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

Lenny Rachitsky:You're CTO of Meta you're co CEO of Salesforce you're chairman of the board at OpenAI how do you think the AI market is gonna play out

Brett Taylor:The whole market is gonna 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

Lenny Rachitsky:So makes me think about it I had Marc Benioff on the podcast you guys were co CEOs he was extremely agent filled

Brett Taylor:It's so hard to sell productivity software which I learned our way

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

Brett Taylor:I was the product manager for what 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

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

Brett Taylor:They sort of gave me another shot to do the v two of it that resulted in Google Maps we got about 10,000,000 people using it on the first day

Lenny Rachitsky:What mindset contributed to you being successful in such a variety of roles

Brett Taylor:Waking up every morning what is the most impactful thing I can do today

Lenny Rachitsky:Today my guest is Brett Taylor Brett is an absolute legendary builder and founder he cocreated Google Maps at Google he cofounded the social network FriendFeed 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 $750,000,000 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 cofounder 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 gonna have on the business world how coding is going to change in the coming years where the biggest opportunities remain for startups lessons on pricing and go to market in AI the story behind the like button and so much more this is a truly epic conversation with a legendary builder if you enjoy this podcast don't forget to subscribe and follow it in your favorite podcasting app or YouTube also if you become an annual subscriber of my newsletter you get a year free of a bunch of incredible products including Replit lovable Bolt n8n Linear Superhuman Descript Whisper Flow Gamma Perplexity Warp Granolah Magic Patterns Raycast Jetpr d Mobbin and more check it out at Lenny'sNewsletter.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 AST grep patterns catching subtle issues that traditional static analysis tools might miss CodeBabbit also provides free AI code reviews directly in the IDE it's available in versus code cursor and windsurf CodeBabbit has so far reviewed more than 10,000,000 PRs installed on 1,000,000 repositories and is used by over 70,000 open source projects get CodeBabbit 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 37 signals most project management systems are either inadequate or frustratingly complex but Basecamp is refreshingly clear it's simple to get started easy to organize and Basecamp's visual tools help you see exactly what everyone is working on and how all work is progressing keep all your files and conversations about projects directly connected to the projects themselves so that you always know where stuff is and you're not constantly switching contexts running a business is hard managing your projects should be easy I've been a longtime fan of what thirty seven signals has been up to and I'm really excited to be sharing with you sign up for a free account at Basecamp.com/Lenny get somewhere with Basecamp

Lenny Rachitsky:Brett thank you so much for being here welcome to the podcast

Brett Taylor:Thanks for having me

Lenny Rachitsky:My pleasure there's so much that I wanna talk about you've done so many incredible things over the course of your career it just boggles the mind of things that you've done and we're gonna talk about a lot of that sort of stuff but I wanna actually start with the opposite I wanna 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

Brett Taylor: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 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 1,000,000,000 web pages to 10,000,000,000 which was a big deal at the time it sort of seems quaint now and then I did a decent job and so my boss Marissa Meyer gave me the opportunity to lead a new product initiative which was a big bet on me and I was you know it's 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 wanna find you know plumbers in Manhattan you wanna 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 with the 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 a me too version of Yahoo Yellow Pages you know a sort of essentially grafting on yellow pages search on top of Google search and with a properly crafted query you could you know see those listings at the top of your search results with a standalone site at local.google.com and it was actually it was an important enough initiative initiative that actually there was a on the Google homepage it had you know web images and and local was up there as 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 than like more than giving you that kind of traffic to give you an ad that as a as a product leader or product manager and the product was fine I like it worked but it really wasn't differentiated and and I think in many ways I think 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 like why use this instead of Yellow Pages you know it was sort of a digital version of 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 like about to get fired or something but it was like you know the I don't know the shine on the on my reputation was sort of waning a little bit and they sort of gave me another shot to do like a v two of it and and and I sort of got the impression it wasn't like my last shot but it was sort of you know I I certainly was feeling a little dejected from going from sort of a hotshot new pm to a new thing we spent a lot of time thinking about how can you make something that's just much more compelling and and not just sort of a digital version of of the yellow pages and not just so so similar to some of the other products out there and that's ended up being the thread that we pulled that 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 if 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 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 and certainly my career but it took kind of I think 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 10,000,000 people using on the first day which at that scale of the internet at the time was huge and then in August 2005 we integrated satellite imagery from a recent acquisition called Keyhole which became Google Earth and we got 90,000,000 people using it on the same day everyone wanted to look at the top of their house you know when the imagery came out

Brett Taylor:And it was really interesting because there's so many subtle product lessons in there you know first is I think as you have these new technologies rather than literally digitizing what came before if you can create an entirely new experience it it creates it sort of answers the question for a new customer like why should I give this a time of day you know and so really disassembling the lego set and reassembling it something new rather than just digitizing what was there before certainly that was the lesson I think in Google Maps it really was native to the platform in a way that like a paper map couldn't be you know and and that was like a a really meaningful break through 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's called Lazy Sunday you know rapped about Google Maps and Lars and I were texting each other we did it we're on Saturday Night Live mission accomplished and it was also showing that you know Azure is there thinking about products there's the you know why you decide to use a product and then what is this the enduring value and those are deeply related but not all the same thing and I just learned so many lessons I took with me for like every subsequent product that I worked on

Lenny Rachitsky:Mhmm

Lenny Rachitsky:As as an awesome story one I think it's really empowering for people to hear even you Brett who I'm gonna share all the successes you've had have had a 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 wanna 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're CTO of Meta you're co CEO of Salesforce you're also CPO at COO at Salesforce at Google you joined as an associate product manager where you famously you didn't mention this but you you built Google Maps on a weekend we're not gonna talk about that you're chairman of the board at OpenAI you were chairman of the board at Twitter you have also founded three different companies one social network one productivity docs company called Quip and now Sierra fun fact at FriendFeed you invented the like button I don't know if people know that and also just the news feed I'll just throw that out there to give you some credit so you're basically an associate product manager an IC product manager an engineer CPO COO CTO of three different companies including a public company very rare that somebody is successful at all these types of roles and all these levels so let me just ask you this question what mindset or habits or just ways of working have you worked on building in yourself that you think have most contributed to you being successful in such a variety of roles and levels

Brett Taylor:Yeah it's actually something I am proud of I I like the fact I've worn different hats it's actually amusing when I meet colleagues that I've known from one of those jobs they'll often think of me through the lens of that job you know and so you know I'll go to meet folks from Facebook and they think of me largely as an engineer they'll meet folks from Google they think me largely as a product you know person at Salesforce you know a lot of the folks there interacted with me as like a for lack of a better word a suit you know like the boss and I I'm not sure they think of me as a as an engineer at all even though you know I was still probably coding on the weekends for fun and one of the things that is a principle for me is to have a really flexible view of my own identity I really think of myself I probably would self describe as an engineer but more broadly I think of myself as a builder and I like to build products and and I think companies are one of the most effective ways to build products there's also things like open source but I think I'm a huge believer in the confluence of technology and capitalism to produce you know just incredible outcomes for customers and as a consequence I think to to really build something of significance you know I think to be a great founder you really need to be able to 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 you know one day I think selling is a big part of being a founder you have to sell investors on wanting to invest in your company you have to sell candidates on wanting to work at your company you have to sell customers to want to use the product that your customer produces you have to have good design taste not just for your product but for your your marketing and you know essentially soliciting new customers you have to have good engineering I mean if you're building a technology company the technology comes first it's you know why this industry is so transformative I probably credit and I've told this story before but I'm 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 a very senior kind of architect you know on on a number of projects and then at some point Mark Zuckerberg reorganized the company and kinda split it into a bunch of different groups I ended up with a very large group under me I was just 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 a big group and it was the largest you know management job 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 meet my quality bar and I was editing it and sort of griping about she sort of pulled me into a room and kinda 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 one zero one and and she she's a remarkable mentor in the sense she can kinda give you feedback that's very direct and like often a bit uncomfortable and it but 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 kinda 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 is she overreacting and then I woke up the next day was like no she's right and I had realized sort of this subconscious like limiter that I that was limiting my success in the job which is I was trying to conform the job to the things I thought I liked to do so I was spending a lot of my time on some product and technology things that were I was passionate about thinking you know I'm the boss you know I should you know focus on what I wanna 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 you know changed an organization and it turned out to be more successful I derived a great deal of joy from seeing that success you know our developer platform had a lot of partners and you know when there's an issue there and it's been time on partnerships and it worked and you know our platform became healthier the partner became more successful I was took pride in that success and then I just started being better at my job and I realized that the actual act of engineering or product design or all the things I thought I liked what I really liked is impact and and and so that conversation led to my sort of waking up every morning sometimes literally but certainly in the broadest sense the word's saying what is the most impactful thing I can do today and really thinking almost like a if you had an external board of advisers you know telling you like where are the what are the things where if you focus on them you can maximize the likelihood that what you're trying to achieve will happen and sometimes it's recruiting sometimes it's products sometimes it's engineering sometimes it's sales and I've become much more self reflective just about what is important to work on and I have become much more receptive to doing things that I previously would have said aren't my favorite things to do because I derive so much joy from having an impact that I enjoy a lot more things now and so I really credit Sheryl I'm 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 kinda like change the trajectory of your career I mean I give her all the credit for it

Lenny Rachitsky:There's so many people that share stories of Sheryl Sandberg giving them advice and that changing their life yeah what a 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 kinda 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 sec absolutely

Brett Taylor: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 is gonna fail so you know why did you lose a deal you know you could talk to the salesperson who is 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 that and that's something a salesperson might say maybe the real reason is they didn't actually see much value in your platform so it was communicated to the salesperson as it was too expensive but in fact 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 and you wanna 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 you know you the the proverbial redesign I joke it's like the dead cat balance of a consumer product like a re this next redesign will fix all of our problems I I don't know if it's ever ever worked and then you if you I've met a lot of entrepreneurs who like come from sort of a business development background they're always thinking about partnerships and and you know oh we just get this partnership done for this distribution channel everything's gonna 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 cofounder do you have a good you know leadership team if you're a product manager like your partner in engineering your partner in marketing you really wanna 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 is 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's interesting being able to answer it accurately is actually the hard part

Lenny Rachitsky:This feels like such an important lesson you've learned is there an example that comes to mind where you learned this the hard way or you actually ended up

Brett Taylor:Oh yeah well you're just worst for this whole thing on my failures but I'm fine with that I'm so

Lenny Rachitsky:You've had too many success

Brett Taylor:Friendly 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 Buhay 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 it was like pretty awesome founding team we made a social network as you said we sort of invented a lot of concepts that became popular in in the news feed 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 Tuesday actually a lot of folks in Silicon Valley are like I love love FriendFeed I'm like that's awesome it wasn't really a successful business there was a we were a follower oriented social network not a friendship oriented social network which meant a lot of our content was more like X or Twitter than it is Facebook in that respect and a lot of sharing newspaper articles interests scientific communities things like that and there was a period when Twitter which was one of our competitors at the time there was a lot more social networks at the time I I probably screwed us a little bit I think Obama Ashton Kutcher and like Oprah Winfrey all went on Twitter like in a in a summer and we just got our ass kicked you know it's like and it was a great example of you I think 11 of those 12 people were engineers and we're just making product and I think it was BizStone I mean if you talk to the Twitter folks they could give you the history on this but I think Biz was really focused on like getting celebrities and public figures onto Twitter which is totally obvious like if you have a a social service that's oriented towards following people put some people on there worth following you know like and instead you know we were exclusively focused on polishing the product and we actually I think you know at our sort of peak of popularity we were very confident just you know I think it was a time when like Twitter had the fail whale and was down half the time and people couldn't even use it and you know we our product we are innovating faster we had more features people liked it we could and and we were up a 100% of the time and we totally lost for no reason related to product at all and and it was an example of and I think somewhat famously not of like a lot of great entrepreneurs have come out of Google because once you like Google is 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 like accumulated these skills over time but 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 man wanted to right yeah was just like he's on my you know on my rolodex but I probably wasn't soliciting advice from the right people you know I think that what's great about the technology industry is there's a lot of advice choosing whom you listen to is actually quite difficult but I think we're somewhat myopic you know we're kind of in our own little world creating this product and we weren't asking people to like from the outside end to say like what what are you seeing that could go wrong what are you seeing that could go right what are you seeing in the industry that we're not doing that you think we might wanna do and this is why boards are important this is why you know finding the right advisers the advisers will actually tell you what you not necessarily wanna hear but you need to hear I think that was probably the missing part I'm not sure I was great at marketing at the time but if I had solicited the right advice I you know could have learned that that was a shortcoming and I think that was a a deep lesson I took from that I'm a huge believer in in boards and and getting good advice

Lenny Rachitsky:Any kind of heuristics or advice for people to know whose advice to listen to what do you pay attention to when you're like okay you know this person but listen to this person

Brett Taylor:Yeah that one's tough it is definitely it does come down to good judgment and being judge of people's character one thing that is particularly hard is there's not a strong correlation between the confidence with which someone expresses an opinion and the quality of that opinion well I don't wanna say it's inversely correlated but you know that's funny with all the podcasts out now if there's topics I know a lot about you know sometimes the most eloquent eloquent confident statements about things I know a lot about are are the least accurate and it sounds extremely persuasive and and the 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 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 why why why and really tried to understand the framework that someone is using to give you advice the interesting thing about advice is people are often extrapolating from relatively few experiences so you know they'll say never do this or always do that and it's because they had one experience where that something backfired or something could have gone better if they had done it so it's it's a useful anecdote but if you don't ask why and understand they had one experience and here's what happened it can come across as a rule when in fact it's it's anic data 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 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 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 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 giving like advice on investing you know for Warren Buffett yeah okay it's statistically significant but that's not most advice is like something happened to you once and and you have regrets so

Lenny Rachitsky: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 wanna go in a kind of a different direction you mentioned that you described yourself as an engineer you 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

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

Brett Taylor:major to learn systems thinking and at the end of the day AI will facilitate, you know, creating the 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 gonna be managing this machine that's doing a lot of the tedious work of making the button or, you know, connecting to the network but as you're thinking of the intersection of a technology and a business problem you're trying to affect the 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 just cheesy simple example but I think it's representative at Facebook we would always, you know, we spend a lot of time designing the newsfeed and have you ever had like a really really good designer and they showed you at the time a Photoshop mock up of of the newsfeed it was just always 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 light 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 news feed like Photoshop is the easy part you need to actually design a system that produces a like both in content and visual design like a delightful experience given input you don't control and that's a system that's not mean it's sort of a design and it's just what we did practically I am sure it's changed a lot since you know I left in 02/2012 but we made a 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 I do think that's a really useful skill I think in general with the advent of AI agents and you know AI approaching superintelligence in certain domains I think the tools with which we do our job will change a lot I think it's very important to have a very loose attachment to the way we do our jobs and you know I that story that we won't talk about when I like rewrote Google Maps like everyone talks about that story because it's like and it's I think it's because of Paul Puhay who told it on some podcast and also made the rounds I think that's gonna end up sort of this vestige of the past like I almost like the human calculators at NASA before the computers were invented like wow a person was a calculator well that's fun like tell me that story I think just like what I was good at will no longer be useful in the future certainly not like valuable in the future and that's okay so I think we need to have a really loose view of it but the idea that you shouldn't study these disciplines it's sort of like people say I don't wanna study math because I'm not gonna use it in my career for x well study math's 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

Lenny Rachitsky:I have a two year old and it feels like there's like a new milestone of there's like when to give them a phone when to give them I don't know Snapchat whatever kids use these days and then it's like when to give them their first ChatGPT account I don't know I wonder I wonder how soon that's supposed to happen

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

Lenny Rachitsky:And so is the form factor for your kids like an iPad or a laptop or some

Brett Taylor:Yeah they use like the computer on the desk

Lenny Rachitsky:Got it alright good tips this is good for me to learn all these things as my kid ages okay I'm gonna 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 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 gonna play out and where do you think founders should focus and also just try to avoid

Brett Taylor:I think there's three segments of the AI market that will end up fairly meaningful markets and then I'll I'll end with how I think it's gonna 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 on these models all of the companies that were startups that tried to do this have already been consolidated or almost all of them inflection adapt character and others and I think it's just not that doesn't appear to be a viable business model for a startup because of the amount of capex required and there's just not enough runway you can your fundraising runway to get to escape velocity 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 gonna end up probably no entrepreneurs should build a frontier model that's my my unless

Lenny Rachitsky:You're Elon

Brett Taylor:Yeah oh yeah he's he's not he's he's different right and he has the capacity to raise billions in capital and my guess is most of your other listeners don't and then he's he's the greatest of all time for a reason and and he's different you don't compare yourself to him you know the other part of the market is the tooling you know I think there's you know a lot of folks selling pickaxes in the gold rush this is data labeling services this is you know data platforms it's eval tools more specialized models like eleven labs has a great set of voice models that a lot of companies use that are really high quality and it 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 stuff like a lot of the amazon and azure and others have competing products in those areas because they're very adjacent to the the to the infrastructure itself and every infrastructure provider is trying to differentiate by moving up the stack and and you're right there and so there's some real meaningful companies as I mentioned like snowflake databricks confluent and others but there's a lot of others that were sort of obviated by technology from the 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 and then there's the applied AI market I think this will play out for companies who build agents I think agent is the new app and so I think that's gonna be sort of the product form factor so there's companies like Sierra we help companies build agents to answer the phone or answer the chat for customer experience and customer service there's companies like Harvey that make agents for both the legal paralegal profession antitrust reviews reviewing contracts etcetera etcetera 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 wind up extremely large scale but probably slightly lower margin and I think you know you you the market for them will be probably less technical I mean if if you know if you think about the purest form of software as a service it's not like you ask like what database do you use right it's it's really about the feature and function I think that's where agents will go I think it's gonna be more about product than it is about technology over time just you know just going back to my metaphor you know in 1998 when Mark and Parker started Salesforce just getting that database running in the cloud was like a technical achievement you know nowadays like you know no one asks asks about that because you can just spin up a database in AWS or Azure and it's like no problem I think today you know getting an orchestration orchestrating an agentic process on top of the models is like sounds really fancy and it's really hard and all that stuff you know I'm pretty sure that's gonna be easy in 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 know talk a little bit less about how you deal with the models in the same way modern SaaS few people ask what database you use but you'll probably ask a lot about the workflows and and what you know business outcomes that you're driving are you generating leads for a sales team are you you know minimizing your procurement spend whatever value you're providing is going to sort of slowly evolve towards that I I'm very excited I don't think start ups should probably build foundation models I think but I I mean you can shoot your shot you know if you have a a vision for the future go for it but I think it's probably a 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 going 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 how the software market has evolved I think we're gonna seek something kind of similar with agents like it's not just gonna be like these huge markets like we're in like customer service and software engineering it's gonna be like a lot of like things where people are spending a lot of time and resources that an agent can just solve but it requires an entrepreneur who actually understands that business problem like in deep deeply and I think that's where like a lot of the value is gonna be unlocked in the AI market

Lenny Rachitsky:That is incredibly helpful this makes me think about it had Marc Benioff on the podcast you guys were co CEOs and he was extremely agent filled all he wanted to talk about was AgentForce clearly you are also very agent filled what is it that

Brett Taylor:You heard the term agent hold on let use that one so

Lenny Rachitsky:Clearly you guys saw something that was just like okay we need to go all in on agents this is the future what is it you think people are missing about just like why this is such a critical change in the way software is gonna work what are what's what are people not seeing

Brett Taylor:If you talk to an economist like Larry Summers who's on the OpenAI board with me they'll talk about like what is the value of technology will 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 nineties and I think a lot of folks I talked to think it was actually that very first wave of computing where people made like ERP systems and just like put accounting in into computers and databases even like mainframes we're talking like the PC era because it was such a huge step up like you know just imagine like the ledgers of you know numbers that you'd have for like a large multinational company before and it truly just transformed departments I'll I'll give you a little toy example my dad just retired he's a mechanical engineer and he was talking about when he first started his career in the late seventies and he went into a mechanical engineering firm the majority of the firm were drafts people so basically you'd take an engineering design but you needed to do all the different vantage points and for all the different floors and to give to the contractor to do the thing now there are zero drafts people at his company you just make the the design in first AutoCAD and now Revit and it you know it's a three D model and you know the drafting has actually been eliminated it's just not a thing one needs to do anymore the the actual design and drafting drafting is not a thing that exists it's just like you can it's just a design I I that's true productivity gains right it's like you know the job of the mechanical engineering firm was to do a design the drafting was like the sort of this necessary output for the contractor but it wasn't really adding value it's just sort of like the the supply chain change if you look at the history of the software industry from the PC on there's been meaningful productivity gains but just not nearly as meaningful as that first huge jump and I'm not smart enough to know exactly why but it is interesting like there has the promise of productivity gains from from technology hasn't been as realized I think as some people thought I think agents will truly like start to bend the curve again like we did in the very early days of computing because software is going from helping a individual be slightly more productive you know to actually accomplishing a job autonomously and as a consequence just like you don't need drafts people in 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 and more productive and you can actually you know a smaller group of people can accomplish more and you know truly drive productivity gains in the economy and you know I think if you've ever sold enterprise software you end up in these discussions as a vendor with the customer where you'll have like a a value discussion and you'll do these like somewhat convoluted you know things like okay it's like you're selling a sales thing okay well if every salesperson sells you know 5% more and you should pay us a million dollars like you know 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 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 like how it like transforms the business model of the software industry where there's gonna be like a before and after like I don't know how many people still sell perpetually licensed on premises software but it's de minimis at this point I think we're gonna go through a similar transition like the whole market is gonna 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 gonna be like the market is gonna pull everyone there because it's just so obviously the correct way to build and sell software

Lenny Rachitsky:Let me pull on that last thread so we had Madhavan 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 and he actually used Sierra as one of the shining examples of of this six 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

Brett Taylor:Yeah I'll start with the example and then I'll broaden it so at Sierra we help companies make customer facing AI agents primarily for customer service but more broadly for customer experience so if you have a problem with your SiriusXM radio you'll call or chat with Harmony who's our AI agent if you have ADT home security and your alarm doesn't work you can chat with their AI agent Sonos speakers a lot of different consumer brands and you know if you think about running a call center the there's a cost for every phone call that you take most of it is labor costs but if you have let's just say a typical phone call is anywhere between 10 and $20 US dollars most of it some of it's software some of it's telephony but a lot of it is just like the hourly wage of the person answering the phone so if an AI agent can take that call and solve it you know that is in the industry often called a call deflection or a containment and that essentially means you saved you know call it $15 because you didn't have to have someone pick up the phone so at in our industry basically we say hey if the AI agent you know solves the customer's problem they're happy with it and you didn't have to pick up the phone there's a pre negotiated rate for that and that's we call it like resolution based there are other outcomes as well we have some sales agents being sales paid a sales commission believe it or not you know we do we we really think of our agents as truly customer experience like the concierge for your brand and we wanna 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 they 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 for 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 priced per token or 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 you wrote every day which every engineer in the world knows is an idiotic way to measure productivity he famously went in with a report that had a negative number so I think he did a big refactoring and deleted a bunch and it his way of saying like fuck 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 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 wanna 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 at the wall because you'll never get paid if it doesn't you have to you know really just your orientation becomes so extremely customer centric when you do this the right way I think it's just a 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

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

Brett Taylor: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 and it's quite counterintuitive so for example I almost every software engineering firm I know uses something like Cursor to help their their software engineers most people use Cursor right now as a kind of coding autocomplete though they have a lot of agentic solutions and there's a lot of like OpenAI's Codex and there's you know cloud has I can't remember the the Anthropic products so there's lots of agentic you know agents coming as well one of the interesting things because the technology is sort of immature the code it produces often has problems so there's a lot of people sort of approaching this to sort of actually realize those productivity gains because as any engineer who's written a lot of code will tell you it's pretty easy to like look at and edit and fix code you wrote reviewing other people's code or particularly finding a subtle logical error in someone else's code is actually really hard it's actually much harder than you know editing code that you wrote yourself so if the code produced by a coding agent is often incorrect it actually can take a lot of like cognitive load and time to fix it and in fact if you end up producing lots of you know issues with your customers you could end up you know producing a lot of features but actually it's 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 in agents is really important having AI supervise the AI is actually very effective just think about it this way if you produce an AI agent that's right 90% of the time that's not that great but how hard would it be to make another AI agent to find the errors the other 10% of the time that might be a tractable problem and if that thing's right 90% of the time just for argument's sake you can wire those things together and have something that's right 99% of the time so the it's just a math problem like you know and it turns out that you can make something to generate code you can make something to review code and you're essentially using compute for cognitive capacity and you can layer on more layers of of cognition and thinking and and reasoning and produce things increasingly robust so I'm very excited about that the other thing though is root cause analysis so we have an engineer at Sierra who exclusively focuses on the model context protocol server serving our Cursor instance and our whole philosophy is rather than if it if Cursor generated something incorrect rather than just fixing it try to root cause it try to get it so like the next time Cursor will produce the correct code so like and essentially it's context engineering like what context did Cursor not have that would have been necessary to produce the right outcome so I think people who are trying to get productivity gains in departments like software engineering need to stop sort of waiting for the models to magically work if they wanna see that gains now and you really have to create like root cause analysis and systems and say like you know how do we sort of go root cause every bad line of code and actually give the right context and produce the right system so the models can do it today the over time that probably like less necessary and and you'll have less context engineering necessary to do it but you really have to think of this as a system and I think people are sort of like waiting for the models to just magically get better and I'm like well that will happen eventually but if you want the gains now you gotta put in the work I mean that's essentially why applied AI companies exist and the work is nontrivial but it's you can do it and so you know for customers using platforms like Sira yeah AI agents aren't perfect but we're creating a system that lets customers create a virtuous cycle of improvement if you wanna 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 of the of the haste at the top of the haystack on your behalf and I think that's really the way to optimize these systems

Lenny Rachitsky:I've never heard of this technique of improving Cursor by adding additional context what's the actual way of doing that you build an MCP server that everything runs through or is it like you add Cursor rules what's the actual approach there

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

Lenny Rachitsky:Got it that is very cool I hadn't heard people doing that model call model context protocol makes sense we've talked about productivity gains outside TX just to give you a chance to share how amazing what you've built is what are some of the gains you see from people using Sierra

Brett Taylor:Yeah we have our customers see anywhere between 50 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 health care provider space banks you can actually refinance your home using an agent one of our customers built on our platform to the telecommunications industry DirecTV SiriusXM 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 you know sign up for a we have a an agent that helps with customer support in one of the big dating applications to you know helping you upgrade or downgrade your 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 we're the leader in this space we're trying to enable every company in the world to create their agent with their brand at the top that I think will become as meaningful of a digital touchpoint as their website or their mobile app in the short term it can really transform the costs of running a customer service team you know 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 of 4.6 out of five which is pretty amazing you know that and and what's interesting about service too it's often people have having a problem and so you know when you have a a clear I don't know if you use them in the airport I think that agent has a CSAT score of 4.7 out of five you know people are coming in with a problem and and being delighted and I think that's really the opportunity here and you know our whole vision is that we're gonna move towards a world where every single one of the interactions with your customers can be instant it could be multilingual it could be over audio it could be over chat it could be digital it could be over the phone and it could be very personalized and I think that's really really exciting and if you think about all the best moments you've had with the 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 a 100,000,000 customers and can you do it in a in a really personal way and I think we're really at on the cusp of enabling that

Lenny Rachitsky: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 B to B companies clearly you guys have figured something out I imagined 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

Brett Taylor: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 we're 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 you know download an a new library or 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 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'll use the example of something like expense reporting software the user of that software is an individual employee but the buyer is often a finance department and so you know having to sign up and buy with their credit card doesn't make sense because the person using this is not the person with the credit card and and it just doesn't work and then there's direct sales and direct sales had gone I don't wanna say out of fashion but if I think of like the best direct sales companies I'd 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 it could it it 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 gonna 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 opportunities in AI are actually meet that qualification where the buyer and the user are not necessarily the same same person and it really requires that go to market motion where I see entrepreneurs stumble is they'll sort of choose a go to market motion without thinking through the what is the process of purchasing the software what is the process of evaluating the 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 it like because of the you know sometimes justified reputation of the quality of products of some of these direct sales companies lot of it sort of had gotten a bad name and I think I think a I I'm sort of thankful to see it coming back in in a lot of the AI market

Lenny Rachitsky:I feel like this message is something a lot of founders need to hear especially founders that aren't from a business background of that or you know sales turns them off they don't think they're gonna 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 Ruth

Brett Taylor:Yeah

Lenny Rachitsky:Brett is there anything else that you wanted to share any last nugget of wisdom anything you wanna double click on before we get to our very exciting lightning round

Brett Taylor:No go ahead

Lenny Rachitsky:Okay let's do it here we go welcome to our very exciting lightning round I've got five questions for you are you ready

Brett Taylor:Yeah go ahead

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

Brett Taylor:I don't read a lot of nonfiction but probably if I had to pick one sort of in the area of the topics we talked about competing against luck which was the the book that produced jobs to be done which is a framework I really believe in my only critique is I think most of these sort of like business books should be like an article so maybe buy the book and punch into ChatGPT and get the summary but buy 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 and I think it's a I I they definitely influenced me on the actually 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 sea all 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 wanna 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.

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

Brett Taylor:That ad that was great thing rather that's true it's like remarkable that's true.

Lenny Rachitsky:It might not be true.

Brett Taylor:I don't know I mean I the internet who knows I.

Lenny Rachitsky:Goddamn deepfakes even back then okay do you have a favorite recent movie or tv show that you've really enjoyed?

Brett Taylor:We 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 a type of movie when you watch your film and you have conversations for two days afterwards about it so just a great film.

Lenny Rachitsky:I saw someone using I think V O three to create their own Inception videos where the world's rapping in 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?

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

Lenny Rachitsky:I think it's the first time someone's actually mentioned Cursor in this answer so it might be the beginning of a trend Michael Trull 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 gonna be this additional pseudo code layer on top of code.

Brett Taylor:Yeah.

Lenny Rachitsky:Very aligned with your thinking do you have a favorite life motto that you often come back to and find useful in work or in life

Brett Taylor: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 love i i am an entrepreneur it's why i love to build things so it's definitely like a life motto for me

Lenny Rachitsky:I feel like many people like 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 at friendfeed 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

Brett Taylor:The context of this was before emoji so there if you read the comments on friendfeed posts at least 70% of them were cool or wow or yeah or neat and one of the principal like uses of friendfeed was to have discussions about things so you'd have a post and then a pretty fulsome discussion underneath and it was a very compared to you know twitter and others it was like a great place to have those discussions and so the product problem we were trying to solve is get all the one word answers out so that the discussion was actually like like actual comments as opposed to acknowledgments that you read the thing so we the original framing was one click comment that was how we thought about it and so we the first version that i made had a heart and there she denies remembering this but there's a anna yang now anna muller who has worked at the company she hated it she said like if i look at a heart like hearts on every post i'm gonna vomit like this is 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 then 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 heard 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 a more complex story but it was all because we needed a one click comment that's where the concept came from

Lenny Rachitsky:Wow have you never heard this 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

Brett Taylor:Have a lot of features

Lenny Rachitsky:So many so many ai features yeah brett this was incredible this was an honor i so appreciate you coming on this podcast two final questions where can folks find you online if they wanna reach out maybe go see if they wanna work at sierra and how can listeners be useful to you

Brett Taylor:If you want an ai agent to help with customer service go to sierra.ai if you want to apply here sierra.ai/careers we're we have offices in san francisco new york atlanta and london and are hiring pretty aggressively in every department so please reach out if you're interested

Lenny Rachitsky:And how can listeners be useful to you is it tryout sierra anything else there

Brett Taylor:Yeah tryout sierra i'm a singlish you better

Lenny Rachitsky:Staying on the message i love it yeah brett brett thank you so much for being here

Brett Taylor:Yeah thanks for having me

Lenny Rachitsky: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 at lennyspodcast.com see you in the next episode