He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor (Sierra)

Bret Taylor’s legendary career includes being CTO of Meta, co-CEO of Salesforce, chairman of the board at OpenAI (yes, during that drama), co-creating both Google Maps and the Like button, and founding three companies. Today he’s the founder and CEO of Sierra, an AI agent company transforming customer service. He’s one of the few people I’ve met who’s been wildly successful at every level—from engineer to C-suite executive to founder—and across almost every discipline, including PM, engineer, CTO, COO, CPO, CEO, and board member. *In this conversation, you’ll learn:* 1. The brutal product review that nearly ended his Google career—and how that failure led to creating Google Maps 2. The question Sheryl Sandberg taught him to ask every morning (“What’s the most impactful thing I can do today?”) that transformed how he approached every role 3. The three AI market segments that matter 4. Why AI agents will replace SaaS products 5. His framework for knowing whose advice to actually listen to—and how that came in handy during the OpenAI board drama 6. The counterintuitive go-to-market strategy most AI startups get wrong 7. Sierra’s outcome-based pricing model that’s transforming how enterprise software is sold (and why every SaaS company should adopt it) 8. What he’s teaching his kids about AI that every parent should know *Brought to you by:* CodeRabbit—Cut code review time and bugs in half. Instantly: https://coderabbit.link/lenny Basecamp—The famously straightforward project management system from 37signals: https://www.basecamp.com/lenny Vanta—Automate compliance. Simplify security: https://vanta.com/lenny *Transcript:* https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor *My biggest takeaways (for paid newsletter subscribers):* https://www.lennysnewsletter.com/i/168905359/my-biggest-takeaways-from-this-conversation *Where to find Bret Taylor:* • X: https://x.com/btaylor • LinkedIn: https://www.linkedin.com/in/brettaylor/ *Where to find Lenny:* • Newsletter: https://www.lennysnewsletter.com • X: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ *In this episode, we cover:* (00:00) Introduction to Bret Taylor (04:10) Bret’s early career and first major mistake (08:24) The birth of Google Maps (11:57) Lessons from FriendFeed and the importance of honest feedback (31:30) The future of coding and AI’s role (45:26) Preparing the next generation for an AI-driven world (48:46) AI in education (52:05) Business strategies in the AI market (01:04:38) Outcome-based pricing in AI (01:09:15) Productivity gains and AI (01:17:35) Go-to-market strategies for AI products (01:21:49) Lightning round and final thoughts *Referenced:* • Marissa Mayer on LinkedIn: https://www.linkedin.com/in/marissamayer/ • “Lazy Sunday”—SNL: https://www.youtube.com/watch?v=sRhTeaa_B98 • Quip: https://quip.com/ • Sierra: https://sierra.ai/ • FriendFeed: https://en.wikipedia.org/wiki/FriendFeed • Sheryl Sandberg on LinkedIn: https://www.linkedin.com/in/sheryl-sandberg-5126652/ • Jim Norris on LinkedIn: https://www.linkedin.com/in/halfspin/ • Paul Buchheit on X: https://x.com/paultoo • Sanjeev Singh on LinkedIn: https://www.linkedin.com/in/sanjeev-singh-20a1b72/ • Barack Obama: https://www.obamalibrary.gov/obamas/president-barack-obama • Oprah Winfrey: https://en.wikipedia.org/wiki/Oprah_Winfrey • Ashton Kutcher: https://en.wikipedia.org/wiki/Ashton_Kutcher • PayPal Mafia: https://en.wikipedia.org/wiki/PayPal_Mafia • Sam Altman on X: https://x.com/sama • Warren Buffett on X: https://x.com/warrenbuffett • Unix: https://en.wikipedia.org/wiki/Unix • Fortran: https://en.wikipedia.org/wiki/Fortran • C: https://en.wikipedia.org/wiki/C_(programming_language) • Python: https://www.python.org/ • Perl: https://www.perl.org/ • Rust: https://www.rust-lang.org/ • Eleven Labs: https://elevenlabs.io/ • The exact AI playbook (using MCPs, custom GPTs, Granola) that saved ElevenLabs $100k+ and helps them ship daily | Luke Harries (Head of Growth): https://www.lennysnewsletter.com/p/the-ai-marketing-stack • Confluent: https://www.confluent.io/ • Databricks: https://www.databricks.com/ • Snowflake: https://www.snowflake.com • Harvey: https://www.harvey.ai/ • Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff • Larry Summers’s website: https://larrysummers.com/ • AutoCAD: https://www.autodesk.com/products/autocad/overview • Revit: https://www.autodesk.com/products/revit/ ... References continued at: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email [email protected]._ Lenny may be an investor in the companies discussed.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lenny Rachitsky?

  You're CTO of Meta, you're a co-CEO of Salesforce, you're chairman of the board at OpenAI. How do you think the AI market is going to play out?

• 0:07Speaker 01

  The whole market is going to go towards agents. I think the whole market is going to go towards outcomes-based pricing. It's just so obviously the correct way to build and sell software.

• 0:15Lenny Rachitsky?

  This makes me think about, I had Marc Benioff on the podcast. You guys were co-CEOs. He was extremely agent-pilled.

• 0:20Speaker 01

  It's so hard to sell productivity software, which I learned a long way.

• 0:24Lenny Rachitsky?

  What's a story that comes to mind when you think about your biggest mistake?

• 0:27Speaker 01

  I was the product manager for, it was called Google Local. I had a pretty tough product review with Marissa and Larry. And to not do that well with a link from the Google homepage is like, kind of embarrassing.

• 0:37Lenny Rachitsky?

  I think it's really empowering for people to hear it's possible to succeed in spite of a massive failure like this.

• 0:41Speaker 01

  They sort of gave me another shot to do a V2 of it. It resulted in Google Maps. We got about 10 million people using it on the first day.

• 0:48Lenny Rachitsky?

  What mindset contributed to you being successful in such a variety of roles?

• 0:51Speaker 01

  Waking up every morning, what is the most impactful thing I could do today?

• 0:56Lenny Rachitsky?

  Today, my guest is Bret Taylor. Brett is an absolute legendary builder and founder. He co-created Google Maps at Google. He co-founded the social network Friend Feed, which invented the like button and the real-time news feed, which he sold to Facebook. He then became CTO at Facebook. He then started a productivity company called Quip, which he sold to Salesforce for $750 million. He then became co-CEO of Salesforce. He's also currently chairman of the board at OpenAI. At one point, he was chairman of the board at Twitter. Today, he's co-founder and CEO of Sierra, an AI startup building agents to help companies with customer service, sales, and more. In our conversation, we cover so much ground, including what skills and mindsets have most helped Brett be so successful in so many roles,

• 1:39Lenny Rachitsky?

  why we're all still sleeping on the impact that agents are going to have on the business world, how coding is going to change in the coming years, where the biggest opportunities remain for startups, lessons on pricing and go-to-market in AI, the story behind the like button, and so much more. This is a truly epic conversation with a legendary builder. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of incredible products, including Repli, Lovable, Bolt, N8N, Linear, Superhuman, Descript, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, JetPRD, Mobbin, and more. Check it out at lennysnewsletter.com and click Bundle. With that, I bring you Brett Taylor.

• 2:22Lenny Rachitsky?

  This episode is brought to you by CodeRabbit, the AI code review platform transforming how engineering teams ship faster with AI without sacrificing code quality. Code reviews are critical, but time consuming. CodeRabbit acts as your AI co-pilot, providing instant code review comments and potential impacts of every pull request. Beyond just flagging issues, CodeRabbit provides one-click fix suggestions and lets you define custom code quality rules using AST grep patterns, catching subtle issues that traditional static analysis tools might miss. CodeRabbit also provides free AI code reviews directly in the IDE. It's available in VS Code, Cursor and Windsurf. CodeRabbit has so far reviewed more than 10 million PRs,

• 3:06Lenny Rachitsky?

  installed on 1 million repositories, and is used by over 70,000 open source projects. Get CodeRabbit for free for an entire year at coderabbit.ai using code Lenny. That's coderabbit.ai. This episode is brought to you by Basecamp. Basecamp is the famously straightforward project management system from 37signals. Most project management systems are either inadequate or frustratingly complex, but Basecamp is refreshingly clear. It's simple to get started, easy to organize, and Basecamp's visual tools help you see exactly what everyone is working on and how all work is progressing. Keep all your files and conversations about projects directly connected to the projects themselves

• 3:48Lenny Rachitsky?

  so that you always know where stuff is and you're not constantly switching contexts. Running a business is hard. Managing your projects should be easy. I've been a longtime fan of what 37signals has been up to, and I'm really excited to be sharing this with you. Sign up for a free account at basecamp.com/Lenny. Get somewhere with Basecamp.

• 4:10Lenny Rachitsky?

  Brett, thank you so much for being here. Welcome to the podcast.

• 4:14Speaker 01

  Thanks for having me.

• 4:15Lenny Rachitsky?

  My pleasure. There's so much that I want to talk about. You've done so many incredible things over the course of your career. It just boggles the mind, the things that you've done. And we're going to talk about a lot of that sort of stuff. But I want to actually start with the opposite. I want to talk about a time that you messed up, a time that you screwed up in a big way. We have this recurring segment on the podcast, like I'll fail corner. And so I thought it'd be fun to just start there before we get into all the great stuff you've done. What's a story that comes to mind when you think about maybe your biggest mistake in building a product?

• 4:43Speaker 01

  It may not be the biggest, but it was my first prominent mistake as a product manager at Google. So

• 4:51Speaker 01

  It's for me, it feels big because it was very formative for me as a product designer. So I joined Google in late 2002, early 2003. And I was one of the earliest associate product managers at the company. And first was working on the search system, essentially expanding our index from one billion web pages to 10 billion, which was a big deal at the time. It sort of seems quaint now. And then I did a decent job. And so my boss, Marissa Mayer, gave me the opportunity to lead a new product initiative, which was a big bet on me. And I was, you know, it was both an opportunity to do something for Google, but I was also being pretty scrutinized just as a young, new product manager.

• 5:36Speaker 01

  And the premise given to me was work on local search. At the time, the Yellow Pages was still dominant. And while Google was really good at searching the web, it wasn't really good for finding a plumber or a restaurant just because it wasn't really a huge part of the internet at the time. So this content wasn't necessarily on the internet. And even if it was, it was, you really needed a different. You didn't really want to find, you know, plumbers in Manhattan. You want to find plumbers in San Francisco, if you're me. And so it was a kind of a, both a technical problem and a product problem and a content problem. We launched the first version of that product that I was the product manager for was called Google Local.

• 6:17Speaker 01

  And it was, you know, I'll be a little bit more critical now than I might have been at the time, but it was a little bit of a me too version of Yahoo Yellow Pages. You know, a sort of essentially grafting on Yellow Pages search on top of Google search. And with a properly crafted query, you could, you know, see those listings at the top of your search results. We had a standalone site at local.google.com. And it was actually, it was an important enough initiative that actually there was a, on the Google homepage, it had, you know, web, images, and Local was up there as well. So, you know, it's got top billing. I mean, you could put almost any link on the Google homepage and get a lot of traffic to it. And despite that,

• 6:59Speaker 01

  It didn't do that well. And to not do that well with a link from the Google homepage is like, kind of embarrassing, you know, it's, it's, I mean, there's not, uh, there's not much one can do other like more than giving you that kind of traffic to give you an at that as a, as a product leader, a product manager. And the product was fine, like it worked, but it really wasn't differentiated. And, uh, and I think in many ways, uh, I think, again, I think I've had these reflections more since than at the time that I had some of the time, but why use this instead of Yahoo Yellow Pages? But more than anything else, like, why use this instead of the Yellow Pages? You know, it was sort of a digital version of, of something that had come before.

• 7:40Speaker 01

  I had a pretty tough product review with Marissa and Larry and others. And it was fine. I wasn't like about to get fired or something, but it was like, you know, the, uh, the shine on the, uh, on my reputation was sort of, uh, waning a little bit. And they sort of gave me another shot to do like a V2 of it. Uh, and, uh, and I sort of got the impression, it wasn't like my last shot, but it was sort of, you know, I, I certainly was feeling a little dejected from going from sort of a hot shot new PM to a new thing. So I spent a lot of time thinking about how can you make something that's just much more compelling and, and not just sort of a digital version of, of Yellow Pages and not just so, so similar to some of the other products out there.

• 8:24Speaker 01

  And that's ended up being the thread that we pulled, uh, that resulted in Google Maps. Um, we had uh, licensed from MapQuest, the ability to put like this little map next to the search results. It was always the ugliest part of the product. And we always, you know, made sort of these like backhanded comments about it internally. And we spent a lot of time saying like, what have we sort of inverted the hierarchy here and made the map, the canvas? We ended up finding Larsen Jens Rasmussen, who had been working on this Windows mapping product. And we sort of got them into the company and started exploring this space. And, and, uh, it ended up where through that exploration, we ended up integrating a lot of different products. We ended up integrating

• 9:08Speaker 01

  mapping, local search, driving directions, like all of these products at the time were actually separate product categories and ended up with something that kind of, uh, Redefine the industry and certainly my career. But it took kind of, I think for me as a product leader, it changed the way I think about product. Just because there's sort of feature and functionality. And then there's like, why should I use this thing in the first place? And it was notable, there was a couple of interesting moments. I mean, when we launched Google Maps, we got about 10 million people using it on the first day, which at that scale, the internet at the time was huge. And then in August of 2005, we integrated satellite imagery from a recent acquisition called Keyhole, which became Google Earth.

• 9:51Speaker 01

  And we got 90 million people using it on the same day. Everyone wanted to look at the top of their house, you know, when the imagery came out. And it was really interesting because there's so many subtle product lessons in there. You know, first is, I think as you have these new technologies, rather than literally digitizing what came before, if you can create an entirely new experience, it creates, it sort of answers the question for a new customer, like, why should I give this a time of day? You know, and so really disassembling the Lego set and reassembling it into something new, rather than just digitizing what was there before. Certainly that was the lesson, I think, in Google Maps. It really was native to the platform in a way that like a paper map couldn't be, you know? And that was like a really meaningful breakthrough.

• 10:35Speaker 01

  And then with satellite imagery, it honestly wasn't the most important part of Google Maps, but it was sort of the sizzle to the steak. And it created, you know, I don't think the term viral was a thing people said back then, but it created a viral moment. We ran Saturday Night Live, which is the coolest thing. Andy Samberg, and I think it was called Lazy Sunday, you know, rapped about Google Maps. And Lars and I were texting each other, we did it. We're on Saturday Night Live. Mission accomplished. And it was also showing that, you know, as you're thinking about products, there's the, you know, why you decide to use a product? And then what is the enduring value? And those are. Deeply related, but not all the same thing. And I just learned so many lessons I took with me for like every subsequent product that I worked on.

• 11:18Lenny Rachitsky?

  That is an awesome story. One, I think it's really empowering for people to hear. Even you, Brett, who I'm going to share all the successes you've had, have had a massive failure with like the CEO of Google versus Meyer. Just like Brett, you screwed up. This is, and it was like such a big bet. So one, just like it's possible to succeed as you have succeeded in spite of a massive failure like this. And then some of the product lessons you shared, just to highlight a few of these things, because I think this is great, is just you will often not win if you just make something that's kind of a better copy of something else. What you want to look for is something that is an entirely new experience, something that's differentiated, something that's a lot more compelling. Let's flip to talk about what you've learned from actually being very successful at a lot of things.

• 12:02Lenny Rachitsky?

  So I was looking at your resume and you basically have been very successful at every level of the career ladder and in such a huge variety of roles. So let me just read a few of these things for folks that aren't super familiar with your background. You were CTO of Meta, you were co-CEO of Salesforce, you're also CPO at COO at Salesforce. At Google, you joined as an associate product manager where you famously, you didn't mention this, but you rebuild Google Maps on the weekend. We're not going to talk about that. You're chairman of the board at OpenAI, you were chairman of the board at Twitter. You've also founded three different companies, one social network, one productivity docs company called Quip and now Sierra. Fun fact, at FriendFeed, you invented the like button. I don't know if people know that, and also just the news feed.

• 12:47Lenny Rachitsky?

  I'll just throw that out there to give you some credit. So you're basically an associate product manager and IC product manager and engineer, CPO, COO, CTO, CEO of three different companies, including a public company. Very rare that somebody is successful at all these types of roles and all these levels. So let me just ask you this question. What mindset or habits or just ways of working have you worked on building in yourself that you think have most contributed to you being successful in such a variety of roles and levels?

• 13:19Speaker 01

  Yeah, it's actually something I am proud of. I like the fact I've worn different hats. It's actually amusing when I meet colleagues that I've known from one of those jobs. They'll often think of me through the lens of that job, you know? And so, you know, I'll go to meet folks from Facebook and they think of me largely as an engineer. They'll meet folks from Google, they think of me largely as a product, you know, person. At Salesforce, you know, a lot of the folks there interact with me as like a, for lack of a better word, a suit. You know, like the boss and I'm not sure they think of me as a, as an engineer at all, even though, you know, I was still probably coding on the weekends for fun. And one of the things that is a principle for me is to have a really flexible view of my own identity.

• 14:05Speaker 01

  I really think of myself, I probably would self-describe as an engineer, but more broadly, I think of myself as a builder and I like to build products. And, and I think companies are one of the most effective ways to build products. There's also things like open source, but I think I'm a huge believer in the confluence of technology and capitalism to produce, you know, just incredible outcomes for customers. And as a consequence, I think to, to really build something of significance, you know, I think to be a great founder, you really need to be able to

• 14:38Speaker 01

  not have such an ossified view of your identity that you can't transform into what the company needs you to be at that point. And every founder you'll talk to, you know, one day, I think selling is a big part of being a founder. You have to sell investors on wanting to invest in your company. You have to sell candidates on wanting to work at your company. You have to sell customers to want to use the product that your customer produces. You have to have good design taste, not just for your product, but for your, your marketing and, you know, essentially soliciting new customers. You have to have a good engineering. I mean, if you're building a technology company, the technology comes first. It's, you know, why this industry is so transformative. I probably credit, and I've told this story before, but I'm very grateful for her,

• 15:23Speaker 01

  but I probably credit Sheryl Sandberg for really changing the way I approach new jobs. The story, and I might be embellishing a little bit, but I think it's broadly accurate. So I had just become the chief technology officer of Facebook. And when I first got the job, it was sort of the flavor of CTO, where I had relatively small group reporting into me, but contributed almost as like a very senior kind of architect, you know, on a number of projects. And then at some point, Mark Zuckerberg reorganized the company and kind of split it into a bunch of different groups. I ended up with a very large group under me, and I was essentially running our platform

• 16:06Speaker 01

  and mobile groups, products, design, engineering. So I went from, you know, a handful of reports to like, I don't know, over a thousand or something. It was a big group. And it was the largest, you know, management job. I had become a manager at Google, but a modest, modest team. And so, and I was doing okay, but not great. And I had this moment where Sheryl saw me, I was, I think I was editing a presentation for a partner, just because the presentation I got didn't meet my quality bar. And I was editing it and sort of griping about. She sort of pulled me into a room and kind of gave me a talking to, like a little bit about holding my team to as high of a standard as I have. If someone wasn't, you know, meeting my expectations, you know, what was my plan to like manage

• 16:51Speaker 01

  them out of the company and, or, you know, just like kind of giving me management 101. And, and she, she's a remarkable mentor in the sense she can kind of give you feedback that's very direct and like often a bit uncomfortable, but you know she cares about you, you know? And so it was the type of feedback you listen to. I sort of went home that night and I was kind of stewing on it and like not very happy. I was like, you know, you get sort of naturally a little defensive in those moments, like, is that really true? Am I really fucking it up or is it, you know, she overreacting? And then I woke up the next day, I was like, no, she's right. And I had realized sort of this subconscious. Like limiter that I that was limiting my success in the job, which is I was trying

• 17:35Speaker 01

  to conform the job to the things I thought I liked to do. So I was spending a lot of my time on some product and technology things that were I was passionate about thinking, you know, I'm the boss, you know, I should, you know, focus on what I want to focus on. Instead of thinking about, okay, I'm running a mobile and platform teams at Facebook. What's the most important thing to do today to make our mobile but mobile and developer platform successful? And when I reframed the job that way, I did different things. And the thing that was the biggest pleasant surprise to me was I liked it. You know, I thought I liked engineering and product, but in fact, when I, you know, changed an

• 18:18Speaker 01

  organization and it turned out to be more successful, I derived a great deal of joy from seeing that success. You know, our developer platform had a lot of partners and, you know, when there was an issue there and I'd spend time on partnerships and it worked and, you know, our platform became healthier, the partner became more successful. I was took pride in that success. And then I just started being better at my job. And I realized that the actual act of engineering or product design or all the things I thought I liked, what I really liked is impact. And, and, and so that conversation led to my sort of waking up every morning, sometimes literally, but certainly in the broadest sense of the words,

• 18:59Speaker 01

  saying, what is the most impactful thing I can do today? And really thinking a almost like a if you had an external board of advisors, you know, telling you like, where are the, what are the things where if you focus on them, you can maximize the likelihood that what you're trying to achieve will happen? And sometimes it's recruiting, sometimes it's products, sometimes it's engineering, sometimes it's sales. And I've become much more self-reflective just about what is important to work on. And I have become much more receptive to doing things that I previously would have said aren't my favorite things to do because I derive so much joy from having an impact that I enjoy a lot more things now. And so I really credit Cheryl. I'm so grateful. And actually, it's interesting. I think a

• 19:43Speaker 01

  lot about this when I give feedback to people now, just like those moments that can kind of like change the trajectory of your career. I mean, I give her all the credit for it.

• 19:53Lenny Rachitsky?

  There's so many people that share stories of Sheryl Sandberg giving them advice and that changing their life.

• 19:58Speaker 01

  Yeah.

• 19:59Lenny Rachitsky?

  What a, what a mensch.

• 20:01Speaker 01

  Yeah.

• 20:01Lenny Rachitsky?

  My biggest takeaway from this, uh, which is this question of what is the most impactful thing I could do today? Such a powerful heuristic, just to kind of keep in mind, to your point, you may realize you don't want to be doing sales or hiring. But if that's the most impactful thing and you end up doing it, you may realize I like this and I'm good at this and.

• 20:17Speaker 01

  Can I double click on that though for a sec?

• 20:19Lenny Rachitsky?

  Absolutely.

• 20:20Speaker 01

  I think it's really hard. Um, one of the dangers for founders and product managers, uh, but I think particularly for founders is incorrect storytelling.

• 20:32Speaker 01

  People don't like my product because of X. And if you tell that to yourself and you tell it to your team, all of a sudden it goes from being an intuition to being a fact.

• 20:42Speaker 01

  Well, you better hope you're right because if you orient your strategy around fixing that problem, uh, and you're wrong, your company is going to fail. Um, so you know, why did you lose a deal? Uh, you know, you could talk to the salesperson who was on the account or perhaps maybe a product manager was involved in the conversation. It's very important to have intellectual honesty in those moments because you could say something like, oh, uh, they didn't buy it because the platform costs too much. Um, that, and that's something a salesperson might say. Maybe the real reason is they didn't actually see much value in your platform. So it was communicated to the salesperson as it was too expensive, but in fact it, the problem was product differentiation.

• 21:28Speaker 01

  And you could end up going into a discussion on pricing when in fact there was a much deeper, much harder problem to solve there. But it's not, you know, just like when you break up with someone, you don't say it's because I don't like you anymore. You say it's not you, it's me, you know, you say all these sort of pleasantries because we're all social, uh, animals. And you, and you want to be pleasant with the people that you, around you. So, you know, literally taking what a customer says or what a user says in like a focus group or usability study is rarely correct. It often is related to what the truth is, but it's very important to get right. And so I think one of the things I've observed with first time founders in particular is

• 22:11Speaker 01

  you're often a single issue voter based on your skillset. So if you're a great engineer, the answer to almost every problem in your business is engineering. If you're a product designer, the answer almost to, you know, the proverbial redesign, I joke, it's like the dead cat balance of a consumer product. Like this next redesign will fix all of our problems. I don't know if it's ever, ever worked. And then you, if you, I've met a lot of entrepreneurs who are like, come from sort of a business development background, they're always thinking about partnerships and, and, you know, oh, we just get this partnership done for this distribution channel, everything's going to change. And I think it's really important when you're a founder to be self-aware that you will naturally, subconsciously, pick the thing that is your strength, your superpower, as

• 22:56Speaker 01

  a solution to more problems. And in fact, if that, you think that's a solution to your problem, it may be right, but you probably by default should question it. Like if you think the thing that you've been doing your whole career is the way to fix your problem, it's at least 30% likely that you've chosen that because of comfort and familiarity, not truth. And so I think it's like, one of those skills, I think, is, it really goes around to like, do you have a good co-founder? Do you have a good, you know, leadership team? If you're a product manager, like you're partner in engineering, you're partner in marketing, you really want to have very real conversations to ensure that you're actually working on the right, the actual correct thing. And I think it's easy to say, what's the most impactful thing to do today?

• 23:39Speaker 01

  My guess is a lot of people try that, they'll lie to themselves more often than not, and it's a very challenging question to answer. The question's interesting. Being able to answer it accurately is actually the hard part.

• 23:49Lenny Rachitsky?

  This feels like such an important lesson you've learned. Is there an example that comes to mind where you learned this the hard way, where you actually ended up?

• 23:58Speaker 01

  Oh, yeah. Well, there's been this whole thing on my failures, but I'm fine with that. I'm sorry.

• 24:02Lenny Rachitsky?

  You've had too much success.

• 24:04Speaker 01

  Friendfeed was my first company. At our peak, we had 12 employees, 12 of the best people I've ever worked with. Started the company with Jim Norris, who's an engineer I've known since Stanford, and Paul Wuhyde and Sanjeev Singh, who Paul started Gmail, Sanjeev was the first engineer on Gmail. So we had the Google Maps people and the Gmail people. It was like, pretty awesome founding team. We made a social network. As you said, we sort of invented a lot of concepts that became popular in the newsfeed. We invented the like button. It was really neat. It was a fun time. We were only really popular in Turkey, Italy, and Iran. And at one point, we were blocked in Iran, so we're only popular in Turkey and Italy and Silicon Valley. To this day, actually, a lot of folks in Silicon Valley are like, I love, love Friendfeed. I'm

• 24:49Speaker 01

  like, that's awesome. It wasn't really a successful business. There was a, we were a follower-oriented social network, not a friendship-oriented social network, which meant a lot of our content was more like X or Twitter than it is Facebook in that respect. And a lot of sharing newspaper articles, interests, scientific communities, things like that. And there was a period when Twitter, which was one of our competitors at the time, there was a lot more social networks at the time. I probably screwed this up a little bit. I think Obama, Ashton Kutcher, and like Oprah Winfrey all went on Twitter like in a, in a summer. And we just got our ass kicked. You know, it's like, and it

• 25:32Speaker 01

  was a great example of you. I think 11 of those 12 people were engineers and we were just making product. And I think it was Biz Stone. I mean, if you talk to the Twitter folks, they could give you the history on this. But I think Biz was really focused on like getting celebrities and public figures onto Twitter, which is totally obvious. Like, if you have a social service that's oriented towards following people, put some people on there worth following, you know, like, and instead, you know, we were exclusively focused on polishing the product. And we actually, I think, you know, at our sort of peak of popularity, we were very confident. Just, you know, I think it was a time when like Twitter had the fail whale and was down half the time and people couldn't even use it. And, you know, we, our product, we were innovating faster. We had more features.

• 26:16Speaker 01

  People liked it. We could, and, and we were up 100% of the time. And we totally lost for no reason related to product at all. And it was an example of, and I think somewhat famously, not of like a lot of great entrepreneurs have come out of Google, because once you like, Google was so successful, I think it's hard as a product manager to sort of see like, distribution and all the product design and even business model when you have AdWords and money's raining from the sky, it's hard to, you know, there wasn't as much scrutiny and I think like it's folks like the PayPal mafia, I think learned a lot more about entrepreneurialism than like a typical PM at Google. So I, we're just getting punched in the face, you know, and learning this the hard way. And so that was probably the most prominent example of it, you know, and

• 26:59Speaker 01

  I think we probably did have a, I can tell you all the flaws of that product, but I don't think that was like the reason why we lost. There's a lot of reasons. I think there was a lot of flaws of the product, but it was a lot of other stuff. And so I've learned, like accumulated these skills over time. But when I say the hard part of that question is answering it correctly is, it's hard when you don't have experience in something to have intuition in it. So I think if there's probably a structural flaw, it wasn't that I, I don't know if I could have figured out how to reach out to Ashton Kutcher, but I wanted to, right? It's not like he's on my, you know, on my Rolodex, but I probably wasn't soliciting advice from the right people. You know, I think that what's great about the technology industry is there's a lot of advice. Choosing whom you listen to is

• 27:42Speaker 01

  actually quite difficult. But I think we were somewhat myopic, you know, we're kind of in our own little world, creating this product and we weren't asking people to like, from the outside end to say like, what, what are you seeing that could go wrong? What are you seeing that could go right? What are you seeing in the industry that we're not doing that you think we might want to do? And this is why boards are important. This is why, you know, finding the right advisors, the advisors who actually tell you what you not necessarily want to hear, but you need to hear. I think that was probably the missing part. I'm not sure I was great at marketing at the time, but if I had solicited the right advice, I, you know, could have learned that that was a shortcoming. And I think that was a deep lesson I took from that. I'm a huge believer in boards and getting good advice.

• 28:26Lenny Rachitsky?

  Any kind of heuristics or advice for people to know whose advice to listen to? What do you pay attention to when you're like, okay, you know this person, but listen to this person?

• 28:34Speaker 01

  Yeah, that one's tough. It is definitely, it does come down to good judgment and being judge of people's character. One thing that is particularly hard is there's not a strong correlation between the confidence.

• 28:48Speaker 01

  With which someone expresses an opinion and the quality of that opinion. I don't want to say it's inversely correlated, but you know, it's funny with all the podcasts out now. If there's topics I know a lot about, you know, sometimes the most eloquent, eloquent, confident statements about things I know a lot about are the least accurate and it sounds extremely persuasive. And so it does require very good judgment. One thing is, I think, not just asking for advice, but asking people, who should I talk to to get good advice? And you'll find some common answers there, and that's often a really strong signal of good judgment. And then one thing I found is when you ask for advice, don't just ask what to do, but why?

• 29:32Speaker 01

  Like being like an obnoxious two-year-old kid, you know, why, why, why, why, why? And really try to understand the framework that someone is using to give you advice. The interesting thing about advice is people are often extrapolating from relatively few experiences. So, you know, they'll say, never do this or always do that. And it's because they had one experience where that something backfired or something could have gone better if they had done it. So it's a useful anecdote, but if you don't ask why and understand they had one experience and here's what happened, it can come across as a rule when in fact it's anecdotal. And if you ask advice of three people and they all have very similar interactions, you can create kind of like a first principles framework from which that advice emerges.

• 30:18Speaker 01

  And when you start applying it, you're applying it with a degree of nuance that you couldn't if you're just following a rule. So I think what is, it does come down to good judgment. I think, you know, I don't know how to teach that. I think it is probably a very, I'm a huge believer in good judgment. It's one of the things I hire for. I just think that's something that, you know, probably comes from a mix of self-reflection. You know, like you really need to hold yourselves accountable, like as an entrepreneur, as a product manager. Like if you made a bad decision, spend time reflecting on it, like number one, and really try to understand why and try to like always improve your judgment. I think at the end of the day, that is why you are a good entrepreneur, a good product manager. And number two, when you get advice, really understand where it's coming from and why,

• 31:03Speaker 01

  so that you can create sort of your own independent view of where that advice came from and recognize that. No one's advice is statistically significant, or very rarely is it. I mean, if you're getting like advice on investing, you know, for more in Buffett. Yeah, okay, statistically significant, but that's not. Most advice is like, something happened to you once and you have regrets.

• 31:26Lenny Rachitsky?

  I love that you're like, I don't, I don't know if I have a great answer. Then you just give us an incredible answer to this question. I want to go in a kind of a different direction. You mentioned that you describe yourself as an engineer. I know I heard you code to relax still. Let me just ask you this question, something a lot of people in college are thinking about. Do you think it still makes sense to learn to code? Do you think this will significantly change in the next few years?

• 31:46Speaker 01

  I do still think it's studying computer science is a different answer than learning to code. But I would say I still think it's extremely valuable to study computer science. I say that because I think computer science is more than coding. If you understand things like, you know, big O notation or complexity theory or, you know, study algorithms and, you know, why, why a randomized algorithm works and, and, you know, why two algorithms with like the same sort of big O complexity, one can in practice perform better than others and why a cache miss matters and just all these little, there's a lot more to coding than, than writing the code.

• 32:29Speaker 01

  The reason I think that is, I do think the act of creating software is going to transform from typing into a terminal or typing into Visual Studio Code to operating a code generating machine. I think that is the future of creating software. But I think operating a code generating machine requires systems thinking. And I think that computer science, there are other disciplines as well, but computer science is a wonderful major to learn systems thinking. And At the end of the day, AI will facilitate creating the software. We may do a lot more in the next few years, we can't even imagine.

• 33:12Speaker 01

  But your job as the operator of that code manager generating machine is to make a product or to solve a problem, and you really need to have great systems thinking. And you're going to be managing this machine that's doing a lot of the tedious work of making the button or connecting to the network. But as you're thinking of the intersection of a technology and a business problem, you're trying to affect a system that will solve that problem at scale for your customers. And that systems thinking is always the hardest part of creating products. I'll just give you like, it's this cheesy, simple example, but I think it's representative. At Facebook, we would always, you know, we spent a lot of time designing the newsfeed. And have you ever had like a really, really good designer and they showed you at the time a Photoshop mockup of the newsfeed?

• 33:56Speaker 01

  It was just all beautiful, the photos, the family was happy and the photo was like a perfect photo and the posts were like all perfectly grammatically correct and of a completely normal length and the comments and the, you know, there was the like button, everything was just perfect. And then you'd like implement that design and you'd look at your own newsfeed and it looked like shit because it turns out like not everyone's photos were made by like a professional photographer, the posts were all these different lengths, the comments were like, you know, you suck and like all that stuff. And then all of a sudden you realize that like designing a newsfeed, like Photoshop is the easy part.

• 34:35Speaker 01

  You need to actually design a system that produces a like both in content and visual design, like a delightful experience, given input you don't control. And that's a system, that's not, I mean, it's sort of a design. And it's just what we did practically, I'm sure it's changed a lot since, you know, I left in 2012, but we made a system so, you know, designers had to show their newsfeed designs with real newsfeed data that was messy rather than, you know, anything artificial because I think it forced the process to be more realistic. But I say that because I think that like whether AI is writing code or doing the design or doing all these other things, like you need to learn how to have a system

• 35:18Speaker 01

  in your head, you need to understand the basics of what's hard and what's easy and what's possible and what's impossible. And AI can help you do that too, by the way. But I do think that's a really useful skill. I think in general with the advent of AI agents and, you know, AI approaching superintelligence in certain domains, I think the tools with which we do our job will change a lot. I think it's very important to have a very loose attachment to the way we do our jobs. And, you know, that story that we won't talk about when I like rewrote Google Maps, like everyone talks about that story because of like, and it's, I think it's because of Paul Puhay who told it on some podcast and it all sort of made the rounds. I think that's going to end up sort of this vestige of

• 36:03Speaker 01

  the past. Like I almost like the human calculators at NASA before the computers were invented, like, wow, a person was a calculator? Wow, that's fun. Like, tell me that story. I think just like what I was good at will no longer be useful in the future, or certainly not like valuable in the future. And that's okay. So I think we need to have a really loose view of it. But the idea that you shouldn't study these disciplines, it's sort of like people say, I don't want to study math because I'm not going to use it in my career for X. Well, studying math is quite important. Like, it teaches you how to think. It teaches you like how the world works, physics, math. And I think computer science, especially at least sort of the foundations of it, will continue to be the foundations of how we

• 36:45Speaker 01

  build software and understanding that when you're interacting, particularly with something that's smarter than you, producing code you may not completely understand, how you constrain it and how you get it to produce these outcomes. I think it will require a lot of sophistication, actually.

• 36:59Lenny Rachitsky?

  That's such a great answer. There's this always sense of this binary, should I learn T code or not? And your point here is learn to understand how engineering works and how systems work and how, what your code does and how it all interconnects. But the way you actually do the coding at your desk will change significantly. This reminds me of something you mentioned on a podcast recently, this idea that you think there's gonna, or there should be a new programming language that is more designed for LLMs versus humans. Can you just talk about that? Because I think a lot of people aren't thinking about that.

• 37:27Speaker 01

  I don't know it's a language, I would call it a programming system because I think language might be too limited. My reductive version of the past, you know, what are 40 years of computers, maybe more, is, you know, you, we created the hardware for computers, then we created punch cards, which is the way, you know, in like the late 70s, you know, you would tell a computer what to do, or maybe mid to late 70s. Then we into, you know, invented early operating systems and time sharing systems. And from the invention of things like Unix at Bell Labs and Berkeley, you ended up

• 38:06Speaker 01

  with the C programming language, Fortran, and a lot of sort of higher level programming languages, I think Fortran and then C. And you, we've sort of moved up the layers of abstraction, so no one does punch cards anymore, obviously. Few people write assembly language. Some people write C, some people write Rust, but a lot of people write Python and TypeScript and things like that. And as we've invented more and more abstractions, we've made it easier to do high leverage things. So, you know, I always look, if you look at how remarkable Google was back in the day, or Google Maps, like you could probably give a lot of React programmers the task of make a dragable map now, and I think a lot of people could do it.

• 38:51Speaker 01

  That was true R and D, you know, back in the day. When Salesforce was created in 1998, just putting a database in the cloud was hard. And, you know, that was just like, that alone was a technical moat that is now trivial with Amazon Web Services. And that technical moat is comically narrow, but the product moat is quite, quite large. I think that if the act of writing code is going from something that is very costly to like the marginal cost of that going to zero, how many of the abstractions that we've built are based on, you know, human programmer productivity? I think a ton. You know, like, I always laugh that I assume Python is probably the most common generated

• 39:36Speaker 01

  code, just because of how much it's in the training data, and data scientists love Python, and I love Python too. It's such a comically bad thing for AI to generate just because it's one of the most inefficient programming languages of all time. If you know the global interpreter lock and just slow. And I've written a lot of high scale web services that it's just quite slow. And it's very hard to verify. Like it's, it's not as bad as Pearl, but like, you know, if you have a big Python program, how many errors will you find at runtime versus, you know, before releasing it? So it was, Python was designed to be very ergonomic, almost look like pseudocode for humans, for me, to write code in a delightful

• 40:18Speaker 01

  way. That's why data scientists love it so much. So as we move to a world where like, let's just postulate, and I'm not sure this will be completely true, that like, we're not going to write a lot of code as people. We're going to be operating these code-generated machines. We probably don't care how ergonomic the programming language is. What we care about is when this machine generates code, do we know that it did what we wanted it to do? And if it doesn't do we want it to do, can we change it easily? I think there's a lot of insights in programming languages that could serve this. So, you know, Rust, I think, is interesting because if I, if I asked you to look at a C program and say, does it leak memory? You probably couldn't do it that well, just because it's really

• 41:00Speaker 01

  hard. And if it's a very, like a million line C program, that's be very, very hard. If I asked you to verify that a Rust program doesn't leak memory, you would just have to compile it. And, you know, because it has compile time, memory safety, just the act of compiling successfully tells you that's true. I think we need more things like that because if a AI is generating this code, by definition, you have to read every line. That is going to be the limiting factor for producing the code. Or worse, you're just not going to read every line and you're going to emit a bunch of unsafe, unverified code into the wild. And so the question is, how do you enable humans to have as much leverage as possible, which means using computers to do the work on your behalf? You could

• 41:44Speaker 01

  have, obviously, the simplest form of this is AI supervising AI and doing code reviews, and that's great. Certainly, self-reflection is a really effective way of improving the robustness of an AI system. But I do think if you, you know, if it doesn't matter how tedious it is to write the code, you could probably layer on some techniques that are sort of out of fashion, like formal verification, unit testing, other things. And if you layer all these on, I'm sort of thinking about it as I as a, it's like the guy in The Matrix with the, you know, green letters coming down. Like, how can I make something so I, as an operator of the code-generated machine, can produce like incredibly complex scale software incredibly quickly? And know that it works. And if you start with that as your design center, I think you probably change the languages,

• 42:29Speaker 01

  you probably change the systems, you probably change all these things. And you're probably going to bring to bear a lot of things. And what's really fun about it is you can loosen a lot of constraints. Like, coding is free. Okay, so that's neat. What, with that in mind, what do you want to do? What would be best suited for the language, the compiler, for testing, for self-reflection, you know, for supervisor models, all these things? I think that's more of a programming system than a language. But I, I think when we create something like that, it can really enable creators, builders, to create incredibly robust, incredibly complex systems. And I'm super excited about vibe coding, but I don't know, like, generating a prototype has been the limiting factor in software ever.

• 43:10Speaker 01

  It's actually like building increasingly complex systems and actually changing them with agility. You know, if you look at the famous like Netscape 1 to Netscape 2 rewrite, they sort of like somewhat, a lot of people attribute that to part of their failure against Internet Explorer. It's like, making these things is not hard, like maintaining them is hard, and ensuring they're robust is hard. And, and I think we've just sort of, we're in the very early phases of defining what this new system for developing software looks like. And I'm, I'm very excited to see what emerges.

• 43:42Lenny Rachitsky?

  I feel like we're definitely living in the future when someone like you is suggesting that we build a matrix-like experience, and that's going to be potentially the future of coding and, and building. I can't wait for that. It feels like a great opportunity and a fun project. This episode is brought to you by Vanta and I am very excited to have Christina Cassiopoe, CEO and co-founder of Vanta, joining me for this very short conversation.

• 44:06Speaker 02

  Great to be here. Big fan of the podcast and the newsletter.

• 44:08Lenny Rachitsky?

  Vanta is a longtime sponsor of the show, but for some of our newer listeners, what does Vanta do and who is it for?

• 44:16Speaker 02

  Sure. So we started Vanta in 2018, focused on founders, helping them start to build out their security programs and get credit for all of that hard security work with compliance certifications like SOC 2 or ISO 2701. Today, we currently help over 9,000 companies, including some startup household names like Atlassian, Ramp and LangChain, start and scale their security programs and ultimately build trust by automating compliance, centralizing GRC and accelerating security reviews.

• 44:46Lenny Rachitsky?

  That is awesome. I know from experience that these things take a lot of time and a lot of resources and nobody wants to spend time doing this.

• 44:54Speaker 02

  That is very much our experience, but before the company, in some extent during it. But the idea is with automation, with AI, with software, we are helping customers build trust with prospects and customers in an efficient way. And you know, our joke, we started this compliance company, so you don't have to.

• 45:10Lenny Rachitsky?

  We appreciate you for doing that. And you have a special discount for listeners. They can get $1,000 off Vanta at vanta.com/lenny. That's V A N T A dot com slash lenny for $1,000 off Vanta. Thanks for that, Christina.

• 45:24Speaker 02

  Thank you.

• 45:26Lenny Rachitsky?

  Okay, one more question along these lines and then I want to zoom out on just kind of where AI is heading. And something I love to ask folks like you that are at the cutting edge of AI is, is what you're teaching your kids. I know you have kids. I feel like the world is going to be very different when they grow up. What are you encouraging them to learn that you think might is different maybe from previous generations to help them be successful in a world of AI abundance?

• 45:53Speaker 01

  I don't know if I'm teaching them differently, but I'm really trying to encourage them to make AI a part of their lives. I

• 46:01Speaker 01

  I was reflecting, actually, when I took the AP Calculus exams in ninety-seven, ninety-eight, AB and BC, I could use a graphing calculator. And I haven't done this research, I actually meaning to plug this into ChatGPT before our conversation, but I'll do it after. Did the calculus exam change before and after they allowed the calculator in the exam? I assume it did. But essentially, to, when you allow the calculator in the exam, you need to make sure that none of the questions, you know, benefit people for having a calculator or not. And which actually forces you to sort of rethink the problems to test calculus knowledge that don't benefit from like rote arithmetic or, you know, the other things you can do on a graphing calculator.

• 46:47Speaker 01

  I think that a lot of education is sort of, doesn't presume you have a superintelligence in your pocket. And so, you know, if you ask someone to write an essay on a book that they read, you could probably hallucinate one pretty easily from one of the big, you know, providers like ChatGPT. And maybe if you are skilled enough that prompting, maybe even your teacher won't know it's written by an AI.

• 47:12Speaker 01

  So what do you do? Like, how do you teach kids differently? It's really hard for teachers right now because I think we haven't gone through the transition of adding calculators to the exams. So I think a lot of the mechanisms we have to evaluate students are broken by the existence of ChatGPT and the like. So I think we're in a very awkward phase. But I, I think we can still both teach kids how to think and teach kids how to learn. And I think our education system can catch up. And I actually think these models can be one of the most effective educational tools in history. I don't know if you're a visual learner or a reading learner. I like to read. I didn't love going to lectures. I don't learn that well from them. I like to like read the book.

• 47:54Speaker 01

  And, you know, if you have a teacher who doesn't teach in your style, you can now go home and ask ChatGPT to teach you another mechanism. I, my kids use ChatGPT to quiz them before a test. You can use audio mode or chat mode. It's like better than cue cards. You, my daughter took home a Shakespeare book. She took a picture of a page she didn't understand and ChatGPT explained it to her way better than I would have as well.

• 48:22Speaker 01

  I think every child in this world has a personalized tutor that can teach them in the way that they best learn visually, over audio, reading. We have a platform that can test you, that can quiz you. I think it's really an amplifier of agency. I think, you know, the folks who like kids who have agency, who have aspirations to to learn something, I think you have what is the best combination of every teacher you've ever had and these these models and you can use it. So with my kids, you know, my oldest daughter learned how to code and she was making a website and every time she had a question for me, I would just make her use ChatGPT. Not because I was trying to be an obnoxious father, but I'm like, she's to learn that

• 49:07Speaker 01

  like to to use this tool because it's it's amazing. And I so I really am trying to have them learn how to use it constructively in their in their lives. But that all that said, I just feel a ton of empathy for public school teachers right now. It's very hard because we're just with the technologies moving faster than our educational system. And I think particularly as it relates to evaluation, it's just really challenging for teachers right now. And I worry, you know, because these technologies amplify agency, the opposite can also be true of you. If you are a student trying to like not learn something, I think these tools probably provide a lot of mechanisms to avoid it as well. And so I think there's a challenge for parents and teachers and I think we're going to end

• 49:50Speaker 01

  up with kind of like a bumpy handful of years here. But I brought up the calculus AP exam because obviously a graphing calculator is not ChatGPT. Don't get me wrong, but I think we've been able to configure out a way to conform, you know, homework and in-class learning and tests around the technologies available to us fairly successfully to date. And I'm fairly confident we'll figure it out, you know, and I like and I and I think it's going to and I on the much more positive side. And I went to public schools. I don't know if you did too. Like you end up with some pretty bad teachers, you know, at times and now you have an outlet. You don't need to be the, you know, rich kid who can afford a tutor anymore to get tutoring.

• 50:32Speaker 01

  You know, if you are a kid who excels in math and your school doesn't have advanced statistics classes, well, now you do. So I think this is just an incredibly democratizing force with kids who have agency. And I think that's very exciting. I'm hopeful that this, there's a 11 year old right now who's going to start a really amazing company. You know, 10 years from now, who's like ChatGPT is going to be like their primary tutor that like led to that, that outcome. And I think that's pretty, pretty cool.

• 51:02Lenny Rachitsky?

  I have a two year old and it feels like there's like a new milestone of there's like when to give them a phone, when to give them, I don't know, Snapchat, whatever kids use these days. And then it's like, when to give them their first ChatGPT account. Oh no, I wonder, I wonder how soon that's supposed to happen.

• 51:16Speaker 01

  I think ChatGPT, my personal take is this different from the former two. I, I don't think mobile phones are great in school or great for kids. And I, I personally advocate for waiting a long time. But I think that ChatGPT is more like Google search and you know, it's one thing to have a device in your pocket that's addictive and has push notifications, but it's another thing to use AI to, to learn. And so I think the two are different. And I really think of AI fundamentally as a utility. And, and I don't think a lot of parents before ChatGPT said, when should I let my kid use Google search? You know, that's like a different type of tool. And I think that, you know, like that is the way I think about these technologies.

• 51:54Lenny Rachitsky?

  And so is the form factor for your kids, like an iPad or a laptop or some?

• 51:58Speaker 01

  Yeah, they use like the computer on the desk.

• 52:00Lenny Rachitsky?

  Got it. All right. Good tips. This is good for me to learn all these things as my kid is. Okay. I'm going to zoom out and let's talk about business strategy, AI. One of the biggest questions a lot of founders think about these days is just where should I build? What will foundational model companies not squash and do themselves? Being someone building a very successful AI business and also being on the board of OpenAI, I feel like you have a really unique perspective on what is probably a good idea and it's probably not a good idea. Why do you think the AI market is going to play out? And where do you think founders should focus and also just try to avoid?

• 52:35Speaker 01

  I think there's three segments of the AI market that will end up fairly meaningful markets. And then I'll, I'll end with how I think it's going to play out. So. First is the frontier model market or foundation model market. I think this will end up the small handful of hyperscalers and really big labs, just like the cloud infrastructure as a service market. So, and the reason for that is that creating a frontier model is entirely a function of capex and you need a company with huge amounts of capex capacity to build one of these models. All of the companies that were startups that tried to do this have already been consolidated or almost all of them inflection, adept, character and others.

• 53:15Speaker 01

  And I think it's just not, it doesn't appear to be a viable business model for a startup because of the amount of capex required and there's just not enough runway, you can, fundraising runway to get to escape velocity. And also the models deteriorate in value fairly quickly as an asset class. And so you need just a lot of scale to make a return on the investment for a model that deteriorates in value so quickly. So I think that's going to end up, probably no entrepreneur should build a frontier model. That's my, my.

• 53:47Lenny Rachitsky?

  Unless you're Elon.

• 53:48Speaker 01

  Yeah, he's not, he's different, right? And he has the capacity to raise billions in capital. And my guess is most of your other listeners don't. And then he's, he's the greatest of all time for a reason and he's different. You don't compare yourself to him, you know? The other part of the market is the tooling. And I think there's, you know, a lot of folks selling pickaxes in the gold rush. This is data labeling services. This is, you know, data platforms, it's eval tools, more specialized models. Like 11 Labs has a great set of voice models that a lot of companies use that are really high quality. And it's sort of like, if you're trying to be successful in AI, what are the different tools and services that you need?

• 54:31Speaker 01

  There is some risk to the tooling market because it's probably, it's pretty close to the sun. So if you look at the. Infrastructure as a service market and the cloud tooling market like the Confluent and Databricks and Snowflake, a lot of the Amazon and Azure and others have competing products in those areas because they're very adjacent to the infrastructure itself. And every infrastructure provider is trying to differentiate by moving up the stack and you're right there. And so there's some real meaningful companies, as I mentioned, like Snowflake, Databricks, Confluent, others, but there's a lot of others that were sort of obviated by technology from the infrastructure providers themselves. So those companies probably are the most at risk for, you know, a developer day from

• 55:16Speaker 01

  one of these big foundation model companies releasing exactly what they do. So you have to, there's probably a lot of people who need your tool, but the question will be if or when is probably the right way to think about it. One of these large infrastructure providers introduces a competitor, why will people continue to choose you? So it's a good market, but it's a little bit close to the sun, as I said. And then there's the applied AI market. I think this will play out for companies who build agents. I think agent is the new app. And so I think that's going to be sort of the product form factor. So there's companies like Sierra. We help companies build agents to answer the phone or answer the chat for customer experience and customer service. There's companies like Harvey that make agents for both the legal, paralegal profession,

• 56:01Speaker 01

  antitrust reviews, reviewing contracts, et cetera, et cetera. There's companies that do content marketing. There's companies that do supply chain analysis. I think this is sort of like this offer as a service market. They'll probably be higher margin companies because you're selling something that achieves a business outcome as opposed to being a byproduct of the models themselves. They will almost certainly pay taxes down to the model providers, which is why those model providers will end up extremely large scale, but probably slightly lower margin. And I think, you know, the market for them will be probably less technical. I mean, if, you know, if you think about the purest form of software as a service, it's not like you ask like, what database do you use, right? It's, it's really about the feature and function. I think that's where agents will go.

• 56:46Speaker 01

  I think it's going to be more about product than it is about technology over time. Just, you know, just going back to my metaphor, you know, in 1998, when Mark and Parker started Salesforce, just getting that database running in the cloud was like a technical achievement. You know, nowadays, like, you know, no one asks about that because you can just spin up a database in AWS or Azure and it's like, no problem. I think today, you know, getting an orchestration orchestrated in an agentic process on top of the models is like, sounds really fancy and it's really hard and all that stuff. You know, I'm pretty sure that's gonna be easy in three or four years. It's just like, just as the technology improves. And so over time, you say like, what is an agent company? Well, it looks a little bit like software as a service.

• 57:30Speaker 01

  You know, talk a little bit less about how you deal with the models. In the same way, modern SaaS, few people ask what database you use. But you'll probably ask a lot about the workflows and what, you know, business outcomes that you're driving. Are you generating leads for a sales team? Are you, you know, minimizing your procurement spend? Whatever value you're providing, it's going to sort of slowly evolve towards that. I, I'm very excited. I don't think startups should probably build foundation models. I think, but I, I mean, you can shoot your shot. You know, if you have a vision for the future, go for it. But I think it's probably a challenging market that's already sort of consolidated. I'm very excited about the other two markets. I'm particularly excited as building agents becomes easier to see a lot of long tail

• 58:14Speaker 01

  agent companies come out. I was looking at a website for the top 50 software companies in the stock market. And obviously, like, the top five are the big, big one ones like Microsoft, Amazon, Google, all that. But like, the next 50 are all SaaS companies. And they're like, some of them are very exciting, some of them are like, super boring. But this is like how the software market has evolved. I think we're gonna see something kind of similar with agents. Like, it's not just gonna be like, these huge markets, like, we're in like, customer service and software engineering. It's gonna be like, a lot of like, things where people are spending a lot of time and resources that an agent can just solve. But it requires an entrepreneur who actually understands that business problem, like, in deep, deeply. And I think that's where like, a lot of the value is gonna be unlocked in the AI market.

• 59:00Lenny Rachitsky?

  That is incredibly helpful. This makes me think about, I had Marc Benioff on the podcast, you guys were co-CEOs, and he was extremely agent-pilled. All he wanted to talk about was agent force. Clearly, you are also very agent-pilled. What is it that you think-

• 59:13Speaker 01

  I've never heard the term agent-pilled on these ones.

• 59:18Lenny Rachitsky?

  Clearly, you guys saw something that was just like, okay, we need to go all in on agents. This is the future. What is it you think people are missing about just like why this is such a critical change in the way software is going to work? What are people not seeing?

• 59:31Speaker 01

  If you talk to an economist like Larry Summers who's on the OpenAI board with me, it'll talk about like, what is the value of technology? Well, it helps drive productivity in the economy. And if you look at the, one of the big jumps in productivity in the economy was in the 90s. And I think a lot of folks I talked to think it was actually that very first wave of computing where people made like ERP systems and just like put accounting into computers and databases, even like mainframes, we're not talking like the PC era. Because it was such a huge step up, like, you know, just imagine like the ledgers of, you know, numbers that you'd have for like a large multinational company before. And it truly just transformed departments. I'll give you a little toy example.

• 1:00:16Speaker 01

  My dad just retired. He was a mechanical engineer. And he was talking about when he first started his career in the late 70s and you went into a mechanical engineering firm, the majority of the firm were draftspeople. So basically you take an engineering design, but you needed to do all the different vantage points and for all the different floors and to give to the contractor to do the thing. Now there are zero draftpeople at his company. You just make the, the design and first AutoCAD and now Revit. And it, you know, it's a 3D model and, you know, the drafting has actually been eliminated. It's just not a thing one needs to do anymore. The, the actual design and drafting, drafting is not a thing that exists. It's just like you can, it's just a design. I, that's true productivity gains, right?

• 1:00:58Speaker 01

  It's like, you know, the job of a mechanical engineering firm was to do a design. The drafting was like the, sort of this necessary output for the contractor, but it wasn't really adding value. It was just sort of like the, the supply chain change. If you look at the history of the software industry from the PC on, there's been meaningful productivity gains, but just not nearly as meaningful as that first huge jump. And I'm not smart enough to know exactly why, but it is interesting. Like, there has the promise of productivity gains from technology hasn't been as realized, I think, as some people thought. I think agents will truly start to bend the curve again, like we did in the very early days of computing,

• 1:01:43Speaker 01

  because software is going from helping an individual be slightly more productive, you know, to actually accomplishing a job autonomously. And as a consequence, just like you don't need drafts, people in a mechanical engineering firm, you just won't need someone doing that thing anymore. It means they can do something else that's higher leverage and more productive. And you can actually, you know, a smaller group of people can accomplish more and, you know, truly drive productivity gains in the economy. And, you know, I think if you've ever sold enterprise software, you end up in these discussions as a vendor with the customer where you'll have like a value discussion

• 1:02:25Speaker 01

  and you'll do these like somewhat convoluted, you know, things like, okay, it's like you're selling a sales thing. Okay, well, if every salesperson sells, you know, 5% more, da, da, da, da, and you should pay us a million dollars. Like, you know, and it's roughly that conversation. And it's so unattributable, you know, especially, and it's why it's so hard to sell productivity software, which I learned the hard way is, you know, it's just hard to know, you know, what's the value of making everyone 10% more productive? Did you actually make them 10% more productive? Or did something else change? You don't really know all these things. But now with an agent actually accomplishing a job, not only is it actually truly driving productivity in a very real way, but it's measurable as well.

• 1:03:10Speaker 01

  So all those things combined means, I think this is actually like a step change in how we think about software, because it does a job autonomously, which is like sort of more self-evident, a productivity driver. It's measurable, so people value it differently as well, which is why I also believe in outcomes-based pricing for software. And all of that combined, to me, it feels like... As significant as the cloud, or I think more technologically, but just in terms of like how it like transforms the business model of the software industry where there's going to be like a before and after. Like, I don't know how many people still sell perpetually licensed on-premise software, but it's de minimis at this point. I think we're going to go through a similar transition, like the whole market is going to go towards agents.

• 1:03:54Speaker 01

  I think the whole market is going to go towards outcomes-based pricing, not because it's the only way, but it's going to be like the market is going to pull everyone there because it's just so obviously the correct way to build and sell software.

• 1:04:08Lenny Rachitsky?

  Let me pull on that last thread. So we had Motiv on the podcast recently, pricing expert legend, monetizing innovation author. And he talked about pricing strategy for AI companies and he was very much in your camp of if you can, you need to price your product as an outcome-based product and the access use is exactly what you shared, which is you can do that if you can attribute the impact and it's autonomous, it's running on its own. Maybe just, and he actually used Sierra as one of the shiny examples of this being successful. Can you just briefly just explain a little bit, what is outcome-based pricing for people that haven't heard this term before? And then just how does it work for Sierra to give an example?

• 1:04:45Speaker 01

  Yeah, I'll start with the example and then I'll broaden it. So at Sierra, we help companies make customer-facing AI agents primarily for customer service, but more broadly for customer experience. So if you have a problem with your Sirius XM radio, you'll call or chat with Harmony, who's our AI agent. If you have ADT home security and your alarm doesn't work, you can chat with their AI agent, Sona speakers, a lot of different consumer brands. And, you know, if you think about running a call center, the, there's a cost for every phone call that you take. Most of it is labor costs, but if you have, let's just say a typical phone call is anywhere between $10 and $20 US dollars.

• 1:05:26Speaker 01

  Most of it, some of it's software, some of it's telephony, but a lot of it is just like the hourly wage of the person answering the phone. So if an AI agent can take that call and solve it, you know, that is in the industry often called a call deflection or a containment. And that essentially means you saved, you know, call it $15 because you didn't have to have someone pick up the phone. So in our industry, basically, we say, hey, if the AI agent, you know, solves the customer's problem, they're happy with it and you didn't have to pick up the phone. There's a pre-negotiated rate for that. And that's, we call it like resolution based. There are other outcomes as well. We have some sales agents being paid a sales commission, believe it or not.

• 1:06:11Speaker 01

  You know, we do. We really think of our agents as truly customer experience, like the concierge for your brand. And we want to make sure that, you know, our business model is aligned with our customers' business model. As you said, these agents need to be autonomous and the outcome has to be measurable. That's not always possible, but I think it's broadly possible. And what's really neat about it is if you talk to any CFO or head of procurement, you know, with their big vendors, they look at the bill of materials and it's like overwhelming and it's impossible to know if you're getting the value that you hoped from that contract. I think consumption based, which was popular particularly in the infrastructure space is closer to it. But I'm not sure like a token is actually a good measure of value from AI either.

• 1:06:57Speaker 01

  I always use the analogy, like right now, most of the coding agents are priced per token or per utilization, but there's this famous story of an Apple engineer who had a bad manager who's like, had you report how many lines of code you wrote every day, which every engineer in the world knows is an idiotic way to measure productivity. He famously went in with a report that had a negative number. So I think he did a big refactoring, deleted a bunch and it was his way of saying like, fuck you to the man. I think tokens are similar. Like, yeah, you used a lot of tokens, like, good for you. Did the, you know, did it produce a pull request, you know, that was good? And, and I think that's the whole point of all this. I don't think, I think there's a huge difference between outcomes based pricing and usage based pricing,

• 1:07:39Speaker 01

  because especially in AI, they're not necessarily even correlated. And you could have a long phone call, not solve the customer's problem, and they give you a negative review online and call the call center again. All that effort was for nothing. In fact, you might have added negative value. And so I am a huge believer in this. And what's fun about it is it really just aligns. I think every technology company aspires to be a partner, not a vendor. And I think at Sierra, we are truly a partner to every single one of our customers, because we're all aligned on what we want to achieve. And I think that is really where software, the software industry should go. It requires a lot of different shape of a company. You just have to have, you have to be able to help your customers achieve those outcomes.

• 1:08:23Speaker 01

  You know, you can't just throw software at the wall because you'll never get paid. If it doesn't, you have to, you know, really just your orientation becomes so extremely customer centric when you do this the right way. I think it's just a better version of the software industry. So I think it's right from first principles. It's right for procurement partners, and I think it's right for the world.

• 1:08:43Lenny Rachitsky?

  We've been chatting a little bit about productivity gains. There's a lot of skepticism in, in the headlines these days of just like, what is AI actually doing? Like, is it actually helping people be more productive? There was a recent study, actually, I don't know if you saw, where they showed engineers were less productive with AI because it was just putting them in different directions. They had to research all what's going wrong here. And so I think CX is a really good example where you clearly are seeing gains. Are you seeing actual gains at your company or any other company you work with outside of CX in terms of productivity that is like, clearly, yes, this is working and a huge deal?

• 1:09:15Speaker 01

  I'm extremely bullish on the productivity gains from AI. But I do think the tools and products right now are somewhat immature and it's quite counterintuitive. So, for example, I, almost every software engineering firm I know uses something like Cursor to help their, their software engineers. Most people use Cursor right now as a kind of coding autocomplete, though they have a lot of agentic solutions. And there's a lot of, like, OpenAI has Codex and there's, you know, Claude, as I can't remember the Anthropic products. So there's lots of agentic, you know, agents coming as well. One of the interesting things, because the technology is sort of immature, the code it produces often has problems.

• 1:09:56Speaker 01

  So there's a lot of people sort of approaching this to sort of actually realize those productivity gains. Because as any engineer who's written a lot of code will tell you, it's pretty easy to like, look at and edit and fix code you wrote. Reviewing other people's code, or particularly finding a subtle logical error in someone else's code is actually really hard. It's actually much harder than, you know, editing code that you wrote yourself. So if the code produced by a coding agent is often incorrect, it actually can take a lot of like cognitive load and time to fix it. And in fact, if you end up producing lots of, you know, issues with your customers, you could end up, you know, producing a lot of features,

• 1:10:39Speaker 01

  but actually like, you know, mucking up the machine a little bit and having something that's not ideal. There's a couple of techniques I think are interesting. Like first, I think there's a lot of AI startups now working on things like code reviews. I think this idea of self-reflection in agents is really important. Having AI supervise the AI is actually very effective. Just think about it this way. If you produce an AI agent that's right 90% of the time, that's not that great. But how hard would it be to make another AI agent to find the errors the other 10% of the time? That might be a tractable problem. And if that thing's right 90% of the time, just for argument's sake, you can wire those things together and have something that's right 99% of the time. So the, it's just a math problem, like, you know, and it turns out that you can make something to generate code, you can make something to review code,

• 1:11:24Speaker 01

  and you're essentially using compute for cognitive capacity. And you can layer on more layers of cognition and thinking and reasoning, and produce things increasingly robust. So I'm very excited about that. The other thing, though, is root cause analysis. So we have an engineer at Sierra who exclusively focuses on the model context protocol server serving our cursor instance. And our whole philosophy is, rather than if cursor generated something incorrect, rather than just fixing it, try to root cause it. Try to get it so like, the next time cursor will produce the correct code. So like, and essentially is context engineering.

• 1:12:04Speaker 01

  Like, what context did cursor not have that would have been necessary to produce the right outcome? So I think people who are trying to get productivity gains in departments like software engineering. Need to stop sort of waiting for the models to magically work if they want to see that gains now. And you really have to create like root cause analysis and systems and say like, you know, how do we sort of go root cause every bad line of code? And actually give the right context and produce the right system so the models can do it today. Over time, that probably less necessary and you'll have less context engineering necessary to do it. But you really have to think of this as a system. And I think people are sort of like waiting for the models to just magically get better.

• 1:12:45Speaker 01

  And I'm like, well, that will happen eventually, but if you want the gains now, you got to put in the work. I mean, that's essentially why applied AI companies exist. And the work is non trivial, but it's you can do it. And so, you know, for customers using platforms like Sierra, yeah, AI agents aren't perfect, but we're creating a system that lets customers create a virtuous cycle of improvement. If you want to go from a 65% automated resolution rate to 75%, we have a billion tools to let AI help you do that. Identify opportunities for improvement, figure out why people are frustrated. What new capabilities can we add to our agent to improve the resolution rate? And you're sort of let AI put the needles of the haste at the top of the haystack on your behalf. And I think that's really the way to optimize these systems.

• 1:13:28Lenny Rachitsky?

  I've never heard of this technique of improving cursor by adding additional context. What's the actual way of doing that? You build an MCP server that everything runs through? Or is it like you add cursor rules? What's the actual approach there?

• 1:13:40Speaker 01

  I'm probably out of my depth here, but it's essentially MCP. But it's essentially, you know, because that's how you provide context to cursor. And I think that almost always when you have a model making a poor decision, if it's a good model, it's lack of context. And so you really want to like, you know, find the intersection of your particular product and code base with the context available to these coding agents and systems and fix it at the root is sort of the principle here.

• 1:14:06Lenny Rachitsky?

  Got it. That is very cool. I hadn't heard people doing that. Model context protocol makes sense. We've talked about productivity gains outside TX, just to give you a chance to share how amazing what you've built is. What are some of the gains you see from people using Sierra?

• 1:14:18Speaker 01

  Yeah, we have, our customers see anywhere between 50 and 90% of their customer service interactions completely automated, which I think is really exciting.

• 1:14:29Speaker 01

  And we serve just a really, really broad range of customers. We serve the health insurance industry, the healthcare provider space, banks. You can actually refinance your home using an agent one of our customers built on our platform to the telecommunications industry, DirecTV, SiriusXM, to a lot of retailers as well, which is really fun. Everyone from Wayfair to clothing retailers like Olacay and Chubby Shorts. What's really neat about it is a pretty diverse range of use cases and it's everything from helping you, you know, sign up for a, we have an agent that helps with customer

• 1:15:10Speaker 01

  support in one of the big dating applications to, you know, helping you upgrade or downgrade your SiriusXM plan. Actually, it's really funny. We do technical support from everything from home alarm systems to Sono speakers to more recently CAT scan machines, which I think is amazing. So technicians going in and fixing the CAT scan machine can chat with an AI agent to help them, guide them through that process. We're the leader in this space. We're trying to enable every company in the world to create their agent with their brand at the top that I think will become as meaningful of a digital touchpoint as their website or their mobile app. In the short term, it can really transform the costs of running a customer service team,

• 1:15:53Speaker 01

  you know, and what's remarkable is do so with really high customer satisfaction scores. You know, that Weight Watchers agent, I believe, has a customer satisfaction score of 4.6 out of 5, which is pretty amazing, you know. And what's interesting about service too, it's often people having a problem. And so, you know, when you have a clear, I don't know if you use them in the airport, I think that agent has a CSAT score of 4.7 out of 5. You know, people are coming in with a problem and Indian delighted, and I think that's really the opportunity here. And our whole vision is that We're going to move towards a world where every single one of the interactions with your customers can be instant. It can be multilingual, it can be over audio, it can be over chat, it could be digital, it can be over the phone,

• 1:16:37Speaker 01

  and it could be very personalized. And I think that's really, really exciting. And if you think about all the best moments you've had with a brand, it's like that store associate who you know. And you know, it's like, for me, it's like the butcher at the grocery store. I love to cook. He knows me, we talk. Like, can you actually produce that at scale for a company with 100 million customers? And can you do it in a really personal way? And I think we're really on the cusp of enabling that.

• 1:17:03Lenny Rachitsky?

  Let me ask you one more question before we get to our very exciting lightning round. There's a lot of founders struggling with go-to-market in AI with their AI apps. There's so many apps these days, so many products, so many things coming at buyers at large B2B companies. Clearly, you guys have figured something out. Imagine your name helps, investors help. But what have you learned about just how to successfully do go-to-market with an AI product? Say, an agent-specific product that you think would be helpful for folks trying to do this better?

• 1:17:35Speaker 01

  I think there's a small handful of go-to-market models that have been proven to work. And I think it's important to choose the right one for the product category you're going after. One category I would say is developer-led. This is sort of famously Stripe and Twilio were probably like two of the original that did this exceptionally. And essentially, the go-to-market notion there is to appeal to an individual engineer, often within the department of the CTO, who have accountability and a fair amount of latitude to choose a solution. This works if your product is sort of a platform product.

• 1:18:16Speaker 01

  It doesn't work, for example, if your product is trying to help a line of business, because lines of business typically don't have dedicated engineering teams, or let alone the latitude to just go, you know, download a new library or start using a web service like that. It particularly works well if you sell to startups, just because startups tend to have engineering teams with quite a bit of latitude to choose services to help them solve the problem given by the founder. Then there's product-led growth. It's a broad term. Obviously, every company's product matters. But product-led growth more specifically means users can sign up from the website, often get put on trial, often you can buy a couple of seats with a credit card.

• 1:18:58Speaker 01

  And those work where your user and your buyer are the same person. So it works for small business software almost always because sole proprietors do everything. And so you're selling small business software like Shopify in the early days. And there's a lot of other products like that where you're trying to sell to small merchants. You know, that's great. It doesn't work well when your buyer and the user of the software are different. So I always use the example of something like expense reporting software. The user of that software is an individual employee, but the buyer is often a finance department. And so, you know, having to sign up and buy with your credit card doesn't make sense because the person using it is not the person with the credit card and it just doesn't work. And then there's direct sales.

• 1:19:41Speaker 01

  And direct sales had gone, I don't say out of fashion, but if I think of like the best direct sales companies, I probably, there's a lot of lineage from Oracle, but you think SAP, Oracle, ServiceNow, Salesforce, Adobe perhaps. And there's others as well. And these were companies that sold into, you know, large lines of business in a relatively traditional sales motion. I think because product-led growth became very popular, I think a lot of companies use that, which is great. That motion produces great products. But if PLG means that you aren't actually engaging with the buyer of your software, like you're not going to grow. And so I've actually seen more recently a lot of AI companies, direct sales come a

• 1:20:26Speaker 01

  little bit more back into fashion because I think so many of the opportunities in AI are actually, meet that qualification where the buyer and the user are not necessarily the same, same person. And it really requires that go-to-market motion. Where I see entrepreneurs stumble is they'll sort of choose a go-to-market motion without thinking through the what is the process of purchasing this software? What is the process of evaluating the value of this software? And I think people just need to be much more like first principles about it and much more thoughtful about it. And candidly, I think like a lot of companies should leverage direct sales more than they do. And even though it like because of the, you know, sometimes justified reputation of the

• 1:21:11Speaker 01

  quality of products of some of these direct sales companies, a lot of it sort of had gotten a bad name. And I think, I think a lot, I'm sort of thankful to see it coming back in a lot of the AI market.

• 1:21:20Lenny Rachitsky?

  I feel like this message is something a lot of founders need to hear, especially founders that aren't from a business background of that, you know, sales turns them off. They don't think they're going to be great at sales, just this push of this might be what you have to get really good at, and this is how you win. And you can't just rely on product like growth.

• 1:21:36Speaker 01

  Yeah.

• 1:21:37Lenny Rachitsky?

  Brett, is there anything else that you wanted to share? Any last nugget of wisdom, anything you want to double click on before we get to our very exciting lightning round?

• 1:21:46Speaker 01

  No, go ahead.

• 1:21:47Lenny Rachitsky?

  Okay, let's do it. Here we go. Welcome to our very exciting lightning round. I've got five questions for you. Are you ready?

• 1:21:53Speaker 01

  Yeah, go ahead.

• 1:21:54Lenny Rachitsky?

  What are two or three books that you find yourself recommending most to other people?

• 1:21:58Speaker 01

  I don't read a lot of nonfiction, but probably if I had to pick one, sort of in the area of the topics we talked about, Competing Against Luck, which was the book that produced Jobs to be Done, which is a framework I really believe in. My only critique is I think most of these sort of like business books should be like an article. So maybe buy the book and punch into ChatGPT and get the summary. But by the book, it's Clayton Christensen talked about it, but it's a really good framework for thinking about delivering value with their products. And I think it's a, it definitely influenced me. On the, actually, one book I do recommend is Endurance, which is the story of Shackleton's

• 1:22:42Speaker 01

  trip to go to the South Pole. Like half the book is him starving to death and eating seal meat with his crew of people frozen in their boat. I've never seen a better story of grit in my entire life. It's like kind of remarkable that it's a true story. And, you know, if you want to like, if you're an entrepreneur going through a hard time, read that, you're like, okay, it could be worse. It's a great book, too. It's just remarkable. It's a true story.

• 1:23:08Lenny Rachitsky?

  And one thing he did a great job at is setting expectations for folks that joined that are that famous.

• 1:23:13Speaker 01

  That ad, that's true. It's like remarkable. That's true.

• 1:23:16Lenny Rachitsky?

  It might not be true.

• 1:23:17Speaker 01

  I don't know. The internet, he knows.

• 1:23:20Lenny Rachitsky?

  God damn, deep fakes even back then. Okay, do you have a favorite recent movie or TV show that you've really enjoyed?

• 1:23:27Speaker 01

  I haven't gotten any new TV shows recently. We just watched Inception with the kids and they loved it and made me appreciate Christopher Nolan. So I, and what a cool movie, cool content. I love, it's the type of movie when you watch your film and you have conversations for two days afterwards about it. So just a great film.

• 1:23:45Lenny Rachitsky?

  I saw someone using, I think, V O three to create their own inception videos where the world's wrapping around each other. Oh, man. Okay. Do you have a favorite product that you have recently discovered that you love or one you've loved for a long time?

• 1:23:58Speaker 01

  I'm really a big fan of Cursor. I think it's like change. I'm, I love creating software and I'm excited, though, for agents. You know, I've been really excited. I was very excited to see Codex from OpenAI and others. So I think Cursor will be, in its current form, is a transition product. And I know they're working on agents as well, but I really enjoyed taking something I love and I'm like, been my life's passion and really diving into this AI tool and like seeing how it transforms, how it creates software. So I've just been like spending a lot of time with the product just because it's so core to my, like, what I love to do. And it's a really well, well crafted product.

• 1:24:39Lenny Rachitsky?

  I think it's the first time someone's actually mentioned Cursor in this answer, so it might be the beginning of a trend. Michael Truitt was on the podcast and he actually had a very similar message as you had at the beginning of this chat about the future of code, what comes after code and this concept that there's going to be this additional pseudocode layer on top of code.

• 1:24:56Speaker 01

  Yeah.

• 1:24:57Lenny Rachitsky?

  Very aligned with your thinking. Do you have a favorite life motto that you often come back to and find useful in work or in life?

• 1:25:05Speaker 01

  The best way to predict the future is to invent it, which I think I attribute to Alan Kay of Xerox PARC, who invented a lot of the core abstractions that we use in computing today. It's why I love, I am an entrepreneur. It's why I love to build things. So it's definitely like a life motto for me.

• 1:25:27Lenny Rachitsky?

  I feel like many people like say this. I feel like you've actually done it so many times. You're reliving this motto. Final question. We talked about you inventing the like button at FriendFeed. Were there other thoughts of what they would call it other than like? Was it just like, obviously like? Was there other thinking there?

• 1:25:47Speaker 01

  The context of this was before emoji. So there, if you read the comments on FriendFeed posts, at least 70% of them are cool, or wow, or yeah, or neat. And one of the principal like uses of FriendFeed was to have discussions about things. So you'd have a post and then a pretty fulsome discussion underneath. And it was a very, compared to Twitter and others, it was like a great place to have those discussions. And so the product problem we were trying to solve is get all the one word answers out. So that the discussion was actually like, like actual comments as opposed to acknowledgements that you read the thing.

• 1:26:30Speaker 01

  So we, the original framing was one click comment. That was how we thought about it. And so we, the first version that I made had a heart. And though she denies remembering this, but there's a Anna Yang, now Anna Mueller, who has worked at the company, she hated it. She said, like, if I look at a like hearts on every post, I'm going to vomit. It's just too, it's like too, too much, you know? And, and it also was interesting, like we were simulating. And it was like an article about a tragedy or something. A heart was just not the right thing. Like, which actually turned out to be really hard to translate, was just a much more neutral sentiment. And that's why it was hard to translate because it was subtle.

• 1:27:14Speaker 01

  And we, so that's how we ended up with this. We started with a heart and I don't know if we ever had the word love, but we definitely started off with the mycognography. And then like, which just felt like this. Positive, yet as neutral as possible within the realm of positive so that it could work for like a more complex story. But it was all because we needed a one click comment. That's where the concept came from.

• 1:27:36Lenny Rachitsky?

  Wow. I've never heard this story before. It makes me think about LinkedIn now. They have, they're basically trying to solve that same problem. They have all these auto reply kind of pill tag things. I don't think people like.

• 1:27:45Speaker 01

  They have a lot of features.

• 1:27:47Lenny Rachitsky?

  So many, so many AI features. Brett, this was incredible. This was an honor. I so appreciate you coming on this podcast. Two final questions. Where can folks find you online if they want to reach out? Maybe go see if they want to work at Sierra? And how can listeners be useful to you?

• 1:28:00Speaker 01

  If you want an AI agent to help with customer service, go to sierra.ai. If you want to apply here, sierra.ai/careers. We're, we have offices in San Francisco, New York, Atlanta, and London, and are hiring pretty aggressively in every department. So please reach out if you're interested.

• 1:28:18Lenny Rachitsky?

  And how can listeners be useful to you? Is it tryout Sierra? Anything else there?

• 1:28:21Speaker 01

  Yeah, tryouts here. I'm a single issue voter.

• 1:28:25Lenny Rachitsky?

  Stay on that message. I love it. Brett, Brett, thank you so much for being here.

• 1:28:28Speaker 01

  Yeah, thanks for having me.

• 1:28:30Lenny Rachitsky?

  Bye, everyone.

• 1:28:32Lenny Rachitsky?

  Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.