The Daily Stoic - You Will Need This to Push Through | Is AI Making Us Stupid?

Episode Date: July 16, 2026

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Welcome to the daily stoic podcast, designed to help bring those four key stoic virtues, courage, discipline, justice, and wisdom into the real world. Look, if you hated the taste, if it didn't feel good, if you weren't getting good results, if it didn't guarantee a dopamine hit, it wouldn't exactly require much discipline to steer clear. That's the whole point of the virtue, though, isn't it? That it's requiring you to resist an impulse or foregoal. a pleasure. As we said recently, discipline is about doing what's hard. Just as courage is the triumph over fear. Discipline is the triumph over another lower part of our nature. A lot of times

discipline is pushing yourself to do something you don't want to do, but the other part of it, which we might call the temperance part, is not doing the stuff you want to do. And in some ways, this takes the most strength. Seneca said that we're all slaves to one thing or another sex or ambition or attention or chaos. And by indulging in the, these passions often enough, eventually we lose the freedom to abstain from them. We just can't not. We need discipline to push through that, to resist the urge to keep going when it's not serving us, to step back when our ambition tempts us to overreach, to recognize that rest and recovery and restraint are not signs of weakness, but also of strength and wisdom. True discipline

means knowing when to stop. It's having the courage to say no, to the extra hour, the extra project, the unsustainable pace, so that we can sustain ourselves for the long haul. Discipline in this way isn't just about action. It's also about control, control over our impulses, our desires, even our most deeply ingrained habits. Without this, we are slaves. Only through temperance can we ever be free. And I talk a lot about that and discipline is destiny. I didn't just want it to be a book about do this, do this. There's a bunch of things we need to stop doing. And discipline is understanding that. You can grab a sign copy at store. Dailystoic.com. But if you are looking for some better overall habits, you might like our daily stoic course

habits for success, habits for happiness, which is a six-week deep dive into better stoic habits, which you can sign up for right now. Or you can join us in Daily Stoic Life and get it and all our other courses for free. I'll link to that in today's show notes. So apparently everybody that works for us at the bookstore and Daily Stoic is using whatnot. And I guess I'm out of the loop. But once you flip open the app, you see why. There's something like super exciting about live sales.

Like, whoa, you can get this right now that they're talking about it right now. And the sellers look like they're having fun. And, you know, it's not edited photos and videos. Like you see it right there in the seller's hands. It's like a really cool estate sale or like the coolest fair you've ever been at. And that's just like not what the online chopping experience is normally like. Check it out today. Just search What Not, W-H-A-T-N-O-T in the app store. Download it and you can start selling right away.

Most leaders have a clear picture of how work is supposed to get done, but they have almost no visibility into how it's actually happening day-to-day. And without that visibility, you're just making decisions, like what to automate or where AI is going to be helpful based on not much more than your gut or, you know, how you've always done things. And traditional approaches, you know, you can hire consultant, you can map out processes. Now, this could take months. By the time you get the data, it's outdated before you can even use it.

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Or is it here to replace us? Is it making us stupider? Is it ruining the world? Is it undermining the sort of basic skills? that we have to develop as people, and then we have to continue to develop as people, you know, sort of being thoughtful, being reflective, thinking, researching,

practicing our skills, right? These are the questions, right? These are the questions that if the people of the AI companies are not asking, or if the politicians are not asking, then we ourselves have to ask personally and professionally. And it's actually one of the things I ask, today's guest, one of my friends, David Epstein, about.

I'm raved about David's work for years. He's the best-selling author of Range and the sports gene. He has this awesome new book called Inside the Box, How Constraints Make Us Better, which came out on May 5th. Loved it. I actually read it back in January, so I had to sit. Speak of constraints, I couldn't tell you how awesome it was until closer to it coming out.

But in today's episode, we ripped a little bit on AI, how we use these tools without letting them use. use us, how you have to use your brain first. Remember that story I tell and wisdom takes work from Seneca about where he says, like, nobody becomes wise by chance. You don't become good at anything by accident. If you don't develop these skills, they atrophy. That doesn't mean you can't use tools. That doesn't mean there aren't some ways that it can make you better. I think that's, in fact, quite clearly the case. But anyways, David and I are talking about using the brain first, then AI, how David uses it in his work, and how we need these concerns.

constraints if we want technology to help us without becoming a crutch. We shape our tools and our tools shape us. And that's what this conversation is about. Like I said, if you haven't read David's work, you absolutely should. He's been on the podcast a bunch. And this is just a short little episode with him and I talking about a very important topic in the news. Talk to me about your thoughts on AI. Because like what it does is it makes a lot of really hard, time-consuming things easy. Yeah. And so is that a good thing or a bad thing?

Yeah. A few things. I mean, we were talking about desirable difficulties, things that make learning more difficult. And so far, this kind of first, let's say, like first run of research on AI looks terrible from that respect. There was an MIT study that got a lot of headlines talking about brain rot, how using chat GPT will rot your brain.

But there was actually more nuance in the study. So these MIT students who had to write essays on. different topics. And they were broken up into groups. One group had to just write using their brains. And one group had write using chat GPT. They could use chat GPT. They didn't have to. And they write on these topics. And then they're brought, I think they wrote three essays. And then they're brought back sometime later. And they have to write on the same topics again. And they're asked about the things they wrote. And this time, they're switched. So the brain first group can now use chat GPT and the chat TPT can now go to brain. And basically everything for the chat GPT group looked worse. They

didn't remember anything they'd written. Their essays were more uniform, homogenous and less interesting. Whereas the group that used chat GPT second did did quite well. You know, there were stuff about their brain activation, but I don't like to lean on fMRI data. So I think the rule that comes out of a suggestion would be brain first tool second. And there's just this mounting evidence that's seeing that people are using it to kind of replace their thinking instead of the supplemented and that that's bad because then they do not learn. Like, if they have it right their first draft, they have real trouble recalling any of the content that's actually,

even if they're like, and then I go over it. Right. The point of writing the essay is to write the essay, not to have the essay. It's to force yourself to do a hard thing, even if it's somewhat arbitrary thing, right? It's to force you into this structure that requires you to think and comprehend and communicate in a certain way.

And then you, on the other side of it, Yes, you do have the essay, but more importantly, you have the skills of having done the thing. A huge portion of the writing that everyone does, especially when they're younger, is not because you care about getting the best essay. Yes. It's because you're learning how to make an argument, how to think, you're learning the material that's in there, you're learning how to organize things coherently. It's not because you need the most polished essay in the world. And so I think this, so, you know, we're still early days for this research, but so far it's looking pretty bad for how to affect people's thinking. I think there are other things.

I find it fascinating. Like, there's a research use I have for it that turned something that would take me 10 hours into one. And what? So I use this site where I can upload a paper, scientific paper,

and it will immediately create a map of other papers that cite my paper with bubbles of different size showing how cited those papers are. So I can see which are the real big deals in this field to cite my paper. It'll try to automatically organize them into supporting a refuting.

I'd say it's right, like, say, 75% of the time. And then it will make list with excerpts a few sentences around the mention of the paper that I'm interested in. That's something I used to control F my way through PDFs for for hours and hours and hours. So that's amazing. And I mean, I find it interesting in all sorts of ways. At the same time, I think there's that issue with the undesirable ease that it does a thinking where it's, you know, it depends how you use it. The task is supposed to be hard. The task is supposed to be hard. That's the nuance that was lost in the brain rod headlines, which is that it did actually show that

if you used brain first, it was good. The other thing I think is it just gives people, infinite capacity to just start a bazillion different things and to create, you know, now you're hearing this term work slop to just create a ton of mediocre stuff that buries the people who are getting it. You know, hey, think you researchers for me, and then they give you 80 pages of shit they printed out

as opposed to thinking about. So it's like creating more work in the system. It might offload from one person. But there's this cartoon where someone is like, AI's great. I gave it these four bullet points. It turned it into a six-page email. And then the other end, the person gets the email.

It's like, AI is great. It took the six-page email and turned it into four bullet points. Yeah, yeah. So I think there's a lot of that going on. And I think, you know, through the lens that I'm thinking of the world right now, I think there's going to be even more need for proactive constraints because you can start so much stuff and do so much stuff. There's actually like one AI company that I've spent a bunch of time with because I know the founder and I wanted to understand how these things work, how they train. So I sit in on their demos and all this stuff.

I've talked to other companies that use them. And one of the things I've noticed is most of these companies are saying, I need AI. I know I need AI. But they haven't, so they implement it. But they didn't identify a problem. Yes. And define it well that the tool matches. Right.

And so they'll end up implementing it and it starts generally instead of specifically. Yeah. And they're generating more of this work slop and all this stuff. And so I think we're often skipping over the define the problem. And then let's see how the tool fits with this because there's this race. You know, you feel so behind if you haven't implemented it everywhere. Yeah. I was thinking about AI in through the lens of your last book about.

range, right? Because obviously, if you only do a very specific thing, the chances of it replacing you, I guess, would be higher in some ways, right? Like, we've spent the last 15 years telling everyone to become coders, and now it's really good at coding. And, uh, that was like supposed to be the safest job five years ago. That's not great. But I've also found it in my use of it, which is that like, if I have a decently broad base of knowledge about an area, actually, I find the, the AI is, is not that great. Because I know. I'm able to spot all the ways that it's wrong. Yeah, yeah.

And it's, it's in fact where if you're a specialist, you're actually more vulnerable to this because you don't know what you don't know. Yeah. And then you're more relying on it. So there's some stuff it does really good, but I'm finding to be good at it. What you have to have is kind of a broad liberal arts knowledge base. Recognizing slop requires a lot of knowledge. Yeah, I think it requires actually knowing a ton of stuff. Yes.

Like, knowing information. Yeah. Yeah. which people think is just obviated now. And just having a good sense of kind of information hygiene, like where to say this doesn't sound right. Two things to your point. First of all, I had this experience recently of conversing with AI, I guess. And it brought up a fact that I was like, how did you know that?

It was about the number of Charles Darwin's pen pals that he kept. And he would like hit them up for information on. And I was like, man, that is a very obscure fact that I happened to know because I read this specific paper that went into range for it. And then it said, and then it talked about the researcher that studied his pen pal correspondence. I'm like, that is so weird. I know both of these things. And that's the right fact, but the wrong researcher. And it's creating this whole narrative about it.

And so I started asking, what's your source? Yeah. It turns out it's me. It was excerpts that somebody put up on good reads or whatever, some part of my book. And it had taken two things that were close in physical proximity, mash them together, and then, like, filled it out with a narrative, basically. Add a slop on top of it. Yeah, and I'm like, you would never, like, I might be the one person that would have caught this particular thing, right? Yes.

So there's that aspect of it. And also, I mean, one thing that's happened, Rain just kind of having a little bit of a second life because the beginning of the book is framed early in this like looking at kind and wicked learning environments in the world. Like these situations that are sort of more stable will you solve the same problem over and over versus those where you have to kind of reinvent and learn new things all the time. And increasingly we're all in that wicked world where like work next year won't look like work last year. And the argument it makes is that people with breath

are particularly prepared for that kind of world and even gets into using human computer chess partnerships as a model like where things are going. And I had no idea where we were going to be with AI now, but people have started to view that as prescient

as this argument for like these synthesizers being more powerful and like, anthropic just said they only want programmers with side quests and the financial times just did this big analysis that showed that the returns to like collaborative and coordination skills are accelerating and how fast they're outpacing like just technical skills. So I think we're going to need people building the LLMs, of course, are specialists. But other people are going to have to learn broadly and reinvent if they want to not just be producing a ton of slop. Well, do you think even like what they call sort of query management or prompt engineering?

Prompt as a term. But like, so if you're like, hey, draw me a picture of this. It's going to give you some random slot, basically. But if you're like, if you're able to go, hey, I want a picture of this in the style of this artist from this era. By the way, I don't want it to look like X, Y, or Z. Like, the more precise you're able to be, and the irony of that preciseness coming from your broad sense of all the different avenues that it could go, it's really important. Like, you have to have this sense of knowledge. I was having to do something on this book I'm researching now.

I'm exploring, like, this brief period in somebody's life when he was on a college football team. And I have this letter where he's listing some of the people he remembered playing with. And so I, like, upload this chunk of the letter and I'm asking, okay, like, what did these people go on to do? Right. And so this is eight, 70 years ago or something, you know, some of the names are common. And some of them are not. And it's telling me, oh, this person went on to do this. And this person went on to do this.

This person went on to do this. And like, it makes this one confusion. You know, Stan Turner goes on to play, you know, as a moderate professional football career. And it's like, okay, no, no, no. This is clearly Stan's Field Turner, who goes on to be the director of the CIA in the Carter administration. Okay.

But I knew enough to go, hey, you've confused this person and this person. It is never going to be like, it could be this person or it could be this person. I don't know. It's going to confidently give me the answer. And I have to not confidently, but be just unsure enough that I go, are you sure you don't mean this? And then we're able. So you have to have this basis of knowledge that allows you to spot bullshit. Yeah.

And then just like anyone that's ever used a research assistant, also you have to have this meta understanding that you can never trust anything, anyone gives you, you have to, if someone is prepping stuff for you, you have to then go independently verify all this information. Or there's a vulnerability in your system. And so, like, the ability to know, have that sort of just intuitive bullshit detector. And then, and then the knowledge base to know what's not working and not working. And then also the standards, which is like, I will never turn in or publish or show anyone, anything that I haven't personally vetted. These are all skills that you're going to need to function in this environment.

I think those take a while to develop, though, too. Totally. When I think about some of the mistakes I made as an early journalist, I didn't recognize certain potholes that are obvious in retrospect until I, like, got something wrong. Yeah. You know, or had to make a correction. And then your editor was like, well, what do you mean? You just printed what they said or whatever.

Yeah. You learn. And you don't even know some of the assumptions you make, right? So in this book, I ran a lot of stuff I was writing through AI, you know, for fact check. But I had hired a human fact checker also. Yes. And I was a fact checker in the past, and I also went through it myself, like line by line, crossing out one line at a time. And when I finished the book, because I kept really good in my first book, the sports scene, I was a little sloppy with citation.

I was like, this is so interesting. I'll remember where this came from. And then it took me six weeks to like recreate my sources and do bibliography. With range, I kept better track, but still sometimes was trying to recreate something. This time, it was like every line I annotated. So I wouldn't. So this time when I finished with inside the box, I was like, do I need? to spend 10 grand on a fact checker?

Like, I think I was really careful this time. I probably nailed it. And then I fact checked myself, I probably made a change on every page, right, going back through. Like, you just are not attuned to all the assumptions you make. And if you're slowing down to catch them all, you never get the thing done also. Well, isn't that one of the benefits, though, to flip your book's premise on its head, right?

Like, there are things that constraints would prevent you from doing, right? So it's like, I would say AI's not great at fact checking. Yeah. But it's also free. Yes. So, so like I, yeah, my last book, I ran it through or even stuff as I'm writing now, I'll run it through. And I'll go, hey, what am I missing?

Where could this be better? What's wrong? You know, is anything jump out at you as being incorrect? And I would say 85% of what it brings up is totally wrong. Like, like, it caught this thing where it was like, hey, you know, it says this. That can't possibly be true. And it's like, okay, well, I have a letter from 1962. Yeah, yeah.

Like, this is, in fact, exactly what happened. And so it's false positives are very high, but if it gives you one or two or three or ten things that you wouldn't have caught otherwise. And all you're paying is this monthly fee, that's great, right? Like you probably wouldn't have hired two fact checkers or three fact checkers. So you still do it the old way,

and then you get this other way on top that's only upside. And in this case, right, only upside because you're just saying, oh, I better take a closer look at that, essentially. You're not saying this is going in, right? So it's just attuning you to things you may have overlooked. which I think is great. And I would say for people that are listening to us

and thinking about using it in this way, I would say use very, very small chunks when you put that in because I think that people have a tendency if it spits out a long answer. The more text you're looking at it once, the less meticulous, I think you're going to be about questioning these. If it gives you a few sentences,

I think you're going to focus in and question the things that it said. If you're like, fact check these 20 pages and it spits out 20 pages, I just don't think you're going to be as careful. So I would do it in very one chunk, two chunk, which, again, is hard because you could throw in hundreds of pages and say fact check this thing, right? And then the other interesting thing about constraints, right, is like, so you can have, read this section, give me your notes, and then it does it. And then you can do, as you wouldn't do with a person because you're not a monster. You'd be like, do it again.

Do it again. Do it again. And what's remarkable is how you'll get different answers each time. And so, again, the lack of constraints, the reduction in cost, let's put aside the environmental impact, which I don't know. enough about to speak authoritative. But you can, you can have it do the task many, many times. So if you're like, hey, I want a picture for a presentation. I want it to look like this. I want it to look like this. And then it does it. And you go, okay, try it again. Actually, give me three options. Make it better. Right. You just say make it better. And you get it. And so the

reduction in constraints allows you to get things that you wouldn't get out of a human, which is five options. And then you can mix and match them together. And there's benefits there. For sure. For sure. And I mean, that's like the march of a lot of efficiency and a lot of our tools, right? Or what Clayton Christians and called empowering innovations. They bring something that, you know, maybe someone with a staff could have done, but now someone without a staff can do it much more cheaply. It's like where, you know, you used to have to have like a desktop computer and then it gets cheaper and everybody can carry one around with them. But I do still think it's incumbent upon people to put a lot of constraints into that process or else you just end up with the work slop stuff and try and outsourcing your thinking completely, which is a whole other problem. Like, I wonder for some jobs now, you know, so five years ago,

as coding is, you learn to code. You'll never need another job. Now it's like the least safe thing. Yeah. Maybe other than some of what we do, I don't know. But I wonder how do you get kind of senior coders if you don't have junior, like, where are the minor leagues for a lot of these things? Or when I didn't end up working as a sign.

I mean, I did when I was a grad student, works as a scientist. But how do you get your hours to be this senior person? Yeah. Like I developed in, so much of my work has been based on research, pure, reviewed research that I thought was wrong that I picked out because I had a strong quantitative background and I got that intuition from being in grad school for geology. Right. Right. And so it's like, where do you, if you just skip over that, you don't really get that intuition that allows you to pick out stuff that's wrong.