AI makes you more creative, AI Roundtable with Steven Johnson and Grant Lee | E2231

25:27Like, let's put JCal out there as a podcaster, as an agent to go talk to people building products in this simulation known as planet Earth. So it does feel like in some ways the LLM is replicating our brains. I don't know if you've been giving this thought, Grant, at all of what this all means. I'm honestly still trying to make sense of it. I think there's interesting things that are happening. When I even go back to think about visual communication and oftentimes when you're trying to communicate a set of complex ideas, you almost need to simplify it for a human. So you're saying, OK, what are the three main things?

26:03You take all this information, you simplify it to three, and then you tell your team, hey, these are the three priorities. Well, if AI doesn't need to simplify it to three, they can go out and do 10 or 20 or the most important things on the list. And then what's the point of actually distilling down to three? These are all things we're all trying to grapple with. How much of it is just human limitations to how we can actually comprehend, synthesize information and make sense of it all versus actually if AI is doing it, then what's the point of that? We can skip over that step altogether. brainstorming.

26:35Jason, if I can, yeah, can I jump in? Since, you know, you started a college dorm conversation about consciousness. I'm never going to like miss out on that. Yeah, here we go. Pass the bog. But, but I think one of the early things that I found really extraordinary, and I might even talk about it when we first talked on the show in 2023 about notebook is that you could give the model, even in the early days, you could give the model a set of sources And you could say, what are the most interesting pieces of information in these documents? And interestingness was a concept that the models kind of natively understood in this amazing way, even when they weren't quite as smart as they are today.

27:15And I think it's because of the nature of like next token prediction. Right. Interestingness is I was surprised by something. I thought there would be this, but I got I thought there would be X, but I got Y. That's interesting. Right. That's how we learn when we see that kind of difference between a prediction or not. So a thinking machine that is built around predictions is going to be like natively really good at interestingness, which I think is amazing. And it connects a little bit to what Grant is talking about in terms of like part of what you want to do when you convey information to another person is pull out the most interesting bits or figure out the most interesting way to convey that information so that their brain remembers and engages.

27:50That's part of like the power of what Gamma does with slides and presentations and so on. But I do think on the consciousness side, it's a little bit of a red herring in the sense that like up until language models, if you were if you understood material, by definition, you were conscious. Right. There was no way to get to understanding without being conscious. The only people could really understand fully semantically things for humans and humans were conscious. I think what's interesting now is that we truly have, I think you have to look at what, you know, if you give Gamma or if you give Notebook like a bunch of information and ask it to create something new based on that content, you have to look at that software and say, it is understanding the material on some level.

28:33Like it is grasping what the core concepts are. But I don't think it's conscious. Like it doesn't have an interior experience of it. And so it's created this new possibility for understanding without sentience, without consciousness. And that's what's confusing to people because we've never had that before. It's mimicking a portion of human experience. It doesn't have the third perspective where you're like, I'm doing this. I can see myself doing this. I see my motivation. I'm in the process of building a slide deck in order to convince a venture capitalist to give us money. It's just saying input, output, here's the data.

29:12And then here's the output that a venture capitalist would like. And then, hey, I'm going to make another deck. And do people do this in the software, say, who's the audience for the deck? Therefore, you know, tailor it to it and make two different decks or three different decks. This is for sales. This is for HR, for hiring. And this is for venture capitalists. Yeah, maybe a little bit to the brainstorming concept here, because I think that's when we think strategically about what this is all about in an organization, what's uniquely human is brainstorming. And this stuff is augmenting brainstorming in some way.

29:44Yeah, absolutely. I mean, so much of the pain point before, especially with slide decks, is you have to almost make a generic version, a one-to-many version. It's impossible to go back and format everything so that you go into the next meeting or you don't have time to personalize the deck. And what we see today is now that we have, if you set up a template in Gamma, now all of a sudden you can personalize it to whatever audience you want and you can specify. If I'm pitching customer X, that has a very different set of needs, different industry. Let's make sure that we're not pitching stuff that's not relevant.

30:15And you can do that at scale. So not only are you doing it as an end user, both via an API, then obviously you can do that automated at scale. So I think it just changes how people approach just this notion, this category for us. It's just we're trying to push the boundaries in ways that weren't possible before when everything had to be manually manipulated. And for us, that ends up becoming a superpower for our customers. It's 2026. It's a new year and I've got a resolution just for you. Stop missing phone calls. The important ones. When your business misses calls, that means you've missed an opportunity.

30:49That's why today's episode is brought to you by Quo, Q-U-O, the smart way to run your business communications. Quo is the number one business phone system that's being used by more than 90 ,000 companies. Here's how it works. Everyone on your team shares a single phone number and that phone number works from an app on any device. And that means no more missed messages because everyone has access to the full thread. Even if it's off hours, Quo's AI agent automatically logs your calls and generates easy to follow summaries. So make this the year when no opportunity and no customer slips away. Try Quo for free and get 20 % off your first six months when you go to Quo.com slash twist.

31:34That's Q-U-O.com slash twist. Quo. No missed calls, no missed customers. There's another piece to this puzzle, Stephen, you put into the group chat. And behind every great podcast is a great group chat. It's really interesting how group chat, like getting off of Twitter, getting off of TikTok or whatever, the group chat is where the interesting discussions go because you can kind of be a little more freewheeling. It's kind of like us going to Eureka Cafe or wherever the hell we went in the 90s to just, you know. What was the place on, what was the dining room, Union Square? Oh, yeah. I was thinking of Limbo.

32:12Yeah, Coffee Shop was another place, but Limbo was cool too, yeah. Yeah. I was just talking to David Hershkowitz from Paper. He's like, when did you start working here? And I was like, and start Silicon Hour Reporter and writing for Paper Magazine. And I was like, well, I pitched you doing Digits Magazine as an insert to Paper. And that was going to be the original name of Silicon Hour. reporter. And he was like, I didn't take you up on that. And I was like, yeah, but I remember the grilled cheese and the French fries at coffee shop. Anyway, this is the equivalent here, which is the group chat.

32:42And there's a resistance to using AI. I'm curious, there's a group of people that you've been talking to, Stephen, that are resisting it, creative specifically. And maybe we could unpack why you think some folks are saying i i don't want to introduce anything digital here i just want to play acoustic in the studio i want to go back to tape i i don't want i want to go back to film i don't want to use a digital recorder and um yeah give us your give us your it's a really interesting one and it what i was saying in the chat was that what What had been striking for me in the last couple of weeks just over the break was I had a couple of those conversations with like, you know, younger 20 somethings who were just out of school who were talking this way about AI.

33:32And I think in some ways that comes out of the sense that. that the threat here because of the challenge and i think we can talk to this a little later like the challenge that ai as opposed to assessment in schools like writing a paper for you and all that kind of stuff um that in some people who've just been in school they see potentially ai as a as a as a way of cheating or a way of like getting around like doing your own thinking and if it whereas when i look at it and i think you know this is i'm sure the way grant looks at it and most of the people who listen to the show think about it, I see it as a way of just augmenting my thinking, right?

34:10I see this as a way of having better ideas and having richer ideas. And I had this fascinating conversation with a friend of mine who's a novelist, who's older, but is a novelist. And she's actually kind of receptive to using AI. But at some point I said to her, I'm convinced that these tools make me a more original writer and thinker. it's unpacked yeah yeah she said that doesn't make sense to me like that seems like a category mistake if you're drawing upon something that is like the average of human knowledge like that should make you less original if you're using it and i was like no but what you don't realize is that i have all these things in as a writer as a thinker where i'm stuck in a familiar pattern or a cliche or i have a certain way of writing a paragraph that i just always do it's really lame and i'm now or i just have a bunch of received ideas about the world that i you know i'm just kind of just, you know, they're codified.

35:02Yeah. Yeah. Yeah. And so now I have this tool where I can say, okay, I wrote this paragraph, like, but I'm so boring. Like, give me five alternative versions of like how I could open that sentence or like, take a look at this like section. And what am I missing? Like, what's the blank spot here? Like, what's not in this document that I haven't thought of yet. And it's able to kind of like unblock me and open up new doors again, to your point about brainstorming. And how would you do that previously? You would have just emailed three friends and said, can you read this, right? You read other books and other thoughts and other articles and research and things like that.

35:38And what you still do when you're actually doing the final piece, but your ability to kind of quickly test the waters of an idea or quickly, you know, kind of explore a parallel path intellectually that you would have normally taken you three weeks to go and find the right materials and then read them through. Now you can just test hypotheses really quickly. So I just feel that I'm just, I don't know, I feel like it's enriching my range in a way that has been just, I don't know, exhilarating to be a part of. You know, when I try to come up with quips from when I'm doing podcasts and, or I'm doing something on stage, I just gave a keynote, or I did a fireside chat at CES yesterday, and I was looking for some jokes, right?

36:24You think jokes, kind of hard for an LLM to do. Au contraire, more frere. You just tell it, here's the five things I want to riff on. And it gives you the worst possible jokes you could imagine, but they do spark an idea. So I was going through these pagers. I had a box of old gadgets, and I picked up the pager. And the first thing I thought of was the Hamas I was getting their nuts blown off with a pager. So I just threw it to the person. I said, oh yeah, you know what we call this in the Middle East? This is called a Hamas vasectomy. And I threw it in their lap. Got a good laugh from the audience.

37:04But when I do brainstorming with LOMs, I just say, give me the couple of idioms. I use the word idioms. This seems to trigger really well. I don't know why. Give me idioms or pop culture references, perhaps because Wikipedia uses those terms and I have them in my brain. And it just gives me, Grant, so many ideas. It doesn't give me the jokes, but it just gives me like a little bit of ideas, like almost like a little bit of a mind map. How do you think about this sort of creativity versus

37:37sterilizing the content? Yeah. It's really, I mean, I think one thing that's interesting, different professions, I mean, part of this I map to crossing the chasm. So in certain professions coding for instance right now it's very clear we've crossed the chasm everybody's using it every developer is using every sort of coding you know agent out there and and i think there is part of that where maybe the chasm was just for that particular profession was just much smaller once you cross and immediately move into mass market and then once you have critical mass there's a tipping point then it just becomes so obvious that the profession itself is going to have to embrace this new technology.

38:17I think other professions where, to Stephen's point, a lot of, you know, creatives, there is much more of that resistance. That chasm feels so much more daunting because the question is, if you are, if these models are trained on prior art and people's work, then you're trying to, you know, take advantage of that somehow. Well, you know, the default mindset today is like, well, that's, you know, copyright or that's IP infringement like you can't do that and i want to be an original creator an artist and so i do believe in many ways that certain professions the chasm itself is just much wider and it will probably take some time when you eventually reach critical mass and like you know for instance photoshop today nobody thinks of it as cheating or like augmenting photos in ways that at least you know the the broad majority of folks and so like now now it's a tool that people can use and maybe Photoshop now is even obsolete, but it's like once people embrace some of these toolings and professions can actually understand that this is just an extension of you, then maybe it's okay.

39:20I think knowledge workers, I think it will be sort of unevenly distributed. Certain professions like educators, for instance, have been surprisingly early adopters, willingness to like actually use this as a tool that they're just sick and tired of having, you know, lack of resources, no budget. and so screw it, I'm going to fast forward and I can pick up this tool, pay a couple bucks a month and like completely augment my day and I'm okay with that. They have no pride in like, hey, this deck or whatever I'm creating for my students. Because they're resource constrained. You know, when you think about it, like startups, whoever's resource constrained, if you give them something more efficient, it's like, okay, I'll use it because I don't have the ability to accomplish the task.

40:02Here's the interesting Pew Research survey. Now, surveys, it's important to put in context. Here they're asking the percentage of U.S. adults who say the increased use of artificial intelligence in society will make people's ability to do each of the following. Think creatively. Worse, better. 53 % say it will make creativity worse. 16 % better. 16 % neither better nor worse. 16 % not sure. So when you look at this piece of data, this is from June, by the way, in 2025. So it's six months old. And that actually is relevant. Because I'm going to guess half the people here have not used AI all that much.

40:43And so how do you read into this, Stephen? Just the first one here. Because you and I just described, and Grant agreed, that, man, this makes you so much better thinking creatively. But people's assumption is the opposite. I would put also that other question about helping you make decisions, because I wrote a whole book about complex decision making, which is a really creative work. Like you have to, to make a complicated decision. Like you have to think creatively, you have to imagine alternative scenarios and come up with, you know, future plans that you might not initially have thought of.

41:15So there's a lot of creativity that's built into decision making too, that I think the models can be super helpful. That's farsighted, right? That's your book. Yeah, yeah, yeah. There we go. Thank you. Thank you. I didn't want to plug too much. Literally every time your book comes out on Audible, I just buy it and go to a hike, I listen to it. I appreciate it. But here's the thing that I think is a factor here as well. I think that outside of hardcore tech users, maybe students, that the idea of using AI with your own context, with your own sources, is actually, particularly in June of this year, that was not as much of a mainstream thing.

41:52So I think part of what people are approaching here, are thinking about here is they're thinking, okay, I want to like work on my novel. I'm just going to go to a generic chat bot and just ask some general questions and it's going to help me, but it's not going to know anything about like the project that I'm working on or my interest or what I'm trying to do. And so they kind of rightfully assume that there is a ceiling for what you can get out of a chat bot that knows nothing about you and knows nothing about your industry or knows nothing about your company or your nonprofit or whatever you're doing.

42:20But I think when people actually experience it and, you know, it's obviously notebook has done this probably longer than anybody, but it's now a part of all the major platforms. You load your own context and then you ask questions based on that context. And when you see that happening, I think people instantly, like I have a lot of friends who make documentaries and they were kind of worried about AI for a while. And then they were like, oh, wait, I can just interview 35 people and put the transcripts into a notebook and then start sketching out like ideas for scenes with actual quotes just right in the notebook instead of having my interns go through like 500 pages of transcripts to pull out the relevant bits for the scene.

42:59And they're just like, that is so much better. But you can't do that with a generic chatbot. You have to have source grounding for that unlock to happen. And I just think most people don't know about this yet. Like we're so in this bubble that we kind of assume everybody knows you can engineer context for your projects. And let's look at those last two here in terms of decision-making, Grant. We'll pull up the graphic one more time. Again, a few studies six months ago, which is the equivalent of six years ago in AI time. It's really like a 10 to 1, I think, at this point. Make difficult decisions.

43:3440 people think that it will make you worse at making difficult decisions. And 38 % think it's going to make you worse at solving problems. 19 % think it will make you better. So that's two to one. and it's you know 60 40 here on solving problems what's your takeaway from this grant you just think it's these are people answering the question who are thinking about the movie terminator or the extinction and just a little bit scared and they don't want this to be true well part of this is yeah obviously it is a moving target to you know six months ago to your point is a lifetime and i think many people that are still skeptical it's it's yeah to both their points is they're just barely exploring.

44:16When you think about a lot of these like shallow use cases, yeah, if you give, you know, gamma like a prompt, like create a presentation on, you know, dinosaurs or something, you know, can give you something that like the argument is, okay, well, you know, gamma is doing all the work for you. You're not learning any of that. And so, you know, are you actually, you know, better off actually, you know, going back and doing the research and doing it all yourself so you actually can comprehend what's going into the slide deck. But what we actually see most people use Gamma for is to actually go much deeper.

44:45It's to actually do a lot of exploration in tools like Notebook LM or ChatGPT, where you're going much deeper in the research, synthesizing that information, trying to figure out for my audience what's going to be important, then porting all of that into Gamma, helping them visualize all that information. And I think these are things where the steps it takes to go from like this first draft to final draft is still a lot of work, but now you're replacing the mundane, tedious tasks with stuff that I feel is actually much richer and allows you to go much deeper into these concepts. I think that's where most people are missing because they're not doing that yet.

45:17They're thinking about the one sentence prompt and give me an output and like, oh, that's cheating. That's fast forwarding. That's like skipping all the important steps. And that's only because they actually haven't done it in the important steps themselves. And you have some expertise in this with the book, Steven, you're trying to map out, if my memory serves me correctly here, like you're trying to map out and build a mental model to make a better decision. And then, of course, as prediction markets like Polymarket help us do, you want to kind of go through some predictions. A great book, Super Predictor or Super Predictions?

45:53What was that book? Super Forecaster. Super Forecaster. Thank you. See that? We just did it right now. Yeah, yeah. And this is what brainstorming is like between humans, but we would have got there quicker than LLM. But Super Forecasting, this is part of making decisions. I know this because when when I took on venture capital as a profession 12 years ago, you have to become a super forecaster. You have to become a, you have to think about your thinking and decisions and then go back seven years later and say, why did I make this terrible decision? Or how did I make this brilliant decision? Well, what contributed to that so I can make the next hundred better?

46:32Yeah. I mean, it's, I think, maybe what's happening when people are asked questions like that and the few things they're imagining that you're just outsourcing the decision to the model and that's it. You're just like, hey, make this choice for me. And, you know, without context and all that kind of stuff. And yes, they wouldn't be very good at that. But the way that people actually use it or actually using these tools is they use the model to create the scaffolding for the decision that you're going to make, where they use the model to test different hypotheses about like the impact of the decision as it could be or to help them think creatively and imagine potential unintended consequences of the decision that they are having a hard time imagining.

47:09And then they gather all that information together and then they can make a choice. But you have far more data, in a sense, to make the choice because the models, they're supporting you. And it reminds me a little bit of, again, what I was talking about using these tools to be a more original thinker. One of the things that the models do that is just mind bug, if you've ever written a historical book or anything that evolves like a chronology is you can feed a tool like notebook, like 100 newspaper articles and say, create a chronology of all the events in sequence here and a list of all the major characters.

47:47so I get a mental map of like everything that happens, which is if you're writing complex, like narrative nonfiction, like that is just seeing the sequences are really, really complicated. And that can take like weeks to build out of a hundred newspaper articles. I would say, you know, you or Malcolm Gladwell, or, you know, pick your thinking author, that is kind of the job is to synthesize all this information and make it so, you know, the gen pop like myself can read your book and be like, okay, Stephen or Malcolm are holding my hand through the data and I can have a fun ride for six or seven hours with them understanding something complex.

48:27And now the LLM can do it for you, yeah? Well, yeah, I think, I'm trying to say something that's slightly different than that in a way, Jason, which is the thing that I do well, let's say, as a writer is like, make the stuff interesting, create a compelling narrative, write the sentences in a compelling way, whatever it is. The thing I don't do particularly well is figure out the exact sequence of dates of events that happen. Like that's just you struggle with that. Like everyone like everyone has to do it, but it's not I don't have a particular talent for it, but you need to do it to write the book.

48:56That's the chore. That's the that's the chore. And so now I'm able to say, hey, actually, notebook, will you handle that? So my brain is freed up to just think about the creative things and the more original writing or the better way to phrase the sentence or the paragraph or the chapter. And that's where I see it as building this scaffolding for you to do better thinking, better decisions, better writing, better creativity. In other words, you're standing on the shoulders of those chores. The chores would be for a typical book. What percentage was chores versus the delightful part of the actual prose and storytelling?

49:33If you just gave an honest... I mean, it's close to 50-50. You know, it's a lot of time just managing the information. And then when you get to the end of the process and you have like bibliographies and footnotes and stuff like that, it's just like it's weeks of like drudgery that I cannot wait to write the next book. Like with these tools, like I was just saying when I wrote my book, it was 60 40. It was 60 percent of me just getting all this data, you know, structured on a whiteboard, on index cards. And then here we are, the this craziness of just standing on the shoulders of the chores in a way.

50:09And yeah, these, it's very weird surveys. I'm wondering what the value of these surveys are. I mean, it's sort of like the initial research. It's a great thing to start with. It's a good building block, but you really have to double click on it to understand what's actually happening, which then I think a good jump off point here is this MIT Media Lab study. So there's an MIT Media Lab study from June saying the use of ChatGPT made you dumber. The team asked three groups to write various SAT essays. One used ChatGPT, one used only their brains, and one used Google Search, but not AI. For the first three assignments, they would then ask to write a fourth essay.

50:52But the ChatGPT group had their tool taken away. The group that had asked to use ChatGPT for help had lower brain engagement and underperformed at neuro-linguistic and behavioral levels on the second assignment, while the Google and brain-only groups had similar results. I wonder if this actually means chat GPT makes you dumber in general or, and this hasn't been peer review or anything, 18 people completed it. So this is just like a first stab out of it. But anybody have a stab at this, Grant? Do people let their guard down and maybe disengage sometimes when using the tools? And maybe they have to be thoughtful about reengaging their brains, almost like, you know, driving a stick shift versus an automatic when you're driving.

51:36That's what this sort of speaks to me, too. I mean, as a dad of two younger kids under 10, this is one where studies like this, you know, obviously you can we can double click into all the different ways that maybe study is self-displugged. But it does concern me. You know, if these tools do become a crutch for students, then are they really learning the concept? I think the onus then becomes on oftentimes the teacher to kind of set the right framing. Like these this is a tool. Use it as a tool. Don't use it to fast forward through the things that are important. And without the right teacher, which, you know, who knows who you're going to going to get as a student, then then you might be tempted to kind of skip over the hard parts here.

52:18And I'm not sure where this all lands. I think we're still so early in how these tools are evolving and especially how tools are being taught in schools and in places where, you know, who knows what's the right way to actually even adopt and introduce some of this stuff. So I'm curious to kind of follow along. I'm curious to hear Stephen's thoughts on like what this means broadly for people. Yeah, we think about it a lot at Notebook and at Google, obviously, because classroom and EDU is such a big part of what we do. And Notebook's, you know, a significant amount of our users are either students or teachers or scholars.

52:53Um, you know, one way to think about it is this, like if you are interested in genuinely understanding and learning something, this is the greatest time to be alive ever, right? These tools are incredible. If you were trying to truly under, to get the information into your brain so that you understand that you have a 24 seven tutor that will adapt to whatever, you know, language or comprehension level you are, turn it into a podcast or a slide deck to help you understand. So that's amazing. If you are interested in creating the illusion that you understand something, it is also the greatest time to be alive, right?

53:29So if you don't care, and that actually isn't that big a deal outside of school, right? Because it's not a really good long-term strategy to go into your job and not actually read anything your boss says to you and just feed it into ChatGPT or Gemini and just output the answer, right? Eventually, your boss will be like, you don't understand anything. We fired somebody for something similar. Or we were about to fire somebody and they resigned. They literally did their notes for a founder meeting with ChatGPT based on the Zoom call. And I had said explicitly, I want you to write the notes. You can use the summary tool, obviously, and any of these things to remember points.

54:07But I want you to actually literally write your thing. And I had them read it out loud in front of the class on the management team meeting. And it had errors in it. And it was like ChatGPT. This is 18 months ago. It was it was AI slop. And man, the person was so embarrassed. They resigned the next week right before I had a chance to fire them, unfortunately, because I was really looking forward to that dismissal. So so to me, in a way, the biggest problem with the education sector, because there's so many ways in which these tools are amazing for teaching and so many ways that they're amazing for the learning experience.

54:43The biggest thing is that they have complicated assessment. Right. It used to be that you could test whether you had learned something by writing a paper. And the paper was an expression of the state of your knowledge. And your teacher could read the paper and decide whether you had learned it or not. And, you know, one of the side effects of this technology is like if you have free access to these tools, they will write a plausible paper for you, particularly if it's on a well, if it's on, you know, Catcher in the Rye, where there's like six billion papers there that have been written that the model knows all about.

55:13It will do a plausibly good job of it. And so then that is a real problem. Like, I don't take it lightly, but I think it's also a solvable problem. Like, we just need to think carefully about how we do it. Like, there are other ways to assess learning. And if you can figure out the assessment piece and maybe use AI to help with the assessment piece and not undermine it, then the education story, I think, is a lot more positive because there's not an incentive to cheat or to skip the learning process if you are going to get responsibly assessed at the end of the semester. And then you're going to have all these amazing tools.

55:46And the easiest assessment possible is to have a person acoustically sit there and write in a blue book. What do they call those little blue books we used to have in school? Remember those little blue books? They were blue books. And they'd be like, here's your blue book. Start writing. And you'd be like, oh, my God, this is terrorizing. But OK, here we go. Or how about this? You know, just speak to me. Oral exams. Oral exams. Oral exams. Just talk. And, you know, it's more fun. And it teaches you a whole nother set of skills, which is humanity. You'll be better on a podcast if you can explain what resonated with you in Catcher in the Rye.

56:24And you could be a better human in the human world. I think we should end on this Jevons paradox, Grant. Explain what it is and explain how it relates to hiring for you. Yeah. So, I mean, the classic example, you know, this is the 1800s is steam engines become super more efficient. and the argument would be, okay, well, if they're more efficient, then we're not going to have to use as much coal. And all of a sudden, coal consumption or usage should go down or at least be flat, and the exact opposite happened. There are many more use cases. Coal usage goes through the roof, demand surges, and I think there's a lot of analogs that have since happened, similar time frame was around Bessemer process and steel production.

57:09and I think what's interesting is like not only do you get potentially more uses of what was happening before but you unlock new use cases. In the steel instance like this is the advent of skyscrapers and so prior to steel being super expensive to produce this didn't exist and all of a sudden now you have skyscrapers and completely modern infrastructure and buildings that weren't possible before It's interesting to then compare that to where we are today and with AI. Certainly with coding, what we're seeing today is many more use cases of software and software development. We're seeing much more internal software being developed.

57:51We're seeing personal softwares, personal apps being developed. And so my question is, yeah, what are the sort of skyscraper moments for AI? I think in many ways, Notebook LM is maybe one of these where things like learning and how people actually understand information. These are things that weren't possible before. And I'm excited to see what else might be born of this sort of generation of software. And my favorite is air travel. When you talk about Jim's power. So like, yeah, let's make these flights more efficient on a fuel consumption basis. And it's like, yeah, you know what you just did?

58:27you opened up more airports and more routes because, hey, I wasn't going to go back for Thanksgiving and Christmas, but YOLO, why not? I mean, it's only 300 bucks. Screw it. I can bring the family. How do you think about this and employment? I mean, I guess this is a good one to wrap on because this is where people are scared. This is where people are wondering, oh my gosh, self-driving cars. Oh my gosh, Optimus, which I got to see Optimus 3.0 two weekends ago. Man, that is going to be game changing. Can't really talk about it too much, but I saw the lab and what they were building. And I was like, oh boy, Tesla's going to be known for one thing a hundred years from now and it's Optimist.

59:04Then people are going to be like, oh, do you know, there's a piece of trivia for you. You know what Tesla originally did? And he's like, I don't know. And they're like, they used to build cars. And he's like, really, they built cars? Oh, okay, I just know there's 8 billion optimists running around here. Yeah, how do you think about this issue? Is this time different or is it just the same? It may be different in some ways in that knowledge workers are the ones that are impacted. Previous historical technological breakthroughs created whole new classes of knowledge workers that didn't exist before and automated more kind of manual labor.

59:36And this is a case where the transformations will actually probably happen, will almost certainly happen to knowledge workers first. The question is, is it additive? Is it, you know, is it replacement? I love the skyscraper analogy that Grant had. The other one I think of here is the light bulb. So like, you know, in the in the middle of the 19th century, I wrote about this a little bit and how we got to now. The state of the art, the state of the art internal lighting was whale oil. Shout out from sperm whales. Right. And like to get the whale oil, they would like get an eight year old kid to go into the brain of a dead whale and scoop out the oil because it had to be small enough to like fit in there or whatever.

1:00:16And when Edison invented electric light bulbs, like it put that job out of work, like nobody made a business, made a living anymore as like a whale oil extractor. But what it did was it created like the electrical grid, created a whole universe of appliances and careers built around those appliances, building them, maintaining them, you know, electricians or being a podcaster. Like you can't do that without electricity, right? Like a whole universe of media that was made possible by electricity. And so I think of what's happening here is that we're going to have like an intelligence grid that we already are starting to have.

1:00:53And the question is, you know, when you tap into that intelligence and are able to like use it to build whatever you want to build, isn't it at least plausible that it will create a whole set of professions that we can't yet imagine, just as you couldn't imagine it as a whale captain sitting there in Nantucket in 1850? Reminds me of there was a Wired cover, Freelance Nation, I think. I don't know if it's Wired or. I think it was Wired. It was Wired. And, you know, we sat there, Grant, a little bit before your time. And it was like, what if you didn't have to work for a corporation? Just stick with me here for a moment.

1:01:35You could be so skilled that you could work from anywhere and provide a service to organizations and then send them a bill and they would pay you for it. But you never had a boss. You would be freelance. And it could be a nation of freelancers. And guess what? Here we are. If you know how to use these tools, you'll be infinitely employable. And this fear and loathing of AI, which we're contending with because so many people are getting so rich so fast and the revenue is going up. And Grant, my God, going from zero to a hundred million in revenue over how many years did that take? Three, four? Yeah, two plus years.

1:02:11I mean, Stephen and I are like, it took us three years to get to a million in revenue. I don't know if I ever got there, Jason. Wait, V didn't have enough advertising from Archer Daniels Midland or whoever was sponsoring the Sunday shows? Yeah, it's a different time. But you don't need to be scared about this stuff. If you embrace these tools, there's an infinite number of possibilities for you. And I see it internally. And I think that's just to end with a little bit of hope here. The people in my organization, and I have a number of them. There's like 12 of these analyst associates in the organization.

1:02:47And I'd say maybe three or four of them are particularly good at using these tools and creating scripts. My Lord, they are 50 times, 20 times more valuable to me as the business owner. And I'm telling the other eight, look at what these people are doing with the databases, exporting the Slack into a LLM and doing this analysis, as you pointed out, Grant, earlier. like those people who find the trailhead and then get to the top of El Cap to the boss. Those people are heroes. And then the other folks, it's like the eight of the other eight people or nine people who can't do it. The sum total of their value to the business is less than the one person who does learn to use the tools and learning to use these tools is as easy as using the tool and saying, how do I use the tool?

1:03:38I mean, it's what's that recursive, I guess would be the word for it. Like the tools will just teach you how to use the tools and tell you what to build. You don't even need to know what to do. Just ask the tool, how do I do this? Less words of hope. And you're, I think, an optimist. Yeah, Stephen. I am. I think there's a lot. I think it's the most profound technological change of my lifetime. And any change this significant is complicated and will have unanticipated negative secondary effects that we have to keep an eye on. But like to be in the middle of it and to be trying to figure it out and to steer it in a responsible way is incredible opportunity.

1:04:13I mean, it was a little sad hearing you talk about that, the freelancer life with no boss and no giant corporation, because that was my life for my entire existence until I turned 55. But I really do love it because we, you know, I feel like we're right in the middle, just as Grant is, of figuring this stuff out and trying to figure out the best way to like empower people with these tools. And so that opportunity is amazing. It's kind of nice, isn't it, to go from being a freelancer, gunslinger, solo act to being part of a band. It's actually, it's a nice change of pace. I learned that with All In.

1:04:48So collaborative. Like being a writer is a very lonely profession in a lot of ways. And our team is just so brilliant. And there's so many things that I would have never thought of. Like starting with audio overviews. Like that would have just, it would never have occurred to me to do audio overviews. but like a bunch of other wizards in our team, like figured it out. And it was one of the best things we ever did. So it's, I really enjoyed that part of the job for sure. Grant, as we wrap, you're assuming optimistic about the impact of AI on society, humanity. Super optimistic, super optimistic.

1:05:19You know, going back to your example, you know, whether it's planes or, you know, cars, when things start taking off and these industries start taking off, you know, new jobs get formed. You need, once cars start taking off, you need roads. Roads start leading to bigger cities. Cities start leading to all different things that just weren't possible before. And so I feel like that will translate into new opportunities, new jobs, hopefully replacing a lot of the mundane and tedious work that nobody wants anyways. I just don't think we yet know what it looks like. And so, you know, I got to give ourselves a little bit of time, but I think we'll get there.

1:05:53And yeah, I'm super optimistic. Stay optimistic, folks. and thisweekina.ai. Sign up. And listen, these kind of paradigm shifts, and this is the largest of our lifetime. I agree with you, Stephen, on that one for sure. It feels like everything up until this point is the epilogue in some ways. These are the building blocks that made this revolution happen. It's very simple. You just join the revolution. You just start using the tools every day. And then you become the most valuable person in your organization, like instantly. And especially if you're in a legacy organization. and we'll see you next time on This Week in AI slash This Week in Startups.

1:06:28Bye-bye.