that we have restrictions about how games designed to be addictive in a casino work, we could imagine a world in which we had similar

restrictives about digital addiction. So they say here, yeah, so they called it design safeguards to mitigate engineer addictiveness. I'm interested in this, right? I would say traditionally I had been skeptical about the idea of regulating reduced addictiveness of technology because my main concern is when I read thinkers and digital ethicist and policymakers talking about engineered addictiveness, they hone in on, they have this mental model where they can hone in on these like particular things you added onto a digital product that made them addictive and you could just turn them down. This was my issue with it is I don't think that model is correct. So they're like, well, you know, in this mental model, they would be like, you know, something like Twitter or TikTok,

the issue there is, well, you have infinite scroll, or you have a particular way that the new post pop up that's like a slot machine and that's addictive. Or they talk about dark patterns, which is really kind of a nonsense term, for like, well, the way it interacts with you emphasizes addictiveness. The whole point here in this type of thinking

and this mental model is we could turn those features off and then have a version of TikTok or Twitter that wasn't addictive. But that's not actually my understanding of how these things work. I think the addictive loop, the thing that makes the basal gangula always vote to pick up that phone and look at that app is actually typically just in the core functioning of the app. I'm showing you videos and I'm selecting the video to show you based on things you like before. It's a very simple feedback loop that pretty quickly locates subsets of the videos in the space of possible videos that generate a novelty or humor or other's type of positive reaction in you,

and then you want to keep looking at it. There's not a feature you turn off that makes that not addictive. That's the issue with the engineered addictiveness approach, is that I think this model just isn't correct. Now, you could say, well, turn off the algorithm, but the algorithm is just a thing to decide what video to show you next

in the context of TikTok. It's using a pretty basic multi-arm bandit style optimization. If you turn off the algorithm, what's it showing you then? Like, what videos does it show you? So that's always been my issue is that like I don't think the mental model of social media is fine and then we added addictive features, made it bad turn those back off again. I just don't think that mental model is right. I think for a lot of people, it's built in, you know, there's this sort of valence switch on things like Twitter where for a while if you're, you know, if you were more like a left-leaning academic, Twitter was great and exciting. And then it kind of got a shitified and got worse. And so you have this paradise loss idea of like something must have made this worse.

So we can go back to the way it was before. And I actually just think it's somewhat fundamental. On the other hand, this is what's interesting to me is like, okay, let's pull this thread. Like if we really were serious about no, you know, engineered addictiveness is bad. So using recommendation algorithms that are using fine-grained observations of your behavior to decide what to show you to be as engaged, like you can't do that anymore. that's not allowed. TikTok goes away, right? That is what TikTok is, but maybe that's not the worst thing.

And if your Instagram, right, or your Facebook, you go back to follower feeds. These aren't algorithmically curated. So I'm just seeing a reverse chronological timeline of like stuff that's being posted by people actually have to follow. That wouldn't be the worst thing. I think these things would be much less addictive. Like the way Twitter used to work is like you would look at your timeline. And if you came back to look at it 10 minutes later, you're like, none of the 100 people I follow who I think are interesting said anything new. There's nothing there. All right. Let me move on with my life. New Axel be like, no, no, no, hold on, hold on, hold on. Let me show you someone getting sucked into a drain pipe to show you or a fight or someone getting hit in a car. Like, I'll just show you, there's always stuff here to see. So actually, the thing I thought was a bug with the engineered addictiveness reduction regulation argument.

the fact that like it's just fundamental to how these things work actually maybe is a feature. Then like if you can't be engineered addictiveness, you can't have a lot of these platforms. Or they have to go back to the way they were before when they were interesting but, but not compelling in this way. Our understanding of brain science that we talked about today helps us understand, you know, why this is, right? If like looking at X, sometimes you get something new that's interesting from someone you follow and most of the time you don't means that it's a reward.

signal is way more inconsistent, and the power of the votes for picking up the phone to look at X is going to be much less than in a world in which it will always find something using a personalized algorithm to show you. So I think that's interesting. But again, there's all devil's advocate. But then someone will say, like, but what if Netflix wants to recommend shows? Does it mean it can't do that anymore? Right? And why don't we want there to be auto recommendations? And hey, maybe not. Maybe we want this all to be human curated. So I don't know. I've had a, Jesse, I've had a complicated history with this type of regulatory path. I don't think people's mental model of engineered addictiveness is right,

but the actual model actually, if we get rid of that, that's a much bigger swing. But maybe it's something we have to consider. I don't know. What would you think about like a Netflix? This is not the addictive issue with Netflix because it's not a close, a rapid feedback loop like with TikTok. But if you got rid of recommendations on Netflix,

I don't think that would really matter. I think most people are just finding, they're happy to be like recommended stuff by people. But I don't know. Maybe I'd be naive here. Yeah. All right. What else do we got?

Our final question comes from Jessica and it's about deep work in the age of AI. All right. Let's see here. Jessica says, my law school assigned deep work as summer reading. Ooh, that's cool. We should find out what law school that is, Jesse. Yeah.

And I'm reading it. Does you know? or is it not? I don't know, no. Okay. And I'm finding it to be incredibly relevant in this current AI-centric climate, despite it being published before the ongoing chokehold that AI has on everyone, especially students.

I really wanted to know if you had some new thoughts or a kind of follow-up to the book in the context of gender of AI currently and its effect on deep work. To me, deep work regarding law is more important than ever. Yet, as I read the book, I can't help but wonder how many of my fellow students will only ask Chat Chapti to summarize it for the, them and not bother reading it for themselves. Do you have any thoughts or insight as to the value of deep work now in relation to many falling back on AI for tasks that had previously been worked through manually? The book is as relevant as ever, but I find myself wanting some commentary in

deep work's place directly in the context of the heavy use of AI among students. It is a good question because the context surrounding deep work has changed from when I wrote that to now. And here's one of the big changes I've seen in the AI age. In 2016, when that book came out the number one issue was that people were undervaluing deep work. So they weren't spending enough time doing true deep work. And because of that, the value they were producing was being artificially capped. By prioritizing other things like responsiveness or meetings or pseudo-productivity, you didn't get enough time for actual pure deep work.

And because of that, you actually was holding you back how much value you could produce. The other issue I was trying to correct in that book is that people didn't understand what deep work was. So even when they thought they were doing deep work, they were doing things like quick checks of email inboxes and Slack channels and not realizing that that was a catastrophe for their cognitive capabilities,

that all of that cognitive context shifting was causing lots of issues. And so if you could be more careful about how you approach deep work and you prioritize it more, you would be happier, you'd produce more value. So it was about people just not doing enough

deep work or understanding how to do it well. In the AI moment, we have this other issue, which, as Jessica mentions, is people outsourcing things that would normally require deep work, like reading a book. Basically, anything that causes cognitive friction, we're like, ooh, which almost always is going to be abstract processing or using our brain in ways that we weren't evolved for. So anytime we're grappling to understand words or the produce words or understand mathematics or produce mathematics, these are examples of activities to cause cognitive friction. people are turning to AI to try to reduce that friction. So typically you would need to use deep work to understand a book in a law school class. But now you could get a summary of it and you have to expend a lot less energy. The reason why this is a problem is that it makes you dumber.

And if you're dumber, you're worse at deep work for when it actually has to happen. The friction is what you want. That is the feeling of the metaphorical muscle getting stronger. If you want to be stronger, you actually have to do the exercising. And so if you don't grapple with a book, which is hard and causes a cognitive, friction. You don't really understand that material very well. And if you don't understand it very well, you can't deploy it very well later when you need to, whatever you're doing in this context and your law context. It makes you dumber. You're just not getting the benefits. Deep work gives you

benefits in terms of understanding. Same thing with writing. It's uncomfortable to have to yoke together many different parts of your brain to put letters onto a blank sheet of paper. But writing is how your brain categorizes, makes sense, and better defines your understanding of things. I mean, we've been doing it this way for a while at law school. You struggle with text. You struggle to write about those texts. That is making your brain stronger so that you can do law. If you have a machine do those things for you, your brain is not getting stronger. You will be a worse lawyer. It's the equivalent of just having another student do your work for you. Yeah, that's easier. But the whole reason why you're doing that work is because you're

trying to create a lawyer brain, which requires a lot of training. Just like if we're going to make you into a special operations operator, we would want you to do all this PT that we're doing at Naval Seals training because we need your body to get very strong and your endurance to be very high. Otherwise, you're going to struggle when we put you in the missions. Same thing. If you want to be a lawyer, we need to do cognitive PT, which is going to be reading these terrible books and going through all these footnotes and wrangling with it to try to write

clear briefs and your mind as you write. Like, this doesn't quite make sense. My logic's not quite there. and in all of that, your brain is getting in the Navy SEAL shape because that's what you need for the equivalent of a Navy SEAL mission in our cognitive world, which is like working on a complicated law case. So yeah, I used to worry that people just weren't doing enough deep work.

And now I'm worrying that they're outsourcing the obvious deep work that remains and are getting out of the benefits because of that. So yeah, I do think it is an issue. You know, it's funny. I have a call right after this about AI policy at the university level. So I've been thinking a lot about this. And, you know, we need to, I think we're at a point now with AI

where we have to think about the human brain why it's important and what we value about and what we're trying to do about it. From a humanistic standpoint, what is the goal? What do we want to do with our brains and why? And then step back and say, so where do I want to use AI or not? Just like we would do with physical fitness. Like I could drive around on a rascal scooter

and make sure that I never moved my body at all. And that would be easier in the moment, but it's not good for my body. It'd make me less healthy and I'd be more miserable about it. We just got to start thinking that same way about our mind. Again, I think the role of AI in education right now should be incredibly limited. Right. Incredibly limited.

It should be basically focused only when there's AI-driven tools that's used in a professional academic context. As you get to the right level of training, you can learn how to use those tools. but otherwise, this is Navy SEAL training for your brain. If you're at law school or you're at a university, it doesn't make sense to bring in polis to lift the weights or to have someone else do the sit-ups for you. So, you know, I think we're going to get better at this.

We're all still trying to grapple with this, Jesse, but my new book, which I haven't, just an inkling in my eye, my indefensive thinking book will get into this. But all I'm doing now is reading books that maybe will help me figure out what this book would be about. It's so far from now I can't even think about it. But in theory, it's something I am going to tackle. So your last book you signed a two book contract.

Are you going to do the same thing? No, I want to just sign. I just want to sell this next book. Because I don't know what's going to come next. I'll do a two book context if I have two good ideas. I mean, I signed my last two book deal. It was basically in the pandemic. And it was slow productivity in the deep life.

And I was like, I knew I wanted to write those two books next. It's many years later. that finally we're almost there. But this time I just want to write this next book and then see what comes next after it. Yep. All right. Well, let's, before we conclude for today,

we like to briefly check in. I'm on the episodes about what I'm up to. Clearly, I'm not in the Deep Work HQ. Jesse is. I'm up in Vermont. I'll tell you what's clutch about this place, Jesse. We were in different places each year for now until I build my Deep Work HQ North,

which will happen. It will happen. Mark my words. This place is walking distance to a trail system. system. Okay. To me, that's important.

I love thinking walks. Thinking walks in the woods are great. And to be able to walk into a trail system and do my thinking walks is that has been clutch. So I'm adding that to my list for things that the Deep Work HQ North is going to have to have. Do you have a thinking walks go to for today? Yeah, I hope so. And let me see.

We work on the podcast. podcast. I have a call. We're going to go into town. Yeah, tonight. If the rain holds off, I'll definitely do, I'll definitely do a thinking walk. Definitely do a thinking walk tonight. Yeah, I've been working on an article up here. So, like, I've been, I just submitted a draft. So that's the only reason why.

So I don't even know what I'm thinking about. I don't know where I am with the book. I think I'm going to have to do like a July book roundup the old-fashioned way next week, Jesse, because I don't know, we put a couple episodes in the can. I don't know what the last book I talked about. I've been reading a lot of books. I'm in the middle of a lot of books. I don't even know.

So I'm going to just wait until next week when I'm back. And I have my reading list from home and I'll go back. But I'm, I don't know where I am. Just as long as the audience knows that you haven't quit reading is. No, I'm about to finish two books in the next couple of days. I think I finished a couple books right before I left. I think this might be a seven book month, I think in the end. But we'll see.

But I don't even remember. I don't even remember where we are. So I have not quit. I'm reading more than ever. I'll do an old-fashioned book Roundup once I'm back to the HQ next week. Otherwise, hopefully the HQ is doing okay. I ran a lot of experiments in there when you were gone, Jesse. You might notice there's like a growing number of circuits in the maker lab area.

Yeah. Let me just say this for people who understand, when I'm thinking about my Halloween animatronics, I successfully before I left for this trip can now. I'm able to program a sequence in X lights that I can then download onto my Falcon player controller on a Raspberry Pi, which can then connect to a light controller. I'm using Elgado Plus light controller over an Ethernet network, and that controller can then control multiple programmable lights. So I now have the full work chain in place for doing professional caliber, sound lights, and actuator motor synchronization and control, so that I can. can achieve my goal for this year of having a fully coherent Disney-style animatronic Halloween scene with movement, lights, and sound all synchronized and a countdown timer in between execution.

So I have all the technical chain in place. Now I actually have to just start working on the actual props. So that's, I've been hard at work of that. I know you follow that closely and you check all my circuitry when you come in. So hopefully it all looks okay. But I am happy about that. As long as you don't turn into Walt Disney and smoke three packs a day, then you're fine. I don't know, ma'am.

He was creative. That's like a Mason Curry book. What an artist do? I'm going to have to start smoking through it. Not just three packs a day. He also, this all I'll have to do as well. He, you know, he had bad back because he had injuries from playing polar or whatever. And so by the late 40s, early 50s, every day he had a nurse.

a full-time nurse named Hazel George. And every day at the end of the workday, she would give him like a pretty extensive back massage in his office, but she would mix him a whiskey-based drink, which he would drink through a straw because he was on the massage table so he could be drinking. And I would bet you, I would bet you, you know, Mickey Mouse's shoes that he had a cigarette in the other hand as well,

even that massage. Let's be honest. This guy's got a... This guy had to figure it out. If you're going to smoke three packs today, you don't have much downtime. Yeah, you got to get after it. So, the thing about that, daily massages while drinking whiskey through a straw and smoking a cigarette. This guy, this guy lived life.

Did die at 65. I am reading a Disney book, by the way, that is excellent. I'll talk more about it, you know, next week in the roundup. It's an academic book. It's a crazy book. an academic book, new book, Princeton University Press, by an art historian at UC Irvine, named Ronald Bethancor,

that is getting into, like, the precise use of industrial automation technology deployed for rides in Disneyland. And kind of like, there's an academic thesis here about Disneyland as being a place to sort of, like, expose people to the logics of automation, et cetera, et cetera.

but man this thing is research he is like in the weeds on and he's art historian but he's in the weeds on my type of nerd stuff like this type of programmable logic controller was used and wired up in this way for the for the matterhorn bob sleds like just all of the technical details of the engineering behind these rides i'm like this is a crazy book but i am happy it exists so i have some good disney content for sure all right that's enough of that we should probably call it here but thank you everyone just listening i believe next week, we'll go back to the HQ, right? This is the only one we're recording while I'm on the road? Yes. All right. So I think next week I will be back in the deep work HQ. No. And probably we will have, I don't know, I think there'll probably be an AI reality check.

I get so mixed up on what we're recording what we're not. You know what we're not. You know what we're going to get. But I'll always be good on the deep questions feed. So until next time, as always stay deep. Hey, if you've made it this far, you must be ready to. join my fight for depth in a distracted world. Now, the best way to do this is to join over 125,000 people who receive my email newsletter each Monday. You can sign up at calnewport.com

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