Lenny's Podcast: Product | Career | Growth - He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more

Starting point is 00:00:00 You're CTO of Meta, you're co-CEO of Salesforce. You're chairman of the board at OpenAI. How do you think the AI market is going to play out? 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. It makes me think about it. I had Mark Benio up on the podcast.

Starting point is 00:00:17 You guys were co-COs. He was extremely agent-pilled. It's so hard to sell productivity software, which I learned in our way. What's a story that comes to mind when you think about your biggest mistake? I was the product manager for was called Google Local. 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. I think it's really empowering for people to hear it's possible to succeed in spite of a massive failure like this.

Starting point is 00:00:41 They sort of gave me another shot to do the V2 of it that resulted in Google Maps. We got about 10 million people using it on the first day. What mindset contributed to you being successful in such a variety of roles? Waking up every morning, what is the most impactful thing I can do today? Today, my guest is Brett 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

Starting point is 00:01:08 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 and 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,

Starting point is 00:01:39 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

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Starting point is 00:02:19 With that, I bring you Brett Taylor. This episode is brought to you by CodeRabbit, the AI code review platform, transforming how engineering teams shift 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

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Starting point is 00:03:22 Basecamp is the famously straightforward project management system from 37 signals. 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 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 long time fan of what 37 signals has been up to and I'm really excited to be sharing this with you. Sign up for a free account at basecamp.com slash Lenny. Get somewhere with base camp. Brett, thank you so much for being here. Welcome to the podcast. Thanks for having me.

Starting point is 00:04:15 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 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? It may not be the biggest, but it was my first prominent mistake as a product manager at Google. So it's, for me, it feels big because it was very formative for me as a

Starting point is 00:04:56 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 Meyer, 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.

Starting point is 00:05:36 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.

Starting point is 00:06:01 You want to find plumbers in San Francisco, if you're me. And so it was kind of a technical problem and a product problem and a content problem. We launched the first, The first version of that product that I was the product manager for was called Google Local. 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, 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.

Starting point is 00:06:37 We had a standalone site at local.gookle.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, 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, I mean, there's not a.

Starting point is 00:07:07 there's not much one can do more than giving you that kind of traffic to give you an ad bat as a product leader, a product manager. And the product is fine, like it worked, but it really wasn't differentiated. And I think in many ways, I think, again, I think I've had these reflections more sense than at the time, though 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 Yellow Pages? It was sort of a digital version of something that had come before. I had a pretty tough product review with Marissa and Larry and others.

Starting point is 00:07:43 And it was fine. I wasn't about to get fired or something. But it was like, you know, the shine on my reputation was sort of waning a little bit. And they sort of gave me another shot to do like a V2 of it. And I sort of got the impression. It wasn't like my last shot, but it was sort of, you know, I certainly was feeling a little dejective. from going from sort of a hot shot new PM to a new thing. So we spent a lot of time thinking about how can you make something that's just much more compelling

Starting point is 00:08:16 and not just sort of a digital version of the L-O pages and not just so similar to some of the other products out there. And that's ended up being the thread that we pulled that resulted in Google Maps. We had 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

Starting point is 00:08:57 into the company and started exploring this space. And it ended up where through that exploration, We ended up integrating a lot of different products. We ended up integrating mapping, local search, driving directions, like all of these products. At the time were actually separate product categories. And it ended up with something that kind of redefined 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 a 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?

Starting point is 00:09:31 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 of 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, 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?

Starting point is 00:10:16 You know, and so really disassembling the Lego set and reassembling it's 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 then. that was like a really meaningful breakthrough. 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 stake. 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.

Starting point is 00:10:48 We run Saturday Live, which is the coolest thing, Andy Sandberg, and I think it was called Lazy Sunday, you know, wrapped about Google Maps. And Lars and I were texting each other. We did it. We're on Saturday Live. Mission accomplished. And it was also sharing that, you know, Azure they're thinking about products. There's the, you know, why you decide to use a product.

Starting point is 00:11:08 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. As 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 my. Just like, Brett, you screwed up. This is, and it was like such a big bet.

Starting point is 00:11:33 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 share 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. Which 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. 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're co-CEO of Salesforce.

Starting point is 00:12:19 You're also CPO at CFO 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 a 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 Friend Feed, you invented the like button. I don't know if people know that, and also just the news feed. 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, CO, CTO, CFO, CFO.

Starting point is 00:12:57 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 of most contributed to you being successful in such a variety of roles and levels? 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 me largely as a product,

Starting point is 00:13:40 you know, person. At Salesforce, you know, a lot of the folks there interacted 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 as an engineer at all, even though 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. I really think of myself, I probably would self-describe as an engineer,

Starting point is 00:14:09 but more broadly I think of myself as a builder. And I like to build products, 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 content. influence of technology and capitalism to produce, you know, just incredible outcomes for customers. And as a consequence, I think, to really build something of significance, you know, I think to be a great founder, you really need to be able to not have such a ossified view of your identity

Starting point is 00:14:41 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 marketing and essentially soliciting new customers. You have to have 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

Starting point is 00:15:22 grateful for her, but I probably credit Cheryl Sandberg for really changing the way I approach new jobs. The story, and I might be embellished in 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. I was essentially running our platform in mobile groups, products, design, engineering.

Starting point is 00:16:09 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 Cheryl 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, my team to as high of a standard as I have. If someone wasn't, you know, meeting my expectations, you know,

Starting point is 00:16:50 what was my plan to like manage the amount of the company? Or, you know, just like kind of giving me management 101. And 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 and you know she cares about you, you know. And so it's 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?

Starting point is 00:17:19 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 was limiting my success in the job, which is I was trying 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 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.

Starting point is 00:17:51 Instead of thinking about, okay, I'm running the mobile and platform teams at Facebook, what's the most important thing to do today to make our 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 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's an issue there and it's been time on partnerships and it worked and, you know, our platform became healthier. The partner became more successful.

Starting point is 00:18:34 I 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 so that conversation led to my sort of waking up every morning, sometimes literally, but certainly in the broadest sense of the word saying, what is the most impactful thing I can do today? and really thinking almost like 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

Starting point is 00:19:31 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,

Starting point is 00:19:42 I think a 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. There's so many people that share stories of Cheryl Sandberg giving them advice and that changing their life.

Starting point is 00:19:58 Yeah. What a mensch. Yeah. My biggest takeaway from this, 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.

Starting point is 00:20:17 Can I double click on that though for a second? Absolutely. I think it's really hard. One of the dangers for founders and product managers, But I think particularly for founders is incorrect storytelling. 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.

Starting point is 00:20:41 Well, you better hope you're right. Because if you orient your strategy around fixing that problem and you're wrong, your company is going to fail. So, you know, why did you lose a deal? 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, they didn't buy it because the platform costs too much. And that's something a salesperson might say. Maybe the real reason is they didn't actually see much value in your platform.

Starting point is 00:21:20 So it was communicated to the salesperson as it was too expensive. But in fact, the problem was product differentiation. 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 animals 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.

Starting point is 00:22:04 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 you're often a single issue voter based on your skill set. 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 the proverbial redesign. I joke is like the deadcat balance of a consumer product. This next redesign will fix all of our problems. I don't know if it's ever worked. And then I met a lot of entrepreneurs who come from sort of a business development background.

Starting point is 00:22:36 They're always thinking about partnerships. And 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 a solution to more problems. And in fact, if that you think that's a solution of 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

Starting point is 00:23:09 30% likely that you've chosen that because of comfort and familiarity, not truth. And so I think it's like one of the 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 your partner in engineering your partner in marketing you really want to have very real conversations um 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 my guess if 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 is interesting and being able to answer it accurately is actually the hard part.

Starting point is 00:23:50 This feels like such an important lesson you've learned. Is there an example that comes to mind where you learn this the hard way where you actually ended up? Oh, yeah. Well, it's worth of this whole thing on my failures, but I'm fine with that. You've had too much success. Friendsview was my first company. At our peak, we had 12 employees, 12 of the best people I've ever worked with.

Starting point is 00:24:11 Started the company with Jim Norris, who's an engineer I've known since Stanford, and Paul Buhit and Sanjeev Singh, who, Paul started Gmail, Sanjee 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 news feed. We invented the like button.

Starting point is 00:24:36 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 that's look on value like, I love, love friend feed. I'm like, that's awesome. It wasn't really a successful business.

Starting point is 00:24:54 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'm probably screwing us a little bit. I think Obama, Ashton Coucher, and like Oprah Winfrey all went on Twitter like in a summer.

Starting point is 00:25:28 And we just got our ass kicks. You know, it's like, and it 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 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 with following. And instead, you know, we were exclusively focused on polishing the product.

Starting point is 00:26:01 And we actually, I think, you know, at our sort of peak of popularity, we were very confident. I think it was a time when like Twitter had the fail whale and it was down half the time and people couldn't even use it. And, you know, our product, we were innovating faster. We had more features. People liked it. We could. And we were up 100% of the time. And we totally lost for no reason related to product at all.

Starting point is 00:26:24 And it was an example of, you know, I think somewhat famously not of like a lot of great entrepreneurs have come out of Google. Because once you're like Google was so successful. I think it's hard as a product manager to sort of see like distribution. and all 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 sort of 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.

Starting point is 00:26:59 You know, and 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 with the product, but it was a lot of other stuff. And so I've learned that 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 and 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

Starting point is 00:27:24 if I could have figured out how to reach out to Ashton Kutcher what I wanted to, right? It's not like he's on my rolydecks. But I probably wasn't soliciting advice from the right people. I think that what's great about the technology industry is there's a lot of advice. Choosing whom you listen to is actually quite difficult. But I think we're somewhat myopic. 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?

Starting point is 00:27:55 What do you see 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 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.

Starting point is 00:28:26 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? 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 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, that'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.

Starting point is 00:29:09 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? Like be it like an obnoxious just two-year-old kid, you know, why, why, why, why, why? And really tried 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

Starting point is 00:29:56 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 it's anecdotia. And if you ask advice for three people and they all have very similar interactions, you can create kind of like a first principles framework from which that advice emerges. 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 one 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.

Starting point is 00:30:34 It's one of the things I hire for. I just think that 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, good product manager. And number two, when you get advice, really understand

Starting point is 00:31:02 where it's coming from and why 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 I'm investing for Moore and Buffett, yeah, okay, it's statistically significant. But that's not, most advice is like something happened to you once and you have regrets. I love that you're like, I don't, I don't know if I have a great answer, and 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 described yourself as an engineer.

Starting point is 00:31:34 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? 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 big o notation or complexity theory or, you know, study algorithms and, you know, why a randomized algorithm works 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 cash mist matters.

Starting point is 00:32:24 And just all these little, there's a lot more to coding than writing the code. 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 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 this software. We may do a lot more in the next few years we can't even imagine. But your job as the operator of that code manager generating machine is to make a product or to solve a problem.

Starting point is 00:33:17 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, you know, 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 system's 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 all, you know, we spent a lot of time design the news feed. And if you ever had like a really, really good designer and they showed you at the time a Photoshop mockup of the news feed, it was just all as beautiful.

Starting point is 00:33:57 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, but 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 of that stuff. And then all of a sudden you realize that like designing a news feed like Photoshop is the easy part. You need to actually design a system that produces a like both in content and visual design.

Starting point is 00:34:42 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. 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

Starting point is 00:35:09 realistic. But I say that because I think that, like, 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 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.

Starting point is 00:35:29 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 that story that we won't talk about when I rewrote Google Maps, everyone talks to that story because of like, and I think it's because of Paul Pouhai, who told it on some podcast and it sort of made the rounds. I think that's going to end up sort of this vestige of the past.

Starting point is 00:36:04 Like I almost like the human calculators at NASA before the computers were invented. Like, wow, a person was a calculator? well 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 um 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 study 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 uh especially at least sort of the

Starting point is 00:36:41 the foundations of it will continue to be the foundations of how we build software and understanding that when you're interacting, particularly with something that's smarter than you, producing code you might 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. That's such an great answer. There's this always sense of this binary, should I learn decode or not. And your point here is to understand how engineering works and how systems work and how what your code does and how to all interconnect, 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

Starting point is 00:37:18 there's going to, 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. 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 ended 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 with

Starting point is 00:38:07 the C programming language, Fortran, and a lot of sort of higher level programming languages, I think Fortran and then C. And we've sort of moved up the layers of abstraction. So no one does punch cards anymore, obviously. A few people write assembly language. Some people write C, some people write REST, 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

Starting point is 00:38:37 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 draggable map now. And I think a lot of people could do it. That was true R&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 mode is, is comic. narrow, but the product mode is quite large. I think that if the act of writing code is going from something that is very costly to

Starting point is 00:39:19 like the marginal cost of that going to zero, how many of the abstractions that we've built are based on, you know, human program or productivity? I think a ton. You know, like I always laugh that I assume Python is probably the most common generated code just because 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 the time. If you know the global interpreter lock and just slow and I've written a lot of high-scale web services and it's just quite slow.

Starting point is 00:39:56 And it's very hard to verify. It's not as bad as Pearl, but like 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 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 of it, like, we're not going to write a lot of code as people. We're going to be operating these code-generating machines. we probably don't care how ergonomic the programming language is. What we care about is when this machine generates code,

Starting point is 00:40:39 do we know that it did 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 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 hard. And if it's a very, like a million lines he 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

Starting point is 00:41:13 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, if 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 have obviously the simplest form of this is AI supervising AI and doing code reviews, and that's great.

Starting point is 00:41:50 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, 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 it's like the guy in the matrix with the green letters coming down. Like, how can I make something? So I as a operator with the code generating 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, you probably change the systems, you probably change all these things.

Starting point is 00:42:32 And you're probably going to bring to bear a lot of things. And what's really fun about is you can loosen a lot of constraints. Like coding is free. Okay, so that's neat. 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 think when we create something like that, it can really enable.

Starting point is 00:42:58 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. It's actually like building increasingly complex systems and actually changing them with agility. 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 as hard and ensuring the robust is hard. 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 very excited to see what emerges.

Starting point is 00:43:42 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 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 Kasyopo, CEO and co-founder of Vanta, joining me for this very short conversation. Great to be here, big fan of the podcast and the newsletter. Vanta is a longtime sponsor of the show, but for some of our newer listeners, what does Vanta do and who is it for? 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 certification. like SOC2 or ISO-2701.

Starting point is 00:44:30 Today, we currently help over 9,000 companies, including some startup household names like Atlassian, Ramp, and Lang Chain, start and scale their security programs, and ultimately build trust by automating compliance, centralizing GRC, and accelerating security reviews. 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.

Starting point is 00:44:54 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. We appreciate you for doing that. And you have a special discount for listeners. They can get $1,000 off Vanta at vanta.com slash lenny.

Starting point is 00:45:18 That's va.n-t-a-com slash lenny for $1,000 off Vanta. Thanks for that, Christina. Thank you. 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 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 is different maybe from previous generations to help them be successful in a world of AI abundance?

Starting point is 00:45:52 I don't know if I'm teaching them differently, but I'm really trying to. to encourage them to make AI part of their lives. I was reflecting actually when I took the AP calculus exams in 97, 98, AB and BC, I could use a graphing calculator. And I haven't done this research. I actually need to plug this into chat, GPT, before our conversation, but I'll do it after. Did the calculus exam change before and after?

Starting point is 00:46:23 They allowed the calculate in the exam. I assume it did. But essentially to when you allow the calculated in the exam, you need to make sure that none of the questions, you know, benefit people for having a calculator or not, which actually forces you to sort of rethink the problems to test calculus knowledge that don't benefit from like road arithmetic

Starting point is 00:46:44 or, you know, the other things you can do on a graphing calculator. I think that a lot of education is sort of doesn't presume you have a super intelligence 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 chat GPT. And maybe if you are skilled enough at prompting, maybe even your teacher won't know, it's

Starting point is 00:47:10 written by an AI. 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 exam. So I think a lot of the mechanisms we have to evaluate students are broken. by the existence of Chatchy, BT, and the like. So I think we're in a very awkward phase.

Starting point is 00:47:30 But 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 reading learner. I like to read. I didn't love going to lectures. I don't learn that well from them. I like to read the book.

Starting point is 00:47:52 and if you have a teacher who doesn't teach in your style, you can now go home and ask Chat ChbT to teach you in another mechanism. My kids use ChatTBTBT BT to quiz them before a test. You can use audio mode or chat mode. It's like better than Q cards. My daughter took home a Shakespeare book. She took a picture of page. She didn't understand.

Starting point is 00:48:16 And Chatty BT explained it to her way better than I would have as well. 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 the folks who have kids who have agency who have aspirations to learn something, I think you have what is the best combination of every teacher you've ever had. add in these models and you can use it. So with my kids, you know, my oldest thought I'd learned how to code and she was making a

Starting point is 00:48:59 website and every time she had a question for me, I would just make me use chat GPT. Not because I was trying to be an obnoxious father, but I'm like, she needs to learn that like to use this tool because it's amazing. And so I really am trying to have them learn how to use it constructively in their lives. But that all of that side, I just feel a ton of empathy for people. public school teachers right now. It's very hard because we're just with the technology is 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

Starting point is 00:49:37 agency, the opposite can also be true 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 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 chat GPT, don't get me wrong. But I think we've been able to figure out a way

Starting point is 00:50:04 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 think it's going to, and I, on the much more positive side, I went to public schools. I don't know if you did too.

Starting point is 00:50:22 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, a rich kid who can afford a tutor anymore to get tutoring. And, 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 there's a 11-year-old right now who's going to start. a really amazing company, you know, 10 years from now,

Starting point is 00:50:55 who's like chat GPT is going to be like their primary tutor that like led to that, that outcome. And I think that's pretty, pretty cool. 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 chat GPT account. I wonder how soon that's supposed to happen.

Starting point is 00:51:15 I think chat to be my personal take is a different from the performer to. I don't think mobile phones are great in school or great for kids, and I personally advocate for waiting a long time. But I think that chatGB is more like Google search. And 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 learn. And so I think the two are different. And I really think of AI fundamentally as a utility.

Starting point is 00:51:45 And I don't think a lot of parents before chatypte said, when should I let my kid use Google search? That's like a different type of tool. I think thinking it like that is the way I think about these technologies. And so is the form factor for your kids like an iPad or a laptop or some? Yeah, they use like the computer on the desk. Got it. All right.

Starting point is 00:52:02 Good tips. This is good for me to learn all these things as my kid. 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 Open AI,

Starting point is 00:52:24 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? I think there's three segments of the AI market that will end up fairly meaningful markets and then I'll end with how I think it's going to play out.

Starting point is 00:52:43 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 is a service market. 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, adapt, character, and others. 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, your fundraising runway to get to escape velocity.

Starting point is 00:53:28 And also the models deteriorate and 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 entrepreneurs should build a front of. tier model. That's my take. Unless you're Elon. Yeah. Oh, 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,

Starting point is 00:54:13 data platforms. It's e-val. 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? 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 is 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.

Starting point is 00:54:52 And every infrastructure provider is trying to differentiate it by moving up the stack, and you're right there. And so there's some real meaningful companies, as I mentioned, like Snowflake, Databricks, Conflin, 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 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.

Starting point is 00:55:31 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.

Starting point is 00:55:56 There's companies like Harvey that make agents for both a legal, paralegal profession, 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 offers 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.

Starting point is 00:56:32 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 really about the feature and function. I think that's where agents will go.

Starting point is 00:56:46 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 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, orchestrating an agentic process on top of the models is like, sounds really fancy and it's really hard and all that stuff.

Starting point is 00:57:20 You know, I'm pretty sure that's going to 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 about Microsoft as a service. You're going to 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

Starting point is 00:57:48 providing is going to sort of slowly evolve towards that. 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 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 or 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

Starting point is 00:58:31 exciting. Some of them are like super boring. But this is. is like how software market has evolved. I think we're to see something kind of similar with agents. It's not just going to be like these huge markets like we're in like customer service and software engineering. It's going to 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 going to be unlocked in the AI market. That is incredibly helpful. This makes me think about, I had Mark Beniof on the podcast. You guys were co-ceos, and he was extremely agent-pilled.

Starting point is 00:59:08 All you wanted to talk about was agent-in-force. Clearly, you are also very agent-pilled. I've never heard the term agent-pilled. I'm going to use that one. Clearly, you guys saw something that was just like, okay, we need to go all in on agents. This is the future. What is that 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? If you talk to an economist, like Larry Summers, who are on the Open AI board with me, they'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 and economy was in the 90s.

Starting point is 00:59:47 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. I was 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. My dad just retired. He was a mechanical engineer.

Starting point is 01:00:18 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 drafts people. So basically you'd take an engineering design, but you needed to do all the, the different vantage points and for all the different floors and to give to the contractor to do the thing now there are zero draft people at his company you just make the the design and first autocad and now rev 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

Starting point is 01:00:52 that exists it's just like you can it's just a design i that's true productivity gains right it's like the job of the mechanical engineering firm was to do a design. The drafting was like sort of this necessary output for the contractor, but it wasn't really adding value. It was just sort of like the supply chain change. If you look at the history of the software industry from the PC on, there's been meaningful productivity games, but just not nearly as meaningful as that first huge jump.

Starting point is 01:01:24 And I'm not smart enough to know exactly why, but it is interesting. 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 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

Starting point is 01:02:03 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 and you'll do these like somewhat convoluted, you know, things like, okay, it's like you're selling a sales thing. Okay, well, every salesperson sells, you know, five percent more, da, 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

Starting point is 01:02:49 our ways. You know, it's just hard to 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 accomplished a job, not only is it actually truly driving productivity in a very real way, but it's measurable as well. 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,

Starting point is 01:03:36 or I think more technologically, but just in terms of 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-premises 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. 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. Let me pull on that last threat. So we had Modavan on the podcast recently, pricing expert legend monetizing innovation author.

Starting point is 01:04:14 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 uses exactly which 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 shining 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? 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

Starting point is 01:04:54 customer experience so if you have a problem with your serious 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 sonos speakers a lot of different consumer brands and you know if you think about running a call center there's a cost for every phone call that you take most of it is labor But if you have, let's just say, a typical phone call is in the order between $10 and $20 U.S. dollars. Most of it, some of its software, some of its 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.

Starting point is 01:05:49 to have someone pick up the phone. So in our industry, basically we say, hey, if the AI agent 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 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. We do.

Starting point is 01:06:12 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.

Starting point is 01:06:56 I always use the analogy. Like right now, most of the coding agents are price per token or per utilization. But there's this famous story of an Apple engineer who had a bad manager who 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 predict. He famously went in with a report that had a negative number because I think he did a big refactory and 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.

Starting point is 01:07:28 Did it produce a poll request, you know, that was good? 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 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.

Starting point is 01:07:57 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.

Starting point is 01:08:23 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. We've been chatting a little bit about productivity gains. There's a lot of skepticism in the headlines these days of just like, what is they actually doing?

Starting point is 01:08:51 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 clear. Yes, this is working and a huge deal. I'm extremely bullish on the productivity games from AI,

Starting point is 01:09:19 but I do think the tools and products right now are somewhat immature and it's quite counterintuitive. So, for example, almost every software engineering firm I know uses something like Cursor to help their software engineers. Most people use Cursor right now as a kind of coding auto-complete, though they have a lot of agentic solutions, and there's a lot of, like, opening I has codex, and there's, you know, Cloud has,

Starting point is 01:09:44 I can't remember the anthropic products. So there's lots of agentic, you know, agents coming as well. One of the interesting things is because the technology is sort of immature, the code it produces often has problems. 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.

Starting point is 01:10:10 reviewing other people's code or particularly finding a subtle logical error in that 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 uh issues with your customers you could end up you know producing a lot of features 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. First, I think there's a lot of AI starts networking on things like code reviews.

Starting point is 01:10:50 I think this idea of self-reflection in agents is really important. Having AI supervised 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 it's just a math problem.

Starting point is 01:11:18 And it turns out that you can make something to generate code, you can make something to review code, 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. And essentially is context engineering.

Starting point is 01:12:04 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 and 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 root cause analysis and systems and say like, you know, how do we sort of go root cause every bad line of code

Starting point is 01:12:28 and actually give the right context and produce the right system so the models can do it today. Over time, that probably like 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 just magically get better. And I'm like, well, that will happen eventually. But if you want the gains now, you've got to put in the work.

Starting point is 01:12:50 I mean, that's essentially why applied AI companies exist. And the work is non-trivial, but you can do it. And so, you know, for customers using platforms like Sierra, yeah, yeah, 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?

Starting point is 01:13:20 And you're sort of let AI put the needles at the top of the haystack on your behalf, and I think that's really the way to optimize these systems. 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 had cursor rules? What's the actual approach? 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.

Starting point is 01:13:47 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 in codebase with the context available to these coding agents. and systems and fix it at the root is sort of the principle here. Got it. That is very cool. I hadn't heard people doing that.

Starting point is 01:14:08 Model context protocol. It 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 for people using Sierra? Yeah, our customers see anywhere between 50 and 90% of their customer service interactions completely automated, which I think is really exciting.

Starting point is 01:14:29 And we serve just a really, really broad range of customers. We serve the health insurance industry, the health care provider space, banks. You can actually refinance your home using an agent, one of our customers built on our platform, to the telecommunications industry, direct TV, Sirius XM, to a lot of retailers as well, which is really fun. Everyone from Wayfair to clothing retailers like Olokai and Chubby Shorts, what's really neat about 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 support in one of the big dating applications to, you know, helping you upgrade or downgrade your serious XM plan.

Starting point is 01:15:19 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 the 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. 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 4.6 out of five, which is pretty amazing.

Starting point is 01:16:05 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 five. You know, people are coming in with a problem in 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.

Starting point is 01:16:33 It can be over chat. It can be digital. It can be over the phone. 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 it's like for me, it's like the butcher at the grocery store.

Starting point is 01:16:50 I love to cook. He knows me. We talk. 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. Let me ask you one more question before we get there very exciting lighting around. There's a lot of founders struggling with go-to-market in AI with their AI apps.

Starting point is 01:17:12 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. I 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? 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 somewhere

Starting point is 01:17:50 famously Stripe and Twilio, where probably two of the original that did this exceptionally. And Essentially, the go-to-market motion 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. 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

Starting point is 01:18:32 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 seats with a credit card. And those work where your user and your buyer are the same person. So it works for small business software almost always because sole providers do everything.

Starting point is 01:19:07 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 was used 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 their credit card doesn't make sense because the person using it's not the person with the credit card and it just doesn't work. And then there's direct sales.

Starting point is 01:19:41 And direct sales had gone, I don't say out of fashion, but if I think of like the best direct sales companies, probably there's a lot of lineage from Oracle, but you think SAP, Oracle, whole service now sales force 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 um but if pl g 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 with a lot of AI companies, direct sales, come a 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 person.

Starting point is 01:20:37 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 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 quality of products of some of these direct sales companies, a lot of it sort of had gotten a bad name.

Starting point is 01:21:15 And I think I think a lot, I think I'm sort of thankful to see it coming back in a lot of the AI market. I feel like this message is something a lot of founders need to hear, especially founders that aren't from a business background that 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. Yeah. 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?

Starting point is 01:21:46 No, go ahead. Okay, let's do it. here we go. Welcome to our very exciting landing round. I've got five questions for you. Are you ready? Yeah, go ahead. What are two or three books that you find yourself recommending most to other people? I don't read a lot of nonfiction,

Starting point is 01:22:01 but probably we've 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 article so maybe buy the book and punch into chat dpti and get the summary um but by the book it's uh

Starting point is 01:22:25 clayton christensen talked about it but it's a really good framework for thinking about delivering value with their products um and i think it's a i definitely influenced me on the um actually one book i do recommend as um endurance which is the story of shackleton's trip to go to the south pole um 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 a remarkable. That's a true story.

Starting point is 01:23:08 And one thing he did a great job at is setting expectations for folks that joined that are that famous that ad that means. I don't know if that's true. It's like remarkable. That's true. Oh, it might not be true. I don't know. I mean, I interviewed. Yeah, he knows. God damn. Deepfakes, even back then. Okay. Do you have a favorite recent movie or TV show that you've really enjoyed? Yeah, haven't gone any new TV shows recently.

Starting point is 01:23:29 We just watched Inception with the kids, and they loved it, and made me appreciate Christopher Nolan. So, and what a cool movie, cool con. It's a type of movie when you watch your film, and you can have conversations for two days afterwards about it. So just a great film. I saw someone using, I think, V-O-3 to create their own Inception video. videos where the world's wrapping in on each other.

Starting point is 01:23:51 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? I'm really a big fan of Cursor. I think it's like change. I love creating software. And I'm excited, though, for agents. You know, I've been really excited.

Starting point is 01:24:09 I was very excited to see Codex from OpenA and other. So I think Cursor will be in its current form is a transition product. And I know they're working on agents as well. 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 I create 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. I mean the first time someone's actually mentioned cursor in this answer. So it might be the beginning of a trend. Michael Trell was on the podcast and he actually

Starting point is 01:24:46 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. Yeah. 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? The best way to predict the future is to invent it, which I think I attribute to Alan K of Xerox

Starting point is 01:25:12 Park, who invented a lot of the core abstraction. 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. I feel like many people like say this. I feel like you've actually done this so many times. You're living this motto.

Starting point is 01:25:35 Final question. We talked about you inventing the like button at Friend Feed. Were there other thoughts of what they would call it other than like? Was it just like obviously like or was there other thinking there? The context of this was before emoji. So if you read the comments on friend feed posts, at least 70% of them are cool or wow or yeah or neat. And one of the principal like uses of friend feed was to have discussions about things. You'd have a post and then a pretty fulsome discussion underneath.

Starting point is 01:26:11 And it was a very compared to, you know, Twitter and others, 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 actual comments as opposed to acknowledgments that you read the thing. 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.

Starting point is 01:26:42 And there she denies remembering this. There's an Anna Yang, now Anna Mueller, who has worked at the company, she hated it. She said, like, if I look at like hearts on every post, I'm going to vomit. It's just too, it's like too much, you know. And it also was interesting, like we were simulating. 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.

Starting point is 01:27:11 And that's why it was hard to translate because it was subtle. 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 start off the microography. 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.

Starting point is 01:27:36 Wow. I've never heard the story before. It makes me think about LinkedIn now. 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 that's very much. They have a lot of features. 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 finding online if they want to reach out,

Starting point is 01:27:56 maybe go see if they want to work at Sierra. And how can listeners be useful to you? If you want an AI agent help with customer service, go to cedar.aI. If you want to apply here, Sierra.a.i. slash careers, where we have offices in San Francisco, in New York, Atlanta, and London, and our hiring pretty aggressively in every department. So please reach out of your interest. And how can listeners be useful to you? Is it tryout Sierra? Anything else there? Yeah. Try out Sierra. I'm a single issue voter. Stay here on the message. I love it. Yeah. Brett. Thank you so much for being here. Yeah. Thanks for having me. Bye, everyone.

Starting point is 01:28:32 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 lenniespodcast.com. See you in the next episode.

Lenny's Podcast: Product | Career | Growth - He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more

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