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 that editorial and tastemaking 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 eats 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 this stuff.
So ahead of everyone, what happened?
I would say... Today my guest is Aparna Srinapragada.
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.
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Well, with that, I bring you Aparna Shanapragada.
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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 standup comedy and you take it semi -seriously.
Just how serious are you about this?
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 I do watch and do standup comedy, I do open mics, I've done a few shows.
I have one set brewing that is around AI, unsurprisingly AI, and tech, and Silicon Valley.
It's really interesting for me.
This was an accidental discovery.
Like I've always been an SNL fan and like just comedy fan.
But I went to an open mic because 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, punch line market fit.
And, actually, there are a couple of things that I do find really powerful and useful, because in OpenMics, or even when you're testing these things, it's a very tight cycle of iteration, and you get live.
OpenMics are the real, live experiments.
You put something out there, you get very clear microfeedback 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 now have a fantastic vision, but the first version is not quite there, right?
And Reid 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 corollaries between these two things.
I didn't realise you actually did shows and you're working on a set.
I wasn't gonna ask you for a joke, but if you're working on a whole thing about AI, is there something 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 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 too that, you know, they're probably more like men in the sense that they hallucinate a lot, they kind of are not yet reliable - I'm afraid to laugh at this a little bit.
This is great. OK. 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?
Commanding. This is great.
OK, let's get serious again.
So you worked most of your career at a lot of consumer internet companies.
You worked at Google, Robinhood.
You're on the board of eBay, on the board of Capital One.
Now you're at Microsoft. I'm curious just what is most different about working in a company like Microsoft and building product at a company like Microsoft?
I think intellectually I knew 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 It's 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 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, 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 then say, we'll just focus on one side of the house.
Or kind of overly crippling the user experience and kind of leaning on the other side.
So I think there's an art and science and nuance in 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.
I think there's a famous trailer from the 2000s on Van Dam on these two trailer, two buses.
Like 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 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. You don't want to kind of be rash of that.
So it's like the future is unevenly distributed, but even within the companies.
On the second bucket of this other, the bust that Van Dam's writing on of governance and adoption and changing behavior and stuff, is there anything 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 we, you know, I've been working with folks to say, 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 just built this world's first deep research agent made for work, right, post trained for work.
And of course, it has 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 company be completely developing different muscles.
This program for Anjiri, you're talking about, I wanted to spend a little time on it.
So 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, right.
And if you did, lived in that environment where you had all the AI tools and really advanced deep research intelligence on DAAP, 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, 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?
What does a team with three people and tons of 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?
Is that how it works?
Yeah, that is the setup.
We are just a few weeks into that setup.
But meanwhile, what we have done is we've actually set up an 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 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, the meta point here 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, right?
You build something and you kind of like roll it out.
You have a general availability for and then you take the time.
And that's really important too, because again, like we're talking about pharma companies, 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 the one year in the future?
Let's follow this thread in a few different directions.
There's like how product change development changes.
There's how engineering changes.
There's also just agents.
I know you're spending a lot of time in agents.
It feels like you're not an AI company these days if you're not working on agents or building an agent.
Then we're doing this wrong, we didn't force, you didn't use the word agents like so far into the conversation.
I try hard to push it out as far as I can.
It's like every conversation San Francisco just like how long until I start talking about AI.
Yeah, exactly. It's like three minutes average, I bet.
Oh, man. OK, 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 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, right?
Yes, underneath it there's stochastic models versus very deterministic programming models.
You can tell I'm a computer scientist, like the way that, 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 formally in the assistance era where there's like human driving the, that's what we think of as co -pilot.
I think that 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 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 could delegate more 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 hand holding and fine mortar stuff, you're saying, hey, here's my goal.
Go make this happen.
I'll give you an example.
We're working on this researcher agent for work.
Last night I said, hey, 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 do, how should I be thinking about like, you know, 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, so far AI as summarizing a document or saving time, right?
This is like fighting synapses that I didn't quite have and I actually giving me new insights and giving me, did I say superpowers, right?
So that's a natural evolution of AI I'd say.
So when I think about agents, I think about three things.
One is, is an increasing level of autonomy and kind of independence that you can delegate higher and higher -order tasks.
Second thing I think of here is complexity, right?
So it's not just a one shot, hey, create this image or do this thing or summarize a document.
It's, you know, build me this prototype that expresses my idea of an augmented reality app, right?
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, well, what are the three again?
When I think about agents, I think about these three things.
So one, it's autonomy.
And it's a spectrum.
It's not a zero -one.
It's how do I actually delegate things that it can do?
Second, I think of as complexity.
It's not a one -shot, summarize this document, generate this image, but it's build me this prototype or help me knock this meeting out of the park.
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 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 or 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 ahead 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, right?
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, right.
What we have with conversational interface and natural language is it's a much more elastic, right.
That doesn't mean it's not designed.
So people often I hear product builder say, oh yeah with AI, like the model needs 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, right?
And actually a lot of startups, as well as big companies is really experimenting with this stuff.
One is if you think about it, prompt itself is a new construct.
And that's a new UI element, just like a dropdown 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.
You see this with different products, right.
You see with the Copilot, you see with Chagmati, DeepSik, this idea of thinking aloud, and it's kind of showing the work, but how much do you do it?
If it's too verbose, it feels like I'm running some cron job and scripts, but if it's too terse, then I don't know if it's going 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, this is a fun new space to be digging in 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 element of the interaction, like how much to share about how much you're thinking, here's the, 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.
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.
So, for example, if I said, hey, create an image and it created a black and white 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?
But too little and 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 Wiel 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 DeepSeek 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 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, like, again, in Inhuman, my API would be very different from some, 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 then like.
Following the start 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, 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 prototypes and prompt sets.
And I think the notion is not to say, hey, now like everybody's just like a biggest version of 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, 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.
When in doubt, as someone put it, demos before memos, right?
I think like 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.
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've been 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 like there's going to be a supply of ideas, right?
Like a massive increase in supply of ideas in prototypes, 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 kind of make sure that this is something that rises above the noise.
So I would say that it's simultaneously thinking about not chasing after every idea.
That's the 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, they will be a few folks that are especially at the prototyping, only idea discovery stage that the lines are blurred, right?
They'll 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 the territorial and tastemaking kind of out here, one or few at the heart of it, because otherwise you just have a Frankenstein product.
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 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.
In some Swedes, maybe we'll have souls, 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 is OSOFRA operator, what is that?
What that stands for?
Yeah, I just made it up, but yes.
Okay, cool. This idea of prototyping as 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?
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 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, if you're trying to change something in the bowels 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 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 than getting adoption for it?
Which is what PMs are really good at.
And so I feel like it's the opposite, like PMs are the most important role and they're, you know, it'll change too, but let me get your take, which is 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. Meaning, look, if you're a TPS report, mostly process person, and 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.
But I think what I do think on the flip side is that tastemaking and kind of the editing function becomes really, really important, right?
In a world where the supply of ideas, supply of prototypes becomes even more, like an order magnitude higher.
You have to think about 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 used to be more gatekeeping, I would say, in terms of like, oh, this is, we should ask the product leader 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 title.
But there's also just like unlock of late and 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 they're not typically skilled at to bring forth 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 ChatGPT to even communicate to me in a 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, what would Satya think about like this particular set of 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 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 just see across the board. And I think the part of product development has to almost rewire ourselves to, I think Toby from Shopify calls it the reflexive AI usage.
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 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 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 the space, they find it hard, is that 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, have deeper and smarter answers.
couldn't do data analysis.
So like my impression of it from 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 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 15 year old in a month.
I think that last point is so important, that we've tried these tools over the years, and 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, 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, and I'm like, this didn't work a few months ago, and keep setting high expectations and like demand more of the AI today, I think you can unlock more.
There's a lot of alpha in doing that.
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I'm going to come back to this cheesy plugin.
Say more about this.
So this is a plugin that just lets you put a custom message on every new tab, and 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 experiment to say, hey, how much more AI pilled can I get, both at work and in personal life, to say, when I'm trying to do anything manual, 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?
No, I built it. 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 live in GitHub and GitHub co -pilot, so it was like, okay, let's build this Chrome extension.
Yeah. Are you releasing this for the general public?
No, I mean, this stuff, that's the amazing thing.
It took me like 10 minutes to do this.
OK, let's link to it.
Let's get it out there, open source this thing.
OK. 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 been going to these two amazing leaders of this generation.
I would say, I mean, again, no surprise there, as you'd expect from CEOs of multitrillion -dollar market tech companies, they are 99 .99 percentile in almost every dimension you think of, right?
Intellect, empathy, leadership, product, strategy.
They are, and of course, flavors of differences.
I was the technical advisor for Sundar for the first at Google and set up, kind of, the office of the CEO there.
And there, again, a matter of time and context because there's a lot more consumer -oriented focus there.
So what I did find Sundar great at is being really calm and measured and thoughtful in terms of, you know, making sure that things are dealing with the complex ecosystems, right?
If you think about the phoney ecosystem or even like the search and publisher and advertiser ecosystem.
It's a very complex ecosystem.
He was a master at that.
He's an asset at that.
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 Zoom levels that he can operate at, the macro, the strategy, what's the game, but also the micro.
Hey, 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 just been like, you know, learning from the firehose, 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, I mean, 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, zero to one, the temptation is to kind of think about, it's like that South Park episode, step one, think about the problem.
Step two is - Underpants, I think it's underpants.
Underpants, exactly, right?
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, right?
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 fate or even at least like in this, roughly in the ballpark.
So to give you a couple of examples, right, I think when you look at the solve stage, there are wide lurches, you gotta 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 foreign language.
This is not hypothetical.
This is what we 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.
Actually, you should be very comfortable, not only tolerant.
I think you 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 what mode you're in, are you in a solved mode or 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 metrics, right?
And I think again, if you have worked on, you know, rule of 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 out, you have the whole shebang.
But when you're looking at something zero to one, if you decide on a metric too prematurely, that's false precision, first of all, right?
like you kind of, I mean, CTR, when you have like thousand people, doesn't mean anything.
You know, retention also may not mean anything.
So really being very wary of like this big guy, big girl of grownup 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?
So if you look at Alexa and Siri and Google Assistant and all these things, it 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.
You could set a timer, you could play music, 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 not a good recipe.
That's exactly what I use my Google home for.
So basic. I don't do the trivia thing, though.
Maybe I got to give this a shot.
Uh -huh, you got to try that, yeah.
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 living in one year in the future, I gravitated towards the zero to one in Solve Mode products, completely thinking about new category of products.
And what I found both the hardware 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?
That's somewhat obvious.
I would say deep learning was one for Google Lens.
Back then speech recognition was a step function for conversational search. I would say for Robinhood, the generational shift was very clearly, and the fact that phones were a primary means for, you could actually have an app, a mobile app for finance that you could use.
So look for that inflection.
What is the tech inflection?
And right now of course, LLMs 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?
To give you an example, when we started working on Google Lens, what we said is, look, people were taking mostly pictures for sharing, selfies and sunsets and so on.
Suddenly when storage became free and mostly free and everybody had phones everywhere all the time, you took pictures of everything and then you had enough of pictures or you use the camera as the keyboard for your world, for the real world.
And so how do you then say, oh, this consumer shift is big.
And so therefore, 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. Is there an inflection point, natural inflection point, in the business model.
So any great products, if you think about all the way from search, again, the second price option and the fact that you had CPCs.
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, the monetization is a whole different, I mean, you've just barely scratched the surface of whether you do, you know, 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, you know, kind of like great, but at least two out of three for a good product.
So this is 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 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.
Yup, absolutely. Awesome.
And you didn't mention Robinhood.
I think in that example, that was another good example of phone use.
about the business model of kind of again, like not having a zero, you know, 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, you know, people will switch to it.
Okay, so speaking of Zero -to -One products, I'm gonna 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 v0 lovable bolt replate that are like the fastest growing companies 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 co -pilot the first tool in the world at the stuff so ahead of everyone You build VS Code, which all these companies are forking to build on.
You have incredible AI infrastructure, incredible AI talent.
So this could've been your market.
What happened? What happened apart now?
You know, it's interesting the framing.
So I'm a big user of GitHub co -pilot, 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, right?
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 prototyping.
It goes from idea to mocks and idea to a clickable prototype 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 have the repo there, right?
And you have kind of...
Of course you have the systems in terms of autocomplete, and you can chat, but now we have the agent board. It's one of the fastest 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 go 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 right so that i think is the way that GitHub is positioned to build on and growing honestly, really well.
That's so interesting.
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 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 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 hand hold.
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 O .G.
Excel product folks, and 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, hey, Excel is a proof that noncoders also have to program.
Programming is really powerful.
And it's the tool that gives all of the noncoders a really powerful programming ability.
And I thought that was just really striking.
And then the second thing that I found out was 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 championships like World Excel 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 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 depth and the attention that the team has given, and again, that's the compounding effect over decades of working on like deep, deep signal from people who live who depend on it day in and day out.
Okay, to kind of start to close out our conversation I wanna ask this question around your career.
I find that most people have like one moment in their career that changes the trajectory of their career.
It could be like 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 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, right?
Like I said, hey, these phones are becoming a thing.
Personalization has to be important.
So I probably bang 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.
It said, hey, actually on the phone, we should be able to push content.
It's not about searching a big personalization.
And for example, if you have a flight coming up, we should be able to say, hey, connect the dots and say you should leave now for the, given the traffic and 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 Hagberg.
Like these are kind of like these really moments that the smartphone should be smarter.
So I let that product through 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 to rise to the occasion way more than, you know, 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, et cetera, we've been thinking of it, but didn't have the horse power 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, they talk about talent density now.
And I think really smart people who have gone on to do amazing things.
And so it gave me a taste of what a small group of people can do.
It's such a great story because it didn't work out right in the end, like 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, it was, it was 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 five years or 10 years to kind of like really unlock the intelligence.
But sometimes it's one or two key clicks tops 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 and still just like moving on and it's not working?
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 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.
It was a Google Now the first agent before agents, that's what it feels like.
It was certainly the idea.
Yeah. You know, but it is fascinating to me that the interface that there we had the opposite problem, like whether you think about all the voice assistants, right?
The interfaces 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, the AOL dialog, 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, right?
I think there is 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.
When do you imagine a coworking 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, 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 figure out, for example, what tasks can we delegate, what can be kind of like inspect, how do we actually have information that flows between people that agents can mediate and so on.
All right, now 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. First question.
What are two or three books that you find yourself recommending most to other people?
Oh, I have recency bias, but I've been reading this book called the Brief History of Intelligence, phenomenal book, and you know, like lots of, lots of underlining for me.
And I think it kind of, the premises to it looks at the evolution of intelligence, like human intelligence and kind of the brain development, and connects that to what we are seeing with the eye.
Do you ever favor a recent movie or TV show that you really enjoyed?
Hacks. I've been watching this.
It's about a woman who was this great stand -up comedian.
I think it's set in kind of like the fact that she grew up, I think, in the 70s and 80s and kind of like really tried to break through in an industry that hasn't traditionally been like very friendly to women.
So really fun and quirky.
Do you have a favorite product that you recently discovered that you really love, could be an app, could be something 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 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, again, like without being intrusive, of just capture the thoughts, notes and structure it.
It felt like one of those things where you have 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 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.
And then just check that out at lenny'snewsletter .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, I don't know, the last 20 years or so, 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, no one knows anything.
Like when you think about like all the folks who are 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 is no substitute to experientially, like building it.
And, 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, is there like a favorite under the radar stand up comedian that you think people should go check out?
Oh, there's a, there's a couple of them.
So one, I think there's a, there's an Indian American or I think I think a British Indian standup comedian, her name is Sindhu V.
Super smart, mom comedy.
And I think the other one that he...
This is definitely not under the radar but like I've just like love his stick is Nate Burgazi.
He's just so good. Aparna, 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?
So you can find me on LinkedIn and Twitter, Aparna CD 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 kind of, you know, this, 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.
Purna, thank you so much for being here.
Thank you. Bye everyone.
Thank you so much for listening.
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