Robby Stein on the PM's Core Skill: Making Great Decisions
Lenny's Podcast
When AI can help build almost anything, what still separates a product people love from one nobody uses? Speaking at the Lenny and Friends Summit, Robby Stein, who worked on Instagram Stories, Reels, feed ranking, and direct messaging and now works on AI-era Google Search, argued that the product manager's defining skill has shifted. It is no longer mainly about getting things done. It is about making great decisions. Stein laid out a three-part process drawn from nearly 20 years of building consumer products: understand people deeply, diagnose root causes, and refine the craft. For each part, Stein also described how AI is starting to change the work.
From Project Management to Judgment
Stein opened with context. After almost two decades of building products, including startups and large products like Instagram and Google Search that now reach billions of people, Stein said they have also "built some things that zero people use today." Across the successes and failures, Stein sees a repeatable pattern in what made products work, and that pattern is the subject of the talk.
Stein said the talk would focus on the craft of product rather than on how AI is transforming products. Stein's view is that things that once took teams of people years to build can now be done by almost anyone with a handful of agents or AI assistants. That changes the PM role. In the past, Stein said, much of a PM's value came from organizing, creating energy and momentum, and acting almost like a project manager. That is still true in some cases. But in a world where almost anything can get built, Stein said it is clearer than ever that the PM's real value lies in judgment and taste.
People often describe PM work as doing many things reasonably well. Stein called that somewhat true, but argued that above everything else, the craft comes down to one thing PMs should be the absolute best at: decision-making. PMs should be students of what makes a great decision. Stein called this humbling, because many decisions turn out to be wrong. The three "chapters" that follow are, in Stein's words, an unwritten book about the process Stein and their teams have used, presented in this format for the first time.
Chapter One: Understanding People Deeply
The first chapter starts with people and their fundamental needs. Stein cited Clayton Christensen's Competing Against Luck and the jobs-to-be-done framework: we don't use products, we "hire" them to do things for us, and builders need to know what those jobs are. The best way to draw out those insights, according to Stein, is through a story. Stein's Instagram team once held an offsite with the people who created the framework and went through the process themselves.
Stein walked the audience through the exercise. Think of your last major purchase, then put yourself back in that moment. When interviewing someone, you ask about the details of their life that day: where they were, what they were reading, who they were with, what they were wearing, whether their kids were there, whether they were in a store or online. The aim is to put yourself fully in that person's position.
Stein's own example was buying a bed. Stein and their wife were trying out beds, and three or four felt great. Then Stein's wife asked Stein to jump on one and "do a barrel roll." After Stein flopped onto it, she said they were buying that bed. What she needed most was a bed that would not wake her up when her partner moved. Stein admitted this was "a little offensive" but real. A PM for beds, thinking about a classic roadmap, might focus on cooling, eco-friendly materials, or price. Stein acknowledged those do matter. But what drove the decision that day was motion transference. Without talking to the customer, Stein said, the team would never have learned that. That is what discovering the job to be done means.
Jobs to Be Done at Google: Visual Inspiration and Trusted Local Information
Stein gave two digital examples from Google. Early in Google's AI experiences, the team realized people were coming for inspirational journeys, such as figuring out what to buy. But two years ago, Stein said, chatbots were text-only, and you cannot really describe a couch to someone in text. Stein said the team was among the first to invest in state-of-the-art multimodal understanding, retrieval, and knowledge systems. The goal was for someone to ask about a couch, get a strong visual response, and continue a multi-turn conversation: asking for emerald green, asking what "fuzzy" means, focusing on a particular throw pillow, then getting a grid of throw pillows. Stein described these as very hard problems at the time. They are common now, but in Stein's account they came from recognizing a core need.
The second example came from early exploration of AI Mode, which lets people have a conversation with Google Search. Early versions gave rich information in response to something like a neighborhood food question, but something was missing: the things people expect from Google. That meant accuracy and authority, seeing places on a map, star ratings, closing times, cuisine type, and price level. Once the team brought that together, a user could ask about food and get a rich experience with trustworthy entity information. Stein called that "the magical product" and said it remains one of the most popular parts of the product based on thumbs-up ratings and other metrics the team tracks. Again, Stein tied it to a human insight about why people used the experience in the first place.
How AI Might Scale User Understanding
Stein was candid that their thinking about AI's role in this first chapter is still developing. One idea Stein is excited to try more is feeding transcripts of user interviews to a model and having it extract jobs to be done, so the process can scale. Another is an agent that interviews people using the same methodology Stein described, asking many more users why they use a product and what their underlying problems were. Stein presented both as ideas under consideration, not finished systems.
Chapter Two: Diagnosing Root Causes
The second chapter begins after you have an initial vision and build something. That first version, Stein said, is usually not great. Almost everything Stein has worked on started out that way. The process that follows has four parts: get a clear, analytical view of the specific problems in the product, rank them by priority, understand them deeply, and fix them. Then ask again, and keep looping. Stein compared this to a training process, something like an epoch of model training, where each round of changes moves you closer to product-market fit until the product works.
Instagram Stories and the Audience Problem
Stein's first example was Instagram Stories. Stein said Stories was one of the few things that worked fairly well out of the box, though there were many internal versions that placed stories in the feed that didn't work well and caused plenty of headaches. Even after launch, it was clear that not everyone was adopting it or felt comfortable sharing. The product's premise was sharing your life throughout the day, so the question became: why aren't you doing that?
Stein described this as the inverse of the jobs-to-be-done question. Instead of asking why people did something, you ask why they aren't doing the thing you want them to do. People answered, and Stein described the answers as a cloud of "leaf nodes." Grouped by theme and sorted, the top theme was roughly an audience problem: my ex is on it, my teacher is on it, my aunt, my sister. People didn't want to post something goofy in front of all of them. The team then ran a large quantitative survey with thousands of people, using the qualitative themes as input, to quantify how much each factor weighed.
The conclusion, according to Stein, was that unless the audience burden could be overcome, no creative tool or clever product idea would work. The team spent two years cycling through ideas until they arrived at Close Friends. Along the way there were many strange versions. One was called "favorites." Another had a back door on your profile that showed a private version of it. Another let you post to your feed under a close-friends label. Stein called these "super confusing": no one understood them, and it made little sense to put a raw photo in the middle of a polished feed. The only version that worked was inside Stories, so the team cut everything else and relaunched it as a Stories experience. That version worked.
Reels: A Failed Launch in Brazil
The second Instagram example was Reels. The team launched an early version in Brazil. Their assumption, which Stein said made sense at the time and which the team shared, was that people posting goofy videos and dances would not want them on their profile forever, so the format was ephemeral. The launch was, in Stein's words, a "huge failure."
It turned out that if you put a lot of time into an impressive dance, you don't want it to disappear after a day. Stein called that obvious in hindsight. The bigger insight was that the people posting, even young people, wanted to be entertainers. They wanted to build businesses from their content, have it last, and have it go viral. The team made a large update to its change list: Reels became its own format that lives permanently and can be seen widely, including by "your whole school." The earlier assumption had been that people wouldn't want that. Stein said Reels has done quite well since.
Using AI to Mine Feedback at Scale
Stein said AI now makes this diagnostic process much easier with internal tools. The team uses Google Antigravity alongside a system in which users who have opted in, and are comfortable sharing as they use Google products, are asked what they thought and where a response missed the mark. That creates a large library of feedback that a model can analyze.
Stein's example was shopping for a backpack. One user said it was great that Google knew about each backpack and its sizing, since Google has canonical product information, but it should have asked about their son and his height, because otherwise the backpack might be too heavy for the child. When the team stack-ranked the feedback, this kind of comment came up often. The insight was that the product needed to be collaborative and hold a conversation, because users don't always know everything they need to know to reach their goal. Stein said this was critical to getting users to rate responses positively, and that investing in it has been one of the biggest wins the team has seen in engagement, usage, and helpfulness.
Chapter Three: Craft, Part One — Removing Pain
The third chapter applies when a product is ready for prime time: you are launching to all your customers and sending the blast email. This is about fit and finish, which Stein said is often overlooked. Stein believes people can sense whether a product's creators cared about it. Two things drive that impression: whether the product works perfectly without user pain, and how it makes people feel, such as joy or excitement.
On pain, Stein said the old approach was for the team to be obsessive power users of its own product. AI now makes it possible to scale this. Stein showed a tool they built internally with Antigravity. It takes a large set of potential queries and uses the product as an agent would: it goes to Google, types a question, enters the AI experience, asks a follow-up, records and screenshots the result, and evaluates it against a comprehensive rubric.
Stein said the model proved very capable. For a math or homework question, it could check whether math and LaTeX rendered correctly or whether stray characters appeared. For a question about bioluminescence, it could check whether the response included a visual tray, as it should for a visual topic, or was only text. This surfaces the best responses and makes breakages easy to spot. Stein said the team has since invested in agents that detect when things are broken or off-spec and increasingly fix them on their own. In Stein's view, where teams once relied on a QA team or people who obsessively used every part of the product, many of those days are now over, because the process can be scaled more elegantly.
Chapter Three: Craft, Part Two — Sparking Delight
The second part of craft is delight. Stein's example was the redesigned Google search box, which Google reinvented for the first time in about 20 years and announced at I/O. The question was what an AI-based search box should be. Stein said it should grow with the user and accept uploads of files, videos, and photos for context, and the team had to consider how daunting that could feel. The goal was for Google to still feel like Google while signaling an upgrade, with AI inside helping you. The design uses a color gradient to represent the AI power behind the product.
Stein pointed to details from the design team. The blinking cursor cycles through Google's colors, and Stein said they have been amazed by how often people notice it and post about it on X. When you tap the search box, it jumps out slightly, as if excited, then loads a ring, and there is haptic feedback. Stein described this as one small example of how motion, color, feel, touch, and intentionality can communicate the humanity behind a product and give it a soul. Stein thinks many people overlook this part of the creative process.
The Playbook, and Where to Spend Your Time
Stein closed by summarizing the playbook for PMs in the AI era. Much of it is not new, but Stein said AI puts it in a new light. Understand people deeply and get at their core needs and problems. Be rigorous and analytical about what needs fixing for the product to work for people and meet its objective. Build it so it is simple, works well, and makes people feel good using it. Stein said the craft of PM, and the skill of making decisions that lead to great products, has never been more important.
Stein also acknowledged this is just one approach. The audience had probably heard ten others that day that were just as valid, and each person should decide what makes sense for them. Stein's final ask was that the next time people think about their products, they spend less time on features out of the blue, technology to throw at the problem, or new distribution tactics, and more time thinking about the people on the other side of the screen.
Hey everyone, it's so great to be here and I love being here to get to talk about one of my favorite things ever, being a PM, product manager, and builder. And what's pretty incredible is that it's never been so interesting to be building products. And after 20 years almost of building, particularly in consumers, by the way, it's very upsetting to be able to say out loud. But after almost 20 years of building, I've been fortunate to build all kinds of different products over my career. I've built startups and I've also helped lead teams and products through really big changes like Instagram and Google Search where now billions of users are using products that our teams have created over the years. Also have built some things that zero people use today, by the way. Absolutely zero.
And what I've learned throughout this process is that there is a little bit of a pattern of the kinds of things that we've done to make the products really great. Sometimes it works out, sometimes it doesn't. But there has been a repeatable pattern that I've seen emerge from the things that have done really well and the things that have not. And that's really what I'm here to share today and in part to all of you. There's lots of things to talk about, but I wanted to give you just a little bit of extra background on me.
So I spent some years at Instagram where my team has built out Instagram Stories and shipped it originally and built all of the experiences around it. We have Reels, feed ranking and direct messaging. So a whole bunch of products that came out of that era. And then now at Google Search where I am working through a brand new experience with the team to help think through what is Google Search in the AI era so that you can truly ask anything on your mind and get the most helpful information imaginable across everything, whether it's videos or audio, text, and you can tap into all of the knowledge of the web and of Google and connect the information that's out there.
But today I'm not talking about the transformation of product, although that is something I really deeply enjoy. I'm talking about the craft of product. What does it take to build? What does it take to have a product that people love and to shape that and make that? And how do you do that in the age of AI that ultimately, from my opinion, any one of you can do now with just a handful of agents or kind of AI assistants, which took us years and whole teams of people to do in the process here.
Let's just mention one thing I wanted to say around the role of PM before we get started, and that is really the fundamental shift that we're all feeling is that we used to be, I think, as PMs, a big part of our value was around getting stuff done, was around organizing, creating energy and momentum, and being almost like a project manager and a product manager. And I think in some cases that's still true, but really, if in the world today almost anything you can think of can get built, it's more clear to me than ever that the actual true value of PM is actually around judging. It's around taste. It's around doing something extremely well. And I think something I've thought about a lot is that people say the PM's main craft is that you do lots of things pretty well. And I think that is somewhat true. But I think above all else, actually the craft of PM is one simple thing that you should be the absolute best at. And that is decision-making. It's about making decisions, and you should be students of what it takes to make great decisions. And it's a humbling thing because most decisions, many decisions are wrong. And these are just the things that I've learned over the years that have helped me and the teams make decisions that have led to some of the great experiences that are now out there today. There's kind of a book that's been unwritten in my mind that I've been kind of thinking about in three chapters around the process that, you know, I've used and teams have used to build, and I'm here to share that today really for the first time in this format.
So chapter one is understanding people deeply. It all starts with people, obviously, and you can figure out lots of things that you can build, but it comes back to the very root of what are the fundamental needs of a person. And I love the book Clayton Christensen wrote, Competing Against Luck. I'm sure many of you have read it and have probably read the jobs to be done framework, which I really do love. The thought is that we don't use products, we hire them to do things for us. And we have to know what those things are. And it's best articulated how to extract those kinds of insights through a story.
And so actually a team that we had at Instagram, we did an offsite with the people who created the jobs to be done framework. And we actually went through this process. So you can all think about this with me. What was the last major purchase that you made? So quickly think about it. And for me it was a bed. So here are a bunch of beds. And then what you're supposed to do, and the book actually talks about how to do this, we've really internalized this with our process, is to put yourself back into that moment. And when you're talking to a person, ask them details about their life. What was it like that day? Where were you? What were you reading? Who were you with? What were you wearing? Were you with your kids? Were you by yourself? Were you in a store? Were you online? And you really deeply put yourself in the position of that user. For me, looking at the sea of beds with my wife. And basically, we were kind of trying different things out. And at the end of the day, three or four felt great, but my wife weirdly turned to me and said, "Can you do me a favor and jump on this?" Basically, she was like, "Do a barrel roll." I was like, "What?" All right. And so I did like a huge flop, I guess. And she was like, "We're getting this bed right now. Thank you. We're done." And I was like, "What? This is so weird." She's like, "No, the main thing that I need is a bed that will not wake me up if you like move." And if you think about it, that's like a little offensive, but also I was like, "Okay, it's real." And if I was a PM for beds, I might be like classically thinking, quick, what's the road map for beds, right? You might be thinking, okay, how about cooling? Everyone's doing cooling. Like, yeah, cooling, sure. Or like eco, or just pure price. Like, those things do matter, but for us the thing that made the decision on that day is a concept of transference. And if you didn't talk to me and you didn't learn that, the PM, the team, they would never know that. And so that's what it means to discover the job to be done for someone and how products, things customers hire, what your services do, your products do to help them.
And very quickly, just talking two quick examples in the digital world, one from Google. Early in our AI experiences, we realized that people actually came for inspirational journeys. But two years ago, chatbots were only text. So we were one of the first teams to look at this need and say, how do you get inspired by what to buy or what kind of couch to get through text? How do you describe a couch to someone? You can't. And so we were one of the earliest teams to really invest in state-of-the-art multimodal understanding, retrieval, and knowledge systems. So you could actually ask about a couch, get an incredible visual response, and do multi-turn conversation with it to say, "Oh, I actually want emerald green," or "I want fuzzy," like, what does fuzzy mean? Or, "I like that throw pillow. How does it create attention on the throw pillow?" And do a turn and then now do a grid of throw pillows. Like, these were really hard things many years ago. And obviously you see them now in many places, but that was a core need and a core insight that we saw.
And then the last example was very early in our AI Mode exploration, which is a way that you can use Google Search now to ask anything and have a conversation with Google. People came and it had all this incredible information, like here, this is a neighborhood and like a food question, but it was missing something. It was missing something that people were coming to Google for and were expecting, which was the knowledge and the accuracy and the experience of actually seeing the places you go, seeing them on the map, getting the Google-level authority of the star rating, the closing time, what kind of food it is, how expensive it is. It was completely missing from the product. And when we brought that together so that now you can talk to Google, ask about food and then get this rich experience that brings all the goodness of seeing the food and clicking on an entity now and actually seeing the description that you can trust, that was the magical product, and it's still one of the most popular things on the product today based on thumbs-up ratings and other things that we follow. But again, back to a human insight of the why behind they were using the experience and what caused them to use it in the first place.
And so AI can actually change a lot of this, and a lot of the talk today is how AI can apply to these. I think on this one I'm still thinking about some of these myself. But one of the things I'm excited to try more is how you can actually take the transcript data of these user interviews and actually have it specifically source jobs to be done and scale that much more. Another idea I've been thinking about is actually an agent that can interview people with the exact methodology that I just described so that it can really ask a user more at scale to talk about why they're using the product they're using and what those underlying problems were.
So that is understanding people. Next up, you have a seed, you have something you're working towards. The next step: diagnosing root causes. So the way I think about it is you have this initial vision of this problem you're solving for people, this initial understanding of what people want, and then you make a thing, and that thing is usually not great. And most of the things that I've ever been a part of building started not great, but everything did, really. And what's incredible about this process is by having a clear and analytical view of the exact problems that are in your product space, ranking them in priority and understanding them deeply, and then actually fixing them and then asking it recursively again and really looping through, you basically are doing your own training process, kind of like an epoch of model training a little bit. And each update you do with this change list gets you closer to the market fit you want until you have that fit and then it works.
And there's two stories I wanted to kind of articulate here, both from Instagram. One was when we originally launched Stories, it actually did take off. It was one of the earliest things that worked pretty well out of the box, but we had many internal versions where Stories were fit into the feed that didn't work great. So there was a ton of headache to get to the final version, but one thing that was particularly clear was that not everyone was adopting it or felt comfortable sharing. And really the whole premise was, hey, this is an opportunity to share your life throughout the day. Why aren't you doing this? And the process that we followed was pretty simple. We asked a very clear root cause of why you were not able to do the thing that was our goal. So it's kind of the inverse. The first one's like, why did you do the thing? Now you have a thing. It's like, well, why aren't you doing the thing? And it turns out that if you asked that question, people answered. Think of all the leaf nodes, like a cloud behind me, of all of the answers people had. And then if you were to group them by theme, it turned out that the top one when you sorted them was something around audience problems, more or less. Like, my ex is on it, my teacher's on it, my aunt is on it, my sister's on it, I'm not going to like just put a goofy thing on, like, people are on it. And so what we realized in seeing that, and then what we did was we then did a very large quantitative survey, thousands of people, taking that as an input, and then we were able to quantify the actual weights across them. And what we figured out was that if we couldn't overcome that burden, there's no creative tool, there's no like interesting product idea that would work. And so the team actually spent 2 years cycling through different ideas in the space until we came across Close Friends and it ended up working. And there were all kinds of weird permutations in this like multiverse scenario where, like, it's called Favorites and it also has on your profile a back door that you can like see a private version of your profile, and you can also post to your feed under that kind of Close Friends label. Super confusing. No one understood it. Like, why would I put this like very raw photo in the middle of like a glitzy feed of all this stuff that's happening? The only thing that worked was within Stories. So we cut out the entire product, relaunched it as a Stories experience and it worked.
Reels was the second example. We had a launch for Reels in Brazil, and we thought at the time that if you're doing goofy things and dances and posting them, why would you want them to live on your profile for everyone to see forever? Which made sense, and the team felt that way. So we launched it. Huge failure. So it turns out that if you invest a bunch of time doing an awesome dance, you don't want it to go away in a day, which was an ephemeral format. It's very obvious in hindsight. But the bigger insight was the people who were posting them, even if they were young, you know, people, they wanted to be entertainers. They wanted to make businesses out of their content and they wanted it to live on and go viral. And so we did a massive update. We talked about this change list, where now it's its own format. Instead of being ephemeral, it lives like forever and it can be something your whole school sees. Instead of the opposite, like, oh, I don't want my whole school to see it. It's like, no, that's actually what you want. You want everyone to get the joy from this product. And then obviously that has taken off and done quite well over the years.
So two really clear examples of how that's happened. And with AI this is actually possible to do a lot easier now using internal tools, and I think things that you can all do every day. And we use, you know, internally, Google Antigravity, but we have a system that effectively lets users that have opted into this, they want to talk to us more and they've decided that they're comfortable sharing as they're using Google products with us. And so what will happen on the side is we will then ask them, hey, what did you think of this? Where did it miss the mark? And they will put comments in. And so imagine this huge library of feedback from people now that's available. Well, now the model can take that and can both take qualitative feedback, like above. This is a shopping example for backpacks. And it's like, it's awesome that you knew about each backpack and it's awesome that you knew which was larger and the sizing, because Google has all this canonical information, but you should have asked me about my son and the height. Like, that's a pretty key thing, or it's going to be too heavy for my kid. And it was so obvious that when you stack rank them, this was a very common kind of feedback. And so this concept of being collaborative and creating a conversation so you can actually help the person make progress toward the ultimate objective, because they don't actually know all the things that they need to know to answer, was absolutely critical to getting the user to vote and say this was awesome. And so we invested a ton of energy in this, and it's been one of the biggest things we've done in terms of engagement, in terms of usage, in terms of value and helpfulness, you know, that we've seen out of the product.
So that's kind of chapter two, which is around understanding the root causes. Now, if you're lucky, you have a great seed idea, you've done this process of finding the market fit by go looping
through, seeing your problems, analyzing them rigorously, and fixing them aggressively. Maybe you get to the stage now where the product is ready for prime time. You're going all out. You're going to all your customers, you're sending the blast email. And really, this is about the fit and finish and final elements of the product, which is around craft.
And I think this is often overlooked, but basically the way I think about craft is the feeling that I think everyone has a general sense, and I believe people can detect whether the creators of a product cared about the product that they created basically. And there's two things that I think drive this. One is does the product work perfectly and is there any user pain basically in that experience? And the second is how does it make me feel? Do I feel something? Joy, excitement, using it. And so I'm going to talk about each real quick.
On the pain side, it's really not just about obsessing over it and you all being power users of your own product, but actually how to scale that now with AI, which is really incredible. And so I actually built a tool that I'm sharing here. This is a real tool that I built internally using the Antigravity system to basically look at all of the potential questions we might ask. So, here's a sample here on the side. Use the product as an agent. So, like literally go to Google, type in a question, go to the AI, ask a follow-up, and then record that experience, screenshot it, and then evaluate what you see based on a really comprehensive rubric.
And what we found out was that the model actually was incredibly capable to look at things like, hey, there's a math question, or this is like a school homework thing. Is it doing a great job rendering math and LaTeX? Or is it emitting weird characters? Or if you ask about bioluminescence, is it adding a visual tray, which is what the response should have if you're asking about something visual, or is it just text? And you can go through and not only can you get to the best response, but you can see breakages really easily. And the team has now invested in a bunch of agents that can go figure out when stuff is just broken or off-spec and then increasingly fix it itself.
And you have this feedback loop, and where you used to have maybe a QA team, you may need a PM or you might need people on your team who are obsessively using every single part of the product. I think many of those days are now over, where we can scale this process much more elegantly for everyone.
And then the last thing I'm going to say is around delight and really sparking joy. And this is a little experience. This is what the Google search box looks like now. We reinvented the search box after 20 years, announced it at I/O, what an AI-based search box should be. It should grow with you. It should be able to upload things and have context around files, around videos, around photos. What should that be like? And how daunting of an experience is that?
And what we did was we came up with a system that said, okay, we wanted Google to still feel Google, but we wanted it to be this upgrade that felt like AI is here. There's AI inside that's helping you. And so there's this concept of this kind of gradient of color that really represents the AI kind of power behind the product.
And you can actually see a couple nice details here that the design team brought together. One was actually in the cursor. If you watch it blinking, it's cycling through the different Google colors. And I can't tell you how many times I've seen this pop up on X, that someone noticed this and felt like posting it. It just blows me away every time. Or if you tap in the search box, it's kind of excited. It jumps out kind of and then loads the ring. It's like, "Ooh, we're getting ready to load. We're here." You know, it has this kind of feeling and it actually has real feeling with haptics.
So imagine all that coming together as an experience, and it's just one small example of just by using the product, and through motion, through color, through feel, through touch, and the intentionality of the experience, you communicate the humanity behind the product and you give it a soul. And I think many people overlook this part of the product creative process.
And then really when you bring this all together, this is kind of the wrap here. I think that this really means that you can have a playbook for what it means to be a PM in this new AI frontier. And you still have to do all the same things. A lot of this is not new, but it's possible in a totally new light with AI. So you have to understand people deeply, get at their core needs and problems that they're feeling. You have to be rigorous and analytical at sorting out what are the things I need to fix in order to get the product to win and to work for people against that objective. And I need to build it so that it's awesome and it's simple to use and it works great and it makes people feel awesome using it. And that's really the playbook.
And I think that now it's exciting that the craft of PM is so critical. I think there's never been a more important time to be a PM and to do product and to be a builder. And I think the skill of making great decisions that can lead to great products has also never been more important.
So hopefully all of you can take a little bit of something. It's just one way. There's so many ways to build products. You probably heard 10 other ones today that were just as valid as this one. You should decide on what makes sense for you, but hopefully you take one thing away that you can take away with you and build as well. And hopefully the next time you're thinking about your products, you're spending a little bit less time thinking about, hey, here's cool features kind of out of the blue, or here's some tech we're going to throw at it, or here's a new way we're going to get it in front of people. And you spend more of your time thinking about the actual people and the humans on the other side of the screen that you're building for. Thank you so much.
[applause]
Article published · Updated