He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more

You are CTO of Meta, you are 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.

Makes me think about I had Marc Benioff on the podcast. You guys were CO CEOs. He was extremely agent pill.

It's so hard to sell productivity software which I learned 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.

They sort of gave me another shot to do a 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 could do with that?

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 News feed which he sold to Facebook. He then became CTO at Facebook. He then started a productivity company called Quip which he sold to Salesforce for $750 million. He then became co CEO of Salesforce. He's also currently chairman of the board at OpenAI. At one point he was chairman of the board at Twitter. Today he's co founder and CEO of Sierra, an AI startup building agents to help companies with customer service, sales and more. In our conversation we cover so much ground including what skills and mindsets have most helped Brett be so successful in so many roles. 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, why where the biggest opportunities remain for startups, lessons on pricing and go to market in AI, the story behind the like button and so much more. This is a truly epic conversation with a legendary builder. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of incredible products including replit, lovable, bolt, N8N, linear, superhuman, descript, Whisper Flow, Gamma, Perplexity, Warp, Granola, magic patterns, Raycast, JetPRD, Mobin, and more. Check it out at lennysnewsletter.com and click bundle. With that I bring you Brett Taylor this episode is brought to you by Coderabbit, the AI code review platform, transforming how engineering teams ship faster with AI without sacrificing code. Quality code reviews are critical but time consuming. Coderabbit acts as your AI copilot, providing instant code review comments and potential impacts of every pull request. Beyond just flagging issues, coderabbit provides one click fix suggestions and lets you define custom code quality rules using astgrep patterns, catching subtle issues that traditional static analysis tools might miss. Coderabbit also provides free AI code reviews directly in the IDE. It's available in VSCode, Cursor, and Windsurf. Codearabbit has so far reviewed more than 10 million PRs, installed on 1 million repositories, and is used by over 70,000 open source projects. Get Coderabbit for free for an entire year at Coderabbit AI using code. Lenny that's Coderabbit AI. This episode is brought to you by Basecamp. Basecamp is the famously straightforward project management system from 37signals. Most project management systems are either inadequate or frustratingly complex, but Basecamp is refreshingly clear. It's simple to get started, easy to organize, and Basecamp's visual tools help you see exactly what everyone is working on and how all work is progressing. Keep all your files and conversations about projects directly connected to the projects themselves 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 37signals has been up to and I'm really excited to be sharing this with with you. Sign up for a free account@basecamp.com Lenny get somewhere with Basecamp Brett thank you so much for being here. Welcome to the podcast.

Thanks for having me.

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 I call 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 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 1 billion web pages to 10 billion, which was a big deal at the time. It 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 it was both an opportunity to do something for Google, but I was also being pretty scrutinized just as a young new product manager. And the premise given to me was work on local search. At the time, the Yellow Pages was still dominant. And while Google was really good at searching the Web, it wasn't really good for finding a plumber or a restaurant, just because it wasn't really a huge part of the Internet at the time. So this content wasn't necessarily on the Internet. And even if it was, it was, you really needed a different. You didn't really want to find, you know, plumbers in Manhattan. You want to find plumbers in San Francisco if you're me. And so it was a kind of a. Both a technical problem and a product problem and a content problem. We launched the first version of that product that I was the product manager for was called Google Local. 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. We had a standalone site@local.google.com and it was actually, it was an important enough initiative that actually there was a. On the Google homepage, it had, you know, web images, and local was up there as well. So, you know, it's got top billing. I mean, you could put almost any link on the Google homepage and get a lot of traffic to it. And despite that it didn't do that well. And to not do that well with a link from the Google homepage is like kind of embarrassing. You know, it's, it's, I mean there's not, there's not much one can do other like more than giving you that kind of traffic to give you an at bat as a, as a product leader, a product manager. And the product was fine, it worked, but it really wasn't differentiated. And I think in many ways, again, I think I've had these reflections more sense than at the time that I had some of the time. But why use this instead of Yahoo Yellow Pages? But more than anything else, why use this instead of yellow Pages? It was sort of a digital version of something that had come before. Had a pretty tough product review with Marissa and Larry and others and it was fine. I wasn't about to get fired or something. But it was the, I don't 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 dejected from going from sort of a hot shot new PM to a new thing. So you spent a lot of time thinking about how can you make something that's just much more compelling and not just sort of a digital version of the yellow 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 Lars and Jens Rasmussen who had been working on this Windows mapping product and we sort of got them into the company and started exploring this space. And 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 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 product just because there's sort of feature and functionality. And then there's like, why should I use this thing in the first place? And it was notable. There's a couple of interesting moments. I mean, when we launched Google Maps, we got about 10 million people using 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 on the same day. Everyone wanted to look at the top of their house when the imagery came out. And it was really interesting because there's so many subtle product lessons in there. 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 sort of answers the question for a new customer, like, why should I give this a time of day? And so really disassembling the LEGO set and reassembling it into something new, rather than just digitizing what was there before. Certainly that was the lesson, I think, in Google Maps, it really was native to the platform in a way that like a paper map couldn't be. And 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. We run Saturday Night Live, which is the coolest thing. Andy Samberg in, I think it was called Lazy Sunday, you know, rapped about Google Maps, and Lars and I were texting each other.

We did it.

We're on Saturday Night Live. Mission accomplished. And it was also showing that Azure Theater is thinking about products. There's why you decide to use a product and then what is the enduring value? And those are deeply related, but not all the same thing. And I just learned so many lessons I took with me for every subsequent product that I worked on.

That is an awesome story. 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 massive failure with, like the CEO of Google, Marissa Meyer. Just like, Brett, you screwed up. This is. And it was like such a big bet. So one, just like it's possible to succeed as you have succeeded in spite of a massive failure like this. And then some of the product lessons you shared, just to highlight a few of these things, because I think this is great, is Just you will often not win if you just make something that's kind of a better copy of something else. What you want to look for is something that is an entirely new experience, something that's differentiated, something that's a lot more compelling. Let's flip to talk about what you've learned from actually being very successful at a lot of things. 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 are CTO of Meta. You are co CEO of Salesforce. You're also CPO and COO at Salesforce. At Google, you joined as an associate product manager where you famously. You didn't mention this, but you built Google Maps on the weekend. We're not going to talk about that. You're chairman of the board at OpenAI. You were chairman of the board at Twitter. You also founded three different companies. One social network, one productivity docs company called Quip, and now Sierra. Fun fact at FriendFeed, you invented the like button. I don't know if people know that. And also just the newsfeed. I'll just throw that out there to you you some credit. So you're basically an associate product manager, an IC product manager, an engineer, cpo, coo, cto, CEO of three different companies, including a public company. Very rare that somebody is successful at all these types of roles and all these levels. So let me just ask you this question. What mindset or habits or just ways of working have you worked on building in yourself that you think 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 of me largely as a product 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, I'm not sure they think of me as a, as an engineer at all, even though, you know, 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, 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 confluence of technology and capitalism to produce just incredible outcomes for customers. And as a consequence, I think to really build something of significance, I think to be a great founder, you really need to be able to not have such a ossified view of your identity that you can't transform into what the company needs you to be at that point. And every founder you'll talk to 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 your customers. You have to have good engineering. I mean, if you're building a technology company, the technology comes first. It's why this industry is so transformative. I probably credit, and I've told this story before, but I'm very grateful for her, but I probably credit Sheryl Sandberg for really changing the way I approach new jobs. The story, and I might be embellishing a little bit, but I think it's broadly accurate. So I had just become the chief technology officer of Facebook. And when I first got the job, it was sort of the flavor of cto, where I had relatively small group reporting into me, but contributed almost as like a very senior kind of architect 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 owner and me, and I was essentially running our platform and mobile groups, products, design, engineering. So I went from, you know, a handful of reports to, like, I don't know, over a thousand or something. It was a. It was a big group, and it was the largest, you know, management job I had. 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. The presentation I got didn't make my quality bar. And I was Editing it and sort of griping about. She sort of pulled me into a room and kind of gave me a talking to, like, a little bit about holding my team to as high of a standard as I have. If someone wasn't, you know, meeting my expectations, you know, what was my plan to, like, manage the math of the company and. Or, you know, just like, kind of giving me management 101 and. And she. She's a remarkable mentor in the sense she can kind of give you feedback that's very direct and, like, often a bit uncomfortable. 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? 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 limiter that 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, I'm the boss, I should know, focus on what I want to focus on instead of thinking about, okay, I'm running the mobile and platform teams at. At Facebook. What's the most important thing to do today to make our mobile, mobile, and. 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 changed an organization and it turned out to be more successful, I derived a great deal of joy from seeing that success. Our developer platform had a lot of partners, and there was an issue there. And I'd spend time on partnerships, and it worked. And our platform became healthier, the partner became more successful. 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. That conversation led to my 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 telling you, 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 product, sometimes it's engineering, sometimes it's sales. And I've become much more self reflective, just about what is important to work on. And I have become much more receptive to doing things that I previously would have said aren't my favorite things to do because I derive so much joy from having an impact that I enjoy a lot more things now. And so I really credit Cheryl. So grateful. And actually it's interesting, 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 Sheryl Sandberg giving them advice and that changing their life. 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. And, and 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. Well, you better hope you're right because if you orient your strategy around fixing that problem and you're wrong, your company's 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 cost 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. 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 a usability study is rarely correct. It often is related to what the truth is, but it's very important to get right. And so I think one of the things I've observed with first time founders in particular is 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 jokes like the dead cat balance of a consumer product. This next redesign will fix all of our problems. I don't know if it's ever, ever worked. And then you, if you. I met a lot of entrepreneurs who are like, come from sort of a business development background. They're always thinking about partnerships and you know, oh, we just get this partnership done for this distribution channel, everything's going to change. And I think it's really important when you're a founder to be self aware that you will naturally, subconsciously pick the thing that is your strength, your superpower, as a solution to more problems. And in fact if that you think that's a solution to your problem, it may be right. But you probably by default should question it. Like if you think the thing that you've been doing your whole career is the way to fix your problem, it's at least 30% likely that you've chosen that because of comfort and familiarity, not truth. And so I think it's like one of the skills I think is it really goes around to like, do you have a good co founder, do you have a good 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 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. Being able to answer it accurately is actually the hard part.

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 or you actually ended up, oh.

Yeah, we're spending this whole thing on my failures, but I'm fine with that.

You've had too much success.

Frontview was my first company. At our peak, we had 12 employees. 12 of the best people I've ever worked with. Started the company with Jim Norris, who's an engineer I've known since Stanford, and Paul Buhite and Sanjeev Singh, who. Paul started Gmail. Sanjeev was the first engineer on Gmail. So we had the Google Maps people and like Gmail people was like pretty awesome founding team. We made a social network, as you said. We sort of invented a lot of concepts that became popular in the newsfeed. We invented the like button. It was really neat. It was a fun time. We were only really popular in Turkey, Italy and Iran. And at one point we were blocked in Iran. So we're only popular in Turkey and Italy and Silicon Valley to this day. Actually, a lot of folks in Silicon Valley are like, I love, love friend feed. I'm like, that's awesome. But wasn't really a successful business. There was a. We were a follower oriented social network, not a friendship oriented social network, which meant a lot of our content was more like X or Twitter than it is Facebook in that respect. And a lot of sharing, newspaper articles, interests, scientific communities, things like that. And there was a period when Twitter, which was one of our competitors at the time, there was a lot more social networks at the time. I probably screwed this a little bit. I think Obama, Ashton Kutcher and like Oprah Winfrey all went on Twitter in a summer and we just got our ass kicked. 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 worth following, you know, like. And instead, you know, we were exclusively focused on polishing the product. And we actually, I think, you know, at our sort of peak of popularity, we were very confident. Just, you know, I think it was a time when like Twitter had the fail whale and it was down half the time and people couldn't even use it. And you Know, we, our product, we were innovating faster, we had more features, 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. And it was an example of, I think somewhat famously, not a lot of great entrepreneurs have come out of Google because once you, like Google was so successful. I think it's hard as a product manager to sort of see like distribution and all product design and even business model when you have AdWords and, you know, 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, 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, accumulated these skills over time. But when I say the hard part of that question is answering it correctly is it's hard when you don't have experience in something to have intuition in it. So I think if there's probably a structural flaw, it wasn't that. I don't know if I could have figured out how to reach out to Ashton Kutcher if I wanted to. It's not like he's on my Rolodex, but I probably wasn't soliciting advice from the right people. You know, I think that what's great about the technology industry is there's a lot of advice. Choosing whom you listen to is actually quite difficult. But I think we were somewhat myopic. You know, we're kind of in our own little world creating this product. And we weren't asking people to like, from the outside in, to say, like, what. What are you seeing that could go wrong? What are you seeing that could go right? What are you seeing in the industry that we're not doing that you think we might want to do? And this is why boards are important. This is why 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 giving good advice.

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, ignore this person, but list to this person, yeah, that one's tough.

It does come down to good judgment and being judged 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 it's funny, with all the podcasts out now, if there's topics I know a lot about, sometimes the most eloquent, confident statements about things I know a lot about are the least accurate and it sounds extremely persuasive. 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 two year old kid, you know, 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 they'll say never do this or always do that. And it's because they had one experience where that something backfired or something could have gone better if they had done it. So it's a useful anecdote. But if you don't ask why and understand they had one experience and here's what happened, it can come across as a rule when in fact it's anecdata. And if you ask advice of three people and they all have very similar interactions, you can create kind of like a first principles framework from which that advice emerges. 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. It's one of the things I hire for, I just think if 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 a, as an entrepreneur, as a product manager. Like if you made a bad decision, spend time reflecting on it. Like number one, and really try to understand why and try to like always improve your judgment. I think at the end of the day that is why you are a good entrepreneur, a good product manager. And number two, when you get advice, really understand where it's coming from and why so that you can create sort of your own independent view of where that advice came from and recognize that no one's advice is statistically significant or very rarely is it. I mean, if you're getting like advice on investing for Warren Buffett, yeah, okay, it's statistically significant. But that's not. Most advice is like something happened to you once and you have regrets. Something.

So 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 describe yourself as an engineer. I know, I heard you code to relax still. Let me just ask you this question, something a lot of people in college are thinking about. Do you think it still makes sense to learn to code? Do you think this will significantly change in the next few years?

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 study algorithms and why a randomized algorithm works and why to algorithms with the same sort of big O complexity, one can in practice perform better than others. And why a cache miss matters. And just all these little. There's a lot more to coding than 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 a code generating machine. I think that is the future of creating software. But I think operating a code generating machine requires systems thinking. And I think that computer science, there are other disciplines as well. But computer science is a wonderful major to learn systems thinking and at the end of the day, AI will facilitate creating 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. And you really need to have great systems thinking. And you're going to be managing this machine that's doing a lot of the tedious work of making the button or connecting to the network. But as you're thinking of the intersection of a technology and a business problem, you're trying to affect a system that will solve that problem at scale for your customers. And that systems thinking is always the hardest part of, of creating products. I'll just give you like, it's, it's a cheesy, simple example, but I think it's representative. At Facebook, we would always, you know, we spent a lot of time designing the newsfeed, and if you ever had, like a really, really good designer and they showed you at the time a Photoshop mockup of, of the newsfeed, it was just all as beautiful. The photos, the family was happy and the photo was like a perfect photo. And the posts were like, all perfectly grammatically correct and of a completely normal length. And the comments and the, you know, there was the like button. Everything was just perfect. And then you'd, like, implement that design and you'd look at your own newsfeed and it looked like shit because it turns out, like, not everyone's photos were made by, like a professional photographer. The posts were all these different lengths, the comments were like, you know, you suck, and like, all that stuff. And then all of a sudden you realize that, like, designing a newsfeed, like Photoshop is the easy part. You need to actually design a system that produces both in content and visual design, a delightful experience given input you don't control. And that's a system. I mean, it's sort of a design and it's a system. What we did practically, I'm sure it's changed a lot since I left in 2012, but we made a system. So, you know, designers had to show their newsfeed designs with real newsfeed data. That was messy rather than, you know, anything artificial, because I think it forced the process to be more realistic. But I say that because I think that, like, whether AI is writing code or doing the design or doing all these other things, like, you need to learn how to have a system in your head. You need to understand the basics of what's hard and what's easy and what's possible and what's impossible. And AI can help you do that too, by the way. But I do think that's a really useful skill. I think in general, with the advent of AI agents and AI approaching super intelligence 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 about that story because like, and it's, I think it's because of Paul Buche who told it on some podcast and they'll sort of made the rounds. I think that's going to end up sort of this vestige of the past. Like I almost like the human calculators at NASA before 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. Certainly not like valuable in the future. And that's okay. So I think we need to have a really loose view of it. But the idea that you shouldn't study these disciplines is sort of like people say I don't want to study math because I'm not going to use it in my career for X. Well, studying math is quite important. Like it teaches you how to think, it teaches you like how the world works. Physics, math, and I think computer science especially at least sort of the, 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 may not completely understand how you constrain it and how you get it to produce these outcomes. I think it will require a lot of sophistication actually.

That's such a great answer. There's this always sense of this binary, should I learn to code or not? And your point here is learn to understand how engineering works and how systems work and how what your code does and how it all interconnects, but the way you actually do the coding at your desk will change significantly. This reminds me of something you mentioned on a podcast recently. This idea that you think there's 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 40 years of computers, maybe more is we created the hardware for computers Then we created punch cards, which is the way in the late 70s you would tell a computer what to do or maybe mid to late 70s. Then we 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 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 few people write assembly language, some people write C, some people write Rust, but a lot of people write Python and Typescript and things like that. And as we've invented more and more abstractions we've made it easier to do high leverage things. So you know, I always look, if you look at how remarkable Google was back in the day or Google Maps, like you could probably give a lot of REACT programmers the task of make a draggable map now. And I think a lot of people could do it. That was true R and D. You know, back in the day when Salesforce was created in 1998, just putting a database in the cloud was hard. And that was just like that alone was a technical moat that is now trivial with Amazon Web Services. And that technical moat is comically narrow, but the product mode is quite large. I think that if the act of writing code is going from something that is very costly to the marginal cost of that going to zero. How many of the abstractions that we've built are based on human programmer productivity? I think a ton. I always laugh that I assume Python is probably the most common generated code just because of how much it's in the training data. And data scientists love Python and I love Python too. It's such a comically bad thing for AI to generate just because it's one of the most inefficient programming languages of all time. If you know the global interpreter lock and just slow. And I've written a lot of high scale web services and it's just quite slow and it's very hard to verify. It's not as bad as Perl, but if you have a big Python program, how many errors will you find at runtime versus before releasing it? 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 let's just postulate and I'm not sure this will be completely true that like we're not going to write a lot of code. As people, we're going to be operating these code generating machines. We probably don't care how ergonomic the programming language is. What we care about is when this machine generates code, do we know that it did? 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 Rust I think is interesting because if I, if I asked you to look at a C program and say, does it leak memory? You probably couldn't do it that well just because it's really hard. And if it's a million line C program, that's 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 because it has compile time memory safety, just the act of compiling successfully tells you that's true. I think we need more things like that because if an 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. Certainly self reflection is a really effective way of improving the robustness of an AI system. But I do think if you, you know, if it doesn't matter how tedious it is to write the code, you could probably layer on some techniques that are sort of out of fashion, like formal verification, unit testing, other things. And if you layer all these on, I'm sort of thinking about it as I, as a. It's like the guy in the Matrix with the green letters coming down like how can I make something? So I, as a operator of the code generating machine, can produce 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 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, 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 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. You know, if you look at the famous like Netscape 1 to Netscape 2 rewrite, like somewhat. A lot of people attribute that to part of their failure against Internet Explorer. It's like making these things is not hard, like maintaining them is hard and ensuring 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.

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 Cacioppo, 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 certifications like SOC2 or ISO2701. Today we currently help over 9,000 companies, including some startup household names like Atlassian, Ramp and LangChain, start and scale their security programs and ultimately build trust by automating compliance, centralizing GRC and accelerating security reviews. 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. That is very much our experience, but before the company and to 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@vanta.com Lenny. That's V A N-T A.com 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?

I don't know if I'm teaching them differently, but I'm really trying to encourage them to make AI a part of their lives. I was reflecting, actually, when I took the AP calculus exams in 97, 98 A, B and BC. I could use a graphing calculator. I haven't done this research. I was meaning to plug this into ChatGPT before our conversation, but I'll do it after. Did the calculus exam change before and after they allowed the calculator in the exam? I assume it did, but essentially, when you allow the calculator in the exam, you need to make sure that none of the questions benefit people for having a calculator or not, and which actually forces you to sort of rethink the problems to test calculus knowledge that don't benefit from, like, rote arithmetic or, you know, the other things you can do on a graphing calculator. 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 ChatGPT. And maybe if you are skilled enough at prompting, maybe even your teacher won't know it's 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 ChatGPT and the like. So I think we're in a very awkward phase, 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 a reading learner. I like to read. I didn't love going to lectures. I don't learn that well from them. I like to read the book. You know, if you have a teacher who doesn't teach in your style, you can now go home and ask ChatGPT to teach you in another mechanism. I my kids use ChatGPT to quiz them before a test. You can use audio mode or chat mode. It's like better than cue cards. You. My daughter took home a Shakespeare book. She took a picture of page she didn't understand and chatbots 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 like 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 and these, these models and you can use it. So with my kids, you know, my oldest daughter learned how to code and she was making a website and every time she had a question for me, I would just make her use ChatGPT. Not because I was trying to be an obnoxious father, but I'm like, she 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 that said, I just feel a ton of empathy for public school teachers right now. It's very hard because we're just with the 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 because these technologies amplify agency. The opposite can also be true if you are a student trying to not learn something. I think these tools probably provide a lot of mechanisms to avoid it as well. And so I think this is 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 chatgpt, don't get me wrong. But I think we've been able to figure out a way to conform 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. And I think it's going to on the much more positive side. I went to public schools. I don't know if you did too. You end up with some pretty bad teachers at times and now you have an outlet. You don't need to be the rich kid who can afford a tutor anymore to get tutoring. If you are a kid who excels in math and your school doesn't have advanced statistics classes, well, now you do. So I think this is just an incredibly democratizing force with kids who have agency and I think that's very exciting. I'm hopeful that this, there's a 11 year old right now who's going to start a really amazing company, you know, 10 years from now who's like Chachi Beat 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 him a phone, when to give them, I don't know, Snapchat, whatever kids use these days. And then it's like when to give them their first ChatGPT account. Oh no, I wonder how soon that's supposed to happen.

I think ChatGPT, my personal take is it's different than the former two. 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 ChatGPT 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. And I don't think a lot of parents before chatgpt said when should I let my kid use Google Search? That's a different type of tool. And I think 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 something?

Yeah, they use the computer on the desk.

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

I think there's three segments of the AI market that wind up fairly meaningful markets and then I'll, I'll end with how I think it's going to play out. So first is the frontier model market or foundation model market. I think this will end up the small handful of hyperscalers and really big labs, just like the cloud infrastructure as a service market. So, and the reason for that is that creating a frontier model is entirely a function of capex and you need a company with huge amounts of capex capacity to build one of these models. All of the companies that were startups that tried to do this have already been consolidated, almost all of them Inflection, Adept, 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 fundraising Runway to get to escape velocity. And also the models deteriorate in value fairly quickly as an asset class and so you need just a lot of scale to make a return on the investment for a model that deteriorates in value so quickly. So I think that's going to end up probably no entrepreneur should build a frontier model. That's my, my.

Unless you're Elon.

Yeah, 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 he's the greatest of all time for a reason. And he's different. You don't compare yourself to him. The other part of the market is the tooling and I think there's a lot of folks selling pickaxes in the gold rush. This is data labeling services, this is data platforms. It's evaluating 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 as a service market and the cloud tooling market, like the confluent And Databricks and Snowflake. A lot of the Amazon and Azure and others have competing products in those areas because they're very adjacent to the infrastructure itself. And every infrastructure provider is trying to differentiate by moving up the stack. And you're right there. And so there's some real meaningful companies, as I mentioned, like Snowflake, Databricks, confluent others, but there's a lot of others that were sort of obviated by technology from the infrastructure providers themselves. So those companies probably are the most at risk for, you know, a developer day from 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, your tool, but the question will be if or when is probably the right way to think about it. One of these large infrastructure providers introduces a competitor. Why will people continue to choose you? So it's, it's a good market, but it's a little bit close to the sun, as I said. 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 Cira, we help companies build agents to answer the phone or answer the chat for customer experience and customer service. There's companies like Harvey that make agents for both the legal paralegal profession, antitrust reviews, reviewing contracts, etc. Etc. There's companies that do content marketing, there's companies that do supply chain analysis. I think this is sort of like the software as a service market. They'll probably be higher margin companies because you're selling something that achieves a business outcome as opposed to being a byproduct of the models themselves. They will almost certainly pay taxes down to the model providers, which is why those model providers will end up extremely large scale, but probably slightly lower margin. And I think the market for them will be probably less technical. I mean, if you think about the purest form of software as a service, it's not like you ask what database do you use, it's really about the feature and function. I think that's where agents will go. I think it's going to be more about product than it is about technology over time. Just going back to my metaphor, in 1998 when Mark and Parker started Salesforce, just getting that database running in the cloud was technical achievement. Nowadays 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 getting an orchestrating an agentic process on top of the models is like, sounds really fancy and it's really hard and all that stuff. 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 like software 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 business outcomes that you're driving. Are you generating leads for a sales team? Are you minimizing your procurement spend? Whatever value you're providing, it's going to sort of slowly evolve towards that. I, I'm very excited. I don't think startups should probably build foundation models, I think, but I just, 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 are the big, big boy ones like Microsoft, Amazon, Google, all that. But like the next 50 are all SaaS companies and they're like, some of them are very exciting, some of them are like super boring. But this is like how the software market has evolved. And I think we're going to see something kind of similar with agents. Like 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 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 deeply. And I think that's where 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 Marc Benioff on the podcast, you guys were co CEOs and he was extremely agent pilled. All he wanted to talk about was agent force. Uh, clearly you are also very agent pilled.

What is it that I've never heard the term agent pilled. I'm going to use that one.

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

If you talk to an economist like Larry Summers, who are on the OpenAI 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 in the economy was in the 90s. And I think a lot of folks I talk to think it was actually that very first wave of computing where people made ERP systems and just put accounting into computers and databases, even mainframes. We're not talking the PC era because it was such a huge step up. Just imagine 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. And he was talking about when he first started his career in the late 70s. And you went into a mechanical engineering firm, the majority of the firm were draftspeople. So basically you take an engineering design, but you needed to do all the different vantage points and for all the different floors and to give to the contractor to do the thing. Now there are zero draftspeople at his company. You just make the design in first AutoCAD and now Revit, and it's a 3D model and the drafting has actually been eliminated. It's just not a thing one needs to do anymore. The actual design and drafting is not a thing that exists. It's just like you can. It's just a design. 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 gains, but just not nearly as meaningful as that first huge jump. And I'm not smart enough to know exactly why, but it is interesting. Like there has been 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 to actually Accomplishing a job autonomously. And as a consequence, just like you don't need draftspeople in a mechanical engineering firm, you just won't need someone doing that thing anymore. It means they can do something else that's higher leverage and more productive. And you can actually a smaller group of people can accomplish more and truly drive productivity gains in the economy. And 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 a value discussion and you'll do these somewhat convoluted, you know, things like, okay, it's like you're selling a sales thing. Okay, well if every salesperson sells, you know, 5% more, da da da da, and you should pay us a million dollars, like, you know, and it's roughly that conversation. And it's so unattributable, you know, especially. And it's why it's so hard to sell productivity software, which I learned our ways, you know, it's just hard to know, you know, what's the value of making everyone 10% more productive. Did you actually make them 10% more productive or did something else change? You don't really know all these things. But now with an agent actually accomplishing a job, not only is it actually truly driving productivity in a very real way, but it's measurable as well. So all those things combined means I think this is actually like a step change in how we think about software because it does a job autonomously, which is like sort of more self evident a productivity driver. It's measurable, so people value it differently as well. Which is why I also believe in outcomes based pricing for software. And all of that combined to me it feels like as significant as the cloud or I think more technologically. But just in terms of how it transforms the business model of the software industry where there's going to be a before and after. 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. 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 thread. So we had Matavan on the podcast recently, pricing expert legend, monetizing innovation author, and he talked about pricing strategy for AI companies. And he was very much in your camp of if you can, you need to price your product as an outcome based product. And the access uses exactly what you shared, which is you can do that if you can attribute the impact. And it's autonomous, it's running on its own, maybe just chalk. 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 Cira, we help companies make customer facing AI agents primarily for customer service, but more broadly for customer experience. So if you have a problem with your Sirius XM radio, you'll call or chat with Harmony, who's our AI agent. If you have ADT home security and your alarm doesn't work, you can chat with their AI agent. Sonos speakers, a lot of different consumer brands. And if you think about running a call center, there's a cost for every phone call that you take. Most of it is labor costs. But if you have, let's just say a typical phone call is anywhere between 10 and 20 US dollars, most of it, some of it's software, some of it's telephony, but a lot of it is just like the hourly wage of the person answering the phone. So if an AI agent can take that call and solve it, that is in the industry often called a call deflection or a containment. And that essentially means you Saved Call it $15 because you didn't have to have someone pick up the phone. So in our industry, basically we say, hey, if the AI agent 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. 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, 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. 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 a Apple engineer who had a bad manager who's like, how'd you report how many lines of code code you wrote every day? Which every engineer in the world knows is an idiotic way to measure productivity. He famously went in with a report that had a negative number because I think he did a big refactoring, deleted a bunch. And it was his way of saying, like you to the man, I think tokens are similar. You know, like, yeah, you used a lot of tokens, like good for you. Did the, you know, did it produce a pull request? You know, that was good. And, and I think that's the whole point of all this. I don't think. I think there's a huge difference between outcomes based pricing and usage based pricing, because especially in AI, they're not necessarily even correlated. And you could have a long phone call, not solve the customer's problem and they give you a negative review online and call the call center again, all that effort was for nothing. In fact, you might have added negative value. And so I am a huge believer in this. And what's fun about it is it really just aligns. I think every technology company aspires to be a partner, not a vendor. And I think at Sierra we are truly a partner to every single one of our customers because we're all aligned on what we want to achieve. And I think that is really where software, the software industry should go. It requires a lot of different shape of a company. You just have to have, you have to be able to help your customers achieve those outcomes. You know, you can't just throw software the wall because you'll never get paid if it doesn't. You have to, you know, really just your orientation becomes so extremely custom 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, in the headlines these days of just like, what is AI actually doing? Like, is it actually helping people be more productive? There was a recent study, actually, I don't know if you saw where they showed engineers were less productive with AI because it was just putting them in different directions. They had to research all what's going wrong here. And so I think CX is a really good example where you clearly are seeing gains. Are you seeing actual gains at your company or any other company you work with outside of CX in terms of productivity? That is like, clearly, yes, this is working and a huge deal.

I'm extremely bullish on the productivity gains from AI, but I do think the tools and products right now are somewhat immature and it's quite counterintuitive. So for example, I. Almost every software engineering firm I know uses something like cursor to help their software engineers. Most people use cursor right now as a kind of coding autocomplete though they have a lot of agentic solutions and there's a lot of like OpenAI has codecs and there's, you know, Claude has, I can't remember the anthropic product. So there's lots of agentic, you know, agents coming as well. One of the interesting things, because the technology is sort of immature, the code it produces often has problems. 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 look at and edit and fix code you wrote. Reviewing other people's code or particularly finding a subtle logical error in someone else's code is actually really hard. It's actually much harder than editing code that you wrote yourself. So if the code produced by a coding agent is often incorrect, it actually can take a lot of like cognitive load and time to fix it. And in fact, if you end up producing lots of, you know, issues with your customers, you could end up, you know, producing a lot of features, but actually like, you know, mucking up the machine a little bit and having something that's not ideal. There's a couple of techniques I think are interesting. Like first, I think there's a lot of AI starts now working on things like code reviews. I think this idea of self reflection and agents is really important. Having AI supervise the AI is actually very effective. Just think about it this way. If you produce an AI agent that's right 90% of the time that's not that great. But how hard would it be to make another AI agent to find the errors. The other 10% of the time, that might be a tractable problem. And if that thing's right 90% of the time, just for argument's sake, you can wire those things together and have something that's right 99% of the time. So it's just a math problem. 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. I'm very excited about that. The other thing though is root cause analysis. So we have an engineer at Cira 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 the next time cursor will produce the correct code. And essentially is context engineering. 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, how do we sort of go root cause every bad line of code and actually give the right context and produce the right system so the models can do it today over time that probably less necessary and you'll have less context engineering necessary to do it. But you really have to think of this as a system. And I think people are sort of like waiting for the models just magically get better. And I'm like, well, that will happen eventually, but if you want the gains now, you got to put in the work. I mean, that's essentially why applied AI companies exist. And the work is non trivial, but you can do it. And so, you know, for customers using platforms like Sierra, yeah, AI agents aren't perfect, but we're creating a system that lets customers create a virtuous cycle of improvement. If you want to go from a 65% automated resolution rate to 75%, we have a billion tools to let AI help you do that, identify opportunities for improvement, figure out why people are frustrated, what new capabilities can we add to our agent to improve the resolution rate? And you 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 add cursor rules? What's the actual approach there?

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

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

Yeah, we have our customers see anywhere between 50 and 90% of their customer service interactions completely automated, which I think is really exciting. And we serve just a really, really broad range of customers. We serve the health insurance industry, the healthcare provider space banks. You can actually refinance your home using an agent. One of our customers built on our platform to the telecommunications industry, directv, Sirius XM to a lot of retailers as well, which is really fun. Everyone from Wayfair to clothing retailers like Olakai and Chubby Shorts. What's really neat about it is a pretty diverse range of use cases and it's everything from helping you sign up for. We have an agent that helps with customer support in one of the big dating applications to helping you upgrade or downgrade your SiriusXM plan. Actually, it's really funny. We do technical support from everything from home alarm systems to Sonos 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. 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 5, which is pretty amazing, you know. And what's interesting about service too, it's often people having a problem. And so, you know, when you have our clear, I don't know if you use them in the airport, I think that agent has a CSAT score of 4.7 out of 5. You know, people are coming in with a problem and ending delighted. And I think that's really the opportunity here. And our whole vision is that we're going to move towards a world where every single one of the interactions with your customers can be instant, it can be multilingual, it can be over audio, it can be over chat, it can be digital, it can be over the phone and it can be very personalized. And I think that's really, really exciting. And if you think about all the best moments you've had with a brand, it's like that store associate who, you know and you know, it's like for me, it's like the butcher at the grocery store, I love to cook, he knows me. We talk like, can you actually produce that at scale for a company with 100 million customers? And can you do it in a really personal way? And I think we're really on the cusp of enabling that.

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

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 famously Stripe and Twilio were probably like 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 download a new library or start using a web service like that. It particularly works well if you sell to startups, just because startups tend to have engineering teams with quite a bit of latitude to choose services to help them solve the problem given by the founder. Then there's product led growth. It's a broad term. Obviously every company's product matters. But product led growth more specifically means users can sign up from the website, often get put on trial, often you can buy a couple 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 proprietors do everything. And so you're selling small business software like, you know, Shopify in the early days and there's a lot of other products like that where you're trying to sell to small merchants. You know, that's great. It doesn't work well when your buyer and the user of the software are different. So I always use the example of something like expense reporting software. The user of that software is an individual employee, but the buyer is often a finance department. And so, you know, having sign up and buy with your credit card doesn't make sense because the person using is not the person with the credit card and it just doesn't work. And then there's direct sales and direct sales had gone, I don't say out of fashion, but if I think of like the best direct sales companies, I probably there's a lot of lineage from Oracle, but you think SAP, Oracle, ServiceNow, Salesforce, Adobe perhaps, and there's others as well. And these were companies that sold into, you know, large lines of business in a relatively traditional sales motion. I think because product led growth became very popular. I think a lot of companies use that which is great, that motion produces great products. But if PLG means that you aren't actually engaging with the buyer of your software, like you're not going to grow. And so I've actually seen more recently a lot of AI companies, direct sales come a little bit more back into fashion because I think so many of the operations opportunities in AI are actually meet that qualification where the buyer and the user are not necessarily the same person. And it really requires that go to market motion. Where I see entrepreneurs stumble is they'll sort of choose a go to market motion without thinking through the what is the process of purchasing this software? What is the process of evaluating the value of the 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. And I think. I think a lot. I'm sort of thankful to see it coming back in a lot of the market.

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

Yeah.

Brad, 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?

No, go ahead.

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

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, but probably I had to pick one sort of in the area of the topics we talked about Competing Against Luck, which was the book that produced Jobs to be Done, which is a framework I really believe in. My only critique is I think most of these sort of, like, business books should be like an article. So maybe buy the book and punch into chatbots and get the summary. But by the book, it's. Clayton Christensen talked about it, but it's a really good framework for thinking about delivering value with their products. And I think it's a. It definitely influenced me on the. Actually, one book I do recommend is Endurance, which is the story of Shackleton's trip to go to the South Pole. Like, half the book is him starving to death and eating seal meat with his crew of people frozen in their boat. I've never seen a better story of grit in my entire life. It's, like, kind of remarkable that it's a true story. And, you know, if you want to, like, if you're an entrepreneur going through a hard time read that, you're like, okay, it could be worse. It's a great book, too. It's just remarkable that it's a true story.

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

I don't Know that that's true. It's like remarkable. That's true.

Oh, it might not be true.

I don't know. The Internet, who knows?

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

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

I saw someone using, I think VO3 to create their own Inception videos where the world's wrapping on each other. Oh, man. Okay, do you have a favorite product that you have recently discovered that you love or one you've loved for a long time?

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

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

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 Kay of Xerox parc. He invented a lot of the core abstractions that we use in computing today. It's why I am an entrepreneur, it's why I love to build things. And it's definitely a life motto for me.

I feel like many people say this, I feel like you've Actually done this so many times you're living this motto. Final question. We talked about you inventing the like button ad Friend feed. Were there other thoughts of what they would call it other than like was it just like obviously like. Or is there other thinking there?

The context. So this was before emoji. So there. If you read the comments on friend feed posts, at least 70% of them were cool or wow or yeah. Or neat. And one of the principal uses of FriendFeed was to have discussions about things. So you'd have a post and then a pretty fulsome discussion underneath. And it was a very. Compared to Twitter and others. It was like a great place to have those discussions. And so the product problem we were trying to solve is get all the one word answers out so that the discussion was actually like actual comments as opposed to acknowledgments that you read the thing. So the original framing was one click comment. That was how we thought about it. And so the first version that I made had a heart. And she denies remembering this. But there's Anna Yang now Anna Muller who has worked at the company. She hated it. She said if I look at hearts on every post I'm going to vomit. It's just too, it's like too, too much, you know. And, and it also was interesting. Like we were simulating. It was like an article about a tragedy or something. A heart was just not the right thing. Like which actually turned out to be really hard to translate was just a much more neutral sentiment. And, 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, and I, I don't know if we ever had the word love but we definitely start off the iconography 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.

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

A lot of features.

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

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

And how can listeners be full to you? Is it try out Sierra? Anything else there?

Yeah, try out Sierra. I'm a single issue.

Better stay on message.

I love it. Yeah.

Brett, thank you so much for being here.

Yeah, thanks for having me.

Bye everyone. 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@lenny's podcast.com See you in the next episode.