Microsoft CPO: If you aren’t prototyping with AI, you’re doing it wrong | Aparna Chennapragada

0:00 A cheesy Chrome extension literally whenever I open a new tab, it just says, How can you use AI to do what you're going to do right now? How do you see the future of product development being different? If you're not prototyping and building to see what you want to build, I think you're doing it wrong. It becomes even more important to have that editorial and taste making at the heart of it because otherwise you just have uh Frankenstein product. There's this acronym that you taught me, N L X. What is that? Natural language interface and LX is the new UX. Often I hear uh product builders say, Oh yeah, with AI, like the model eats the product. That doesn't mean it's not designed. You and I are having a conversation, it's a podcast. I'll have another conversation at Microsoft, and that's a meeting. Conversations also have grammars, they have structures. They have UI elements. They're invisible. What are the new principles, new constructs in natural language as a interface? I just saw that Cursor hit 300 million ARR in two years. Interestingly, you guys were very well positioned to do really well in this AI coding tool space, you guys said. Copilot, the first tool in the world at this stuff. So ahead of everyone, what happened?

1:08 Today my guest is Aparna Shine Pragada. Aparna is Chief Product Officer at Microsoft, where she oversees AI product strategy for their productivity tools and their work on agents. Previously she was Chief Product Officer at Robin Hood, Vice President at Google, where she worked on Google Lens, search, shopping, augmented reality, AI assistant, and a lot more. She was also a longtime engineering leader at Akamai and on the board of eBay and Capital One. In our conversation, we chat about how working in B2B is like being Jean-Claude Van Damme doing the splits across two moving trucks, how she's operationalizing her team living in the future so that they're building towards where things are going, why people still need to learn to code, why the PM roll isn't going anywhere, why NLX is the new UX, and so much more. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

1:55 Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of products, including Linear, Superhuman, Notion, Perplexity, and Granola. Check it out at lenny's newsletter.com and click bundle. With that I bring you a parta Shatapragada. This episode is brought to you by Epo. Epo is a next generation A B testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams. Companies like Twitch, Miro, ClickUp, and DraftKings rely on Epo to power their experiments.

2:28 Experimentation is increasingly essential for driving growth. and for understanding the performance of new features. An Epo helps you increase experimentation velocity while unlocking rigorous deep analysis in a way that no other commercial tool does. When I was at Airbnb, one of the things that I loved most was our experimentation platform. where I could set up experiments easily, troubleshoot issues, and analyze performance all on my own. Epo does all that and more, with advanced statistical methods that can help you shave weeks off experiment time, an accessible UI for diving deeper into performance, and out of the box reporting that helps you avoying, prolonged analytic cycles. Apple also makes it easy for you to share experiment insights with your team, sparking new ideas for the A B testing flywheel. Epo powers experimentation across every use case.

3:14 Including product. Route. machine learning, monetization, and email marketing. Check out Epo at getepo.com slash Lenny. and ten x your experiment velocity.

3:24 Let's get EPPO. dot com slash 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.

3:44 For over thirty years, they've trained more than two hundred fifty thousand product leaders. at companies like Google, Microsoft, and Salesforce. Equipping them with practical strategies to build and scale market winning products. Pragmatics 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.

4:08 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 twenty percent off with code Lenny Twenty. At Pragmatic Institute. Dot com slash Lenny.

4:29 Aperna, thank you so much for being here and welcome to the podcast. Thank you, Lenny. Thanks for having me. When I asked a lot of people that work with you, uh, what I should ask you about and what's that what I should know about you, something that came up Again and again. It's something that I think most people don't know about you.

4:47 Which is that you're uh you're big into stand up comedy. And you take it semi seriously? Uh Just how serious are you about this? How how much of your life is this? And Most importantly, how does this help you build better products? It's hard to say I'm serious about like a funny business, but uh I I do I do watch and

5:04 do stand up comedy. I do open mics. I've done a few shows. Wow. Um I have uh one set brewing that is around uh Uh AI unsurprisingly AI and tech and Silicon Valley. You know, it's really interesting for me. This was an accidental discovery. Like I'd always been an SNL fan and like just comedy fan. But I went to an open mic because you know, my son sings and he went to the open mic for singing and his like

5:31 Mom, you should go do this and I was like, Oh, let me go give it a try and I found That I Enjoyed it and was good at it. To your question though about building better products, I'd say Both have PMF

5:42 I mean product market fit. Punchline market fit. Uh Uh but I I actually there are a couple of things that I I do find really powerful and useful because, you know, in open mics or even when you're testing these things, it's a very tight cycle of iteration and you get live Like

5:59 Open mics are the real life experiments, right? You you put something out there, you get very clear micro feedback from Users and then you get tough feedback sometimes. And I I think as product builders that's actually one of the great skills to have, which is yeah, you you sometimes launched stuff that You know have a fantastic vision, but the first version is not quite there, right? I think Reed Hoffman says this, hey, if you don't

6:23 Launch the first version and are not embarrassed. You're doing it too slow. Just that gap and closing that, it's good resilience. Yeah. I never saw these corollaries between these two things. I didn't realise you actually did like Shows and you're working on a set.

6:36 Uh I wasn't gonna ask you for a joke, but if you're working on j on a whole thing about AI, is there something Uh that you can share from that set. One uh joke I'd uh maybe share is uh People think about these AI chat products as uh Women uh because uh

6:53 You know. You don't know what's going on, it's a black box. And uh You don't know what it's what what uh they're thinking. There's like an entire set around that. But obviously on the flip side too that

7:04 You know, they're probably more like men in the sense that They hallucinate a lot, they're uh They kind of are not yet reliable. I'm afraid to laugh at this a little bit. Okay. And they're even when they don't know the answer, they make up stuff. They're very confident.

7:19 Yeah. This is good. Where are we gonna be seeing the show, by the way? Uh. Okay. This is great.

7:28 Okay, uh let's get serious again. So You worked at most of your career at a lot of consumer internet companies. You worked at Google, Robinhood, you're on the board of eBay, or on the board of Capital One. Now you're at Microsoft. I'm curious just what is most different about working at a company like Microsoft and building product at a company like Microsoft.

7:46 I think intellectually I knew uh that hey enterprise, particularly the the the area that I look at most at Microsoft is Focused on Enterprise and productivity and transforming Companies to EI. And to me, uh I think two things really strike uh as

8:02 Very different. One In fact I just posted about this the other day saying In consumer you're kind of like, Oh, we have a playbook for Make the product work or make the feature work and make it delightful.

8:14 But I think in the enterprise you almost have e every time you ha you think you have one use case, you have really two. Which is how do you make sure that the feature works well and there's governance of the feature. Right. If you think about like even something as simple as sharing a Link to a document. You want it to be easy, frictionless.

8:32 But at the same time you want that to be secure and kind of uh safe and being able to have auditability and all of those things. And often I find that When you go from consumer to enter a consumer to enterprise, you fall into a trap of either disregarding that I didn't say, Oh, you know, we'll just focus on one side of the house.

8:50 Or kind of overly crippling the user experience, right? And kind of s you know, leaning on the other side. So I think the there's a art and science and nuance and playbook there too. So that's one big learning for me. The other learning and especially in the AI era for me has been about This You know, I think there's a famous trailer from the

9:09 Two thousands on Van Dam on these like two Trailer, two buses. Yeah, doing the splits exactly. I feel like a lot of the the companies, including the tech companies, but certainly the enterprises that I talk to, are in these two modes where on one hand, this is the most compressed tech cycle that we've ever experienced, right? It's all in the order of weeks and months versus years and decades, if you think about like

9:32 Mobile and cloud and internet. And There's just like so much happening, uh the intelligence overhang. Uh on the other hand, there's also like humans and habits that productivity habits change uh It's hard to change.

9:48 And Change management through the company is also hard, right? You don't want to kind of be rash on that. So it's like You know, uh the future is unevenly distributed, but even within the companies. On the second bucket of this other this the the bus that Van Damme's riding on of governance and and adoption and

10:05 Changing behavior and stuff. Is there something you've learned about how to Get past that, help help that along more. The thing not to do is uh hold back. uh folks who are early adopters. Right.

10:18 I think that's the that's the other one learning. In fact, I think that's one of the reasons why recently we've you know. I've been working with folks to say Can we have Can we have both, which is the longer term change management, being able to do it in a trust uh trusted way.

10:32 At the same time do this program we're calling frontier program. And Roll out. Cutting edge experimental features. We learn we just built this world's first Yeah.

10:43 Agent. For deep research agent made for work, right? Post train for work. And of course it has, you know, all sorts of edges, rough edges. But if there are only adopters in an enterprise or outside, how can we kind of put that in the hands of those folks? Without kind of insisting that all of the

10:59 uh all of the company B completely different n developing different muscles. This program Friends here talk you're talking about, uh I wanted to spend a little time on it. So Uh what is the idea? The idea here is like people are Working in this futuristic

11:12 environment. How how does that actually work? Yeah, I think the idea is exactly this, which is like I wanna kind of institutionalize and operationalise my personal model of like living one year in the future. And say what does this uh imagine a a company or a setup.

11:26 Um Like Frontier in consulting group or Frontier Inc. Right. And if you did.

11:32 li lived in that environment where you had all the AI tools. And really advance deep research intelligence on the app. What are the kinds of questions you'd be asking? What are what's the kind of work you'd be doing? How would you change? How you're going about your work day? Uh so that's the premise. And you'd say, hey, how does it change an individual? But also down the lane, we want to think about

11:53 What does a frontier team look like? We talk a lot about frontier labs and models. I think models layer is amazing and obviously like you know that's what empowers all these product building to happen. But I wanna push us to think about what is a Frontier product. Look like

12:10 Uh and more importantly, how does a frontier way of working, right? Like what does a team It's Three people and tons of like compute and AI tools look like. So how exactly does this work? There's like a team within Microsoft. That's like your job is to use all of our latest tools and build product using that.

12:27 Yeah, that is that is the setup. We're just a few weeks into that setup. But meanwhile what we've done is like we've actually uh set up in Like a Compu external Like a fake company and said, Hey, if you are

12:40 somebody who wants to come play with some of the cutting edge science projects and beep research uh agents in Aidens at work. Yeah.

12:51 Wow. Okay. And it's only a few weeks in. Okay, so T B D how it all goes. Yeah, yeah. And again, like these are micro that's the meta point here, I also is that, you know, in the traditional way, we've kind of always thought about across the companies, across industries. Really thinking about roll outs in these uh macro ways, right? You build something and you kind of like roll it out, you have a general availability for And then you take the time.

13:14 And that's really important too, because again, like we're talking about pharma companies, legal companies relying on this. So we do want to have that. But at the same time, given the compressed cycles of AI How do we start to have people experience what's What's the one year in the future? Let's follow this thread in a few different directions.

13:31 There's like how product chain Development changes, there's how engineering changes. There's also just agents. I know you're spending a lot of time in agents. Feels like you're not an AI company these days if you're not working on agents or building an agent. Many we're doing this wrong. We didn't force you didn't use the word agents s uh like Push it out as far as I can.

13:54 Uh it's like it's like every conversation in San Francisco, it's just like how long until I start talking about AI? Yeah. Three minutes average, I bet. Oh man. Okay, so So with agents, I know that you're leading a lot of this work at Microsoft. And a lot of people are wondering what the hell what the hell does this mean? W what is gonna change.

14:11 Give us just a glimpse into how you see the world. being different in a world of agents being around more. But there's a short term and there's a long term, right? There's a lot of you know, hyperventilated, excited talk about kind of the w the eventual future and all of that. I take a much more practical product building lens on this, right?

14:32 And I think about these. At the end of the day, they're tools. Right. Yes, underneath it, there's stochastic models versus very deterministic programming models. You can tell I'm a computer scientist with my like the way the that that word view definitely shapes how I think about this. To me the short term is uh there's an evolution. Like we had apps. Right.

14:52 And now I think we are firmly in the assistance era where There's like human driving the uh you know, that's what we think of as co pilot, right? Like I think the human driving kind of the uh in the driver's seat, but having a lot of assistance from AI. So I think of this as

15:09 then you you you look at the dimension of almost like autonomy and delegation and intelligence, as the intelligence, for example, when deep reasoning Unlock happen. Of course, then you could say you can delegate more, right, to to the agent. So I think to me I think there's one dimension where you say, Hey, agents are

15:27 Somewhat independent software processes. Right, that can kind of like run tasks, and you're not just thinking about hand hold hand holding and fine motor stuff. You're saying, Hey, here's my goal. Go make this happen. Uh like I'll give you an example, right? So we're working on this researcher agent for work. And last night I said, Hey I you know I'm really I I have an important meeting coming up with the leadership team.

15:50 I really want to present these frameworks here and this is the roadmap here. go back and look at all the people that are in the meeting. What are their views on this topic? and kinda come up with how do you how I should can be thinking about like You know, the right.

16:04 Persuasion pitch here, right? And what's magical about this is not just that it's saving time. Typically we think about the so far AI as summarizing a document or saving time, right. This is like Fighting synapses that I didn't I didn't quite have and like actually giving me new insights.

16:20 And giving me Then I say superpowers. Right. So that's a natural evolution of AI, I would say. So when I think about agents, I think about three things. One is is an increasing

16:31 Um level of autonomy and kind of uh uh independents that you can delegate higher and higher order as Second thing I think of it is complexity. Right? So it's not just a one shot. Hey, create this image or do this thing or summarise the document. It's

16:48 You know, build me this Prototype. uh that expresses my idea of a in a augmented reality app. Right.

16:54 Uh it's a complex task. And then the third thing I would say is asynchronous. It works when you're not working. Right. I think that's the other big thing about these things, that you're not h you don't have to s So didn't find us it. This is answer the question of what is an agent, essentially, these three ballpoints.

17:09 So it's uh order the three again. When I think about agents, I think about these three things, right? So one, it's um Autonomy. Like being and it's a it's a spectrum, it's not a zero one. It's how do I actually uh delegate things that it can do. Second I think of as Complexity, right?

17:26 It's not a one shot, hey, summarise this document, generate this image, but it's you know, build me this prototype or help me Knock this meeting out of the park. I Um and then the third one I think of is uh it's a much more natural interaction.

17:39 That doesn't just mean chad, but it may be actually jumping on a meeting with the agent and being able to like talk through all of it. or point it to things that I wanted done differently. So I think all three things the autonomy, the complexity and the natural interaction. Or at least product principles that'll shape really good ones, good agents.

17:58 That is really helpful. Along this line of agents, there's this acronym that you taught me as we were chatting ahead of this podcast, uh N L X. What is that and how does that relate to uh agents and why are people not thinking about this enough? Oh, that's one of my Roman Empires these days, the the uh natural language interface. N L X is the new UX. I

18:18 So I think Here's the here's the uh uh here's the deal. To me, I think traditionally we've thought very uh consciously about GUI. Because uh the graphical interfaces are not something natural and so they have had to be explicitly designed. But they're rigid interfaces.

18:34 Right. Uh what we have with conversational interface and natural language Is it's a it's a much more elastic. Might.

18:42 That doesn't mean it's not designed. So people had often I hear uh product builders say, Oh yeah, with AI, like the model eats the product. So it's just You check with it. Uh you and I are having a conversation. It's a podcast. I'll have another conversation at Microsoft.

18:57 And that's a meeting. So Conversations also have grammars they have structures, they have UI elements, they're invisible And so one of the things that I see and I'm really excited about is What are the new principles, new constructs in

19:12 Natural language. as an interface. Um, I'll give you a few examples, right? And actually like a lot of startups as well as big companies are really Experimenting with this stuff. One is if you think about it, prompt itself.

19:24 Is a is a new construct. And that's a new way that's a new UI element, just like a drop down was, uh or a menu was. But others that are emerging especially for agents I think are plans. So when you give a h high high level goal what we are seeing is that when the agent comes back with a plan, preferably an editable plan,

19:42 That's a new construct. Right. The other one that's um that I think about a lot. Yes. uh showing the work.

19:50 Right? Progress. You see this with the Uh different products, right? You see with the co pilot, you see with Chat GPT, Deep Seek, this idea of thinking aloud.

20:00 And it's kind of it's showing the work. Uh but how much do you do it? If it's too verbose, it feels like I'm running some cra job and scripts. Uh, but if it's two turse then I don't know if it's going in the right path and I don't have the confidence yet. So there are all these new elements. So if you're a product builder

20:17 This is a fun new space to be digging in. Um For product design. This is really interesting,'cause I think people chat with all these chat bots and it just feels like This is just the way it is, but you actually are designing every

20:31 element of the interaction, like how much to share about how much you're thinking. Here's the my plan, what do you think? Yeah. So I think I think This s will surprise a lot of people just realizing there's so much

20:43 That goes into just designing even these what seemingly are simple conversations. Yeah, I another good example is follow ups, right? You could say, look, you have a qu you you asked me a question And then I I could a ask a follow up uh set of things. Endax Explicitly should be designed for success.

21:00 Right? So for example, like if I said hey, create an image. And it created a black and white, you know, out of like a uh clipart version of something. What are the next obvious follow ups that It should be suggesting proactively.

21:13 Now Too much. And you're kind of annoying me, right? Like uh but too little and i in in some sense you've lost an opportunity to direct me or guide me Into a happy path.

21:24 Yeah. This resonates a lot with when we had Kevin Wheel on the podcast, he talked about this question of just how much to sh show about what you're saying and you know Uh and it's interesting that Deep Seek went the extreme of just showing everything and people liked it too. I think that was interesting. Yeah, and I think it's a point in time thing, uh too, Lenny, because in some sense right now these things are such black boxes.

21:45 They're almost like peeking uh under the hood Uh for anything, even if it's verbose. feels like, Oh, I know what's happening, especially because the compute inference time, it's taking long to think. Uh, so it just feels like if you just went silent I'd be Baby.

22:00 Uncomfortable. I think. Mm-hmm. Uh exactly. Uh so I do feel like there's that point in time. But over time I also feel like this is an area ripe for personalization.

22:11 For example, right, like again in inhuman like My API. would be very different from some my interfaces. probably different from others and I might just want the direct hey Give me the TLDR.

22:23 Uh versus the oh so I went here and then I went there and then like Mm-hmm. Following this throughout a little bit, we're talking about just how the future is gonna be different. There's like designing for these chat experiences, there's agents. Uh kind of zooming out to just product development in general. I feels like you're at the forefront of a lot of the tools that are gonna change the way we build products and also your teams are working.

22:44 with a lot of these tools that no one else has access to. So let me just ask, how do you see the future of product development? Being different. From today most, and what do you think product builders should be Preparing for doing to kind of

22:56 To succeed in that future. Yeah, I I will I'll start with one stark uh z statement uh that I say internally and externally and I'm trying to live it, is that In this day and age, if you're not prototyping and building To see what you want to build. I think you're doing it wrong.

23:16 I call it the prompt sets are the new PRDs. Right. Like I really insist on folks saying if you're building new projects New features. Of course come with prototypes.

23:27 And prompt sets. And I think the the the the notion is not to say hey now like everybody is just Uh you know uh like a biggest version of a like a software engineer, right? It is to say You know

23:41 You have the fastest path. to kind of seeing and experiencing what's in your mind. uh to uh to be able to communicate. Right? It's a much more high bandwidth way of communication. I think about that as a really a loop accelerator in terms of product building. That's number one.

23:57 uh when in doubt, uh as uh someone put it. Memos befor memos. Right. I think like that's uh that's really number one. I would say number two This one is a little bit t tricky, I'd say, is that

24:09 What I'm seeing is that the time to first demo the is much Sharder. Right. But the time to like a full deployment Is

24:19 uh is going to take longer. So I I think that there's gonna be an uneven cadence. So typically I think there was much more of a, hey you win this thing, you take a few weeks and then you kinda trade and so on. Now but that inner loop of like prototyping and iterating and getting even user research. uh through AI conversations, all of that. Gets shortened.

24:40 But I think the bar for scale Therefore becomes much high, right? In some sense, if you look at it, like there's gonna be a supply of ideas, right? Like a massive increase in supply of ideas in in prototypes. I And so which is great. It raises the floor.

24:56 But it raises the ceiling as well, right? In some sense, like how do you break out? In these times that uh you have to You have to kind of make sure that this is um This is something that rises above the noise. So I would say that it's simultaneously thinking about

25:10 Uh not chasing after every idea. Like Right, I think there's a second one. I'd say the third thing is you know, there's a lot of conversation around full stack builders. Right, what does the team of the future look like? Be a product building team.

25:23 What I think about is um I think that is inevitable in terms of like There will be a few folks that are especially at the prototyping early idea discovery stage that The lines are blurred. Right.

25:35 you ha there'll be a few pacemakers at the same same time. I think you can still have A lot of people experimenting. it becomes even more important to have the territorial and taste making And of Arthur. Uh one or a few.

25:49 Uh At the heart of it. uh because otherwise you just have uh Frankenstein product, right? That d that Definitely doesn't change. I have um one other additional bonus thing which is

26:02 A lot of folks think about, oh, you know, don't bother studying computer science or you know, the coding is dead and uh I I just fundamentally disagree. If anything, I think You know, we've always had layer h higher and higher layers of abstraction.

26:18 in programming. You know, like we don't program in assembly anymore. uh like most of us don't even program in C and like and then you're you're kind of you know higher and higher layers of abstraction. So to me they will be Ways that you will tell

26:32 The computer, what to do. uh right, it'll just be at a much higher level of abstraction, which is great. It democratizes you there'll be an order of magnitude more Software operators. Like instead of Swedes, maybe we'll have Souls.

26:45 Bye. Uh, but that doesn't mean you don't understand computer science and it's a way of thinking and it's a mental model. So I I strongly disagree with the whole like coding is dead. That's awesome. I love that. Uh and is so is uh is a software operator, was that what that stands for? Yeah. I just made it out quite yes.

27:04 Okay, cool. Uh this idea of prototyping is being kind of core to Building these days. Is there anything you do within Microsoft to operationalize that and make that just like a thing everyone has to do? Is it just like culturally do it or is it like you must Show me a prototype before you show me a

27:19 You know, I think it's again, like the future is here unevenly distributed, even in Microsoft, I would say, but there is certainly a strong cultural uh momentum and shift and desire say hey let's let's actually look at Live demos, live prototypes, and to even like communicate the ideas.

27:37 Fight. Uh And and to me, I mean it's not always possible because obviously there are like things that are Deeply like if you're trying to change something in like the bowls of Excel. You probably don't

27:50 There's even enough depth. In the uh in the uh product that You know what you need to do and you don't need to prototype that. But if you're especially thinking about new things and new products, new features, absolutely. Okay. Let's talk about product management. There's this

28:04 uh fear that emerged as soon as all these AI coding tools came out of just like PMs are dead. We don't need PMs. We could just build things ourselves with what are these people hanging around for? And what I found Is it's actually the opposite. That now that coding is easy. Now the question is more and more, what should we be building? Why should we be building it? Is this right? Is this the right solution, then getting adoption for it.

28:28 Which is what PMs are really good at. And so I feel like it's the opposite, like VMs are the most important role and there, you know, it'll change too. But I but let me get your take. Just what do you think the future of product management looks like? Do you think it's dead? Do you think it's Gonna thrive, do you think it's gonna change? Yes.

28:42 Uh meaning look. I mean if you're a TPS report uh mostly process uh person and like a lot of companies do

28:54 uh get confused about product management and process and project management. I think then you do have a question of like, hey, what is the value add here, right? Especially if like Yeah, I can read and write like Fifty thousand. meeting notes and uh, you know, tr track things and send emails and so on.

29:12 Uh but I think what Uh what I do think on the flip side is that taste making and kind of the edit editing function becomes really, really important, right? In a world where The supply. of ideas, supply of prototypes becomes even more like an order of magnitude higher.

29:31 you'd have to think about like what is the editing function here. Uh so that does mean that the bar is higher. For uh for you for product uh folks. But I think there's a there's an interesting side effect I am observing in, you know, startups that I'm advising companies and even within the companies that

29:47 There's There used to be more gatekeeping, I would say, in terms of like Oh, this is you know, we should ask the product. Later what they think. And again, like there is a role for that editing function, but you have to earn it now. You just don't get it because of the s uh title.

30:03 But there's also just like unlock of Latent really good ideas from smart engineers Smart User researchers. smart designers who can now who now have like this expert in their pocket, right? To kind of round out all the other things that they're not

30:18 They're not uh typically skilled at. to bring forth their ideas. And that's f amazing, I think. And I think that expert, it's interesting, I w I'm working with an engineer and some stuff. And he uses uh chat GPT to even communicate to me. In a more effective way.

30:34 pitch into something that Will convince Lenny this is a good idea. By the way, that is actually one of my uh common use cases, which is Uh WW XD, I call it what would X do. Like I used to say, Hey, um, what would uh Satya think about like this particular

30:51 set of uh uh uh conversations or i ideas that we are pitching and so on. This is the power of like I think beprisoning plus relevant context, right? This engineer you're talking about has that context. uh about you and so it's kind of uh very interesting. If only everyone was uh as famous as Satya and had so much information out there, but I guess you can import all their emails or

31:12 Whatever tools exist to just like understand from the conversations you've had with that person. Yeah, and I think this is this goes back to actually what you were saying too, which is I think this idea of what is the there's like a coil spring, there's an intelligence overhang that I I just see across the board. And I think the Part of product development has to almost rewire ourselves.

31:32 to I think Toby from Shopify calls it the reflexive AI usage. Uh and that's not as easy. And I've been thinking about why. Like I basically I mean, I have a cheesy Chrome extension literally whenever I uh open a new tab, it just says How can you use AI to do what you're going to do right now? Just to like it's very cheesy, but it kinda helps to pause and think, Oh, what w what what am I trying to do here?

31:56 But um the reason I find it hard and when I talk to even like people who are living and breathing in this space They find it hard is that You know, the updating of the priors Is really hard. Like the models couldn't do some things one year ago. Like

32:09 I mean image generation was full of spellings. Or like reasoning. You just couldn't like you know, have deeper and smarter answers. You couldn't do data analysis. So like my impression of it from change trying it a few months ago. That trial needs to be updated and it's hard to do that, right?

32:26 You have to kind of do something almost uh counterintuitive and against the grain to say, No no, like ignore what you learned about like what this can or cannot do. Like the baby just grew up to be a fifteen year old. In a month. I think that last point is so important.

32:41 That we've tried these tools over the years and It Many like so far it hasn't been amazing and then all of a sudden it is and you kind of Don't know that. And you've given up almost in

32:51 And things change. I think that's actually if if you're a product builder listening to it, that's a really interesting Arbitrage. Like if you can kind of cut against the grain and say, No, I won't have that scar tissue around like

33:04 you know, this didn't work a few months ago and keep setting high expectations and Like demand more of the AI today? I think uh y you can you can unlock more. There's a lot of alpha in In doing that.

33:18 That's right. Today's episode is brought to you by Coda. I personally use Coda every single day. to manage my podcast and also to manage my community. That's where I put the questions that I plan to ask every guest that's coming on the podcast. Sorry about my community resources.

33:33 That's how I manage my workflows. Here's how Koda can help you. Imagine starting a project at work. And your vision is clear. You know exactly who's doing what and where to find the data that you need to do your part.

33:44 In fact, you don't have to waste time searching for anything, because everything your team needs from project trackers and OKRs The documents and spreadsheets lives in one tab. All in coda. With Coda's collaborative all in one workspace, you get the flexibility of docs. The structure of spreadsheets.

34:00 The power of applications. And the intelligence of AI. All in one easy to organiz. Like I mentioned earlier. I use Coda every single day.

34:09 And more than fifty thousand teams trust Koda to keep them more aligned and focused. If you're a startup team looking to increase alignment and agility, Coda can help you move from planning to execution in record time. To try it for yourself, go to coda.io slash Lenny today and get six months free of the team plan for startups. That's C O D A.io slash Lenny.

34:30 to get started for free and get six months of the team plan. Coda.io slash Lenny. I'm gonna come back to this cheesy plug in. Say more about this. So this is a plug in that just lets you put a Custom message on every new tab and it just You have it say

34:43 How can you use AI to do this? Yeah, it's as as cheesy as that. And it's interesting because it works uh in the last few uh weeks alone. I've been doing this like Experiment to say hey, how much more AI pill Can I get like both at work and in

35:01 A person life to say You know when I'm trying to do anything manual, my Should I be demanding the AI to do this? That's so cool. Do you know the name of this Chrome extension by any chance? Otherwise No, I didn't. You build the Chrome extension.

35:16 That's so cool. Uh okay. Uh did you use AI to build it? Of course. Wow. Which tool did you use to do that? Some kinda Microsoft tool, I imagine.

35:25 Yes. Uh yeah. No, actually it it was just like I mean, I'm I live in GitHub and GitHub co pilots, so it just like was like okay. That's the Windows Chrome extension. Yeah. Are you releasing this for the general public? No, I mean this this that's the fun that's the ma m amazing thing. It took me like Ten minutes to do this.

35:41 I Hm. Okay. Let's link to it. Let's get it out there. Open source this thing. Okay. Uh you mentioned Satya, I have a question about this. So you're one of the very few people that have worked

35:51 very closely with both Satya and Sundar at Google. Let me ask you this. How do their leadership styles Differ. And is there just like a fun story you could share about each of them? Yeah, I do feel uh I do feel lucky to have uh you know, kind of have a window into

36:07 He's two amazing leaders um of this generation. I would say I mean again, no surprise Yeah. as you'd expect from CEOs of multi trillion dollar uh market cap tech companies, they are ninety nine point nine nine percentile in like almost every dimension you'd think of, right? Intellect Empathy, leadership.

36:25 you know, be product uh strategy Um there are of course flavors of differences. Uh I was uh the technical advisor for Sundar for the first uh at at Google and set up in the office of the CEO there. Uh, and there again a matter of like time and context, because a lot there's a lot more consumer oriented uh focus there. So what I did find s great ideas is being

36:49 really calm and measured and thoughtful in terms of um, you know uh taking making sure that things have dealing with the complex ecosystems Right, if you think about the phone ecosystem or even like the search and publisher and advertiser ecosystem. It's a very complex ecosystem. He was a master at that.

37:06 He's an asteroid then. And I think on Sapia I find it amazing the appetite he has for learning and fine tuning his mental models and just like the The zoom levels that he can operate at. uh the macro, the strategy, what's the game

37:22 But also the micro hey, wh why are we not? Like here's like a specific insight that I saw on Twitter. And like you can count on the fact that he's ahead of pretty much everybody else. In terms of spotting those early things too. So it's uh it's just been like Like uh you know, learning from the fire hose as as they put it. What a cool opportunity to work with two incredible folks.

37:42 Okay, let's go in a in a whole different direction. Let me just ask you this question that I've been asking people more and more. What's the most counterintuitive lesson that you've learned about building products that goes against common Start up wisdom common. Product building wisdom. I don't know if it's um I mean as common as it should be and it's like a counterintuitive thing, but I've repeatedly learnt

38:04 That When you're doing something New, zero to one. Uh

38:10 Uh uh. The temptation is to kind of think about You know, it's like that South Park episode step one. uh think about the problem. Step two questions. Step three breath. Underpants is step two. Underpants. Exactly. Right. Uh so I do feel like there's a temptation to rush and say Uh

38:25 To go to scale before so I've always said uh to my teams. solved before scale. I

38:33 So what that what that does mean is there's a different posture and different mode. When you're trying to solve a problem. versus scaling something that's either post product market fit or even at least like in the roughly in the Ball ballpark. So to give you a couple of examples, right? I think when we when you look at the solved stage

38:53 There are wide lurches. You gotta be very comfortable with the fact that your day one thinking about, hey, a plant detection uh tool. And then Day fifteen, you're like, Oh, actually the tech is really good for translating

39:08 in a foreign language. By the way, this is not hypothetical. This is what we kind of like looked at in Google Lens. back back then and said, Okay, like where what is the intersection and so on? So from the outside it looks like chaos. But actually in the ins and you should be very comf not only tolerant, I think you should be like should have an appetite for that.

39:26 Because the last thing you want is prematurely like You know, fix on one Local hill? And then you're climbing that. And startups and entire product areas and companies, big companies make that mistake. And three years later you're like, Oh, how do I get off this hill? So I'd say that's one big competitive thing.

39:41 Like when you're trying to think about uh what mode you're in, are you in a solved mode? Are you in the scale mode? Uh One example is kind of making sure that you're comfortable with the chaos. I think the other lesson I've learnt is

39:55 The dang of metrics. Right. And I think uh again If you have work on You know, Google search or if you're well done

40:03 You know, like after products, you'd really have like a very fine grained sense of What are the metrics? For this product. You have the input metrics, out you have the whole shebang. But when you're looking at something zero to one

40:17 If you decide on a metric too prematurely, That's false precision, first of all, right? Like you kind of uh I mean CTR when you have like thousand people doesn't mean anything. uh you know, retention also may not mean anything. So really being Very very

40:33 Of like this Uh Big guy, big girl, grown up metrics as I call it. Right, you're looking for more qualitative The sound of click.

40:42 And what is your as the other uh anecdo you know, kind of the handler uses What is your set timer and play music? I So if you look at like Alexa and like Siri and Google Assistant and all these

40:54 Things. They had a very promising broad interface. You could say anything. But I think There was one or two things that it was really good at, right? Like you could set a timer You could play music.

41:04 And you could play trivia. And so you've got to nail those things before you say, Oh yeah, here, you can do anything with it. Which is why recipe. That's exactly what I use my Google home for. Uh So basic. Uh I don't do the trivia thing though. Maybe I gotta

41:19 Give this a shot. There's something along these lines that I've also seen you talk about, which is how to go zero to one with something. Just kinda little framework for helping you know uh if this is the right time for this idea. How do you think about that? Yeah, I a and when we when you think about the salt mode, and this is again like uh sticking with my whole you know, living in one year in the future, I I gravitate towards the zero to one and solve mode

41:44 Products completely thinking about new category of products. And what I've found both the hard way I would say is that you do want to look for At least two out of these three factors uh inflection points here. If you want to make a really good product. Number one.

41:59 Ізері Shift. Is a step function in the tech. Right. That's Somewhere, I would say, like, you know, deep learning was one for Google Lens. back then speech recognition was a step function for like conversational

42:12 a search I would say for Robin Hood. You know, the the generational shift was very clear and the fact that phones were uh a primary means for you know, you could actually have an app mobile app for finance that you could use. So look for that inflection, right? What is the tech inflection? And right now, of course, like

42:30 N LMs and reasoning models are that Step function. But that's not enough. I would say the second factor that we should look for is What is the consumer behavior shift? I

42:41 So uh to give you an example, when we started working on Google Lens What we said is look. People were taking mostly pictures for sharing, right? Selfies and sunsets and so on. And s suddenly when storage became free And mostly free and

42:56 m everybody had phones everywhere all all all all the time. You took pictures of everything. Right? And then you had like enough of pictures or in you use the camera as the As the keyboard for your for your world, right? For the real world.

43:11 And so how do you kind of then say, Oh, this consumer shift is big And so there are four kind of like as it. As you go order of magnitude more Photos then you want more to come out of them and you can apply AI to that.

43:24 And I'd say the third r inflection point, particularly I would say in enterprise, but also in consumer, is the business model shift. Right, how do you is there an inflection point, natural inflection point in the business model? So any great products if you think about like you know, all the way from search. Again, like the the second price option and the fact that you had like

43:43 you know as C P Cs. Same thing with SAS. And the fact that you could actually charge uh or monetize Enterprise products in a different way.

43:53 And with AI, of course, like the monetization is a whole different like I mean you've we've just uh barely scratch the surface of uh whether you do Yeah. uh monetization usage like on tap And then of course outcome based stuff, outcome based monetization. Hey, have you s solved the problem for me?

44:12 And then I will pay you some fees. Right. So all three, like to me, are you know kind of like great. But at least two out of three. But a good product.

44:21 So this essentially when investors look at startups, they're always asking why now? Why is this the time to start this thing? And so your advice here is. You should there's three ways to look at it and you should Two of these three should be true. There should be a a shift in technology, some new technology that has enabled this. Now, recently.

44:39 There's a shift in consumer behavior. And then there's maybe a new sort of or you've invented a new business model. Like any way to monetize something that Uh It gives you an advantage over folks trying to do it too. Awesome. And uh you didn't mention Robin Hood, I think, in that example. That was another good example of

44:55 Yeah. Yeah, I mean talk about the business model of kind of uh again like uh not uh having a zero um uh you know zero fees, right? And again, like b that combination of all of these things. Is what can unlock it. Not it you can't just say, Oh, we'll just have a much s much more better intuitive interface. And hope that

45:14 Uh you know, people switch Do it. Okay, so speaking of zero to one products, I'm gonna take us to uh occasional segment on this podcast that I call Hot Seat Corner. And I have a question for you that is on my mind and it's come up in a

45:27 A couple recent podcasts actually. So there's these companies like Cursor, V Zero, Lovable, Bolt, Replit that are like the fastest growing companies' history. Uh I just saw that cursor hit three hundred million ARR in two years. Interestingly, you guys were very well positioned to do really well in this space, this AI coding tool space. You guys said Copilot, the first tool in the world at this stuff. So ahead of everyone.

45:50 You build VS Code, which is all these companies are forking to build on. You have incredible AI infrastructure, incredible AI talent. So this could have been your market. What happened? What happened to Partner? You know, it's interesting the framing uh

46:04 So I'm a big user of GitHub Co pilot. Uh and I would say Look, uh the the f l if you unpack I think the thing the the beauty of this is that Cold generation It's become an amazing tool. uh that LMs have unlocked.

46:18 Right. So it is not so it is actually really good excitement and action that now code generation has just opened up all of these things. That we talked about the whole idea of like prototyping. Go go from idea to marks an idea to kind of a clickable prototype in like in a few minutes.

46:35 Those are the kinds of things that of course we should expect code generation to uh enable. The way I think about uh uh you know how we we are positioned and like what we what we do with GitHub is So It's a system, not just a product or a set of features. If I think about GitHub it's for

46:54 Folks who are Who have the re repo there, right? And you have kind of uh of course you have the assistance in terms of auto complete and you can chat. But now we have the agent board. It's one one of the Mm fastest um you know kind of loops that we are seeing.

47:07 really strong positive feedback. So in some sense when you have a system uh what you are looking for in terms of building and designing it is not just a single product that can Go, but it's the What is the repository?

47:21 What is your context? Whatever. The set of features that grow from your expertise, right? If you're a really expert coder, you want kind of like the You know, assistance, the sk this product needs to scale for that.

47:33 If you're a wipe coder. You should still be able to do that and so on. Right. So that I think is the way that um that GitHub is positioned to uh build on and like growing Honestly, really well. That's so interesting. So ba it' like the core of this is everyone ends up in GitHub anyway, no matter what tool they use. And that's kind of the

47:52 Yeah, and I think the the yeah the idea again is that You know Code generation as a tool will unlock a lot more products. I mean, they're not all competitors to the fact of um they're not all kind of um uh you know. Doing the same job. I think when you're at the end of the day, like you're building

48:10 Uh code for companies to run on. You need to have a system, you need to have kind of the I believe an entire Swiss Army tool toolkit, right? Not just the autocomplete, not just a chat, not just like a software agent that runs and you kind of like handhold. You need all of this to work together and that's what the GitHub um product is going after. All roads uh lead to GitHub.

48:34 Uh On the flip side of this question. Mm-hmm. Uh there have been uh probably five thousand startups that have tried to disrupt Excel. And you guys just keep waiting.

48:43 So something there is working really well. That is so interesting you say that. So when I um came to Microsoft, uh and I'm an Excel fan, so I actually had a conversation with one of the OG uh Excel product folks I was like man what is it about this this uh product And he's saying a couple of things that were really interesting for me that just stuck with me. One is I said

49:04 Hey. You know, Excel is a proof that Non coders also have to program. Right? Programming is really powerful. And it's the tool that gives all of the non coders really powerful programming.

49:16 Uh You know, ability. And I thought that was just like really uh striking. And then the second thing that I found out super cool. Uh, I don't know if you know this, but I didn't know at least before uh two years ago that there are these amazing Excel championships. Like world XM championships.

49:33 Where you see folks who can do just magic. And To me I think the insight here is also that some tools are Harder to learn Perhaps in the beginning there's friction in terms of learning.

49:47 But great to use. Right. So it it's a very good uh um uh case of Hey, the learning curve initially, the one time learning curve might be Tricky but it is because there's so much power uh and depth.

50:01 In the in the tool. That's so interesting. I never thought of Excel as a programming language, but but it makes sense. And I feel like once you get used to it and this is just the way things work, you're kind of stuck there and everything else has to basically copy that model, which is hard. to be as good. Yeah, and I think the the depth and the attention that the that the team is given, and again, that's the compounding effect over you know, decades of working On like deep, deep

50:23 a signal, right, from people who live who depend on it day in and day out. Yeah. Okay. To s to kind of start to close out our conversation, I want to ask this question. around uh your career. I find that most people have a per like one moment in their career that changes the trajectory.

50:41 of their career. It could be like a manager they had, it could be a project they worked on. Could be just a job they landed. What would you say is the most pivotal moment in your career that eventually led you to becoming Chief Product Officer at Microsoft.

50:54 Actually th there is one moment where you know, it was a turning point for me. I was in Google uh search, I was working On this idea that I thought should just work. And it didn't. Right? Like I've I s I said, hey, um, these phones are becoming a thing.

51:10 Personalization has to be important. So I I I wouldn't bang my head against the wall for a year or so. uh trying to make personalization work. And it turns out when you have a you know query uh that you put in to go search, like the personalization didn't matter as much.

51:27 And so You know, we disbanded the team. But then I think Um I started working on this product called Google Now. Which was

51:36 A twist on that which said, Hey, actually on the phone We should be able to like push content. It's not about like, you know, searching uh with personalization. For example, if you have a flight coming up, it should we should be able to say Connect the dots and say you should leave now for the you know, g given the traffic and where where you need to go and so on. Or if you're deeply interested in an old stand up comedy with deadpan artists, you should

51:59 Check out my checkbook. Like these are kind of like these uh really Moments that The smartphone should be smarter. So I w I led that product through the kind of the initial zero to one phase.

52:11 And that was a pivotal moment. It made me realize two things. One I really love seeing around the corner and kind of seeing where where things go and building the product rise to the occasion. Uh way more than You know, the the scaling and sustain sustaining uh products.

52:28 Second, it's harsh, but Being early is is the same as being wrong. You know, this is pre LMs, pre deep learning. uh lot of the really amazing ideas in terms of next token predictor, et cetera. We'd we've been thinking of it, but You know, didn't have the horsepower.

52:42 to go uh the interface was great, the intelligence wasn't there. And I'd say the third uh thing that stuck with me is I got to work with some really smart like they talk about talent density now, right? And I think really smart people who've gone on to do like amazing things. And so kind of like it gave me a taste of what a small group of people can do. Such a great story because'cause it didn't work out right in the end, like Google now kinda went away, right?

53:06 And by the way, I super remember that product. It was very cool. I remember looking at it as very like delightful and happy. And so I I also have this segment on the podcast called Failure Corner, where people share a story of failure and how that helped them and I love this as a combination of those two. Yeah, I mean I I'm not gonna lie, I think it was uh it was um it was painful when you do that because you s you see The vision of what can be.

53:28 And what is and sometimes it's hard limitations. Sometimes it takes like You know, in this case it takes uh five years or ten years to kind of like really unlock the intelligence. But sometimes it's a it's one or two key clicks click stops away from

53:43 The product being great. Uh and part of figuring out is knowing when uh when you're in what situation. How long was that period from From starting out until just like move it on and it's not working. Yeah, I would say in that case, one of the good things is again, like the f the it led to the foundation of it was one of the foundations of the Google Assistant and of course as the LMs

54:04 you know, step function happen now with Gemini it it kind of like works out. And I think it's the same thing across the board. Which is Uh sometimes you want to kinda figure out The invariants that do work.

54:16 Right, that can then that then go on to the next version of the product. And other times you just have to start over. Is Google now the first agent? Before agents, that's what it feels like. That was certainly the idea. Uh you know, inter But it is f f fascinating to me that the interface that there we had the opposite problem. Like whether you think about all the voice assistants, right?

54:37 The um interface is like Be overshot. And the intelligence wasn't there. Today I feel like there's an opposite problem. I think these these things have amazing intelligence. And the interface we have largely is like

54:51 The AL AOL dialogue modem chat. But We've covered a lot of ground. Is there anything that you wanted to chat about or leave listeners with maybe a last nugget of wisdom.

55:04 Before we get to our very exciting lightning room. I think I would say one thing that I'm really excited about is this Idea of figuring out how we As People and agents.

55:16 Collaborate together. Right, I think there's like some great set of products and experiences to be reimagined. That's my other Roman Empire, which is how do we actually have this co working space? Where uh You know, you have kind of like the

55:30 uh the humans and uh agents and how do you actually kind of have an output that's much, much more significant than what any one of us or any few of us can I don't produce. Well I need to hear more about this. What do you when do you imagine a co working space of humans and agents? What does this look like? Is this like Microsoft Teams or is this like a physical place with little robots? Oh I hadn't thought of the physical place, but I I I I am think I am thinking a lot about kind of You know, right now all of these experiences are very single player.

55:59 Right. And I do think there's an opportunity to think about How do we Again, I'm leaving one year in the future. How do we actually have like

56:07 you know, collaborate with each other, but with also with agents and really Uh figure out for example U What tasks can we delegate? What can we kind of like inspect?

56:17 How do we actually have information that flows between people. That agents can mediate and so on. Mm. All right. I'm curious to see what you guys got cooking.

56:27 With that, we've reached our very exciting lightning round. Are you ready? Let's do it. Let's do it. First question, what are two or three books that you find yourself recommending most to other people? Oh, I have recency buys, but I've been reading this book called uh The Brief History of Intelligence. Um

56:44 Phenomenal book and uh you know Like lots of uh lots of underlining for me. And I think it kind of uh the the premise is to it it looks at the evolution of intelligence, like human intelligence and kind of the the brain development and up kinda connects that to what we're uh what we're seeing with the eye. Do you have a favorite recent movie or TV show that you really enjoyed?

57:05 Hacks? I've been watching uh this. It's about a woman who uh Uh Who this Like it.

57:11 Great stand up comedian. of uh I think it's set in kind of like the um The fact that she she grew up uh I think in the uh six seventies and eighties and kind of like really tried to break through. In an industry that hasn't

57:25 Traditionally being like very Uh friendly to women. So Really fun and uh quirky. Do you have a favorite product that you recently discovered that you really love? Could be an app, could be some 'Cause I can't

57:36 I ha I do use a lot of Microsoft products, GitHub Copilot being one of them. But I think the one that I maybe I I'll think is um Granola, I think is the name of the app. I I found it really useful. Uh I just gave it a spin the other day and I'm like, oh, this is really useful in terms of being able to um You know, again like

57:55 without being intrusive, just uh just capture The uh Thoughts, notes and structure it, put some it it it felt like one of those things where yep the Confluence of a few things like we were talking about, right? Like the transcription, real time transcription tech has gotten really good. Voice recognition is great.

58:13 And then enough of the L M magic on top of it to kind of make it structured and contextual. I am a huge fan of granola. I'll give a quick pitch here. If you become an annual subscriber of my newsletter, you get a year free of granola. For your entire Company.

58:28 Did not know that. There we go. So and then just check that out, Lenny's newsletter dot com and you click the word bundle and you'll see how to do that. Very cool. Uh two more questions. Do you have a favorite life motto that you often come back to when

58:41 You're dealing with something Maybe you share with folks they they find useful as well in work or in life. Uh I have one in fact actually this is my email signature for I don't know for the last uh twenty years or so. Uh says the best uh way to predict the future is to invent it. I think it's a quote by Alan Kay.

58:58 I find it useful for two things. One is You know, no one knows anything. Like when you think about like all the folks who are program you know, kind of think about hey, this is the this is exactly how everything is going to look and this is exactly the sequence and so on. I think there is no substitute to experientially like building it. And uh and I think the

59:17 Second part is You know, like if you think there's something that is that should exist. Go build it. I love that. Final question.

59:26 We've talked about stand up comedy a bit. Is there a Is there like a favorite under the radar stand up comedian? That you think people should go check out. Oh, uh there's a there's a couple of them. So one I think there's a um there's an Indian American or a I think I think a British Indian

59:42 Uh stand up comedian. Her name is Cindu Vee, super smart, you know, mom comedy. And I think the other one that Hey, this is definitely not under the radar, but like I'm just like Love his um

59:55 Stick is uh Nate Bergadzi. He's just so good. Aparnot, this was amazing. Two final questions, where can folks find you online if they want to reach out, maybe, and follow up on anything you shared? And how can listeners be useful to you?

1:00:08 You can find me on LinkedIn and uh Twitter, uh Purna C D. Uh is the handle. Uh I do post stuff a lot more on LinkedIn these days. So um you know, would love uh would love to hear um thoughts, comments, conversations there. I'd say one thing that would be super interesting is if any of this stuff spark conversations, particularly around like kind of You know, this uh

1:00:32 What do what can a small team with a lot of AI tools do. Or new products that folks are really excited about, saying that they should exist. Hit me up. Amazing.

1:00:42 Perna, thank you so much for being here. Thank you. Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast.

1:01:02 You can find all past episodes or learn more about the show at Lenny's Podcast.com. See you in the next episode.

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