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

I have 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 the territorial and taste making at the heart of it.

because otherwise you just have a Frankenstein product. There's this acronym that you taught me, NLX. What is that? Natural Language Interface. NLX is the new UX. Often I hear a product builder say, oh yeah, with AI, like the model, it's the products. 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 an 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 the stuff. So ahead of everyone. What happened? I would say...

Today, my guest is Aparna Shanapragada. 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 Robinhood, 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 role 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

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 Aparna Shanapragada. This episode is brought to you by EPPO. EPPO is a next generation A-B testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams.

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Aparna, 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 what I should ask you about and what I should know about you, something that came up again and again is something that I think most people don't know about you. which is that you're big into stand-up comedy.

and you take it semi-seriously just how serious are you about this how how much of your life is this and most importantly how does this help you build a better problem it's hard to say I'm serious about like a funny business but I I do I do watch and do stand-up comedy I do open mics I've done a few shows I have one set brewing that is around unsurprisingly AI and tech in 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 he's like, 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. I mean product market fit, punchline market fit.

Actually, there are a couple of things that I do find really powerful and useful because in open mics or even when you're testing these things, it's a very tight cycle of iteration and you get live. open mics are the real live experiments right you put something out there you get very clear micro feedback from users and then you get tough feedback sometimes and i think as product builders that's actually one of the

great skills to have, which is, yeah, you sometimes launch stuff that, you know, have a fantastic vision, but the first version is not quite there, right? And Reed Hoffman says this, hey, if you don't 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 coral areas between these two things. I didn't realize you actually did like shows and you're working on a set i wasn't going to ask you for a joke but if you're working on a whole thing about ai is there something uh that you can share from that set one joke i'd maybe share is People think about these AI chat products as women because, you know, you don't know what's going on. It's a black box. and you don't know what they're thinking.

There's like an entire set around that, but obviously on the flip side to that, you know, they're probably more like men in the sense that they hallucinate a lot. They're, they kind of are not yet reliable. And even when they don't know the answer, they make up stuff. They're very confident. This is good. Where are we going to be seeing the show, by the way? Uh...

This is great. Okay, 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, you're 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.

i think intellectually i knew uh that hey enterprise particularly the area that i look at most at microsoft is focused on enterprise and productivity and transforming companies through ai And to me, I think two things really strike as very different. One, in fact, I just posted about this the other day and 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. But I think in the enterprise, you almost have every time you think you have one use case, you're really true, 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, but at the same time you want that to be secure and kind of safe and being able to have auditability and all of those things. And often I find that when you go from consumer to enterprise, you fall into a trap of either disregarding that and say, oh, we'll just focus on one side of the house.

or kind of overly crippling the user experience, right? And kind of leaning on the other side. So I think there's an 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 you know i think there's a famous trailer from the

2000s on Van Damme on these like two trailer, two buses. Doing the splits. Yeah, doing the splits. Exactly. I feel like a lot of 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 mobile and cloud and internet and

There's just like so much happening. The intelligence overhang. On the other hand, there's also like humans and habits that productivity habits change. It's hard to change. 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, the future is unevenly distributed, but even within the company.

On the second bucket of this other, the bust that Van Damme's riding on of governance and adoption and changing behavior and stuff, is there something you've learned about how to get past that, help that along more. The thing not to do is hold back.

folks who are early adopters, right? I think that's the other one learning. In fact, I think that's one of the reasons why recently 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 trusted way.

At the same time, do this program we're calling Frontier program and roll out cutting edge experimental features. We learned we just built this world's first agent for deep research agent made for work, post trained for work. And of course, it has all sorts of edges, rough edges. But if there are only adopters in an enterprise outside, how can we kind of put that in the hands of those folks? without kind of insisting that all of the company be completely developing different muscles.

this program friends here you're talking about 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 environment how does that actually work Yeah, I think the idea is exactly this, which is like, I want to kind of institutionalize and operationalize my personal model of like living one year in the future and say, what does this imagine a company or a setup like?

Frontier Consulting Group or Frontier Inc. And if you lived in that environment where you had all the AI tools and really advanced deep research, intelligence on task, What are the kinds of questions you'd be asking? What's the kind of work you'd be doing? How would you change?

how you're going about your workday. 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 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 want to push us to think about what does a frontier product look like and more importantly how does a frontier way of working right like what does a team with 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. Yeah, that is the setup. We are just a few weeks into that setup. But meanwhile, what we have done is like we've actually set up in like a company external like a fake company and said hey if you are somebody who wants to come play with some of the cutting edge science projects and deep research agents and agents at work. Come have a come party here.

Hmm. Wow. Okay. And it's only a few weeks in. Okay. So TBD, how it all goes. Yeah. Yeah. And again, like these are micro, that's the meta point here. Right. Also is that, you know, in the traditional way, we've kind of always thought about across the companies, across industries. really thinking about rollouts in these macro ways. You build something and you roll it out, you have a general availability and then you take the time.

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 the thread in a few different directions.

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. We're doing this wrong. We didn't force, you didn't use the word agents.

so far into the conversation i try hard to push it out as far as i can it's like it's like every conversation in san francisco just like how long until i start talking about ai yeah it's like three minutes Oh, man. Okay, 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 does this mean? What is going to change? Give us just a glimpse into how you see the world. being different in a world of agents being around more.

There's a short term and there's a long term, right? There's a lot of hyperventilated talk about the eventual future and all of that. I take a much more practical product building lens on this, right? And I think about these at the end of the day, they're tools. Yes, underneath it, there's stochastic models versus very deterministic programming models. You can tell I'm a computer scientist. That worldview definitely shapes how I think about this.

To me, the short term is there's an evolution. We had apps, right? And now I think we are firmly in the assistance era where there's like human driving the, you know, that's what we think of as copilot, right? Like I think the human driving kind of the, in the driver's seat, but having a lot of assistance from AI.

so i think of this as 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 happened of course then you could say you can delegate more right to the agent so i think to me i think there's one dimension where you say hey agents are somewhat independent software processes.

right that can kind of like run tasks and you're not just thinking about hand holding and fine motor stuff you're saying hey here's my goal go make this happen like i'll give you an example right so we're working on this researcher agent for work And last night I said, hey, you know,

i'm really i have an important meeting coming up with the leadership team 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 kind of come up with how I should be thinking about the right persuasion pitch here, right? And what's magical about this is not just that it's saving time. Typically, we think about the SOFAR AI as summarizing a document or saving time.

This is like... Firing synapses that I didn't I didn't quite have and I actually giving me new insights and giving me, dare I say, superpowers. So that's a natural evolution of AI, I would say. So when I think about agents, I think about three things. One is an increasing... level of autonomy and kind of Independence that you can delegate higher and higher order tasks

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 summarize the document. you know, build me this prototype that expresses my idea of an augmented reality app.

It's a complex task. And then the third thing I would say is asynchronous. It works when you're not working. I think that's the other big thing about these things, that you don't have to sit in front of it. This answers the question of what is an agent, essentially, these three ballpoints.

So it's order the three again. When I think about agents, I think about these three things, right? So one, it's... 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 it's not a one shot hey summarize this document generate this image but it's you know build me this prototype or help me knock this meeting out of the park right

And then the third one I think of is it's a much more natural interaction. That doesn't just mean chat, but it may be actually jumping on a meeting with the agent and being able to 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 are at least product principles that will shape really good ones, good agents. That is really helpful.

Along this line of agents, there's this acronym that you taught me as we were chatting out of this podcast, NLX. What is that? And how does that relate to agents? And why are people not thinking about this enough? Oh, that's one of my Roman empires these days. The natural language interface. NLX is the new UX. So I think

Here's the deal. To me, I think traditionally we've thought very consciously about GUI because the graphical interfaces are not something natural and so they have had to be explicitly designed, but they're rigid interfaces. What we have with conversational interface and natural language is it's a much more elastic. That doesn't mean it's not designed. So people had, often I hear a product builder say, oh yeah, with AI, like the model leads to the product. So it's just, you chat with it.

You and I are having a conversation. It's a podcast. I'll have another conversation at Microsoft 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 natural language as an interface? I'll give you a few examples. And actually, a lot of startups as well as big companies are really experimenting with this stuff. One is if you think about it, Prompt itself. is a new construct. and that's a new way that's a new ui element just like a drop down was or a menu was

But others that are emerging, especially for agents, I think are plans. So when you give a high level goal, what we are seeing is that when the agent comes back with a plan, preferably an editable plan, that's a new construct.

The other one that I think about a lot is showing the work right progress when you see this with different products right you see with the copilot you see with chat gpt deep seek this idea of thinking aloud 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 cron job and script

But if it's tutors, 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. If you're a product builder, this is a fun new space to be digging in. for product design this is really interesting because i think people chat with all these chatbots and it just feels like this is just the way it is but you actually are designing every element of the interaction like how much to share about how much you're thinking

Here's my plan. What do you think? So I think this will surprise a lot of people just realizing there's so much that goes into just designing even these what seemingly are simple conversations. Yeah, another good example is follow-ups, right? You could say, look, you asked me a question and then I could ask a follow-up set of things. And that explicitly should be designed for success. right so for example if i say hey create an image

and it created a black and white, you know, like a clip art version of something. What are the next obvious follow-ups that it should be suggesting proactively now too much and you're kind of annoying me right like uh but too little and in in some sense you've lost an opportunity to direct me or guide me into a happy path here

this resonates a lot with when we had kevin wheel on the podcast he talked about this question of just how much to show about what you're saying and you know and it's interesting that deep seek went the extreme of just showing everything and people liked it too and i think that was interesting Yeah, and I think it's a point in time too, Lenny, because in some sense right now, these things are such black boxes. They're almost like peeking under the hood 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 so it just feels like if you just went silent i'd be very uncomfortable i think Exactly. 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. For example, right, like again, in human, like my API.

would be very different from somewhere. My interface is probably different from others and I might just want the direct, hey, give me the TLDR versus the, oh, so I went here and then I went there and it's like... Following this thread a little bit, we're talking about just how the future is going to be different. There's designing for these chat experiences. There's agency.

Kind of zooming out to just product development in general, it feels like you're at the forefront of a lot of the tools that are going to change the way we build products and also your teams are working. 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 succeed in that future?

I'll start with one stark statement 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. I call it the prompt sets of the new PRDs.

right like i really insist on folks saying if you're building new projects new features of course come with prototype prompt sense and I think that the notion is not to say hey now like everybody is just like a biggest version of a like a software engineer right it is to say you know you have the fastest path

to kind of seeing and experiencing what's in your mind to be able to communicate. It's a much more high bandwidth way of communication. I think about that as really a loop accelerator in terms of product building. That's number one. When in doubt, as someone put it, demos before memos. I think that's really number one.

i would say number two this one is a little bit tricky i'd say is that what i'm seeing is that the time to first demo the is much shorter right but the time to like a full deployment is going to take longer. So I think that there's going to be an uneven cadence. So typically, I think there was much more of a, hey, you build this thing, you take a few weeks and then you can iterate and so on.

Now, but that inner loop of like prototyping and iterating and getting even user research through AI conversations, all of that gets shortened. But I think the bar for scale therefore becomes much high, right? In some sense, if you look at it, there's going to be a supply of ideas, a massive increase in supply of ideas in prototypes.

right and so which is great it raises the floor but it raises the ceiling as well right in some sense like how do you break out in these times that you have to you have to kind of make sure that this is This is something that rises above the noise. So I would say that it's simultaneously thinking about

not chasing after every idea. I think there's a second one. I'd say the third thing is there's a lot of conversation around full stack builders. What does the team of the future look like, the product building team? What I think about is 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.

There will be a few tastemakers at the same time. I think you can still have a lot of people experimenting. it becomes even more important to have that editorial and taste making kind of partier one or few at the heart of it. Because otherwise you just have a Frankenstein product, right? That definitely doesn't change.

I have one other additional bonus thing, which is a lot of folks think about, oh, don't bother studying computer science or the coding is dead. And I just fundamentally disagree. If anything, I think. We've always had higher and higher layers of abstraction in programming. We don't program in assembly anymore. Most of us don't even program in C, and then you're kind of higher and higher layers of abstraction. so to me there will be ways that you will tell the computer what to do

It'll just be at a much higher level of abstraction, which is great. It democratizes. There'll be an order of magnitude more software operators. Like in Swiss, maybe we'll have source. 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 strongly disagree with the whole coding is dead. That's awesome. I love that. And so as a software operator, is that what that stands for? Yeah. Interesting.

Okay, cool. 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 it?

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 momentum and shift and desire to say hey let's let's actually look at live demos live prototypes and to even like communicate the ideas right

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 vowels of Excel, you probably don't. There's even enough depth. in the 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, new products, new features, absolutely.

Okay, let's talk about product management. There's this 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. 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, which is what PMs are really good at. And so I feel like it's the opposite. IPMs are the most important role and it'll change new. But let me get your take. What do you think the future of product management looks like? Do you think it's dead? Do you think it's going to thrive? Do you think it's going to change? Yes.

Meaning, look, I mean, if you're a TPS report, mostly process person and like a lot of companies do 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 AI can read and write like 50,000 meeting notes and, you know, track things and send emails and so on.

What I do think on the flip side is the taste making and kind of the editing function becomes really, really important, right? In a world where the supply ideas supply of prototypes becomes even more like an order of magnitude higher you have to think about like what is the editing function here

So that does mean that the bar is higher for product folks. But I think there's an interesting side effect I am observing in startups that I'm advising companies and even within the companies that 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're saying. 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 title.

But there's also just like unlock of latent really good ideas from smart engineers, smart user researchers. smart designers who can now have like this expert in their pocket right to kind of round out all the other things that they're not they're not typically skilled at.

to bring for their ideas and that's amazing i think and i think that expert it's interesting i'm working with an engineer and some stuff and he uses chat gpt to even communicate to me in more effective ways, like turn this pitch into something that will convince Lenny this is a good idea.

by the way that is actually one of my common use cases which is the wwxd i call it what would x do like i used to say hey um what would the satya think about like this particular set of uh conversations or ideas that we are pitching and so on this is the power of like i think deep reasoning plus relevant context right this engineer you're talking about has that context

about you. And so it's kind of very interesting. If only everyone was as famous as Satya and had so much information out there, but I guess you can import all their emails or whatever tools exist to just like understand from the conversations you've had with that person. Yeah, and I think this goes back to actually what you were saying too, which is

I think this idea of what you see, there's like a coil spring, there's an intelligence overhang that I just see across the board. And I think the part of product development has to almost rewire ourselves. I think Toby from Shopify calls it the reflexive AI usage. And that's not as easy.

And I've been thinking about why. I basically, I mean, I have 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 just like it's very cheesy but it kind of helps to pause and think oh what what am i trying to do here But the reason I find it hard and when I talk 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 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 and trying it a few months ago that prior needs to be updated and it's hard to do that right you have to kind of do something almost

counter-intuitive 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 15 year old in a month I think that last point is so important that we've tried these tools over the years and 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 and things change. I think that's actually, if you're a product builder listening to it, that's a really interesting arbitrage thing for you. Like if you can kind of cut against the grain and say, no, I won't have that scar tissue around like. you know, this didn't work a few months ago and keep setting high expectations and demand more of the AI today, I think you can unlock more. There's a lot of alpha in doing that. That's right.

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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 how can you use ai to do this

Yeah, it's as cheesy as that. And it's interesting because it works in the last few 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 a personal life to say you know when I'm trying to do anything manual like 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, you built a Chrome extension. That's so cool. Okay. Did you use AI to build it? Of course.

Wow. Which tool did you use to do that? Some kind of Microsoft tool, I imagine. Yes. Yeah, no, actually, it was just like, I mean, I live in GitHub and GitHub Copilot, so just like was like, okay, let's go build this Chrome extension. Are you releasing this for the general public? No, I mean that's the amazing thing. It took me like 10 minutes to do this.

Okay, let's link to it. Let's get it out there. Open source this thing. Okay, you mentioned Satya. I have a question about this. So you're one of the very few people that have worked 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 lucky to have a window into these two amazing leaders of this generation. I would say, I mean, again, no surprise.

As you'd expect from CEOs of multi-trillion dollar market cap tech companies, they are 99.99 percentile in like almost every dimension you'd think of, right, intellect.

empathy, leadership, product, strategy. There are, of course, flavors of differences. I was the technical advisor for Sundar with the first at Google and set up kind of the office of the ceo there uh and they are again a matter of like time and context because a lot there's a lot more consumer oriented focus there so what i did find some great at it is being

really calm and measured and thoughtful in terms of making sure that things are dealing with the complex ecosystems. If you think about the phone ecosystem or even like the search and publisher and advertiser ecosystem, it's a very complex. He was a master at that. he's an astronaut and i think on satya 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

the macro, the strategy, what's the game, but also the micro. Hey, why are we not? Here's a specific insight that I saw on Twitter. And you can count on the fact that he is ahead of pretty much everybody else. in terms of spotting those early things too so it's uh it's just been like like you know learning from the fire hose as as they put it what a cool opportunity to work with two incredible folks

Okay, let's go 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 startup wisdom, common product building wisdom?

I don't know if it's as common as it should be and it's like a counterintuitive thing, but I've repeatedly learned that when you're doing something new 0 to 1 the temptation is to kind of think about, you know, it's like that South Park episode, step one. So I do feel like there's a temptation to rush and say to go to scale before solve. So I've always said. to my teams. solve before scale.

So 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 roughly in the ballpark. So to give you a couple of examples, I think when you look at the solve stage, There are wide lurches. You got to be very comfortable with the fact that

You're day one thinking about, hey, a plant detection tool. And then day 15, you're like, oh, actually the tech is really good for translating in a foreign language. By the way, this is not hypothetical. This is what we kind of looked at in Google Lens back then and said, OK, what is the intersection and so on. So from the outside, it looks like chaos.

But actually in the end and you should be very comfortable not only tolerant I think you should be like should have an appetite for that 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 like when you're trying to think about uh what mode you're in are you in a solve mode are you in the scale mode One example is kind of making sure that you're comfortable with the chaos. I think the other lesson I've learned is the danger of metric. right and i think again if you have worked on you know rule search or if you worked on you know like office products you really have like a very fine-grained sense of

What are the metrics for this product? You have the input metrics. You have the whole shebang. But when you're looking at something 0 to 1, If you decide on a metric too prematurely,

That's false precision, first of all. CTR, when you have 1,000 people, doesn't mean anything. Retention also may not mean anything. So really being very wary of this uh big guy big girl grown-up metrics as i call it right you are looking for more qualitative the sound of click And what is your, as the other kind of the handler uses, what is your set timer and play music?

right so if you look at like Alexa and like Siri and Google assistant and all these 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 and you could play trivia And so you've got to mail those things before you say, oh, yeah, here, you can do anything with it. That's exactly what I use my Google Home for.

So basic. I don't do the trivia thing, though. Maybe I gotta 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 kind of a little framework for helping you know if this is the right time for this idea. How do you think about that?

Yeah, and when you think about the solve mode, and this is again like sticking with my whole, you know, living in one year in the future, I gravitate towards the zero to one and solve mode products completely thinking about new category of products. And what I found about the hardware, I would say, is that you do want to look for at least two out of these three factors inflection points here. If you want to make a really good product. Number one, is there a shift? is a step function in the tech.

Somewhat obvious, I would say, like, you know, deep learning was one for Google Lens. Back then, speech recognition was a step function for conversational search. I would say for Robinhood, you know, the generational shift. was very clearly and the fact that phones were a primary means for, you know, you could actually have an app or 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 nlms 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 right so 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 suddenly when storage became free and mostly free and everybody had phones everywhere all all all the time you took pictures of everything right and then you had like enough of pictures or you use the camera as the as the keyboard for you for your world right for the real world and so how do you kind of then say oh this consumer shift is big and so therefore 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.

And I'd say the third 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 you know a cpc Same thing with SaaS and the fact that you could actually charge or monetize enterprise products in a different way.

And with AI, of course, like the monetization is a whole different, like, I mean, you have just barely scratch the surface of whether you do seat monetization usage like on tap and then of course outcome based stuff outcome based monetization hey have you solved the problem for me and then i will pay you some fees So all three, like to me, are kind of like great, but at least two out of three for a good product.

So it's 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 There's three ways to look at it, and two of these three should be true. There should be a shift in technology, some new technology that has enabled this now, recently. 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 it gives you an advantage over folks trying to do it today. Awesome. You didn't mention Robinhood, I think, in that example. That was another good example. Yeah. Yeah. I mean, talk about the business model of kind of, again, like not having zero fees, right? And again, like the combination of all of these things.

is what can unlock it. You can't just say, oh, we'll just have a much more better intuitive interface and hope that people will switch. Okay, so speaking of zero-to-one products, I'm going to take us to an 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 on a couple recent podcasts, actually.

So there's these companies like Cursor, vZero, Lovable, Bolt, Replit that are like the fastest growing companies in history. 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 space, this AI coding tool space. You guys said, Copilot, the first tool in the world at this stuff. So ahead of everyone. You build VS Code, which is what 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 So I'm a daily user of GitHub Copilot. And I would say, look, if you unpack, I think the thing, the beauty of this is that Code generation has become an amazing tool that NLMs have unlocked. So it is actually really good excitement and action that now code generation

has just opened up all of these things that we've talked about the whole idea of like prototyping. Go from idea to marks and idea to kind of a clickable prototype in like in a few minutes. Those are the kinds of things that, of course, we should expect code generation to enable. The way I think about how we are positioned and 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 folks who are who have the repo there right and you have kind of of course you have the assistance in terms of autocomplete and you can chat but now we have the agent board it's one of the fastest you know kind of loops that we are seeing really strong positive feedback so in some sense when you have a system

What you are looking for in terms of building and designing it is not just a single product that can grow, but it's the what is the repository? What is your context? What are 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, this product needs to scale for that. If you're a wide coder,

You should still be able to do that and so on. So that I think is the way that GitHub is positioned to build on and growing honestly really well. That's so interesting. So like the core of this is everyone ends up in GitHub anyway, no matter what tool they use. And that's kind of the... Yeah, and I think 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 they're not all kind of you know doing the same job. I think when you're at the end of the day, like you're building code for companies to run on. You need to have a system, you need to have kind of the ability, an entire Swiss Army 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 product is going after. All roads lead to GitHub. On the flip side of this question, there have been probably 5,000 startups that have tried to disrupt Excel, and you guys just keep winning. So something there is working really well.

That is so interesting you say that. So when I came to Microsoft and I'm an Excel fan, so I actually had a conversation with one of the OG Excel product folks. I was like, man, what is it about this product? And he said a couple things that were really interesting for me that just stuck with me. One is, you know, he said, 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. you know, ability. And I thought that was just like really striking. And then the second thing that I found out super cool, I don't know if you know this, but I didn't know at least before two years ago that there are these amazing Excel championship. like World XM Championships, 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, but great to use. right so it's a very good case of hey the learning curve initially the one-time learning curve might be tricky but it is because there's so much power and depth in the in the tool

That's so interesting. I never thought of Excel as a programming language, 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 has given and again that's the compounding effect over you know decades of working on like deep deep a signal right from people who live and who depend on it day in and day out Okay, to kind of start to close out our conversation, I want to ask this question around your career. I find that most people have

one moment in their career that changes the trajectory of their career. It could be a manager they had, it could be a project they worked on, it 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? Actually, there is one moment where you know it was a turning point for me i was in google search i was working on this idea that i thought should just work and it didn't

I said, hey, these phones are becoming a thing. Personalization has to be important. So I probably banged my head against the wall for a year or so trying to make personalization work.

And it turns out when you have a query that you put into Google search, the personalization didn't matter as much. And so we disbanded the team. But then I think... I started working on this product called Google Now which was 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

hey connect the dots and say you should leave now for the you know given the traffic and where where you need to go and so on or if you're deeply interested in stand-up comedy with deadpan artists, you should check out Mitch Hedberg. These are kind of like these really moments that the smartphone should be smarter. So I let that product through the kind of the initial zero to one phase.

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 things go and building the product rise to the occasion. way more than the scaling and sustaining products. Second, it's harsh, but being early is the same as being wrong.

You know, this is pre-LLMs, pre-deep learning, a lot of the really amazing ideas in terms of next token predictor, etc. We've been thinking of it, but, you know, didn't have the horsepower to go. The interface was great. The intelligence wasn't there.

And I'd say the third thing that stuck with me is I got to work with some really smart like the talk about talent density now right and i think really smart people who have 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

It's such a great story because it didn't work out right in the end. Google now kind of went away, right? And by the way, I super remember that product. It was very cool. I remember looking at it as very delightful and happy. And so 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'm not gonna lie i think it was uh it was um it's painful when you do that because you see the vision of what can be 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 one or two key click stops away from the product being great. And part of figuring out is knowing when you're in what situation.

How long was that period from starting out until just like moving on and it's not working? Yeah, I would say in that case, one of the good things is again, like it led the foundation of, it was one of the foundations of the Google Assistant. And of course, as the LLMs, you know step function happened now with gemini it kind of like works out and i think it's the same thing across the board which is sometimes you want to kind of figure out

the invariants that do work, right, that can 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. But it is fascinating to me that the interface, that there we had the opposite problem. Whether you think about all the voice assistants, the interface is like...

we overshot and the intelligence wasn't there. Today I feel like there's an opposite problem. I think these things have amazing intelligence and the interface we have largely is like the aol aol dialogue modem chat bot 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

before we get to our very exciting lightning round. 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 collaborate together. I think there's 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 you have kind of like the humans and 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... produce well i need to hear more about this what do you when 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 had a thought of the physical place, but I am thinking a lot about kind of, you know, right now all of these experiences are very single player, right?

and i do think there's an opportunity to think about how do we again i'm living one year in the future how do we actually have like you know collaborate with each other but with also with agents and really uh figure out for example uh what tasks can we delegate what can we kind of like inspect how do we actually have information that flows between people that agents can mediate and so on.

all right i'm curious to see what you guys got cooking 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 I have recency bias, but I've been reading this book called the brief history of intelligence and now i'm in a book and uh you know

Like lots of underlining for me. And I think it kind of, the premise is too, it looks at the evolution of intelligence, like human intelligence and kind of the brain development and kind of connects that to what we're seeing with AI. Do you have a favorite recent movie or TV show that you really enjoyed? I've been watching this.

it's about a woman who was like a great stand-up comedian of I think it's set in kind of like the the fact that she she grew up i think in the uh 70s and 80s and kind of like really tried to break through in an industry that hasn't traditionally been like very uh friendly to women so really fun and quirky Do you have a favorite product that you've recently discovered that you really love? Could be an app, could be some physical.

i do use a lot of microsoft products github copilot being one of them but i think the one that i maybe i'll pick is um Granola, I think is the name of the app. I found it really useful. I just gave it a spin the other day and I'm like, oh, this is really useful in terms of being able to, you know, again, like.

without being intrusive just just capture the thoughts notes and structure it put some it felt like one of those things where you have the confidence 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 And then enough of the LLM 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. Did not know that. There we go. so and then just check that out lenny's newsletter.com and you click the word bundle and you'll see how to do that

Very cool. Two more questions. Do you have a favorite life motto that you often come back to when you're dealing with something? Maybe you share with folks they find useful as well in work or in life. I have one. In fact, actually, this is my email signature for the last 20 years or so. It says the best way to predict the future is to invent it.

I think it's a quote by Alan Kay. 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, you know, kind of think about, hey, this is exactly how everything is going to look and this is exactly the sequence and so on. I think there's no substitute to experientially building it. And I think the 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. We've talked about stand-up comedy a bit. Is there 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 i think i think a british indian

stand-up comedian. Her name is Sindhu V. Super smart, you know, mom comedy. And I think the other one that, this is definitely not under the radar, but like, I'm just like, love his... stick is uh nate margazi he's just so good apart from it 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? You can find me on LinkedIn and Twitter, AparnaCD.

is the handle. I do post stuff a lot more on LinkedIn these days, so I would love to hear 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 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. Perna, thank you so much for being here. Thank you. Bye everyone.

Thank you so much for listening. as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.