Why Do Digital Detoxes Fail? What Works Better? | Monday Advice

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 called 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 Kondo when I did this. 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 contrast tended 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 what I called it at the time. All right, 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 multicellular 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. All right. 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 and 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 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 lampreys 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 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 basal ganglia. So as Bennett writes, the basal ganglia accumulates votes for competing choices with different populations of neurons representing each competing action, ramping up 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 past 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 a 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 and 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 has its own dopamine neuron inputs. And this is what's leading us to pick up our phone. Okay, with that in mind, sub-question 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. 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 food behind it that's 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 the vote puppeteer actions pick up the phone same thing with like a digital shabbat 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 basal 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 the participants of my experiment who just treated it as like a white knuckle experience is it that basal 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. Look, if you listen to my podcast, then presumably you're interested in ideas for taking control of your mind to produce deep results in a distracted world. But there could be a difference sometimes between the type of scattered advice you might get on a podcast and what would be delivered in a carefully produced class. 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One more time, gusto, g-u-s-t-o dot 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 and 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 that to 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. All right, 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 our 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 is going to happen as you step your foot forward and if that matches what happens, you move on happily. But if it doesn't, because there's a loose rock or something, it's immediately that discrepancy is like, okay, problem. What's happening in 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 primary subregion that is going to matter for our discussion here is what's known as the agranular prefrontal cortex or the APFC. Now, this is something that evolved 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 agranular 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, it's just simulating everything it thinks is about to happen just to make sure that the world matches our understanding. But we can use the sensory neocortex to run all simulations of stuff that not about to happen And there other parts of the brain we can also involve in these simulations including other parts of the frontal neocortex that allows us 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, where 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 these 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 ganglia which doesn't know if it's seeing 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 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 an 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 ganglia, 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 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 of times, but 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 the 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 the 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 uh our phone behavior all right so uh your phone is here and you don't want to pick it up the basal ganglia knows there's tick tock on the phone and picking up the phone is um you know it's reinforced because it's going to give us that little hit and that hit will be 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 the simulation the basal ganglia would just say like picking up my shoes doesn't seem very rewarding but picking up the phone does you'll pick up the phone but the simulation is going to run through this whole simulation that ends in a very rewarding end state where you finish the run and you have the endorphins and you're you feel a sense of accomplishment from having you know made progress in your training and that's really rewarding and And the basal ganglion 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 end state in the simulation. And if it's strong enough, if that reward state was strong enough that you experienced at the end of your simulation, picking up the shoes now outvodes 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 uh expose ourselves to rewards right 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, setting down the path to that deep reward can outvote 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 were 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 – it's Rococo and it's abstraction of just stuff that's like, ooh, that's kind of weird. It's 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, et cetera, or big sense of accomplishment, these can way outweigh the phone. but you have to have been exposed to them enough time that your basal ganglia knows about them and then therefore it'll rate the simulations 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 activities the better is that they're basically training their basal ganglia 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 to possibly win out. The first step towards whatever you're doing that's not the phone has to win out against picking up your phone. So 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 can literally just take a few steps and you can be there working on the art. Whereas if you have to drive across town to an art studio, that first step is not proximate. So 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 leading you 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 are 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 win 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 ganglia as a real reward is if you've actually experienced it before and it's that's why i say it's like a bootstabbing 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 wouldn't 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 bull on X. So it boop straps on each other. So this is why the digital declutter 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. 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 261. Is this part of the thinking research or is it just a random book? It is, yeah. So 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 I'm reading this book. It's like my fourth or fifth book. My plan is to read maybe 10 books this summer that are just about thinking and 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. Hey, let's take another quick break to hear from some of the sponsors that make this show possible. The problem with these huge tech companies is that they don't just want your money. They want to know everything about you. 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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 contributions at podcast at calnewport.com. All right, Jesse, what do we got to get going today? Our first note comes from Paul, who is 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 Lo-Fi 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 why I been trying to live a summer of LUD a term I 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 OK. 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, I have a complicated take on 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 uh yeah she goes on to say she sits in the chair 17 hours a day con she just sits there scrolling constantly just put in my veins actually it's a hard question to ask you because I have sort of mixed reactions. The thing I think that is good about this advice, if we're going to put on our neuroscience hat, is that what you're tuning down is the 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 sort of convinced herself that's the only place you use the phone. So 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 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, But 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. 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've seen I'm around this person 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 they put around their, their drinking. So if you're having to have a scrolly chair and all these other times, an old phone or this or that, I mean, I mean, at some point you're like, maybe I just want to use social media. So I'll throw that out there as well. But anyways, I liked the idea. I liked that it's successful. 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 you don't i mean it's all bootstrapping yeah you you use it less other things build more rewards um 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 him i was like you have to show me on your phone because i'm not going to download the tiktok app so 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 Tik TOK. I have Tik TOK on here. I think from the article I wrote for the New Yorker last year where I use Tik TOK. Um, and I have zero interest in like, I'm clicking on it now. Does this even work anymore? All right. Clicking on Tik TOK, update your app. So there we go. It's like, I can't. He expected me to bury an entire airplane in my backyard. 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. And 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 this is 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 burying 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 heart's 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 Goodhart's law? There's three key takeaways. Let's just look at these. Takeaway number one, Goodhart's law warns of distorted metrics 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 over-focusing 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 Goodhart's law. Organizations should 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, 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, right? 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 effort. 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 to burn endless tokens and produce code that it checks the code and this code goes to 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 uh 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 pseudo-productivity, 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'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 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 palmieri 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 um so i'm going to read a core paragraph here uh let's see where did i find this okay i i read this earlier so i'm just going to hone in on this paragraph, which I think kind of gets to 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 access restricts availability to ensure that only patients with appropriate clinical indications can 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. Da-da-da. Okay, so up here it says, time to maximize 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 um gambling and uh sir 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 um tobacco use we had age gating right it was uh 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. So there are restrictions on what you can and can't do when you're doing gambling games, 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 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 ethicists and policymakers talking about engineered addictiveness, they hone in on – they have this mental model where they can hone in on these 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, 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 like the new posts pop up that's like a slot machine and that's kind of 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 ganglia always vote to pick up that phone and look at that app is actually typically just in the core functioning of the app. Right? I'm showing you videos and I'm selecting the video to show you based on things you liked 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 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 non-addictive. That's the issue with the engineered addictiveness approach, is that I think this model just isn't correct. now you could say we'll turn off the algorithm but that the algorithm is just the thing that decides what video to show you next in the context of tiktok it's using a pretty basic multi-armed bandit style optimization if you turn off the algorithm what it showing you then like what videos does it show you So that always been my issue is that I don think the mental model of social media is fine and then we added addictive features made it bad turned us back off again I just don't think that mental model is right. I think for a lot of people, it's built in – there's this sort of valence switch on things like Twitter where for a while, if you were more like a left-leaning academic, Twitter was great and exciting. And then it kind of got as 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 uh you know engineered addictiveness is bad so using recommendation algorithms that are using uh 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 you're instagram right or you're facebook you go back to follower feeds uh 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 um and then 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 acts will 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. 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 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. They have to go back to the way they were before when they were interesting, but not compelling in this way. Our understanding of brain science that we talked about today helps us understand why this is. If like looking at X, sometimes you get something new that's interesting from someone you follow. most of the time you don't, means that its 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, but what if Netflix wants to recommend shows? Does it mean it can't do that anymore? And wouldn'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. Now, 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 a Netflix? This is not the addictive issue with Netflix because it's not 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 recommended stuff by people. But I don't know. Maybe I'm being 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. Did 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 ChatGPT to summarize it for them and not bother reading it for themselves. Do you have any thoughts or insight as to the value of DeepWork 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 on DeepWork'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. 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. We're using our brain in ways that we weren't evolved for. So anytime we're grappling to understand words or to produce words or understand mathematics or produce mathematics, these are examples of activities that 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 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 when you whatever you're doing in this context in 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 refines your understanding of things. 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. So just like if we were 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 deep, 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 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 like i never moved my my body at all um and that would be easier in the moment but it would it's not good for my body it'd make me less healthy and i'd be more miserable about it we just gotta start thinking that same way about our mind i again i think the role of ai and education right now should be incredibly limited right incredibly limited it should be it should be basically focused only when there's ai driven tools that's used in a professional uh 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 you're at a university it doesn't make sense to bring in polio 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, um, my new book, which I haven't just an inkling in my eye, my in-defensive thinking book, we'll 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 is like basically at the end, it was in the pandemic and I, it was a slow productivity in the deep life. And it 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 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. 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 a 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 walk schedule for today um yeah i hope so let me see we work on the podcast i have a call that we're going to go into town yeah tonight if it doesn't if the rain holds off I'll definitely do a thinking walk. Definitely do a thinking walk tonight. Yeah, I've been working on an article up here. So 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 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 don't know where I am. Just as long as the audience knows that you haven't quit reading is efficient. 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. 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, 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, I am 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 Elgato 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 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 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 i am happy about as long as you don't turn into walt disney and smoke three packs a day then you're fine uh i don't know man he was creative that's like a mason curry book what artists do i have to start smoking through it not just three packs a day he also this this all i'll have to do as well he you He had a bad back because he had injuries from playing polo 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 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. Yeah. Let's be honest. This guy's got it. This guy had it figured out. If you're going to smoke three packs a day, you don't have much downtime. Yeah. You got to get after it. So think 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 it's uh 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 bethencore uh 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 expose people to the logics of automation, etc., etc. But, man, this thing is research. He is in the weeds. He's an art historian, but he's in the weeds on my type of nerd stuff. This type of programmable logic controller was used and wired up in this way for the Matterhorn bobby sleds. Just all of the technical details of the engineering behind these rides. i'm like this is a crazy book um 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 uh but thank you everyone who's listening i believe next week we'll be back in 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 um 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? Just stay tuned. Stay tuned. You never know what you'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. 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