OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter)

0:00 The AI models that you're using today Is the worst AI model you will ever use. For the rest of your life. And when you actually get that in your head, it's kind of wild. Everywhere I've ever worked before this, you kinda know what technology you're building on, but that's not true at all with AI. Every two months, computers can do something they've never been able to do before, and you need to completely think differently about what you're doing. You're chief product officer of maybe the most important company in the world. world right now. I want to chat about what it's just like to be inside the center of the storm. Our general mindset is in two months there's gonna be a better model and it's gonna blow away whatever the current set of limitations are. And we say this to developers too. If you're building and the product that you're building is kind of right on the edge of the capabilities of the models, keep going, because you're doing something right. Give it another couple months and the models are gonna be great, and suddenly the product that you have that just barely worked is really gonna sing. Famously you led this project at Facebook called Libra. Libra is

0:56 Probably the biggest disappointment of my career. It fundamentally disappoints me that this doesn't exist in the world today because the world would be a better place if we'd been able to ship that product. We tried to launch a new blockchain. It was a basket of currencies originally. It was integration into WhatsApp and Messenger. I would be able to send you Fifty cents in WhatsApp for free. It should exist. To be honest, the current administration is super friendly to crypto. Facebook's reputation is in a very different place. Maybe they should go build it now. Today my guest is Kevin Wheel. Kevin is chief product officer at OpenAI.

1:32 which is maybe the most important and most impactful company in the world right now, being at the forefront of AI. and AGI and maybe someday super intelligence. He was previously head of product at Instagram and Twitter. He was co-creator of the Libra cryptocurrency at Facebook, which we chat about. He's also on the boards of Planet and Strava and the Black Managers Network and the Nature Conservancy. He's also just a really good guy.

1:57 And he has so much wisdom to share. We chat about how open AI operates, implications of AI, and how we will all work and build product. Which markets within the AI ecosystem companies like OpenAI won't likely go after and thus are good places for startups to own. Also, why learning the craft of writing evals is quickly becoming a core skill for product builders, what skills will matter most in an AI era and what he's teaching his kids to focus on, and so much more, this is a very special episode and I am so excited to bring it to you. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

2:33 If you become an annual subscriber of my newsletter, You get a year free of Perplexity Pro, Linear, Notion, Superhuman, and Renola. Check it out at Lenny's Newsletter.com and click bundle. With that, I bring you Kevin.

2:47 We'll This episode is brought to you by Epo. Epo is a next generation A B testing and feature management platform. built by alums of Airbnb and Snowflake for modern growth teams. Companies like Twitch

3:00 Miro, ClickUp, and DraftKings rely on Epo to power their experiments. Experimentation is increasingly essential for driving growth. and for understanding the performance of new features. An Epo helps you increase experimentation velocity while unlocking rigorous deep analysis in a way that no other commercial tool does. When I was at Airbnb, one of the things that I loved most was our experimentation platform. where I could set up experiments easily, troubleshoot issues, and analyze performance all on my own. Epo does all that and more, with advanced statistical methods that can help you shave weeks off experiment time, an accessible UI for diving deeper into performance, and out-of-the-box reporting that helps you avoid annoying, prolonged analytic cycles.

3:41 Apple also makes it easy for you to share experiment insights with your team, sparking new ideas for the A B testing flywheel. Epo powers experimentation across every use case. Including product. Right. Machine learning, monetization, and email marketing. Check out Epo at getEpo.com slash Lenny.

3:59 And ten X your experiment velocity. Let's get EPPO. dot com slash Lenny This episode is brought to you by Persona, the adaptable identity platform that helps businesses fight fraud. Meet compliance requirements and build trust.

4:14 While you're listening to this right now, how do you know that you're really listening to me, Lenny? These days, it's easier than ever for fraudsters to steal PII, faces, and identities. That's where Persona comes in. Persona helps leading companies like LinkedIn, Etsy, and Twilio securely verify individuals and businesses across the world. What sets Persona apart is its configurability.

4:38 Every company has different needs depending on its industry, use cases, risk tolerance, and user demographics. That's why Persona offers flexible building blocks that allow you to build tailored collection and verification flows that maximize conversion while minimizing risk. Plus, persona's orchestration tools automate your identity process. so that you can fight rapidly shifting fraud and meet new waves of regulation. Whether you're a startup or an enterprise business, Persona has a plan for you.

5:06 Learn more at with Persona dot com slash Lenny Again, that's width. P E R S O N A. dot com slash Lemmy.

5:16 Kevin, thank you so much for being here and welcome to the podcast. Thank you so much for having me. We've been talking about doing this forever and we made it happen. We did it. I can't imagine how insane your life is. So I really appreciate you that you made time for this. And we're actually recording this uh the week that you guys launched your new image model, which is a happy coincidence.

5:36 Uh my entire social feed is filled of with Giblifications of everyone's life and family photos and everything. So good job. Yep, mine too. My wife, uh Elizabeth sent me one of hers, so I'm I'm right there with you. Uh let me just ask, did you guys expect this kind of reaction? It feels like this is the most viral thing that's happened in AI, which is a high bar uh since I don't know, Jet GPT launched, just like did you guys expect it to go this well? Uh what does it feel like internally? You know, there have been a handful of times in my career when you're working on a project or a product internally.

6:09 And the internal usage just explodes. Uh this was true, by the way, when we were building stories at Instagram. More than anything else in my career, we could feel it was gonna work. Because we were all using it internally and we'd go away for a weekend, you know, before it launched. We were all using it and we come back after a weekend and we would

6:26 Know what was going on and be like, Oh hey. I saw you were at that camping trip. That w how was that? You were like, Man, this thing really works. Image Gen was definitely one of those. Uh, so we've been playing with it for I don't know, couple months and um

6:41 Uh it it when it first went live internally to the company There was kind of a a uh A little gallery where you could Generate your own. You could also see what everyone else was generating. And it was just like nonstop buzz.

6:54 So yeah, we had a sense that this was gonna be a lot of fun for people to play with. That's a really cool like that should be a measure of just like Uh confidence in the something going well that you're launching is internally everyone's going crazy for it. Yeah, especially social things because you have a very tight network as a company. Socially. So you know each other and your experts in your product, hopefully.

7:16 And so there's some sense in which if you're doing something social and it's not taking off internally You you might you might question what you're doing. Yeah. Uh, and by the way, the Ghibli thing, is that something you exceeded where how did that even start? Was that like an intentional example? I think it's just a style people love and model is is really capable at

7:34 Uh emulating style or understanding what you know, it's very good at instruction following. That's actually something that I think people I'm starting to see people discover with it. But you can do very complex things. You can give it two images, you know, one is your living room.

7:47 And the other is a whole bunch of photos or memorabilia or things you want and you say like Tell me how you would arrange these things. Or you can say, I'd like you to show me what this will look like if you put this over here and this thing to the right of that and this one to the left of this, but under that one. And the model actually will understand all of that and do it. It's incredibly powerful.

8:07 So I'm I'm I'm as excited about all the different things people are gonna figure out. Yeah. All right. Well good job. Good job team OpenAI. Uh let's get serious here and let's kinda zoom out a little bit. The way I see it is you're chief product officer of maybe the most important company in the world right now.

8:22 Uh, just not to set the bar too high, but you guys are ushering in. AI A GI at some point, superintelligence at some point, no big deal. Uh I've had I have more questions for you than I've had for any other guest actually put out a call out on Twitter and LinkedIn and my community just like what would you want to ask Kevin and

8:39 Uh three hundred over three hundred well formed questions. And we're gonna go through every single one. So let's just get started. I'm just joking. Cool. I picked out the best and there's a lot of stuff I'm really curious about. Well it's it's one PM here. It doesn't get dark for a while, so let's do it. Okay, there we go. Okay, so first of all, I'm just gonna take notes here. Uh when is AGI launching? When is the sign? I mean, we just launched a good image gen model. Does that count? Yeah.

9:01 It's it. It's uh it's getting there. It's getting there. There's this um there's this quote I love, which is AI is whatever hasn't been done yet. Because Once it's been done, when it kinda works.

9:14 Then you call it machine learning. And once it's kinda ubiquitous and it's everywhere, then it's just an algorithm. Um so I I've always loved that that we call things AI when they still don't quite work and then, you know, by the time it's like An AI algorithm that's recommending you follow, you know, oh that's just an algorithm. But this new thing, like self driving cars,

9:35 Uh I think to some degree we're always gonna be there. And the next thing is always gonna be AI. And the current thing that we, you know, use every day and is just a part of our lives. That's an algorithm. It's so interesting'cause yeah, like uh in in the Bay Area you see self driving cars driving around, and it's so normal now. When like th four years ago and I know three years ago you would have thought you would have seen this and you'd be like, Holy shit, what is

9:58 Wh how we're in the future and now we're just so take it for granted. It's I mean there's something like that with everything. If I showed you When GPT three launched Right, I wasn't at open AI then. I was just uh I was just a user. But

10:11 It was mind blowing. And if I gave you GPT three now, I just plug that into chat GPT for you and you started using it, you'd be like What is this thing? It's like mess. Uh slop. Slop.

10:23 There's I had the same experience when I when I first Got into a Waymo. Right, your your very first ride, at least my very first ride. My first like

10:33 Ten seconds in a Waymo. It starts driving and you're like, Oh my God, watch out for that bike. You're you're holding on to whatever you can. And then like Five minutes in, you've calmed down.

10:43 And you realize that you're getting driven around the city without a driver. And it's working. You're just like, Oh my God, I am living in the future right now. And then like another ten minutes, you're bored, you're doing email on your phone, answering Slack messages, and you know, suddenly this miracle of human invention.

11:01 It's just a expected part of your life from then on. And I there is really something in the way that we all are adapting to AI that's kinda like that. These miraculous things happen and computers can do something they've never been able to do before. And it blows our mind collectively for like a week. And then we're like Oh yeah.

11:19 Like Oh yeah, n now it's just machine learning on its way to being an algorithm. The craziest thing about what you just shared actually is like I don't know chat GPT, which is like now feels terrible, uh three point five. was like a couple years ago and Uh

11:32 Imagine what life will be like in a couple years from now. We're gonna get to that, where things are going, what you think is gonna be the next big leap. But I wanna Start with the beginning. of your journey at OpenAI.

11:43 Uh so you Worked at Twitter, you worked at Facebook, you worked at Planet, Instagram. Uh At some point you got recruited to go and come work at OpenAI. I'm curious just what that story was like of the recruiting process of joining OP OpenAI as CPO. Is there any

11:58 Are there any fun stories there? Uh, if I'm running remembering the timeline right. We communicated uh Planet, I was leaving. And I was planning to just go take some time. You know, like I wasn't gonna stop working, but um But I was also happy to take the summer. This is like maybe April or something. It was like cool, I'm gonna have the summer with my kids, we're gonna, you know, go to Tahoe or something and I'll actually get to hang out rather than

12:21 What I usually do going up and down and all that. I and and then you know, Sam and I had known each other lightly for a bunch of years and He's He's always involved in so many interesting things, you know, like companies building fusion and and all these things. So

12:37 He'd always been somebody that I would like call occasionally if I was starting to think about my next thing. Um, because I like working on big like tech forward sort of, you know. Next next wave kind of things. And um

12:51 And so Uh I called him. I think Vinode also helped uh put us in touch again and And this time it wasn't like oh you should go talk to like these guys working on fusion. Yeah.

13:01 He said, Actually Yeah, the we're thinking about something, you should come talk to us. I was like, Okay, that sounds amazing, let's do it. And it goes really fast. Really, really fast. Like I met

13:13 Uh, you know, most of the management team in a brief period of time, a few days. And they were telling me, look, we're gonna we're basically gonna move as fast as we as we want to move and uh It kind of

13:26 I if every if you talk to everyone, everyone likes you, we're ready to go. Uh Sam came over for dinner. Uh and we had we had a great Evening together just

13:37 Like Talking about open AI in the future and getting to know each other better. And at the end I was like I and I I I was gonna go in the next day for like a bigger round of interviews. And uh um

13:49 Sam was saying, you know, hey, it's going really well, we're really excited. And I said, Cool, so how do I think about tomorrow? And he said, Oh, you'll be fine, don't worry about it. And if it goes well, like we're basically there. And so I go in the next day. Meet a bunch of people. Have a great time. Like I really enjoyed everybody I met with.

14:05 In any interview. you can always second guess yourself, you know, like Oh, I shouldn't have said that thing or I uh that thing I gave a bad answer on. I wish I could redo But I I I came away feeling like I think that went pretty well. And

14:20 Ex I was expecting to hear like that weekend basically,'cause they'd sort of set expectations as soon as You know, if this goes well, we're ready to go. And Uh I didn't hear anything.

14:33 And then it was like Monday, Tuesday, Wednesday. I still didn't hear anything. And uh I reached out to uh to folks on the open AI side a couple of times, still nothing. And I was like, Oh my God. I screwed it up. Like I don't know where I screwed it up, but I totally screwed it up.

14:52 I can't believe it. And I was going back to Elizabeth, my wife, and being like, What did I do? Like where where do you think I You know, getting all crazy about it and um And then It's still nothing. And finally it was like It was like nine days later.

15:06 They finally got back to me and it turned out, you know, there was like a bunch of stuff happening internally and this, that, and the other thing and Uh you know, there's just a million things happening at and they finally were like, Oh yeah, that went well, let's do this. And I was like, Oh, okay, cool, let's do it. Uh

15:21 But uh it was like nine days of agony. And they were just super busy on some internal stuff, and uh there I was like fretting every single day and re re going over every line of our interview process. It makes me think about when you're like dating someone and you've texted them and then they're just you're not hearing anything back and all like you assume something is wrong. Yeah, totally. They might just be busy. Uh I I give them a hard time about it still. Uh that's wild. Uh I love it. I love that it worked out.

15:49 Uh And I guess I guess the lesson there is don't don't jump to conclusions. Yeah. Have a little bit of chill. Speaking of that, I wanna chat about what it's just like to be inside the center of the storm. Again, you worked at uh a lot of let's say traditional companies, even though they're not that traditional.

16:09 Twitter and Instagram and Facebook and Planet. And now you work at Open AI? I'm curious what is most different about how things work in your day to day uh life at OpenAI. I think it's probably the pace. Uh maybe it's two things.

16:22 One is it's the pace. The second is, you know, everywhere I've ever worked before this You kinda know what technology you're building on. So you spend your time thinking about what what problems are you solving, who are you building for. You know, how are you gonna make their lives better? How are you gonna Is this a big enough problem that

16:41 You're gonna be able to to change habits. You know, do people care about this problem being solved? All those like good product things. But the stuff that you're building on Is like kinda fixed, you know? You're talking about databases and things and I bet The database you use this year is probably five percent better than the database you used two years ago.

17:00 But that's not true at all. AI. It's like every two months computers can do something they've never been able to do before, and you need to completely think differently about what you're doing. Th there's like something fundamentally Interesting about that. Makes life fun here. There's also something uh w you know, we'll maybe like talk about evals later, but

17:20 But It also really in this world of um Yeah, a everything we're used to with computers. is about giving a computer very defined inputs. You know, if you look at Instagram, for example, there are buttons that do specific things and you know what they do.

17:36 And then when you give a computer defined inputs, you get very defined outputs. You're confident that if you do the same thing three times, you're gonna get the same output three times. LLMs are completely different than that, right? They're good at fuzzy, subtle inputs, then all the nuances of human language and communication they're pretty good at. And also they don't really give you the same answer. You you probably get spiritually the same answer for the same question, but it's certainly not the same set of words every time. And so you're much more it's

18:04 Fuzzier inputs and fuzzier outputs. And It. When you're building products It really matters.

18:11 W whether you know, y y there's some use case that you're trying to build around If The model gets it right sixty percent of the time. You build a very different product than if the model gets it right. Ninety five percent of the time versus if the model gets it right, ninety nine point five percent of the time.

18:29 And so there's also something that you have to get really into the weeds on your use case and the evals and things like that. In order to understand the right kind of product to build. So That is just fundamentally different. You know, if your database works once, it works every time. And that's not true in this world. Let's actually follow this thread on evals. I definitely wanted to talk about this.

18:49 So we had this uh legendary panel. Uh at the Lenny Friends summit, it was you and Mike Krieger. And Sir Guo uh That was moderating. So fun. And uh

18:59 The thing that I heard that kind of stuck with people from that panel was a comment you made. Where you said that writing evals is gonna become a core skill for product managers. Yeah. And I feel like that probably applies further than just product managers. A lot of people know what e valves are. A lot of people d have no idea what I'm talking about.

19:15 So could you just briefly explain what is an eval? And then just why do you think this is gonna be so important for people building products in the future? Yeah, sure. I I think the easiest way to think about it is almost like a a quiz for a model, a test to to gauge how much it How it knows a certain set of subject material or how w how good it is at responding to a certain set of questions.

19:36 So in the same way you You know, you take a calculus class and then you have calculus tests that see if you're You've learned what you're supposed to learn, you have evals that test How good is the model at at creative writing?

19:48 How good is the model at uh that you know, graduate level science. How good is the model at Competitive coding. Uh and so you have these set of V vals that basically, you know, perform as benchmarks for how smart or

20:02 Capable the model is. Is like a simple way to think about it like unit tests. For Yeah, unit tests, tests in general for models. Totally. Great, great. Okay. And then

20:12 Uh, why is this so important for people that don't totally understand what the hell's going on here with evals? Why is this so so key to building AI products? Uh well it gets back to what I was saying. You need to know whether your model is going to there are certain things that models will get right Ninety nine point nine five percent of the time and you can just be confident. There are things that they're gonna be ninety five percent right on and things they're gonna be sixty percent right on. If the model's sixty percent right on something, you're gonna need to build your product totally differently.

20:39 And by the way, these things aren't static either. So a a big part of evals is If you know Yeah you're you're building for some use case. So let's say let's take our deep research product, which is one of my favorite things that we've released. Maybe ever.

20:54 Um Right. The idea is With deep research for people who haven't used it. You can give Chat GPT now.

21:01 A An arbitrarily complex query like it it's not about returning you an answer from you know a search query Which we can also do. It's it's Here's the thing that if you were gonna answer it yourself.

21:13 You'd go off and do, you know, two hours of reading on the web. And then you might need to read some papers, and then you would come back and start writing up your thoughts and realize you had some gaps in your thinking, so you go out and do more research. Yeah, you might it might take you a week. Answer to this question.

21:30 You can let Chat GPT Just like chug for you for twenty five, thirty minutes. You know, it's not the immediate answers you're used to. But it might go work for twenty five, thirty minutes and do work that would have taken you a week. So as we were building that product

21:44 We were designing Evals Uh. Uh it sort of it at the same time as we were thinking about how this product was gonna work and we were trying to go through Like hero use cases. Yeah, h here's a question you want to be able to ask. Here's an amazing

22:00 Answer for that question. And And then turning those into evals. And And then hill climbing on those evaluation. So it's not just that the model is static and we hope it does okay on a certain set of things.

22:12 You can teach the model, you can make this a continuous learning process. And so as we were fine tuning our model for deep research to to be able to answer these things. We were able to test is it getting better on these evals that we said were important measures of how the product was working. And it's when you start seeing that and you start seeing performance on evals going up, you start saying, Okay, I think we have a product here.

22:35 You made a kind of a comment along these same lines around e balls that uh that AI is almost like capped in how amazing it can be by That how good we are at E bells. Does that resonate? Any more thoughts along those lines? These I mean these models are are

22:50 their intelligence is and intelligence is so Fundamentally multi dimensional. So you can talk about a model being amazing at competitive coding. Which may not be the same as that model being great at front end coding or back end coding or

23:06 taking a whole bunch of code that's written in COBOL and turning it into Python, you know? Like and that's just within the software engineering world. And So the I I think there's a sense in which you can think of these models as

23:18 Incredibly smart. Very like factually aware. Uh Intelligence is But Still most of the world's data, knowledge

23:29 process is Is not public. It's behind the walls of companies or governments or other things and Same way, if you were gonna join a company You would spend your first two weeks onboarding. You'd be learning the company specific processes. You'd get access to company specific data.

23:45 Yeah. You can teach these m the models are smart enough, you can teach them anything. But They need to have the the sort of the raw data. Uh to

23:56 To learn from. And so there's a there's a sense in which Um Yeah, I think the future is really gonna be Incredibly smart.

24:05 Broad base models. that are fine tuned and and and um Tailored with company specific or use case specific data. So that they

24:16 perform really well on company specific or use case specific things. Um and You're gonna measure that with custom evals. And so you know what what I what I was referring to is just like these models are really smart. You need to still teach them things

24:31 If the data's not in their training set and there's a huge amount of use cases that are not gonna be in their training set. Because they're relevant to one industry or one company. I'm just gonna keep following the thread that you're leading us down and but I'm gonna come back'cause I want more questions around some of these things. So you c you came to a uh a space that I think a lot of AI founders are thinking about is just where is open AI not gonna come squash me in the future.

24:53 Or one of the other foundational models. And so it's unclear to a lot of people just like should I build? A start up in the space or not? Is there any advice you have or any guidance for where you think open AI Where just foundation models in general likely won't go and where you have an opportunity to build a company.

25:09 Well, I one of my so th this is something that Ev Williams used to say um back at Twitter that's always stuck with me, which is No matter No matter how big your company gets, no matter how like incredible the people are. There are way more smart people outside your walls than there are inside your walls. And

25:27 That's why we are so focused on building a great API. We have three million developers using our API. Uh No matter How ambitious we are, how big we grow.

25:38 By the way, we don't want to grow super big. There there are so many use cases, places in the world where AI can fundamentally make our lives better. We're not gonna have the people, we're not gonna have the the you know the the know how to build most of these things.

25:54 And I think like I was saying, the data is is industry specific, use case specific, you know, behind certain company walls, things like that. And there are immense opportunities in every industry and every vertical in the world to go build AI based products that improve upon the the state of the art. And there's just no way we could ever do that ourselves. We don't want to. We couldn't if we did want to. And we're really excited to power that.

26:20 For three million plus developers and way more in the future. Coming back to your Earlier point about the the the tech changing constantly and getting faster and not exactly knowing what you'll have.

26:32 by the time you launch something, in terms of the power of the uh the model. Uh I was I'm I'm curious what Allows you to ship. quickly and consistently in such great stuff.

26:41 And it sounds like one answer is bottoms up empowered teams. versus a very top down roadmap that's, you know, planned out for a quarter. What what are some of those things that allow you to ship such great stuff so often? So quickly. Yeah, I mean we try and we try and have

26:56 uh a a a sense of where we're trying to go, you know, point ourselves in a direction so that we have Some rough sense of alignment. Um Like thematically. Uh

27:08 I don't for a second and we do quarterly roadmaping, you know, we we laid out sort of a year long strategy. I don't for a second believe that what we write down in these documents is what we're gonna actually ship, you know, three months from now, let alone six or nine. But that's okay. There's a um I think it's like an Eisenhower quote. Plans are useless, planning is helpful. Uh, which I totally subscribe to, especially in this world.

27:30 It's really valuable if you think about quarterly roadmapping, for example. really valuable to have a moment where you stop and go Okay. What did we do? What worked, what went well, what didn't go well, what did we learn?

27:42 And now what do we think we're gonna do next? And by the way, everybody has some dependencies. You you know, you need the infrastructure team to do the following things. partnership with research here. And so you want to have a second to kinda check your dependencies, make sure you're good to go, and then start executing. We try and keep that really lightweight.

27:59 Because It's not gonna be right. You know, it we're gonna throw it out halfway because we will have learned new things. So the moment of planning is helpful, even if you're only gonna you know, it's only partially right. So that's p I think be just expecting that you're gonna be super agile and that there's no sense writing a three month roadmap, let alone a year long roadmap because the technology's changing underneath you so quickly.

28:23 We really do try and go Like very strongly. Bottoms up. Kind of subject to our overall directional alignment. We have great people.

28:33 Um we have engineers and PMs and designers and researchers who are passionate about the products they're building. And have strong opinions about them. Uh and are also the ones building them. And so they're they have a They have a real sense of what the capabilities are too. Which is super important.

28:49 And so I think you wanna be more bottoms up in in this way. And so we operate that way. We are happy making mistakes. We make mistakes all the time. It's one of the things I really appreciate about Sam. He pushes us really hard to move fast. But He also understands that with moving fast comes

29:06 uh we didn't quite get this right, or, you know, that we launched this thing, it didn't work, we'll roll it back. You know, look at our naming. Our naming is horrible. There was a lot of questions people had for you. Yeah. Um and we'll we'll get around to fixing it at some point, but it's not the most important thing and so we don't spend a lot of time on it.

29:27 But it also shows you how it doesn't matter. Uh again, Chat GPT the most popular Fastest growing product in history. uh models are it's the number one AI API and model, so clearly it doesn't matter that much. And we name things like O three mini high.

29:45 Oh man, I love it. Um okay so you talked about roadmaping. I'm And bottoms up. And I'm really curious how you Is there like a a cadence or ritual of Aligning with you or Sam or he or

29:58 You review everything that's going out, like is there a meeting every week or every month where you guys see what's happening? On key projects, so we do product reviews and things like that, like you would expect. Um There isn't a ritual because there isn't uh we we I I would never want us to be blocked on

30:14 Launching something. You know, waiting for a review with me or Sam if we can't get there. I'm traveling or Sam's, you know, busy or whatever, that's a bad reason for us not to ship. So Yeah, obviously for the biggest most high priority stuff we have a pretty close beat on it, but

30:31 We really try not to, frankly. Um like we want to empower teams to move quickly. And Uh I I think it's more important to ship And iterate.

30:42 So we have this philosophy that we call iterative deployment. And the idea is like we're all learning about these models. together. So there's a real sense in which it's way better to like Ship something. Even when you don't know the full set of capabilities.

30:57 And iterate together, like in public. And we we kind of co evolve together with the rest of society as we learn about these things and where they're different and where they're good and bad and weird. I really like that philosophy. Um There's also a bit of I I think the other thing that that

31:14 Like ends up being a part of our our product philosophy is Uh the sense of like model maximalism. The models are not perfect. They're gonna make mistakes.

31:25 You could spend A lot of time building all kinds of different scaffolding around them. And by the way, sometimes we do because sometimes there are things, you know, kinds of errors that you just don't want to make. But We don't spend that much time building scaffolding around the parts that don't match that.

31:43 Because our general mindset is in two months there's gonna be a better model and it's gonna blow away whatever, you know, the current set of limitations are. And so if if you're building and we say this to developers too. If you're building And and the product that you're building is kinda right on the edge of the capabilities of the models.

32:02 Keep going,'cause you're doing something right. Because y you give it another couple of months and the models are gonna be great. And suddenly the the product that you have that just barely worked is really gonna sing. And uh you know, that's that's kinda how you make sure that you're really pushing the envelope and building new things. I had uh the founder of Bolt on the podcast, uh Stack Blitz is the company name, and he

32:23 He shared the story that they've been working on this product for seven years behind the scenes and they was failing. N nothing was happening. And then all of a sudden, uh I was sorry to mention a competitor, but Claude uh Came out or a Sonnet three point five came out. And all of a sudden everything worked. And they've been building all this time and finally it worked.

32:40 And I hear that a lot with Y C just like things are But never were possible now are just becoming possible every few months with the updates to the models. Yeah. Absolutely. Let me actually ask this. I wasn't planning to ask this, but I'm curious if you have any quick thoughts. Just why why is uh Sonnet so good at coding and kind of thoughts on

32:57 Uh Your stuff getting as good and better at actual coding. Yeah. I I mean kudos to Anthropic. They've built very good coding models, uh, no doubt. We uh

33:08 We we think that we can do the same. Um maybe by the time this uh podcast is shipped we'll we'll have more to say. But Either way, uh

33:18 All credit to them, I think. Uh this intel intelligence is really multi dimensional and so I think there's The the the model providers I it used to be that OpenAI had this like massive model lead, you know, twelve months or something ahead of everybody else. That's not true anymore.

33:35 Yeah, I like to think we still have a lead. I'd argue that we do. But it's certainly not a massive one. And that means that there are gonna be different places where, you know, the Google models are really good. Or where anthropics models are really good. Or where we're really good and the and our competitors are like, ah, we gotta get better at that.

33:51 And it actually is easier to Get better at a certain thing once someone's proved it possible. than it is to, you know, forge a path through the The jungle. In doing something brand new.

34:03 So I just think yeah As an example, it was like nobody nobody could break four minutes in the mile. And then finally somebody did, and the next year twelve more people did it. I I think there's that all over the place. And it just means that competition is really intense.

34:19 And consumers are gonna win and developers are gonna win and businesses are gonna win in a big way from that. It's part of why the industry moves so fast, but um You know, all respect to to the other big model providers. Models are getting really good. We're gonna move as fast as we can and I think we've got some good stuff coming. Exciting. Uh

34:37 This makes me also think about Uh. in many ways other models are better at certain things, but somehow Chad GPT is like the Like if you look at all the awareness numbers and usage numbers, it's like No matter where you guys are in the rankings, people seem to just like

34:52 Think of AI and Chat GPT almost as As the same. What do you think you did, right? To Kinda win in consumer mindset, at least at this point in awareness in the world. I think being first helps, which is one of the reasons why we're so focused on moving quickly. Um

35:06 Yeah, we like being the first to launch new capabilities, things like deep research. Uh We've also our models are very they can do a lot of things, right? So they can They can take real time video input. They can you have speech to speech. You can do speech to text and text to speech.

35:23 Um, they can do deep research, they can operate on a canvas, they can write code. And so Chat GPT can kinda be this one stop shop where All the things that you want to do are possible. Um And as we as we go forward in it, you know, uh we have more agentic tools like Operator where it's

35:40 browsing for you and doing things for you on the web. Yeah, more and more you're gonna be able to come To this one place to chat GPT. Give it instructions and have it accomplish real things for you in the world. There's just like something fundamentally valuable in that. And so

35:55 Yeah, we we think a lot about that. We think And it it we we move We try to move really fast so that we are always the most useful place for people to come to. What would you say is uh The most counterintuitive thing that you've learned.

36:08 After building. AI products are working at open AI. Something was just like I did not expect that. I don't know, maybe I should have expected this, but one of the things that's been funny for me is um The extent to which you can kind of reason when you're trying to figure out how some product should work with AI. You can often

36:27 Or even why some AI thing happens to be true, you can often reason about it the way you would reason about another human. And it kinda works. Yeah, so uh maybe a couple examples. When we were first launching our um our reasoning model. Right. We were the first to to build a r a model that could reason, that could that could instead of giving you just a quick, you know, system one answer right away to every question you asked.

36:53 It was the third emperor of the The Holy Roman Empire like You know, here's an answer. You could ask it hard questions and it would reason the same way that if I asked you to do a crossword puzzle. You couldn't just like snap fill in everything. You would be well, okay, I'm

37:08 This one across, I think it could be one of these two, but that means there's an A here, so that one has to be this, a way, you know. Like back track, kinda step by step build up from where you are. Same way you answer any Any difficult uh logistical problem, any scientific problem. So

37:25 This reasoning breakthrough was big. But it was also the first time that a model needed to sit and think. And that's a weird paradigm for a consumer product. You don't normally have something where You might need to hang out for twenty five seconds after you ask a question. And And so we were trying to figure out we know, what's the UI for this?'Cause

37:43 It's also not like with deep research where the model's gonna go and think for twenty five minutes sometimes. It's actually not that hard. Because You're not gonna sit and watch it for twenty five minutes. You're gonna go do something else. You're gonna go to another tab or go get lunch or whatever. Uh and then you'll come back and it's done.

37:58 When it's like twenty, twenty five seconds or ten seconds. It's A long experien it's a long time to wait. But it's not long enough to go do something else. So you actually need so and

38:09 Yeah, so you could you can think, like w if you asked me something that I needed to think for twenty seconds to answer What would I do? I I wouldn't just like Go mute. And not say anything and kind of um you know shut down for twenty seconds and then come back.

38:25 So we shouldn't do that. We shouldn't just like have a slider sitting there. That's annoying. But I also wouldn't just start like babbling every single thought that I had. Um, so we probably shouldn't just like expose the whole chain of thought as the model's thinking. But you know, I might go like Huh, that's a good question. All right, I might approach it like that. And then think and you know, you're sort of like maybe giving little updates.

38:46 And that's actually in what we ended up shipping. Yeah similar things where you can like you can find situations where Um you get better thinking sometimes out of a group of models. Uh that all try and attack the same problem and then you have a model that's looking at all their outputs.

39:03 And integrating it and then giving you a single answer at the end. I mean sounds a little bit like brainstorming. Right. I I certainly have better ideas when I get in a room and brainstorm with other people'cause they think differently than me and So anyways, there's just like all these situations where

39:18 You can actually kind of reason about it like a group of humans or an individual human and it sort of works. Which I don't know. Maybe maybe I shouldn't have been surprised, but I was. That is so interesting because when I see these models operate. I like I never even thought about you guys designing that.

39:34 uh experience. Like it to me just feels like this is what the L M does. It just sits there and tells me what it's thinking. And I love this point you're making of like We uh like let's make it feel like a human operating And well, how does human operate? Well, they just talk out loud, they think, here's the thing I should explore. And I love that deep sequent, like to the extreme of that, right? Where they're just like, Here's everything I'm doing and thinking in a

39:55 And people actually like that too. I guess was that was that surprising to you? Like, oh maybe that could work too. People seem to like Everything. Yeah, we learned from that actually. Um Because we um When we first launched it, we kinda gave you like the the subheadings of what the model was thinking about, but not much more.

40:12 And then Deep Seek launched and there were it was a lot. And we kinda went, you know, I don't know if everyone wants like that. There's some novelty effect to seeing what the model's really thinking about. We felt that too when we were looking at it internally. Interesting to see the model's chain of thought. But it's not I I you know, I think at the scale of like four hundred million people, you don't want to see

40:30 The model kind of like babble a bunch of things. Um And so what we ended up doing was summarizing it in interesting ways. So instead of just getting the subheadings, you're kind of getting like one or two sentences about How it's thinking about it, and you can learn from that. So we kinda tried to find a middle ground that that we thought was an experience that would be meaningful for most people.

40:51 But you know showing everybody like three paragraphs, uh, is probably not the right answer. This reminds me of something else you said at the summit that has really stuck with me, this idea that chat people always make fun of like chat is not like the future interface for how we interact with AI. But you made this really interesting point, um May argue the other side.

41:09 Which is Like as humans, we interface by talking and the IQ of a human can span from really low to really high. And it all works because we're talking to them. And chat is the same thing and it can work on All kinds of intelligence levels. Uh maybe just share maybe I just shared it, but uh I guess anything there about just Why chat actually ends up being such an interesting interface for LMs.

41:29 Yeah, I don't know if uh maybe I'm uh maybe this is one of those things I believe that most people don't believe, but I actually think chat is an amazing interface. Because it's so versatile. Um people tend to go, Oh, chat, yeah, well that's just like you know, we'll figure out something better. And I kinda think I kinda think this is uh it it's it it's it's incredibly universal because it is the way we talk. Like I can talk to you

41:54 verbally like we're talking now, I can you know, we can see each other and interact. Uh we can talk on WhatsApp and you know, be texting each other. But All of these things is this sort of like unstructured

42:08 uh, you know, method of communication and that's how we operate. If I had to And if I had some more rigid interface that I was allowed to use when we spoke, I would be able to speak to you about, you know, far fewer things and it would actually get in the way of us. having like maximum communication bandwidth. So there's something magical. And and by the way, in the past it never worked because models there there wasn't a model that was good at understanding

42:32 all of the complexity and nuances of human speech, and that's the magic of LLMs. So to me it's like a an interface that's exactly fit to the power of these things. And that doesn't mean that it always has to be just like I don't necessarily always want to type. But

42:47 If you you do want that very open ended, flexible communication medium, it may be that we're speaking and the model's speaking back to me. But you still want that like that the very sort of lowest common denominator Um No restrictions way of of interacting.

43:04 That is so interesting. That's really changed the way I think about the stuff is that point that chat is just so good. For this very specific problem of talking to superintelligence, basically. By the way, I think there are like it's it's not that it's only chat either. Like there are i i if you have high volume use cases where

43:21 It they're more prescribed. And the y you don't actually need the full generality There are there are many use cases where it's better to have something that's less flexible, more prescribed, faster at a specific task. And those are great too. And you know, you can build all sorts of those and

43:38 Um But You still want chat as like this baseline for anything that falls out of whatever you know vertical you happen to be building for. It's like a catch all for like every possible thing you'd ever want to express to a model.

43:50 I'm excited to chat with Christina Gilbert, the founder of One Schema, one of our longtime podcast sponsors. Hi Christina. Yes, thank you for having me on, Lenny. What is the latest with one schema? I know you now work with some of my favorite companies like Ramp, Vanta, Scale, and Watershed. I heard that you just launched a new product to help product teams import CSVs from especially tricky systems like ERPs.

44:15 Yes, so we just launched one scheme of file feeds, which allows you to build an integration with any system in 15 minutes as long as you can export a CSV to an SFTP builder. We see our customers all the time getting stuck with hacks and workarounds, and the product teams that we work with don't have to turn down prospects because their systems are too hard to integrate with. We allow our customers to offer thousands of integrations without involving their engineering team at all. I can tell you that if my team had to build integrations like this, how nice would it be to be able to take this off my roadmap. and instead use something like one schema, and not just to build it, but also to maintain it forever. Absolutely, Lenny. We've heard so many horror stories of multi-day outages from even just a handful of bad records.

44:54 We have laser focus on integration reliability to help teams end all of those distractions that come up with integrations. We have a built-in validation layer that stops any bad data from entering your system, and OneStream will notify your team immediately of any data that looks incorrect. I know that importing incorrect data can cause all kinds of pain for your customers and quickly their trust. Christina, thank you for joining us. And if you want to learn more, head on over to oneschema.co. That's oneschema.co. I wanna come back to the you talked about researchers.

45:26 And Their relationship with product teams. Uh, I imagine a lot of innovation comes from researchers just like I having an inkling and then Building something amazing and then releasing it. And some ideas come from PMs and engineers.

45:39 How does how do those teams collaborate? Like does every team have a PM? Is it a lot of research led stuff? Just like what Give us a sense of just where ideas and products come from mostly. It's an area where we're evolving a lot. I'm really excited about it, frankly. I I think if you go back you know, couple of years when ChatGBT was just getting started.

45:59 Uh obviously I wasn't an open AI, so um But Uh Mm. It we were more we were more of a pure research company at the time.

46:09 Yeah, ChatGPT, if you remember, was a low key research preview. Um. Yeah. It it wasn't a thing that the team launched thinking it was gonna be this massive product. Oh, Chat GPT, yeah.

46:21 And it i it was just uh a way that we were gonna let people, you know, play with and iterate on the models. Um And so we were we were primarily a research company, a world class research company. And As chat GPT has grown and as we've built

46:37 our B to B products and our APIs and other things. It now we're more of a product company than we were. I still think we can't we're Yeah, open AI should never be a pure product company. We need to be both a world class research company and a world class product company. And the two need to really work together. And that's the thing that's um that I think we've been getting much better at over the last like

46:58 Six months? If you I if you treat those things separately. And you know, the researchers go do amazing things and build models. And then they get to some state and then the product and engineering teams go take them and do something with them.

47:13 We're effectively just an API consumer of our own models. The best products though are gonna be is like I was talking about with deep research, it's a lot of iterative feedback. It's understanding the products you're trying to solve or the the problems you're trying to solve. Building evals for them. using those evals to go gather data and fine tune models to get them to be better at the these use cases that you're looking to solve. It's a huge amount of back and forth.

47:38 uh to do it well. And I think the best products are gonna be inch product design and research. working together as a single team. To to build novel things. So that's that's actually how we're trying to operate with basically anything that we build. It's a new muscle for us because we're kind of new as a product company.

47:56 But um But it it's one that's not a people are really excited about because we've seen every time we do it. We build something awesome. And so, you know, now every product starts like that.

48:07 How many product managers do you have at OpenAI? I don't know if you share that number, but You do. Not that many, actually. I don't know. Twenty five?

48:16 Um Maybe it's a little more than that, but Uh. My personal belief is that you want to be pretty PM light as an organization, just in general. I say this with love because I am a PM, but

48:27 Too many PMs. Causes problems. You know, we'll like fill the world with Decks and ideas versus execution. So I think that the the

48:37 I I think it's a good thing when you have A PM that has uh that that is working with maybe slightly too many engineers because it means they're they're not gonna get in and micromanage. You're gonna leave a lot of of you know, influence and responsibility with the engineers to make decisions.

48:54 It means you want to have really product focused engineers, which we're fortunate to have. We have an amazingly product focused, like high agency engineering team. But when you have something like that, you have a team that feels super empowered. You have a uh PM that's You know, trying to really understand the problems and kinda gently guide the team a little bit.

49:12 But has too much going on to get w too far into the details. And you end up being able to move really fast. So that's kind of the philosophy we take. Uh we want

49:23 We want product E Enge leads and and product E engineers all the way through. Um We want not too many PMs, but really awesome high quality ones. Um And so far that seems to be working pretty well. I imagine being a PM at open AI is like a dream come true for a lot of people.

49:40 Uh At the same time, I imagine it's not a fit for a lot of people. There's researchers involved. Very product minded engineers. What do you what do you look for in the PMs that you hire there? For folks that are like, maybe I probably I shouldn't go work there, I shouldn't even think about that.

49:54 I think uh I I've said this a few times, but like high agency is something that we really look for. People that are not gonna come in and kinda wait for everyone else to allow them to do something. They're just gonna See a problem and go do it. Um That's i i it's just a core part of how we work. I think people that

50:13 that are happy with ambiguity because there is a massive amount of ambiguity here. is not the kind of place and and we have we have trouble sometimes with um with more junior PMs because of this because It's just not the place where s someone is gonna come in and say, Okay You know, here's here's the landscape. Here is your area. I want you to go do this thing.

50:32 And that's that's what you want as a as an early career PM. We just I mean, no one here has time. And the nobody the the problems are too ill formed and we're figuring them all out as we go. And so um high agency, very comfortable with ambiguity. ready to come in and help execute and move really quickly.

50:53 That that's kind of our our recipe. And I think also Happy leading through influence. Because I mean it's usual as a PM people don't report to you. Uh your team doesn't report to you, et cetera. But you also have the the

51:08 The complexity of a research function, which is even more sort of self directed And it's really important to build a good rapport with the research team. Uh, and so it you know, that I I think the EQ side of things is also super important for us. I know at most companies a PM comes in and they're just like, Why do we need you?

51:28 And as a PM you have to uh earn trust and How people see the value and I feel like at Open AI it's probably a very Extreme version of that where they're like, Why do we need this person? Wave researchers, engineers, what are you gonna do here? Yeah, I think people appreciate it done right. Um, but you got you bring people along. I I think one of the most important things a PM can do well is be decisive.

51:47 So It's It's There's a real fine line. You don't wanna be making it I mean it's kind of like I I don't love the

51:56 PM is the CEO of the product uh illusion all the time, but But just like Sam in his role. would be making mistakes if he made every single decision in every meeting that he was in. And he would also be making mistakes if he made no decisions in any meetings that he was in, right? It's a it's the it's understanding when

52:17 To defer to your team and to like Let Let people You innovate? And when there is like a decision to be made that people either don't feel comfortable with or don't feel empowered to make or

52:30 A decision that that You know, has too many different like disparate Pros and cons that are spread out across a big group and someone needs to be decisive and make a call. It's a really important trait of a CEO. It's something Sam does well. And it's it's also a really important trait of a PM kind of at a at a more microscopic level.

52:47 And so y because there's so much ambiguity, it's not obvious what the answer is in a lot of cases. And so having a PM They can come in and like And by the way, this doesn't need to be a PM. I'm perfectly happy if it's anybody else, but I kinda look to the PM to say like If there's ambiguity and no one's making a call. You better make sure that we get

53:04 A call made and we move forward. This touches on uh a few posts I've done of just Where is AI gonna Take over work that we do versus help us with various work. So let me come at this question from a different different direction of just how AI impacts. Product teams and hiring things like that.

53:20 So first of all there's all this talk of Uh LM's doing our coding for us. And ninety percent of code is gonna be written by AI in a year. Dario and Anthropic said that. the same time, you guys are all hiring engineers like crazy, PMs like crazy, you know, every dysfunction is dead, but you're still hiring every single one.

53:36 Uh I guess just first of all, let me just ask this. How do you how do you and the team Like say engineers, PMs use AI in your work. Is there anything that's like really interesting or things that you think people

53:48 And how you use AI in your day to day work. We use it a lot. I mean, every one of us is in chat GPT all the time. It summarizing docs, using it to help write docs with GPTs that, you know, write product specs and things like that. All all the stuff that you would imagine. I I mean talk about writing evals like

54:06 y you can actually use models to help you write e balls and they're pretty good at it. That all said, I still don't I'm still sort of disappointed by by us and despite I really mean me. Um

54:19 Yeah If I were to g if if I were to just like teleport my five year old self leading product at some other company, into my day job, I would recognize it still. And I think We should be in a world certainly a year from now, probably even more now that

54:35 Um where I almost wouldn't recognize it because the workflows are so different and I'm using AI so heavily. And I still recognize it today. So I think in some sense I'm not doing a good enough job of that. Yeah, just to give an example, like Why shouldn't we be like vibe coding

54:51 Uh Demos right, left and center. Like instead of Showing stuff. In like Figma. We should be showing prototypes that people are vibe coding, you know, over the course of thirty minutes.

55:02 To illustrate proofs of concept and to explore ideas. That's totally possible today. And we're not doing it enough. Are actually Art.

55:11 Chief People Officer Julia. Was telling me the other day She vibe coded an internal tool that she had at a previous job that she really wanted to have here at OpenAI. And she opened, I don't know, windsurf or something and

55:25 Vib coded it. I How cool is that? And if our chief people officer is doing it We have no excuse to not be doing it more.

55:34 That's an awesome story. Okay, and some people may not have heard this term by coding. Can you describe what that means? Yeah, uh I think this was uh I think this was Andre's uh term Carpathi, yeah. Uh Andre Carpathi, yeah. Um where it's just so you have these tools like Cursor and Windsurf that

55:51 and get up co pilot that are very good at suggesting uh what code you might want to write. So you can give them a prompt and it'll write code and then As you go to edit it, it's suggesting what you might want to do. And The the the way that that Everyone started using that stuff was

56:06 Give it a prompt. Have it do stuff. You go edit it. Give it a prompt. You know, and you're kind of like really going back and forth with the model the whole time. As the models are getting better And as people are getting more used to it, you can kinda just like Uh

56:20 Let go of the wheel a little bit. And When the model's suggesting stuff, it's just like tap, tap, tap, tap, tap, like keep going, yes, yes, yes, yes, yes. And of course the model makes mistakes or it does something that doesn't compile, but when it doesn't compile, you paste the error in and you say go go go go go. And then you you test it out and it like

56:39 does one thing that you don't want it to do. So you enter in an instruction and say go go go go go. And you just kinda like Let the model do its thing. And it's not that you would do that for production code that needed to be super uh tight. Today yet.

56:54 But for so many things you're trying to get to a proof of concept, you're getting to a a demo. And you can really take your hands off the wheel and the model will do an amazing job and that's what That's that's vibe coding. That's an awesome explanation. I think like the pro version of that, which is I think the way Andre even described it, is you talk, you do like a There's a step

57:13 Like Whisper, Super Wisp, or something like that, where you're like talking to the model, not just not even typing. Yeah, totally. Oh man. So let me let me just ask, I guess When you look at product teams in the future, you talked about how you guys should be doing this more.

57:26 Instead of designs, having prototypes. What do you think might be the biggest changes in how product teams uh are structured or built, where do you think things are going in the next few years? I think you're definitely gonna live in a world where you have more um where you have researchers built into every product team. And I don't even mean just at

57:44 at like foundation model companies. Because I I think the future actually frankly one thing that I'm sort of surprised about or about our industry in general is that There's not a greater use of fine tuned bottles. Uh like a lot of people

58:00 You know, th these models are very good. So our API does a lot of things really well. But When you have particular use cases You can always make the the model performed better on a particular use case by fine tuning it.

58:12 Probably just a matter of time, you know, a lot folks aren't like quite comfortable yet with doing that in every case. But to me, there's no question that that's the future. Every Yeah, models are gonna be everywhere just like transistors are everywhere. AI is gonna be just a part of the fabric of everything we do, but I think there are gonna be a lot of fine tuned models because why would you not want uh to to more

58:33 specifically customize a model against a particular use case. And So I think you're gonna want sort of quasi researcher, uh, machine learning engineer types Uh as part of pretty much every team because Fine tuning a model is just gonna be part of the core workflow for building most products.

58:50 So that's that's one change that maybe you know, you're starting to see at foundation model companies that will propagate out to more teams over time. I'm curious if there's a concrete example that makes that real and I'll share one that comes to mind as you talk. Sure. When you look at Cursor and Windsurf, something I learned from those founders.

59:07 Is that they they use like a sonnet. But then they also have a bunch of custom models that help along the edges. That make the specific experience That's not just generating code even better, like autocomplete and looking ahead to where things are going. So maybe is that one or any other examples of what you're what what what is a fine tuned model that you're that you think teams will be building?

59:28 With these researchers on their teams. Yeah, I mean, so when you're fine tuning a model, one of the you're you're basically giving the model uh a bunch of of examples of the kinds of things you wanted to be better at. So it's it's Here's the problem, here's a good answer. Here's a problem, here's a good answer. Uh or here's a question, here's a good answer. You know, times a thousand or or ten thousand.

59:48 Uh, and suddenly you're you're teaching the model to be much better than than it was out of the gate at that particular thing. We use it. Everywhere internally. Um we also

1:00:00 We we use ensembles of models much more internally than people might think. Um So it's not Here is I I have ten different problems. I'll just ask, you know baseline GPT four O about a bunch of these things.

1:00:14 If we have ten different problems We might We might solve them using uh, you know, twenty different Model calls. Some of which are using specialized fine tuned models. They're using models of different sizes,'cause maybe you have different latency requirements or cost requirements at different

1:00:30 For different questions. They are probably using custom prompts for each one. Like basically you want the to teach the model to be really good at you want to break the problem down into more specific tasks. Versus some broader set of high level tasks. And then

1:00:46 You can use models very specifically to get very good at each individual thing. And then you know, you have an ensemble that sort of Tackles the whole thing. I think a lot of good companies are doing that today. I still see a lot of companies uh Yeah, kind of giving the model.

1:01:03 single generic broad problems. Versus breaking the problem down and I think There will be more breaking the problem down, using specific models for specific things. Including fine tuning. And so in your case, the'cause this is really interesting, is is that you're using different

1:01:19 uh levels of chat GPT like a one, oh three and Yeah, that's really. There'll be parts uh of our internal stack. So we do if you Give you an example. Uh

1:01:30 Customer support. With four hundred plus weekly uh four hundred plus million weekly active users, we get, you know, a lot of inbound tickets, right? I don't know how many customer support folks we have, but it's not very many. Thirty, forty, I'm not sure.

1:01:46 Way f way smaller than you would have at any comparable company. And it's because we've automated a lot of our flows. We've got, you know, most questions. Using our internal resources, knowledge base, you know, uh guidelines for how we answer questions, what kind of personality, et cetera. You can teach the model those things. And then have it do a lot of its answers automatically.

1:02:09 Or where it doesn't have Uh you know the full confidence to answer a particular question, it can still suggest an answer. Request a human to look at it, and then that human's answer actually is It's own sort of fine tuning data for for the model. You're telling it

1:02:25 the right answer in a particular case. And Uh we're using it various places. You know, some of these places you want a little bit more reasoning. It's not super latency sensitive. So you want a little more reasoning and we'll use one of our O series models. In other places

1:02:39 You want a quick check on something, and so you're fine to use like four oh mini, which is super fast and super cheap. And in general, it's like specific models for specific purposes. And then you you you ensemble them together to solve problems. By the way, again Not unlike how we as humans solve problems.

1:02:57 A company is arguably an ensemble of models. That have all been. You know, fine tuned and based on What we studied in college and what we have like learned over the course of our careers, we've all been fine tuned to have different sets of skills. And you like

1:03:13 group them together in different configurations and the output of the ensemble is much better than the output of any one individual. Kevin, you're blowing my mind. That sounds exactly correct. Uh and also different people are you pay them less, uh they they cost less to talk to. Some people take a long time to answer.

1:03:33 Some people hallucinating. This is a you. Yeah. This is like uh this is a mental model that really does work in in thinking about it. This is great. Some people are visual, they want to draw out their thinking. Some people want to talk word cell. Wow. This is a really good metaphor. So again, coming back to your advice here, because I love that it we circle back to it. It's you're finding

1:03:54 Uh a really good way to think about how to design great Yeah, experiences and LMs, I guess, specifically. So think about how a person would do this. Well it's it's it's maybe not always the answer is to think about how a person would do it. But but sometimes to gain intuition for how you might solve a problem, you think about what an equivalent human would do in those situations.

1:04:13 And use that to To You know, at least gain a different perspective on the problem. Well. This is great.

1:04:24 There's a lot of prior art because we talk to other humans all the time and encounter them in all sorts of different situations and And so like there there's a lot to learn from that. Okay, so speaking of humans, I wanna chat about the future a little bit. So you have three kids. And someone uh community member asked me this.

1:04:42 Hilarious question, but I think it's it's something a lot of people are thinking about. So this is Patrick's Srail. I worked at him with a bit Airbnb he has. C says ask what he's encouraging his kids to learn to prepare for the future. I'm worried my six year old by the year twenty thirty six will face a lot of competition trying to get into the top roofing or plumbing programs and need a backup plan.

1:05:02 That's funny. Um So our kids are we have a ten year old and eight year old twins. So they're they're still pretty young. Uh they're they're kinda I mean, it it's amazing how AI native they are. Like They just it's completely normal to them that there are self driving cars that they can talk to AI all day long.

1:05:23 Um, they have full conversations with Chat GPT and Alexa and everything else. I don't know. I think Who knows what the future holds. I I think, you know, things like coding skills are gonna be relevant for a long time. Who knows? But I I think

1:05:39 If you teach your kids to be curious To be independent, to be self confident, you teach them how to think. I don't know what the future holds, but I think that Those are gonna be skills that are gonna be important in in any configuration of the future. And so

1:05:55 You know, uh it's not like we have all the answers, but that's how Elizabeth and I think about Uh our kids. And do you find that AI there's a lot of talk about AI tutoring. Is that something you guys are doing? Anything you're I know they're using Chat GPT, I love the I love all the photos you post. Or they're playing with prompt and stuff. But I guess is there anything there you're you're experimenting with or you think is gonna become really important? Th this is something that

1:06:17 Uh It's maybe the most That A I could do, maybe that's a maybe that's a grand statement. There are lots of important things that AI can do and including like

1:06:30 speeding up the pace of fundamental science research and discovery, which I m maybe is actually the most important thing AI can do, but But one of the most important things would be Personalized tutoring. And it kinda blows my mind that there is still I I know there are there are a bunch of good products out there. Like

1:06:48 You know, Khan Academy does great things. They're a wonderful partner of ours. Uh Vinod Kozla has a nonprofit that has uh that that's doing some really interesting stuff in this space and is making an impact. But I kinda want like I'm kinda surprised that there isn't like a two billion

1:07:04 Kid. Uh AI personalized tutoring Thing. Because The models are good enough to do it now.

1:07:13 And every Every study out there that's ever been done seems to show that when you have you know classrooms is still classroom two is like education is still important But when you combine that with personalized tutoring You get like

1:07:27 Multiple standard deviation improvements in learning speed. And So it's just It's Uncontroversial

1:07:35 It's good for kids. It's free. Chat GPT is free. You don't need to pay for I and the models are good enough. Like It still just kinda blows my mind that there isn't something amazing out there that you know, our kids are using and your future kids are using and like People in

1:07:51 All sorts of places around the world that aren't uh as lucky as our kids to be able to like have this sort of built in solid education. Again, chat GPT is free. People have Android devices everywhere. Like this could I I really just think this could change the world and I'm surprised it doesn't exist and I want it to exist. This kinda touches on something I wanna spend a little time on, which is a lot of people

1:08:12 Also worry a lot about AI, where it's going. They worry about jobs it's gonna take. They worry about, you know the superintelligence. Squashing Humanity in the future. What's kinda your perspective on the on that and just kind of the optimistic case that I think people need to hear?

1:08:27 I I mean I'm a big technology optimist. I think if you look over the last Two hundred years, uh Maybe maybe more. Technology has driven a lot of the advancements that have made us the the world and the society that we are today. It drives economic advancements, it drives um

1:08:44 uh geopolitical advancements, quality of life, longevity advancement. I mean Technology's at the root of Of just about everything. So I I think there are

1:08:55 Very few examples where uh where where this is anything but a great A great thing over the longer term. That doesn't mean that there aren't Like temporary dislocations or where there aren't individuals that are impacted. And that's like that that matters too.

1:09:10 So it can't just be that the average is good. You've got to also think about how you take care of each individual person as best you can. So uh it it's something that we think a lot about and as we, you know, work with the administration, as we work with policy like We we try and Help where wherever we can.

1:09:28 We do a lot with education. Um I you know, one of the one of the benefits here is that Chat GPT is also perhaps the best like re skilling app you could possibly want. It knows a lot of things. It can teach you a lot of things if you're interested in learning new things. So But I these are

1:09:46 Issues. I'm super optimistic about the long run. And we're gonna need to do everything we can as a society to ensure that we like make this transition Yeah, a a as graceful and as well supported as we can. To give people a sense of where things might be going, that's a big question a lot of people's minds. So someone asked this question that I love.

1:10:06 Which is uh AI is already changing. creative work in a lot of different ways, writing and design and coding. What do you what do you think is the next big leap? What should we be thinking is the next big leap? in AI assisted creativity specifically, and then just broadly, like where do you think things are gonna be Going in the next years.

1:10:23 Yeah. I this is also an area where I'm I'm a big optimist. Like it If you if you look at Sora, for example, I mean we talked about image gen earlier and the the The absolute like fount of creativity that people are putting across Twitter and Instagram and other places. Uh I'm I am the world's worst

1:10:40 Artist. Like the worst. Maybe the only thing I'm worse at than than Then art is singing. And I you know, I like give me a pencil and a pad of paper and I can't draw

1:10:53 Better than my five than our eight year old. You know, it's just like it's But Give me Give me image, Jen, and you know, I can think some creative thoughts and put something into the model and suddenly have output that I couldn't have possibly done myself. That's pretty cool.

1:11:08 Even even you look at um At folks that are really talented. Uh, I was talking to a director recently about Sora, someone who's directed films that that that we would all know. And uh And he was saying, you know, for for a film that he's doing, like say

1:11:25 Sa uh take the example of some sort of sci fi ish, you know, think of like Star Wars And you've got some scene where there's a There's a plane zooming into some Death Star like thing. And so you've got the plane looking at the whole planet, and then you want to cut to a scene where the The plane's like, you know, kind of at the ground level and

1:11:44 all of a sudden you see the city and everything else, right? How are you gonna manage that cutscene? And and and that transition. And he he was saying, you know, in in the world of two years ago I would have Paid.

1:11:58 Uh uh, you know, a three D effects company. Uh a hundred grand. And they would have taken a month. And they would have produced two versions of this cutscene for me. And I would have evaluated them. We would have chosen one because what are you gonna do? Like pay another fifty grand and wait another month?

1:12:16 And uh and we would have just gone with it. And you know, it would be fine. Like m movies are great. I love them and and Um there been Obviously we can do great things with the technology that we've had. But

1:12:29 you now look at what you can do with Sora and and his point was now I can use Sora, our video model And I can get fifty different variations of this cutscene, just you know, me brainstorming into a prompt and the model brainstorming a little bit with me. I've got fifty different versions. And and then of course I can like

1:12:47 iterate off of those and refine them and take different ideas. And now I'm still gonna go to that that three D effect studio. To produce the final one. But I'm gonna go having brainstormed and like this had a much more creative approach. With a with an outcome that's much better. And and like I did that assisted by AI. So

1:13:07 My personal view on on creativity in general is that it's No one's gonna you you don't type into Sora like make me a great movie. It requires creativity and ingenuity and all these things. But it can help you explore more. It can help you get to a better final result. So

1:13:24 You know, again, I tend to be an optimist in in most things, but I'm actually I I I think I think there's a very good story here. I know Sam Altman, I think it was him who tweeted recently the creative writing piece that you guys are working on, where it's uh say hi is very bad at writing creative stuff. And he shared an example where it's actually really good. I imagine that's another area of investment.

1:13:43 Yeah, there's There's some exciting stuff happening internally. Um With some new research techniques. So uh we'll have more to say about that at some point, but yeah. Sam uh Sam sometimes uh

1:13:56 Likes to show off some of the stuff that's coming. Um, by the way, it's like very sort of indicative of this iterative deployment. uh philosophy. We don't have some breakthrough and keep it to ourselves forever and then, you know, bestow it upon the world someday. We kinda just talk about the things we're working on and share when we can. And launch early and often and then iterate in public. And I I I I really like that philosophy.

1:14:22 I love all these hints at a few things coming. I know you can't say too much. You talked about how there might be a coding leap coming in the near future, maybe by the by time this comes out. Is there anything else people should be? Thinking about might be coming in the near future, any things you can tease that are Interesting, exciting.

1:14:38 Man, this hasn't been enough for you? Uh Oh. Only everything is getting better every day. Yeah. I'm like, man, I hope uh I hope we get some of this stuff out before the the episode launches. I don't piss people off. Um No, uh it's

1:14:53 Yeah. The the the the amazing thing to me is We Uh we were talking earlier about how far models have come in just a couple of years. If you went back to GPT three, you'd be like disgusted by how bad it was, even though Lenny of two years ago.

1:15:08 was, you know, mind blown by how good these were. Um And For a long time, we were iterating every, you know, six to nine months on a new GPT model. It was like GPT three, GPT three point five, four And

1:15:25 Now with this O series of reasoning models We're moving even faster. Where like every roughly, you know, three months, maybe four months, there's a new O series model and each of them is a step up in Incapability.

1:15:41 And so The capabilities of these models are are increasing at a massive pace. They're also getting cheaper as As they scale. Yeah, you you look at uh at where we were even like a couple of years ago

1:15:55 The original I think the original I don't know, what was it? GBT three point five or something. was like a hundred X the cost of GPT four oh mini today. In in the API. So couple of years you've gone down two orders of magnitude in uh in cost. For much more intelligence.

1:16:14 And so I like I don't know where there's another series of trends like that in the world. Models are getting smarter. They're getting faster, they're getting cheaper. And they're getting safer, too. Uh you know, they hallucinate less. Every every iteration.

1:16:28 There's just you know, the the Moore's Law and And and Transistors becoming ubiquitous. That was a law around doubling the number of transistors on a chip every eighteen months.

1:16:40 If you're talking about something where you're getting ten X every year. That's a massively steeper exponential. And Uh it just you know it's It tells us that the future is gonna be very different than today. I I I still

1:16:54 Th the thing I try and remind myself is The AI models that you're using today is the worst AI model you will ever use for the rest of your life. And when you actually get that in your head, it's kinda wild. I was gonna actually say the same thing. That's that's the thing that always sticks with me when I watch this thing. Like you're talking about Sora.

1:17:13 And I imagine many people hearing that are like, No, no, it's it's not actually ready. It's not good enough. It's not gonna be as good as a movie I see in the theater, but The point is what you just made, but this is the worst it's gonna be. It will only get better. Yeah. Model maximalism.

1:17:27 Like Keep Well, you know, building building for the capabilities that are almost there and the model's gonna catch up and be amazing. Mm. Escape to where the puck's going to be.

1:17:38 This reminds me I was just using I was Jiblifying everything the other day and I was just like Why is it taken so long? W what was that? I said as one does. That's what it does. These days I was just like it's taken a minute to generate this image of my family. In this amazing way. Like, come on, what's taking so long? You just get so used to Magic happening in front of you.

1:17:57 Yeah. Totally. Okay, final question. This is gonna go in a completely different direction. A lot of people Asked about this. So famously you led this project at Facebook.

1:18:08 Called Libra, which is now called. Nobody's gonna be able to do it A lot of people always wondered what happened there. That was a really cool idea. I know some people have a sense, there's regulation challenges, things like that. I don't know if you've talked about this much, so I guess just

1:18:21 Can you just give people a brief summary of just like what is Libra the this project that you're working on and just what happened and how you feel about it. Yeah, I mean David Marcus led it and the I you know, I happily work uh for him and with him. Uh I think he's a visionary and um also a mentor and a friend. Uh you know, honestly, Libra is probably the biggest disappointment of my career. Uh when I think about the problems we were solving, which are very real problems, you if you look at, for example the remittance space, people sending money to family members in other countries.

1:18:52 It is maybe I mean it's incredibly regressive, but people that don't have the money to spend are having to pay twenty percent to send money home to their family. So outrageous fees. It takes multiple days. You have to go then pick up cash from yeah, it's just It's all bad.

1:19:11 And Here we are with like Three billion people using WhatsApp all over the world. Talking to each other every day, especially friends and family, and exactly the kind of people who'd send money To each other.

1:19:23 Why can't you send money? As Immediately, as cheaply As simply as you send a text message. I it just it's one of those things when you when you sit back and think about it

1:19:36 That should just exist. And that was what we set out to try and do. Yeah, w I I don't think we played all of our like cards perfectly. If I could go back and do things there are a bunch of things I would do differently. Yeah, we we We tried to kinda get it all at once.

1:19:52 We tried to launch a new blockchain. It was a basket of currencies originally. It was immigration into WhatsApp and Messenger. And I think the whole world kind of went like Oh my God, that's a lot of change at once. And you know, it happened also to be at the time that Facebook was at the absolute like mater of its uh reputation. And so that didn't help, right? It was it was also not the messenger that people wanted for this kind of change. We knew all that going in, but we we went for it. I think if we I I think there are a bunch of ways that we could do that that would have introduced the change a little bit more gently.

1:20:27 You know, maybe still gotten to that same outcome. Um but fewer new things at once and introduce the new things one at a time. It Who knows? Um You know, those were decisions we made together. Um, so we we all own them. Certainly I own them.

1:20:42 But it just it fundamentally disappoints me that that's that this doesn't exist in the world today because the world would be a better place if we'd been able to ship that product. I would be able to send you You know, fifty cents in WhatsApp. For free, it would settle instantly. Everybody would have a balance in their WhatsApp account. We'd be transact I mean, it was just

1:21:01 It should exist. I don't know, to be honest, like I mean the the current administration is super friendly to crypto. Facebook's reputation, Meadows reputation, is in a very different place. Maybe they should go build it now. I was looking at the history of it and uh apparently they sold the tech to some private equity company for two hundred million bucks.

1:21:19 Yeah. Yeah. So And then we're going to be able to There are a couple of uh of current uh blockchains that are built on the tech because the tech was open source from the beginning.

1:21:31 Uh Aptos and uh Mistin are two companies that are built off of this tech. So You know, at least the all all of the work that we did did not die, but and and lives on in these two companies and they're both doing really well. But Still, uh, you know, we should be able to send each other money in WhatsApp and and we can't today. Here, here. Well thanks for sharing that story.

1:21:51 Kevin, is there anything else you wanna share? Or Maybe a last nugget of advice or insight before we get to our very exciting lightning round. Ooh, the lightning round. Let's just go do that. Let's do it. With that, Kevin, we reached our very exciting lightning round. Are you ready? Yeah.

1:22:06 Let's do it. Okay. What are two or three books that you find yourself recommending most to other people. Co intelligence by Ethan Moloch. A really good book about AI and how to use it in your daily life as a student, as a teacher. Yeah. Uh I he's super thoughtful. Also, by the way, a very good follow on Twitter. Um

1:22:24 The accidental superpower. By Peter Zion. Uh, very good if you're interested in geopolitics and the the forces that sort of shape the dynamics happening. Um And then uh I really enjoyed cable cowboy.

1:22:37 I don't know who the author is, but uh the biography of John Malone. Just fascinating if you like business, especially if you want to get into like I I mean, the man was uh an incredible deal maker and shaped a lot of the modern cable industry. So that was a good Biography. These are all first time mentions, which is always great.

1:22:55 Oh good. Next question. Do you have a favorite recent movie or TV show that you really enjoyed? Um Huh. I wish I had time to watch a T V show. Um so I'm just Sora videos.

1:23:07 Yeah, right. Um I I don't know, I read uh when I was a kid I read the Wheel of Time series. Um and now Amazon has it. uh as they're in like the third season of it. So I I want to watch that. I haven't yet. Um

1:23:23 Top Gun Two was an awesome movie. Um, I think that's no longer new, but you know that shows up my last time you watched the movie was. Um but I like the idea. Like I I want Uh I I want more like Americana. I want more like being proud of being strong. Uh, and I thought Top Gun Two did a really good job of that. Like, you know. Uh Pride and patriotism, I think I think the US could use more of that.

1:23:48 Is there a favorite product that you recently discovered that you really love other than your uh super intelligence internal tool that you all have access to. There I'm I'm just joking. That's right. I internally to Right. Uh Well, I think I think like vibe coding with with products like Windsurf is just super fun. Um I'm I'm having a great time doing that.

1:24:08 I still just love that our chief people officer Vib coded some tools. Maybe the other one is Waymo. Uh I every chance I get, I'll take away Mo. It's just a better way of writing and it's still feels like the future. Um so

1:24:22 They've done an amazing job. That's awesome. By the way, I had the founder of Windsurf on the podcast. They might come out before this or after this. And also Cursors CO is coming on the podcast either before or after this. Oh, cool. I have a ton of respect for what those guys are doing. They're they're Those are awesome products. Just changing the way everyone builds product, no big deal.

1:24:40 Uh couple more questions. Do you have a favorite life motto? That You often repeat yourself, find really useful in worker in life. Yeah, uh so actually this is um Interestingly enough, it's it's more of a philosophy, but then I thought

1:24:53 Zuck encapsulated it one time on a on a Facebook earnings call. Um so I actually had this made into a poster. Uh it sits in my room, but um But somebody was asking Mark.

1:25:08 You know, it was some quarter when Facebook had grown a lot. This is back in the twenty teen sometime, I think. But it's like you know, so what what did you do? What you know, what was it that you launched? What was the one thing that drove all this growth for you? And he said something to the effect of You know, sometimes it's not any one thing. It's just

1:25:27 Good work. Consistently. Over a long period of time. And that's always stuck with me. And I I think it is I mean I you know, I run ultra marathons.

1:25:36 It's like It's just about grinding. I think people too often look for like the silver bullet when a lot of life is and a lot of like excellence. Is actually showing up day in and day out. doing good work, getting a little bit better every single day.

1:25:52 And you know, you may not notice it over A week Or even a month. And a lot of people then, you know, kinda get Like dismayed and stop.

1:26:00 But actually you keep doing it, the gains keep compounding, and over the course of a year, two years, five years It adds up. Like crazy. So Good work consistently over a long period of time.

1:26:12 Damn, I love that. I gotta make a poster of this now. That is I've got to resonate with that. Okay, I'll take it. That is so good. Okay, final question. Uh I'm gonna ask if you have any prompting tricks and I'm gonna set it up first, but think about if you have a trick that you could recommend to people for prompting LMs better.

1:26:28 Uh there's this I had a guest, Alex Kamarowski, come on the podcast. He's from Stripe and writes his weekly Reflections on what's happening in the world. A lot of them are AI related. And he he once described an L M as a zip file of all human knowledge. And all the answers are in there and You just need to figure out the right question to ask to get the answer to every problem, basically.

1:26:47 And so it just reminded me how important prompt engineering is and knowing how to prompt well. You're constantly prompting chat GPT? Uh what's one tip, one trick that you found to be helpful in helping you get what you want? Well I'll say first of all I wanna kill

1:27:02 The idea that you have to be a good prompt engineer. I think if we do our jobs, that stops being true. Yeah, it's just one of those like sharp edges of models that Experts can learn, but then you just over time you shouldn't need to know all that. The same way you used to have to get deep into like, you know, what's your storage engine in MySQL? Are you using InnoDB four point one or like

1:27:23 And you know. There's still use cases for that if you're at the at the sort of deep edge of my SQL performance, but most people don't need to care. And you shouldn't need to care about Minute details of prompting if

1:27:35 AI is really gonna become You know, broadly adopted. But um Yeah, today we're not totally there. I think it's I think by the way we are making progress there. I think there is less

1:27:45 prompt engineering than there had to be before. But Uh I in line with some of the fine tuning stuff I was talking about and the importance of giving examples. You can do like you know, effectively poor man's fine tuning by including examples

1:28:00 In your prompt. Of the the kinds of things that they that that you might want and the and a good answer. So like here's an example and here's a good answer. Here's an example, here's a good answer. Now go solve this problem for me. And the model really will listen and learn from that. Not as well as if you do a full fine tune.

1:28:18 But Much more than if you don't provide any examples. And I think people don't do that often enough. That's awesome. One tip that I heard, I'm curious if this works, is you tell it this is very, very important to my career.

1:28:31 Make it like really understand like someone will die if you don't answer me correctly. Does that work? It you know, it's really weird. I there's probably a good explanation for this, but You can also say things, so yes, I think there is some validity to that. You can also say things like

1:28:49 I want you to be Einstein. Now answer this physics problem for me. Or you are the world's greatest marketer. The world's greatest brand marketer. Now here's a naming question.

1:29:01 And It's there is something where it sort of shifts the model into a certain mindset. Um that can actually be Really positive. I use that tip all the time actually. I I always uh when I'm coming up with questions for interviews and I use it a l occasionally to like come up with things I haven't thought of.

1:29:18 I actually type You're the world's best Podcast interviewer. Right. I have Kevin Kevin Wheel coming on the buck. Yeah, and it actually works. Yeah and by the way. Back to our other point that we've made a few times. Like you do do that sometimes with people, right?

1:29:31 Um you you sort of put them you you frame things, you get them into a certain mindset, and the different. So I think there are like human analogs of this one more time. Kevin, this was incredible. Uh I was thinking about a way to end this. The way I feel like I feel like not only are you at the cutting edge of the future like your

1:29:51 You and the team are kind of like actually the edge. That is creating the future. And so it's uh a real honor to have you on here and to talk To you and to hear How you think things are

1:30:01 Wh where do you think things are going? And what we need to be thinking about. So Thank you for being here, Kevin. Oh, thank you so much for having me. I feel real I get to work with the world's best team and you know All credit to them, but uh

1:30:14 Really appreciate you having me on. It's been it's been super fun. Uh I forgot to ask you the two final questions. Where can folks finding online if they want to reach out? And well, how can listeners be useful to you? I am at Kevin Wheel. K E V I N W E I L on pretty much every platform. You know, I'm I'm still uh Twitter D A U after all these years.

1:30:33 I guess an X D A U. Uh LinkedIn, wherever. And um I think The thing I would love from people. Give me feedback. People are using chat GPT. Tell us where tell me where it can be where it's working really well for you and where you want us to double down.

1:30:46 Tell me where it's failing. I'm I'm very active and engaged on Twitter. I love hearing from people what's working and what's not. So uh don't be shy. And I learned following you, uh Helps you figure out all the stuff that you're launching. Like you share all the things that are going out of you. Day, week, month. So that's also benefit.

1:31:04 And by the way, four hundred million weekly active users all emailing you feedback. Here we go. Yes, let's do it. Okay. Uh well thank you, Kevin. Thanks for being here. All right, man. Thanks so much. See you soon. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show.

1:31:33 at Lenny's podcast dot com. See you in the next episode.

What you see above is a preview of the first minutes. One unlock costs 10 credits and covers this episode forever: full segment and word-level timestamps on this page, plus .txt, .srt, .vtt and word-level JSON downloads, as many times as you like.