# The AI Skills Nobody is Teaching (And Everyone Needs) with AI Expert Ethan Mollick

An episode of [A Bit of Optimism](https://invisiblenetwork.ai/podcast/1515385282), 16 Jun 2026, 59 min.

Audio: https://episode.flightcast.com/01KV6WT7XE7H83HZ0TE447PV6F.mp3

Be honest: AI makes you a little nervous. Maybe you're afraid it'll take your job. Maybe you're overwhelmed by all the advice about prompts and agents and which chatbot to use. Or maybe you're just quietly hoping it'll all slow down. Ethan Mollick says we're underestimating our own agency in the age of AI. Instead of worrying about what AI will do to us, we should focus on what we choose to do with it. Ethan is a Wharton professor, the author of the bestseller Co-Intelligence: Living and Working with AI, and the writer behind “One Useful Thing,” one of the most popular newsletters on AI, work, and education. He's spent twenty years studying how people actually use technology, and he's become the go-to voice for making sense of AI without the hype or the doom. And in his new book, Co-Existence: The Next Phase of AI, he explores what comes next as AI moves from a tool we prompt to a presence we live and work alongside. In this conversation, Ethan shares the practical playbook most of us are missing and makes the case that our experience, taste, and point of view aren't things AI replaces. They're exactly what make us better at using it. In this episode you'll learn: ➡️ Why young people are NOT "AI natives" (and why experience is the real AI advantage) ➡️ The $20 decision that instantly upgrades how you use AI ➡️ Why AI agrees with everything you say + the simple prompt that fixes it ➡️ How to make AI write in YOUR voice instead of sounding like everyone else ➡️ The "jagged frontier": what AI is surprisingly bad at (and why that's your opportunity) ➡️ Why taste may become the most valuable skill of the AI era ➡️ How much agency we really have over where AI takes us Ethan believes that the future of AI isn't something that will just happen to us… It's something we get to build together. This… is A Bit of Optimism. + + + To pre-order Ethan’s new book, Co-Existence: The Next Phase of AI, head to: https://co-existence.ai/ Want to hear more from Ethan? Check out his Substack “One Useful Thing”: https://www.oneusefulthing.org/ + + + Chapters Chapters • 00:00:00 Why are AI Experts Are Either Doomers or Zealots? • 00:02:05 From Video Games to AI: Ethan's Unexpected Journey • 00:09:16 AI's Profound Impact on Knowledge Workers • 00:14:50 How AI Kills Traditional Talent Pipelines • 00:15:57 Why AI Art Doesn't Bother Me, But I'd Never Hang It on My Wall • 00:20:40 How To Overcome AI's Complication of Competitive Edge • 00:22:06 The 20 Dollar Investment That Changes Everything • 00:24:40 The 84 Percent Rule: Why AI Can Now Do Your Seven-Hour Job in 15 Minutes • 00:25:59 Your Voice Matters More Than You Think: Why AI Can't Replace Taste • 00:19:53 The Discomfort-Avoidant Generation Meets the Efficiency Machine • 00:13:08 Why Young People Are Worse at Using AI • 00:43:35 The Brain We're Sacrificing: From Phone Numbers to Critical Thinking • 00:51:39 Two Prompts That Will Transform How You Use AI • 00:52:58 How to Use AI As Co-Intelligence • 00:54:57 The Agency You Have Right Now: It's Not About Policy, It's About How You Use It + + + Simon is an unshakable optimist. He believes in a bright future and our ability to build it together. Described as “a visionary thinker with a rare intellect,” Simon has devoted his professional life to help advance a vision of the world that does not yet exist; a world in which the vast majority of people wake up every single morning inspired, feel safe wherever they are and end the day fulfilled by the work that they do. Simon is the author of multiple best-selling books including Start With Why, Leaders Eat Last, Together is Better, and The Infinite Game. + + + Website: http://simonsinek.com/ Leaderful: https://simonsinek.com/leaderful Podcast: http://apple.co/simonsinek Instagram: https://instagram.com/simonsinek/ Linkedin: https://linkedin.com/in/simonsinek/ Twitter: https://twitter.com/simonsinek Facebook: https://www.facebook.com/simonsinek

## Transcript

[0:00] So when you're interviewing for a job, and everybody's good because of AI,
[0:05] how do I stand out and get a job that they still need to hire for? I'm fascinated by that.
[0:11] Like, if Claude is really good at running your company,
[0:14] Claude's also good at running every other company, and there's no variation between them,
[0:18] and generically high quality with no variation means there's no votes or competitive edge,
[0:23] I think humans who bring competitive edge to this one way or another just by providing
[0:27] variation if nothing else is a useful way to think about problems, right? You start to
[0:31] care a lot more about the taste of that person than you do about the entire organization built to
[0:36] deliver the product. We're all a little afraid of AI. Even the people who love it are a little
[0:44] bit afraid. Some of us are afraid it'll take our jobs or make us dumber or change everything so
[0:49] fast we'll never be able to catch up. It doesn't help the most AI experts fall into one of two camps.
[0:55] They're the dooms or they're the zealots. That's why I wanted to talk to Ethan Mollock. He's an AI
[1:01] expert who refreshingly is neither. Ethan's a Wharton professor studying how AI,
[1:07] entrepreneurship and innovation impact our work. He focuses on how employees actually use these
[1:13] tools rather than how they're theoretically supposed to improve our work. His New York Times best
[1:18] seller co-intelligence and his popular sub-stack one useful thing have become the go-to resources
[1:25] for all those people trying to make sense of AI without losing their minds. Ethan believes we
[1:30] have more agency over AI than we think. It's our experience, our taste, and our genuine points of
[1:37] view that are things that AI can never replace. And that's important because the choices we make
[1:45] about how we integrate AI personally and at work as a society will shape what it becomes.
[1:52] If you like this episode, please remember to subscribe. This is a bit of optimism.
[2:05] You are very popular right now. What would you, I know you teach entrepreneurship and other things
[2:13] before AI, what was the, what were you teaching that was the hot thing?
[2:18] In addition to AI stuff, my other thing I really care about is games and education and education
[2:23] scale, two related things. I've done a lot of work on using video games for teaching. So I was,
[2:28] I was, as the brand says, not famous but known in that space of thinking about games and teaching
[2:33] at scale how do we teach transformationally. So that is what they have worked on for a long time
[2:37] and the AI work was sort of in parallel to that. And was it your own personal curiosity about
[2:42] AI that sucked you in or was it that you were forced to use AI in the work that you were doing because
[2:48] it had to, because it made it better? Went to business school and my PhD program at MIT and I
[2:52] worked with the MIT Media Lab with their AI group. So Marvin Minsky was one of the fathers of the
[2:57] field and I was the non-technical guy in the group. So I was the person who had to explain to other
[3:02] people what AI was, how it works, when we go talk and have technical conversations, I'd come
[3:06] along. I had been AI adjacent or involved for 20 years but always in the sort of non-technical,
[3:12] like how do we use this, how do we explain it role and everybody else was technical because it
[3:16] wasn't really working out, like AI had limited use cases. And so when GPT-3 came along and
[3:21] started to make a splash and then chat GPT after that, I was sort of well positioned in this world
[3:26] of someone who had been thinking about explaining this for a while in a non-technical world.
[3:30] The reason I was really interested in talking to you, because to be honest with you, I try not to
[3:34] have AI guests on. And the reason is because they generally come in one of two flavors, right?
[3:42] It's the greatest thing that ever happened and it's going to make the world a better place
[3:46] or it's the worst thing that ever happened we're all going to die. And the conversations are for
[3:51] obvious reasons a little lopsided and one sided and they usually have some vested interest in one
[3:57] opinion or the other. And the reason I wanted to talk to you, to be honest with you, is yours is
[4:03] just more practical. It's not, it's the savior, it's not, it's going to kill us all, but it's kind of
[4:08] like, well, it's here, you know, kind of like the internet showed up. What's the best way to use
[4:14] this thing? And it's a little more down the middle. Yes. It's nice to be in a place where being
[4:21] somewhat pragmatic makes you unusual. That's not usually the place where you get publicity for
[4:26] being the non-bombastic version. It's true. But also the other one with those two opinions is
[4:31] they tend to eat the world, right? If you think that we're getting a machine god who's going to save
[4:35] us all, then, you know, all of it matters is discussing that, right? Just like any other sort of religious
[4:40] belief. And if you believe that we're all going to die, how can you have a conversation about anything
[4:45] other than, you know, this is going to do us all? And the fact is, this is general purpose
[4:48] technology. It's going to affect everything we do one way or another. It's worth spending some
[4:52] time on that. And some of those effects will be good, some will be bad. I do remember the sort of
[4:56] the rise of the internet. I'm old enough to remember it. And the conversations were somewhat similar,
[5:02] maybe less, less dramatic on either side. There was a lot of positive energy at that. Mostly
[5:08] positive. But there was lots of conversation like, I remember people who, you know, there were
[5:12] the zealots who believed that the internet and everything online was going to replace everything.
[5:16] You know what it is? It's the difference between eating to live and living to eat. You know,
[5:20] and if you leave it to the technologist, they think all technology is living to eat.
[5:24] I mean, they literally made protein powder shakes. I don't remember a soy end to your
[5:28] spouse. I remember a soy end. I remember for people who thought eating was too annoying. Why are
[5:32] we spending all our time worrying about eating when we could just get it taken care of, right? It's
[5:36] a different view of efficiency. But it's also a view that kind of blinds you to the fact that
[5:40] technology is the most human activity, right? We're making tools for ourselves, how we use them,
[5:45] how we adapt them, how we regulate them. Those are going to have big influences. You know,
[5:49] AI is more self-directed than most technologies, but we still have a lot of agency over what happens
[5:54] next. I mean, I know from my own experience, most people are misusing or underutilizing the
[6:00] technologies that are available to us. And I'm already getting to the point where I'm turning on,
[6:06] you know, social media or listening to friends or reading an article. And I'm already feeling
[6:10] overwhelmed by all of the advice of people telling me that I should be using it like this. And I
[6:15] be sitting up an agent to run my life and sitting up an agent to do my marketing and sit up an agent
[6:19] to do my finances. I'm already overwhelmed by all the advice of how I should even be prompting
[6:23] the machine to the point where I'm almost backing off and shutting down, because one minute
[6:28] demonized that you got to only use that one, the next one, you got to only use Chatchy BT,
[6:31] then if you only got to only use Claude, it's all too much already. And for anybody who's not
[6:37] predisposed to like be all in, that reaction, I think, is pushing some people away, believe it or not.
[6:44] Well, I absolutely, I mean, look, I am a nerd of the old school, so I like getting into the details
[6:49] of stuff and partially like explaining them, which is I think part of why you have me here. But also,
[6:53] it is overwhelming. I mean, part of what's actually interesting is AI's got an easier, right? Not
[6:57] to be too, you know, too evangelistic about it, but like it used to be the stuff mattered. Like,
[7:01] you had like all those little details, like prompt engineering mattered. So it mattered how I
[7:06] phrased things and matter. If I said, you know, you are a physicist, it was better physics. If I
[7:11] said, think hard about this, that matter, if I offered bribes, that would matter. We've been testing
[7:16] all that. None of that matters anymore. The models have gotten good enough that if you're good at
[7:18] giving instructions, like a human to humans, you probably do okay with this. And suddenly,
[7:23] all the models are getting good quite quickly. So whether you open AI or chat with you there,
[7:27] like chat with you or Gemini or Anthropics, they're all three of those are pretty solid.
[7:32] There's like one or two hints I'd give people, but otherwise it's really just use it for stuff
[7:36] and don't stress how you're using it because you'll figure it out.
[7:38] Also, when we talk about like AI is going to take away all of our jobs. And by the way,
[7:42] those are the technologists saying that when one of the things that I also love is the technologists
[7:47] are also very fond of saying, you know, things like 80% of the jobs today didn't exist 20 years ago.
[7:53] Which means it's fair to say that 80% of the jobs in 20 years, we can't even imagine.
[7:57] And to your point about prompting, it wasn't that long ago where the technologists were telling us
[8:02] prompting is going to be the thing. And people are saying, I'm going to get a degree in prompting
[8:06] and the technology got good enough in just a few minutes that that literally went away.
[8:10] Yeah, and I think that that is part of what makes this kind of interesting, right? We can't
[8:14] imagine the jobs we'll keep trying. And also not imagine the jobs does get nerve-wracking when like,
[8:20] you know, AI is here and it's like, what is the job? Well, I'm not 100% sure. I mean,
[8:23] I think there's both legitimate reasons to worry about jobs. These systems are very capable and
[8:27] very confident. And they do change their impact real work. This is not electricity in the same
[8:32] way of like we need to figure out a way to harness or use this thing. There's a real impact.
[8:36] I've talked about this a little bit. I actually think that, you know, nature pours of vacuum,
[8:41] markets correct. When we have a bubble, the stock market will at some point, not of our choosing,
[8:46] correct itself. And all systems seek equilibrium at some point. I'm fond of saying that in the 70s and
[8:52] 80s, robots started enter factories. And the blue collar world said, hey, we're going to lose our
[8:58] jobs. And the white collar world said, it's the future baby. Progress stops for no one. Reskill,
[9:04] you know? And, you know, the pendulum duff swing. And because your plumber doesn't care about AI,
[9:10] the carpenter doesn't care about AI. The mechanic doesn't care about AI. The people who care about AI
[9:16] are the knowledge workers. Yes. And, you know, it's the future baby. Progress stops for no one.
[9:22] Reskill, baby. Yeah, I mean, that's a really Saturday. I mean, there is a come up, but there is
[9:26] another thing, right? Like, if you look at the last three industrial revolutions, there's either
[9:30] two or three. I mean, that's all we've got, right? And the reason they worked out wasn't because
[9:35] the technology made everything great alone. It was because it was also labor fought against,
[9:40] you know, capital. And you had a whole bunch of fights happening. Unionization is how the benefits
[9:45] got spread around, right? The technology doesn't naturally do them. What's really interesting is
[9:49] we're going to have a similar fight here, right? Like, AI, we already have good data, right? AI is
[9:53] pretty good at being a doctor. It's getting better being a lawyer all the time. Like, most lawyers
[9:57] I talk to see a trajectory where not too long from now, you will be able to get as good advice for
[10:02] not the most complicated issues from the AI. If you're not already, right? And there's
[10:05] ongoing debate about this. But the thing is lawyers are not going to be quiet about losing their jobs.
[10:11] And it turns out a lot of Congress's lawyers and a lot of people don't have money or lawyers. And
[10:15] I'm willing to bet that you're going to see laws passed in every state that you can give a lawyer
[10:19] officially a human sign off of it. So, even if they're worse than AI, right? Yeah. So part of this
[10:24] there is a little bit of, oh, it's your come up into white color workers. But it's also like, no,
[10:27] no, they've got their fields that are not going to go easily, right? And doctors, lawyers,
[10:34] it's going to be interesting. Coaters really do not have the protection. The doctors or lawyers
[10:37] or actors or other kind of guilds or associations do. So we'll see a lot of conflict over these issues
[10:43] in the near future. Lobbing for our own interest is not a new thing. So, for example, for years,
[10:48] there have been people who try to raise money for the government where we can get more income.
[10:53] The average lifespan of a dollar bill is about one year. The average lifespan of a coin is
[10:57] something like 30 years. And so the proposal to move dollar bills to dollar coins,
[11:02] because it would save the government, you know, untold billions per year in printing and all
[11:06] of the rest of it. And the reason we haven't done it is because of the income paper lobby,
[11:10] because they like those billions being spent on them to make new dollar bills every year.
[11:16] And so, you know, we're used to doing things that are not to our advantage because of lobbyists.
[11:22] And this is just another thing where those who have access to power and those who have
[11:26] influence will protect their interests, as you said. And not necessarily badly, right? Do we want
[11:30] all lawyers out of a job? Is that open question? A lot of people will be like, yes, yes, please. But like,
[11:34] you know, I mean, already, actually, there's some early evidence that the number of cases being
[11:39] submitted to judges is exploding exponentially, where people are pleading their own cases with AI
[11:44] law. I'm like, how do you deal with that? It used to be that we had a filter, right? So the
[11:48] secondary thing is you had to give it a lawyer to take your case. And if they did a bad job,
[11:51] they would be punished. And if you got a hundred-page legal brief, a person wrote that. And that was
[11:56] the indicator of real efforts. But now it is it. How do we deal with that kind of system? So we're
[12:00] going to have all of these ripples and all kinds of areas that are going to require policy of
[12:03] changes, regulation changes, societal changes, even if we don't have sort of a apocalyptic AI
[12:09] event the way we were talking about earlier. So let's go from the theoretical to the practicals.
[12:14] And as we sort of said before, which is a lot of people who aren't technologists are under
[12:19] utilizing or misutilizing this remarkable technology. Some are still using it as a glorified
[12:25] Google. Google to search. This is more of an answer machine. But still, I mean, we all do that
[12:30] because it is a very simple and fantastic use case. The more nuanced and complex prompts,
[12:36] I think I know I'm under utilizing. Like, I ask for an answer, even if it's something that has
[12:42] some depth. But I don't ask it to write a report or make an interactive, you know, dashboard
[12:48] of the data that I'm not, I know that I'm not doing that. And I'm already ahead of some in terms
[12:54] of my utilization of the thing. I have two questions. One is you're teaching students, you're teaching
[12:59] a lot of grad students at business school. Is it a false belief that just because people are younger,
[13:04] they're all in on this technology or are they also sort of bumbling and fumbling their way as
[13:08] they learn about it? So we actually have a paper on this. So I think what people think, I hear this
[13:13] term in the internet, we're talking about digital native all the time. People talked about this,
[13:16] right? Like kids these days are good at using the internet. And indeed, like if you talk to somebody
[13:20] who grew up with TikTok, they will know all these intuitive ways that, you know, as an older person,
[13:25] you will not understand, like, no, that's cringe. There's a bunch of rules and slang and
[13:28] approaches that you want to take. And I think that that model kind of carries through the younger
[13:32] people get the technology. It does not hold for AI. Like I talked to like SCHRO and it was like,
[13:37] the kids these days, they're, they're AI native. I mean, they're not AI native. You're just talking
[13:40] to Claude. They're conduits to Claude. Like if you ask for a report, they'll give you a beautiful
[13:44] report. They have no idea what's in that report. How could they? They have no knowledge of this.
[13:48] They're just giving you a Claude says. And we actually found some evidence on this when we did a
[13:51] study at BCG at Boston Consulting Group. We found junior employees were often much worse at using AI.
[13:56] They seemed like they were using it while they were adopters. But how could they judge whether
[14:00] something was good or bad? I think this is a rare case where the more experienced you are,
[14:03] I'm sometimes the older you are, the better you're going to be at using AI if you decide to use it.
[14:08] Because you can intuitively grasp how do I give instructions? AI works enough like people that
[14:12] if you give an instruction and you're good at a field, those who would be like, no, no,
[14:16] I understand what went wrong there. You're thinking about this. You should be thinking about that.
[14:19] Even though the AI is not a person, it doesn't think. You'll know what kind of information to expect
[14:23] to get good answers. So I think actually experience really does matter. If you think about how
[14:27] education says about we're actually schools are in chaos. I mean, they're always in chaos,
[14:30] right? Universities have always been in chaos. This is not new. You know, people are cheating with
[14:33] you at GBT. But we actually know the pathway forward, right? Like it's going to be a little bit
[14:37] messy and it's going to be like, we'll do it more. We did this with the characters. We'll do more
[14:40] in-class assignments. Outside of class, we'll use AI tutors which turn out to be very effective
[14:44] at controlled experiments. We'll make them work better than we do now. In-class, we active learning.
[14:48] We'll figure it out. I'm not worried that we can't figure it out. I am worried about the next
[14:52] stage. I teach people in general, I said, working and they become specialists the same way we've taught
[14:55] specials of 4,000 years, which is apprenticeship, right? I send them off to work for whomever, right?
[15:01] They go work at, you know, Bank of America or whatever. And they learn the job and everyone gets
[15:06] a good deal. They get a little bit of income, but not as much as they would probably deserve,
[15:10] but they get a chance to prove themselves. And they learn the ropes by doing run, work over and
[15:14] over again. The middle manager assigns them run work that they don't want to do anymore. And
[15:18] gets to evaluate whether this person is any good or bad for moving up the ladder. And it's been a
[15:22] great mechanism. And that just broke, right? Because every junior person knows less than chat
[15:26] GPT. And they would rather just use chat GPT. And they'd kind of dumb not to use chat GPT or
[15:30] claw to give you answers because it's better than what they could do. And every middle manager would
[15:34] rather delegate to the AI than a flawed human who takes forever to give them an answer. And it
[15:39] is just good. And so everyone's just doing AI work to each other. And I think that that is the
[15:43] problem that you're talking about here, which is the danger is that we lose the talent pipeline.
[15:47] There are solutions to it, but they're going to require fairly radical change in how we think
[15:51] about talent pipelines. How much of this is kind of like art? And here's where my brain is going,
[15:57] right? Which is I am an art fanatic. It's the thing that I love more than most things. And I am
[16:04] totally fine with AI making art. It doesn't bother me. I'm totally fine with AI making music. It
[16:08] doesn't bother me. However, when I hang something on my wall, I like knowing that a person conceived
[16:15] of it. I like knowing that a person made it. Because when I buy a piece of art, I'm not just
[16:19] buying the visual thing on the wall. I'm buying the story that goes along with it. Or for example,
[16:23] I was listening to some music this weekend. I was listening to John Batista's Beethoven Blues
[16:29] album, which is if you haven't heard it spectacular. Now could AI make a blues version of a Beethoven
[16:35] Sonata 100% it could? But the joy that I got from listening to that music was not just the music
[16:42] that I was listening to, but I was smiling that a person had the creativity to come up with this.
[16:49] And that was part of my joy when we look at the work product. You know, there's two things we're
[16:55] neglecting, which is I like thinking. I enjoy debate. I enjoy making my head hurt at difficult
[17:04] things. I enjoy learning the same way a painter likes painting and a musician likes playing music
[17:11] and composing. Where is the human desire to want to learn? And then we'll our schools, but especially
[17:19] our places of work allow for that to happen where they all become so obsessed with efficiency
[17:25] that we actually even if we want to learn, you see where I'm going with this.
[17:28] There's 10 billion directions from here, right? So I want to put a pin on the art thing because
[17:32] it's actually really important and interesting, which is obviously the weapon rise of our more
[17:37] artisanal human-made things. The most direct version of this, by the way, is when you have AI
[17:42] in poetry or long-form fiction, there's often a lot of things wrong with it, but because we're used
[17:48] to, if we read something that reads beautifully and it's effortful to read, we assume that there's
[17:53] a purpose behind it. So we spend our own effort figuring out the whole. Like, for example,
[17:57] the eyes are very famous at weird analogies, right? So it might say this conversation is like a gap
[18:02] too smile. Now, that is not meaningful, but if you spend some time thinking that you're like,
[18:06] oh, how is it like, and you will reach a feeling of meaning, right? If that was, you know,
[18:10] Lazdo Krasnikai or someone else writing this set of stuff, and I was reading, I'd be like,
[18:13] oh, this person thought hard about that analogy, and I just spend the work to do it. If it's the AI
[18:18] doing it, you know, in some ways, it's beautiful, but the meaning comes from me, and am I being cheated
[18:23] because I have to create the meaning that has no intention. The intention no longer belongs to
[18:29] the artist, the intention now is shifted to the list of art, right? And maybe it always has,
[18:34] definitely novel stuff, but like, so that's one angle that's kind of interesting, right? And then
[18:38] I think the sort of second one is, you know, is on thinking about developing some of these kind of,
[18:42] how do you develop intuition? And we actually have a way of doing that. We know how to train people.
[18:46] Like, we could teach people the experts, but the problem is it's effortful. And so, you know,
[18:52] there's always been this sort of view that I told you earlier on, I made games for education.
[18:57] One of the most depressing things you learn is you can make something incredibly fun,
[19:01] but first of all, it's only 80% fun. It's not as fun as actually doing a thing for fun.
[19:05] And second of all, learning is effortful. And if you're not doing effortful work, then you're in
[19:09] trouble. And now, for the few areas that we intrinsically care about, for you, you know, it might be
[19:13] art history or music, or maybe for some people, it's math and science. Maybe for some people,
[19:17] it's, you know, it's a sport they care about. Whatever you, we are effortful about, and intrinsically
[19:21] motivated, you're like, why isn't all learning like this? And the problem is you don't care enough
[19:25] about it. Like, and so, but I still want you to learn math, even though you don't want to learn
[19:28] math, I still want you to learn American history. And if you shortcut that through AI,
[19:33] giving you an answer is you learn nothing. We've been enough experiments to show that.
[19:36] So making people essentially lift mental weights becomes the problem in a world where there are
[19:42] shortcuts. You know, I find there's a great irony in all of this, which is the problem actually
[19:46] doesn't lie with AI, which is we've been on the steady drum beat, this path to this point where
[19:53] we are, you know, discomfort avoidant. The concept of ghosting is the thing where you just
[19:59] avoid a difficult conversation, or you see it now, particularly among young people, where
[20:04] they're more comfortable with quitting a job than having a difficult conversation or getting
[20:08] negative feedback. And, and then the idea that we've become so end result oriented, you know,
[20:14] as as capitalism has become short-term focused and more focused on a shareholder supremacy,
[20:19] shareholder value over the quality of the product or customer satisfaction or employee satisfaction,
[20:24] you start to see we become more results oriented and we've left out the work product.
[20:30] This is not a new concept. AI is just the most exaggerated form of being results obsessed
[20:36] at the expense of the effort, the work, or the journey to get there.
[20:40] Well, and just to take another path from that, I mean, part of this is what makes AI
[20:44] work so challenging, right? Because if you want productivity gains, you just, you'll get
[20:49] 100 times more power point, right? If my job is producing power point, like so it requires you
[20:54] to rethink what the work is. And so what the work product is can't be the same thing. I mean,
[20:59] even the most basic way, coders can write 100 times more code than they could before. If they
[21:03] are embedded in an organizational process where it takes two weeks to do, you know, a product sprint
[21:08] as they as they often call them, right? So each of the stand-up meetings every day and the
[21:12] assumption is the code will write X number of work, the product manager will do this,
[21:17] the designer will do this, the marketing people will do this, and suddenly one person's 100
[21:21] times more productive, what does that even mean for an organization becomes a problem?
[21:24] So part of this is our systems, we're coming back to that theme we've been developing throughout,
[21:29] which is human systems are not built for an AI world. We have, we're effort, you know,
[21:34] school wasn't built for a place where anyone could write your essays, right? Like, you know,
[21:38] like, work wasn't built for people to be able to produce PowerPoint on demand without thinking
[21:42] about it, right? Fiction wasn't built that I could write as many papers. The lock clerks and
[21:46] courts weren't built for anyone to be able to bring up our case. That's not a problem. That happens
[21:50] in every industrial revolution. It's just all happening once everywhere. And sometimes the AI wins,
[21:55] sometimes human systems win, sometimes we both lose, but that's where I watched the adjustment
[22:00] happen. I'm going to go backwards a little bit here. Let's go back to practical, which, based on
[22:06] classroom and in the business world, because I know you study that as well. What are better
[22:11] simple ways that we could be using the technology available to us? Like I said, most people are
[22:15] miser under utilizing the tool. And, you know, I'm overwhelmed by the people giving me advices how I
[22:21] should be doing things and what I could be doing. But, you know, from, from just a basic standpoint,
[22:27] how can somebody level up just one to 10 percent? You can get more than one to 10 percent. I take
[22:34] no money for AI lab, so you don't sound like a, I don't sound like a shill. But you have to end up
[22:37] paying 20 bucks a month to one of the big three companies is where I'd recommend either Google's
[22:41] Gemini, OpenAI's Chatchapetee or Anthropics Cloud. And you have to actively pick the best model
[22:47] available at that point, which is what you'll have access to what are called thinking models. And
[22:51] those will change over time. But you have to actively select that, a default to a lower one.
[22:55] You will get huge impact improvements just from picking the most recent model and using it.
[23:00] The second thing I would say is AI has gotten quite good. So there's kind of three phases of AI.
[23:06] There's prior to Chatchapetee, where mostly we talk about AI, we'll be talking about how you
[23:11] use, you know, data analysis, basically. If there's all this, my algorithmic fairness and price
[23:15] mining and all the, like, you know, customized pricing, all of that came from prior to Chatchapetee.
[23:20] Then Chatchapetee kickoff, Generative AI, and what I will grandiosely call it because it's
[23:24] out of my book, my previous book, co-intelligence, where you'd work back and forth of the Chatbot to get
[23:29] an answer, right? I typed out the Chat, which you mean answer. Now we're in a new phase, which
[23:33] is called a Genetic AI. And it's really just three or four months old practice. What does that mean?
[23:37] What does that mean? An agent is an AI system that can independently go do work if you ask it to.
[23:41] So a Genetic is just the, the adjective of an agent. Agent, right? So a Genetic is an AI agent.
[23:47] And there's marketing in terms of random. But it's an AI that can do work. The most important thing
[23:51] to realize is how good the work is and how long the worker is. There's this paper and test by
[23:56] open AI. So you always take with the grain of salt, but there's been independent enough assessment.
[24:00] I feel good about it called GDP value. And what they did was they took people representing 5% of
[24:04] the US economy. So journalists and product managers and lawyers and private investigators.
[24:10] And with an average of 14 years of experience, they had them each create a really hard problem
[24:15] that they face in their field. They had another set of people with 14 years of experience to do it,
[24:19] took them on average seven or eight hours to do the work. And then the AI did the same thing,
[24:23] took about 15 minutes for the AI. Then they had a third set of experts come in and spend an hour
[24:27] evaluating the outputs from each of these, not knowing who's who's who's and voting on which they
[24:31] liked better. And when this came out a year ago, the best day eyes in the world were getting about
[24:35] 48% 40% of the time they were tying or beating humans. Then the latest models as of when we're
[24:40] recording this are about 84%. So 84% of the time, the work that they do seven hours of human work
[24:47] are equivalent to or better than a human. But that means going back to practical pieces,
[24:52] you would probably save three times effort and three times a cost. If every complex job you
[24:58] would give it to AI. And even if it took you an hour to put it together and evaluate, even if
[25:02] you had to give up 30% of the time, you would still save time and effort. So one of the things I
[25:06] think you're doing is not using AI and giving it hard enough task to do. Okay. And so that's the
[25:12] other thing, which is the value of the AI that where efficiency is not how quickly it can solve
[25:17] the problem, but how quickly you can evaluate whether it got the problem right. Which again,
[25:21] brings back to expertise. An expert can look at this right away and be like, not just as wrong,
[25:26] but like often it's wrong because of a specific problem that you should have either specified
[25:30] better or the AI is stupid about something. And sometimes you can instantly get, oh, it's never
[25:33] going to get this because it's too subtle and I can't communicate the point. I'm just going to do
[25:37] this myself. But sometimes you're like, oh, yeah, yeah, this is a rookie mistake. And I should
[25:41] remind it that when it writes articles, it not just factually explain everything, but explain it
[25:45] with a story or whatever your thing is. And then it's better, right? So evaluation feedback,
[25:50] these are things experts are good at. And the AI responds really well to that.
[25:54] He was the other problem, which is I remember when I wrote my first book, right? Everybody told me,
[25:59] everybody in the publishing world said the most difficult thing for any author is quote-unquote
[26:03] to find their voice, right? To have a voice. Now, it's a very hard concept to understand,
[26:10] you know, what voice is. Essentially, it's when you read my words, they are of me. They might,
[26:16] whether my personality, they're, they're my point of view. It's not just nicely written,
[26:21] but it is, it is of me, right? And it's very hard to do for an author. And I have found that AI
[26:28] can write beautifully, but it has no voice. And if you ask it to have a voice, it's going to always
[26:33] have voices that are available to it in the, in the world, in other words, published people,
[26:39] but not you. And so most writing will start to just sound the same. I mean, I'm already seeing it.
[26:46] I'm getting AI-generated emails in my inbox, and they're all basically the same email.
[26:50] It's not X, that's Y. It's doing the heavy lifting here. The thing that keeps me up at night.
[26:56] This is a load-bearing argument that's the kind of three sentences, but, you know, word, word,
[27:02] word. And I'm starting to just delete them all because they're all familiar. Yes. And none of them
[27:06] stand out. Right. And I push back. I'd say it's not that it doesn't have a voice. It has a voice,
[27:11] right? A singular voice that it has all voice, or a tragedy between, and it's actually not a bad
[27:15] voice. Like, if I was get, if I didn't see it for a billion times, your voice, it's all voice.
[27:20] And it's a perfectly good voice, right? It's a little dramatic. Sometimes it's like, it loves
[27:25] transitions too much. Obviously loves, you know, M dashes too much, but it's not your voice. And
[27:29] that is another thing that is like developing your voice. Now, a lot of people can't, right? Like,
[27:34] not everyone's a good writer. No matter how much we teach them writing, they don't get it. Ghost
[27:38] writers have been around forever, right? I'm glad, you know, that I'm writing as something I do
[27:41] and I have established voice. I know plenty of people who use ghost writers to do their kind of
[27:45] work. I agree on the AI voice. Now, I will say, you can get it significantly more like you. Not
[27:51] for the kind of long form work of a book, but a tip here, if you want to do this, is give AI a large
[27:57] sample of your writing and then say, write two pages summarizing the style of this and the
[28:04] instructions and how to write in the style. And then you paste that into your custom instruction,
[28:08] you say, write in the style. It will not, it will be slight parody of you, but it will be infinitely
[28:12] better than if you just say, you know, write like this famous person. Right. I mean, I did something
[28:20] recently as an experiment, which is I walked around the living room just talking into Claude
[28:26] and said, write an op ed in the style of Simon Sinek. Here's the idea. And I just walked around
[28:31] the living room for about three or four minutes and then it gave me a pretty remarkably written
[28:38] article. Then I said, fact check it. And it said, well, that's wrong, that's wrong, that's wrong.
[28:43] I said, okay, go offer me what I could say to make it factually correct. And the thing that I
[28:50] enjoyed about doing it, which is, you know, it takes 80% of my time to make a shitty first draft.
[28:58] And then editing is reasonably efficient and a lot more fun to really just clean something up.
[29:05] Most of the time is the, is the first draft, right. And so here I got a shitty first draft
[29:10] in a few minutes. And then I sat down and with it, you know, it fact corrects it, which is so
[29:17] efficient, I didn't have to go do all the research myself, although I did double check all the
[29:21] research just to be sure. As you point, the error rates of these things would drop. If you use
[29:25] not making mistakes, the way it's not making mistakes the same way, I have to say it was actually
[29:30] kind of fun to edit it in my voice with my sense of humor. And the last finishing touches,
[29:35] I realized I could put my voice. I was pretty impressed. I was pretty impressed. Now I could cheat
[29:40] because I have written enough that it can know my style. And I wouldn't say it was perfect,
[29:45] but it was, it was scary good. Yeah. I mean, there's a few things going on there. One of those is,
[29:51] this idea of disruption to writing. And I mean, there's costs to everything, right. So one option
[29:55] of writing the crappy first draft is it's your crappy first draft. So I always recommend some crappy
[29:59] first draft, because otherwise the AI's ideas will take over your ideas. It's very good ideas. And
[30:04] you're like, you will find you can't brainstorm. But with that said, I find this kind of a similar
[30:09] loop of like editing is a weird way of approaching. It's not how we used to do it before, which is like,
[30:14] I can get something written in my form. And then I edit it. You know, I'm sure some writers
[30:17] have worked that way for years, but that's a disruption to writing that might be better, right.
[30:22] And might be worried. It's hard to know. It's something you're a factory, you know, producing
[30:25] first drafts that all the time. And or maybe some people are just really good at editing. And
[30:30] they weren't good at draft writing. And something they're more productive than they were before.
[30:34] I think this is one of the future jobs that we underappreciate, which is not just that jobs are new,
[30:38] but that the weight of the job will shift. Yes. To your point, you know, we've always celebrated
[30:44] the writer and editors have always been like, just there. If you work for public relations,
[30:49] or you work in magazines, like the person who's the writer who wrote the press release,
[30:53] they're the person who went to school to write the press release. And we just sort of like the editors
[30:57] are just the lower paid, you know, you know, failed writers, you know, quote unquote. But now I think
[31:04] the writers, I think that the balance will shift. I think we're going to see the cross with lots of
[31:08] jobs, by the way, to come back to the job thing. So jobs are many tasks, right? A writer, like, as a writer,
[31:13] you're in charge of writing and editing and fact checking all of it. And the AI does some of that
[31:16] work. It shifts the burden of what you do, but it doesn't take away everything. And I think what we're
[31:21] going to see in a lot of jobs is this idea of bottlenecks that the AI is good at some stuff,
[31:24] but bad at other stuff. We call it the jagged frontier of AI in our early papers on this,
[31:30] which is it's good at some things bad at some things you wouldn't expect. Where it's bad, right?
[31:34] Writing perfectly in your voice, getting a joke right, like suddenly the demand for your labor
[31:39] is higher there, right? And your value is higher. It might have been that your jokes were not what
[31:43] was getting you, like that was not your main deciding factor. But if you're better jokes now,
[31:47] something there's value. Same things happening in coding, by the way, it used to be that writing
[31:51] really clean code was a really good skill. Now the AI is right most of the code, being an architect
[31:56] is good, being an engineer manager is good. The jobs change what's important and what isn't
[32:01] important changes. And that changes who's good about it at two, between new opportunities and new
[32:06] risks. You may have instead of 100 coders, you might have 50 or 30 working on the team,
[32:11] but there's still human beings with egos and securities, you know, lack of sleep, all of this stuff.
[32:16] And there's still somebody overseeing the project who has to manage all the messy human stuff,
[32:20] regardless of how good the technology is. I for one believe that doubling down on human is going
[32:24] to become even more important now because we still have to take care of the people who are working
[32:30] on the products with their AI agents. Oh, absolutely. And also when I went to the coded in the past,
[32:36] I had to hire a company to do it. Now there might be a code of working for every two person team,
[32:40] and more software is being created than ever, right? So the jobs are unimaginable in the future,
[32:45] sort of an annoying thing to say. I think it is annoying because I think we actually have some idea
[32:49] of what this looks like, which is not that coders are replaced by, you know, prompt engineers,
[32:53] but that the job of coding changes. The demand for coding shifts from giant organizations for
[32:58] a thousand people worked together programming to now disperse to your card dealership might have
[33:03] a code or buildings customized software for you around what the managers want to the team. You
[33:08] might have two developers working for you rather than outsourcing web development that are evolving
[33:13] things, the nature of software and the job change. And I think that is a missing piece of this puzzle
[33:17] also. And I think the other thing we aren't appreciating, which is the more things get good,
[33:22] right? Because it used to be that quality would help you stand out. Yep. That if you were smarter,
[33:27] a better coder, a better writer, a better this, a better that, whatever it was being good at
[33:31] something made you stand out from the crowd, right? If the quality of, let's just say everything
[33:37] gets slightly higher or a lot higher, then it commoditizes so many products. And so what I'm
[33:44] curious about and cannot predict and don't even have a thought about what happens here. But if
[33:50] if everything just becomes generically good, then how do you stand out in a market now? And we've
[33:57] kind of seen this with the rise of social media. We're in the last generation that has movie stars.
[34:01] I think it's the death of the movie star. Nobody's really buying a ticket to go see a movie because
[34:07] a particular actor is in it. Like one battle after another, lots of people went to see the movie,
[34:12] very few went to see it because Leonardo DiCaprio was in it. That's what the movie stars used to
[34:18] do. They used to make people go see the movie. Now we'd rather see the franchise when we're
[34:22] interested in Marvel than who's in it. And this is what I mean by commoditization. I'm so curious
[34:28] as everything becomes better and commoditized, TV channels, everything's commoditized. What's the
[34:34] thing that makes companies, products, and people stand out? So when you're interviewing for a job,
[34:41] and everybody's good because of AI, how do I stand out and get a job that's still that they
[34:47] still need to hire for? I'm fascinated by that. I think a few things are there's a lot of things
[34:52] there again, right? Part of this, by the way, is writ large, right? Like if Claude is really good at
[34:58] running your company, Claude's also good at running every other company and there's no variation
[35:02] between them. And generically high quality with no variation means there's no votes or competitive
[35:08] edge. I think humans who bring competitive edge to this one way or another just by providing
[35:12] variation if nothing else. Yes. It's a useful way to think about problems, right? Like your sense of
[35:16] taste matters, right? And presumably it's why, you know, why people listen is like your sense of
[35:21] taste, so who to talk to, the kinds of questions you have. You know, it's similar to the sense like,
[35:25] do you like Rothko or do you like Rembrandt? Like there's different tastes that have different kinds
[35:28] of outcomes. The second thing is developing taste, right? It's a bigger issue, which is how do we
[35:32] get people to develop taste? It's usually a casual, casual lifetime thing. That might be one of the
[35:36] new talents we teach people is developing a sense of taste, which requires, you know, experiencing
[35:41] broad things and making choices and having them vocabulary describe your sense of taste and choices.
[35:46] I think that as people become bigger creators and they can do more, their taste matters more.
[35:52] Like directors man to matter more than ever because I understand what I'm getting with a
[35:56] West Anderson experience, right? And if he can direct the whole thing the way you want to do,
[36:00] what would that look like? And we might find the same kind of thing with all kinds of other
[36:04] stuff. There's someone who has a particular taste in ice cream styles. Now you can make ice cream
[36:08] on demand because they have what connect you through the APIs to a, you know, to a vendor that makes
[36:12] that product for you. So it kind of fits in of enabling one person to do much more. You start to
[36:17] care a lot more about the taste of that person than you do about the entire organization built to
[36:22] deliver the product. So good. I mean, I have my own biases and opinions, but I'm curious,
[36:28] is there actually a difference between Gem and I chat GPT and Claude? I know Claude has a much more
[36:32] beat a B focus business model. That means security is more of a thing because business wouldn't
[36:39] stand for, you know, any lapses in security, maybe like customers might. Is there actually a
[36:44] difference? So there are just a half step back on the boring educational side of this. When you
[36:49] think about AI now, you want to think about three things, the model, which is the brains of the
[36:53] bunch, right? At the time of recording this, that's Opus 47 from Anthropics, that's a Claude model,
[36:59] chat GPT 5.5, and Gem and I threw up in one pro. By the time you hear this, they will be
[37:04] slightly higher numbers on all of those things based on how things are going, right? But those are
[37:08] the brains, right? The better your AI model is, the smarter is it, everything. It's better
[37:12] negotiations, better poetry, it's better math, it's better, like, but that's the brains. Then you
[37:16] want to consider apps, apps are the tools you access these. For most people, when I say app,
[37:21] what they should be thinking of is chat GPT.com or Claude.ai or Gem and I.google.com, that is an app.
[37:27] But the apps that people increasingly talk about when they use AI are things like Claude code,
[37:31] OpenAI, codex, notebook, LM, which you haven't used for Gem and I is free and very impressive for
[37:36] research and gathering data. And those are very specific tools built for certain purposes.
[37:41] And then finally, there's what we call harnesses, which are how the AI can do things, right?
[37:45] So a harness lets the AI write code or do internet searches or make images for you.
[37:49] So right now, the three big companies all have roughly equally good. We'll probably,
[37:54] you know, jockey from position, but they're all making very good brains. The models are all very
[37:59] good. Right now, Google has the most diverse set of products of apps, but their main apps are
[38:06] probably weaker than Anthropic or OpenAI. And they have worse harnesses for the main apps. So
[38:12] if you want to use AI to do things, right now, the most powerful tools are Claude code or
[38:16] co-worker on your machine if you're using Anthropic or OpenAI's codex tool. And what makes
[38:22] those different is they use your computer. So like you can give it access to your files,
[38:27] to your email, it can do work for you using your machine, your web browser, whether you like
[38:34] this or not, right? And do work. So because of that, those two are kind of jockeying back and forth
[38:40] for the lead, but all three of them are quite good. The models are good across all of them.
[38:43] So now let's talk about security, right? So we're all tired of meta and all the other companies,
[38:49] you know, filling our computers with cookies, tracking our every movement on every website,
[38:54] even after we've left their website and their product. And we've all become very sensitive to
[38:58] turning off cookies and data privacy is now a thing, you know, do I want to give any of these AI
[39:04] models? Do I trust any of these companies to have access to all my computer, all my browse
[39:08] history, all my finances, et cetera, et cetera, et cetera. So it's a hard question, right? I mean,
[39:13] there are more secure versions where you can even run your own version of these tools, but they
[39:18] will not be as good as OpenAI, Anthropic, and Google's. There are a couple kinds of security
[39:23] conditions you might have. One of them is, are they taking your data and using it to train their
[39:26] next model? If you pay 20 bucks, all of them have an option to turn off that training feature.
[39:31] Is that enough privacy for you? It's hard to know, right? There's open questions about whether or not
[39:36] someone's AI history will be searchable. Is it, you know, is it something that lawyers can demand
[39:41] to look at it, right? Discoverable. There's open questions about, you know, what will companies do
[39:45] with this in the long term, even though they sign agreements with you? But in the other hand,
[39:48] Gmail probably has all your email in it, right? Like these look like enterprise software
[39:52] applications at this point, rather than sort of invasive individual tools. So do you try how much
[39:57] you trust, you know, Google with your information or Instagram with your information? We're in that
[40:02] same kind of boat over again. The difference is, as much as I don't want Google to have access
[40:08] to my Google, my email, I know that it does, but I know that nobody can go out onto the web and
[40:15] ask a query in a Google search to read my email and tell me something. I think a lot of us are
[40:21] afraid that somebody could just go on to chat GPT or open, you know, one of these. So they can't do
[40:26] that. They're not there. You, there's no, it's just like Gmail in that way, right? There's no
[40:30] bleed over where there's just one giant inbox and you're just barely holding it together, right?
[40:35] It works the same way. I mean, it looks like enterprise software. So that has its own risk, right?
[40:39] But the basic risk of like can someone just ask for something and get access to your chat GPT? No.
[40:44] If they log in with it, you know, you have to do all the same things you do set up.
[40:47] Got it. We've got your authentication. Don't leave yourself logged on to a computer, but it works.
[40:52] The analogy I would have is Gmail, right? Like Google has all this information. They're obviously
[40:56] processing it and using it for their own purposes, but they're also not going to, you know,
[41:01] they've anonymized it in some way to try and create trusts. It takes effort to hack into someone's
[41:05] Gmail. It's the same kind of boat, right? Now, whether or not we want you want a company ever,
[41:09] even more power over it, those are choices you get to make. But I don't think we should put this
[41:12] in a separate privacy category. The actual risk is if I let it have access to my computer
[41:18] and it could use my web browser, you know, could someone convince my AI to send them all my money
[41:24] if it's, you know, if it's reading all my emails? And that is, you know, hasn't happened yet,
[41:28] but it's not impossible. Right. So obviously because you are, you know, you teach this, you embrace this,
[41:34] you allow your students to use it, I assume. I don't know how to ask this, which is how do you
[41:39] ensure that your students are learning if they are allowed to use these tools to learn?
[41:47] So I went viral first in education with my syllabus right after the Chatchee Wiki came at the
[41:52] first version, which is what we call GBG 3.5. And, you know, that was around for a few months.
[41:56] GBG 3.5 was pretty flawed. Like you would make up arguments all the time, but obviously,
[42:00] who's an aide, it felt like, you know, I like a smart, you know, ninth grader or something like
[42:05] that, right. And so I teach college courses, I could tell. So my original policy was,
[42:09] use AI for everything you want. You're accountable for the output. That was great for four months,
[42:13] until GPT 4 came along, which is now obsolete. It wasn't good for a while. And it was as good as my
[42:17] students across something. It's not across all things, but enough that a low effort student was
[42:22] worse than GPT 4. And I can no longer tell people, just use AI, I can tell, because the AI was giving
[42:27] them the answers, not being the answers. And we've seen this over and over. There's a lot of studies
[42:31] that show if you just use GPT to get answers to questions, you think you're learning, even if you're
[42:35] not cheating, you think you're learning and you're not learning because the AI gives you the results.
[42:38] But it turns out we actually know pedagogically how to solve this problem. We did this with
[42:42] calculators, right, within school, which is we could do in class testing. We can make you use the AI
[42:47] for some stuff and not for others. So for my class, I'm looking at TD entrepreneurship. So output is
[42:51] in some cases, like I gave all my students, for example, what I called the bootcamp test, which is
[42:57] the name of the Blade Runner human test, but it made my own version of it. And they had to launch
[43:01] their startups using AI, but based around areas they were experts in, experiences they had had,
[43:07] knowledge of the world they had had, a viewpoint they had, which kept them in the picture. And then
[43:12] they also had to do a lot of in-class stuff, right? We had to have a discussion about these things.
[43:16] I actually had them use AI tutors that asked them questions. They had to use an AI to build the
[43:20] case study with it. I set up the AI, so it won't give them all the answers. It would challenge
[43:25] them to come with the case study information. So there's things we can do, but it does require
[43:29] changing how we teach. But the reality is technology does affect our brains. I'll give you a real
[43:35] life one-to-one example, right? My mind, I used to have a steel trap for phone numbers. I knew
[43:43] everybody's phone number. You told me, gave me a name. I'll tell you their phone. And I didn't
[43:47] have to memorize it. I just heard the phone number and I had a steel trap of phone numbers. It was
[43:50] just how my brain worked. And I, in the early days of, I bought a Casio digital diary. I got it
[43:56] from my birthday. I remember, you know, it had 2K of memory. I think I upgraded to the 6K when it
[44:01] came out. It was like hardcore, right? And it was the most remarkable thing. And I programmed all
[44:06] the phone numbers from my memory into the device and then slowly added more and more phone numbers
[44:11] as I learned them. And my brain was like, okay, if that's what you want, fine. I can't remember a
[44:18] single phone number anymore. And if we have to remember that the, like, the Iliad and the Odyssey
[44:23] were oral traditions. Yes. You know, this book that we were forced to read in school that's
[44:28] like, you know, 800 pages, you know, go back a, you know, a couple of hundred years. And it was
[44:34] like, son, it's time I tell you the story of the Iliad. And you will tell your son the story of
[44:40] the Iliad. It was the oral traditions that people remembered. But because the printing press,
[44:44] our brains just stopped remembering stuff. Yes. So this has to have an impact on our intelligence.
[44:50] There's no getting around it. Absolutely. I mean, look, my, my grandfather was an engineer who built
[44:57] like the fire suppression systems for Cape Canaveral. And his like dissertation was writing,
[45:02] doing a single piece of matrix multiplication. I have no idea how to do what he just did. And he
[45:07] did slides rules to do it. I've known to use a slide rule. My kids have not learned cursive,
[45:11] right? Like we give up stuff all the time. The whole idea of technology is on purpose. We give up
[45:17] things that we used to be able to do. Yes. The machines. So we don't have to do them anymore.
[45:21] And every time we face the same choice about what's value and what's not. And what I worry about,
[45:26] like the default version of that is bad, right? I mean, we've seen this happening with like,
[45:29] you know, you can argue short from video is killed reading because it's more entertaining to do that.
[45:33] I don't need to spend the effort reading the book to get there. Okay. That was a bad choice.
[45:38] We are going to have a ton of these choices right around AI. It doesn't hurt your brain,
[45:43] but it is a choice that you can hurt your brain with, right? As an educator, part of my job is
[45:49] to get around that problem anyway. Yeah. People can survive a lot without reading very well.
[45:53] They can survive pretty well without doing math. They don't have to learn American history. Like
[45:57] there's some degree of making this requirement. But this is a slippery slope, right? Because now
[46:02] now we go down the path of, oh, you don't need university. And there's a whole movement that
[46:06] you don't need to go to college. And what we forget is you may not need the subjects that you learn
[46:10] at college, but going to higher ed teaches you to think critically. It teaches you to argue with
[46:17] people who have way more education than you and form strong arguments to take them on. It also
[46:23] teaches you adulting. And so my problem isn't that technology replaces that they're sacrifices.
[46:29] Like I accept that I don't have to have a memory for phone numbers because of technology anymore.
[46:34] That I accept that. My concern is that thinking, the ability to think is the sacrifice here.
[46:42] And that's way more damaging than remembering phone numbers or remembering the Iliad.
[46:49] So I push back. I don't think it destroys your ability to think. I mean, I think for a lot of people,
[46:53] it gives them even more ability because they have a conversation partner at their level
[46:56] who's willing to discuss the topic. As long as people have any curiosity about the world,
[47:00] all of this is processes for them. I mean books. Someone else came up with an argument for me
[47:04] in an opinion that doesn't mean that there aren't negative effects on that. It doesn't
[47:08] mean we won't give up things we shouldn't give up. Part of what heartens me is like we've got 12 to
[47:13] 16 years of schooling to try and get some of this right. And if we do it right, AI accelerates some
[47:20] of that. And if we get to make choices as society, now will people make bad choices? Yes. And so I
[47:25] do worry about this, right? I'm thinking a lot about how we, what do we give up? How do we
[47:29] stay human? It's going to require effort, just like a lot of other things. And there is danger there.
[47:34] But I guess I feel like that feels like a big leap to we're not going to think anymore. The AI
[47:39] will tell us what to do. We'll just obey its instructions. I don't think we'll stop thinking. I
[47:43] think it'll hurt thinking like the quality of thinking, critical thinking gets hurt. And I mean,
[47:47] look, you know this is as somebody who studies education. You talk to any college professor and
[47:52] they'll tell you forget about AI, just the introduction and distractability of a phone,
[47:56] you know, that they'll say that, you know, the writing is abysmal these days. Every college
[48:00] professor is complaining about the writing being abysmal. So kids don't know how to write. And when I
[48:04] say right, I don't mean like, but I mean form an argument. And they'll say like the first paragraph
[48:09] was fantastic. Second paragraph was fantastic. The third paragraph was fantastic. The problem is
[48:13] the paragraphs have nothing to do with each other because it's clear that they're like getting
[48:17] distracted in between paragraphs. I guess I would say I'm on hand, you're right. But we would
[48:21] very bad in education for a long time. Like a bad way to teach is stage on a stage where I go
[48:26] up and give a lecture, right? And a hundred people write things down. But we've done it for a couple
[48:29] thousand years because there's a lot of other constraints that make it the way we do work.
[48:32] There is a negative side, but like one of the things that really excites me is AI tutoring.
[48:35] We have some early evidence that it has big effects on learning, right? Like instead of me
[48:40] lecturing to a classroom and assuming the height of learning is I lecture to what the upper
[48:44] part of the classroom, people who really knows if the middle, I you know, I lecture to the person
[48:48] who doesn't know things as much, personalized education is now an actual possibility. Like
[48:53] there is a cure as well as a poison in this thing. And I think that it's worth paying attention
[48:58] to both. Like if we don't change anything, the effects will be bad on education, right? But that
[49:04] implies that we're all going to sit down and just be like, I guess it's done, you know, like yeah.
[49:08] And I don't I think for the first time, I suppose a short form video where you had to do this very
[49:12] elaborate thing of like, we'll do TikToks for education and that never works. We actually have a
[49:16] tool that is a pretty good tutor that can talk to you at your level. They can make you get into an
[49:21] argument. That's part of why I do my classes. And we see schools adapting, right? First,
[49:24] they put computers in all the schools and now they're still really taking them out. And it was
[49:27] partially because we just like comes back to the human thing. It's completely like the thing that
[49:31] makes AI interesting. Yeah. Is it understands, understands and quotes, right? For those who are
[49:36] just listening to this, I'm making air quotes in my hands. It understands humans. It has theory of
[49:41] mind effectively. And that's what the others are going to technologies don't. Like it can
[49:46] teach to your level. It can understand what you're confused by. It can help you make this interesting
[49:51] for you. If you're only interested in basketball or, you know, basket weaving, it can give you
[49:56] basketball and basket weaving analogies and problems. This was the holy grail of education. And I
[50:01] think it's one thing to say, yeah, technologies have lit risk and everything else. I also think we
[50:05] undersell some of the positive impact that we can get from this. The strong argument that you're
[50:09] making here is, and I don't know how many people have done this, which is where you use the talk
[50:13] function where you can actually have a conversation backwards and forwards with the AI as opposed to
[50:17] typing. And I think the case you're making is the idea and I like is that you can debate with someone
[50:22] at your level. So you're not explaining to somebody who's not at your level. You're not feeling dumb
[50:26] or trying to keep up with somebody who's more experienced or smarter than you, but rather that you
[50:30] can go backwards and forwards and learn it the way you like to learn. And I've tried this where I
[50:35] am having a debate or conversation backwards and forwards, backwards and forwards, backwards and forwards
[50:39] backwards and forwards. And I'll say things like, oh, wait, or is what you're telling me this?
[50:43] But I think this, that I think is really, really interesting to your point.
[50:47] There's two other tricks there. One is the AI's sycophantics. So if you're having a debate with it,
[50:51] it's going to agree with you. So you have to talk to act like a critic, right? Like you say,
[50:54] and then the second is you want to take advantage of the meta piece also, the learning piece of saying
[50:58] actually have a way to tell me what I'm doing wrong with my arguments. How can I be more persuasive?
[51:02] What patterns am I missing in discussion? Give me some examples of those patterns and how
[51:07] I could have used them. So again, pack the effort piece. If you're willing to do the lifting
[51:11] yourself of asking the questions, like everyone always says they want to come to office hours and
[51:15] have this debate with professors. That's most people don't come to office hours. You sit as a
[51:20] professor and you're waiting for somebody to come and debate to you in the great issues of the day
[51:23] and you sit alone in your office during the hour. The people are allowed to come to you because
[51:27] they've other things to do. I think that we overestimate how this sort of shining city in a hill
[51:32] where it's sit down and debate and have these discussions. That's not how most things work.
[51:36] Now we have a tool that can do that. If you're interested, you can do that without having
[51:39] to come to my office hours. I want to double click on the two points you made because I think
[51:43] they're really valuable, which is remember that the AI is a sick event and you've got to tell it
[51:47] to criticize or critique and ask it to evaluate your thinking and help make your thinking stronger.
[51:53] Those are two brilliant, brilliant prompts that I think more of us should remember to improve the
[51:57] quality of our interaction with the technology. We're talking early about AI and writing. A piece
[52:01] that was missing in your conversation, you're talking about user fact checking. It's very good
[52:04] at that. I would use it more for initial research. All of the AI models have a deep research mode
[52:08] that's quite good and we'll actually do research for you. But the thing you're also missing from
[52:13] that is when I write something, I have the AI evaluate from different perspectives. So I will have
[52:17] the AI read it through as a reader who doesn't understand much about this topic and tell me what I
[52:21] need to change. Read this through as an expert who is out to get me on social media. Where would they
[52:27] nitpick my arguments? Am I being irresponsible anywhere? Did any of my humor fall flat? So
[52:32] giving the AI, personas don't change the AI's ability. So you say your good at physics doesn't
[52:38] make it good at physics, saying you're a physicist, but it doesn't make it talk like a physicist,
[52:41] or parody of a physicist. Talk like a cynic. Talk like a critic. Talk like an naive person.
[52:46] You won't get answers. You couldn't get without going to wide range of readers. Also true
[52:50] in entrepreneurship, by the way, get feedback from the AI and different personas about your idea.
[52:54] This is very practical and very good. What are you actually afraid of?
[52:58] I think we're in for a period of chaos. Let's say the Industrial Revolution works out like the
[53:03] last three did, like the AI revolution. Living through it still sucks. Charles Dickens is basically
[53:09] just a story about how miserable the Industrial Revolution was. You have halves and have nots,
[53:14] you have social change. Even if everything works out fine, now we have better tools as a society.
[53:18] But I don't see a lot of action. You've led this conversation by saying that people either
[53:24] doom and gloom or everything is going to be great. I find policy-making is in the same place right
[53:29] now. Either it's all going to work out great or we have to stop this whole thing. Neither of those are
[53:34] realistic outcomes. How do we help cushion people if they're unsure? Turns out training programs for
[53:40] new jobs never really work. Is there something we can do better this time around to re-skill people?
[53:44] We're going to have negative effects on deep fakes are going to be everywhere. How do we deal with
[53:49] who we trust for information? There's a thousand little good and bad things that are going to be
[53:54] happening all at the same time that are going to be very complicated and they're going to get boiled
[53:58] down because of how social media and everything else works to either AI all bad in which case you have
[54:02] a list of all these things that are a mix of real things and fake things about AI water use or whatever
[54:08] it is and it's going to be AI is bad or AI is great and it is a thing. It's a technology and
[54:13] interactive people. Technologies are neither good nor bad nor are they neutral. They have effects on
[54:18] our world and I worry that we're not taking this seriously. The other thing I worry about is people
[54:22] don't know how good these systems are. They are better than you think, right? I have a doctorate.
[54:26] I'm a professor for a while. I publish journals like the AI writes a pretty damn good academic paper
[54:31] now. Not just a parody of an academic paper if you give it a status set to work from. It is proving
[54:36] math at a level where you really need to be one of the best math professors in the world to know
[54:39] whether the system is right or wrong. It's often right at this point. It is doing really good
[54:43] images and marketing work that beats most marketers and studies that we have of this. These are
[54:48] really good systems. Their development is not slowing down. We have to start thinking about what we
[54:53] want the world to look like rather than just assuming it's all either going to work out or not.
[54:57] How much agency do we as the general population have or are we just the subjects? Are we just the
[55:02] pawns in this game between these three major companies, Microsoft, Open AI and Anthropic?
[55:09] I think that we have, there's two levels of agency we have. Level of agency number one is
[55:15] societal, right? There's a reason why people are floating data center bands because they think
[55:20] that would be popular. The usual mechanism of policy making of organizing, of writing letters to
[55:25] your congresspeople, those still work. The second is where I think there's even more agency.
[55:29] The AI labs are full of coders and they have found an unreasonably effective way of making a tool
[55:35] than mimics human thought. It's weird the large language models work as well as they are. We know
[55:40] they work technically but we don't know why this is so unreasonably good. How can it do poetry and
[55:44] offer interior decoration and cash flow analysis and a pitch deck about the Gettysburg address?
[55:53] It shouldn't be able to do these things. It does all of this stuff. We give them too much credit.
[55:56] They don't actually know much about how AI is useful in your field. Remember, there's a
[56:00] giant frontier. It's good at some stuff, bad at some stuff. Your biggest source of agency is
[56:04] actually using it to positive use in your own job and work. A large part of what I post
[56:09] about is this is a way to help humans thrive with AI if we use it this way, rather just
[56:14] automating away human work. I think our biggest sense of agency is, okay, you have access to these
[56:18] tools, Simon, how do you use that to expand your business to make sure that all the people who
[56:22] work for you have the focus of amazing folks that are overly smart. How do they do more than they
[56:26] did before? How do they do more satisfying jobs? There's a lot of agency there, and if
[56:31] you talk about it through your platforms, that changes things. A lot of what I do is talk to
[56:36] executives and leaders of companies where I'm like, you have to show people how augmentation,
[56:40] how this can be used to make humans thrive, how can make your business thrive,
[56:44] rather than the default plan of like, if I fire everyone in a place with AI, profits will be higher.
[56:48] That's the dangerous thing. To me, the real agency right now is let's find positive examples,
[56:53] and there are tons of them out there, use them and build them to make AI make the world a better
[56:57] place and not worse. I really appreciate this. You've given me, you've enriched how I can use
[57:02] this product. I'm going to take you on. I'm going to have the agency that you recommend.
[57:07] I think this is a moment for transformation. I think people aren't being ambitious enough.
[57:12] Everyone's like, what if I record my work? It's not, how would you reach every one of your
[57:17] audience members separately if you could do that? And why don't you just build it rather than
[57:21] waiting for it to happen? Well, I'm not sure I'm going to use it that way because I like
[57:24] the artist. I take pride in the fact that when somebody's talking to me, that it is actually me,
[57:30] my opinions. I don't think it's about automating Simon, like creating a Simon clone. I never
[57:36] like that. There's people who create Ethan bots. I don't think that's the way. As you're talking to
[57:41] a fake version of parody of yourself, I'm saying, what do I want people to accomplish in this world?
[57:47] Like, how do I build a tool for everybody? Yes, that I believe in. And like I said,
[57:50] I would do a Simon AI with a very specific application that it lives alongside.
[57:55] But I like people knowing that when they see me and they think it's me, it really is me.
[57:59] I agree. I mean, it's the same thing with my writing. I write all my own, you know,
[58:03] my own Twitter posts and everything else. Yeah. You know, it's important to keep the muscles alive
[58:07] for nothing else. Ethan, such a joy. Thank you so, so much for taking the time. I really appreciate it.
[58:11] Thank you so much. Pleasure.
[58:19] As always, thanks for listening. And if you liked this episode, please do remember to subscribe
[58:24] to a bit of optimism wherever you enjoy listening to podcasts. And remember, new episodes drop
[58:28] every Tuesday. A bit of optimism is a production of the optimism company, lovingly produced by our
[58:35] team, Lindsey Garbenius, Phoebe Bradford and Devon Johnson. And if you want more cool stuff or
[58:41] just to find out what I'm up to, visit SimonCinic.com. Until next time, take care of yourself. Take care of
[58:48] each other.

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