B (2:06)

Yeah.

A (2:06)

Or they just go full first person shooter. That's like a Palmer lucky. Like you know, a certain type of entrepreneur who's just going to go straight for the jugular.

C (2:18)

That's an episode right there.

A (2:20)

All right everybody, welcome back to this Week in startups. It is January 7, 2026. We're back and just a little soft launch here in 2026. I decided for my own entertainment and for my own intellect and interest, I wanted to start another roundtable just like, you know, the format I created for all in three, four smart people talking about week's topics and I wanted to do it in AI. So I came up with a very unique name this Week in AI. I don't know how I came up with it, but it just felt like the right name. And we're going to officially launch it later this year. But I'm testing it here on the this Week in Startups feed. And what I'm trying to do is find people who are actually builders, not journalists, not commentators, but people actually building product. Today we are extremely lucky to have two people who are deep inside of the game. First, Grant Lee, who is the co founder and CEO of Gamma Gamma is an amazing AI first presentation product that I became aware of because all my founders started using it. We'll get into that in a minute. And one of my oldest friends in the technology business, Stephen Berlin Johnson, he co created Feed, which was a zine in the 90s and. And then we rediscovered each other. I was always listening to his great books and audiobooks when I was hiking around San Francisco. But a great thinker, but also a product guy. And he wound up somehow as the editorial director of NotebookLM at Google Labs, which is one of my favorite products. Just some housekeeping. You can sign up for a daily newsletter I started called this Weekinai AI. Essentially a substack. We're just putting 10 links together of the top 10 stories of the day, just so you don't miss them. You probably know six or seven of those stories, but the two or three you don't know, we suffered to find them. In terms of an editorial format, it's very concise. This week@AI.com top 10 links of the day, you can subscribe to the YouTube channel, etc. We're also going to be publishing LinkedIn, TikTok, Instagram, blah blah, blah, blah, blah. And we're taking suggestions for panelists. I'm going to rotate the panel, try to get to a group of like 5, 6, 7, 7 great folks so that I can have the same people on essentially every week or so. So suggestions at pitch at this Week in AI. AI. All right, gentlemen, welcome to the program. How are you doing, Stephen? It's been a minute.

B (4:38)

Yeah, I mean, I last saw you like shortly after we Launched the first kind of public experiment of Notebook, and I think it was December of 2023, really. So it's been. It's been quite a ride since then, but yeah. Great to see you.

A (4:55)

Good to see you, man. We've known each other forever, huh? It's like, when did we first meet? 96, you think?

B (5:01)

Yeah, that's what I was thinking. I was actually just looking at feed in the Wayback Machine at one of the editions from like 1997 that we did, and just seeing all the people we were publishing back then who've gone on to have like a great authorial career. It's called.

A (5:16)

It's one of who's on the top of the list. It really is one of these things when you start a publication in the 90s, Gen Xers and you curate. Wow. It's like drafting or something. Because you can't afford the top tier writers. We couldn't afford the people who Graydon Carter was going after, so we had to find the scrappy ones. Who did you find that went on to great things?

B (5:36)

Oh, my God. Multiple Pulitzer Prize winners like Alex Ross, now the music critic for the New Yorker, was in there in the early days. Our friend Clay Shirky, he wrote for us all the time.

A (5:49)

Now Clay also did a column for Silicon Island Reporter. I remember.

B (5:52)

Yeah, yeah.

A (5:53)

I read it as the editor, and I'd be like, I didn't go to graduate school, Clay, you gotta help me out. Tell me these references. There was no Wikipedia at the time. What is Clay up to?

B (6:03)

Clay Sher, he's the. He's the Provost of AI and Technology at nyu. And so no joke, we've been working with him a lot because they're, they're big champions of Notebook. And he's personally like a lovely champion of Notebook as well. So I kind of reconnected with him in the last year or two because of this project, which is great. So I'm getting back to my like 90s Silicon Alley roots.

A (6:24)

It's.

B (6:24)

It's beautiful.

A (6:25)

Crazy. What an amazing moment in time that was. You know, when you think about it, a bunch of young people who knew what the Internet and online services were, who were just a year or two ahead of the public. But if you're a year or two ahead of the public, it's like being, you know, like a God, it's. Or a demigod. You just know everything. And we were given a ton of money to go do really interesting things, which I think is a good tee up for. For our guest, Grant Lee. Grant how old are you, by the way? I can't tell. I don't how old.

C (6:50)

I'm just over 40. 42.

A (6:52)

Oh, you're 42. Okay. So you're a millennial.

C (6:54)

I guess that's the term.

A (6:56)

Yeah, we're generous. You're a millennial. And of course, you're the co founder and CEO of Gamut. Explain to the audience what Gamma is, why you built it.

C (7:05)

Yeah, so you can think of us as being, you know, the visual storytelling product for work reimagining how people present and share their ideas. The default tool for so long has been PowerPoint, and we're trying to come up with something to sort of modernize the way people are communicating their ideas.

B (7:20)

Got it.

A (7:21)

And you recently raised a ton of money. Yeah, if I remember correctly.

C (7:24)

Yep, raised a bit. Raised the Series B with led by Andreessen.

A (7:28)

Oh, congratulations. And the valuation there, not that valuations really matter, but it is indicative of the moment in time.

C (7:34)

Right. 2.1 billion.

A (7:35)

Incredible. And the company, I believe there was a whisper broke. 100 million in revenue or something in that zone.

C (7:41)

That's right, yeah. We passed 100 million last year. Still making great progress. And yeah, it's still, you know, early days, but a lot of fun. Team is great, excited to keep building.

A (7:50)

All right, so this will lead us, I think, to our first topic, which is AI and productivity inside the enterprise for non developers. We all know developers. You know, you put a co pilot on them, you. And they obviously had Stack overflow. You got GitHub. There were many ways for them to be augmented, Stephen. But you and I are starting to see people use tools like yours, tools like grants, to become superhuman, to use a term, not the company, but the descriptor. What are you seeing in the field with how people are using NotebookLM? And I guess just as a little preamble, how would you explain NotebookLM to just an office worker, somebody who uses Microsoft Office, who uses the Google Suite or uses Gamma? How should they think about NotebookLM and where it fits between Notion, Coda, you know, Microsoft Office, et cetera?

B (8:45)

Yeah, I mean, Notebook does a couple of things. One of the things that we, you know, I think we're pretty ahead of the curve on is just the whole concept of context engineering and context management that, you know, AI is really useful when you're talking to a general purpose chatbot, but the real power comes when you load it up with the sources that are crucial to whatever you're working on. So that whether it's, you know, the legal depositions You've, you've recorded for the brief that you're trying to file as a lawyer, whether it's the assignments for the class you're, you're taking, the like information that's specific to you. The model needs to be able to see that. And so we really built Notebook from the ground up as a tool for kind of managing that information and then allowing you to query that information. We still have kind of the best system I think out there for, you know, actually having grounded citations back to the original passages. If you ask a question, you can go back and read the original source in its entirety. And then increasingly these amazing transformations that, you know, parallel some of the great stuff that Granite's doing at Gamma where, you know, you initially the, the one that kind of broke out was audio overviews where you could take your sources and turn them into this kind of magical two person podcast conversation that kind of went viral in the fall of, of 2024. But now we have a whole host of transformations you can do, including these new slide decks which I'm sure we'll talk about that are, that are pretty amazing. So yeah, that's, that's the product launch.

A (10:11)

Is a fast growing organization. We've got more than a dozen employees working with me here in Austin and another dozen spread out all over the world. But there's so many moving parts when it comes to hiring and managing employees. There's the onboarding, of course, payroll. You got to pay them. And listen I' all these podcasts to do. I don't have time for payroll, benefits, hr, taxes, answering questions. Nor do I want to hire a full time person and then have them do five hours of work a week. No, I have the perfect partner. Gusto. They're the all in one payroll and benefit product that's built just for your small business. Easy to use, it's incredibly fast to get started. And it's designed specifically with remote offices in mind. And Gusto is not just giving you helpful tools. They're going that extra mile to keep your workers happy and keep everything running smoothly. And they're now offering level funded health plans to keep your insurance costs down and on demand pay to help workers get access to their cash faster without paying extra interest or hidden fees. So here's your call to action. We want you to try Gusto today. So we're giving you three months free when you run your first payroll. That's right, three months free. F R E. That's my favorite price, folks. Go to gusto.com twist that's gusto. G-U-S-T-O.com twist. What an amazing service and a partner, great partner. And it's a pretty powerful product. As an example, I had my team when we had the All In Summit, and I have to interview, you know, 30 people. This was the All In Summit 2025. And I said to my team, because I have two Athena assistants, which I'm an investor in that company. These are the top 1% of the knowledge workers in the Philippines. Go to athena wow.com, get an assistant. I said, here are the speakers. Go find me every time they've spoken, you know, Alex Karp, etc. And put them into Notebook LM. And then I can ask questions and do output from it. Right. So here you have every YouTube of everybody speaking. And so then, you know, when I'm on the road or I'm in line at Starbucks, whatever I'm doing, I could ask questions of it. I could get summaries, and I could even make an output of a podcast. Is a really interesting how you expand the context window and you can build like essentially a brain. It's kind of like the brain.com, if you remember that. Yeah, Jerry Mikowski's brain. Our friend Jerry. But the brain company, which I think still exists. Super fascinating. Do you have like, I guess the. The real world example was Fred Wilson's. Maybe you could explain that a little bit and then we'll talk a little bit about Gamma as well.

B (12:44)

You know, Fred's been putting basically all the kind of background on their investments at Union Square Ventures and actually using it in some cases to simulate having a lawyer review the documentation. So it's like bringing in all the documents for the investment and then saying, hey, analyze this as if you were a lawyer to make sure that everything looks sensible. And he's been seeing great results from that. But we're seeing a lot of venture funds actually use it just as a way of kind of like analyzing all the pitches that are coming in and, you know, kind of including a investment thesis for the firm that's. That's included there. Yeah, there's Fred's. Fred's blog post about it. Excellent little rendering of him. And basically, you know, using it to. As a tool for pulling the key insights out of a bunch of different documents and also increasingly, I think, as a creative tool. So, you know, we were chatting a little bit before this. I just had this whole experience over the last four or five weeks where I found some old kind of sketch of an idea for a story kind of along the lines of like a book like Sapiens, like about the kind of birth of agriculture and things like that. And I just stumbled across it like five weeks ago and I'd written it maybe like four, four years ago and I'd forgotten about it. And I was reading it and I was like, oh, since, you know, over the last four years something fundamental has changed, which is that I now have Notebook and Deep research, which is now integrated into the notebook, which, you know, is just totally transformative. And I was like, I can explore the ideas that I just sketched out here in this document so much faster than I ever was able to do before. I can kind of stumble across something and be like, oh, what's the deal with that? And send Deep Research off to figure out, you know, pull down the most important sources, you know, synthesize that information, summarize it for me and then I can kind of show the model in notebook, like what I'm writing and say, okay, what, what's interesting here? What am I missing? Like what are the holes here? Like how could I build on this? And I ended up writing like a 13,000 word piece like in my spare time. That would have taken, you know, months and months of like full time concentration because these, these tools have just transformed the workflow. And I think, you know, as you're saying that like coders have, have started to talk about how revolutionary this is. But I think that knowledge work workers are just beginning to understand like what an amplifier these, these tools are. I think 2026 is going to be a year, we're going to see a lot more of that.

A (15:13)

I think that's spot on because the knowledge workers, they're so, they're intimidated I think by the concept of scripting or coding. It's been this giant wall or this giant mountain to scale and scaling the I'm going to become a developer mountain, it's just too intimidating. It's like going up like what's the one that you assume like El Capitan or something, but you know, on the back of El Capitan you could walk up this very, you know, gingerly trail and get to the same view. That's I think what these tools are. It's just another way to climb the mountain. But you don't have to scale it straight up. Grant your thoughts on what you're seeing with people using your tool again sometimes making great presentations, making great infographics. This was something a knowledge worker had to go hire somebody, they kind of give their feedback, they wait a week, they get some slides back. But now you have this sort of AI first process, explain how people are using it. In your take on Steven's position that 2026 is the year of the knowledge worker embracing these tools.

C (16:16)

Yeah, I'll start by just saying, you know, NotebookLM, we're also huge fans. We use it internally. I think many of these tools just unlock so much possibility. So for us, one use case we've had is we have a private Slack workspace for all of our power users. We're constantly trying to get feedback. They're oftentimes giving us input on things that are working well, not all of a sudden. Now we can take that entire chat history, all the messages that we've ever had, dump that into NotebookLM, and that now allows you to map that world in a very different way. You can start building Personas like, okay, all the feedback we're getting, how do you categorize what type of pain points we're actually trying to solve for and for whom within these Personas? You can then start asking deeper questions. What are the value props that really matter for this Persona? If we were to pursue X, Y and Z feature, are we actually delivering on a certain Persona's use case or pain point? And is it really going to move the needle for them? So for us, I think that's just been an empowerful way. It really is this thing where it allows you to learn and synthesize information that frankly wasn't possible before. And I love just the notion of actually mapping this to something that feels so much more tangible. Once you map it, you can figure out where on the map do you even want to explore, go deeper? And for us, that's been great.

A (17:32)

And that requires somebody to know how to either authenticate NotebookLM into Slack, which I don't know if that exists right now, or if you have to use Zapier to do it, or if you just export the entire course.

C (17:44)

Right now we're just doing exporting. Yeah, Like a little bit. A little bit more manual. There's some intermediate steps, but, you know, not. Not the craziest thing.

A (17:51)

And once you think about that, there's this intermediate step. And this is kind of like finding out about that trail on the back of El Cap. Right. Like you don't know where the trailhead is. And then somebody says, by the way, there's an export feature in Slack. You just export the whole thing and then you upload it and then you do that one little simple step. It's kind of like Stephen, when we taught somebody how to like install TCPIP on their Mac and authenticate onto the Internet. And all of a sudden they got on Usenet and their brain exploded. But that one thing, where's the trailhead? How do I get in to the trail and find everybody else who's climbing the mountain? That's the piece that is such a blocker.

C (18:25)

Yeah, totally.

B (18:26)

Yeah, yeah.

C (18:26)

Once you unlock that. Yeah, go ahead, Stephen.

B (18:29)

Oh, no, I think you were. I want to hear more about Gamma.

C (18:32)

Yeah.

A (18:33)

Thank you for the roses for NotebookLM. That's an incredible use case. But tell us about Gamma as well.

C (18:38)

For you know, where this all started was for Gamma to almost try to solve my own problems. So I started off early in my career consulting, investment banking, living in slide decks. Slides are an incredibly powerful tool to communicate to others. You're in many ways trying to visualize concepts, trying to synthesize information, make sure that's spreadable and that when it gets in the hands of someone else, you're able to take what's on your head and translate that into someone else's head and transport that. And so we've been on a mission to really kind of democratize that visual storytelling. How do we make it dead simple for anybody to be able to convey what's in their head and really get that information out there? And so I think over the past few years we've made good progress. For us, it's about the building blocks. How do we, every step of the way, simplify it, reduce the amount of tedious and mundane tasks, a boring task that nobody wants to do, the formatting, the aligning of boxes and get them to a better output. And so a lot of, you know, the past five years has just been about getting to a better starting point for our end user.

A (19:42)

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B (22:53)

It's an incredibly interesting question. And it's funny to be thinking about it inside of Google like a giant organization. That is my actual first contact with a company this size in my career. Like, I'd never worked with an organization this Big. So I'm looking at it from a completely different perspective in a way. But, you know, one of the things that's so interesting about what you could do now as a startup to what Grant was saying about adding the Slack conversations and building kind of Personas off of that is one of the, like, first parts of the extended Notebook team that really, really, besides me, that really started embracing the product internally using Notebook, was user research. And so, like, one of the things that's amazing is, like, in addition to, like, thinking about the organization itself, thinking about all your base of users and all of their needs, right? So, you know, we have all these interviews with potential or current users, just talking at length about their, like, pain points and what's working for them, what products they're using. And the UXR people would just be like, let's put all the transcripts of those things into a notebook. And then that meant that product people could then kind of be like, hey, I'm thinking about this feature. Not only do I have, like, my lawyer on tap here as an agent, and not only do I have my, you know, whatever, like HR person virtually as an agent, I have my whole audience, like my user base or potential user base here as something that I can, like, have a conversation with and be like, hey, if we changed this feature so that it worked this way, how would that be received? And you get these amazing, you know, kind of deep, profound answers again, all kind of taking you back to the original quotes from the user so you can read in their voices as well. So you're not just like getting a synthesis or a summary from a model, but you're actually like, hearing the quotes from the original users. So any, yeah, anything like that is just. It completely changes the way you do.

A (24:45)

The work, which kind of brings up the big C philosophically. Consciousness. And what is consciousness? I mean, I hate to level up the discussion here because we're. We're in such a great tactical and strategic place, but what is human consciousness but the ability, ability to build a world or a mental model and then play out a scenario in your brain, what would happen if we launched this new feature? And that's what these things seem to be doing when they play the. Guess the next word. And here's the context window and here's my. My resources. So it makes me wonder if we're actually in a giant simulation here. We're all just somebody's agents. Like, 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. 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.

C (25:47)

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, okay, what are the three main things? You take all this information, you simplify to three, and then you tell your team, hey, These are the three priorities. Well, if, you know, 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 3? 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, you know, AI is doing it, then what's the point of, you know, we can just skip over that step altogether.

A (26:33)

Brainstorming.

B (26:34)

Jason, if I can go ahead. 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, but. But I think one of the early things that I found really extraordinary, and I might have even talked 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. And I think it's because of the nature of next token prediction 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. Information so that their brain remembers it and engages. That's part of like the power of what like 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 flavor fully, semantically things for humans and humans were conscious. I think the, what's interesting now is that we truly have, I think you have to look at what you know. If you give Gamma, 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, like 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.

A (28:49)

It's mimicking a portion of human experience. It doesn't have the, 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 and then here's the output that a venture capitalist would like. And then hey, I'm going to make another deck. And grant, 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 brain. So 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.

C (29:44)

Yeah, absolutely. I mean so much of the pain point before, especially with slide decks is you have to almost, you know, make a generic version, a one to many version. It's impossible to go back and like, you know, format everything so that you go into the next meeting or you're going to have time to personalize the deck. And what we see today is like 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. And. And you can do that at scale. So not only are you doing it, you know, as an end user, both via an API, then obviously you can do that, you know, automated, at scale. So I think that 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, you know, ends up becoming a superpower for our customers.

A (30:38)

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C (32:00)

It's kind of like us going to.

A (32:02)

Eureka Cafe or wherever the hell we went in the 90s to just, you know, what was the place on. What was the dinosaur? Union Square.

B (32:10)

Oh, yeah, I was thinking of Coffee Shop. Yeah, Coffee Shop was another place, but Limbo was cool too. Yeah.

A (32:14)

Yeah. I was just talking to David Hershkovitz from Paper. He's like, when did you Start working here. And I was like, and start Silicon, our reporter and writing for Paper magazine. And I was like, well, I pitch you doing Digits magazine as a insert to paper. And that was going to be the original name of Silicon Valley Reporter.

B (32:31)

That's amazing.

A (32:32)

I didn't take you up 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. And there's a resistance to using AI.

C (32:48)

I'm curious.

A (32:48)

There's a group of people that you've been talking to, Steven, that are resisting it. Creative specifically. And maybe we could unpack why you think some folks are saying, I don't know, 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, I want to go back to film. I don't want to use a digital recorder. And yeah, give us your, give us your.

B (33:14)

It's a really interesting one. And it, what I was saying in the chat was that 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 and I, 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 bit later. Like the challenge that AI is posed to assessment in schools, like writing a paper for you and all that kind of stuff 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, 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 listen to the show, think about it. I see it as a way of just augmenting my thinking. Right. I think I see it as a way of like having better ideas and having richer ideas. And I had this, I had this fascinating conversation with a friend of mine who's a novelist, who's older but is a novelist and she's, she's actually kind of receptive to using AI. But at some point I said to her, it's like, I'm convinced that these tools make me a more original writer and thinker. Yeah, yeah, 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 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 that's really lame and I'm now. Or I just have a bunch of received ideas about the world that, you know, I'm just kind of just, you know, they're codified. Yeah, 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 alternatives 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. And open up new doors. Again, to your point about brainstorming of just.

A (35:29)

And how would you do that? Previously, you would have just emailed three friends and said, can you read this?

C (35:33)

Right?

B (35:34)

You read other books and other thoughts and other articles and research and. And things like that, and what you still do when you're actually doing the final piece, but your ability to. To kind of quickly test the waters of an idea or quickly, you know, kind of explore a parallel path intellectually that you. 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. My range in a way that has been just, I don't know, exhilarating to.

A (36:09)

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. It's. Or did a Fireside Chat at CES yesterday, and I was looking for some jokes. Right? You think jokes kind of hard for an LLM to do? Au contraire. Like, you just tell it like, here's the five things I want to riff on, and it gives you the worst possible jokes you could imagine. Or the. But they do spark an idea. So I, like, I was going through these, like, pagers. I had, like, a box of old gadgets, and I picked up the pager, and the first thing I thought of was the Hamas getting their nuts blown off with a pager. So I just throw it to the person. I said, oh yeah, you know, 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. But when I do brainstorming with LLMs, 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. 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, you know, sterilizing the content?

C (37:38)

Yeah, it's really, I mean, I think one thing that's interesting, you know, different professions. I mean, part of this I map to, you know, crossing the chasm. So, you know, 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 I think there is part of that where maybe the chasm was just for that particular profession was just much smaller. Once you cross it and you immediately move into mass market and then once you have critical mass, there's a tipping point, then it just becomes so off obvious that the profession itself is going to have to embrace this new technology. I think other professions where to Stephen's point, a lot of creatives, there is much more of that resistance, that chasm feels so much more daunting because the question is if these models are trained on prior art and people's work, then and you're trying to take advantage of that somehow. Well, 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 and 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 broad majority of. 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, then maybe it's okay. I 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, lack of resources, no budget, and so screw it, I'm going to fast forward it. I can pick up this tool, pay a couple bucks a month and like completely augment my day. And I'm okay with that. And they have no pride in like, hey, this, this, you know, this deck.

A (39:49)

Or whatever I'm creating 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. Here's the interesting Pew Research survey now surveys. It's important to put in context here. They're asking the percentage of a US 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'll make creativity worse. 16% better. 16. Neither better nor worse. 16. Not short. So when you, 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.

B (40:38)

Yes.

A (40:38)

I'm going to guess half the people here have not used AI all that much. And so how do you read into this? Stephen? Yeah, 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.

B (40:57)

I would put also that, 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. So there's a lot of creativity. The, that's built into decision making too, that I think the models can be super helpful.

A (41:20)

Right. That's your book.

B (41:21)

Yeah, yeah, yeah. There you go. Thank you. Thank you. I didn't know.

A (41:23)

I've learned literally every time your book comes out on audible.

B (41:26)

I just appreciate it. So. But here's the thing that I think is a factor here as well. I think that outside of, you know, hardcore kind of 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, like, that was not as much of a mainstream thing. So I think part of what people are approaching here, 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 chatbot 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, do. And so they kind of rightfully assume that there is a ceiling for what you can get out of a chatbot 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. But 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. 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. And they were just like, that is so much better. But you could that 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. But.

A (43:17)

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, Pew 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. Forty 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 will make you better. So that's 2 to 1. 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.

C (44:04)

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, it's just. They're just barely exploring. When 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, it can give you something that. And like the argument is, okay, well, you know, Gamma's 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 4 is to actually go much deeper. It's to actually do a of exploration and 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. They'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, the important steps themselves.

A (45:28)

And you have some expertise in this with the book, Stephen, you're trying to map out if, if my memory serves, reading 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? Great book. Super predictor or super predictions? What was that book?

B (45:54)

Super forecaster.

A (45:55)

Super forecaster. Thank you. See that? We just did it right now. And this is what brainstorming is like, you know, between humans. But we Would have got there quicker with than LLM. But super forecasting, this is part of making decisions. I know this because 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? What. Well, what contributed to that so I could make the next hundred better?

B (46:32)

Yeah, I mean, it's. I think maybe what's happening when, 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, who are 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 imagine. And then they, you know, gather all that information together and then they could make a choice. But you have, you have far more data in a sense to make the choice because the models, they're supposed to supporting you. And it, you know, it reminds me a little bit of again what I was talking about being about using these tools to be a more original thinker. One of the things that the models do that is just mind blowing. 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. So 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 sequence is really, really complicated. And that can take like weeks to build out of a hundred.

A (48:01)

I would say, you know, if you are 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, you know, holding my hand through the data and I can have a fun ride for, you know, Six or seven hours with them understanding something complex. Now the LLM can do it for you.

B (48:29)

Yeah, well, yeah, I think I'm trying to say something 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 this stuff interesting, create a compelling narrative. You know, 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.

A (48:49)

You struggle with that.

B (48:50)

Yeah, 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.

A (48:56)

That's. That's the chore.

C (48:58)

That's the chore.

B (48:59)

And so now I'm able to say, hey, actually, notebook, will you do 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.

A (49:20)

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? If you just gave an honest.

B (49:35)

I mean, it's close to 50. 50. You know, it's, it's. 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.

A (49:51)

Like I was just saying when I. It was 60% 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. And 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 makes you dumber. The team asked three groups to write various SAT essays, one use ChatGPT, one used only their brains and one used Google search but not AI for the.

C (50:48)

First three assignments they would then ask.

A (50:50)

To write write a fourth essay. But the Chat GPT 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 made you dumber in general or. And this hasn't been peer reviewed review or anything. 18 people completed it. So this is just like first stab out of it. But any 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 re engaging their brains. Almost like you know, driving a stick shift versus an automatic when you're driving. That's what this sort of speaks to me too.

C (51:38)

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 the study itself is flawed. But it does concern me, you know, if these tools do become a crutch for students, then yeah, 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 forward through the things that are important. And without the right teacher, which you know, who knows who you're gonna, gonna get as a student then, then you might be tempted to kind of, you know, skip over the hard parts here. And 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 the stuff. So I'm curious to kind of follow along and yeah, curious to hear Steven's thoughts on like what this means broadly for people.

B (52:41)

Yeah, we think about it a lot at Notebook and at Google obviously because we classroom and edu is such a big part of what we do and Notebooks, you know, significant amount of our users are either students or teachers or scholars. 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, 7 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? So if you, if you don't care, like, and that, that actually isn't that big a deal outside of school, right? Because it doesn't. It's not a really good long term strategy to go into your job and like not actually read anything your boss says you and just feed it into Chat GPT or Gemini and just output the answer, right. Eventually your boss will be like, you don't understand anything.

A (53:46)

You're just, we've hired 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, but I want you to actually literally write your thing. And I had them read it out.

C (54:12)

Loud in front of the class on.

A (54:13)

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.

B (54:30)

So. So to me, the, in a way, the biggest problem with the education sector because there's so many ways in which these are tools are amazing for teaching and so many ways that they're amazing for, for the learning experience. The 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 6 billion papers there that have been written that the model knows all about, it 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.

A (55:45)

These amazing tools, and 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.

C (55:56)

Books we used to have in school?

A (55:57)

Remember those little.

B (55:58)

They called them blue books.

A (55:59)

They were blue books. And they'd be like, here's your blue book. Start writing. And you'll be like, oh, my God, this is terrorizing. But okay, here we go. Or how about this? You know, just speak to me.

B (56:11)

Oral exams, oral exam, oral exams.

A (56:14)

Let's 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 could explain what resonated with you in Catcher in the Rye. And 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.

C (56:36)

Yeah, so I mean, the classic example, you know, this is the 1800s, is steam engines become super more efficient. And, you know, the argument would be, okay, well, if they're more efficient, then, you know, we're not going to have to use as much coal. And all of a sudden, you know, coal consumption or usage should go down or at least be flat. And the exact opposite happen. There are many more use cases. Coal usage goes through the roof, demand surges. And I think there's a lot of analogs that, you know, have since happened. Similar timeframe was around, you know, Bessemer process and steel production. And I think what's interesting is like, not only do you get potentially more, you know, uses of what was happening before, but you unlock new use cases and the steel and 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. We're seeing, you know, personal software, personal apps being developed. And so, you know, my question is. Yeah, what, what are the sort of, you know, skyscraper moments for, for AI? I think in many ways NotebookLM 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, see what else might be born of this sort of generation of software.

A (58:17)

My favorite is air travel, when we talk about Jevons Paradox. 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? You 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 is going to be known for one thing 100 years from now and it's optimist. Then people are going to be like, oh, do you know, here's a piece of trivia for you. You know what Tesla originally did and like, I don't know. And they're like, they used to build cars and it's like really? They built cars? Oh, okay. I just know there's 8 billion Optimus is running around here.

C (59:17)

Yeah.

A (59:18)

How do you think about this issue? Is this time different or is it just the same?

B (59:23)

It may be different in some ways in that knowledge workers are the ones that are impacted for previous historical technological breakthroughs, created whole new classes of knowledge workers that didn't exist before and automated more kind of manual labor. And 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, 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, um, the state of the art.

A (59:59)

That was good.

B (60:00)

The state of the art internal lighting was whale oil. Yeah.

A (60:04)

Shout out Kentucky from sperm whales.

C (60:07)

Right.

B (60:07)

And like to get the whale oil, they would like get an 8 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, whatever. And 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, electricity 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 have. And 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, in 1850.

A (61:13)

Reminds me of. There was a Wired cover, Freelance Nation, I think. I don't know if it's Wired or.

B (61:20)

I think it was Wired.

A (61:21)

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. You 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. I mean, Grant, my God. Going from 0 to 100 million in revenue over how many years did that take? Three, four?

C (62:09)

Yeah, two. Two plus years.

A (62:10)

I mean, Steven and I are like, it took us three years to get to a million in revenue. And like you're.

B (62:15)

I don't know if I ever got there. Jason.

A (62:18)

Wait, V didn't have enough advertising from Archer Daniels Midland or whoever was sponsoring the Sunday shows. It was, yeah, 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 end with a little bit of hope here. The people in my organization, and I have a number of them, there's like a 12 of these, like analyst associates in the organization, and 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, like, look at what these people are doing with the databases. You know, exporting the slack into a LLM and doing this analysis. As you, as you pointed out, Grant earlier, like, those people who find the trailhead and then get to the top of El Cap, like, 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? I 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 build. You just ask the tool, how do I do this? Last words of hope. And you're, I think, an optimist.

B (63:53)

Yeah, Stephen, I am. I think there's a lot of. I think it's the most profound technological change of my lifetime. And any change this significant is complicated and we'll 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. I 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 almost 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. 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. Is amazing.

A (64:37)

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, I learned that with all in like so collaborative.

B (64:49)

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 was 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 enjoy that part of the job for sure.

A (65:10)

Brandt as we wrap, you're assuming optimistic about the impact of AI on society, humanity.

C (65:17)

Super optimistic. Super optimistic. You know, going back to your example, you know, whether it's planes or, you know, cars, when, when things start taking off and these industries start taking off, you know, new jobs get formed. You need, once the cars start taking off, you need roads. Roads are leading to bigger cities. Cities are leading to all different things that just weren't possible before. And so I feel like that will translate into new opportunities, new jobs, 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. And yeah, I'm super optimistic.

A (65:54)

Stay optimistic, folks. And this week in AI, AI sign up and you know, listen, these kind of paradigm shifts and this is the largest of our lifetime. I think I agree with you, Stephen, on that one for sure. Feels like everything up until the this point is the epilogue in some ways. Like 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. Bye bye.