Mathematicians Just Proved How to Prevent AI Brain Rot

1. 0:00 – 38:08

   An unlikely team tackles AI brain rot

   1. CNCal Newport0:00

      There's a new AI paper that's making waves. It's called Large Language Models as a Cognitive Virus. Now, it's drawing attention in large part because of who the authors of the paper actually are. It's not technologists, and it's not social scientists. It's actually a group of applied mathematicians, largely from the fields of biophysics and complexity theory. And if those sound familiar, it's because that's the same group of fields that included the Ian Malcolm character from Jurassic Park. Now, what they do in this paper is they, they set out to model mathematically the spread of LLMs through our culture, as well as the impact of LLMs on our ability to think using something known as a nonlinear dynamical system. Now, if you don't know what that means, you're in good company. Most people who tried to read this paper didn't know what that meant either. But I want to look closer at it with you today and explain what's really going on, because when you get through all the complex mathematics, what you find is actually an elegant identification of the three primary interventions that us as a culture have available right now to prevent our brains from becoming completely dependent on this technology. So we're gonna go through the paper, I'm gonna cut through the details, we're gonna get to these interventions, we're gonna discuss each. I'll tell you which one the authors of the paper like best, I'll tell you which ones I like best, and then I will leave you with some concrete advice for actually starting to take action on the intervention I like best right away. All right. So we have a lot to get into, and this is an important conversation because when it comes to the, the frontier labs creating these LLMs, as Ian Malcolm told us... Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should. All right, so let's get into it. All right. So I want to tackle what's going on here in three parts. Let's start with the basic question of what is it actually that this paper is trying to model? Um, I'll bring it on the screen here for a second to show you the key equation. So here's the paper. Um, as we scroll down, we get to what are the, uh, key equations for the world that they are going to model. Um, I know this looks a little bit complicated, so for those who are, uh, watching instead of just... uh, who are listening instead of just watching, there's three equations. One is the du over dt, another is dc over dt, and another is dd over dt. Those are derivative symbols. What's actually going on here? Um, I'm gonna try to explain it. So we can take this off the screen here for a second. There's three key variables that this system of equations is using that describe the world. One is u, one is c, and one is d. And all this captures is u is the percentage of the population that doesn't use LLMs. C is the percentage of the population that use LLMs but also are still capable of independent thought on their own, and d is the percentage of the population that is dependent on LLMs and have largely lost the ability for them to think, uh, for themselves. They've offloaded all of this to LLMs, right? So they- they've divided the population in this system into three parts. Now, what they do is they're gonna use these equations to simulate how those proportions shift between those three categories over time. So they'll go from time step to time step, updating how many people are in the first category, how many are in the second, and how many are in the third. So it's population dynamics. How does the population evolve over time? So let's go back for a second here, Jesse, to the equation. So what we're, what we're actually looking here when we look at these equations is when you see like right here, um... Oh, that color changed. All right. So when you see right here du over dt, that's capturing, um, how much is the variable u changing. What is the rate of change of the variable that describes the amount of people, the proportion of the population that doesn't use LLMs? And then they have some equation you use to calculate in this time step, how much does that change? The second equation captures what's the rate of change in this current moment of the second group, the group that uses LLMs and, uh, uh, but is not dependent on it, and then this does the same thing for the third group. I'm going to, uh... let's bring this down for a second because I'm gonna shift over, God help us all, uh, to my drawing tablet here, and I'm gonna try to illustrate how you use these equations to actually calculate this simulation. All right. So what I'm gonna draw on the screen here is, uh, the three sets. So let's see. Here is u, so we'll assume u. I've drawn kind of a big circle for it, so we'll kind of assume that's big. And then maybe when we start the simulation, c and d are very small. So we start with some proportion of people in these three categories. Now, what we do is each of these categories becomes the input to those equations we just saw. So I'll just write, you know, the word eq over here for equations. So each of those equations, the input to it is these proportions. At the current time step, how many people are in u, how many are in c, how many are in d? And what the equations tell you is how much each of these groups should change going into the next time step. So what we might get is, according to those equations, it might say u gets smaller, c gets much bigger, and d stays the same. So it's the output of the equation tells you how much to change each of these set sizes for the next time step. And then we could just repeat. Okay, now we have new sizes of U, C, and D. We feed those as input into the equations, and it will give us how much to change them for the time step down here. And then we do that again and do it again. All right, so we can bring that down for now, Jesse. So what we're gonna get then is a way to go step by step. We keep updating the size of these proportions, non-users, competent users, and dependent users. And then we go the next time step and update it again, go the next time step, update it again. So over time, if you keep updating these proportions using these equations, you see the proportions, uh, fluctuate until, if it's a stable system, they'll eventually stabilize to a sort of steady state, whereas you keep updating them, they stay about the same. That's all they're really doing here. Okay, so that's, uh, how these simulations work. Let's go back to the equations one more time, Jesse, 'cause there's a key part of these equations which will be critical for the discussion we're about to have. So if we look at the equations themselves, we do see, when calculating these change values, as I mentioned, the current proportion in U, current proportion in C, current proportion in D, these show up in the equation. But there's these other things that show up as well, these, uh, Greek letters. Like, there is a lambda, there's a rho, there's a kappa. So we have these variables. Here's a mu over here. Here's a sigma. So we also have these Greek letters showing up in these equations. What are those? Uh, those are fixed values, okay? So those are, are fixed values that you have to fix as the experimenter before you run the simulation that we're talking about. These are critical, because each of these fixed values capture something about the world that is being simulated. So if we go back to the equations, I'll show you briefly. This lambda, for example, uh, captures the, the rate, you know, the pressure to move from a non-user of a LLM to a user of the LLM. So as you change that variable as the experimenter, you're changing how much pressure in your simulated world a non-user will feel to switch to using LLMs. We have, uh, rho over here is the disengagement rate, so at what rate do regular users go back to becoming non-users? Down here we see mu. That's the rate at which a regular user becomes a dependent user in which they no longer really think for their own self at all. Uh, sigma is the rate at which dependent users go back to becoming regular users, so they start thinking for themselves again. Um, kappa captures the degree to which being around other non-users gives you social pressure to not use LLMs yourself, okay? So these are the... Those values capture the dynamics of the world, and they're just set by the experimenter. So, so what do you do, um, you know, what do you do if you're gonna run these experiments is you set those values, and then you run the experiments to see what happens. And then you change some of those values. So you change something about the real world in your, that you're simulating, and then you run the simulation again, and you see what the system does. So you're really looking at, when you, that, when you use these sort of, uh, dynamical systems and you simulate them, you're looking at how changing these different numerical descriptions of your world impact what ends up actually happening. Now, this is not new. Epidemiologists and biophysicists, they, they've been, and complexity theorists, have been using these type of dynamical systems and exploring them for, for a very long time. So, uh, you could use a similar system, and people do, for example, to study viruses spreading, like what's gonna happen with COVID. This is how epidemiologists, uh, were, were trying to predict what would happen as, you know, COVID spread. And in those models, like in a standard virus model, you have similar equations, and you still have three groups, except for now the groups are not affected, infected and actively contagious, and infected but recovered. And then you run these simulations over time to see how those group change, and, and it matters because, for example, early on, when most people are uninfected and there's a small number of infected people, the infected group grows really large probably, if you set your equations up right, because there's, there's very little immunity and there's lots of people that could potentially get sick. But, for example, over time, if the group of recovered people gets really large, then you're gonna see much less growth of the infected people because there's just many fewer people that are left to infect and so on. So we use these type of dynamical systems to measure, uh, lots of different real world systems, and what really matters is, is the equations, but mainly also these, these hardwired values you put into there. So again, if you go back and look at how they use systems like this for COVID, you know, one of the hardwired numbers they might have in there captures the willingness of the population to do, uh, social distancing, for example. And so then you could change that one variable and say, if the population is not really social distancing much, how does this change the way the disease spreads versus which if they are? All right, so that's the whole setup here. These equations describe change at a given time step, and so you can apply them again and again and again to simulate populations changing status over time. And the key thing you change as the experimenter are these hardwired values that describe the world in which you're simulating. I want to take a quick break to hear from some of the sponsors that make this show possible. Look, fall is here, and with it comes the chaos of the back to school season. Now, if you're anything like me, when faced with these new time demands, one of the first thing to fall off might be your diet. This is where Factor enters the scene. They make chef-crafted, dietician-designed, ready-to-eat meals that are, uh, prepared in only two minutes, meaning that you don't have to choose between real food- and convenience. When I'm in a rush, I know I can still get a lunch I love, like the Santa Fe chicken with peppers and potatoes, that not only tastes great but is good for my body. Now, this last part is important. With over 175 banned ingredients, every Factor meal is designed around what supports a healthier lifestyle and nothing that does not. So let's eat real. 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In a four-week consumer perception study of 30 men aged 40 to 60, 97% agreed that the overall appearance of their skin was visibly improved. So if you're a man who no longer looks baby-faced, consider Caldera Lab and in particular their anti-aging serum, The Great. If you want to look and feel like the best version of yourself, go to calderalab.com/deep and use code DEEP for 20% off. All right. Let's get back to the show. Okay, so let's go back to this paper again. What did they find when they ran these various experiments with different values? Well, a core value that they, they changed systematically was this, uh, lambda value that captured what is the pressure if you're a non-user to use the LLMs, and they, they would look at for different values of lambda, how does this system stabilize? And the... What they found, and I'll show you. If we bring this up, Jesse, I'll show you the main chart, which is honestly a little inscrutable. But if you want to know their main chart, this is their main, uh, their main chart showing their main result, where you see lambda down here is changing, and here you see how, uh, U, what U stabilizes to, which is the, um, the people who don't use LLMs, and over here you see what D and... that's the dependent, and C stabilizes to. I find that chart a little bit inscrutable, so we could bring it down, but I can give you the high level description of one of their core findings. They found that when, uh, lambda's very small, so there's very little pressure to, to adopt LLMs, it stabilizes to a world where, you know, most people don't use LLMs. As you increase the lambda, there's a tipping point. So there's a particular value of lambda, so a certain amount of pressure, where you get a drastic change. So before that tipping point, if you use a slightly smaller value of lambda, most people don't use LLMs. You go a little bit beyond that key tipping point, and most people are using LLMs. They're either in the C or D group, so either using and still thinking or using it and completely dependent and not thinking at all. And then how, in those experiments, uh, how the LLM users, so most people become LLM users once you get past that threshold, how they divide among people who still think for themselves and who are completely dependent on LLMs, um, depends on the relationship between those other factors that describe the relationship between those two groups. The other thing they found is something called hysteresis, which says, okay, if you move that lambda just past the tipping point, the system will evolve to where most people are using LLMs. If you then run the simulation starting with most people using LLMs and move that lambda right back before the tipping point, it doesn't change things. People continue using them. You actually... Once most people are using LLMs, you have to reduce that pressure a lot more to get them to come back. So it's, it's, uh, easier to get everyone using LLMs in this mathematical model than it is to get everyone using LLMs back to a state where a lot of people don't. And so their conclusion at a very high level is we should look at prevention. If you care about certain outcomes, you want to prevent it from happening in the first place as opposed to trying to figure out how to get things back. All right. So what interventions do they look at? Well, first of all, we have to know what is it that they want to stop. What is the, the bad outcome for this simulation according to the authors? And they would say the bad outcome they care about is most of the population ending up in that final category, the D category, of people who are so dependent on LLMs that they stop thinking for themselves. They said that's basically what we want to avoid. Um, people using LLMs but still thinking for themselves is not a problem. People not using LLMs is not a problem as well. So they're looking at what interventions prevent the simulation using these equations from ending up with most of the population in D. What does an intervention mean in a simulation? It's changing those fixed values, those values represented by those Greek characters. So what different ways can you adjust those values to prevent from ending up with everyone in D? The interesting thing is that for each of the useful ways you can change those values to prevent the bad outcome, it corresponds to real world stuff we could do. It corresponds to a real world intervention. So this is where the paper gets interesting. So I've written down here, um, the three major interventions identified in this paper, or the ones I thought were most interesting, um, based on this mathematical model So the first intervention is to try to reduce the pressure to use LLMs to a, a point that's so low that most people just remain non-users. So if we go to the equation, that would be making the lambda variable small enough that when you run the simulations, we sort of... most people stay in U. Now, the authors of the paper say that intervention is not very likely. LLMs are useful. They're already pretty ubiquitous. You know, good luck convincing most people not to use LLMs. So they sort of discard that particular intervention. I don't think we should be so quick to do so. I think there are certain contexts where we're actually seeing that exact intervention being deployed, so it's worth giving serious thought. One of those contexts is universities, right? So a university is a place where it might make complete sense to say, "In this context, we are gonna have a lot of restrictions and pressure against using LLMs for a lot of these cognitive activities," because the goal of the university education is to cultivate your mind, not to finish assignments as efficiently as possible. So that's an example of a context in when, when actually that first intervention identified by this model actually makes sense. We also see it in journalism. So there are, uh, certain really well-known publications, 'cause I write for a lot of publications, so I sort of know this, um, who have very clear rules about LLMs for their reporters and their journalists. They say y- you can't... basically, don't use them. Do not write with them. Do not edit your text with them. You know, um, maybe you can do some one-off brainstorming with them, but really when you're sitting down to write, it needs to be a human writing, which makes complete sense if you're a mainstream publication because the main thing that you're selling is we have professional thinkers and writings expressing th- themselves. So that's another context in which you're effectively reducing within that context the lambda value. The pressure to use LLMs is very small because you have restrictions, rules, and a culture there, um, to make that something that is non-desirable. So that first intervention of really reducing the pressure to use LLMs maybe is not a globally applicable intervention, but it makes sense in different contexts. All right, the second intervention that this mathematical model reveals is working is to try to increase the pressure to turn LLM users back to non-users. So there's a particular Greek symbol in those equations, uh, it's the Greek symbol, the lowercase rho, that captures the pressure of users to come back to not using it. So again, the authors of the paper don't see that as, like, very reasonable. LLMs are popular. People like 'em. But there are contexts in which we've actually seen this intervention begin to emerge, and that we should continue to con- uh, consider it, right? So if you look at Gen Z, for example, young people, they have a really mixed relationship with these AI tools, in large part because they don't like the people running these AI companies. They don't trust them. They don't trust their motives. Um, they're... They find it kind of inauthentic. Like, really, if you look at who's most enthusiastic often about LLMs, it's, like, middle-aged dads, you know? It's the 55-year-old CEO of some media company that's like, "This is great. We all should be using LLMs to write really quick memos," and they're all experimenting and having a good time. The kids are like, "I don't know about this. Like, what? I don't trust you. I grew up in social media. I don't trust any of you people over in Silicon Valley telling me I have to use something." So that's an example where you could see a cultural pressure grow to, uh, retreat back from LLMs to a non-LLM world. Now, if that sounds fanciful to you, we've actually seen in other types of systems you can model as dynamical equations, um, uh, differential equations. We've seen other systems where this has actually happened. This is what effectively happened with youth smoking. So the Ad Council got all this money from the tobacco settlement in the '90s, and they invested them in high-end ads like the Truth campaign that were made to change the culture, youth culture's relationship to cigarettes, to make it seem like cigarettes are a product that these old white guys in suits are using to try to get you hooked to make money, and they're exploiting you and are laughing at you, and it kinda changed the culture where kids were like, "I don't see smoking as countercultural and cool. I see it as sort of I'm being exploited by, you know, the Philip Morris CEO or something, and I don't wanna be a part of that." So by changing the culture around youth smoking, you saw a retrenchment, a, a retreat from, um... Youth smoking levels got smaller as people in that culture were, felt more pressure, cultural pressure not, to not use it or to step back from using it to being a non-user. So actually, that's not a far-fetched intervention. Um, maybe it's not gonna work globally, but something to keep in mind. All right, the third intervention this model identifies would be to reduce the pressure to go from a user of LLMs that still thinks for themselves to a fully dependent user that doesn't think at all. There are, uh, sort of two Greek symbols in that, those equations that were relevant. Sigma and mu capture moving, uh, the pressure to move from regular users to dependent, as well as the ability to move from dependent back. So both of those variables matter for keeping people in regular users who still think for themselves and preventing too many people from ending up in the completely dependent category. This is the intervention that the authors of this paper like best. Like, they're basically saying, "We don't need to keep people from using LLMs. We're completely happy with a world where most people use them, so long as we don't have too many people who are dependent and lose their ability to think for themselves." So they, they sort of identify that as the best strategy. How would you actually implement in the real world Putting up that firewall between C and D. Well, the authors get into it in the paper. They said, "Oh, you would have to, uh, explicitly introduce human contemplation activities and preserve them." Even if there are some activities that an LLM could do for you, you have to have this, this sort of scaffolding, this cognitive scaffolding that says, "This type of thinking is important, so, like, humans are still gonna do it. Here's another type of thinking that's hard that humans are gonna do it, even if LLMs could." And by having personal habits and organizational structures around thinking in certain places where humans should keep thinking, you'll be in a world where you're, you're, you're, uh, you're changing those... effectively changing those hardwired values so that there's, uh, s- m-many fewer people actually move the full dependency. So if we, uh, explicitly preserve human cognition as an activity that we practice and make a part of our lives, you will prevent the bad case of most people ending up in D. Now, if that sounds familiar, that's basically the intervention I've been calling for, perhaps most notably in my, my New York Times op-ed for March about we need a cognitive fitness revolution. We need to treat cognitive fitness, our ability to use our brains to do complex thought, as a tier one skill or health or property that we should all actually be actively preserving and, and, and improving like we do with our physical health. We care about what we eat, and we exercise. We need to care about what information we consume and exercise our brains. So it's pretty interesting that the mathematical model, the sort of simple three-part compartment model, as they call it, compartmental model, of LLM spreading between these categories, it, it identified three possible interventions, and the intervention that the authors like best is the same intervention I came up with observationally, just actually working with real people and seeing what, th- what's happening and what would actually help it. So this is always interesting to me when you have different types of investigations ending up at the same conclusion. So which one do I think is best? Well, the way I see this, the way I see this is, uh, A, it's cool that you can use mathematical models to identify interventions. B, I think this is a useful way of thinking. This type of categorical clarity is a useful way of thinking because in the discussion of AI, and in particular LLMs, it's muddled. It's all over the place. It's anger at the AI companies themselves or getting caught up on a particular usage of LLMs that we don't like and, and in this sort of muddle or just boosters that are like, "Look, I can make video games with good graphics with just a prompt," and that seems really cool, and it's all so muddled. I like the clarity that the mathematics bring into it. There's different types of interventions that work in different places, and this type of intervention might make sense in a university. This type of intervention might make sense in youth culture. This type of intervention might make sense in people's individual lives. That clarity of what are the different types of things we could do? Why might we do them? Where might it apply? Why might it be a problem? Why might it be good? That's the right way to think about it. All right. Then C, obviously, I like this final intervention because I've been writing about it a lot in non-mathematical terms, and the reason why I like that final intervention is probably for different reasons than the authors of the paper. So the authors of the paper discard the first two because they say it feels unreasonable on a societal-wide level to sort of keep people from using LLMs. As I just argued, I don't really believe that. I think there's specific groups in which that still makes sense. So I'm not discarding those, but I think they're organizational interventions, and they apply for some organizations and not others. I like the final one because it's something you can do right now. Cognitive fitness, like physical fitness, is something that you as an individual can take action on right now. You don't have to wait for your school district to make a change. You don't have to wait for your employer to make a change, like with those other interventions. You can start i-implementing those interventions right away. And when it comes to techno-selectionism, I'm a big believer in you have to have that personal substrate, stuff that people can do right away to gain autonomy over technology in their lives. The other levels matter, organization, societal matter, but the fastest moving one is what individuals can do. So I wanted to end this discussion by giving you three quick ideas about if you wanted to implement this final intervention in your own life, that is to build a scaffolding of cognitive fitness in your life that prevents you from falling into the dependent category of LLM users. What are three simple rules you can apply to your LLM use in your own life that'll help you get there? I have three... I, I think about this list a lot, but I wanna give you the three I'm thinking about right now. First of all, when using LLMs, your goal should be to eliminate tedium, not cognitive friction. So if you're, if you're coming up to a use of an LLM where, hey, this is going to take a tedious task that I'd rather not do, I just need it done, so I can move on to do something more interesting, that's a good use of LLM. You're automating tedium, which has, in itself, no real benefit. But if you're using an LLM to avoid the friction of having to think, I don't wanna come up with an idea. Can I just go back and forth with an LLM and get one? Writing, that's kinda hard. Can I have it write a draft, and, and I can just edit it a little bit? If you're avoiding cognitive friction, that's a problem. Cognitive friction is not bad. That's you using your brain in difficult ways, makes your brain smarter, gets the best results. Do not outsource hard thinking or creativity to an LLM. Friction is good. You wanna feel the strain in your brain the same way you feel a strain on your muscle when you're trying to get stronger. If you're automating tedium, that's okay. All right, second piece of advice. Do not let LLMs write on your behalf except in instances where it's clearly marked as LLM output. I do not agree with the trend of let me let an LLM write this email for me because it'll do a better job and it's kind of hard to write emails. Or this memo that I'm sending out to my employees, if I have the LLM write it, um, it'll probably be nice and clean and it'll save me some time. Uh, I think that is psychological fraud. I think when we read text that we believe a human wrote, we simulate the mind of the reader in our mind. We're actually like interacting with this sort of conception of another entity. If you didn't actually write those words, there's a sort of psychological fraud going on there. I think the rule should be any LLM-generated text needs to be identified as such. So if you want to have, for example, an LLM summarize like here's like the three action steps we should take based on, uh, the meeting we just had, you should write an email that says, "Good meeting. Below is an LLM summary of the, of the meeting and the three action points we should have," and then you can paste that text. Or if your memo is written by LLM, be like, "Here is an LLM-generated memo based on the following sources." So now the, the human recipient knows this here is not from a person, this was written by an LLM. This means you're going to write more, and that is a key way to keep up cognitive fitness is when I'm writing on behalf of me, I have to do that writing. I have to clarify my thoughts and try to put into clear words. That's a very important scaffolding if you want to avoid falling into the fully dependent. Like the easiest route in the full dependency on LLMs and losing your human birth rate of original thought is to outsource writing to LLMs. Like, ah, that's hard. And then a- once you do that, you're, you're seeding your brain. All right. Rule number three, read human writing, not AI summaries of human writing. All right? The thing is, if you're reading an article, what is an article? It's a summary of a very complicated reality that an expert came to, understood that reality and said, "I'm now going to summarize this down to the level of compression that I think gets the main points across as accurately as possible." Do not add another layer of summary on top of that. That's al... It's already summarized. I had this conversation with someone recently where like, "Why would I ever read a book? I'm g-- I run my books through an LLM to get a summary of the main points of the book." The book is already a summary of the points. This is an author who's an expert on that topic saying, "I am getting this down to the level of detail that's enough to have mastery of the subject. In four or five hours, you can be a master of this subject. I've compressed it for you. I've spent years doing this for you." You don't need to summarize it further. You're over summarizing. You're over summarizing because maybe there's cognitive friction in the, the actual reading of the summary level that the author chose and you want to make your life even easier. So I would say avoid that if you want to... Again, if you want to avoid m- going from C to D, from use to dependency, read things at the level of summarization that their authors have already determined to be appropriate. I would only use LLM summaries where, you know, it's something, A, it's very long and there's only some points in there that matter to you. Like here's a massive report and I just need to know these conclusions. You could have the LLM pull that out for you. That would make sense to me. You don't actually need to have all that information. I think that would make sense to me. Um, I guess if you have a terrible communicator in your organization who's sending you these super long emails that are wordy and complicated, but there's only like one point in there, I guess you could summarize that. But otherwise, for the most part, the text you're reading has already been summarized down to the level that the writer thinks maintains the optimal ratio of maintaining useful information to not having too much. So, uh, don't run away from reading human writing. That strain is not a problem. All right? So those are three rules I've been implementing if you want to make sure that you don't fall into that final category. So there we go. It's an interesting paper. You know, there's, there's, there's a lot of controversy around it. I don't know. I, I think this is just sort of standard. The controversy around it is people saying, um, how do you know those are the right equations to describe LLMs and how do you know the right values for those hardwired values? You didn't go out there and empirically measure those and stick them in. But this is really common with, um, you know, biophysicists do this type of thing all the time. These mathematical models can capture general population dynamics and then you mess around with the parameters to understand how does changing this part of the world affect these general dynamics? It just gives you insights about how a world might unfold. They're coarse, but it's a way of mathematically getting at dynamics that, as we're seeing here, we're seeing show up in the real world. The interventions suggested by this math were actually seen in the real world. So I thought it was, uh, a useful paper, but probably my summary is enough unless you really want to try to understand, you know, some of these pages. Like here, Jesse, let's load this up one more time. Unless like... I spend a lot of time with pages like this where it's... But this is what complexity theorists do. They set up these differential equations and they try to solve for different equilibrium points, which is actually like not conceptually that hard. You're basically trying to find where does the change become zero because that represents an equilibrium point and you do algebra. So, but you don't need to know all that. I think I got to the key, the key conclusions. [whooshing sound] All right. Before we move on to opening my inbox, I want to take another quick break to hear from the sponsors that make this show possible. But stay tuned because I have a pretty fiery response to an article coming up next. So let's talk about our friends at Notion. Let's be real. In knowledge work, AI shouldn't be just about going as fast as possible and maxing out token budgets. It should also help you do the important things better. This is why I like what Notion is building. Notion is the AI workspace where your team's knowledge, projects, and agents all come together in one place. Teams using Notion move faster, cut friction, and cost by consolidating tools and staying aligned. Because everything lives together, Notion has the context it needs to answer questions instantly, automate busy work, and keep work moving. It's AI designed to strengthen teamwork, not replace it. With over 100 million users, Notion is trusted by 98% of the Forbes Cloud 100 and 95% of the top AI companies, including OpenAI, Cursor, Lovable, and more. Learn more about how Notion can support your business at notion.com/cal. Now you gotta type that all in lowercase letters, notion.com/cal to try Notion today, and when you use our link, you're supporting our show. I also want to talk about our friends at My Body Tutor. Do you wanna get in better shape but struggle to follow through? Here's the solution, My Body Tutor, T-U-T-O-R, an online coaching program that actually delivers results. Now how does it work? When you sign up, you're assigned a coach, and they help you devise a nutrition and exercise plan custom fit to your specific goals and your specific circumstances. And then, and this is the key part, you quickly check in each day on the My Body Tutor app to let your coach know how things went. This accountability really helps you follow through. It also puts an advocate in your corner who can help you adjust your plan to whatever life throws your way, or maybe they'll notice you're struggling and can offer to help. You need someone in your corner, and your coach can be that person. So if you wanna get healthier, go to My Body Tutor today, and if you do, mention I sent you, and they will give you $50 off your first month. That's mybodytutor.com. All right. Let's get back to the show. All right. Well, uh, because it's a Monday, I like after we do our deep dive to, to open up our inbox to hear what you're interested in. Again, if you have questions, comments, or reactions, you can send them to podcast@calnewport.com. All right. What are we doing first this week, Jesse?

   2. SPSpeaker38:00

      We have a question about a recent article that characterizes your stance on AI.

   3. CNCal Newport38:05

      All right. So someone sent a note... Actually, a lot of people sent

2. 38:08 – 56:29

   What are Cal’s thoughts on how Jesse Singal describes AI?

   1. CNCal Newport38:08

      similar notes, but someone sent a note specifically that says, "What does Cal think about how, uh, Jesse, um, Singal describes his AI views in The Dispatch?" So a different Jesse, not this Jesse.

   2. SPSpeaker38:20

      [chuckles]

   3. CNCal Newport38:21

      Jesse, uh, Singal. Here's the article. I read this actually. I'll load it up here in The Dispatch. It's called "Will AI Kill You?" Um, and then it gets into it. Okay. Uh, it talks about me in there, so I figured it's probably worth, uh, getting into. I pulled out the key quote. I'll read it off here. So the... I mean, the key quote re- related to me was the following, so I'm reading here. "People who should know better are letting stereotyping get the best of them. The computer scientist and podcaster Cal Newport, a leading anti-doomer..." I'm a leading anti-doomer now, Jesse. I like that. "... recently had a column in The New York Times in which he decried the doomers and their strange ideology that certain leading AI figures talk in apocalyptic tones about the power of their technology, he argued, doesn't mean that they've seen evidence we haven't, rather that it's representative of how rationalists always think about AI in these circles. It's taken for granted that AI capabilities will rapidly accelerate and completely transform the world, and to talk about it any other way would be considered uninformed." All right, that was quoting me. It's a little confusing, uh, for people who are listening. [chuckles] He contra- Now we're back to Jesse talking. "He contrasted this supposedly out-there movement with the cooler, chiller stylings of the Chinese government, Jensen Huang, and, quote, 'American AI leaders who have minimal connections to rationalism.' End quote. My only response to the claim that we should defer to the judgment of the CCP on trillion-dollar matters of technology and industry and human flourishing and/or extinction is bafflement. As for Huang, it's extremely silly to treat him as unbiased just because he says we shouldn't freak out." But he does have the cool jacket. He's got that cool leather jacket. I told you... Someone told me. Was it Ed Citron? The, he told me what type of jacket that is. It's like a motorcycle racing jacket or something. It's very expensive.

   4. SPSpeaker40:07

      Mm-hmm.

   5. CNCal Newport40:07

      I gotta wear that. I overheat easily though. I don't think I could go around in like a heavy leather jacket. All right. Um, so this is probably worth talking about. Um, for what it's worth, I like Jesse actually. Um, he's a good reporter. He actually does reporting. A lot of people don't do that anymore, like the reading reports, talking to sources. He's a good reporter. Um, he's also surprisingly funny if you listen to his podcast. He's a funny guy. Um, a lot of people have had similar reactions though that Jesse had there, so I think it's worth getting into it, um, to try to explain what's what or what my, what I'm actually trying to argue here. Okay. So again, you can go back and watch my episode from last, uh, two Thursdays ago. I got into this in, in more detail. Last Thursday I also talked about this. There's plenty of recent podcasts of me trying to get in more detail about this. But as a really quick reminder, the, the, the complicated thing about the current AI conversation is that you have these two related movements which are essentially, um, utopian futurist movements. So you have rationalism and large parts of the effective altruism movement that subscribe to a utopian futurist belief that superintelligent AI, if shepherd, shepherded properly, i.e. by leaders of their movements, will bring us to this utopia in which like a chosen few will, um, be able to fuse with this superintelligent AI god, and we can, um, transfer and s- expand these digital consciousness throughout the stars and, and, and the galaxy, right? So this is the sort of futurist ideological view that comes out of futurism movements from the '80s and '90s, from which leaders of EA, EA and the rationalism emerged. The flip side of this futurist utopian view is the dystopian fear that if we, you know, superintelligent AI, which they again believe is going to be the Godhead, if it is not shepherded properly, so if these leaders of these movements aren't allowed to do it right, um, it will kill us all. So it's eschatological, right? We are going to have this sort of apocalyptic deliverance from superintelligent AI that will either kill us all or give us this utopian future where the select few are going to spread our consciousness stored in digital forms throughout the light cone of the universe, right? So they believe very strongly. Here's what's confusing about this. Most of the debate right now is between people in the same group. So Anthropic and OpenAI are heavily influenced by those ideologies. I've, I-- This is what I was trying to capture in that column that Jesse was talking about, um, but it's now been extensively reported on by many technology journalists at The New York Times, at The Wall Street Journal, at The Economist, um, very extensively captured. Those two, of all the AI companies working on hyperscaled LLMs, those two frontier labs, as we call them in particular, are heavily influenced by that ideology. They come out of that ideology. It is essentially their raison d'etre is to ensure that we get the good superintelligent God and not end up with the bad God. That's why they were formed. The people who are yelling at them now are also from the EA rationalist movements, but all they're disagreeing on, it's a family squabble. The, the, the EA and rationalists, so you have the Yudkowsky and all the different EA institutes, they're arguing, "You guys," being the labs, "we're not ready yet. You're going too fast. We're gonna end up with the bad God. We're not ready yet to go for the good God." And then Dario Amodei or, you know, Greg Brockman over at OpenAI are like, "No, no, no, we can do it. We'll get the good God." So it's a family squabble. So all of this debate going on is all from people who, who are believing the same ideology, and I don't think people recognize the degree to which especially the effective altruist, um, version of this ideology is incredibly well-funded. They're funding fellows that are on congressional staffs. They're funding, straight up just funding journalists, giving fellowships to journalists to make sure that their point of view, um, will show up in the journalism. These are very powerful groups, and they're, they're in the same group. They're just having a family squabble about whether we're ready or not to do it, and also, uh, you know, there's now profit motives. The, the people in the c- in the frontier labs are discovering you can buy things with money, and so there's, you know, these interesting tensions going on. So this really matters. If the entire debate over a technology is being shaped on both sides by people from a particular ideology, well, we really need to understand that ideology so we can understand how to parse what is being said, what we should believe, what we've shown it, what it really means. So it's very important. This is not an unrelated lifestyle quirk of otherwise just sort of normal engineers trying to build this technology. It is central to understanding what's going on in the AI debate right now. It's similar to, you know, if you're, if you were, um, to ignore it or to say it's just a quirk would be like in the 1950s, you're trying to cover, uh, you know, a, a, a school district in the South, and the leader of that, the school board is really against desegregating schools, and they also happen to be a member of the Klan. And if you ignored that, like, well, that's... What does that have to do with... You know, that's just some... Yeah, they're weird in their beliefs. What does that have to do with their, their work on the schools? Like, no, that, that ideology of the Klan would really impact the way they're thinking and talking about school segregation. We need to understand that. So it's important that we understand these ideologies and their impact. So for example, there's two things I really care about. One, if you subscribe to these ideologies, it's really gonna change the way you talk about the technology. So for decades, these groups, you know, since really the early 2000s, these groups have been talking about superintelligent AI is inevitable. It's gonna be fueled by recursive self-improvement, which is an idea from their movements. It's not a computer science idea. And they're going to rapidly grow in power until they're dangerous. They've been talking that way for years, long before LLMs came along. So now we have to ask the question, if you're listening to Anthropic or OpenAI or to the, their friends, EA rationalist friends who are on the other side who are yelling at them, to what degree are they seeing things that we should really be scared about? And to what degree are they just continuing to say the same things they've been saying since well before LLMs came along? Like, that's an interesting question. Now, how might you resolve that question? Well, one of the things I suggested was the following. There's other AI companies that have hyperscaled LLMs. There's other researchers that know this technology inside and out, right? There's oth- there's other AI industries in other countries that are also working at the cutting edge of LLMs but that are not connected to this particular ideology. So a good litmus test would be if you hear the people in this group saying a fear, let's see if the people who have access to similar technologies but aren't in that group are saying the same things. That seems like a good litmus test. And guess what we're finding? They're really not. That's interesting. So when I mention, like, Mark Zuckerberg, look, I'm not a fan of Mark Zuckerberg, but Meta has hyperscaled LLMs. They, they, they have top researchers there. They've been working on this for a while. And what has his reaction been to the two particular companies that are within the futurist utopian ideology? He's like, "Guys, you gotta chill out. There's not an inevitable thing here. Just don't build dangerous things. We're building tools over here. We're just not building the dangerous ones. Like, what are you talking about?" He pushed back, "Why are you doing... Why are you talking so much about recursive self-improvement? What does that have to do with building useful products?" So it's basically, uh- A kid from the Silicon Valley lunch table, you have the, the EA rationalist over here, and then you have the other kids over here. They're like, "Dudes, chill out." Like, "Don't tell the teacher that we've been, whatever, stealing the french fries." That's a weird analogy, Jesse, but I'm, I'm, I'm trying here. Why do I care about Jensen Huang? Because they are deeply involved in the LLM industry since they provide the technology that all of these models run off of, and they also run their own hyperscaled models internally. But Huang comes from a, an old-school hardware world that has very little connection to the utopian ideologies that, uh, are big at OpenAI and Anthropic. And all year he's been like, "Chill out with the sci-fi guys," right? Again, I'm sh- they have their own biases. Huang doesn't want export controls. They want to sell as many chips as possible to China. I know this. I'm not being naive about it. But what matters is we need to double-check what the ideologically oriented labs say versus the non. That's a useful check. They all have their biases, but that's a useful check. Why do you point to China? Is it a celebration of China? No, they're doing terrible things with AI over there. They're using it to surveil and persecute minority groups with a level of Orwellian rigor that would turn your hair, hair gray, right? Like it's terrible stuff they're doing over there. I'm no fan of the CCP. But what's interesting is the CCP, if they believed for a moment that this technology was gonna create a deity that was more powerful than the government, people would be going out the windows, right? They would just take over those comp- they would compl- you know, they, they would not tolerate that for a moment. But you don't hear that rhetoric out of, uh, the CCP AI industry, in part because they have no connection to those futurist ideologies, right? So I think these are important data points. This is not a moral judgment on these groups are somehow morally better. I just think this is important that if you have this group believes to this ideology, which has long been saying AI, whatever form it takes, is going to improve through recursive self-improvement and become super powerful, and they're saying that, and they've always been saying that, we want to check that against other groups who work with the same technology. Are they saying the same things? And right now we're seeing not really. I think that's an important data point. The other thing I worry about here that makes these ideologies important to understand is that I think it is actually impacting the specific experiments that those two labs are running, so it's relevant. Notice OpenAI, they ran these very dangerous hacking, uh, experiment, experiments. We know they're dangerous because it was, uh, negligent the degree to which they let these agents run for very long amounts of time with minimal supervision and plenty of evidence that they were gonna do unpredictable, dangerous, or hazardous things, and they said, "We don't care. We need to keep going." Other AI labs aren't doing this. Meta had like a little bit of an incident with one of these tests, and they were like, "Shut it down. We're gonna... Okay, obviously don't do that." But OpenAI just kept going. Why were they running this particular band of tests that was clearly hazardous and was probably gonna cause unpredictable results? Well, if you come out of those futurist ideologies, you say collateral damage along the way is the price you pay to get the good God. We gotta win the race because we have to summon the right God to prevent the destruction of humanity. So if you believe those ideologies, it affects the experiments you run. Anthropic is obsessed with recursive self-improvement. That is not some computer science idea. That's not an engineering idea. It's an idea that was latched onto by futurists in the late '80s and early '90s as a way of being able to, uh, make predictions about superintelligent AI without having to do engineering. I mean, if you're Eliezer Yudkowsky, right, if you're Nick Bostrom, you want to talk about superintelligent AI and utopia, but you're not engineers. So what do you do? They latched onto the idea of what they called recursive self-improvement. We don't have to tell you how those systems would work. They'll just make themselves better till they get there. So it's an idea that came out of futurism, not out of computer science. Anthropic is obsessed with it. So is OpenAI. We have to do this. We have to do this. Now, it's a really bad idea. Why? Is it because we're gonna have a takeoff and get a digital God? Most computer scientists I know say, no, that's not how that works. But what will you get if you try to increasingly have AI agents modify model training runs or the actual code of the agent harnesses? What are you gonna get is obfuscated systems, systems that it's harder to figure out how they work, and they're less predictable. Why would we want that? The only reason why you would do that is if you've been immersed your entire l- adult life in an ideology that told you recursive self-improvement is how you summon the God. And again, Mark Zuckerberg, who I am no fan of, but, like, he's useful because he has hyperscaled LLMs and is not a part of that ideology, he called them out on this recently. He called out Anthropic and OpenAI. He said, "Why are you, why are you working on recursive self-improvement? What does that have to do with building useful AI tools?" Maybe... I, I, I'm paraphrasing, but he basically said, "Maybe if you spent less time chasing recursive self-improvement and more time trying to build useful, safe tools, we wouldn't have this problem." So we're seeing this inner scene war happening between the future ideologist and the non-futurist ideologist in Silicon Valley and beyond, where the non-ideologists are increasingly vocally calling out the ideologists and saying, "This is your Dungeons & Dragons weirdo stuff, guys. Like, build useful products. Why would you try to do recursive self-im- It's not a way to build a useful product. That's part of just, like, the, The Sequence has told you that's part of how you're gonna end up with a digital God." So I think these ideologies really, really matter, not because of weirdness. I'm a computer scientist. My people are weird. Spoiler alert, we're all weird. We all, we all do weird things. It's not... This is not exactly, you know, a, a cool crew, and I don't mean to throw shade on me and my fellow computer scientists. We're not cool people. We're all weird. Mark Zuckerberg is weird. Jensen Huang is weird. He wears a motorcycle racing jacket. Why is he doing that? Because he's... The engineers are weird. It's not about weird or not weird It's about if your industry and then the primary critics of your industry are all from a very specific ideology that affects how you talk and how you act, then the rest of us how to-- have to understand it. In fact, I don't understand how you can be reporting on AI right now without knowing that deeply through and through. Timnit Gebru has a good analogy here where she said, look, if we're in a world where most of these two AI labs were run by Scientologists, and the loudest voices that were yelling back at the AI labs were also Scientologists, and they had a disagreement about, like, when, um, the lizard gods were gonna be revealed or this or that, those of us covering AI would really, really wanna understand, like, how much of what they're doing and what they're saying is coming out of Scientology versus, like, what's really happening with the technology. This is the same thing. We have to understand the futurist ideology that is completely shaping the discussion right now. Like, you can't understand Jacob Croxson without understanding he comes completely out of that ideology and talks that way. You-- He makes no sense to people if you don't understand him and Anthropic are from the same group, and they're having a family squabble. And, and, and, uh, Jacob is leaving the members of his family who think they can summon the good God and going over to the other groups, the other cousins, who are like, "Anthropic's not ready to do it yet," but they're all from the same group. They all have the same utopian vision. You have to understand that to be able to cover what is happening in the AI doom debate. So that's why I think I'm seen as an, an, an anti-doomer. Um, I'm a big believer in various AI systems. I think it's a very powerful, interesting technology, and I'm very worried that the discussion around AI and two of the leading labs are caught up in this ideology, which is completely affecting lots of things negatively. And so that's why I really care about that i- ideology. We're all weird, but that goes beyond weirdness. It's what explains your behavior, the way you talk, the actions you take as a company. We have to be able to at least understand that so that we can have as much clarity as possible in understanding the landscape. So I don't know. I hear this same question a lot, Jesse, so I, I figured I'd give some clarity. Um, you know, there's, like, major journalists right now who are taking fellowships from effective altruist organizations. I don't think people really understand the, how all these things connect together.

   6. SPSpeaker56:16

      Mm-hmm.

   7. CNCal Newport56:18

      All right. Uh, what else do we have?

   8. SPSpeaker56:20

      Several listeners sent in their vision documents for the fall.

   9. CNCal Newport56:24

      Yes, I asked about this. All right, completely changing gears here. Um, earlier this fall,

3. 56:29 – 1:00:42

   A look at some vision documents

   1. CNCal Newport56:29

      I did an episode about what's a lightweight system for organizing your efforts so that given all the distractions and digital whirlwind and all the stuff that we have to face in our typical lives, what's a simple way to make sure that you're still making progress on things that are important? I talked about creating a vision document that captured the key things you were working on in the fall and then the key tools, practices, or projects you were gonna use to, to make progress on them. I asked people to send in, um, their own sample visions, and some people did. So I thought we would, um, look at a couple, and I've anonymized... These are sufficiently anonymized. All right. So here's a sample vision, um, looking at fall semester 2026. So this, I guess, comes from a student. And we see a, a, a summary of the goals up top. "My goal this semester is to complete my coursework and go into December ready to study for the bar and begin my legal career." Under projects and goals, we have fall coursework, MPRE, post-grad job search, move out of apartment, Marine Corps Marathon. So, like, these are specific things to be accomplished in the fall to move closer to that vision. And then under methods and tools, so what will you be using day to day, week to week to organize all these efforts? We see, for example, they'll use Things for task managements, Google Calendar for events, Garmin Connect, uh, for marathon training, weekly planning, daily planning in the morning, daily planning in the PM with a shutdown routine, and they will track... They have a list of metrics they're gonna track. So that's, like, a example of a good vision document. One page. You can keep that in your sock drawer. That's where I suggest you have it. And every day remind yourself, "What am I trying to do? What are the big projects or goals that'll move me closer? What are the tools and methods I'm running daily so that I can actually make progress towards these things?" All right, so then we have one more we can look at. Uh, this one I've, I've redacted some of the details just to keep things sufficiently anonymized, um, because this is a vision plan from a CIA assassin, so interestingly it has... No, it's not a CIA assassin, but I wanted to get rid of identifying details. All right, so the high-level summary of this sample vision says, "This fall I will implement the new CMS" and something redacted because it was too identifying. "I will create protocols and procedures to ensure staff understand how to operate these new systems." Under major projects and practices, the ones I haven't redacted includes organizing request and feedback, identified required protocols, create video and documented procedures. So, um, these... The, the thing I blacked out is the bigger picture project, and these are sort of the sub things to get done. And then under daily, daily practice, they have organized folders and files. "I'm gonna organize one folder daily." Then under materials and methods, we have Time Block Planner, using, uh, Tasklist, Loom to capture processes to, uh... So if you haven't used Loom before, it's a way to create, like, videos describing how to use a digital tool that you can then use for other people to learn it. Using Whisper Flow to capture discussions and brainstorming. It's an AI tool that can create transcriptions from voice, and a Remarkable tablet for meeting notes and drafting sketches. So we have goals up front- And then we have materials and methods below. Um, this individual had a separate vision for their personal life. So that was professional. For their personal life, they said, "This fall, I will move my family to our new home so our existing home can be rented for additional revenue. The major projects and practices include move into new home, prepare current home for renters, and have a daily practice of reading one chapter daily." In terms of materials and methods relevant to this vision, they have OmniFocus for tracking projects and tasks, um, and then they have a summary of some other stuff below. All right, so those are just examples of, like, what a one-page vision document looks like. One-sentence summary, major projects or goals you're working on, then the methods and tools you'll be using to help organize your time and effort so that you can make progress on those particular goals. So thank you, the two of you who sent in their sample visions. All right, I think we have time for one more question, Jesse. What else do we have?

   2. SPSpeaker1:00:33

      Our final question is about applying your advice in a parenting situation.

   3. CNCal Newport1:00:38

      All right, this is gonna be volatile. All right, so here's what the, the message says, "My

4. 1:00:42 – 1:03:16

   Cal’s advice for a parenting situation

   1. CNCal Newport1:00:42

      wife stays home with our eight-month-year-old baby. I've been seeing how much time blocking helps me, and I'd like my wife, uh, I'd like to help my wife organize her days, but I'm struggling. Her time is mostly shaped by the baby's naps and feedings, and anything can change in a moment. Should a stay-at-home parent with an infant use time blocking at all? If so, what would it look like when so much of the day is out of her control?" So I don't know, Jesse, why... Here's my question. Why isn't the baby time blocking her day?

   2. SPSpeaker1:01:14

      [laughs]

   3. CNCal Newport1:01:15

      Right? I mean, there's a lot of efficiency that baby is leaving on the table. If you optimize your feedings, it leaves more time available for moving blocks around. Um-

   4. SPSpeaker1:01:26

      And naps.

   5. CNCal Newport1:01:26

      And naps. So you gotta optimize your naps. They all should be time blocking. Um, let's step back for a second. What's the goal of time blocking? Well, it was invented largely for the knowledge work context, where we were... it was trying to help solve a particular problem, which is due to persistent overload, we often have endless things coming at us as knowledge workers, um, things we could always be reacting to, things that could always hijack and take over lots of our time. And if we're not intentional about how we use our time, we get caught in a reactive loop, and we make very little progress on the things that actually move the needle. So time blocking is a way of taking back control of your time and saying, "I'm just gonna face the reality of all the things I have to do, and what's the best way for me to balance reaction with proactive work?" That's what it was invented for, so I don't think it's relevant for someone who's at home trying to raise an eight-month-year-old baby. There, the goal is pretty clear, is, like, keep the baby alive and try to maintain your sanity as the person. It's not a problem you're gonna solve with time blocking. You don't need to time block, right? So time blocking really is something that is deployed for complicated professional circumstances. It's why I recommend don't time block your evenings, don't time block your weekends. It's not a way to go through all of your life. It's a way to tackle the very difficult challenge of the modern knowledge work, workload, and communication style. So, um, my main recommendation is if you keep telling your wife to time block her baby care, you're gonna end up with a baby bottle smashed and stabbing you in the throat. So I would, uh, I would cool it on suggesting that. I think whatever she's doing is just fine. Your job is to, you know, do whatever you can to make her life easier, because that ain't easy dealing with infants. I like to conclude on Mondays by talking about what I'm up to. Uh, read a new book recently, Jesse, Simplify by Jim

5. 1:03:16 – 1:05:41

   What Cal is up to

   1. CNCal Newport1:03:16

      VandeHei, Mike Allen, and Roy Schwartz. So if those names are familiar, they are the founders of Axios. They left Politico to find Axios, and they wrote a book about simplification in work or in your own life. Uh, they know about it. Axios is famous for having the smart brevity approach to articles, where it's bullet points and bold and, like, just get the key information as quickly as possible. People are more likely to read it. So they're sort of expanding that theory to how you can apply it to the rest of your life. Um, you know, obviously I'm a fan of simplification. I think that book's out. It might not be. They sent me a copy, so, uh, either it's out now or it'll be out soon. But I'm always happy to hear how, like, different business leaders are thinking about things like simplification. They, they embrace a lot of ideas I would a- agree with. You know, simplifying priorities, deep work, minimizing stuff that's taking up time, that's shown in... Very anti-shallow work, very anti-pseudo productivity. So, uh, those are definitely kindred spirits.

   2. SPSpeaker1:04:16

      What would an LLM summarize an Axios column be like?

   3. CNCal Newport1:04:21

      I mean, an Axios column is basically already like an LLM summary.

   4. SPSpeaker1:04:24

      That's what I'm getting at. [laughs]

   5. CNCal Newport1:04:25

      Yeah. I, I, I think they're down to it. They also heavily recommend in the book that their, their employees use LLMs for, like, a lot of things, including writing, which I wouldn't typically recommend. Um, yeah, they were like, they were like the LLMs before there were LLMs. An Axios column is a political column put through an LLM. Maybe that's the way to think about it, except for LLMs get things wrong and hallucinate. In Axios, you have local professional reporters, so it's like an LLM that doesn't hallucinate, I suppose. All right, um, that's all the time we have for today. Uh, we'll be back soon with another episode. Always here on Mondays. Sometimes we have other episodes as well. Uh, until then, as always, stay deep. Hey, if you made it this far, you must be ready to join my fight for depth in a distracted world. Now, the best way to do this is to join over 125,000 people who receive my email newsletter each Monday. You can sign up at calnewport.com/ideas, and when you do, I will send you a free guide to my seven best ideas about cultivating a deep life. Sign up today, calnewport.com/ideas. [outro music]

Episode duration: 1:05:41