Ep 73: General Partner of Felicis Peter Deng on on AI Pricing Tactics, Reaction to GPT-5 & Why Voice is Underrated

In this episode, Jacob sits down with Peter Deng, General Partner at Felicis and former Product Leader at OpenAI, Facebook, and Uber. Peter shares his insider perspective on building ChatGPT Enterprise in just seven weeks and leading voice mode development at OpenAI. The conversation covers everything from why traditional SaaS pricing models are broken for AI products to how evals became the new product specs, the "AI under your fingernails" test for founding teams, and why current agents are massively overhyped. They also explore how consumer AI will fragment across multiple winners rather than consolidate into a single super app, the coming integration between ChatGPT and apps like Uber, and why voice AI will unlock entirely new categories of applications. Plus, insights on the changing dynamics between foundation models and startups, and what it really takes to build defensible AI companies. It's a comprehensive look at AI product strategy from someone who's been at the center of the industry's biggest breakthroughs.

• 0:00Speaker 00

  Peter Deng has led product at an ensemble of some of the most important companies in Silicon Valley, Uber, Facebook, OpenAI, and now is investor over at Felicis. I'm Jacob Effron, and on Supervised Learning, we talked about a ton of interesting things. It was particularly fun just to get his perspective, given all these different roles he's had, on some of the key questions in AI, like what the future of OpenAI looks like, and how ChatGPT interacts with apps like Uber over time. We talked about the future of product management and how AI changes that, and what those roles will look like going forward. We talked about pricing AI products and some of the challenges in that, and new models that might work, as well as what Meta's future AI strategy could look like. Just fascinating to be able to riff on all these topics with someone with such an interesting perspective on them all. I think folks will really enjoy it. Without further ado, here's Peter. Well, thanks so much for coming on the podcast.

• 0:43Speaker 01

  Thank you for having me.

• 0:44Speaker 00

  You've had these incredible product leadership roles across the board, Uber, Facebook, OpenAI. And a lot of people are discussing now and thinking about what these new AI businesses are, and what lessons, I guess, are still relevant from the previous SaaS world, and then what makes these businesses unique and different. And so I figured, we're starting in your domain of expertise on the product side.

• 1:06Speaker 01

  Sure.

• 1:07Speaker 00

  What do that feels like, maybe is, should be thought of differently in this new era?

• 1:11Speaker 01

  I actually think one of the things that changes the most is how people think about value, and specifically, I would say subscriptions are probably going to be something that's going to evolve pretty dramatically. So I'll give you specific examples, right? So when I joined OpenAI, I led consumer products, product marketing and design. And the first project I jumped into was Cheech B for Enterprise. I wanted to kind of like roll up my sleeves and kind of get my hands dirty and actually do something useful. So I remember writing the first spec for that, or kind of vision document, and just kind of the team ran with it really quickly. I think we launched it in seven weeks, which is crazy. And I remember around the time that we were about to launch it,

• 1:54Speaker 01

  I had COVID, so I was just working in my basement, kind of quarantined. And I was having calls about how we're going to price this. And maybe this is a COVID hallucination or whatnot, but it's like, I feel like there's something wrong with pricing it per seat. And I had this like really strong feeling that We were undervaluing sometimes and overvaluing sometimes because the unit of work is not just the fact you get to use a tool, which is what SaaS usually is. Fundamentally, the unit of work is the work provided and work done. So it never sat right with me. And yeah, you can price it per seat high enough so that the whales who use a lot of GPUs, you can still make the cost work out. But I really thought of it in terms of how can we actually get closer to the unit of value, which is how much work is actually done.

• 2:38Speaker 01

  So I think in the age of UI, delightful products still going to matter. You're still going to be able to have to do something super useful. But I want to see companies and startups start to pivot towards what does valuable work creation look like? And I don't have the exact answer. I don't think it is actually usage based pricing because tokens does not equate value. Right? It's a level of work that's done, but it's not maybe the value that's actually created and captured. So I think that's going to evolve a ton from the SaaS world into the new world of AI.

• 3:10Speaker 00

  Well, it's such an interesting thread because I feel like people talk about a lot about outcomes based pricing, but realistically, I think you've seen it in the customer support space, maybe a few others, but it still feels like seat based pricing is probably the dominant, predominant way of doing things today in AI apps. Have you seen anything that you thought is interesting in terms of ways of pricing?

• 3:27Speaker 01

  Well, I've seen some startups who've basically done sort of a platform fee and a kind of value, a sort of, I would say they would have a platform fee and a usage fee-ish, but they would work backwards from the value that they create. So the formula would be roughly like, hey, how many people were we able to make more efficient? What was the impact of that? And then do some math backwards to be like, well, this would be the platform fee and this would be the usage fee. I think that gets at most of the problem. And I think that we're gonna, buyers are always going to have a mental hang up from going to outcome space pricing. And it's a weird kind of perverse incentive in a way. But I think that there will be some more acceptable ways of pricing a product where

• 4:09Speaker 01

  people are not going to balk out a platform fee or a usage fee because they're going to realize the outcomes that they can work backwards from after a year. It just makes sense.

• 4:18Speaker 00

  What would that have looked like with ChatGPT Enterprise? Like, how might you have priced that?

• 4:21Speaker 01

  I think, I actually think that we couldn't have done it with it, ChatGPT Enterprise. I think the reason for that is because the use cases were so broad. And and for me, I'm I'm not a daily active user. I think I'm like a bi hourly or hourly active user of chat GPT. It's so useful from like taking a look at, you know, an investment memo and saying like, hey, where can I tighten up or, you know, do some research on X, Y, and Z thing, or even just being that thought partner, which I find to be just where it's always with me, right? Whether it's voice mode, driving down home after a long day of work at on the 280 and just going into my own private podcast because I can just go deep on a on area I'm researching, right? So I think it's really hard with chat GPT because of the broad widespread

• 5:06Speaker 01

  use of it. But I think for a lot of different startups, it's going to be a lot more targeted.

• 5:10Speaker 00

  You kind of mentioned, you know, that you didn't think usage based pricing was or pricing per token is is the right way to do it, which makes sense. It's always kind of counterintuitive to the value of the end user. There's this tension, I guess, of both allowing people to experience the power of these models and and in some sense, all you want is a startup is people to use it as often as possible. And then at the same time, some people can run up some pretty high bills. And have you seen anything interesting there on the product side?

• 5:32Speaker 01

  I mean, look, we dealt with a lot with this with limits on chat GPT. There are definitely those whale users that are just using, you know, kind of taxing the service at at peak times. And I think the trick is on the product side is to make sure that people don't feel any constraints where they're using it because 80% 90% of people will never run into those limits. But when you get close to those limits and when you maybe exceed them, it's really important to figure out what is that experience that people have that doesn't feel like, oh, you did something wrong. Oh, I broke something. Or, oh, I'm a bad, you know, user. It's it's just gently guiding them to be like, oh, well, here's the next tier, right? And I think that in a sense, that's where the simplicity of of seat based pricing works is that people can

• 6:14Speaker 01

  just think of, you know, I have pretty much all I can eat because that's all I can eat is most likely under this limit. And they never experience that limit. And then that's a really good way for them to upsell to the next level. So I think for a generalist product like that, I think that if you if you are pricing by seat, that is a great way for you to kind of get to the point where you you find that efficiency and people can can self opt into an overage cost or the next tier.

• 6:39Speaker 00

  Right. So it'll be interesting, especially at the enterprise level where you have, you know, maybe within these, you know, some broad platform fee and then a few power users in the enterprise and how enterprise admins figure out, you know, the right the right tier to be in.

• 6:50Speaker 01

  Right, exactly. And then, you know, Yeah, that's the thing is I think that any company I would give strong advice not to basically purely price on on usage. I think the when you start to think of the unit as the GPU usage, to put it bluntly, I think you get into a commodity, like a race to the bottom type thing with your competitors, right?

• 7:10Speaker 00

  Yeah, now it feels like a common pattern we see is like a broad platform fee with maybe like a cap on.

• 7:14Speaker 01

  Yeah, a cap, some kind of cap, yeah.

• 7:16Speaker 00

  You know, another area I was curious to get your thoughts on the product side is it feels like in this wave of AI companies, a lot of the best kind of products that are being built are really partnerships between, you know, the research and post-training teams and then the folks that are actually building products. And you did this at probably the company that does it best at OpenAI. Would love your thoughts on just like, what does this collaboration look like when it's done really well?

• 7:35Speaker 01

  Love that question. So ever since I was, so I grew up as a product person. That's like kind of where my career kind of started off. And I've always believed in this principle that the PM fills in the white space. Like, we are a service organization. We basically help bring various different disciplines together. And in different companies, it just looks different. So I'll give you an example. At Facebook and Instagram and and those companies like that, it was a triad of the product manager, the designer, and the engineer. And everyone was kind of like pulling for different objective functions. And that's where you get the best work is that people are challenging each other. And then at Uber, it's actually was a little different. It was the product manager, the engineer, the designer, and the ops person.

• 8:20Speaker 00

  Yeah.

• 8:21Speaker 01

  Because you operate in the physical world. And I love that experience going from purely digital to digital and physical. And then you switch gears into OpenAI and kind of the new wave of products, you have a new configuration, right? It really is the product person, the designer, the engineer, and the post-training lead. Because you need to, in a large sense, the model is the product. And also, the job of the PM starts to shift as well. Because, you know, the new spec is the eval. And when you're, because you know, you're talking about behaviors and you're talking about, you know, what, what does this agent do to, if you wanted to talk about agents?

• 9:04Speaker 01

  You can't do that in just pure words without actually putting pen to paper on evals of how to, how you define the boundaries of what success looks like. So absolutely, you know, the, the game changes with all these companies. And it was really important that the post-training lead, the product lead, the design lead and the engineering lead are just all kind of operate as one unit. But that's, that's classic. I think with every single wave, you've seen the shift, right? And at the, at the kind of the core, the PM is going to be the one who picks up any piece that is not being picked up.

• 9:36Speaker 00

  Right. And so it feels like evals are kind of the, the, the big new piece in this wave.

• 9:40Speaker 01

  Hundred percent.

• 9:40Speaker 00

  Yeah.

• 9:41Speaker 01

  Hundred percent.

• 9:41Speaker 00

  And then bringing them obviously to, you know, to the post-training team, you know, to, to then more effectively build, build models for those use cases. It's interesting. I feel like we've gone through, you know, different waves of generalization where obviously, you know, we had these, you know, for the last few years, these tremendous step changes and model improvements. And across the board, you just got, at any task you were working on, it just got better as the next wave of models came out. And now it feels like we're in this era of, you know, much more specific post-training for brand tasks. And it's like, cool, even with G P five, it came out, it was, you know, better for coding. It's not necessarily better for, you know, every task that every company ends up doing. And so it feels like more and more this loop that maybe existed within the foundation models is, you know, going to increasingly, I think, exist across the AI app ecosystem.

• 10:23Speaker 01

  Hundred percent. I think that's, that's, that's going to be a huge part of it. I think that like, when you take a look at sort of some of the behaviors that the models take on and what you want to teach it to do, one of the things that the models has gotten really good at is just, you know, RL has gotten really good.

• 10:39Speaker 00

  Yeah.

• 10:40Speaker 01

  Tool use has gotten really good. And those two combinations, I just basically, the model's getting really good at that, opens up a whole new level of abstraction where startups can build on top of. The secret is, there's a lot of data that actually isn't on the open web that the models have not been trained on. And so being able to apply those data sets and the unique insights, the models are good enough to mold to that now, and that's a really good time for folks to be building companies.

• 11:08Speaker 00

  Yeah. When you look at startups, like how much AI-native talent ends up being important? I mean, I could imagine a spectrum where you say, "Look, all you really need to bring to the table is a smart go-to-market, your own evals, and a really smart, you know, understanding of what actually matters in a domain, and then some data you can bring and use RFT on, you know, the OpenAI models and be okay." Obviously, there's then the other end of the spectrum, which is like, hey, folks that are training their own, you know, or doing some serious post-training and adapting their models.

• 11:37Speaker 01

  I think it matters a ton. Because, so at OpenAI, back when I was there, I remember one of the analogies, this is during the time when people are still scared of AI. I feel like maybe people still are, but it's just, it's, the temperatures come down a little bit, right? And the way that I've explained it, the analogy I used was, it's as if humanity has discovered a new element in the periodic table of elements. The people who truly know what to do with this are the people who've played with it, with their hands, and gotten it under their fingernails and really figured out, like, how malleable is it? And what are the properties? And in that time that I was there, we were still discovering the wild capabilities that were locked in the model that we just, you know.

• 12:20Speaker 01

  And so that is a huge part of what I, my answer to your question is, I think it's important that any founding team has to have really deeply played with this new element. And the founders that don't, haven't are pretty evident when you meet them, and it's just not very interesting, right? Because... You have to really understand what the model is capable of. You have to try a bunch of things. And you don't have to be a researcher per se, but the best builders will be the ones who really know the raw materials they're working with. And you got to see that under their fingernails.

• 12:54Speaker 00

  Yeah, this is one of my favorite parts of this whole wave is that, you know, it's like these models get developed and then they're released into the wild. And, you know, the companies themselves are surprised by all these things that people learn as they get the models under their fingernails.

• 13:05Speaker 01

  Totally. Yeah. And like for me, I remember, you know, having this experience firsthand of just, you know. And I say this a lot. It's like, I feel like we still are discovering the capability, the capabilities that are within these models. And that's why it's such a great time to build a company now is that you can actually go and dive deep and see what is the model really capable of in this context. Right? When given this a little bit of this type of eval, when given this, these set of datas.

• 13:33Speaker 00

  Yeah, we're discovering all those capabilities. Well, simultaneously, the models are getting better, which is crazy. Like either one of those threads would be crazy to pull on.

• 13:38Speaker 01

  Exactly.

• 13:39Speaker 00

  And the fact that they're both happening simultaneously.

• 13:40Speaker 01

  Simultaneously.

• 13:42Speaker 00

  Is really, is really interesting.

• 13:43Speaker 01

  Precisely.

• 13:43Speaker 00

  I feel like the question I think about a lot, and you have this inside insight, is the extent to which the foundation model companies have an advantage in building these products. Because, you know, the folks that build these models everybody uses sit next to the folks that build product.

• 13:55Speaker 01

  Hundred percent.

• 13:56Speaker 00

  And, you know, on the one hand, I'm of two minds where it seems like that should be helpful. And then you hear the stories about how things like ChatGPT or Claude Code emerged. And they seem sometimes like it's just there were people in these organizations that were just hacking on things that like anybody could have done. Like they didn't necessarily have to live in OpenAI or Anthropic to build some of these products. And so I also wait back and forth, but I'm curious what your take is on this.

• 14:18Speaker 01

  Look, I think there's definitely an advantage. No doubt about it. There's always advantages and disadvantages, I would say. The advantage is that when you sit next to the partners who are coming up with novel methods and making these breakthroughs. There's no doubt that you're able to discover what the model is capable of in a product context faster than anyone else. So that's, that's a pure advantage.

• 14:39Speaker 00

  Is that just like early access though? Or like what about it specifically? Because it does feel like a lot of times when these models come out, it's like, you know, within a week, you talk to the researchers and like, whoa, we're really surprised that like all these people have discovered all these these things. We had no idea.

• 14:51Speaker 01

  I mean, part of it, a lot of it is, I would say, early access, but it's also being able to like tweak the behavior of the models to do the thing that the product is most beneficial to the product with the post training team. And there's fantastic, there are fantastic researchers at OpenAI and in Frogback and Meta AI. And so they're going to be able to apply some of those goals that come from the, you know, the applied side and really make sure that the product can, and the model can really shine in those, in those ways. So it's more than just early access.

• 15:24Speaker 00

  You think with the availability of RFT and other things, like companies get some of this?

• 15:28Speaker 01

  Yeah, 100%. And then that, and here lies the advantage too, because when you're at one of these model companies, there's always a bit of like, you know, there's, you're either shooting for general intelligence, which is great. And then you become the smartest thing for everyone. But where you really don't have time to dive deep, and this is where the opportunity for startups is, is when you have that unique insight of like, this industry has, founders have to apply a bit of elbow grease to really get a product and a real business going. And the fact is, they can dedicate 100% of their time in a certain use case or some insight they see from the market. And they can move incredibly fast. And they're always going to be able to build a specific product,

• 16:11Speaker 01

  I believe, that is tailored to that problem, right? And you're seeing this in some of the investments you've made, and you know, some of the investments as well. You see this in some of those applied companies that really are going deep and it's stuff that you can't do with ChatGPT. The workflow is completely broken.

• 16:28Speaker 00

  The workflow is certainly, and then, you know, and to your point, it also seems increasingly clear. You know, there's an aspect of kind of post-training and fine tuning that is bespoke to the evals you care about.

• 16:37Speaker 01

  100%.

• 16:38Speaker 00

  You know, that that combination, because in the, in the early days, it felt like it was just going to be workflow. And like, the models were going to be the models. And you were just like, and then, and then that was probably enough. But it feels like there's now even more of an opportunity to differentiate versus the general horizontal players.

• 16:51Speaker 01

  And you can move faster when you have a more focused view of what is it that you care about, to your point. So I think that's the advantage that startups have. There's a big advantage that the model companies have, and there's a big advantage that the startups have, and there's a lot of value to be created. So it's a great time to be alive.

• 17:05Speaker 00

  Are there any spaces that you like won't touch, just you're like, God, that feels like the model companies will will will own or figure out?

• 17:10Speaker 01

  Maybe it's just the bias of just the the founders that I've met, but I feel like the founders get it. I I haven't seen any pitches that really uh feel like they're they're right down the fairway for for OpenAI or Anthropic.

• 17:24Speaker 00

  Yeah, I mean, obviously, you you do have people going head to head on the consumer side or on the coding side, and it feels like some mental model people use is just if it's, you know, if it's a trillion dollar plus bucket of spend, then maybe it's something that the model companies will care about. Ultimately, lower than that, it's it's hard to imagine them.

• 17:38Speaker 01

  It's that I love the way you put it. That's pretty much what it is, because when you're at one of those companies, your your incremental effort is so valuable, because you can make so many, you know, boats rise, that it's, again, they're very valuable boats that need special scaffolding to rise, and that's where the startups come in, and those are not small markets. Those are like

• 17:57Speaker 00

  No, it's amazing.

• 17:58Speaker 01

  Some pretty big ones.

• 17:58Speaker 00

  Two billion dollar market may be too small for

• 18:01Speaker 01

  Yeah, exactly, exactly.

• 18:02Speaker 00

  You obviously have run product at a bunch of organizations, and I'm curious, we've alluded to a little bit around evals, but how do you think about how the PM role will change and product orders will change, you know, as as more and more companies become these like AI-first organizations?

• 18:16Speaker 01

  Yeah, I think a lot of for any role, it's about where you spend your mental power, horsepower, right? So if you think of our brains as GPUs, like where do you where do you point those GPUs?

• 18:27Speaker 00

  And we have a limited compute budget like everyone else.

• 18:29Speaker 01

  Exactly. Limited compute budget, there are eight hours that you're really sharp in the day, maybe, and then you start getting waiting a little bit, you gotta recharge. But I think that at the end of the day, this is why I think role specialization will still exist, because someone who needs to really pay attention and care about the craft of the code and how it's abstracted, even if code is dripping from your fingertips from cursor, you need to think about the architecture and really and put some deep thought into that. Now, there will be a day when, actually, every day that goes by, I feel like every human being who's working is like increasing the level of abstraction that they're operating at, but

• 19:07Speaker 01

  Make no doubt, there's gonna be somebody who really needs to really own and feel like they're the the D R I on the engineering, on the design, who's advocating for, you know, on the design side, simplicity and craft, and on the engineering side, robustness. Those things won't go away. And I think that for the P M, for example, I think a lot of it is just the mental simulation of where, what is the vision of where I want to go? What is the next step? How do we, how do we get there? And I think that Chat G P T has been the P M's best friend. Can't tell you how many times I've been working late at night. Maybe it's like, actually, many late nights. One, one A M. Everyone in my house is asleep. I'm just sitting there. It's lonely. You know, maybe people are off slack at that

• 19:49Speaker 01

  point, but I have a thought partner right there. And I can just bounce ideas off of, and my brain's already not very, you know, fresh at one A M. But I have someone who's like, never tired, and just there with me, and just able to kind of meet me where I am, and just help me crack that one last word of the, of the spec or the vision doc that's like, really is trying to get at what I'm trying to, to, to say. That is really powerful. So I think that every role will have so much leverage. Every human's gonna have so much leverage. And for P M's, you're just gonna get a round the clock thought partner is, is a big part of it.

• 20:27Speaker 00

  Does that mean over time, you think we have more P M's, less P M's? Like, how do, how do actually org structures change?

• 20:31Speaker 01

  I, I don't know if I could predict that future. Roles change all the time and just role descriptions. It's very possible that there's an archetype that comes out, which is essentially a solo, I mean, we've seen this, solopreneurs, who are, have a sense of exactly what they want to build, and have, you know, all of the leverage tools to design it, to build it, to code it, to make sure that the, the operations are successful, everything's up and running. And it's possible that, you know, we don't even call it P M or engineer anymore. You might just have a hybrid role, right? Where you could have one person developing a feature by themselves. And I think that's gonna be the future. Whether that person comes from a technical background, or comes from a, you know, a, a design background,

• 21:13Speaker 01

  it doesn't really matter. I actually was one of the very talented.

• 21:20Speaker 01

  Engineers that I really enjoyed working with at OpenAI. I didn't realize this, but he went to design school. I was just like, I was just looking and catching up with him. I was like, wow, that's great, right? And but that's what's beautiful about all the superpowers that we have with all these technologies is that you can really kind of, you know, fully fulfill your vision, whatever that vision is. And you can use AI to supplement and complement your skills. So I think that the role PM may not exist and be called a PM. It may still exist, but it just might get more technical or not. But we'll see how it evolves. I think even you can see the waves of PMs and how it's evolved, right? So in some companies, the PM is the product manager is more of a marketing person.

• 22:05Speaker 01

  I'd say like an Apple, right? And Microsoft even. And then at a place like Facebook, where we built it, the PM was a very technical, design oriented person. So I think the archetypes of PMs have shifted already. And we've seen it in our lifetimes, right? And in a span of two decades. So there's ample reason to believe that it will shift again.

• 22:26Speaker 00

  I guess shifting gears to some of the different modalities that exist out there. And obviously you've worked on some of these directly and invest across them. I'd love to just start with voice, because I think you've spent a lot of time working on voice when you're at OpenAI. How do you envision, you know, voice fitting into the broader ways consumers interact with AI over time? I think people are wondering, are we all going to just be talking to our computers, you know, in an office in, you know, near months? Or how do you envision it?

• 22:48Speaker 01

  Yeah, I mean, I'm glad you mentioned voice. So this water bottle, I put the, this is the original voice code sticker.

• 22:54Speaker 00

  Love that.

• 22:54Speaker 01

  Launch. And yeah, I didn't realize that, but I carried this water bottle with me. It's one of my favorite ones, but just, just kind of nice and simple. I love these new, these different modalities because with, as someone who like kind of did a bunch of human interaction as a, as a college student and grad student and kind of evolved into that in my early product career and really valued the way that we interact with computers. Each modality has its own, you know, kind of pros and cons. And it is really interesting to have voice be an unlock for so many reasons. Number one, I think voice is, I mean, if you, if you take a look at just this podcast and you do a straight up transcript,

• 23:36Speaker 01

  you will see how many times you and I both start and stop on a thought. Because when you form words, I feel like the brain just. Is forming the thoughts at the same time. There's no barrier, there's no lag time. And you tend to think and talk and think and talk at the exact same time. So that opens up a huge possibility for when you're interacting with AI. The models, the voice models are getting better at tonality and sort of warmth. And it's becoming more and more natural. If you take a look at the stuff that Brendan and team are doing at Sesame, it's fantastic. It is magical, right?

• 24:15Speaker 01

  And you try the demo and it was talking to it, it just, it just, there's a je ne sais quoi about it of it's just so well crafted. And that gives you a sense of how nuanced this medium is, just from a pure model capabilities perspective of emotion. Right? If you think of somebody who's trained as a, as an actor or actress, like they have to be able to inflect their voice in certain ways to communicate certain emotions. It's really powerful. And on the level of, on the applied level, it's being, you know, brought into so many different workflows that could not be done before.

• 24:48Speaker 00

  I do think the interesting question with voice is what will persist, right? Because I think we're, we're seeing these voice agents emerge and it's like, okay, my voice agent calls, maybe they'll call your voice agent. And, you know, over time, if, if there's voice agents on both sides, there probably ends up being more effective ways to communicate information back and forth. And so I do wonder, you know, uh, over time, which voice use cases actually, you know, make the most sense via voice. And I love this idea of, one, it's just an incredibly engaging way to, it's way more engaging than going in text back and forth, uh, in a lot of ways. And then two, this idea of, you know, rather than staring at the blank box, formulating your thought and then putting the thought in, you can kind of in real time, you're like, I don't know where I'm, what I'm thinking, but I'm kind of just ripping and maybe I'll get somewhere.

• 25:27Speaker 01

  Yeah. And one of the things actually I love to do on the drive is use granola because what, what granola does is it lets me kind of, I'm, I'm like working on an investment thesis, for example, and I'm just talking and doing so many false starts and, oh, try this idea, that idea. And the end of it, I get this like somewhat structured, you know, kind of, uh, it's someone, some, it's, it's an agent that sort of processes it and helps me see my own thoughts and where, where, where the insights that were, uh, that came to be at what point in the drive and which ones are really going to stick. But it is a really interesting medium, uh, and, and I, I don't know exactly which ones are going to stick or not, but the fact that voice has stuck around for us as humanity for a long time, I think it's going to be a pretty important one.

• 26:05Speaker 00

  Yeah, I mean, brainstorm sessions clearly remain an incredibly effective way of generating new ideas. I mean, one thing I've heard you riff on before, I think on Lenny's podcast, was just the power of using these models for education. And I think your experiences with your own kids.

• 26:18Speaker 01

  Yeah.

• 26:18Speaker 00

  I'm curious, like, what the most interesting things you've seen around here. And as you think about that, like, whole surface area of potential products, things that get you excited in that area.

• 26:25Speaker 01

  Yeah, I think the education part, well, first of all, I think that there's a lot of interesting startups out there that are being formed now. And it's very clear that this is one of the things that the models are just, again, talking about discovering the capabilities of the models. To be able to just tell, like, don't give me the answer, help me think through it. I used to do that all the time with the early versions of ChatGPT with my son of like, honestly, there was some long division. When was the last time you did long division, you know? And I just got to get reminded of how to do it. And I can sit there and watch five minutes of a YouTube video, or I could be like, hey, ChatGPT, like, tell me, remind me what this thing is again. Or, hey, there's a problem here, I'm not sure how to parse it. The example I gave is like, you know, sometimes helping my kids, my daughter, my sons with their homework.

• 27:10Speaker 01

  And my brain is fried. And just to be able to, and you're about to be a parent, like, it's gonna be, you're gonna, in five years, you'll be like, I don't remember how to do this. And also, my brain is like, it's way too late, I just need an assist. And I think that's where a lot of, that's one of the workflows that I would say would be out there.

• 27:28Speaker 00

  Yeah, totally. I mean, it feels like you've seen, maybe the first most successful wave has been these like language learning apps and the ability to speak, just being so much more effective as a way to learn languages. You had Sal from Khan Academy on the podcast, and you know, what he was saying is obviously the problem to really be solved is just the engagement problem, right? Of how do you get, there's some, there'll be some subset of motivated folks that want to use these products to the fullest. And then the question is, you know, who, how do you, you know, get people engaged to actually use these products?

• 27:54Speaker 01

  Yeah, I think that's, I think actually that's where AI can really help because, like I said, so much of learning, you have to be kind of really interested in it. And some, you know, this is great because I think it's great when you follow kids' passions of what they want to learn. And, you know, this is something my wife and I do, so two of my daughters were really interested in horses. We did horseback riding camp. Luckily, they don't love it enough that we have to buy a horse, so I think that we dodged a bullet there. But, you know, following the lead of where students want to go is going to be really the way that you kind of unlock that curiosity and teach that lifelong love of learning, I would say. And that's where.

• 28:35Speaker 01

  AI can help not just adapting something, a problem, you know, if the kid likes soccer, being able to frame a math problem in a soccer setting, that's one very simple tactical thing. But I think you're right, Sal's right, that there's a problem that exists that how you draw people in. And I'm really curious if there's a way that we can make these agents or AI kind of, you know, take the first step and talk first. That was one of my favorite discoveries of what the models could do at OpenAI was what happens when the model talks first?

• 29:09Speaker 00

  Say more about that.

• 29:10Speaker 01

  Well, because, you know, traditionally, when we use ChatGPT, it's like I say something and it says something back. But there are ways that you can prompt the model to like talk first. And just like, so this is no longer, you know, the analogy is it's no longer like an assistant who's at your beck and call, but really like, what if it could be proactive? And I'm really excited to see a lot more of that arc play out.

• 29:33Speaker 00

  Have you seen that play out in products to date?

• 29:36Speaker 01

  Oh, yeah. So we're in a company called Utori, and they're proactively going out there and like, you know, alerting you of things that are happening out. But that's just the beginning. And I think there's going to be a lot of stuff that happens along those lines. But I think that's another frontier that's yet to be discovered.

• 29:51Speaker 00

  Totally. It does feel like a lot of the product experiences today are like, here's a blank box, do with it whatever you want. And, you know, 10% of people get so much value out of that because they know what to do. And for maybe 90% of people, it can, I don't know, but the exact percents, but it's confusing. And so I do think that shift is something proactive. It's really interesting.

• 30:05Speaker 01

  Yeah.

• 30:05Speaker 00

  Relative to when you started at OpenAI, how was the diffusion of these AI models into, you know, both consumers and enterprises compared to what you might have expected?

• 30:15Speaker 01

  It's funny, because when I joined OpenAI, after I joined, they told me, oh, we hired you because you've done both consumer and enterprise, and we needed someone who did that. And I was like, oh, that's really interesting, because it is a work product. It's a product for work. It's a productivity tool. And so I think, you know, that's probably the reason why when we Joined, it was, it was, it was the consensus that we got to build Chadrew for enterprise because there are just unique features that are needed for enterprises. Where you have security, you have integrations, you have audit log. There's like a bunch of stuff that's expected that, you know, the team is still building today. But, and, and the fact that they are working within an organization and like,

• 30:57Speaker 01

  there should be some privacy within the organization and, and not have things leak out. It's not a surprise that for all of these companies, the adoption is just now catching up because you kind of had to build some of those basic features and the basic constructs and mental models that this is a work product that will make it so that it can be adopted by, by, by enterprises. I think on the consumer side, again, when I first joined, people were scared of, of AI. Predominantly, that was the, the, the consensus. And I remember when we made our first product marketing hire, Coley, she's fantastic. And I remember when I sat down for the final call with her, I was like, well, so, you know, do you have any questions?

• 31:42Speaker 01

  And she just said proactively, she's like, yeah, I spent a bunch of time calling users in the Midwest or just people that I know in the Midwest, just get their take. Cause that's, at the time, it's like, that was unheard of. If you're not in Silicon Valley and you're like, oh, you're bullish on AI, then the, the general consensus was, uh, this is very scary. But this, exactly what always happens, happened, which is once people start playing with the technology, I think that some of the usefulness, some of the fear dissipates and some of the usefulness becomes really evident. And I think that's what's happened in the last 18 months.

• 32:16Speaker 00

  You know, obviously, one of the developments in the last few months has been these escalating research talent wars. And it feels like there's, yeah, some of these salaries are, are getting quite high and folks moving back and forth. How do you think that ends up impacting the broader ecosystem?

• 32:28Speaker 01

  Yeah, I think there's some, definitely, first of all, I trust the market because it's very clear that this, uh, you know, research talent is, is so important to get right for each of these big labs. Um, and, uh, it is. Absolutely imperative for each of these labs to make sure they retain and recruit each other's talent. I think who ends up winning is everyone else, like us as investors and other and startup founders primarily because, you know, the competition to push towards general intelligence, superintelligence is is great for everyone. I think that's great for humanity. I think it does have second order effects that are just, you know, there are some people who are not researchers who feel like they're contributing a bunch

• 33:12Speaker 01

  to the product or the company or true return of value, but the discrepancy in salaries and RSUs and all that is becoming wider and wider. So that is a secondary effect that I'm sure folks will have to address, HR departments will have to address at some point.

• 33:29Speaker 00

  Do you think that will trickle into other roles? I've been struck in talking to some founders where they're like, you know, I put my Zuckerberg hat on and I wondered like, maybe I could bring in this person and, you know, pay him way more than I might have otherwise.

• 33:40Speaker 01

  Yeah, I saw a job listing, you know, just the other day of, you know, and we want a research engineer and this is the salary we're paying. And, you know, they were very public about it on Twitter, on X, sorry. Old habits die hard. And, you know, there's a little bit of debate in the comments. Like, hey, like, you're paying this much? It's like, yeah, that's. So I think the market's always going to reset. And right now, that's that is the role that is in high demand and as it should be. But at some point, there might be some other some others.

• 34:11Speaker 00

  Yeah. I mean, this is also to bring it all the way back to our conversation on pricing. This is like the bull case for a lot of these agent companies where I think there's there's this outstanding question of, you know, if you're replacing labor, but you're all doing something kind of similar. Do you just compete each other down? And is this dream of replacing labor budgets actually just way overstating the size of some of these markets? But I think more and more you're seeing, hey, if you have something that is meaningfully better across the board for a role, then you're able to kind of maintain some serious pricing power.

• 34:39Speaker 01

  But I also think whatever present power people have right or companies have right now, they just have to model that out, you know, two or three more degrees and see what do they need to do? What do those companies need to do to stay competitive and to kind of figure out what the right enough next level of abstraction is going to be?

• 34:56Speaker 00

  What like, is there a specific example of that that that you think of?

• 34:59Speaker 01

  You know, so many companies have just made pivots and changes and I always have this analogy of the springboard in my mind, like each person, each company has something they're really good at, and they use that to springboard to the next level, and then they have another level, and they're going to springboard to the next thing. I think a great example is, you know, I would say Facebook, like Mark is extremely ambitious. I have a super huge amount of respect for him, and it started off as a college site, but then it became a, you know, young person, including high school, and then it became, you know, uh, you know, the way that most people in the world, regardless of your age, communicate, um, and then it became a lot of the way that advertisers are able to reach

• 35:41Speaker 01

  the right audiences, and then, you know, now they're building the next platform with, with the hardware team. At the same time, they're building superintelligence. So I think it's a great example of like, a incredible founder, uh, that is able to see around the corner and start making the bets in the next thing before it's too late.

• 36:01Speaker 00

  Yeah. Where do you think this Facebook AI strategy is going?

• 36:04Speaker 01

  You know, I, I can't say for sure because I'm not there. Um, I don't really talk to people there about the specific, I have friends there, but I don't talk to them about the specific strategy. But from what I gather, it makes sense because when Mark says personal superintelligence, I believe it. I mean, there's, there's, everyone needs something, somebody or something to assist them in their everyday lives. And, you know, if you are able to create that, uh, the agent, if you will, for each person that's personalized to them, and able to do meaningful things and to accomplish their goals, that is really, really valuable. Um, at the same time, there are a lot of startups who are trying to do pieces of that, and it's going to be a bit of a race.

• 36:41Speaker 00

  Is that not the same thing as ChatGPT?

• 36:44Speaker 01

  Um, I think that, well, I, I'll give you one quick hot take is I think that the word agent is really over, um, uh, uh, overhyped, I would say, because nothing, the, the stuff that things do today are actually agentic. They are, maybe they can go think for a little bit longer, but they don't have all the tools to go in the world and do things. So they can think, they can talk, they can see, um, and they can help you think, but they haven't yet had the tools to be able to make that reservation for you quite yet, or, um, rearrange your schedule. So I think everyone's still

• 37:15Speaker 00

  Right.

• 37:16Speaker 01

  trying for it. I don't think that anyone's accomplished it. I don't think anyone at OpenAI and ChatGPT would say that they're there yet either, but that is the holy grail. That's where everyone's going.

• 37:25Speaker 00

  I guess that's another like, yeah, are Google, Facebook, OpenAI all competing over the same consumer prize? Or do you think there's room for like multiple consumer products? Are we gonna just have like one, you know, day-to-day consumer agent?

• 37:35Speaker 01

  Great, great question. I don't think there's gonna be one. I think we as humans demand so much variety and like, you know, that, you know, there's not, there's not just one. Like, I use Facebook and Instagram. Like, there's, there's not one, right? And, yeah, I think that the framing matters. This is where product matters, like how you actually enter into the experience, what you expect, what's the mindset that you're in. These are all the nuances. They're not, there's not one app for the iPhone. There's so many.

• 38:01Speaker 00

  Yeah. Any really thoughts on like the ways they might break down?

• 38:04Speaker 01

  I think all the big companies are gonna really leverage their distribution points. And rather, if they're, if they're smart, they should. And if they execute well, they can, they can have a ton of impact. And, you know, Google with Chrome, for example, is, is pretty big. Facebook with the feed or with Instagram, et cetera, is, is pretty, is pretty big. So I think every big company has a certain springboard they can use to kind of jump into that really assistive thing. And I, I feel like it's gonna, it's gonna roughly be the same division, if you will, like.

• 38:44Speaker 00

  So whatever, like the jobs to be done that you're using these tools for today?

• 38:47Speaker 01

  Yeah, it's a good point. I think it's gonna be a jobs-to-be-done thing. I think it's gonna be a workflow thing, a jobs-to-be-done thing. It's gonna be a unique data thing, which is gonna be really important, right? And it's an orientation thing. Like, I, I mean, I use Perplexity. I think it's great. It's, I still also use Google. And sometimes the Gemini search is like, good enough, right? And I don't really think about it. So there's just so many different choices for consumers, I would say, that I think that the companies will figure out how to differentiate. And if they're smart, they're gonna leverage their distribution.

• 39:20Speaker 00

  I guess shifting gears to the investing side, curious for your take on, on, you know, the VVC question of the day. It feels like it's all about defensibility within these AI apps. And, you know, everyone likes to call them rappers and, you know, here's for your own mental model of, you know, how you think about what's defensible and not, and what get to you kind of more excited or less excited?

• 39:40Speaker 01

  Yeah, totally. Um, I'm, I feel like I have a clarity of what I believe in this one and I could be wrong, but you know.

• 39:48Speaker 00

  I feel like in the AI world, you have to have these strong opinions, but then reserve the right to change them every month when there's like a plethora of new information.

• 39:53Speaker 01

  Exactly. And given just the way that the, you know, uh, society is now, everyone's going to forget what I said anyway, in three months. Um, the first thing is, I don't think any of this stuff is, is a hot take. I think people have said this before, but I deeply believe that unique data is super important. There's so much data that is not out in the web that the models have not been trained on.

• 40:14Speaker 00

  Yeah.

• 40:14Speaker 01

  And I'll give you some examples, right? So, uh, let's say that you are someone who's doing, you know, scheduling for a health company. For example, I'm just making this up, right? Or you, maybe that's the job that you have is scheduling. There's, there are no real YouTube videos out there that, that like, are just like, no one watches people like go back and forth on a transcript on scheduling and the tonality of voice and, and that specific problem. Or let's say that you're cold calling for a sale. There's, you know, there are books you can read that the models probably read, but like how that act, the nuances of how you actually know when the pauses are a little bit too long to jump in as a salesperson on the phone, that data does not exist in the world, uh, right now in the model. So I think there's unique data there.

• 40:59Speaker 01

  There's also enterprise data, like there's, you know, those, you know, uh, McKinsey or Bain, like they have mounds of like research they've done internally and thought process and chain of thought, if you will, that is not written in any book, really. Uh, that's, that's, that's, you know, out there that the models can train on. Um, so that's, that's, I mean, that's, that's unique data and, I, I always like to say this, but I want to remind people that Facebook had unique data. The unique data was nobody really mapped out who you were really friends with. It was all in people's heads and just to digitize that and you have like, what a two, two trillion dollar company, I, I lose track now.

• 41:40Speaker 01

  That is where I think it all starts. And then the second thing is the data flywheel, which again, if you have the right workflows, you're, you have an ability to make sure that the models and your product gets continuously better. And then the third thing is how fast can you get to market and blanket the market? Because. With one and two, if you can get out there and just really take over the market very quickly, you will have a compounding advantage that will be very hard for people to catch up to.

• 42:09Speaker 00

  Totally. I mean, you get this data flywheel, obviously, from being in market and being deployed at scale. And then, you know, you basically earn the right to, you're fully deploying these customers. You're going to end up being this translation layer between whatever the latest model capabilities are.

• 42:21Speaker 01

  Well, it's actually more than that. I actually think your fine tune model, if you will, gets better at the job that you're trying to do.

• 42:27Speaker 00

  Yeah.

• 42:28Speaker 01

  Right? Because one, if you start with something differentiated, it gets you that distribution and people will sign with you. But as people work with your tool, right, and they're essentially, you can see them as just mini lablers, right? When the model gets something wrong, let's say that the agent is doing some kind of scheduling task for you. And it does one or two things wrong and you correct it, that is a unique set of data that you only get if you are the entrenched tool within that company to learn the preferences of how to actually do the scheduling and the reasoning there.

• 43:01Speaker 00

  Right. And some of that, obviously, is ways that you can update the underlying model. But then some of that is just updating the harness around the model and the workflow.

• 43:08Speaker 01

  Yeah.

• 43:09Speaker 00

  You know, because I do think that one thing, you know, talking about strong opinions loosely held is just on the proprietary data side and how much that matters, I feel like I've gone back and forth. But I think I've generally been, you know, maybe a little more bearish than you where it does feel like the number of examples you need to feed these models has never seemed to need to be massive. And in some ways, the learnings from like, oh, this weird edge case that happened because I'm deployed at 50 clients now. You know, maybe ends up updating the scaffolding you put around the model or something. But the model itself, maybe if we take the scheduling in healthcare example, there may be five different companies or 10 different companies that have enough data. And they have more than certainly the 30 other people that are starting out. But I always wonder, it feels like not sufficient on its own to differentiate.

• 43:54Speaker 00

  And I feel like in the early days, there was this hope maybe that actually you needed 10s or hundreds of thousands of examples. And it turns out, I feel like in any space we look at, there ends up being, you know, a handful of companies.

• 44:03Speaker 01

  Well, that's, again, that's the thing is that the models aren't good enough that you can just, you can fine tune a model. On like a hundred different data points.

• 44:09Speaker 00

  Totally.

• 44:10Speaker 01

  So it's not, you're right. You're totally right that the unique data may not be super necessary for you to start that flywheel. But I do think that, you know, getting into the hands of customers is super important.

• 44:21Speaker 00

  Totally.

• 44:21Speaker 01

  To start.

• 44:22Speaker 00

  Because you may end up saying like, oh, we need, you know, in this like system we're orchestrating together, we need an extra model call that checks this weird thing that happens in like this specific context that you would only learn from having deployed these models at massive scale.

• 44:33Speaker 01

  Totally. And yeah, and I think the other thing I would say is that there's a you know, when I meet founders, I can tell which founders have that grit.

• 44:43Speaker 00

  Yeah.

• 44:43Speaker 01

  I call it like, you gotta put in the elbow grease. You gotta really have the elbow grease to actually make this thing work and it can't just like, you know, one-shot the model and hope it works. You have to really understand the use case there and go from there. I think what I'm, it's more than just like the, the model's ability. But I also think that, remember the models learn behavior.

• 45:03Speaker 00

  Yeah.

• 45:03Speaker 01

  And I do want to put out there that behavior shift over time. Right? And if you just look at just the consumer market.

• 45:09Speaker 00

  Yeah.

• 45:10Speaker 01

  You know, we shifted from looking at what, you know, landscape videos to vertical videos. So I think there's always going to be an evolution of what consumers and workers and enterprise demand. And there's going to be shifting those tides, which is why I think that flywheel is important. Less so because it makes it more defensible from a, the model capabilities get better. It's more so that you can keep up and really detect when, when, when preferences change.

• 45:34Speaker 00

  Right.

• 45:34Speaker 01

  And that's, that's, that's kind of where really the magic happens.

• 45:37Speaker 00

  One thing I think, um, John from South Grass said on the podcast a while ago that I loved is like, he's the best, he's like, the best evals are just like, throw these things at your users and see what they do.

• 45:44Speaker 01

  Yeah.

• 45:45Speaker 00

  Like you'll learn way more than standing up in your lab and trying to figure out.

• 45:48Speaker 01

  Yeah. You have to have an initial hypothesis, but after that, then you've just got to go out to market and see what's, what's happening. And that's the flywheel.

• 45:53Speaker 00

  And you obviously spend a lot of time working on chat GPT. I think, you know, with GPT five, it was fascinating to see the reception because clearly, you know, this product is now used by so many different subsegments of consumers. And so, you know, I feel like some of those segments were incredibly fired up about it and others were like frustrated with it in some ways. Uh, and it was just a really interesting manifestation of just the, so many different people using this for different things. What'd you make of that all?

• 46:16Speaker 01

  Well, first I want to say is like, you know, really, ChatGPT was done by the entire team when I was there. And so I take zero credit for versus a lot of brilliant post-training, brilliant product minds, brilliant designers who really kind of made it all come together. But what you're seeing with GPT-5 is not, in my opinion, anything of a surprise. Because this is the same pattern that I dealt with when at Facebook, when we worked on revamping the homepage and everyone's like, you moved my cheese.

• 46:42Speaker 00

  Yeah, well, also News Feed famously like incredibly poorly received the first time it was launched.

• 46:48Speaker 01

  Yeah, and so that was the first version. And I worked on the version that is pretty much live now. Internally, we called it the Nile, which is the new stream architecture. And that was really hard to receive too, because it's like, oh, you moved this, or this, you know. It's a classic change management. And I don't want to belittle it, but I just think that every company that's consequential and has a really deep following is gonna come, is gonna go through that. So my thoughts are with, you know, my friends who are still on the team there of like working through that. But honestly, I saw the backlash, I was like, this is not a surprise. This is exactly what's gonna happen because, and that's the right, the right direction is to go towards a world in which there's no model picker. Because why, why, why do you need to pick that model?

• 47:30Speaker 01

  And I remember being there, we had a really successful time where we cleaned up the model picker. And I remember after I left, I was like, wow, that thing just starts growing again. And then I'm glad that we're like, hey, we're gonna do it all together. And as a user, I just love that they had that goal of collapsing it. But then here it's creeping back up again, because people do want to control. And this is the same, again, same pattern I've seen in so many different products. There's, there's a fine balance between how much magic you provide the person so that you remove a bunch of the friction and how much control is really needed and by whom. We had the same problem at News Feed. People are like, I'm seeing too many friend stories, I'm seeing too many of this. And people are like, I used to bring it back to the way it was, like I had control over it, right?

• 48:15Speaker 01

  Same thing you see on Twitter, it's like, oh, like, I want, I don't, I want, I don't want the for you thing, I just want the live stream. Everyone has a different level of control that they want. And this is gonna be an age-old question in product design of how do you figure out the most tasteful way of doing that?

• 48:29Speaker 00

  One thing I was shamelessly curious to get your take on is obviously you've, you know, before working at OpenAI, you were working at Uber. And I think one of these things that I'm really curious to see how it plays out is just the interaction between, you know, ChatGPT is this like consumer homepage and then all of these other applications that are out there. Because I can imagine if you're on the ChatGPT product team, being able to integrate Uber, DoorDash, all these other products, you know, into better agentic models will just make that that much more valuable to end consumers. And then I'm sure if you're on the product team over at Uber, you're like, please do not put me into some commodity like API call. How does this all go? And how would you think about this on both sides?

• 49:05Speaker 01

  I mean, I really think that you have it framed spot on. It's just about the incentives. And, you know, there's going to be some company out there who has more incentive to partner with some of the, you know, integration and do the integrations than others, which will force some of the others to come in. At the end of the day, I think everyone has their own business that they're optimizing for. And it just becomes a, you know, cost benefit, you know, analysis at some level. The world is going to a world where we are, we are going into a world in which simplicity and instantaneousness is what consumers demand. At some point, there won't be really a choice. If you're one of these companies, you'll just have to

• 49:49Speaker 01

  provide the best service at what you can provide, which is, why would I choose Uber versus Lyft? Right? Are my ETAs lower, et cetera? And how do I make sure that the agent is able to access that? So honestly, you know, if you look at the vision, I think that interfaces are largely going to go away at some point. You're going to have these devices in your ear that are going to be able to talk to. They're going to be proactive, not just, not just reactive. And it's going to be really interesting to see like which one of the players takes off which piece. But at the end of the day, I do think it all kind of collapses a bit. And then the interface becomes that voice model. Or then the interface becomes that, whatever that device is, which is why I think you see a lot of these companies

• 50:31Speaker 01

  going after the device. Apple, Google have an advantage there, obviously. And you see a lot of hardware initiatives in some of the other companies.

• 50:39Speaker 00

  Is that just net bad for, for these companies like Uber? Like, obviously, one of the, you know, powers in the past was having this homepage that people went to. And you're like, oh, maybe you thought about using a courier, or you thought about, you know, food or other things that you can cross sell. Is there any world in which this doesn't end up as like a net negative thing for viewers of the world?

• 50:56Speaker 01

  We'll see. I think that like the, you know, just the iPhone paradigm gave birth to, gave Uber a chance to succeed.

• 51:05Speaker 00

  Right.

• 51:05Speaker 01

  When you have GPS in every pocket.

• 51:07Speaker 00

  But arguably, the tech wasn't good enough to consolidate around one single consumer homepage that could just then access all these other apps for you.

• 51:13Speaker 01

  Yeah, yeah. I mean, I think that there are early signs of this. So in Southeast Asia, there's a lot of super apps that developed and in Asia as well. And this was born, I believe, out of, you know, the roots were in just even the Web 2.0 or Web 1.0 days, where for some of these countries, typing was harder than clicking. And so you just had this emerging behavior in those languages to, to just have a super app like what we would consider cluttered, but you know, it was highly utilitarian and useful for, for some other parts of the world. So it's been done before. So if you're saying like, is there going to be a super app for the consumer? Probably in, in the voice world. But then, again, Uber's going to have to find a way to adapt to that. And they have to figure

• 51:56Speaker 01

  out how to differentiate on some other level. So they don't get commoditized at, at that level.

• 52:00Speaker 00

  I just wonder if in aggregate, it's like, you know, you end up with a few really big super app homepages and then, you know, smaller applications that maybe are really good at, at ride ETAs, but without controlling that homepage are, are just less valuable.

• 52:11Speaker 01

  Yeah, I think the UI will become less and less important, even less so than, than today for some of those companies. But again, there has to be some value prop that, that makes it so that your agent would choose Uber on your behalf.

• 52:25Speaker 00

  Yeah. One thing I'm really curious for your take on is I feel like Ben Thompson's written a lot about the challenges of OpenAI being both a consumer and enterprise company. And like the interesting trade offs that happen in doing that, whether it's where to prioritize compute or relative effort across these things. I'm not sure if you, if you read that stuff, but I'm curious.

• 52:40Speaker 01

  I do. I love, I love that stuff.

• 52:42Speaker 00

  What's your, what's your reaction to what he's written?

• 52:44Speaker 01

  Well, one, I'm glad that I'm not there to be a part of it, just because it'll be really messy to solve. Two, I have the utmost faith in Fiji to figure out how to solve that problem. But, you know, joking aside, I think that it is going to be, it was a struggle internally because you're totally right. It's not just about enterprise and you can cut the work in many different ways. There was consumer and there was enterprise maybe and there was API and platform, right? And it was funny because when I went to OpenAI, I remember it was really hard to come up with a title because it wasn't consumer because it includes chat GPT for enterprise.

• 53:27Speaker 01

  And there's no good word for it and that maybe just shows you how complex like that place is in terms of all the work that needs to be done. But yeah, does enterprise include API? I don't know, some enterprises might want an API. So it's really hard to figure out how to organize that and I trust that Fiji will help figure that out. But it is, it is a really big conflict because at some point in time, you know, they have to figure, and the organization structure might shift from one time to another. When I left, they broke up enterprise and consumer chat GPT into two different groups. Now I hear it's back together, which makes a lot of sense because it's the same product. So I think there's going to be undulations of organizational structures that map the priorities

• 54:12Speaker 01

  of what the company is going to do. But at the end of the day, there's, there's one CEO of applications and there's going to be, there's going to be, they're going to need to figure out what is the most important next part of the strategy. But in the companies that I've been at, reorgs are not a bad thing. They are just a, reorgs are a way to express your strategy. So I think that it will become clear once they figure out exactly the strategy of what's important in the next, you know, nine months to a year, that they will make the organizational stuff match accordingly.

• 54:38Speaker 00

  Yeah, I'm always fascinated just the breadth of of things that, you know, that OpenAI tries to tackle, right? And the fact that, you know, there's still kind of a hand in the robotics space and still a hand in all sorts of different areas. And it'll be fascinating to see, you know, in some senses, it feels like we might be getting toward a new of the foundation models, like picking their lanes, like the Anthropic is like, you know, kind of seems to be punting on consumer and like doubling down on code. And but OpenAI seems as vicious as ever and like covering, you know, they're fighting like hell to win code as well alongside everything else.

• 55:04Speaker 01

  Yeah.

• 55:04Speaker 00

  It feels like OpenAI just hasn't missed, you know, in the last few years, it's like, it seems like everything, you know, the company just has done so well in so many ways. I'm curious if there's any example you can think of while you were there of something that maybe didn't work as well as you as you might have thought.

• 55:17Speaker 01

  I think, you know, I don't think it's things that didn't work, it's more just like, oh, well, we need to do it differently. I think one of the things that I was there was I was there for SearchGPT, which was a separate entry point to search. I think it was really necessary for the organization to rally behind a singular goal. Again, just again, expressing the strategy of just getting people aligned that like searching the web by default or with every call is really important, and that's what some people expect. And then over time, it's just kind of been folded in. So I don't think that was, there's no failure there, it's just like, oh, well, we found a better way to do things. And I think that's the same thing with startups and early stage when I, you know, talk to founders who are pre-part market fit.

• 55:59Speaker 01

  It's like, great, you know, figure out like, what did you learn from that? And just be really willing to do that pivot. And that adaptability, that growth mindset is just common between every single generational company that's out there.

• 56:13Speaker 00

  Totally, but it feels like it's on, you know, taken to the even more extreme in this era with just the pace at which things which do change. And and the idea that like you may have built something that was really helpful to gather earnings at learnings at that time, but then is completely obviated by the model six months later. But it's like, you don't regret having built that because that's what was required to to have all those great learnings and distribution that you ended up having.

• 56:33Speaker 01

  A hundred percent, and that's what's what's awesome about, you know, folks at OpenAI, Anthropic, and like all the companies and all the startups as well is that, you know, the founders are the ones who, you know, are just like, oh, yeah, that that I learned something, let's on to the next one.

• 56:49Speaker 00

  Yeah.

• 56:49Speaker 01

  And that's that's what's awesome about, you know, kind of being in this in the space and seeing a bunch of awesome founders. And that's how amazing products are built.

• 56:58Speaker 00

  I mean, you've worked at some of the most legendary cultures in Silicon Valley. I'm curious how you contextualize the OpenAI culture to some of the other places you've worked.

• 57:05Speaker 01

  In all the cultures I've had the pleasure of working in, ambition was there. I think OpenAI, I would say, dreamed bigger than any of the other companies that I've been at. Where, you know, the all hands there, you would just leave with your feeling like your brain just got stretched a little bit in terms of what's potentially possible when AGI comes and like really shooting for that of like agents that can do research that will self-repetuate and kind of keep on making advancements. That level of optimism, I'm an optimistic person and sometimes as an investor you got

• 57:47Speaker 01

  to tone down that optimism. I'm a very optimistic person, but that stretched my level of optimism and it was so inspiring and that's that was truly just different. And I'm not saying the other cultures I've been a part of were not optimistic. It's just a completely different level.

• 58:03Speaker 00

  Yeah. This has been a fascinating conversation. We always like to end our interviews with a quickfire round where we stuff in some overly broad questions at the end.

• 58:09Speaker 01

  Great.

• 58:10Speaker 00

  So maybe to start, what's one thing that's overhyped and underhyped in the AI world today?

• 58:14Speaker 01

  Oof. I've said this before, overhyped is agents today. Today. It's overhyped because they don't, they don't, they don't do things that agents do yet. And we're building towards that, but we're, but I'd love for us to use that word when we get closer to when that feels to be true intellectually honest and true. Underhyped, I ought to go with evals. I've said this before, but like truly it's one of those things. It's like, wait, that's what you, what are you talking about? Like that's what you need? It's like, yeah, that's the elbow grease that you put in to make and shape these models. And it just sounds so simple and maybe sounds so like kind of busy worky, but that's the unlock. And I think it's completely undervalued even today.

• 58:55Speaker 00

  Yeah. Do you think any, like, obviously there's all this tooling that's emerged around evals and it still seems kind of unclear. Is that like a problem that is so bespoke to each domain that there's not a common set of characteristics or that there is like?

• 59:06Speaker 01

  I think there's a common, I think that there is. I think it's just, it sometimes just doesn't get the love that it requires. And you see some of these, like, there's some startups out there who do RL environments and, you know, kind of, they're really in that space. And so it's getting some love, but I think that it's still under, it's not talked about as much because it doesn't feel as sexy.

• 59:26Speaker 00

  Totally. I mean, it seems like the most important thing for a lot of these applications. I feel like the big question is whether it can be productized or whether it ends up being these like more bespoke consulting-like arrangements.

• 59:33Speaker 01

  Sure, sure.

• 59:33Speaker 00

  But I guess to be determined.

• 59:35Speaker 01

  Yeah.

• 59:35Speaker 00

  If you weren't investing right now and you were to go start an AI app and build one, what, you know, what would you go build?

• 59:43Speaker 01

  Oh, my goodness. I think I would probably

• 59:48Speaker 01

  go deep into some business that a lot of people think is boring, that no one pays attention to, that is deeply optimizable in terms of wasted human effort or human effort that could be better leveraged. I'll be more specific. I think that this AI voice thing is incredible. Like, the voice, I don't think we've even scratched the surface of what AI voice can unlock in terms of helping business processes, et cetera. I know that sounds like probably out of character, given I've worked at Uber and Instagram and Facebook,

• 1:00:27Speaker 01

  but I've always been super fascinated by how the people with the right insights and just how deep some of these industries go, right? And maybe that's why I was really attracted to Uber when I joined, is that the, the, the amount of inefficiency in the world of just transporting, you know, atoms from one port to another, it was such a great, you know, nerdy problem that I love. So I think that would be something in that, in that way where, whether it's some true agent or some voice agent that's able to start to free people from work that, you know, it's not the work that they want to be doing anyway. And, and, you know, again, it doesn't sound super exciting, but like the call centers is like one of those areas that I'm like, wow, like, we can, we can up level that.

• 1:01:12Speaker 00

  You've obviously, you know, you mentioned before this OpenAI culture of extreme optimism and you've, I imagine over the last few years, thought a lot about, you know, post AGI world and where things are headed. You know, we were talking before the show about, about your kids. I'm curious, like, as you've reflected on where this, where the world is headed, has it changed the way you've thought about like parenting at all or, you know, the skills you want to impart on your kids or anything like that?

• 1:01:33Speaker 01

  Yeah, I have. And look, I don't know if I'm doing it right or not, but I think that it's really important to foster curiosity in the kids because and also just to show them that they can realize their vision, whatever it may be. I see this in my kids when they go and like create an art project that they're really proud of. But guess what? When they grow up, they're going to have tools that are beyond a current imaginations of what's possible. I can't wait to see the creativity that they, you know, bring upon the world with the tools of tomorrow, or even the tools of today that they don't have access to already. So that to me, the idea of curiosity and the boldness to know that whatever you dream up, you can for

• 1:02:14Speaker 01

  certain do. For the kids of today, they can for certain achieve those visions. That's, I think, a big part of it. And I believe the fundamentals are important. I mean, you have to deeply understand mathematical concepts to appreciate, you know, what, to think, you know, in a structured way. Coding is one thing that like, even though, you know, we have Cursor and the coding models, I believe that coding is about learning a way of thinking, of taking a problem and subdividing it into component parts, rather than the actual code writing itself. And that way, Cursor is such a blessing, right? To be able to say, well, it's not just a high level idea,

• 1:02:58Speaker 01

  but like, to be able to know, well, what is the level of abstraction that I want to architect this thing? And how do I quickly get that to fruition? So that this, this company or this product or this tool I want to build is framed in the right way and has all the right affordances.

• 1:03:14Speaker 00

  Yeah, I love that. Well, this has been a fascinating conversation. I'm sure there's all sorts of threads folks want to pull on. I'll just leave the last word to you. Like, where can folks go to learn more about you? Anywhere, the floor is yours.

• 1:03:24Speaker 01

  Yeah, so I'd love to meet some founders. I love people who have that clarity of thought, that grit, that ambition, that elbow grease to like, they see something really unique in the market. I get a lot of inbound, so the best way to reach me is to find a person who knows me and get a warm intro. And I respond pretty quickly, but I'm really looking forward to seeing all the creativity of these early-stage founders that are out there, and I can't wait to work with them.

• 1:03:50Speaker 00

  Amazing. Well, thanks so much. Really appreciate it.

• 1:03:52Speaker 01

  It's been really fun.