30-Minute Masterclass on Product Thinking | Instagram Co-Founder & Anthropic CPO, Mike Krieger

1. 0:00 – 1:21

   Intro

   1. MKMike Krieger0:00

      When is it time to call it? When does the chapter close? I think one big piece of advice is I think we're often, as entrepreneurs, we have a lot of weight on our shoulders, and between Instagram and Artifact, I did some angel investment, and it's never positive news when an entrepreneur tells you that a company doesn't work. But I've seen it before, you know, and so it's not a surprise that some companies don't work out. That's baked into the model. And so I guess, uh, the advice I would give is really check in with your investors and don't-- I've seen entrepreneurs get stuck in this idea of, like, "I have to keep going because my investor expects me to." And I think what they'll find is sometimes investors are like, "You're trying-- You've tried it all in this space. I think it's time to, to call it, and that's okay." And, you know, it's not-- it's never a celebration, but it doesn't have to be, you know, a tragedy either. [ on-hold music] I'm Mike Krieger. I'm currently the Chief Product Officer, um, at Anthropic, which is an AI company based here in San Francisco. I co-founded Instagram and was its Chief Technology Officer from about 2010 to 2018, so a nice crazy journey that we had there, too. Started one other company with the same founder as Instagram called Artifact, which was a AI news startup, and then, uh, joined Anthropic just under a year ago. So it's been the kind of third chapter in my professional career, and it's been, it's been a lot of fun so far. [on-hold music]

2. 1:21 – 10:57

   A Brazilian Kid’s Journey to Creating a Product for 2 Billion Users

   1. MKMike Krieger1:24

      Credit my dad for a lot of this. He brought home a computer for us when I was about four. I remember it had it ran Microsoft DOS still. It didn't even have Windows. But one of the things that was very cool is you can just type edit, and you would be able to open up the files and all the-- even the apps that came with the computer. And so I think I liked taking things apart and putting them back together, and I think I really liked seeing how things worked. So it sparked very early for me when I was just four. When it came time to decide what my career was gonna be, what was interesting was in Brazil at the time, there wasn't really the same kind of technology industry as there is today. And so I didn't have any friends or parents' friends who were working in tech. And so I didn't really see it as a career for me. I just thought it was a thing other people did. And so it actually only really took me until I came to California that I realized that this childhood int-interest that I had actually was something that I could do as a career and, and make a whole living off of. When I got to Stanford, I discovered that they had this degree program called Symbolic Systems, which only Stanford has, and it's kind of this little strange program that when I was there, I think per year, they had about forty or fifty students, and now I think it's more than two hundred, so it's really grown, which is great. What I loved about it is that it wasn't just computer science, which I was interested in, but it also included design, it included philosophy, it included psychology, and it was really-- You think about the idea of the program, it's not just to study computing, it's to study the, the, the whole context around why we build software. That was really powerful for me because I knew I liked design, and I liked making things useful for people, but I also liked the actual building, you know, of those things. So I think a few of the things I really came out of that program with, one was this idea that everything you build should solve a problem for somebody, right? It's, like, kind of the core of design thinking, that you identify problems, you figure out how you can best solve them after doing the research, and then you validate what you've built to make sure you actually solve those problems. I think that is really important, and that's a big piece of what I, what I came out with. The other is the value of prototyping. So, you know, instead of just working for six months on a project and then showing it to somebody for the first time, and then it turns out you got something really fundamentally wrong, willing to build prototypes, show them along the way, and open up the design process a lot more. And maybe the last one is the value of a good team, um, and having the right partners. So person I co-founded Instagram with is somebody I actually met back at Stanford. At the time, we didn't know we were gonna start a company together, but I knew that partnering with the right person, especially that-- somebody that has similar skills in some ways but different skills in other ways and they can complement you, um, can make the difference as well for the company succeeding.

   2. SPSpeaker4:03

      For example, there's a brand-new built-in camera. Now, the camera is another way that the iPhone 3G has really changed how people use their cell phones. The new iPhone 3GS, there's a brand-new three-megapixel autofocus camera. [audience applauding] It takes pictures that are even larger, even more beautiful.

   3. MKMike Krieger4:24

      You have to kind of rewind your, uh, time machine back to 2009. Now we take for granted that everybody uses a lot of apps on their phone, that we take a lot of photos on our phone, and that we network, or we, you know, use social media on our phones. But in 2009, that wasn't the case quite yet. So the cameras were getting better, but they were still quite bad. Facebook had a mobile app, but, um, it was kind of that early one where it was kind of just trying to recreate the website on the mobile side, and it wasn't quite as a native. And there were very few sort of new social products coming out. And the context is important because the reason I got really excited to collaborate with Kevin, who ended up being my co-founder, is that he wanted to change all three of those things. So he was building Time was a location-sharing site, a little bit like Foursquare, if you remember Foursquare, but with photos and videos attached as well, and really creating a social experience when you're out and about. That was really exciting to me because I saw the potential for a mobile device to create a much more personal connecting experience. So first moment was reconnecting with Kevin and seeing that he was interested in these ideas. Second important moment was me getting my work visa so I could go work with him. So I, I was first had to transfer my work visa, and that actually took four months. And then the third important moment was us realizing that product that we were working on, it was called Burbn, B-U-R-B-N, which was a, that sort of location-based check-in, location-sharing app, was good, but it was not product that was really gonna to where we wanted to go. And so we had a moment where we had to take a pause, really peel back all of what was not working about the product, and realize that at the core, the piece that was really working was this, uh, aspect about taking photos, sharing what you were doing, and connecting with people that way. And that was really the, the moment where we took a lot of distracting things, stripped them all away, and then focused on what would eventually become Instagram. I think a lot of the art, and it's a part both art and science of building great products, especially with emerging technologies. So Instagram, it was the rise of the mobile phone. Finding the technologies that are ready for broader adoption than they currently have, and then finding how do you build the product around it to make it usable for many, many people. That's a common sort of through line. And I think the kinds of things you can look for in terms of that early energy is, are there some people already starting to do something interesting in the space, but it's all the early adopters? That's an interesting kind of area. Um, the other thing I look at often is the rate of change. So if you look at the iPhone 3 to the 3GS to the 4, and you look at the quality of the photos and the quality of the networking, um, stack, and the ability to connect, you can see the jumps. So you're like, "Okay, if it goes one or two more jumps, it's gonna be incredible." Same with LLMs. I joined Anthropic, we had just come out with Claude 3, and Claude 3 was our first model that really started to get more usage. It was still, you know, limited in a lot of ways. But then you see the next we did Claude 3.5, and that was a big leap. So you can imagine that we're gonna continue to get these large jumps. And so as a product builder, you have to start thinking, "I wanna build a product that's useful today, but is also ready to catch the wave of what the next big leap is." You've closed, uh, the Instagram acquisition. Why did you do it, and what are you going to do with it? Yeah, so Instagram is great, right? I mean, they're, um, they're this super talented group of, of engineers. They're building this amazing product. Um, they just crossed a hundred million registered users, and they're killing it. They started off building on top of our platform. You know, you can take pictures with Instagram, and you can share them to Facebook. Um, and it's, it's really first class, right? So sharing a picture from Instagram, it basically appears exactly the same as if you shared a photo on Facebook. So it's great. They did a really good job with that. Our mission w- around Instagram is we think Instagram is amazing, and we wanna help it grow to hundreds of millions of users. We wanna help them out, um, with, with whatever we can. Um, but we, we have no agenda in terms of making them go onto our infrastructure or something. I know a lot of times companies, um, force i- companies that they're integrating to do stuff like that. I think it's primarily a waste of time. We're not gonna do any of that. We're gonna just try to do the things that, that we would've done if they were an open graph partner, but now we can prioritize them more highly. I mean, they can do a lot of them directly because they have access to our code directly. Uh, I like to say that at Instagram, you know, before the acquisition, we didn't yet have a company. Um, and what I mean by that was we were 13 people, had some ideas around revenue, but we hadn't built any of them out yet. Still very much that, like, early startup. Joining Facebook was really interesting to me because they were, at the time, thousands of people. They had just crossed a billion users, so much bigger than, than us in terms of company size. But they were really focused on preserving as much of the startup culture as they could. So for example, uh, every three or four months, they would do a whole company hackathon where everybody would get to focus on what they were interested in for a couple days instead of the, the ordinary roadmap. They really prized experimentation. They really tried to, you know, famously had the move fast and break things kinda slogan. So I think it was good that we went to a company that was bigger but not super big company or slow, and it taught us that you can grow your team and grow your ambition, but still focus on the things that keep you moving fast. I think the value of the team is something that will stay with me forever because, you know, you can have the right strategy, and you can have a good product. Ultimate, the details that go into those products, the, the pace and speed that you're able to execute on, and, like, how much fun it is, which is also really important, ultimately comes down to having the right team around you. And that's a mixture of people that are talented but don't have a lot of ego, which is a interesting and difficult combination to find sometimes. People that are willing to be generalists rather than just be caught in their sort of individual silo. And so, you know, at Instagram, we had people who would start off, you know, writing the backend for a feature but would also then build the iOS or the Android part. And it's important, I think, for you to have that fluidity, especially on engineering, or designers that would code or product managers that would design, and it's just not getting everybody boxed in. So that aspect is really important. And then also a team that really cares about the product they're building, which again, is easily-- more easily said than done. It's about-- I've seen companies where everybody is working, and they could be working hard, but they don't have a passion or really an attachment to the thing that they're working on. You're never gonna get great products that way. It just never happens because the great product breakthroughs come from people being close to the details, understanding what could be better, coming up with the next idea themselves, not waiting for a, you know, strategy meeting. So that value kind of throughout the right kind of people and the right sort of setup of team is something that I think about all the time.

3. 10:57 – 17:30

   How to Know When It’s Time to Stop : Lessons Learned from Closing an Artifact

   1. MKMike Krieger10:58

      In 2021, I founded Artifact with the same co-founder as Move, Kevin. Our goal, our observation was there's been a rise, and at the time it wasn't, you know, LLMs yet, but there was a lot of rise in machine learning and the beginning of some of these neural networks. For all of that interest and rise, there were still not a lot of products being built that felt very personal. Because if you think about it, the promise of some of this machine learning was great. With a lot of signals, we'll be able to incorporate what's personal to you and also what's aggregated out, you know, among a broader group and be able to tailor the experience. But you looked around, and there actually wasn't that many personal experiences. So our bet with the company was by combining cutting-edge machine learning and good product design, we'd be able to build products that felt very personal to you. And we started with news, uh, and articles in general because that was something that we felt most people are readers. Even if they're not, like, book readers, they like reading articles online. And there's an existing kind of ecosystem of blogs and newsletters and news sites that was out there that we-- if we could just connect people to those sites, we'd be able to deliver a good product. And the idea behind the company was this is the first product, but we'll kind of take the same personalization technology and be able to map it to other products that are also at this intersection of personalization and content. So we thought about shopping recommendations. We thought about local recommendations, all these ideas around how to personalize information using machine learning. We launched, um, the product after about two years in private beta, which I think was too long because it took us too long to get out to the market to start learning. And it also meant that by the time we launched, the team was already quite exhausted for having worked really, really hard on this product for two years nonstop. And then we let the product run for about a year. And after a year, what we noticed was the energy was not there in the system. Like, we would work really hard to, like, improve a feature or add social features and comments and reshares or posts and user-generated content. Like, we, we, we were trying big ideas. Like, it wasn't like the product was standing still, and it was very hard to shift the energy in the system. And I think that was because there was not a fundamental fit around what we were doing and what people were wanting. And I think there was probably two aspects of that. The first one was even if our algorithms, I think, were very good, the mobile web kinda websites that we were sending people to for this news, they were often, like, full of ads or not formatted very well or full of, like, pop-up videos. It was just not a very good experience once you actually click through, and that was, like, I think, a hard experience to deliver. And I think the second part was our product got really good if you put enough data in there and you read enough articles. We'd get very personal to you at the level of, all right, Mike is interested in Formula 1, but not just Formula 1. He's interested specifically in this driver. And also, he likes Brazilian modernist architecture, but also when that's paired with, you know, Scandinavian design. Like, that level of, like, really specific knowledge. But to get that, you had to read a lot of articles. And so most people would come in, they would read a couple of articles. They would say, "This isn't very different than Apple News," and then they would bounce off. So I think we bet too hard on personalization without remembering that you also have to be good at the very beginning before you've really gotten a lot of personal content. I think one big lesson is that users are not gonna adopt a feature or a product just because the, the technology underneath or just because it has intelligence. It actually has to still solve the problem. It goes all the way back to what I had learned at Stanford. So as we build products at Anthropic, of course, the models are very intelligent, and they can do a lot. But you have to do more than just say, "This model is so smart." You have to go beyond that and say, "And here's what it can do for you, and here's how it can connect to you, and here's how it can be useful to you even if you're just getting started with it," right? It goes back to that feeling at, at Artifact of, yeah, the product is good if you put a lot of work into it. Like, we need to design things with Claude that are useful for somebody that's just discovering LLMs kind of for the first time. So a lot of the work that we're doing right now is how do we lower the barrier for people who aren't yet familiar with all the ways that AI can help you in your daily life to just give them some really concrete things that they could do where it's useful to them today rather than in theory. So that's a big lesson, lesson learned. I think another one that's important as well is how do you get user intent and the personalization more quickly. And so for us, it's, you know, when, when you sign up for Claude, we ask you some questions. And it's nice because, you know, you can actually chat, start chatting with Claude and get a feeling of what it's like to talk to Claude. Um, and hopefully, you know, the questions we ask in that onboarding process in your first day let us be able to be more tailored and personal to you quickly, and that was-- kinda came out directly from the Artifact experience. That's a great question around, like, when is it time to call it? When does the chapter close? The way that we did this with Artifact, um, and I think this helped, was we sat down, me and Kevin. We made a list of, like, what are the ideas that we still have in this space that we will feel really silly not having tried before shutting it down? And we wrote them down, and then we prioritized like three big ones that we said, "We wanna try this. And after we try this, we'll take a step back and say, 'Did it change? Did it change the, the trajectory of the company?'" Um, and we did that, and then we kinda wrapped up 2023 and entered 2024. And we said, "We tried them. It's still not, you know, it's still not changing the, the trajectory." Then, you know, it's time to, to move on. But it is quite tricky. I think it's-- I've seen entrepreneurs get stuck at companies for years that they feel, you know, they owe it to themselves or their investors to keep going. But it's not likely to, to shift the direction, so I think being concrete either with a date or with a set of projects helps you, um, know when to move on. I think one big piece of advice is I think we're often, as entrepreneurs, uh, we, we have a lot of weight on our shoulders. And, you know, between Instagram and Artifact, I did some angel investment. And it's never positive news when an entrepreneur tells you that a company doesn't work, but I've seen it before, you know? And so it's not a surprise that some companies don't work out. That's baked into the model. And so I guess, uh, the advice I would give is really check in with your investors and don't... I've seen entrepreneurs get stuck in this idea of, like, "I have to keep going because my investor expects me to." And I think what they'll find is sometimes investors are like, "You're trying-- You've tried it all in this space. I think it's time to, to call it," and that's okay. And, you know, it's ne- it's never a celebration, but it doesn't have to be, you know, a tragedy either. [outro music]

4. 17:30 – 27:02

   Essential Lessons from Building a World-Class AI Product

   1. MKMike Krieger17:33

      I think what was interesting with the Anthropic role is that it was both joining an existing company, the company was about three years old at the time, had already launched models, had Claude.ai, but there was still a lot of zero to one to be done. So I don't think we had our mobile apps yet. Uh, we hadn't built things like Claude Code, which is our agentic coding tool. So there was a lot of empty space still in the product. So it was kind of a combination of zero to one and established. Uh, we have a Claude character, um, effort and team, and that team is really focused on what are Claude's philosophies, what's Claude's vibes, how should Claude respond? And that's been, uh, an effort even from early days, but it's something we've focused even more on recently. In the rest of the AI landscape, you can often have these evaluations or evals, right, where you say, "All right, how is, you know, the model doing at math or competition coding or agentic coding?" It's a challenge to say what kind of vibes does the model have. We have some internal ways of doing that. Often you can try to use Claude to listen to Claude and then say, you know, what kind of vibe does it have? But it also comes from just using Claude a lot internally. So every day there are hundreds of conversations that, you know, people at Anthropic are having with Claude with the intention of understanding how is Claude's personality evolving, uh, when we're doing training, how could it be different going forward? Even things like should Claude be verbose, like, should it say a lot or should it be concise? And the answer varies on the s- d-depending on the situation. I think one of the hardest ones is, um, we are building a fundamentally a dynamic model and a dynamic system. And so as an example, you know, we have-- in Claude.ai, we have a, a feature called Artifacts where, uh, you can sort of work on a document or even a website along with Claude. You know, the model learns how to make artifacts, but the taste that it has in artifacts and how it changes and what language it uses and exactly what it does evolves from model to model. And sometimes you don't know how it's evolved until very late in the model training process. So one of the biggest challenges with building products alongside doing model development is they're both moving targets. Our product is evolving, the models are evolving. Often the models are evolving up to like a week before launch, and we're trying to build products alongside. It's a very interesting dynamic system. It's what makes it exciting because the model can be creative and it can surprise you in a lot of ways. Uh, but it's also, um, a lot more challenging than tr- a classic product development. I think it's really important to, as a product development team, to be really upfront about the capabilities and the limitations and the risks. So you'll see when you sign up for Claude and you go through the, the first conversation, we really emphasize, here's what models are good at and here's where they can still make mistakes. Um, and here's the limitations. And I think that's important to lead at the beginning because I want people to have the right mental model when using these models. They're not perfect. They don't know everything, um, but they can be very helpful in-- for a given task. Similarly, I think it goes back to the kind of the Claude character piece. It's important for the model to also be aware of its limitations. And so often you'll find if you ask Claude about medical questions, it might say, "All right, I can tell you more about this, but first, I'm not a doctor, and if you're worried about this, then you might want to consult a professional." And it's not just a thing we put in for liability. It's a thing we put in because we think it's really important to have Claude recognize its own limitations as much as possible. Yeah. So it's an interesting question for us, which is: How do we gather user feedback on Claude in a way that is, like, most helpful? And the way we've currently found, although I think there are probably other ways that we can continue to evolve it, is for every answer in, in Claude, we have a, you know, thumbs up, thumbs down. How did we do? For both thumbs up and thumbs down, you can write a little paragraph about why it was good or was it bad. And it's so interesting because, um, and that, that extra bit of signal w-around why is the most important part. And so we have a product manager, and she spends a lot of her time aggregating and evaluating and looking at how the model is doing out in the wild. And then you start seeing themes. So an individual answer maybe doesn't give you the clue about how you need to improve the model. But then in aggregate, you say, "Okay, Claude is being too, too verbose here. In this particular case, we're gonna change it for the next model." Or, "Claude is changing its mind too quickly." So when the user disagrees with Claude, maybe the user is actually looking for a debate. Sometimes Claude is like, "It's fine." Like, "You're right. I'm sorry, I was wrong." But the user was actually looking for more of a back and forth. So that kind of conversational feedback is incredibly valuable. And we basically, for every new model training, we'll first look at all the feedback in aggregate for the previous one and say, "What do we want to keep the same and what do we want to change? Or what do we want to double down on and what do we want to make different?" So that feedback process is very, very important, and it's actually the main feedback we get. So we don't train on any user data, um, including conversations for anybody. But we do use the, the thumbs up and thumbs down as a really helpful signal to figure out what we need to improve. I think there's two areas of consolidation that I expect to see. One is on the model development side of things. As these models, they're already very expensive to, to build and train at scale. As they reach even higher levels of intelligence, they're gonna re-require that same kind of jump in compute and resources. And I think we'll probably come down to three, four, or five companies doing that, not as many. You're already seeing some kind of consolidation in, in that side of things. I think Anthropic is well-positioned there. We've got really great partnerships with Amazon, with Google, that I think will unlock a lot of compute for us. On the other side is, um, consolidation in more of the apps side of things, because I spoke at the Y Combinator batch of startups recently, and everybody's working on something that is AI or directly, uh, adjacent to AI, and that's great. There's a lot of products still to be built in this space, but not all of them are gonna work. So in the same way as you saw that with mobile and social media, where it's useful to have that explosion because no one company is gonna map the whole space out, then you start naturally seeing, you know, what's getting traction, what's gonna get consolidated, what are good idea-- what were good design ideas but wrong sort of business ideas that then will evolve into better business ideas. That evolution, I think, is very healthy. Uh, I saw that process with mobile. I saw it with social media. I think now is the time to start seeing it. Well, maybe, you know, in a six-month time you'll start seeing more of that within the app space as well. I think there's-- when I think about where we are and where we need to get to, there's a couple of avenues I think are really important. The first one is understanding people for more than just a single conversation or even a couple of conversations. So we're not static, right? We are dynamic. We have relationships, we have moods, we have challenges, we have wins. And I think for models to be-- feel really like they can work alongside us to really be part of our lives, they're gonna need to develop a sort of empathy beyond a single conversation and really have that sort of longer-horizon interaction with somebody. I think that's really important, and I think that comes from contextual understanding and memory, for example. The second part is the models need to learn when they can be proactive and when they should sit back. As people, we often know if somebody's heads down working, we're trying to-- we're not gonna go interrupt them. Or if they come to you for help, we can answer a question and maybe get back to work ourselves. So how do we have that collaboration with models in a way that feels really natural? As an example, we have Claude as a participant in our Slack channels at Anthropic, which is great. It can chime in and, and be part of the conversation. But we're finding it still either will not participate enough or say too much. So how do we get it to feel like a more natural participant in these conversations is important. And then the third one is around agency and independence. So how do we get the models to be able to take direction from people, but then go off and either do work or do research, or be waiting for additional input, and do that over a much longer time horizon in the background? I think that's very exciting, and there's a lot of things that will be enabled by that capability, but it's going beyond the sort of chat box with a single interaction to more of a long-running agent in the background. One of the big changes that's going to happen is the models will start being able to give you insights about your own self, which I think is very powerful, right? Um, I, I have worked with a coach for many years. I think it's one of the most important ways in which I improve as a leader. I want that for everybody, right? Everybody has-- can benefit from reflection and learning and iterating on the way that they, you know, approach the world. Right now, you can go to Claude and tell it about how things are going, and it will probably be able to give you some coaching, but it's very point in time. When I think about the potential, it's to be much more of an ongoing sort of, uh, improver or, you know, conversational, um, you know, coach. I use this right now with Claude a lot. Anything that I write, I will put-- you know, if I'm writing a strategy document, I'll put it into Claude and say, "What am I missing? What did I forget? Like, what-- Poke some holes in my argument." And it's great to have that partner. Um, I think that'll be one big evolutionary step. The other one that's m- this is more speculative, but if, uh, models go from being single conversations, you know, single chats, to being more of, you know, long-horizon almost personalities, I'm curious what kind of relationship we'll form with the models. Like, will it feel more like a friend, uh, in some ways or a coworker in some other ways? I think that remains to be seen. [upbeat music]

5. 27:02 – 30:10

   Advice for Young and Searching

   1. MKMike Krieger27:06

      I think in your early 20s, you have a unique place in the world because you've-- you're pretty in touch with the trends that, like, teenagers have, which is important, especially if you're building in consumer. But you're now able to, like, take those insights and make them into something like a project or maybe even a company. So it's a very special time in your life where you're, like, at the intersection of being a teenager, being really tuned into that, but being able to do those things. I'd say the two things I would, I would, I would add is, one, not worrying so much that every step needs to make logical sense. There's definitely things that-- side projects I did or explorations I did where at the time I was like, "Am I wasting my time here?" But actually, later, I find that it was connected to the next thing I did. So just trust that if you're driving towards a, a direction, it might take some very strange routes to get there, but as long as you're learning, I think that that's fine. And then the second one is advice that I got when I was 21, and I still think about to this day, is the companies change, the projects change, you know, the economy changes, but the relationships you build in your career will be the relationships that you have over and over again. And so I was working at a startup as an intern, and it was full of people who were in their late 30s who had worked together probably three or four times since they were 20, and that really stayed with me. So, like, remembering that those relationships build on each other and recur, which means that it's, you know, it's worth investing in the-- in that time. [upbeat music] Uh, I think for me, and I was talking to, to Daniela, who's one of the co-founders of Anthropic. I, I told her, and I meant it, I, I hope Anthropic is the last job that I have. I think I see the opportunity here to be paired with research and build interesting products for many, many years 'cause I think every year will be different. When I was 18, I had this, like, promise to myself that I wanted every year to feel different, and this year has felt very different than any other year that I've had being at Anthropic, and I think that the year ahead is gonna be quite different as well. And so that's how I really think about the next five years is just making sure I'm set up for consistently learning, evolving, working with interesting people on interesting products, and then taking the space at least once a year to step back and say, "Am I still learning? Am I still..." You know, I eventually had Instagram, you know, eight years in. There was a time where I said, "Okay, I think I've, I've gotten what I need to get out of this experience, and now it's time to try something else." To me, entrepreneurship is about finding ways in which the world could be different and better and in feeling empowered to go make that change. It's something that really drove me from a pretty early age, and as I've seen that manifest both in the ways you can change your city, the way you can change, you know, the way people relate, of course, with Instagram having global impact, large or small, it's really about identifying, like, what could be different. Like, as-- getting curious about the world and then also getting curious about how you or you and a small team can see if there's a, a change to be made there. Um, and I'm very excited about the way AI will empower people to ask that question and also answer it. [upbeat music]

Episode duration: 30:10