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Peter Deng: Why Uber's product was price and ETA, not pixels

How chess-move planning beats sprinting once a product scales up; Uber Reserve grew into a $5 billion line because operations carried the rider, not UI.

Lenny RachitskyhostPeter Dengguest
Jun 22, 20251h 55mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 5:41

    Introduction to Peter Deng

    1. LR

      You built and led Facebook newsfeeds. You shipped the Messenger app as its own app. You launched ChatGPT Enterprise. What's an important lesson you've learned about what it takes to succeed building something from idea to one to billions?

    2. PD

      You have to plan your chess moves out in advance. You have to really think before you act and build systems that were going to let you go sustainably faster.

    3. LR

      What's the most counterintuitive lesson you've learned?

    4. PD

      Sometimes your product actually doesn't matter. At Uber I learned this because really the price and the ETA at Uber was the product. Looking at it from a holistic perspective, we humans consume the entirety of the product. It's not to say that you shouldn't fix the bug, but it doesn't have as much of an impact as something that is more important to people.

    5. LR

      What's one specific thing you think will change in a big way with AI that people don't think enough about?

    6. PD

      Education is going to change. My son, he was nine at the time, built a custom GPT that you can type in any topic and it would give you a sentence that had every letter of the English alphabet. Isn't that mind-blowing? I can already see his brain rewiring.

    7. LR

      What's one thing you look for in people you hire?

    8. PD

      In six months if I'm telling you what to do, I've hired the wrong person. It helps me and the person operate on a different level where the goal is not, "Did you hit this OKR?" The meta goal becomes, "Are we calibrating enough? Are we actually getting to a spot where in six months you're the one telling me what needs to be done?"

    9. LR

      What's something you've learned about what it takes to be a great product person?

    10. PD

      I think there are five different types of product managers. Number one is...

    11. LR

      (instrumental music) Today my guest is Peter Deng. Peter is maybe the most under-the-radar impactful product leader that you have never heard of. I often say that the best product people are not the people on Twitter and LinkedIn sharing advice, but the people who don't have time to do that because they're too busy doing the work. Peter is the epitome of this. He was VP of product at OpenAI where he oversaw product design and engineering for ChatGPT and helped ship ChatGPT Enterprise, Voice, Memory, Desktop, Custom GPTs, and more. He also oversaw and built their first growth team. He was the first head of product at Instagram where he worked closely with Mike and Kevin and oversaw all product development including on content sharing, ads, growth, even helped build out their design and user research functions. He was also head of the Rider product team at Uber where he oversaw everything in the Rider app including big improvements to pickups and drop-offs at Uber Pool and airports. He also helped the team launch new products including Uber Reserve which is now approaching a $5 billion a year business. He also spent nearly 10 years at Facebook as their fourth ever product manager where he built and led the team behind the current newsfeed product, the standalone Messenger app, also Photos and Groups and Homepage and Profiles. He was also chief product officer at Airtable where he helped the company systemize how they build products and transitioned to Enterprise. He also led product management at Oculus. These days, he is general partner at Felicis where he's able to bring everything he's learned to more founders as an investor. He has never done a podcast before or shared any of these lessons or stories publicly, so you are in for a real treat. A huge thank you to Eric Antonow, Nick Turley, Lauren Mohammedi, Joanne Zhang, and Sandeep Jain for contributing questions and topics for this conversation. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter you get a year free of a bunch of amazing products including Bolt, Linear, Superhuman, Notion, Perplexity, and Granola. Check it out at lennysnewsletter.com and click Bundle. With that I bring you Peter Deng. Many of you are building AI products, which is why I'm very excited to chat with Brandon Fu, founder and CEO of Paragon. Hey, Brandon.

    12. NA

      Hey, Lenny. Thanks for having me.

    13. LR

      So, integrations have become a big deal for AI products. Why is that?

    14. NA

      Integrations are mission critical for AI for two reasons. First, AI products need context from their customer's business data such as Google Drive files, Slack messages or CRM records. Second, for AI products to automate work on behalf of users, AI agents need to be able to take action across these different third party tools.

    15. LR

      So where does Paragon fit in to all this?

    16. NA

      Well, these integrations are a pain to build and that's why Paragon provides an embedded platform that enables engineers to ship these product integrations in just days instead of months across every use case from RAG data ingestion to agentic actions.

    17. LR

      And I know from firsthand experience that maintenance is even harder than just building it for the first time.

    18. NA

      Exactly. We believe product teams should focus engineering efforts on competitive advantages, not integrations. That's why companies like You.com, AI21 and hundreds of others use Paragon to accelerate their integration strategy.

    19. LR

      If you want to avoid wasting months of engineering on integrations that your customers need, check out Paragon at useparagon.com/lenny. This episode is brought to you by Pragmatic Institute, the trusted leader in product expertise. Pragmatic Institute helps product professionals turn ideas into impact through proven courses, workshops and certifications designed for real world success. For over 30 years they've trained more than 250,000 product leaders at companies like Google, Microsoft and Salesforce, equipping them with practical strategies to build and scale market winning products. Pragmatic's full-time instructors each bring over 25 years of hands-on leadership experience, teaching strategies proven to deliver real world results. And it's not just about what you learn, it's also about who you learn it with. Completing a course connects you to an active community of over 40,000 product professionals. You'll engage in meaningful conversations, collaborate with peers and mentors and gain direct instructor access to refine your strategies and stay ahead of trends. Get 20% off with code LENNY20 at pragmaticinstitute.com/lenny.

  2. 5:41 – 11:35

    AI and AGI insights

    1. LR

      Peter, thank you so much for being here and welcome to the podcast.

    2. PD

      Thank you. I'm so thrilled to be here, really honored. Looking forward to having a great time here.

    3. LR

      As we were preparing for this conversation we were jamming on what we should focus on, there's so much that we're going to talk about, but something that you said was really interesting and I'm really excited to start with this, which is that you've, uh, you've always felt that you haven't been able to say all the things you really think and feel because you've been within corporations, PR people keeping you on message.... and this is-

    4. PD

      Yep.

    5. LR

      ... the first time that you feel free, to share.

    6. PD

      First time.

    7. LR

      Okay. So, first of all, just how does that feel? Second of all, tell us w- something that you've been wanting to share that you can finally talk about.

    8. PD

      Well, it, it feels really good. So I, I, let me ask, uh, I love it that you're starting with a spicy question here. Um, and, um, let me share some more context behind it.

    9. LR

      Yeah.

    10. PD

      Um, it's, you know, I'm here to speak more freely but it's not really what you think. I'm not here to divulge any secrets, uh, from the companies. But naturally, I'm kind of a storyteller, I'm kind of an introvert, so this podcast, I feel like I have the ability to go deeper with you on, uh, any topic and kind of add the context. Because I think the n- without some of the context, some of my spicy takes or whatnot might be taken out of context. And just not having the time pressure, not feeling like there's some, you know, PR message I have to hit is just really freeing. So, it feels awesome. Really anything that is on your mind that you th- find interesting to your, to your listeners, I'm here for it. And yeah, excited.

    11. LR

      Something I always tell guests and I don't want people to take this out of context also, but I always describe myself as a re- a reverse journalist, where I want the guest to be the best version of themselves. I never want to catch people off guard or just say-

    12. PD

      Yeah.

    13. LR

      ... something they never meant to say. So, uh-

    14. PD

      That's great.

    15. LR

      ... it's a safe space. Okay, but still, is there anything that you (laughs) want to share or that might be interesting to share that you've been wanting to share that you haven't been able to? Is there anything along those lines?

    16. PD

      I mean, I always get this question around sort of, you know, AGI, is it coming? Is it gonna, is it gonna-

    17. LR

      Yes. (laughs) Tell us.

    18. PD

      ... solve everything, right?

    19. LR

      What have you seen?

    20. PD

      I mean, it's so interesting because, you know, when I was at OpenAI, it was around the time that people were really scared of AI and, you know, "Oh, it's gonna, you know, get rid of humans," or, "It's gonna just, you know, do all these things." But with every technology, I think everyone's been just kind of taking some time to acclimate to it. And I think with AGI, it's a similar thing, which is it's so far out that everyone's like, "Well, is it, is it, what's, what's our world gonna be like?" And the real answer is, like, none of us really know. But in terms of solving problems, I think some people believe AGI's gonna solve everything, but I don't think so. Um, AGI is just necessary but not sufficient. A lot of the value is still gonna require a bunch of hustle from a lot of builders to really turn that new source of energy and channel it into something that we humans want to use that solves some of our problems, and that hustle is gonna be required, that elbow grease is gonna be required to really make AGI something useful.

    21. LR

      Your point is that people think AGI hits, all of a sudden all the jobs are gone, AGI's doing everything. Like, 'cause I think this is a o- optimistic message that things will be okay if AGI... Basically, uh, AGI being, uh, and I'm curious (laughs) if you have a clear definition, but AGI being, uh, AI being just basically as smart as humans.

    22. PD

      Look, I'm, I, I won't-

    23. LR

      Generally.

    24. PD

      ... claim to be an expert on this at all. Um, but I, I just, I think that with every technology that's come out, we've been able to harness it and it takes a lot of harnessing. I think I'm gonna use that word very deliberately, right? I'll, I'll use something really basic. What seems, uh, obvious today is that, you know, there was a time when databases were all the rage. It's like, oh my goodness, you can store a bunch of data and you can query it really quickly, and like, imagine all the possibilities. And I think that a lot of amazing entrepreneurs and builders, you know, built some really great products on top of databases, right? (laughs) In fact, that's kind of the basis of all the stuff that we're seeing today. And it seems so obvious today, but I, I don't know, maybe in, in, you know, 10 years, 15 years when we look back, it's like, of course it made sense that we have this super intelligent, you know, thinking machine, but it requires, uh, product builders to be able to go in there and say, "How do we channel this energy to make it something that we as humans love to use and want to use?"

    25. LR

      I love the optimism around this. It's just like, things will not go crazy once, uh, computers are as generally intelligent as, as humans.

    26. PD

      I, I, I think that's, that's exactly the, the, the, what I'm trying to say. And I think that e- again, every technology people, uh, have this fear, right? And I remember reading or s- uh, watching a documentary once, and they were talking about how when the bicycle came out people were like, "Oh my goodness, this is gonna be the end of all things." And it, again, it sounds-

    27. LR

      The bicycle.

    28. PD

      ... silly today. (laughs) Right? Because you're like, "Bicycles? Really?" But then if you put yourself in the context and the mindset of a previous generation, which, you know, our, the next generation will be looking back at this podcast (laughs) in that previous generation, I think that, you know, again, I, I think optimistically things are gonna be okay. We're gonna adapt. Um, and this was actually one of the things that I talked about with my fresh- friend Josh Constine at, uh, South by Southwest is this idea that humans will always co-evolve with technology. And I think that that co-evolution is already happening. If you take a look at sort of, um, there was a lot of, uh, a fear of AI just when ChatGPT came out, but you know, when you start to get familiar with it things, that kind of things change, and then you are able to, to evolve from being, you know, fearful to, uh, familiar and to, and to go all the way to having this, this mastery of this thing of like, oh my goodness, like, look at all the startups that are happening now, all the things that we can build, right? In just over 18 months, I would say we look back and there's been an attitude shift, right? And so, I guess part of my optimism comes from if you look back 18 months and you look forward 18 months, like, might it be the same thing for something that we're, we're chasing now?

  3. 11:35 – 16:53

    The future of education with AI

    1. PD

    2. LR

      Let me follow this AI thread a little bit more and then we can move on to other things. I feel like-

    3. PD

      Sure.

    4. LR

      ... every c- conversation (laughs) there's like a time to AI conversation then it's like, okay, look, there's other things that also matter. So let me ask you this, the question, what's, what's one specific thing you think will change in a big way with AI that people don't think enough about?

    5. PD

      I think education is gonna change in a big way. And I think a lot about this because, um, I'm involved in my kids' school, uh, quite a bit. And that's something I've done after I, I left OpenAI. And what's fascinating to me is that, you know, watching my son who got to, you know, dog food a bunch of the OpenAI stuff before it was public, I think that was, uh, I think I can sa- safely say that, that seems okay. And, uh, when he was...... was playing with, like, you know, ChatGPT and some of the, the latest models, and he's, he's, uh, he was nine at the time. I can already see his brain rewiring, right? He was starting to ask questions, and he never heard the word prompt before, but his, like... Just, this is how awesome the human mind is. Because he was exposed to this technology at an early age, some things just are unlocked, um, and I think that you're able to think differently. And I'll, I'll give you a specific example of, of what I mean here. You know, he, you know, he goes to Python class, right, and he's, he's coding. Now, I don't actually think he's gonna have to code when he grows up, I think that's gonna be a solved problem, but it's a val- very valuable skill because I think learning to program is learning how to think struct- in a structured way, right, in a very semantic way, a, um, a systematic way. And, you know, he was, he, he was prompting, uh, ChatGPT with some really crazy things that I never even thought of, and one of the things was, "Hey, ChatGPT, can you give me a sentence that has every letter in the alphabet along the theme of oceans or along the theme of space?" And the reason this kind of blew my mind is because in traditional programming, you couldn't write that program. You can't say to, you know, in, in Python, like, "Oh, write a function that goes and, and formulate..." I mean, it's a really difficult function to write, but for, you know, him to be able to think of that prompt, which is really cool 'cause he built a custom GPT that you can type in any topic and it would give you a sentence that had every letter of the English alphabet, kind of like the, uh, the quick brown fox jumped-

    6. LR

      Mm-hmm.

    7. PD

      ... over the lazy dog.

    8. LR

      I was gonna mention.

    9. PD

      Right? Like, isn't that mind-blowing? It's like, that, that- that he can now, he, at age nine he could think about that, whereas me at age nine, I was playing with LEGOs and, like, maybe QBasic, right? And so this idea of how young humans' brains will evolve because of this new tool we have is gonna change the way I think we're gonna do education, right? And I'll be very honest, I'm not an expert in education, but I just thought a lot about it, and, you know, one, one thing I'm gonna be, I think is gonna be really important in the future is being able to figure out how to ask the right questions. You know, we humans are in- i- inherently inquisitive, but, you know, being inquisitive and turning that into the right questions to, you know, prompt or ask AI, which is gonna be, again, something that everyone's gonna have access to, is gonna be a, a differentiator for sort of what kind of work can be done, right? And I, I, I, I, um, the, the analogy I'll draw is when, when the calculator was invented, you know, people didn't stop doing math, right? They just did higher level math, and it frees the mind up to do other things and think more at a, at a higher level of abstraction, and I think we gotta prepare our kids on thinking about, "Well, how do you think at a higher level of abstraction?" And this has happened before, right? I think Google has made memory kind of obsolete, like, you don't have to memorize facts anymore, you can just Google it, right? And the next phase will be something around, "Well, code will just appear if you summon it, so what are the things that, you know, people will think about and the skills we have to develop, uh, that are at the next level of abstraction, right, that tap into our creativity, that tap into our curiosity?" That's gonna be really interesting, so I think education is gonna change dramatically, just like how progressive education in the past switched from memorization of, like, multiplication tables into something that's a little bit more, you know, kind of higher level, um, a higher level of thinking, and I think that's gonna, that's gonna be one of those big areas.

    10. LR

      Hmm. This makes me think about an NPR story I was just listening to where they were following professors using ChatGPT to create their curriculum. There's a lot of talk of students using ChatGPT, cheating, you know, having ChatGPT write their, uh, essays, but teachers are using ChatGPT in a big way, and, and then, uh, (laughs) students are rating professors, uh, badly 'cause they notice they're using ChatGPT for their curriculum.

    11. PD

      Yeah.

    12. LR

      So it's kind of this, like, arms race.

    13. PD

      Well, well, but it's also interesting because then that's, that goes further, it shows further, though.

    14. LR

      It's a revolution.

    15. PD

      Like, you know-

    16. LR

      Yeah.

    17. PD

      ... the whole system has to change, right? Because, again, I still believe that human brains are inc- inherently inquisitive and that we still need development in some way, but how that's gonna develop, I'm, I'm fascinated to watch how that plays

  4. 16:53 – 21:01

    The power of language in leadership

    1. PD

      out.

    2. LR

      I want to get back to product, but first of all, I know something that, uh, you think a lot about along these lines. This came up in many conversations I had with folks that you worked with, is your, uh, emphasis on the power and importance of language, being really good at thinking about th- the words you use, both in writing and speaking. Just talk about how you think about that, just the importance and power of language as a leader.

    3. PD

      I remember taking this class that really stuck with me in college. It was called Language and Thought, um, and it was taught by Herbert Clark, and he had this thesis that kind of blew my mind, which is that, you know, language actually affects the way you think. That's one of the parts of the thesis. And I, once I heard that and read that in his book and listened to the lecture, I couldn't stop thinking about that because it just rang so true, right? I grew up speaking Chinese and I think that there's a lot of things of just the Chinese language that, you know, I feel like I noticed I thought differently when I learned English, right? And there were some studies around this too, I think that there's, um... I think in, in, in, in... I, I, I'm not sure exactly if this is true, I have to go ch- check up on this, but I think in Russian there, there are two different words for, like, a blue. There's like a greenish blue and a bright blue or something.

    4. LR

      I speak Russian, uh, and I-

    5. PD

      Oh.

    6. LR

      ... my, my, but it's like, uh, I was u- I s- I moved to the US when I was six, and so my Russian's not great, so I'm trying to think of this as you say it, but keep, keep going.

    7. PD

      Well, I mean, I, so, so then this is great, so I, I, I need to get a way to, to, to validate this, but...You know, from what I remember, because there were these two different words for this different shades of blue, Russian speakers who then learned English had an easier time distinguishing between these two shades of blue than... And a faster time doing so than people who had just grown up speaking English. Um, so I read some studies on that. And also, there's some other languages that don't actually have a word for blue, I think, and then that's actually really hard for them to distinguish over time. So, that really stuck with me and, and I think that it's... It kind of rings true. So when I, you know... How I put it in practice is that when I make slide decks, I gave a presentation to a, a class a couple of weeks ago, and there were probably a total of 20 words on the entire slide deck. And I spent hours obsessing over them because I really wanted to make sure I captured the right essence of what I was trying to say. And I think that crafting is really important when you're working in product because if you're sitting down and you're writing a vision doc or you're writing a PRD, and y- if you don't pay attention to the words you use, and you're not intentional about it, those have downstream effects. Like, people might misinterpret things, the connotations may not actually come through. And so I, I really am very careful about it because I think that the... There's a multiplicative effect and a downstream effect for using the wrong word. Um, and I, I, I really believe in that kind of language affecting thought, um, uh, thesis, which is why I've just really, really paid attention to that.

    8. LR

      Mm-hmm. Yeah. And I, I feel like AI can help you with that too. Um-

    9. PD

      Yes, exactly.

    10. LR

      ... we had an episo-

    11. PD

      Oh, actually-

    12. LR

      Yeah.

    13. PD

      ... speaking of AI, actually, that's a really interesting point.

    14. LR

      Mm-hmm.

    15. PD

      I think it's really interesting and kind of poetic that... And, and, and fitting, that, uh, the breakthrough in artificial intelligence came from large language models, right? Like, that's... Uh, it's interesting to me because, you know, there's... With every word and every sentence, so much of the knowledge is encapsulated and shaped. And when ChatGPT does something really interesting, I, I tell people it's oftentimes just writing Python code and interpreting it. And Python is a language, yet again, right? So, I think that there's something really interesting where, like, the condensation of human thought in language has... Is related to the LLMs and the advancements in area that we have today.

    16. LR

      I think it was Ilya on, uh, Dwarkesh's podcast where he was talking about how you may think LMs are just like, oh, just predicting the next word, what's the big deal? But in order to do that, it has to understand the universe-

    17. PD

      Yes.

    18. LR

      ... and everything in the world that has ever happened and existed and everything anyone's ever written-

    19. PD

      Yeah.

    20. LR

      ... to predict the next word.

    21. PD

      Yeah.

  5. 21:01 – 36:44

    Building iconic products

    1. PD

      Love it.

    2. LR

      Yeah. Okay, so let me, let me zoom out a little bit and shift a little bit to just product in general.

    3. PD

      Sure.

    4. LR

      You've worked o- at and built some of the most iconic products in history. You worked at OpenAI, Facebook, Uber, head of product at Instagram. So, let me just ask you this question and see where this goes. What's the most counterintuitive lesson you've learned about building products or leading teams that goes against common wisdom?

    5. PD

      I think one thing that... It's a really hard lesson that I learned at Uber, uh, which is sometimes your product actually doesn't matter. And by product I mean sort of the pixels you put on the screen or things that you build in your, in your, in your, um, uh, mobile app. Um, and at Uber I learned this because, you know, it, it, it pains me to say this, but really, like, the price and the ETA at Uber was the product. And I think a lot of times, you know, people at tech companies think of the product as just this digital manifestation. But looking at it from a holistic perspective, you know, we humans consume the entirety of the product, and I think that's... That was one of the things that I, I learned, the lessons that I learned that was, like, really kind of hard-hitting, right? That, um, sometimes the pixels don't matter as much as you think, right? And you fix a certain bug, um, it's not to say that you shouldn't fix the bug, but it doesn't have as much of an impact as something that is more important to people, like a price or ETA. And this w- happens a lot in, you know, B2B products where it's, uh, not just about, you know, how, uh... It's great that your product is, is well-loved by its end users but, you know, doesn't make good business sense is one of those, those hard lessons I learned as a very, uh, bright-eyed, bushy-tailed sort of design-based, uh, product manager, uh, going into Uber. I think the other insight that I had, or rather, uh, other thought I had the other day was just the idea that, like so many of the tech companies today... This is kind of counterintuitive. So many of the tech companies that are most valuable today didn't really start with any technological breakthrough. They were built on some kind of technological breakthrough and they ended up building a lot more technology, but really, a lot of these companies, like Facebook for example, just put in the hard work, right? The elbow grease to... And especially in the early stages to take, you know, essentially a database of human connections and build something valuable on top of it, and then keep on polishing and iterating that product and, and coming up with new ones like news feed and photo tagging, were just... You know, kind of came out of just really paying attention to what people wanted. And some of the ideas are super simple, and it's not something that came out of the lab, right? So Uber, for example, took the fact that everyone had these GPS devices in their pockets, and they didn't invent the GPS device, but they were able to take that and the fact that people had cars and people wanted to kind of, uh, um, you know, get around, and there was a human need, and they just put the... Connected the dots and put everything together. Um, and eventually built a ton of tech to predict the right marketplace and pricing, et cetera. But largely, like, that's a very valuable...... tech company, but it's largely an operations company. And I, I want to give a huge shout-out to my colleagues there who run, you know, kind of Uber Eats and, and Uber, uh, rides from a, from a, a operations perspective, 'cause truly like, that was one of the biggest kind of business model hacks that I've seen, right? And so, I, I think that's, you know, at Silicon Valley it gets lost a lot. It's like, "Oh, this is a new tech company." Oftentimes, some of the most valuable ones are just the ones that are just building what people need on top of existing tech.

    6. LR

      This is such, there's so much (laughs) to say here. I, I, I love it. Uh, and this is coming from p- someone that led the Uber Rider product team, uh, and worked at Facebook and head of product Instagram. You know, it's like, it means a lot coming from someone like you, not someone, you know, that's like, not in product especially.

    7. PD

      Yeah, I mean, um, just to go further on the Instagram part, like, it's the, the idea was super simple. It was, it was showing photos and, and visual sharing, but the craft that Mike and Kevin had in putting in the hard work to get the product just right, that's what made it really take off, right? That's a great example. I, I'd forgotten about Instagram, um, but, uh, how could I? But, you know, it wasn't anything that any other company couldn't have done, but it was that product taste that Kevin and Mike had, and the conviction that there's a certain sort of vibe, if you will, uh, that people wanted, and building that and iterating. I mean, and l- look at it now. It's, it's a, it's a core part of our lives. Visual sharing, they really solved it. (laughs)

    8. LR

      Yeah, I just had Mike, uh, Krieger on the podcast. Um, so it's interesting, there's two tensions here. One is just like, the product doesn't matter in a lot of really successful companies. It's secondary to the cars, the drivers, the, the GPS in the phone. And then on the other hand, uh, technolo- there doesn't need to be a technological breakthrough for, to build a huge business. Is there, it's almost like, if the, uh, if there's no technological breakthrough, then the product matters, like Facebook is an example. Basically, it's like a database of connections, but what allowed the, and Instagram, what allowed them to be breakthrough, and there was, you know, classically competitors at the time, uh, was the experience was a lot better, and then maybe on the flip side, if the, if the experience doesn't matter, then it's, the breakthrough is on the operations and other... Does that resonate? Is that kinda what you're saying?

    9. PD

      It does resonate. I think, I think both, uh, have to be true, but I also, I would say that like, even if you did found a company that has a huge technological breakthrough, uh, very shortly, I think that, you know, kind of the, the, the product experience will start mattering, right? Because, you know, how long does that technological advantage last, right? Before humans wisen up to be like, "Well, this is not the product I want to use. I want to use it a little bit differently, and this is more ergonomic for me," et cetera. So, I think, I think that that's, what you said is, is, is a beautiful summary. I, I also think that a point in time in a company's history will also determine what is gonna be more important.

    10. LR

      This, this is all especially interesting for companies building on top of LLMs and AI infrastructure, where you're essentially saying you don't need to have some kind of technological breakthrough to build something valuable if-

    11. PD

      Mm-hmm.

    12. LR

      ... you can create a really special unique experience that unlocks the potential of this super intelligence.

    13. PD

      I think that's right. And, and I have some more thoughts on just sort of the companies that are building on top of LLMs that are just, you know, e- uh, that's a slightly different thing, I would say. I think that for them, you know, having the right data and the right data flywheels is so important.

    14. LR

      Like proprietary data, especially.

    15. PD

      Exactly.

    16. LR

      Yeah.

    17. PD

      Um, and, and the flywheel part is, is, is just-

    18. LR

      Mm-hmm.

    19. PD

      ... you know, you can start with proprietary data, but the flywheel is really just sort of how do you continue to maintain that and generate that? And the second thing is, again, it's, it's the workflow. So it's the, it's the ergonomics of, how does it actually integrate into people's lives? And that is gonna be, uh, more and more important.

    20. LR

      Let's actually spend more time there, 'cause I have, a lot of people are thinking about this. Feels like, feels like everybody's trying to start a company these days with a- you know, with AI, um, enabling so much more. And so, I think a lot of people are just curious, where should they spend time? And so I think this is actually really interesting. So what I'm hearing here is, two things to think about to create s- any kind of moat, defensibility against, say, foundational models coming to eat your lunch at other companies. Uh, what sort of data can you, uh, acquire that is proprietary and create a flywheel to generate more of that data? And then, um, the other piece is, how do you fit into a very specific, like basically vertical that, uh, you understand really well that fits into their existing workflow? Is that-

    21. PD

      Yeah.

    22. LR

      ... roughly right?

    23. PD

      Well it's, again, this is, this is, this is something we can unpack for a long time, right?

    24. LR

      Yeah.

    25. PD

      Because, um, you know, with any product that you wanna build, there's gonna be incumbents that have distribution advantages, but I do have this thesis that there are certain products that will be able to break through those advantages of the distribution of the other companies. Uh, but you have to kind of overcome a pretty high bar of, your product has to be so much better, right? That's, I think that's, that's one thing. But yeah, I think the data flywheel thing is really interesting because w- you know, the, the, the models will get really good at whatever data you show it. And, and that's, that's one of the things, that people just think that AI's such a magic wand, but no, it's like, if it's been trained on the right data, it's gonna do the thing that it's been trained on. Um, it's very malleable, um, so being very mindful of the data that you have access to to start your flywheel going, and what you can do to keep on going with that flywheel is gonna be a, a critical thing for, for anyone who's starting a company today.

    26. LR

      So let's make that even more specific. When you talk about this, I think about this, the CEO of Windsurf was on the podcast, and he talked a lot about how they have all this really unique data about which recommendations of code, uh, snippets people accept and reject, and actually launched their own model, I think, based on that. Is that-

    27. PD

      Yeah.

    28. LR

      ... is that example? Any other examples to make this tangible?

    29. PD

      That's a perfect example. Um, there are some companies I've invested in that aren't public yet that have their own sort of take on that, which is, um, really interesting to be able to, uh, to take, um, sort of whatever activity is in their product to get smarter at the thing that they are doing. Again, which is why I think the data flywheel and the, the, the workflow go so hand-in-hand together, right? Because...If you are solving something actually valuable for businesses, for people, and there's a lot of that, um, uh, attention that's being paid to it, a lot of work is being done through it, you're gonna have that edge. And, you know, this is where I see, again, startups in very different, uh, markets who have this insight, who understand this very deeply, and are not just trying to zero shot everything and be like, "No, no, no. Like, this is how we're gonna build it to make the product genuinely useful so that it can get genuinely more useful over time." And that is gonna be amazing because, you know, as a consumer of any of these products, we're gonna benefit.

    30. LR

      What I'm hearing here is also if you don't have proprietary data or unique data, you can still have a chance by building this flywheel where you collect that data through your usage. For example, Windsurf, they all built on Claude 3.5 and then now they have all this unique data, and now they're launching-

  6. 36:44 – 41:56

    Scaling from zero to 100

    1. PD

    2. LR

      Yes (laughs) . That's a good sign. Okay. Uh, so we're talking about startups, building new companies. I wanna follow this thread a little bit.

    3. PD

      Sure.

    4. LR

      I feel like you've built more products th- from zero to one to scale than may- maybe most anyone else across all the companies that you've worked at. I'm gonna do a quick rundown of some of the things you've done, and, uh, I'm, I'm gonna miss a bunch, but let's see. You built and led the Facebook news feed, the current version of it. You built the new groups experience, chat, and messages. You shipped the Messenger app as its own app. That was a, that was one of your projects. You, uh, led UberPool low-cost rides. Uh, you launched ChatGPT Enterprise. You shipped Voice and Vision, memory-custom GPTs, just refreshing the whole design of ChatGPT. Uh, many more things. A lot of work at Airtable. Obviously, also, uh, Oculus. Uh, these are just some examples. In the intro, I'm gonna try to go through all these things. So, all that to say, I feel like you've seen a lot of what works and doesn't work building from idea from zero, essentially, to one to scale. So let me just ask you this question. What's a, what's an important lesson you've learned about what it takes to succeed building something from idea to one to billions?

    5. PD

      Yeah. Um, thank you. And that was a good trip down memory lane, too, um, uh, wh- when you read that off. So I think the first thing I would say... And, and, you know, going from zero to one is different than going from one to 100. And when you are in the one to 100 phase, which is a lot of the time that I spent, you know, is, is in the one to 100 phase, um, we, you know, were able to... We quadrupled Instagram, uh, usage in two years. That was very much a fun ride, and there's a bunch of other examples at other, uh, at, at other companies. But when you go to one to 100, I think one of the things that you really gotta take into account is that you have to plan your chess moves out in advance. You have to really think before you act and build systems that were gonna let you go sustainably faster 'cause the zero to one is you're trying to find that product market fit, and then when you get to one to 100, you're trying to make sure you can get to hyper-scale and, and as fast as you can, right? And I've been very fortunate to be along the ride, uh, of, of many of these products as they were going through that hyper-scale. And the analogy I always like to use is that when you do that, you feel the G-forces, right? And, you know, some people are like, "Oh, yeah, I'm a pilot. I can fly at, you know, 35,000 feet," but, like, the, the, you know, feeling the G-forces of takeoff of a rocket is very different, right? And, uh, one thing that I've learned there doing that a few times is you gotta build the systems that help you move sustainably faster, right? And sometimes you have to go slow to go fast. Um, and here's an example. So in building the news feed, the current version that we have today, it, it really hasn't changed much from the time that we, uh, built it, uh, I don't even know, it was like 12 years ago or something. Uh, I don't know the reason why it hasn't changed much, but I like to think that it's because we put a lot of time and craft into thinking about the whole sharing loop and what are, what is the, uh, what is the, what are the key pieces of it, and how is it architected? What's the information architecture, and then what does that whole flow look like? How does it go from posting something at the top of the page to showing up in the news feed to someone clicking Like and then that notifications thing lighting up red and then that repeating over and over again? And I like to think that news feed has stood the test of time, uh, the current version of it, because we thought very carefully about how people wanted to interact and how people wanted to consume information and also that whole loop. Um, and so when, when that happens, then I think things are built to last, right? And I think the, uh, th- this, I think this is the case at a lot of different companies. So when I was at Uber, we, we had a bit of a spaghetti string code situation on the rider app, but, you know, taking a step back and re-architecting things of like, well, what are the core components, and how do you actually make it so that the product selector can scale around the world? And here's a little-known fact. Like, you know, talk about grit and elbow grease. Like, Uber's not just as simple as, like, finding a ride. If you've ever been to another country, like in India sometimes, there are no street signs, so you have to, like, pick up in front of this, you know, mini mart or whatever it might be. So there's a whole team that worked on pick-up and drop-offs. This was a large effort, and it sounds so boring, but it was so critical to Uber being able to scale because pick-up and drop-offs team thought about, "Well, how do you do it for venues?" And that venues and finding that right abstraction means that you can have, uh, a scalable way to, to do pick-ups at airports and, you know, configure different venues. And those systems, when you take the time to build them in the one to 100 phase, help you speed up massively, and that's how you get 4X, you know (laughs) , users in two years. Or on Messenger, we put a lot of th- thought into the infrastructure around push notifications, et cetera. We grew that product from zero to 4.7 billion messages sent per day, uh, in about two and a half years. Um, and I think it really is, requires that, that forethought in, in building the right systems.

  7. 41:56 – 47:12

    Balancing short- and long-term goals

    1. PD

    2. LR

      Let me follow that thread real quickly 'cause that's really interesting. So essentially what you're saying is once... There's like a phase of once you find product market fit, and I'm, I won't actually ask you this. Uh, before you start planning, when you're starting to scale, going from one to 100, your advice here is basically don't move fast and break things. Don't ship MVPs. This is the time to really think many chess moves ahead about what you're gonna need to get this to, say, a billion users.

    3. PD

      Yeah. Yeah, it's building the systems, and then, and that, that systems thinking will, will, will carry you really far, or at least that's been my experience, and hopefully, hopefully you f- find the same way, but, you know, um, your mileage m- may vary. But yeah, that's exactly right.

    4. LR

      What's your guidance on just, like, when to do that? Because, you know, you can't... You know, you build something. Okay. Well, it's working. There's also this just like, okay, let's just keep it going. Let's scale it as far as we can. Is there... In your experience, is it-... just like what's the guidance on when to really step back and really think years and years ahead?

    5. PD

      Great question. I'll say, the first thing I will say is that it's not a binary switch. It's actually a ramp rate. Um, and so when I've led teams, I've always believed strongly in this portfolio approach, right? And so, you know, famously, Google had the 70/20/10 portfolio approach. That may be the right thing for a more mature company. Uh, maybe it's 50/50 if you're a startup, right? But you have to think about this, uh, in a non-binary way, in- in- in a way that's about scaling up, and when do you need to put more resources behind- uh, behind that? So, every startup is gonna be different, right? Every product, uh, that you're launching is gonna be different. And then thinking about your portfolio approach and how much you allocate your time, that would be my- my advice. And it's your, you know, it's- it's really dependent on the stage that you're in. I think that actually is a nice dovetail to my second thing, if I- if I may, um-

    6. LR

      Yeah.

    7. PD

      ... which is, uh, you know, when you're going from that stage of- of, uh, maybe, you know, one to five or one to 10, so not just fully one to 100, one thing I found to be, uh, very helpful is to measure everything. And this sounds, again, very simple, but, you know, just like how you wouldn't fly a plane without instruments, like why would you run your product without understanding the instrumentation and, uh, how it's doing, right? And so one of the things I did in pretty much all the teams that I led, whether it was Instagram, Uber, Airtable, was all about, and ChatGPT too, uh, the- one of the first things I did was always to build a growth team. And building a growth team is really interesting because it actually is a simple razor. It's a simple thing to think about. It's like, I'm gonna build a growth team. But then you're gonna uncover a lot of things, right? You're gonna uncover how much stuff you have not yet logged and how non-rigorous you've been looking at your entire product. And it's- it's so funny 'cause I've seen this movie so many times, the same movie so many times at every one of these companies, where I remember walking into Instagram and I think asking Kevin and Max, like, "So how many users do we have?" It's like, "Well, we don't really know." And- and so it's like, well, there are a lot and we don't really know. And so when you build a growth team and you hire the right growth leader, I've had the pleasure- uh, the- the pleasure of working with George Lee at Instagram, um, you know, some early growth folks at- at Facebook, Andrew Chen at Air- at- um, at Uber, uh, Airtable. Um, I had the privilege of working with, uh, Lauren, um, who is currently now leading growth at- at Notion. So I've- I've been very fortunate to work with some really amazing people on my team. And when you hire the right person, they start asking all the right questions because when, you know, the- the archetype of person who is a- a growth PM will be like, "Well, wait, why is this happening? And let's get the data on X, Y, and Z thing." And that's when you realize, you don't have X, Y, and Z thing logged. And after you have X, Y, and Z thing logged, you look at the data and you're like, "Wait, well, why is that happening?" And then you're- you're forcing yourself to go deeper into the analysis of doing some analysis of like, well, you know, what's correlated with what and what are some hypotheses? And because growth leaders- growth product leaders are so into this experimentation side, it- it actually is this really easy thing to do, is when you start building a growth team, it just begets all of the right questions being asked, and then it starts, uh, you know, kind of turning into all the right behaviors of- of- of taking something you've been building, which is seems like it's working into a more rigorous system. So that's like the zero, so the- the 1 to 10 phase, I would say, that really sets you up for the 10 to 100.

    8. LR

      What- what I like about this growth team advice is that a lot of people think of a time to hire a growth team to, we need to drive growth. What you're saying is there's a lot of second order benefits, which is they help you figure out what the hell is going on and inform a lot of- uh, of other things that are happening, people just actually understanding how things are going on.

    9. PD

      Totally. And I think that the reason why growth team is- is- is the advice I would go with rather than to build an analytics team is because if you build an analytics team or a data science team, it's possible no one's gonna listen to them, right? It's like, "Oh, I have these insights." It's like, well, no one really cares, but if you have- if you hire a growth leader, they are now tied to outcomes of driving growth. So they're gonna be the ones who are listening and asking, you know, more questions and really partnering with that data science team to make your entire product and business more rigorous, and that just changes the DNA of- of your entire team.

  8. 47:12 – 50:02

    Creating a healthy tension in teams

    1. PD

    2. LR

      I wanna talk about hiring, but is there anything else along these lines that you wanna share of building new products, scaling products?

    3. PD

      I guess the- the last thing I would say is like I- I wanna make sure that, you know, sometimes in the- um, in the pursuit of numbers, product folks lose sight of the importance of taste and craft. So, uh, maybe this is actually the dovetail into kind of building teams, but like you gotta have the counterbalances, right? And it's really important to give two people on your team different charges. One is like, go grow the product, and the other one is wait, maintain that design, that beautiful aesthetic, that- that- that, uh, the- the- the craft that your- that your product is known for. And that tension is extremely healthy, right? And so I- I've saw- I've seen this at- at- at- at Facebook. I've seen this at Instagram. I- I helped create this at Instagram, this kind of healthy tension. Airtable, same thing, but just having, ChatGPT, same exact thing, you have to have that push and pull on both sides to really stretch the gamut.

    4. LR

      That begs the question, how do you actually do that? You know, a lot... You could talk about it. You could be like, "Okay, we need to make sure the experience is awesome, but also grow this number. Here's your goal." How do you operationalize that? Is it like a performance review attribute thing? Is it culture or something else?

    5. PD

      As a leader, you have to set up your team the right way. You have to really think about your team as a product and what are the various pieces you need to really stretch the gamut of what you're- what you're thinking about. Um, and the teams that I've helped build...... are, the most successful ones are a team of avengers that are just, like, very different, have very different superpowers. But together, you as the leader are the one who's helping adjudicate any differences or, uh, any, any disagreements. But you're, you know you're getting the best outcome when everyone's pulling and obsessing over a different thing, right? And that's important. It's important to, to create a balance and, and really kind of increase the space that you're looking at, and create those healthy debates. And I think a lot of people overlook that. I think some people think of, you know, people on a team as, like, warm bodies to do a job. But my philosophy has always been to think about, well, what is the, what is the company need to be successful? And who's the best person who spikes at that one thing? And how do I make sure that, that we get that person, and how do we make sure we get the other person and the other person? It's almost like you're playing an RPG where everyone has different sliders, and you have to create this super team where everyone actually spikes in different, in different ways. And that is something that I've had a lot of success with, in terms of when you create that environment, and you create that, uh, vibe, you're gonna get a lot of mileage out

  9. 50:02 – 55:39

    The five archetypes of product managers

    1. PD

      of that team.

    2. LR

      That is a really interesting answer. It's not one I've heard before. Essentially, you're, it's not like create the right incentives. It's hire people that naturally want us see the world in a certain way, and that creates a balance and tension, uh, healthy tension between, say, a PM and a designer, an engineer. That is really interesting, 'cause that feels a lot more sustainable than, like, here's your goal, okay. But also when your goal is make sure, uh, experience is great and people support tickets or de... It's just, like, naturally they need to want this to happen.

    3. PD

      Totally. And actually, there was a, I, I, I have a, a, a sort of a, a framework around, like, I think there are five different types of product managers that has kind of held true. So this is a, a, a framework that just came out of a random jam at Uber when I was talking to some, some of my, my colleagues there. And we formulated this in terms of helping, uh, with hiring practices. Everywhere I've gone, I've also been, like, best friends with the recruiters because honestly, my whole thing is, like, I had to build the right team. So we have to really partner very deeply. At Uber, we developed this, uh, this, this, this five archetypes of a PM. Um, and I've, to this day, I still think it's, like, actually exactly true. And, and it still holds true to this day. But is that interesting? You want me to kind of go into that?

    4. LR

      Absolutely. (laughs) I'm so excited-

    5. PD

      Okay.

    6. LR

      ... to hear what these are.

    7. PD

      These are the five that I found to be most enduring and actually the most, like, kind of different, right? And, and when you talk about I love the way you put this, Lenny, which is when you hire the right people and, like, how, they're, they're naturally motivated by different things, right? And so these are the five that, that we came up with. Number one is the consumer PM. So this is the person that's, like, half designer, half product person, really obsessed over the details. Is it delightful? Is it crafted enough? Oh my goodness, this is three pixels off, I can't stand it. This is, like, making, driving me nuts. Like, why is this so complex? I mean, these are the people that you think of as, like, you know, you know, sometimes the criticism of PM is the consumer PM. But that's just one type, right? And, um, another type, just go down on the other side, we talked about before, is the growth PM. These people are, like, half data scientist, half product person. They are kind of wired to think numbers first, and they have this kind of air about them that's, like, the best ones do, which is like, "I'm really skeptical. Show me the data. Let's run a test and prove it. I don't believe you." Right? And it's, and, and I start with these two in the framework because they're actually really different, right? One is like, "I have vibe, I feel the vibe this is better," and the other one's like, "No, I don't believe you. We should test this and prove it." And that's, like, a really healthy tension. I love, you know, having two people in a room, like, debating that. I'm like, great, we are gonna get some good things done, and we're gonna, we're gonna move the product forward. The third type is, um, you know, kind of, uh, what I call the GM PM or the business PM, right? These are, like, kind of half MBA, half product person. These are folks that are kind of naturally wired to start with the business model and think about what are the margins? Like, where are the opportunities? Where's the value being created? And we had a lot of these at, at, at, um, at Uber, and they were the marketplace PMs, and they're just like, I loved working with them 'cause their, their minds just worked differently. They just thought about problems from, like, well, what is the incentive here, right? And you, this is a fascinating type of mind to, to work with. Um, another one I, I found, uh, this is, it's, it's, it's actually more nuanced than you think. It's like there's a certain sort of archetype that I call the platform PM, which is someone who's, like, really deeply wired to, to kind of build tools for other people. And at Uber, we had, like, internal platforms for, like, messaging or for, you know, building internal tools. And oftentimes these folks are overlooked, but it's, like, actually a really deep wiring because these are the people that are gonna build the systems that are gonna make you go faster, right? And that's what they love doing. Um, and the last one, I would say I used to call an algorithms PM, but now in the, in the, uh, in the, uh, the, the, the world of, of AI, I'm gonna rename this to research PM. And these are, like, half researcher, half engineer, half product person. And these, these minds are amazing. So, like, basically they think, you know, you know, traditional Google search algorithm PM, right? But nowadays it's like who are the people who really have that product taste but deeply understand the tech and the, you know, the way the models are trained to go and effect that, uh, and build the most amazing product? So those are the five. I still think I, to this day these hold true, and we might have been onto something the day that we brai- brainstorm this at Uber. But, uh, yeah, I'm curious to hear your feedback.

    8. LR

      This is great. As you're talking, I'm just like, here's that person, here's that person. Okay, they fit here. Uh, this super resonates. This episode is brought to you by ContentSquare, the analytics platform that helps companies build better digital experiences.Ever wonder why customers drop off before converting or why some pages perform better than others? Contentsquare takes the guesswork out of digital experiences, giving you real-time insights into how users interact with your site or app. With AI-powered analytics, automatic frustration detection, and clear visualizations, you'll know exactly what's working and what's holding your customers back. Whether you're optimizing an e-commerce checkout, refining a B2B lead flow, or improving a mobile app experience, Contentsquare pinpoints exactly what needs fixing and why. Contentsquare powers better customer journeys across 1.3 million websites and apps. Discover the insights you've been missing at contentsquare.com/lenny.

  10. 55:39 – 58:47

    Primary and secondary archetypes

    1. LR

      So just to summarize, there's consumer PMs, growth PMs, business/GM PMs, platform PMs, and sort of research PMs.

    2. PD

      Yeah.

    3. LR

      Uh, a lot of people call them AI PMs now. I feel like that's the term that's really popular now.

    4. PD

      Yeah, we have to evolve with the times, yeah. But also, the other part of the framework I find, uh, uh, kind of interesting is that everyone's like a primary, has a primary one and a secondary one.

    5. LR

      Hmm.

    6. PD

      It's kind of like one of those, like, personality tests, right? And maybe we kind of did this just because it was hard to pigeonhole people, and I, I myself don't think I was pigeonhole-able, but I, I do think that people likes, you know, kind of lead with one type of thinking, and then also have the secondary thing that keeps them in balance. And so if you believe that and you apply it to your team, I'm curious to hear, you know, from your listeners, like, sort of if, if this does resonate or not. And, you know, maybe this framework will help you realize that you're missing someone that, that you should be not missing.

    7. LR

      What was your archetype when you were a PM?

    8. PD

      This is... And, and that's the other thing about personality types is the ones you hear, you're like, "This is me. This is... I, I own this," right? There's no doubt about it, I am a consumer PM, uh, and also a growth PM. That's, that's my... I'm primarily consumer. I just, I, I can't... And this is what I told you about, you know, the other products I've loved and I've see the, I can see the details that people put into it, and I so appreciate that, but at the end of the day, it's like, you gotta measure things, right? So that's what I am, but, you know, again, everyone's different.

    9. LR

      I love your point about how a lot of people think of PM, like they hear that first example and they're like, "Oh, I guess that's what I need to be," 'cause that's what everyone talks about when they're amazing product managers. But you're saying there's many other ways to be a successful PM. We did a personality test, uh, at Airbnb when I was there, and one of the biggest takeaways was it's like this color test and you get a color, green or yellow or red, and, like, the team was all over the spectrum. And s- and it was a really good reminder just you can be a different type of person and still be really successful in this role of PM. And it's probably because of these different archetypes and different needs and roles of PMs. Like, there's this word product manager, but there's many things that PMs do.

    10. PD

      And also as an investor now, it's really important to see the fit of the founder to the market, because if you put a consumer PM into, like, a really, you know, boring, regulated industry, they're probably gonna get frustrated and they're probably not gonna see it through. Whereas, like, there's people that you look at, you know, the pitch and you're like, "Wow, this is... You are really passionate about this problem, and you really care about building tools for others, and this is exactly..." or this is the Twilio PM or, you know, whatever it might be, "You're a perfect fit for this business," and, like, that's awesome, right? So I think that, yeah, I, I love, uh, that what you just said in, in the summary, because I think there's no one way to be a PM, and I think this is sort of the, hopefully this framework will gi- give people a little bit more space to be, you know, express who they really are.

    11. LR

      I'm curious if other functions also have these sort of archetypes, like designers and engineers, but we don't need to get into that. How about if you're listening to this on YouTube, leave a comment of which of these archetypes you think you might be? What's your primary and secondary? I'll read 'em again. Consumer PM, growth PM, business/GM PM, platform PM, research/AI PM.

    12. PD

      Love

  11. 58:47 – 1:15:52

    Hiring for growth mindset and autonomy

    1. PD

      it.

    2. LR

      Okay. Uh, I wanna talk about hiring. So this actually came up a lot when I was chatting with folks that you've worked with, especially, uh, Nick Turley, who's head of product at ChatGPT-

    3. PD

      Yeah.

    4. LR

      ...who we're trying to get on the podcast, 'cause that's an, uh, what I hear.

    5. PD

      He's awesome.

    6. LR

      That's what I've heard. Uh, so he told me that the current head of engineering, the lead product engineer, the head of design, and head of marketing at ChatGPT are people that you hired. Uh, also, many of the people you hired have gone on to do incredible things. You've shared a few of those names. Many of them have been on the podcast, which is the ultimate measure of success. So let me just ask you this. What's, what's one thing you look for in people you hire that you think are, that you think people sleep on, that you think people aren't paying enough attention to that helps you find amazing stars?

    7. PD

      That's really flattering to hear that from Nick. Um, uh, Nick is one of the best people I've worked with, period. In fact, I want to just do a quick shout-out to, like, folks at OpenAI, uh, are, are pretty much the best people I've ever worked with in my career. When I took the job, I told the team, "This is gonna be my last operating role, and I'm gonna leave it all on the field, and I'm just gonna go all ou- all out." And basically, I spent probably as much time, if not more time, on recruiting and building the team, um, than I, uh, as I did sort of thinking about the product. And this is going back to sort of what I said earlier about I think you gotta bring the right people together to have a huge impact. And oftentimes leaders overlook this, and they're like, "Oh, it's just a warm body," but truly, you know, people who have strengths in certain areas complement others with strengths in other areas. And when you build that team, amazing things happen. It's the mo- it's the best investment you can make. It's gonna pay off so many dividends. So I think that's my opening salvo in terms of like, you know, you gotta get the, e- everyone who's listening out there, you gotta make sure you look at everyone on your team, you sort of look at what you need, and you have to get the best in each. And, uh, truly, like, you know, uh, in, in, in, in my for- farewell dinner, uh, at OpenAI, I think I, I, I closed with just the, that like, "Look, I don't even know what I would do after this 'cause all the best people I've worked with are here." We have Ian Silber running design there, Thomas Dimson, you know, Joey Flynn, Ry- Ryan O'Rourke. Nick Turley was an amazing person I met there. Joanne, uh...I mean, there's- I have so many people I'm missing, but you know, Koli on product marketing, Er- Antonow on the marketing comms side, Solomon on engineer. I mean, you, you, you li- the name, the list goes on. Product operations is, is, is stellar. I'm so proud of, like, honestly, the pro- the team that I built there more than, than the products. Um, so I just wanted to say that if it's like- it's- it's a- it's a big thing that I really care about and I hope more leaders think about that too, is like, really be mindful of putting your team together and, and thinking about that as a product. And you have to really craft that. You have to really care about the team, right?

    8. LR

      Just to double down-

    9. PD

      So-

    10. LR

      ... on that point actually before you get to the next tip here-

    11. PD

      Yeah.

    12. LR

      Uh, I just love this answer which is in s- you know, if I were to ask someone here's hire- what's your hiring advice? What do you look for that people may not be looking for enough? Uh, I love that most of it would be like, "In that person, here's what you need to focus on, and here's the interview question," and but, uh, the like kind of your broad answer so far is, it's not actually about the person so much as what is the team gonna look like and where do we need spikes? Where do we need to balance out the composition of this Avengers that we're building?

    13. PD

      Totally.

    14. LR

      Awesome.

    15. PD

      Totally. That's exactly right. And so, so that being said, I, I guess I have, uh, I guess on brand, I have two things I wanna share about, about sort of f- hiring the right team. Um, I have this saying, um, I actually have this, like, doc that I've taken around various companies called the PXD API which is like, here's how to work with me. And in it, there's, there's, um, there's a saying that I have which is what I really optimize for for everyone that I support and everyone I hire which is, "In six months, if I'm telling you what to do, I've hired the wrong person." And it's just kind of served me really well as on three different levels, right? Number one, it's a reminder for myself when I'm either hiring or looking for the person is to keep my bar super high and just not settle because if I do, most likely in six months, it would not be true that I- it, it- that I would be able to let this person run and I would still be telling them what to do which is not what I want. That is not my desire. The second sort of effect of that is that it's- I say that to people, you know, when they come on the team or when... Or as we're making the fire- hire because, you know, it communicates to them that that's my bar and that's how they know they'll be successful, right? And something to kind of work towards, right? And the third thing is kind of a joint thing for the both of us which is, it kinda gives us m- it m- it helps me and the person operate on a different level where it's not- the goal is not like, did you hit this OKR? Did you hit this goal? The- the- the meta goal becomes, hey, are we building, you know, are we calibrating enough? Are we actually getting to a spot where in six months like, you're the one telling me what needs to be done? Like, like that... Are we, are we getting there, right? Because then if, if that's the framing every, you know, mistake that, you know, is made or whatever on either of our, our parts is becomes a learning opportunity in terms of like, "Well, how do we grow to- to- from this to where we wanna be in six months?" Right? And how is it possible that, you know, I as a, as a manager can do the right things to set this person up for success so that I only have to be involved in six months? Right? And I think that those, those three things like and, and, and being able to have that second order effect of like this simple razor. In six months if I'm telling what to do, I've hired the wrong person, it puts pressure on me, it puts pressure on the person, and it creates this really interesting environment and, and this kind of safe space to really think about are we heading towards that goal? And again, every place I've been at, as much as I've loved building the product, I've taken so much pride in building the team and it's just been so much of a pleasure and I think this is my, uh, one of the two secrets that I have here.

    16. LR

      This is so good. I wanna- I have a follow-up question, but just to point out why I think this is so genius is it- there's kind of a assumption here of this person, uh, you can trust them. So there's like a-

    17. PD

      Yeah.

    18. LR

      ... do I trust this person? Do I feel like they're gonna be proactive? Do I feel like they're gonna have, uh, correct insights? Essentially taste and gut feeling. Uh, it's like the layer below this question which is great. And also just this like autonomy. It feels like you- autonomy almost implies so many important traits of somebody that you want to hire and I love just how simple this question is for both you and them.

    19. PD

      Th- thank you.

    20. LR

      To do all that.

    21. PD

      And, and really with that autonomy, I love what you said about autonomy because truly if without a- as, as a leader, as a manager, your goal is to scale and if you don't have- if this thing, this simple statement is not true, how are you able to build the best company, the best product?

    22. LR

      So here's the follow-up question. Is this mostly for leaders like say head of product at ChatGPT? Say someone's not a CPO, they're just like, I don't know, a manager of a PM team. Do you find... Is there a version of this that you think might be useful to them or is this mostly for leaders?

    23. PD

      I think this is for everyone.

    24. LR

      Hmm.

    25. PD

      I think it's for everyone who is a manager, right? Because you know, if you're gonna be a successful manager at any company, um, or a leader at any company and if you're, if you're kind of starting as a line manager or whatnot and you're kind of, you know, uh, s- you know, wanting to grow or even just wanting to, you know, if, if you're early at a company, you have so much institutional knowledge and so getting more, uh, sort of, uh, leverage in terms of being able to pass on the wisdom that you've learned is so crucial, uh, into being successful that I think every manager should, should approach their, uh, you know, their, their approach with this because truly like, that's- it's just good for everyone. It's good for the company to have more kind of leverage and, and scale, it's good for, uh, the, the, the person who is being brought onto the team because they know what success looks like and it gives them a path to kind of keep on growing and it's great for you as a leader, as a manager to be able to basically scale up the entire, uh, uh, um-... sort of expertise of your team.

    26. LR

      And I imagine you don't even need to plan to not tell them what to do. Like, it's just a good lens into, are they, um, gonna be amazing-

    27. PD

      Yeah.

    28. LR

      ... even if you plan to be telling them sort of what to do.

    29. PD

      Yeah. Uh, exactly. And, and, and the other thing is like, again, in your interview process, you kind of end up looking for these insights, right? And you look for, like, the behaviors of like, oh, are they actually gonna be potentially able to, to, to achieve this in six months? And that's gonna give you a really good lens on the picking side, not just the development side as well.

    30. LR

      Peter, what's your second secret? This is, uh, one for one.

Episode duration: 1:55:28

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