B (23:34)

I read that.

A (23:35)

Yeah, right. Isn't that cool?

B (23:36)

New Yorker.

A (23:36)

Yeah, where he like goes out and they don't end up catching a squid in the inn, but like, it's just hanging out with this guy who's hunting a giant squid. That's like quintessential.

B (23:44)

They look for the babies and stuff.

A (23:45)

Yeah. Google article. I wrote an article about email recently though, so I comparable. He spent two years, like hanging out with people hunting giant squids and like on this like high tense adventure, like, is he going to catch them or is he not? But in my most recent New Yorker article, and I don't want to brag, I let an AI agent filter my inbox. And you didn't know, like maybe it was going to miss messages, right? I don't know. I didn't know what was going to happen. We kind of got into like the technology of querying language models. So like, here's the thing. Granny and I call him Granny. Granny and I are like. And I think I've established this basically the same person in terms of like, talent and the importance of our results. I mean, we kind of do the same sort of things and, you know, some cards fall his way instead of mine. But me and Granny, like, really, it's like, get me Granny's not available. Get me noobs. Like, it just goes back and forth. We're basically the same person. I know he doesn't listen to this podcast.

B (24:42)

I think he went to Toughs oh, yeah, Yeah.

A (24:45)

I will say he probably didn't play lacrosse for Tufts. Doesn't look like an athlete, but he's got a brain. Yeah. All right, well, good for him. Smart guy. Smarter than me. Cool guy. All right, let's keep rolling. See what do we got here? All right, my fourth piece of advice comes from the master Stephen King. I'll put this quote up on the screen here for people who are watching this from a 2014 interview for Rolling Stone. Here's what King says. I wake up, I eat breakfast, I walk about three and a half miles. I come back, I go out to my little office where I've got a manuscript, and the last page that I was happy with is on top. I read that, and it's like getting on a taxiway. I don't spend the day riding. I'll write. I'll maybe write fresh copy for two hours, and then I'll go back and revise some of it and print what I like and then turn it off. It's a cool article, Jesse, because it gets into his HQ because the reporter shows up. And the cool thing about it is King has security concerns and he's just so famous and whatever, he has to not have an easily identifiable place he lives or works. So it's a really nondescript office building. He owns an office building. A nondescript office building in, like, a nondescript part of Bangor, Maine, that you go into this building, which they say looks like Dunder Mifflin. And it's like all this King memorabilia and things from his movies and his Gothic Victoria. Like, he's like, there's really cool space where him and his staff are. But he couldn't put it in a cool place because otherwise people just start showing up. That's why. That's why we're above a restaurant, because otherwise people knew where we were. Jesse, the problem that me and Granny both have, people are always trying to chase us down. Leo is always like, I want to know about your latest book. Maybe we'll do a movie about it. Like, Leo, man, chill, chill. Okay. You gotta do a podcast about fixed schedule productivity. I don't have time to talk about your movie ideas, but I thought that was cool, so I like that piece. All right, so what was King talking about here? The question that he was answering is, like, what's your schedule as a writer? Writing is hard. Waiting for the muse or being like, hey, I'm going to get some writing at some point today when I feel up for it. Like, that's amateur hour. The real, the writers who write a lot, like King and King, he writes a lot. He's definitely a high volume writer. They do the same thing every day. Some days are better than others, but that's what adds up over a lifetime to a lot of books. Have your schedule put aside the time for things to matter. Do that time, have a ritual around it. You can have a taxiway as he talks about it, for your mind to get into the mode and then do the work well. You can imagine this is something I'm happy to generalize. Make regular time for the things that matter most in your life. Whatever your equivalent is of working on book manuscripts. Don't wait for having a lot of free time or feeling inspired for it. Put it on your calendar, have a ritual surrounding it and just do it on a regular basis, even if inconvenient, even if it doesn't always go well, and even if sometimes nothing of interest is produced. That is how you make progress on hard things. Require a lot of time if we're going to make this concrete. The particular tactic I want to talk about is autopilot scheduling. This is where you actually have the same time, same place, same day for certain activities and they're on your calendar in repeat for week, weeks, weeks into the future so that time is protected. Nothing gets scheduled there. If you went to the day and say, I really want to prioritize my big project, that day might already be fractured too much for you to make Progress. But if 10 to 12 for the next six months on Tuesday is put aside for like, I work from home and then I go to this library and that's where I work on whatever the thing is, the new skill you're learning or whatever, it doesn't get taken up because you protected that when you're making meetings or appointments, that time hasn't been available and you have a routine, you do it week after week. You don't have to think about it. You don't have to burn mental energy or convince yourself that actually gets done. You got to autopilot to schedule the stuff that you know you have to do or the stuff you know you really want to do on a regular basis. That is the absolute best way to make progress on it. So you follow something like Stephen King's lead. All right, we got one more idea here for the fifth idea. I'm kind of pulling a fast one, so I apologize. I am going to turn away from the pantheon of quote unquote legendary writers. And our final idea is interesting, but it's going to come from an idea who is much less impressive than the other four. And I'm talking about myself. So I actually have a quote from an interview I did a couple years ago from which I will draw my final suggestion. I couldn't help myself. Like, I don't know, I've done a lot of interviews, so I put one of mine in there as well. Before we get to that final piece of advice that comes from yours truly, however, we have to take a real quick break to hear from the sponsors that make this show possible. Are you looking for a gift for your family that they will find meaningful? Let me tell you exactly what you should get. An aura frame. Now these are digital picture frames to which you can easily upload photos. You can do it straight from your phone with just a few clicks after you take the picture and then the digital frame will rotate through the images that you have uploaded. So we bought one of these aura frames for my parents and my wife's parents. They love them, right? It's become a habit for us. If we're doing something interesting, we upload a couple photos. And now my parents and my in laws, they feel like they're kind of a part of what's going on. They know what's happening in our life on a sort of, you know, hey, you guys went on this interesting trip this weekend. We got to see it, we got to know what's going on. It really is a great gift and they love it as well. Now here's a cool thing. If you're buying an aura frame as a gift, there's a feature where you can pre upload a bunch of photos before it ships. So when someone opens it, there's a bunch of photos to show right away. Because otherwise little known fact Jesse, the default photos in the aura. It's a lot of me giving like thumbs up and finger guns. A lot of like this and a lot of this. It's part of my sponsorship deal. You don't want your in law seeing a lot of like me doing finger guns. So you got, you can pre upload the photos ahead of time. I wonder if there's something in our ad copy that says do not imply, do not use, do not imply that Cal Newport finger guns is anything to do with this product. Here's the thing, you can't wrap togetherness but you can frame it for a limited time. You can save on the perfect gift by visiting auraframes.com to get $35 off Aura's best selling Carver mat frames. These were named number one by wirecutter. You also need to use the promo code deep questions at checkout to get that discount. So that's a U R A frames.com promo code deep questions. This deal is exclusive to listeners. Our listeners. And these frames sell out fast. So orders your now. Orders your order yours now to get it in time for the holidays. Support the show by mentioning enough. By mentioning us at checkout. Terms and conditions apply. Also, want to talk about our friends at caldera lab? Here's the thing about being an aging guy. Not that Jesse and I are aging, but, you know, we're in our upper 20s now, so we have to start thinking about these type of things. When you're young, you don't think about your skin, but then one day you look up and you're like, yikes. Look like a grizzled old pirate captain. What happened? This is true, Jesse. I was doing a. I told you, I won't get into the details, But a professional movie shoot with, like a professional crew and professional makeup artist. Really interesting makeup artist. Real pro. She's worked with, like, tarantino. She worked on sinners, like, whatever. She had to put makeup on me for so long before I could get on those cameras. I was like, oh, my God, I'm old. Like, after a while, it was like she was holding like a sandblaster and just going on. So here's the thing. We get older, her skin starts to age. This is why men need to know about caldera labs high performance skincare line, which is designed specifically for. For men. Their products include the good, which is an award winning serum packed with 27 active botanicals and 3.4 million antioxidant units per drop. The eye serum, which help reduce the appearance of tired eyes or dark circles and puffiness. And the base layer, a nutrient rich moisturizer infused with plant stem cells and snow mushroom extract. The makeup artist told me, like, you got to moisturize every day. Like, you got to moisturize. So anyways, I told and I told her about caldera lab. I was like, I have exactly the product. All right, so anyways, this stuff works in a consumer study. 100% of men, so their skin looks smoother and healthier. Skin care doesn't have to be complicated, but it should be good. Upgrade your routine with caldera lab to see the difference for yourself. Go to caldera lab.com deep and use code DEEP at checkout for 20% off your first order. All right, Jesse, let's get back to our deep dive. We're on to our Final piece of writer advice that we're going to apply to our lives. This one comes, God help us, from me. I'm going to kind of cheat here, Jesse, because I also directly give life, like, advice about your life more generally than just writing. I'm going to give a quote here. This comes from an interview I did for the New York Times magazine back in 2023. Speaking of photos, by the way, there's another collision of worlds, because the photo for this New York Times magazine was David Marches, talk column. I've told you about it, but you had to go to New York. They had a fashion photographer who do the photos. And he was so disappointed with me to have a writer come in. I'm like a gargoyle who, you know, can't control his face. And he had just done a photo shoot with Iggy Pop who had, like, come in shirtless. It was just, like, fascinating to look like. And I'm like. He's like, look less terrifying. Light's bad. Anyways, so this is a quote that is both about, like, writers, but also about everyone's life outside of writing. So I'm jumping straight to the life advice with a double acting piece. I'm kind of cheating here, Jesse, but it's my own quote so I can get away with it. All right, let me load this up here. Here's what I said. The critical mindset shift is understanding that even minor context shifts are productivity poison. When you looked at an email inbox for 15 seconds, you initiated a cascade of cognitive changes. So if you have to work on something that's cognitively demanding, the rules has to be zero context shifts during that period. Treat it like a dentist appointment. You can't check your email when you're having a cavity filled. You have to see it that way. All right, so that's kind of about writing. It's kind of about life outside of writing all at the same time. But here's the generalized point I'm making. When doing something important to you, professional or personal. It's like having a conversation with someone on the phone, watching something meaningful, or you're like, doing something professional. I'm coming up with a business strategy. I'm writing, like, a really important memo. You have to remember that the absolute poison that you need to avoid is context shifts. This is what we get wrong about productivity in the digital age. It's what we get wrong about efficiency in the digital age. We're used to productivity being about getting producing things faster. What's going to make me move faster? Because when I was building Model T's the way I got the productivity ratio, cars produced per input put in. The way I increased that ratio was, was moving faster. I had a faster, more efficient way of putting things together. So we think that's what productivity means. But in the digital age, that is not our problem. Our problem is not that we move too slow and that we need to find ways to move faster. The problem is, is in the digital age, the things we're creating, we're creating with our brain. And our brain is a really fickle thing to work with. If you change the context, even briefly, you show it a brief distraction. You look at an email inbox for 15 seconds to check for a message. You just go to see what's going on on social media real quick. The brain goes haywire. It initiates a context shift. This is an expensive cognitive operation. And before this shift is complete, you try to come back to the thing you're doing. So it tries to abort that shift. But before you can fully lock into what you're doing, you check something else. It's a cognitive disaster. Your cognitive capacity is reduced, you're exhausted. The stuff you produce isn't, you actually end up producing is not very good. It would be like if in a fact we discovered like the way the human body worked was like, let's say you're, you're like moving a wrench. Like, let's say the way our human body happened to work is that if you turned your head too far, your, your arm strength disappeared for 10 minutes. Like you, you have a hard time holding a wrench. Let's just say that was true. We would keep that in mind when designing factories. Like, okay, let's be careful, let's not build our factory in such a way that the, that the workers keep having to turn their head because they're going to lose their arm strength and they're going to have to wait for a while until they can really speed up again and get the stream wheels back on. Well, this is what's happening cognitively speaking, in knowledge work in a digital age. Context shifting takes all the proverbial arm strength out. Our ability to concentrate really dies down. But instead of protecting ourselves from this, we make it even worse. We've added even faster, more ready forms of context shifting and distraction. So that's what productivity is in the digital age. Avoiding context shifts, not trying to go faster, avoiding context shifts. In fact, I've talked about this in multiple books of mine. I will take on significantly more cumbersome processes. Slower, more Structured processes that are a pain in the moment. I'll take on a huge amount of hardship if it saves me from context shifts. That's the productivity poison. So that's the advice I want to give here at a high level, is you need to think about context shifting as the thing you want to avoid to be productive. Not getting faster. Now we want to get more concrete. You know, read deep work, read a world without email, read slow productivity. These books of mine get into it. But I'll give you two. I will give you two concrete ideas you can do just to sort of get more used to thinking about that. In the world of work, if you're working on something deeply, you should have a set rule for what happens in deep work blocks. How long do I want to go? And my rule is no context shifting. Start a timer. Put the timer on your phone. Let me tell you why, because now your phone is, yeah, it's in front of you, but it's full screen timer. That's the purpose, is it has a timer on it. And the way you're gonna, your way you're gonna run this process is that if you context shift, that means looking at anything that's not the work you're doing. So email, slack, your text message on your phone, social media, YouTube, you have to start and restart that. Stop the timer. Okay, here's how far I got. And then you have to restart it again. I only got seven minutes. I only got 15 minutes. So now your phone, which will be one of the biggest sources of context shifting, is pre mocking you. You see that number on your. That's what's on your phone is seven minutes. Like the timer's going. And so to look at something else on your phone, you have to swipe that time to the side like you know exactly. Oh, my God, I only made it seven minutes. Like, you have to confront what you're doing. So I think have your phone with a timer full screen. That's what you're timing. And be like, I want to get an hour. I want to do a half hour, whatever it is. And like, hey, if I contact shift, I'm going to restart at home. The answer is really simple. You got to plug your phone in the kitchen. Just plug it in. The charge, if you need it, go to the kitchen. It's not with you while you do other things. It's not with you while you're watching tv. It's not with you at the dinner table. It's not with you when you're reading, when you're doing Other things, you actually have to do those things. There is no easy distraction to turn towards. You'll lose a lot of your taste for the context shifting. This really has to do with your short term motivation systems. What's happening in your brain. Don't worry about the neurological details. Keep your phone plugged in the kitchen when you're at home. I can tell you what though, it's going to make certain shows more boring, especially if you're watching Netflix. Netflix does more of this. Supposedly Netflix is doing more of this engineering shows with stimuli stacking in mind. They assume that their viewers are going to be looking at their phones while they watch the show. So they have to telegraph much more clearly what's happening. So you can kind of listen out of one ear or be brought back up to speed of what's happening. My wife and I are watching the, the Claire Dane show the Beast Within Me or something like that about an author and et cetera, et cetera. I feel like they're doing a lot of that in that show. Like, oh my God, you just told me this and that is bad for these reasons. And I worry because I worry that I'm now responsible for the thing that you did. You know, like there's a lot of this type of telegraphing because I think they're assuming so you might find Netflix kind of boring actually if you do this, but it's worth doing. All right, those are my five ideas, I think. Jesse, it's time for some takeaway music. All right, so when it comes to shaping a more intentional and meaningful life, there are all sorts of different sources of wisdom today. We looked at one source that's particularly close to my heart, which is writers. We took five pieces of reasonable advice about the craft of writing and we transformed them into five more general ideas about cultivating a deeper life. This resulted in five concrete suggestions for actually putting these ideas into action in your own life. Let's review these. Jesse, can you bring this up on the screen here? I have them here. Our five pieces of advice were have a lifestyle vision and use an insight journal to help edit it. Practice diligence and deliberateness with seasonal projects. Make sure that you have a training plan. Use evidence based planning when trying to make decisions in your life. Turn every page. Actually study the thing closely before you go after it. Use autopilot scheduling to make sure that you're making progress on a regular basis without having to expend any decision making energy on the stuff that matters. And finally, minimize context shifts and almost anything you're Doing important in your life, whether it's personal or professional, you need to not be trying to shift your context between different things. Do those five things. Those are five ideas that come out of the world of writing, but it will apply to the life of basically anyone who's listening to this show, and you will find some more depth. So whether you're writing a book or authoring a deeper existence, how you approach the task matters. And it was worth taking a moment to learn from those who have spent their time mastering this craft. All right, there we go. Jesse.

B (42:42)

I love the generalization part.

A (42:45)

Yeah. I don't know why. Mainly because I know writers, but I feel like writing advice often is pretty generalizable to life. The other field where this is true is athletics.

B (42:53)

Yeah, golf.

A (42:54)

Golf. Like all lessons come from golf, but in athletics, there is a lot of reasons. In golf and other places, there's often these lessons that carry out other sorts of things.

B (43:03)

Do you think Alan, if he had known Robert Carroll's career trajectory and writing these epic biographies, he would have said to her every page, yeah, yeah, he was writing because, I mean, that was advice when he was writing for a magazine. Right?

A (43:16)

Yeah, I know. I think, like, he created a Frankenstein's monster. Sounds like I'm gonna go away for 12 years to the Johnson Library. Just, like, reading, you know, the menus from the White House Chef menu. Yeah. I guess you can take these things to an extreme. I think I would be good at that type of lifestyle.

B (43:35)

It's like in Indiana Jones, and they go into the basement. There's, like, never ending.

A (43:39)

Yes. And then in the very back. So here's the thing you don't know about that scene in the very back of that giant warehouse where they're taking the ark. You can see in the distance Robert Caro just opening up the boxes and looking through.

B (43:51)

I'm turning every page.

A (43:52)

Turning every page. Hey, there's an arc here. What's in there? Face melt. That'd be a twist. The ark ends up killing and melting the face of Robert Carroll. That would be a deep dive. You know, that's a matte painting, that scene. So they. It's a warehouse, just like a sound stage. That's how they did all the effects back then, the 70s and 80s. They painted that warehouse. You paint it on a piece of glass, and then you leave a little bit unpainted at the bottom, and you put that later. So you film just someone pushing that cart on an empty concrete floor and a soundstage, and then you film it through that Matte painting and then that gets on the film. So now it looks like you're walking into these vast background. And they're big. These matte paintings are big. Like the size of our wall in here. Yeah, old school special effects are pretty cool. All right, let's see what else we got going on. We got a bunch of questions coming up. So what's happening our show? We got questions from you about these type of issues, etcetera, like living deeper life, etc. And then we have call, we got a case study. And then by back by popular demand in the third act, I'm going to react some more comments. So kind of get, get back, get the word of the people and respond. Some I agree with, some I don't. Some I think are funny and some they change my mind on. So we'll, we'll get into all of that as well. My good friend Granny might stop by at some point to talk writing. It's gonna like make that people think that's true, that me and David Grant are just like really tight. Granny's gonna stop by, we're gonna talk some Tufts lacrosse, you know, research. Talked about researching and Tufts lacrosse. It's gonna be a good time. All right, enough of that nonsense. Let's get into some questions.

B (45:29)

All right, first question is from friend of the show, David Duane. I'm currently reading Jim Lehrer and Tony Schwartz's book the Power of Full Engagement, which encourage you to manage your energy with equal focus as you manage your time. What are your thoughts on managing energy versus time?

A (45:45)

I like that book a lot. I remember that book from the 90s, the powerful engagement. And that was the idea. We talk about time management, but not energy management. But if you're careful about increasing your energy and then matching your energy to the things you want to produce, you're going to get a lot more out of your business. So it's a really smart idea. These are the type of books I came up on in like the 90s when I was reading all these business books as a teenage entrepreneur. They really influenced the way I write today. I think it was probably for the good. It's a style of book that's not as. Maybe it's not as popular as it once was. It really was big in the 80s or 90s. Books that are just like, hey, here's an idea. I mean, you see this so much in my writing. Here is an idea for like, organizing a part of your life or doing this type of work or organizing your tasks or trying to maximize, like, what you get out of your day. I'm just going to walk through it. It's like a concrete idea. We get this wrong and we talk about it. We have steps to it and matrices. It was just like this sort of business pragmatic. Nonfiction was like a really big thing because people are like, yeah, it's hard to be in business and I want to know how to do better. These books are a little bit less common now, but it's definitely. My writing is basically a mixture of Jim Ler and Tony Schwartz style writing with New Yorker writing. And I just put those two things together so they were both very influential. So I liked the power poll engagement. I met Tony Schwartz actually back in God. It would have been like 2015. I did an event at Lincoln Center 99U Conference and I was speaking at one of the stages at Lincoln center about so good they Can't Ignore youe. And I remember it because like I was backstage was me and Brene Brown and so we talked about. Yeah, that was that event. Well, it was a cool event and I might be mixing events up. I think that was an event. I met Gretchen Rubin was there and we were hanging out and I met Tony Schwartz there. Interesting guy. I think he co wrote one of Donald Trump's books like the Art of the Deal type books or whatever and then he during the first Donald Trump election like came out against them and it was like a big controversy at the time or something. So I don't know. Tony Schwartz, interesting guy. I like powerful engagement. I'm a big believer in energy management. This is an idea. Maybe we should do a deep dive on this book at some point. It's an idea that really should come back. It's a really smart one. Low energy. You can't do much work so it matters. So do important stuff when you have high energy, less important stuff when you have low energy. But also manage your energy itself with diet and exercise and sleep. That point that the physical matters for the cognitive. I think we accept it more now but we don't do much about it. So I think it was a good book. I recommend it. It's like again it's 90s style business books. You didn't have to have stories. Not like an Adam Grant book where you're going to like let me have stories and ideas or whatever. It was just like here's something else that's important. We worked with our clients on it. Here's like the five things we tell them to do. That's what these books were. Maybe they're still Writing these books, I don't know. All right, who do we have next?

B (48:39)

Next up is Lauren. I struggle with me time because of my family, work and hobbies. When I sit at home, I feel antsy. What should I do?

A (48:47)

I think it's a bias towards action. I think is absolutely fine. And if anything helps stave off anxiety for a lot of people, the sort of sit still, like, let me just have an empty evening, go for a slow walk or take a bath, like some people, it makes them really anxious and activity makes them feel better. And I think there's nothing wrong with that. And I think this is really a personality type. I know people that have to constantly be working on projects and other people who really would like to minimize work altogether. They're like, I want to just sit and enjoy the sun in the afternoon and listen to a ball game and I don't want to do things. And there's people that are in between. So there's nothing wrong with the bias towards action as long as, here's the caveat, if all of that action is imposed from outside, it's extrinsic, it's inflexible and it's deadlight driven, then you're going to burn out and be stressed. Like that's just the people, like the people who have the really hard jobs have to work 100 hours a week. They have a lot of action. They're also burnt out. What you need, if you have an action bias is a lot of action that is autonomous and flexible. You're choosing to work on it because intrinsically it's interesting to you, but it's also flexible. Like if you don't do it today or you're feeling sick and you're like, I'm just going to cut this short. Nothing happens. It's just you choosing to work on these projects and you make progress as you can and you're not stressed out if something you can't make progress on for a while. That works really well for action oriented people. Intrinsic, autonomous, flexible action. Then fill your time with as much action as you want. That's not going to stress you out because you don't have deadlines, you're not falling behind. You don't have a sense of being forced to do something you don't want to do. So I like it. I wish I do have an action bias, but I don't have an extreme one. I wish I did. It does help my anxiety though, when I try to stay busy with intrinsic, flexible, autonomous things. I think it's a Pretty good. This is sort of Arnold Bennett's how to live on 24 hours a day where he's like, yeah, you should really be filling your time pretty intentionally. He's like, there's nothing really good. You recharge when you sleep. Otherwise you're supposed to be doing things and it should be outside of work. Things you choose that are meaningful, flexible and autonomous. That's basically his book. I think it's a good idea. All right, who we got next?

B (50:56)

Next up is Frank. How should someone think about Reddit, which blends meaningful information and attention traps?

A (51:03)

Yeah, I think Reddit, it falls into a category of multiple other online information sources in today's day and age. Where on the one hand it is true that the information source has a lot of good information in it, that it's. There's a lot of people who are contributing information or if it's a. Maybe it has really good creative output. Like Reddit will be an example of a place where there's a lot of really good information, like people talking about stuff they know about. And on YouTube is another example. There's like a lot of really good creative output. Right. There's junk on there, but you can also see really interesting cool stuff as well. At the same time, it's also true of these services. They have attention traps. They can put you in addictive loops, they can give you that consistent purity signal, reward signal that overwhelms your short term motivation system. And now you just turn to it for numbing or distraction. It's just, if I just get lost in Reddit, there'll be debates and arguments and they're suggesting new things to look at. What about this argument over here? And you go read that and next thing you know you've been on it for hours. YouTube, of course, is the same way. You go down those auto recommendations and God knows where you show up for those type of information sources. I always say, like, you need to use them, like you would use a reference library and not like you would use your television. So if you use them as I am bored or uncomfortable or feeling strained and I'm looking for numbing or distraction, let me just go and see what I find. You're exposing yourself to the attention traps. If instead you say, I am looking for this particular thing, let me go see if I can find it on here, you're extracting the valuable information. So like, oh, I want to, I want to know more about. I'll give a concrete example where Reddit was useful to me. This was back when I was writing my book World Without Email. I was looking for a good, concrete example about the arrival of email. What was it like when email arrived at companies in the 80s and 90s? I mean, I'd done a lot of archival research about the business journalism about email and how it was being written about, but I was like, what was it like in the companies? And I was just searching around Reddit on just using Google searches and I found a great Reddit thread where an engineer from IBM was like, let me tell you, someone asked that question. He's like, let me tell you, I was involved and let me tell you the story. And then I kind of tracked him down and it was really useful. That's good Reddit. Bad Reddit would be if I then was like, let me just stick around and now see people just like fighting about, you know, email flame wars and Usenet and people getting like whatever, like nonsense that's like, not really relevant to what I was trying to do. YouTube is the same way. If you want to look something up, it's like a great resource library. Also, if there's like a particular show you like to say, yeah, I know that show comes out on Mondays and I, I'm gonna watch the video on YouTube of it. That's actually, that's fine as well. You're like, look, I'm a big fan of like Mark Rober science videos or I'm a maker and I like the Wicked Makers channel or something like this and they have a new video out, I'll go and set it up and watch it on my tv. Like, that's fine as well. But if you're just sitting there on your iPad, following the recommendation, you're following the recommendations and you end up in some sort of like weird corner where something like completely unusual or weird or mean is happening, that's a bad use. So that's the way I think about those type of dual use platforms for appointment viewing or looking things up. They're great for distraction or numbing danger Will Robinson. So don't use them for your distraction or numbing. Have higher quality things that you use for distraction or numbing and use those that have the attention traps just for looking things up or appointment viewing. I talk about that in my book Digital Minimalism. There's a chapter on joining the attention resistance. So that's one of the strategies of like actively resisting the sort of attention trap economy is having this particular uses for those platforms and in particular being very careful about what you allow to be used for distraction and numbing. All right, what else do we got here?

B (54:53)

Next up is Maya. Can you explain the difference between the Lincoln protocol on reading specific books for a purpose and the more expansive view of reading for the sheer joy and mental workout of reading? Should I shift my own eclectic reading habit towards a linkin protocol or some hybrid of the two?

A (55:09)

You should do a hybrid. These are two related but not completely the same type of purposes. The linkin protocol is to seek out books that are going to make your life better and that you maybe have to stretch to understand or build up to being able to consume. And that's like an important way to self improve. And it also makes your mind very sharp. Not all of your reading should be trying to pour over Euclid like Lincoln did to try to learn geometry. You should also have reading you do for fun and you do both like this. I just like this. It's elegiac, it's fun, it's an escape or it's just really interesting. And also I'm working on something over here that it's hard and pointed towards a particular purpose. Do both those things and you know what, that ratio can change depending on the season of life and your energy. If it's a busy period like I'm about to enter thriller, December, reading a lot of dumb stuff, January, you know, some other periods or in the summer sometimes when like I have a lot of energy, I'm maybe reading a lot more linked protocol type expansive. So the ratio can change. But they're two separate related but separate types of reading. And I think it's important to have both. The the big thing to avoid is having none because then you're letting the devices win. They are just going to get those tentacles into your brain. That's not good. All right, what do we got?

B (56:23)

Do you know any of the books that you're going to be reading for thriller December?

A (56:26)

I'll read the Dan Brown book. You got me for sure.

B (56:29)

Some folks have been asking. They want to read along with you.

A (56:31)

I don't know yet. I'm going to read that one. I haven't really.

B (56:34)

It's Secret of Secrets. That's the new one that came out.

A (56:36)

Okay, so I'll read that. I got to see what the new crop of thrillers are that I should know about and then I go back and look for classics. I like first books often. Oh, here's someone's been writing for 25 years and I haven't read their first book. I like first books, so I haven't got into it yet, but maybe Maybe I should try to announce. Try to remind me.

B (56:54)

I will. I'll remind you.

A (56:55)

I'm working on this one. Or here's an interesting one I'm thinking about. Yeah, Thriller, December. All right. Do we have one more question?

B (57:01)

Yep. Next up is Welby. Do you think that active podcast listening, done with focus and reflection, can ever rank high in the learning process? Or is it, more often than not just another form of distraction disguised as productivity for the knowledge class?

A (57:15)

Well, I don't think it's pure distraction. I don't think it's productivity. I think it's entertainment. What I mean by that is it's like any other source of entertainment. Watching a TV show or listening to a radio show, it's something that's interesting to you. And some of the stuff might be kind of dumb and some of it might be kind of smart, but I'm not being productive. I'm not trying to master. If I really want to learn a new skill that's important for my job, I should actually learn that skill and get directed practice and do deliberate practice. So think of it as an entertainment. But it's a good source of entertainment because you can focus it and target it. Right. Unlike TV or radio, you could find shows about exactly what you are interested in. Like, my kids and I were getting excited about going to one of our local theme parks. There's. Tell you what podcasts are. All they do is review theme parks. Oh, there's a great one. Jesse. I hadn't heard this format before, and I feel bad because I forgot what it was called. But it was a travelogue format. So he would go to an amusement park and take audio and then he would edit it and narrate it and it would be like he would hear the audio in the background. And then I went to the such and such ride. The line was not that bad. I talked to someone in the bed. Then when I clicked in it, da da da. And the turns, la la la. He's just kind of like narrating. Then I got this food and it was actually pretty good. He's narrating his day with audio clips in it. And it's like super interesting, relaxing aspiration. Like, man, I just kind of want to be at the park with this guy.

B (58:41)

New Yorker does similar stuff with their podcast. I just listened to that one. Darkness in the Darkness with a. She did the research of that crime.

A (58:48)

Yeah, my wife was talking about this. Yeah, I like that. That's a cool. But so here's what I thought was cool about that format is, it's a great format, but in like true crime you have to do a lot of work because you have to like actually have a crime that you're investigating. This guy was just like traveling, but it reminded me of the travel channel. Yeah, it's like any show that's popular on a travel channel, but on this very particular thing I happen to care about at that moment. And there are shows like that for anything that you're interested in and there's probably good shows on it as well. So it's like hyper targeted cable tv. That's the way I think about podcasts. And you're like, oh, this is good. I have a good source of. There's not a tv. I'm not watching a lot of TV shows, but I have podcasts. It's a higher quality source of entertainment. So it's not about productivity, but it's not like mindless distraction either. So I'm a fan of it. No attention engineering either. You got to win fancy old fashioned way like be interesting, be useful, be entertaining, be somewhere where people want to keep coming back to. They have to just hear about it and subscribe and choose to stay. So there you go. Wellbee listen more podcasts, especially ours. All right, we got, speaking of podcasts, we got reactions coming up where I'm going to actually read, God help me read the comments. And before that we have a case study and a call as well. So a lot of good material still to come. But first we got to take another quick break to hear from our sponsors. Look, if you run a small business, you know, there's nothing small about it. Every day there's a new decision to make and even the smallest decisions feel massive. So when you find the decision that's a no brainer, you should take it. And when it comes to selling things, using Shopify is exactly one of those no brainers. Shopify's point of sale system is a unified command center for your retail business. It brings together in store and online operations across up to 1,000 locations. It has very impressive features like endless aisle or ship to customer or buy online pickup in store. And with Shopify pos you can get personalized experiences that will really help shoppers keep coming back. And they will, based on a report from EY businesses on Shopify's POS see real results like a 22% better total cost of ownership and benefits equivalent to an 8.9% uplift in sales on average relative to the market set surveyed. So get all the big stuff for your small Business right with Shopify. Sign up for your $1 per month trial and start selling today at shopify.com deep go to shopify.com deep that's shopify.com deep also, want to talk about our friends at Vanta? Customer trust can make or break your business and the more your business grows, the more complex your security and compliance tools and demands become. That's where Vanta comes in. Think of Vanta as your always on AI powered security expert who scales with you. Vanta automates compliance, continuously monitors your controls and gives you a single source of truth for compliance and risk. This is really important stuff. Compliance and risk monitoring is one of those overlooked time taxes that can really weigh down businesses, especially during phases where all of their focus should just be on growing. Vanta makes all that so much easier. If you know what the term SOC2 compliance means, then you really should be checking out Vanta. So whether you're a fast growing startup like Cursor or an enterprise like Snowflake, Vanta fits easily into your existing workflow so you can keep growing a company that your customers can trust. Get started@vanta.com deepquestions that's V A N T A.com deepquestions all right Jesse, let's get back to the show. All right, so we're going to do a case study here. This is where people send in their descriptions or accounts of using the type of advice we talk about the show in their own life. It is also an excuse to play our often requested case study music. All right, now we can relax. Today's case study comes from Owen. Owen writes the following. In 2024 I received some hard hitting feedback where I almost lost my job as a project manager. I could barely focus for 15 minutes at a time and I wasn't making progress on my projects. I was behind schedule and way over budget. My problem was that I stayed up late every night scrolling social media. I came across your YouTube video how to reinvent your life in four months. I started with discipline in the fall of 2024. I prayed, set my bedtime alarm and connected with friends and family every day. I tracked those core habits. I wrote out my lifestyle centric career plan. I implemented multi scale planning. I started a values document. These adjustments helped decrease my daily screen time from 9 hours to 1.5 hours per day. This past March, I decided to let these new systems run for a bit. I really like Cal's advice. Run the systems for a while, take note and then start from the beginning again. So it's November 2025 and it's time for me to start back at discipline. I read Slow Productivity over the summer I started implementing the ideas. I got pushback on not opening emails immediately. I got pushed back on muting my team's notifications. However, all this pushback came from colleagues at the same level. My bosses and managers didn't notice. In fact, they just commented on the improvement in my work. My systems and habits provide rigor and efficient structure to my professional endeavors. These systems allow me to use my time efficiently and lift the cognitive load. But most importantly, my systems continue work confine work to designated times and locations. My work shutdown routine ensures that I can confidently put work aside. I have many pursuits outside of work that energize me on a weekly basis. Oh, and I love that case study. It both shows the problem of living a fully distracted life. It will just keep pulling more and more from you until like oh, and you're up all night long scrolling social media like you're literally toiling for free in the attention factories of Mark Zuckerberg, right? So he can buy the other half of Kawhi while your main job is suffering. Like look, you can barely focus. So the danger of living with uncontrolled distractions there in the story. But also we see some realities about fixing in that video. Reinvent your life. One of the things I talked about is like let's go in order like discipline and organization. I had a few things I said start with some simple habits. In each just a signal to yourself, do you take those seriously? And then rotate through and keep working on those parts of your life. There's sort of a rotation to that. Well, look at the difference it made in Owen. It like it slowly. If you focus on the right areas, start small but are continuously going through them and improving slowly over time, real change can happen. The other thing that happened here is he put into place the type of ideas I talked about slow productivity and his boss is like, wow, your work's better. Because ultimately that's what matters. The people who are in charge of your job is how good is the work you're doing to your colleagues, it might be make my life easier by answering my email. Make my life easier by being on teams all the time. But for your bosses, for your customers, for your clients, the value of what you do matters. And taking your time doing things well, obsessing over quality, spending more time on each thing you do, not doing too much things at once. Those principles of slow productivity, it produces better work over time it produces more Work over time because you have less of the overhead tax eaten away at your schedule. It's a great example of the type of things I talk about. The motivation for my work is the digital age is eating us alive and we don't even realize why we weren't ready for it. But there are ways, if we're systematic and careful, that we can push back against that erosion and still build successful lives that are meaningful and not overwhelmed. Again, I hate to harp on this, but it's why I hate this straw man that any talk about systems and organization or improvement is somehow hustle culture or productivity culture. Or it's coded as like online bros or something like that. Not about hustling, not about how do I produce more and crush it or whatever. It's about surviving our current digital moment that's eroding all the things that makes life meaningful and we didn't even realize it's happening until it's too late. It's about thriving as a human is incredibly humanistic. What I talk about in an age that's increasingly anti human and digital. So there we go. Owen, great case study. All right, do we have a call this week?

B (66:55)

We do.

A (66:55)

All right, let's hear this. Like to get your opinion on where somebody like me should be focusing their.

B (67:04)

Efforts for the next five years. And when I say somebody like me.

A (67:08)

I mean somebody who works in technology, not necessarily cybersecurity. I'm doing all the stuff I can.

B (67:15)

With optimizing my time and trying to.

A (67:18)

Develop a deep life.

B (67:21)

And a lot of the things you've.

A (67:23)

Covered, they've been transformational.

B (67:26)

But I just thought if anybody knows it would be you guys.

A (67:31)

All right, so Jesse, he's asking about what he should focus on professionally. Yeah, okay.

B (67:36)

I cut off some of the beginning of it because it was a little long.

A (67:39)

All right, so he's saying like, look, I don't know what he's doing. All the advice I talk about about your life and being intentional, but he's like, what should I be focusing on? Probably is worried about AI if I had to guess. Yeah, this is where going back to the deep dive evidence based planning matters. You want to identify what are the skills that are in the same arena where you play that are producing value and are useful? Actually producing value and useful. Like if you do this thing, customers will pay for it. If you know how to do this thing, other companies are going to try to hire you away. I saw this person get hired away at double the salary because they have those skills. So things that you have evidence, are valuable and are adjacent or in the same arena where you already play. Always be thinking about that and surveying that and be ready to make moves every like two to seven years. And that's like deliberate diligence to get there. Okay, now I need to diligently and deliberately so over time, with focus and deliberately working on things that actually make me better, not the things I want to do, move towards those new skills. You probably should be doing that every two to five years, maybe every two to seven years in most tech industries. The important thing here is the evidence. You need evidence that this is producing money. Otherwise you'll get lost in trends and then you'll end up writing all of these pytorch calls to LLMs to build these simplistic agents that everyone and their brother is building. And they don't really do much and there's no actual value proposition there. And it doesn't help where what you really need to be learning was some sort of new virtualization technology that is being used on the new class of servers that are happening for like hot swap, whatever, web apps. Oh, this is really a better way of doing things. And it's highly valued. And because it's hard, not everyone knows how to do it. You got to constantly survey that arena. You don't want to change all the time. If you're changing every year to something new, you'll never catch up. But every two to seven years, maybe every two to five, you should be like, am I moving diligently and deliberately towards a new skill that's in the same arena that I play so I'm not learning something completely new and that I have evidence which is going to be people are willing to spend money to hire or pay me clients or people trying to hire me away. Do I have evidence like this is actually valuable? All right, good call. Let's go on to our final part. Recently we've been having a tradition here of me going back and responding to comments from recent episodes. We're continue doing that today. We actually have them all on screen today as well. If you're watching instead of listening. Our last episode was about LLMs and whether or not they could be conscious. I was responding to Bret Weinstein's discussion of AI consciousness on the Joe Rogan podcast. And I said, look, I think there's something sort of somewhat pre modern about this. I think he told a story to explain what he sees, and now he's reacting to his own story, not to the reality of how these systems are actually architected as with all things AI, this generated some responses. I don't know if the inboxes were like Jesse, but, you know, generated a lot of comments. And so I figured I'd go through and let's respond to some. Some are really smart, some are smart, but I disagree with, some I agree with, and some I think are just funny. All right, Lance, bring the first one up on the screen. Here from Brandon Harrell, 962, who says LLMs do have a goal, understand the world well enough to predict the next token. All right, so that was in response. I was saying in that last piece, they're not. LLMs don't have, like, goals. They're not trying to. Weinstein said something about LLMs are doing experiments to try to understand how different things affect the world and what things affect the world in ways they like versus not is sort of implying that they, they would have goals which they might like, manipulate us eventually. And I say, well, they don't have goals. It's once trained, they're static. What Brandon's talking about is like, well, technically you could say they do have a goal, which is to predict the next token. That's true, but I don't think I would use the terminology goal. I would say LLMs are optimized in training such that once deployed, the tokens they generate tend to minimize loss on the token guessing game. That's what's really happening during training is we're adjusting weights in a way that reduces our average loss. When we give it a bunch of token guessing examples from our testing set. And we, we kept adjusting these weights until we were happy with the average loss. We were getting the testing set. To me, that's different than talking about goals because goals to me require some notion of agency. Right? You need like a dynamic system which is evaluating possible actions based on competing priorities and some sort of notion of value judgments. A static LLM doesn't have that. It was optimized until it did well on a particular game. And then once you deploy it, it doesn't change again. So somehow that seems different to me than having a goal. So it's like saying, I guess saying like LLM, their goal is to token predict is like saying a lawnmower's goal is to cut the grass. Yes, they optimized in the development and training of the lawnmower to be good at cutting grass. But then once you deploy the lawnmower, it has no agency. Right. It doesn't evaluate the world and make decisions and pursue and evaluate different options. It just runs Nothing about it could ever change. It just runs the same way every time you turn it on. And the way it runs tends to be good at cutting grass. We'll see. With an LLM, after trained, it is static and so once it's deployed in the world, it produces tokens. It does pretty well because we optimize to do well at that. But it's without agency. I don't like the terminology of goals. All right, put up another comment here, put on the screen. This one's from Leonard Yousebi. He says, hi, Cal. I just recently discovered the show and I'm enjoying it a lot. I think you are misinterpreting what Weinstein said about doing experiments. The way I hear it, he's referring to the way the RL post training works. Let the LLM generate a bunch of different outputs and reinforce the ones that work. This is similar to doing experiments and learning from them, but it only applies during training. Well, it's a good but nuanced comment, Leonard, so I'm glad you brought it up. I wouldn't based a lot of, I wouldn't bet a lot of money based off of the discussion that Weinstein had with Rogan that he's really deep in the weeds on how sort of post training, post pre training, RL based tuning actually functions and the different ways it runs. I don't know that he was thinking that closely, but it is a good point that you're. So let me respond to you about this because I think it's worth, I think it's worth talking about. Let me tell you the way I think about reinforcement learning. That's what he means by rl. Let me tell you the way I think about reinforcement based tuning and it's a bit of a simplification, but to me it kind of gets at the core of what's going on here during pre training, which is that really long period of time where you use real text and you knock out the words and the model tries to guess the word and then you do gradient descent to adjust its weights that make its output be a little bit closer. So this is where you keep doing this until the average loss on the token guessing game testing set is practical. This is where, you know, you run this for days and days in these giant Data Centers with 50,000 or 100,000 GPUs. That's where you're optimizing it to produce guest tokens. If I give you real text with a word knocked out, you do a pretty good job of guessing what that word Is right. That's this massive pre training. This was, by the way, the thing that was making these. This is my New Yorker piece from August. The thing that was making language models more capable over the last few years until more recently was they made them larger and they pre trained them longer. And so for a while we had this sort of scaling effect that as you made language models bigger and you trained them longer and on more data, they seem more generally capable on all sorts of different things. And what that article was about in August is that those scaling benefits actually really started to die off about a year ago. And now just purely making it bigger and pre training longer is not making the models better. There's actually like a big figure from OpenAI's past, Ilya S because I don't know how to pronounce. I don't have his last name handy. He actually came out recently, Jesse, and said yeah, the age of scaling is over. He had this kind of nice quote where he's like we had this long period of research and then a five year period, 2020 to 2025, which is about scaling. Now we're back to a period of research because scaling is not giving us any more rewards. So what did they do once? Just making the networks bigger and train them longer didn't make them more capable. They turned to Toonie, which happened after all the pre training is over. Without getting too much in the details because there's different ways to do this. The, the one way to think about the more common types of tuning is that you have prompts and correct answers to that prompt. Here's a question and here's a correct answer. That's very different than how pre training works where you're just, it's unsupervised, you're just guessing a missing word and you know what that word is. So I guess that word is the correct answer. But in tuning you might have a math problem and you might have like a detailed proof as like the correct answer or you might have a question about building a bomb and the correct answer you paired with that is. I'm not allowed to talk about that. And what you do, very roughly speaking, is it's like you load that prompt into the language model and it's activating all of the relevant sort of circuits and attention and pattern recognizers that are sort of parsing and figuring out what is this question and what are the types of things we could say next. And then you used a correct answer at that point in a reinforcement learning based algorithms to sort of zap those final logics and please don't yell at me, AI people. This is just simplifying, but to kind of zap those final logics that help you select among the right words to come next once you've identified, like, what type of answer that should be giving here to really. So let's reinforce the logics that are closer to this particular right answer. Now if you do that, it's like, so you're not teaching it how to understand, you're not teaching it in tuning. You're not making it smarter in some sense. Right? It's not in this phase. It's not the type of training where it's like learning whole new concepts or it's learning to recognize things it couldn't recognize before. What you're doing in tuning is saying like, okay, you have all this pre training that was very expensive to recognize and understand. You know what you're doing. You know this is like a joke and that you're at the punchline and that the punch line has to refer back to this thing that was a cat. All the pre training has all these circuits, like, lined up. And then if you come back in and you're reinforcement learning with like, I don't do cat joke answers, you're then going to have like these final wires at the end of all these layers when all these other things are activated, really push it towards whatever token is next and saying like, I don't do cat jokes. And if you do that reinforcement learning on enough examples, you're kind of like brute force training it. Hey, whenever you find yourself with a cat joke, again, just say, I don't like cat jokes. Right. So it's like a final layer of tuning. So you're, you're being more precise about how it should use the smarts that it learned in pre training. That's roughly how I think about the different forms of reinforcement learning and the impacts they have. No, so this is not. I really don't think about this here as a language model experimenting to figure out more about the world. I think about this more like a trainer coming in, like in a dog training example and shocking the dog to prevent them from doing certain things. Or like, you better always go and chase after the lure when it's thrown. Because when you don't like I like, I hit you with a training crop. It's like more of that type of thing. So it's not a language model. Like it's out there like learning and trying to understand how different things do. It is having its behavior curtailed pushed down particular avenues and shutting off other avenues. We don't want you going this way, go this type of way. This is where like a lot of the reasoning reinforcement learning was about. It was give answers that have lots of steps in it as opposed to just giving an answer. So it kind of shuts down the circuits that one of its opportunities. And all the different ways I might answer a question. There's ways where it might answer briefly with like, yeah, the answer is seven. Let's turn down the weights on those types of circuits and turn up the weights on the circuits. You already had that. Like you're going to show your work. And so it's all about aiming the way these things behave towards the end. And that's really simplification. But to me that again, I don't see this as running experiments to learn more about the world. The learning really happens in pre training. The reinforcement learning is more about now constraining and focusing what you want to do with those smarts it already has. But in all cases, here's the key thing. Yes, but that's when humans are building this thing. Once deployed, it's static. It's a lawnmower. I don't care how many super advanced laser studies of grass you did that you could build in these sensors to the lawnmower to change the different modes it goes in. Like the lawnmower is always just going to like statically do these things every time you run it. So to me, that's the key. The key thing is Weinstein is clearly thinking about this as an active agent that's learning. As it learns more, who knows what it's going to do. And it's static once it's trained. But if we even look at how it's trained, and we're careful about that, and this is semantic, but I don't see that as a machine running experiments to learn more. And there are machines that do that, but language models don't. That's not what reinforcement learning is. All right, I don't know. There's my technical answer. All right, let's see what we got here next. Gubby G. Punked7028 says your definition of consciousness appears strictly functional, whereas Brett seems to be discussing qualia, the subjective experience of what it is like to exist. The philosophical consensus regarding whether synthetic structures can possess qualia is currently agnostic. We simply do not know. Asserting that AI cannot be conscious based solely on the difference between biological and artificial information integration is philosophically premature. Well, Gubby G Punk T728 that that's not what I'm arguing. I, I am also agnostic on the idea about whether different non neuronal substances can have the emergent phenomenon of awareness that we, you know, in cognitive science and brain circles they call qualia. I'm agnostic on that as well. I don't think it's impossible or for example, a computing machine, an artificially intelligent machine to maybe have something like emergent consciousness. That's not my argument. It's not about the substance and it's not about the underlying operations that happen in the substance. My argument about consciousness and I think almost any consciousness researcher, except for there are philosophical theories of consciousness, it's basically like everything has some amount of consciousness and it's all on the spectrum, it's a little bit more out there. But the brain researcher side of consciousness thinkers, they would all agree, like look, we don't know exactly all the things you need to get recognizable consciousness, but we know you're going to need some sort of dynamic collection of ongoing, dynamic interconnected processes. And we don't know exactly what they are, but some sort of world model or updatable state and drives and memory and you need some sort of sensing and action and being able to react to action and update information. And all of this has to be pretty deeply interconnected in the right ways. And if you have the right processes and the right dynamism, then you get somewhere, you're going to get some sort of emergent consciousness. My argument about language models is it doesn't have nearly enough of those things. I mean, nothing in the language model is dynamic, it is static, it is statically deployed. It's not even deployed on a single machine. How can you talk about interconnection? They're too big to fit in the memory model of a particular gpu. So now you have pre cast weights for different layers spread out over all sorts of different GPUs which are just running through multiplications for multiple different queries on those layers, for multiple different queries at the same time and then eventually passing those numbers back around. They're integrated different. There is no even, there is no even like hole. There's, you can't even point to like this is where the computation is happening for this word that was just generated. But more importantly, it's static. There's no state that upstates, there's no world model, there's no drives, there's no actuation or reaction, there's no online learning. So my argument about LLMs not being conscious is a Purely architectural argument that's engaging specifically with the architecture of LLMs, which are static, feed forward, unchanging. You gotta have a lot more things that are dynamic and deeply interconnected before you have a chance of the subjective experience of consciousness arising. We don't know where that happens, but almost no one would say a static table of numbers that are divided among a couple hundred GPUs. So dealing with a lot of things at the same time that this somehow has any sort of notion of emergent consciousness. If that does like everything does, every data center does, Google does, every distributed system does, My phone does. Right, like that's too loose of a definition. So I have a very particular functionalist architects based argument for consciousness here. I'm not falling into reductionist traps. All right, this is fun. We got next. Mabel4367. The biggest risk of AI today is people in organizations not understanding the technology and applying the problems they can't handle in a satisfactory way. Like profiling people to determine things like credit ratings or profitability of committing a crime. It's kind of interesting. So this, these examples, they might hit like the average listener who's just been watching or listening to a lot of LLM content online. Super intelligence, all jobs will be automated. Like what are you talking about? Credit ratings or profitability of committing a crime, or profiling, et cetera. Where this comes from, and it's just an interesting story to know is sort of pre LLMs, there was a robust academic AI safety community that was looking at AI. Now we're talking. This is much broader of all these different types of AI systems that have been around for a long time. Decision systems recognizers, neural network based recognizers, machine learning based tools. A lot of these systems existed. They weren't particularly sexy, but a lot of them existed. This is a field in which we've been building lots of tools. And traditional AI safety was like, we should be nervous about tools that make decisions using machine learning or AI because they're often obfuscated decisions. We don't know how they're making the decision. So how do we know that they aren't encoding things that we would never want a human to do? How do we know they're not encoding some sort of, of something that we would think if a human was doing is clearly discriminatory. We don't know if we're just. It's dangerous just to suck up a bunch of data, build a model that data may be biased and then it builds up a model that's discriminatory and now the AI is spitting out loan decisions and you've just encoded discrimination into AI. It's a real issue. It's also like a perfect topic if you're a humanist, critical theorist, or someone who cares about these issues, right? You're like, here's technology going to, you know, you're being too fast. And my expertise can help make sure we don't create trouble. And it was like a really good issue, very niche, very academic, whole conferences on it, but not something that had, like, a lot of discussion in, like, the broader population. LLMs came along and it kind of sucked for the AI safety people because now the discussions were like, is this an alien mind that's going to liquefy our children to power its server farm batteries? Like, it became this, like, outlandish. Are all jobs going to disappear within a year? And now the AI safety researchers are like, we've been very carefully building these frameworks about how to evaluate data from a Kantian perspective. And now you have people going on the Dorkish podcast, and I think if we use wooden tip spears, we can get past the Terminator robots, human sensors, when the wars begin, when the data center wars begin next year. It's just like this crazy, outlandish whatever, and they kind of got left behind a little bit. But those issues are still out there. That community is still out there. That's what this, this commenter is speaking about. And yeah, those are real issues. Again, most AI is not language models. It's not stuff that we think is going to automate our jobs or become alive, but it's integrated into lots of things. And so those are still interesting questions, but it's a hard time, you know, it's a hard time to be talking about these pre LLM systems. All right, let's see. Let's do a couple others here. Felix MD5Cy says, if LLMs can be compared to the speech centers of the brain, is it possible to design complementary mathematical systems that model other brain regions and integrate them into a unified whole? Great follow up question. What I was saying before my architectural argument against LLM consciousness says, like, we know, or at least we suspect, that consciousness, artificial or natural, requires, like, a sufficiently, sufficiently diverse collection of dynamic processes that are deeply interconnected. So Felix MD5CY is basically asking, like, so why, like, what if we just build, you know, LLMs are one component of our artificial brain and we build these other types of processes digitally and we connect them enough, can we get consciousness? The answer is maybe yes, but we don't really know how to do this for so many of those components, it's like not something that we're close to. Part of the problem here is LLMs is so which are focused on language, understanding, production. We have a lot of data to train them. We can use the whole corpus of human written language, this whole massive corpus of humans recording like thoughts and ideas. We can train them on that. And that allowed us to get like something that was really good at that purpose. The vision is similar. We have a lot of good data sets where it's basically pictures and descriptions of what that thing is. They get these off the web because there's often tags that explain what an image is, or they look at text near an image. And so you can train models to like recognize images, what's in an image, or if you use these sort of diffusion models, you can reverse it and have it learn how to go the other way and produce an image from text, which is like how these image producing AI's work or how SORA works. The way they train those is there's a lot of different ways they do it, but like they'll also. One way is you get a picture and you take a bunch of pixels out and they try to fill in the missing pixels. And then another way is you have them go from text to like an image. You compare it to what? Like I have a real image of that thing. There's all sorts of different things you can do. So we have data for vision, we have data for language, but for all these other things, we can't just do unsupervised learning with data. That's the problem. How do we build a world model that generalizes how the world functions? The roboticists are working on this. The LLM people are like, oh, it's easy, we'll do it from text. Nah, it's not working. We train these models with 100,000 GPUs and all the text ever written by humans, and they have no real consistent model of the world. The roboticists are trying to figure out how to do this from vision data and experience data. This is really, really hard. And I've talked to a bunch of roboticists about it. It's hard and they're not close to having this answer. How do we model things like short term motivation or drives? We're not sure. I don't know. Yes, I think it is theoretically possible. In fact, maybe one day we'll do it. I bet we will. But most of the other systems maybe are much harder than language models to build. So it's probably better to think of language models as great breakthrough on the thing they do really well. It's like we built an artificial language processing center of our brain that could be really useful. But that language processing center from our brain, if we destroy the rest of our brain, you ain't conscious. Like, things aren't going to go well for you. You can destroy the language part and probably still have enough things left for, you know, like an Oliver Sacks case study. I have a hard time like remembering things. I can't read, but I can still have like some subjective experience. But if you destroy the whole brain but the language processing part, I don't care how developed that thing is, he ain't going to be conscious. All right, move quicker here. User gizmo, says Cal, Aren't you stating a version of the Chinese room argument? The counter to that is something like if you look at individual neurons, it's just a cell passing electrical signal, so it can't be conscious. But of course it is at the macro level. But I do agree with you nonetheless. There's some key things missing elements, like continuous learning. I like that you agree the first part. No, this is not like John Searle's Chinese room because I'm not making a reductionist argument about the actual underlying substrate as being relevant to consciousness. Again, I have an architectural argument. Enough sufficiently dynamic systems doing the right dynamic things, sufficiently interconnected, LLMs are static and only to really do one thing aren't going to satisfy. That Chinese room argument's interesting. It has a lot to say. It's very relevant in the age of LLMs. It's a thought experiment, Jesse, where you're basically translating, I guess, from English to Chinese. And there's a person inside this room and he has all these manuals he can look through that give him steps and rules that he follows. And the other end, he has a well written, the proper Chinese version what came in. But you wouldn't say that anything in that room understands Chinese. That was John Searle's argument about what does it mean to know something. It's a good argument, but it's not what I'm drawing upon here. All right, Gragas 1337. I feel like these comments are getting longer as before. Eventually it's going to be like four or five columns and just continuously scrolling. People who think about AI are verbose. My God. All right, Gragas 1337, says Cal. You're making the classic reductive argument. LLMs, it's just a bunch of numbers that go through matrix operations etc. All right, I guess I'll stop there because at least that particular question, I'm not making the reductive argument. I don't care about the substrate on which LLMs are built. It has to do with like specifically the processes they implement and whether they're sufficiently diverse, dynamic and connected. And they're not. I don't care that they're numbers or ions. All right, Spider man in London, he says language models don't run experiments. But he's quoting me. Language models don't run experiments. And then he says, but ChatGPT Pro literally performs multiple parallel operations, then compares them to pick the most right answer. That's what distinguishes Pro from the standard model. All right, let me just stop right there. What he's talking about there is what's known as inference time compute, which is again one of these host of features along with a lot of reinforcement. But learning based tuning that was introduced after scaling failed in the last year to try to find some way to get some sort of measurable improvement on something out of these models so they could keep trying to keep excitement investment going. Inference time computing. So this particular type of inference time computing says the control program that calls the LLM, calls the LLM multiple times, gets multiple answers and then decide which one is best. This is like wildly impractical from like a financial perspective. Like people don't. This feature is not widely used because it's, it's super incomplete. Increases the cost of inference since you're doing the inference like four or five times on certain benchmarks that OpenAI cared about. Hey, if you have an answer four times and for example, one of the ways they did this was like ask it five times and whatever answer it gets, the majority return that, hey, These things hallucinate 20% of the time. You get a better answer that way because it's unlikely to hallucinate in the same way three times in a row or something like that. Right? And so you can, on certain benchmarks, the performance was better if you asked the same. The control program asked the same question three or four times and then returns the most common answer, or sends all of those answers to another LLM and says which one of these looks best? But it's a marginal improvement and it was expensive, so it's not widely used. But no, that is not the same as the LLM runs experiments to better understand the world. Because they're static, they're static. Once they're optimized and tuned, they cannot change, they cannot learn, they cannot. The LLM does not learn. It doesn't know that you asked it to run four times. It's just a table of numbers. You get a bunch of answers and another program chooses one of those. That's not learning. There's nothing that's stored. There's no model of the world that's updated. So. All right, let's roll on here. Now we get a couple. I think we're ending with a couple more interesting or funny ones. No more arguments about reductive models of consciousness. Jules5347 says this all reminds me of the Kostrup quote. These people are simulating a kidney and expecting that with sufficiently complex model P is going to come out of the CPU. The funny quote we got here, Mark DEV1 Brother, if you continue down this path, you'll have to mention on a pod that you would never kill yourself. Took me a second to understand that what he's saying is the computers and or OpenAI are going to come after me to kill me. So I better make it really clear. If they try to make it look like a suicide, look to Sam Altman. If something suspicious happens to me, if I die of like severe electrical burns coming out of my computer, go see where Sam Altman was up to. All right? Or maybe some of these other commenters. Baptized imagination says anyone who has to use these things at their job knows they aren't conscious in any way. A good way to probably end this discussion. It is true. Weinstein's fears aside, you actually use these, you realize, like there's something weirder going on here than just conscious being. So there we go. I don't know. I think these are really smart questions, Jesse. I think, like, the comments are really smart. We're getting the deep things. I don't know if I'm right about all these, but it's a cool back and forth and it belies the idea that Internet comments, I guess, are always, you know, just, you're a poop clown and then it's a gif of Taylor Swift. You know, it's smarter than that. So, you know, take it a grain of salt. Some of my stuff might be right. Some of it might. I love the comments. I love getting into it. And I want to emphasize the thing I said in that podcast episode too is I wasn't picking on Bret Weinstein. He actually was saying things that a lot of people are saying. He just talks really clearly. So this is a great. And he was on a big platform, so this is like a great way to get into. To summarize a common thought about fears about AI. So it's not like Weinstein has some particularly pathologically bad thinking. He just stated really clearly something I think a huge percentage of AI users would if you push them. Like, yeah, probably something like that's true. So there we go. All right, that's all the episode we have time we have for today. I need to write my next episode, which is going to be three hours on the architecture based model of substrate agnostic consciousness. I just feel like, I feel like there's now going to be a demand for that. Maybe I'll get my friend Granny to come ride it with me. All right, that's enough of this nonsense. See you next week week. I guess it's coming out after Thanksgiving, so I hope you had a good Thanksgiving. We'll see you two weeks after Thanksgiving. Deep and Thriller December. Until then, as always, stay deep. Hi, it's Cal here. One more thing before you go. If you like the Deep Questions podcast, you will love my email newsletter, which you can sign up for at Cal Newport. Each week I send out a new essay about the theory or practice of living deeply. I've been writing this newsletter since 2007 and over 70,000 subscribers get it sent to their inboxes each week. So if you are serious about resisting the forces of distraction and shallowness that afflict our world, you gotta sign up for my newsletter@calnewport.com and get some deep wisdom delivered to your inbox each week. Sam.