You're CTO of Meta, you're a co -CEO of Salesforce, you're chairman of the board at OpenAI.
How do you think the AI market is going to play out?
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.
This makes me think about I had Mark Benioff on the podcast. You guys were co -CEOs.
He was extremely agent -pilled.
It's so hard to sell productivity software, which I learned in a hard way.
What's a story that comes to mind when you think about your biggest mistake?
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.
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 today?
Today, my guest is Brett Taylor.
Taylor. Brett is an absolute legendary builder and founder.
He co -created Google Maps at Google.
He co -founded the social network FriendFeed, which invented the like button and the real -time newsfeed, 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, where the biggest opportunities remain for startups, lessons on pricing and go -to -market in AI, the story behind the like 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, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, JetPRD, 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 Code Rabbit, the AI code review platform, transforming how engineering teams shift faster with AI without sacrificing code quality.
Code reviews are critical, but time consuming.
Code Rabbit acts as your AI co -pilot, providing instant code review comments and potential impacts of every pull request. Beyond just flagging issues, Code Rabbit provides one -click fix suggestions and lets you define custom code quality rules using AST grep patterns, catching subtle issues that traditional static analysis tools might miss.
CodeRabbit also provides free AI code reviews directly in the IDE.
It's available in VS Code, Cursor, and Windsurf.
CodeRabbit has so far reviewed more than 10 million PRs, 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 LENI.
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 longtime fan of what 37signals has been up to and I'm really excited to be sharing this with you.
Sign up for a free account at Basecamp .com slash Lenny.
Get somewhere with Basecamp.
Brett, thank you so much for being here.
Welcome to the podcast. Thanks for having me.
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.
It just boggles the mind, the things that you've done.
And we're going to talk about a lot of that sort of stuff.
But I want to actually start with the opposite.
I want to talk about a time that you messed up, a time that you screwed up in a big way.
We have this recurring segment on the podcast called 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 um it's uh for me it feels big because it was very formative for me as a product designer so i joined google in uh late 2002 early 2003 and i was one of the earliest associated 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 uh which was a big deal at the time it sort of seems quaint uh now and then I did a decent job and so my boss Marissa Meyer um 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 uh 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, you really needed a different, you didn't really want to find plumbers in Manhattan, you want to find plumbers in San Francisco, if you're me.
And so it was 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 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, essentially grafting on Yellow pages search on top of google search and with a properly crafted query you could you know see those listings at the top of your search results we had a standalone site at local .google .com and it was actually it was an important enough initiative that actually there was a on the google home page it had you know web images and 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 home page 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 home page is like kind of embarrassing you know it's it's uh i mean there's not uh there's not much one can do other like more than giving you that kind of traffic to give you an at -bat as a as a product leader a product manager and um the product was fine like it worked but it really wasn't differentiated and uh and i think in many ways uh i think again i think i've had these reflections more 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 something that had come before and a pretty tough product review with Marissa and Larry and others and it was fine I wasn't like about to get fired or something but it was like you know the uh I don't know the shine on the uh on my reputation was sort of uh waning a little bit and they sort of gave me another shot to do like a v2 of it uh and uh and and i sort of got the impression it wasn't like my last shot but it was sort of you know
i i certainly was feeling a little dejected from going from sort of a hot shot new pm to a new thing so you spend 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 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 this little map next to the search results.
It was always the ugliest part of the product.
And we always made these backhanded comments about it internally.
And we spent a lot of time saying, what if we sort of inverted the hierarchy here and made the map the canvas?
We ended up finding Larsen Jens Rasmussen, who had been working on this Windows mapping product, and we got them into the company and started exploring this space.
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.
All of these products at the time were actually separate product categories.
and it was something that kind of redefined the industry and certainly my career but it took kind of uh 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 the internet at the time was huge.
And then in August of 2005, we integrated satellite imagery from a recent acquisition called Keyhole, which became Google Earth.
And we got 90 million people using it on the same day.
Everyone wanted to look at the top of their house 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 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 that was like a really meaningful breakthrough um and then with 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 uh 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 sandberg and 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 live mission accomplished And it was also showing that, you know, as you're thinking about products, there's the, you know, why you decide to use a product.
And then what is the enduring value?
And those are deeply related, but not all the same thing.
And I just learned so many lessons I took with me for like every subsequent product that I worked on.
That's an awesome story.
One, I think it's really empowering for people to hear.
even you Brett who I'm going to share all the successes you've had have had a massive failure with like the CEO of Google Mercer 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 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're a CTO of Meta, you're a co -CEO of Salesforce, Salesforce, you're also CPO at COO at Salesforce.
At Google, you joined as an associate product manager, where you famously, you didn't mention this, but you rebuild Google Maps that 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're also founded three different companies, one social network, one productivity docs company called Quip, and now Sierra.
Fun fact at friend feed, you invented the like button.
I don't know if people know that and also just the newsfeed.
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, CEO of three different companies, including a public company.
Very rare that somebody is successful at all these types of roles and all these levels.
So let me just ask you this question.
What mindset or habits or just ways of working have you worked on building in yourself that you think have most contributed to you being successful in such a variety of roles and levels.
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.
And so 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, a lot of the folks there They're interacting with me as, for lack of a better word, a suit.
The boss and I, I'm not sure they think of me as an engineer at all, even though I was still probably coding on the weekends for fun.
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.
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.
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, 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.
juices um you have to have good design taste um not just for your product but for your your marketing and you know essentially soliciting new customers uh you have to have a good engineering I mean if you're building a technology company the technology comes first it's you know why this industry is so transformative I probably credit and I've told this story before but I'm very grateful for her but I probably credit Sheryl Sandberg for um really changing the way I approached new jobs.
I had just become the Chief Technology Officer of Facebook.
When I first got the job it was sort of the flavor of CTO where I had a relatively small group reporting into me but contributed almost as a very senior 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.
I was essentially running our platform in mobile groups, products, design, engineering.
So I went from a handful of reports to over 1 ,000 or something.
It was a big group.
And it was the largest management job.
I had become a manager at Google, but a modest team.
And I was doing okay, but not great.
And I had this moment where Cheryl saw me.
I was I think I was editing a presentation for a partner just because the presentation I got didn't make my quality bar and I was editing it and 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 manage the company?
the company and or you know just like kind of giving me management 101 uh and and she uh 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 that really true am I really fucking it up or is it you know she overreacting and then I woke up the next day I was like
no she's right and I had realized sort of this subconscious like limiter that I that was limiting my success in the job which is I was trying to conform the job to the things I thought I liked to do so I was spending a lot of my time on some product and technology things that were I was passionate about thinking you know I'm the boss you know I should you know focus on what I want to focus on instead of thinking about okay I'm running the mobile and platform teams at Facebook what's the most important thing to do today to make our mobile mobile and developer platform successful and when I reframed
the job that way I did different things and the thing that was the biggest pleasant surprise to me was I liked it 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 when 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.
And so that conversation led to my sort of waking up every morning, sometimes literally, but certainly in the broadest sense of the word, saying, what is the most impactful thing I can do today?
day. And really thinking almost like if you had an external board of advisors telling you like, 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 doing things that I previously would have said aren't my favorite things to do because I derive so much joy from having an impact that I enjoy a lot more things now.
And so I really credit Cheryl.
I'm so grateful. And actually, it's interesting.
I think a 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 then changing their life.
What a mensch. 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 you end up doing, you may realize, I like this and I'm good at this.
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 is going to fail so you know why did you lose a deal you know you could talk to the salesperson who was on the account or perhaps maybe a product manager was involved in the conversation it's very important to have intellectual honesty in those moments because you could say something like oh they didn't buy it because the platform 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 it's not you 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 you know the proverbial redesign i joke's like the dead cat balance of a consumer product like this next redesign will fix all of our problems i don't know if it's ever ever worked um 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 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 been doing your whole career is the way to fix your problem, it's at least 30 % likely that you've chosen that because of comfort and familiarity, not truth.
And so I think it's like one of those skills, I think, is it really goes around to like, do you have a good co -founder?
Do you have a good 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 work in 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 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. Well, you're supposed to be this whole thing on my failures, but I'm fine with that.
you've had too much success Friends 3 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 and Sanjeev Singh who Paul started Gmail, Sanjeev was the first engineer on Gmail so we had the google maps people and the Gmail people it was a pretty awesome founding team we made a social network, as you said we sort of invented a lot of concepts that became popular in the news feed.
We invented the like button.
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 friend feed.
I'm like, that's awesome.
It wasn't really a successful business.
business, 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, though there was a lot more social networks at the time.
I'm probably screwing this up a little bit.
I think Obama, Ashton Kutcher and like Oprah Winfrey all went on Twitter like 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 were just making product.
And I think it was Biz Stone.
I mean, if you talk to the Twitter folks, they could give you the history on this.
But I think Biz was really focused on like getting celebrities and public figures onto Twitter, which is totally obvious.
If you have a social service that's oriented towards following people, put some people on there worth following.
Instead, we were exclusively focused on polishing the product.
We actually, I think, at our peak of popularity, we were very confident.
I think it was a time when Twitter had the fail whale and it was down half the time and people couldn't even use it.
Our product, we were innovating faster.
We had more features.
People liked it. we could and and we were up 100 of the time and we totally lost for no reason related to product at all and uh and it was an example of you know i think uh somewhat famously not of like a lot of great entrepreneurs have come out of google because once you're like google was so successful i think it's hard as a product manager to sort of see like distribution and all the product design and even business model when you have adwords and you know money's raining from the sky it's hard to a you know uh 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 i'm 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 of the product but it was a lot of other stuff and so i've learned like accumulated these skills over time when i say the hard part of that question is answering it
correctly is it's hard when you don't have experience and something to have intuition in it um so i think if there's probably a structural flaw it wasn't that i i don't know if i could have figured out how to reach out to ashton kutcher but i wanted to right yeah i was like he's on my you know uh 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.
We're kind of in our own little world creating this product.
And we weren't asking people from the outside in to say, 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 you know uh could have learned that that was a shortcoming um and i think that was a deep lesson i took from that i'm a huge believer in boards and getting good advice any kind of heuristics or advice for people to know whose advice to listen to what do you pay pay attention to when you're like okay ignore this person but listen to this person yeah that one's tough it
is definitely 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 you know 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.
And so it does require very good judgment.
One thing is, I think, not just asking for advice, but asking people, who should I talk to to get good advice?
And you'll find some common answers there.
And that's you ask for advice don't just ask what to do but why like being 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 um 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 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 uh it can come across as a rule when in fact it's it's anecdata um 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 one of the things I hire for I just think that's
something that you know probably comes from a mix of self -reflection you know like you really need to hold yourselves accountable like as an entrepreneur as a product manager like if you made a bad decision 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 very rarely is it.
I mean, if you're getting like advice I'm investing, you know, for More and 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.
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, 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?
I do still think 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, uh, you know, uh, study algorithms and, you know, why, uh, why a randomized algorithm works and, and, you know, uh, why two algorithms with like the same sort of big O complexity, one can in practice perform better than others and why a cache miss matters.
And just all these little, there's a lot more to coding than, than writing the code.
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 uh major to learn systems thinking um and at the end of the day ai will facilitate uh you know 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, you know, connecting to the network.
But as you're thinking of the intersection of a technology and a business problem, you're trying to affect a system that will solve that problem at scale for your customers.
And that systems thinking is always the hardest part 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 news feed 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 but everything was just perfect and then you'd like implement that design and you'd look at your own news feed and it looked like shit because it turns out like 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 a like both in content and visual design, like a delightful experience given input you don't control.
And that's a system that's not.
i mean it's sort of a design uh it's just what we did practically i'm sure it's changed a lot since you know i left in 2012 but we um 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, 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 uh attachment to the way we do our jobs um and you know i that story that we won't talk about when i like rewrote google maps like everyone talks to that story because it's like and it's i think it's because of paul who told it on some podcasts and i'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 the computers were invented like wow a person was a calculator later whoa 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 want to study math because I'm not going to use it in my career for x well study math is quite important like it teaches you how to think it teaches you like how how the world works, physics, math.
And I think computer science, especially at least sort of the foundations of it, will continue to be the foundations of how we 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 to all interconnect but the way you actually do the coding at your desk will change significantly this reminds me of something you mentioned on a podcast recently this idea that you think there's or there should be a new programming language that is more designed for lms 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 uh my reductive version of the past you know what are 40 years of of uh computers maybe more is you know you we created the hardware for computers then we created punch cards which is the way you know in like the late 70s you know uh you would tell a computer what to do um or maybe mid to late 70s uh then we end you know invented early operating systems and 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.
A 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 &D back in the day.
When Salesforce was created in 1998, just putting a database in the cloud was hard. That alone was a technical moat that is now trivial with Amazon Web Services.
That technical moat is comically narrow, but the product moat is quite large.
large i think that if the act of writing code is going from something that is very costly to like the marginal cost of that going to zero how many of the abstractions that we've built are based on you know human programmer productivity i think a ton you know like i always laugh that i assume python is probably the most common generated code just because how much it's in the training data 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.
Like it's not as bad as Pearl, but like, you know, if you have a big Python program, how How many errors will you find at runtime versus, you know, before releasing it?
So, Python was designed to be very ergonomic, almost look like pseudocode for humans, for me, to write code in a delightful way.
That's why data scientists love it so much. So, as we move to a world where like, let's just postulate, and I'm not sure this will be completely true, 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 you know rust i think is interesting because if i if i asked you to look at a c program and say does it leak memory you probably couldn't do it that well just because because it's really hard. And if it's a million -line C program, that's going to be very, very hard. If I asked you to verify
that a Rust program doesn't leak memory, you would just have to compile it.
And 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 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 A .I.
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 like incredibly complex scale software incredibly quickly and know that it works and if you start with that as your design center i think you probably change the languages you probably change the systems you probably change all these things 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 what with that in mind what do you want to do what would be best suited for the language, the compiler, for testing, for self -reflection, you know, for supervisor models, all these things.
I think that's more of a programming system than a language.
But I 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 one to netscape to rewrite they sort of like somewhat a lot of people attribute that to part of their failure against internet explorer it's like making these things is not hard like maintaining them is hard and ensuring the robust is hard and and i think we've just sort of we're in the very early phases of defining what this new system for developing software looks like.
And I'm 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 Cassioppo, 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 SOC 2 or ISO 2701.
Today, we currently help over 9 ,000 companies, including some startup household names like Atlassian, Ramp and Langchain, start and scale their security programs and ultimately build trust by automating compliance, centralizing GRC and accelerating security reviews.
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 some extent during it, but the idea is with automation with AI with software, we are helping customers build trust with prospects and customers in an efficient way.
And you know, our joke, we started this compliance company, so you don't have to.
We appreciate you for doing that.
And you have a special discount for listeners, they can get $1 ,000 off Vanta at Vanta dot com slash Lenny.
That's VA nta .com slash Lenny for $1 ,000 off ANTA.
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 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 part of their lives uh i was reflecting actually um when i took the ap calculus exams uh in uh 97 98 ab and bc i could use a graphing calculator and uh i haven't done this research i actually meaning to plug this into to chat gpt before our conversation but i'll do it after did the calculus exam change before and after they allowed the calculator in the exam
i assume it did but essentially to when you allow the calculator an exam you need to make sure that none of the questions you know benefit people for having a calculator or not and which actually forces you to sort of rethink the problems to test calculus knowledge that don't benefit from like road 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 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 Chachi, BT, 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 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.
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.
My kids use ChatGPT to quiz them before a test. You can use audio mode or chat mode.
It's better than cue cards.
my daughter took home a Shakespeare book she took a picture of a page she didn't understand and ChatGPT explained it to her way better than I would have as well 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 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 in these models and you can use it.
So with my kids, 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 it's amazing um and I so I really am trying to have them learn how to use it constructively in their in their lives but that all that said I just feel a ton of empathy for public school teachers right now um 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 uh it's just really challenging for teachers right now and i worry you know because these technologies amplify agency
the opposite can also be true of you if you are a student trying to like not learn something i think these tools probably provide a lot of mechanisms to avoid it as well and so i think there's a challenge for parents and teachers and i think we're gonna end up with kind of like a bumpy handful of years here but i brought up the calculus ap exam because obviously a graphing calculator is not chat gpt don't get could be wrong but i think we've been able to configure out a way to conform you know homework and in -class learning and tests around the technologies available to us fairly successfully
to date um and i'm fairly confident we'll figure it out you know and i like and i and i think it's gonna and i on the much more positive side and i went to public schools i don't know if you did too like you end up with some pretty bad teachers you know at times and now you have an outlet you 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 there's an 11 -year -old right now who's going to start a really amazing company 10 years from now whose chat GPT is going to be their primary tutor that led to that outcome, and I think that's pretty cool.
I have a two -year -old, and it feels like there's a new milestone.
There's when to give them a phone, when to give them, I don't know, Snapchat, whatever kids use these days, and then it's when to give them their first ChatGPT account.
I wonder how soon that's supposed to happen.
I think ChatGPT, my personal take, because it's different from 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 chat gbt 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 um and and i don't think a lot of parents before chatting said when should i let my kid use google search you know
that's like a different type of tool i think thinking like that is the way i think about these technologies and so is the form factor for your kids like an iPad or a laptop or 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.
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 will end up fairly meaningful markets and then I'll end with how I think it's going to play out.
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 just like the cloud infrastructure as a service market.
And the reason for that is that creating a frontier model is entirely a function of CapEx, and you need a company with huge amounts of CapEx capacity to build one of these models.
All of the companies that were startups that tried to do this have already been consolidated, or almost all of them, Inflection, Adapt, Character, and others.
And I think it's just not...
It doesn't appear to be a viable business model for a startup up because of the amount of capex required and there's not enough runway you can get 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 um so i think that's going to end up probably no entrepreneur should build the frontier model that's my my unless you're elon yeah oh yeah 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 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.
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 eval tools.
tools, um, 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 I, 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, uh, if you look at the infrastructure as a service market and the cloud tooling market, like the confluent and data bricks 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, and others.
But there's a lot of others that were sort of obviated by technology from the infrastructure providers themselves.
sells. So those companies probably are the most at risk for, you know, a developer day from one of these big foundation model companies releasing exactly what they do.
So you have to, there's probably a lot of people who need your tool, but the question will be if or when is probably the right way to think about it.
One of these large infrastructure providers introduces a competitor, why will people continue to choose you?
So it's a good market, but it's a little bit it 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.
I think that's going to be the product form factor.
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 Carvey that make agents for both the legal, paralegal profession, antitrust reviews, reviewing contracts, contracts, et cetera, et cetera.
There's companies that do content marketing.
There's companies that do supply chain analysis.
I think this is sort of like this offers a service market.
They'll probably be higher margin companies because you're selling something that achieves a business outcome as opposed to being a byproduct of the models themselves.
They will almost certainly pay taxes down to the model providers, which is why those model providers will end up extremely large scale, but probably slightly lower margin.
And I think, you know, the market for them will be probably less technical.
I mean, if, you know, if you think about the purest form of software as a service, it's not like you ask, like, what database do you use, right?
It's really about the feature and function.
I think that's where agents will go.
I think it's going to be more about product than it is about technology over time.
Just, you know, just going back to my metaphor you know in 1998 when mark and parker started salesforce just getting that database running the cloud was like a technical achievement you know nowadays like you know no one acts ask 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 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 talk a little bit less about how you deal with the models in the same way modern sas 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 it's going into sort of slowly evolve towards that um i i'm very excited i don't think startups should probably build foundation models
i think uh 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 um long tail agent companies come out um i was looking at a website for the top 50 software companies in the stock market and obviously like the top five or the big big one ones like microsoft amazon google all that but like the next 50 are all sass companies and they're like some of them
are very exciting some of them are like super boring but this is like how the software market has evolved i think we're 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 uh it's going to be like a lot of like things where people are spending a lot of time and resources that an agent can just solve.
But it requires an entrepreneur who actually understands that business problem 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 Mark Benioff on the podcast. You guys were co -CEOs.
And he was extremely agent -pilled.
All he wanted to talk about was agent force.
Clearly, you are also very agent -pilled.
I've never heard the term agent -pilled.
I'm going to use that one.
clearly you guys saw something that was just like okay we need to go all in on agents this is the future what is 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's on the open ai board with me they'll talk about like what is the value of technology well it helps drive productivity in the economy and And if you look at the, one of the big jumps in productivity in the economy was in the 90s.
And I think a lot of folks I talked to think it was actually that very first wave of computing where people made, like, ERP systems and just, like, put accounting into computers and databases, even, like, mainframes.
We're not talking, like, the PC era, because it was such a huge step up.
Like, you know, just imagine, like, the ledgers of, you know, numbers that you'd have for, like a large multinational company before and it truly just transformed departments i'll give you a little toy example my dad just retired he was mechanical engineer and he was talking about when he first started his career in the late 70s and he went into a mechanical engineering firm the majority of the firm were drafts people so basically you'd take an engineering design 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 draft people at his company you just make the the design and first autocad and now rev it and it you know it's a 3d model and you know the drafting has actually been eliminated it's just not a thing one needs to do anymore the the actual design and drafting drafting is not a thing that exists it's just like you can it's just a design i 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 it is interesting.
Like there has the promise of productivity gains from technology hasn't been as realized, I think, as some people thought.
I think agents will truly start to bend the curve again, like we did in the very early days of computing, because software is going from helping an individual be slightly more productive, you know, to actually accomplishing a job autonomously.
And as a consequence, just like you don't need drafts people in 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 or you'll have like a value discussion and you'll You'll do these somewhat convoluted things like, okay, it's like
you're selling a sales thing.
Okay, well, if every salesperson sells 5 % more, da -da -da -da -da, you should pay us a million dollars.
It's roughly that conversation.
And it's so unattributable.
And it's why it's so hard to sell productivity software, which I learned the hard way.
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 like how it like transforms the business model of the software industry where there's going to be like a before and after like I don't know how many people still sell perpetually licensed on -premises software but it's de minimis at this point I think we're going to go through a similar transition like the whole market is going to go towards agents I think the whole market is going to go towards outcomes -based pricing not because it's the only 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 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 this being successful.
Can you just briefly just explain a little bit, what is outcome -based pricing for people that haven't heard this term before?
And then just how does it work for Sierra to give an example?
Yeah, I'll start with the example and then I'll broaden it.
So at Sierra, we help companies make customer -facing AI agents, primarily for customer service, but more broadly for 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, 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 is software, some of it is 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.
payment. And that essentially means you saved, you know, call it $15 because you didn't have to have someone pick up the phone.
So in our industry, basically we say, Hey, if the AI agent, you know, solves the customer's problem, they're happy with it.
And you didn't have to pick up the phone.
There's a pre -negotiated rate for that.
And that's we call it like resolution based.
There are other outcomes as well.
We have some sales agents being paid a sales commission, believe it or not, we do.
We really think of our agents as truly customer experience, like the concierge for your brand.
We want to make sure that 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.
What's really neat about it is if you talk to any CFO or head of procurement with their big vendors, they look at the bill of materials and it's like overwhelming and it's impossible to know if you're getting the value that you hoped from that contract I think consumption based which was popular particularly in the infrastructure space is closer to it but I'm not sure like a token is actually a good measure of value from AI either I always use the analogy like right now most of the coding agents are priced per token or per utilization but there's this famous story of a apple engineer who had a bad
manager who's like how do you report how many lines of code you wrote every day um 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 refactory and deleted a bunch and it's 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 I think that's the whole point of all this.
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 uh 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 customer -centric when you do this the right way.
I think it's just a better version of the software industry.
So I think it's right from first principles.
It's right for procurement partners.
And I think it's right for the world.
We've been chatting a little bit about productivity gains.
There's a lot of skepticism in the headlines these days of just like, what is it actually doing?
Like, is it actually helping people be more productive?
If 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 uh almost every software engineering firm i know uses something like cursor to help their their software engineers most people use cursor right now as a kind of coding autocomplete though they have a lot of agentic solutions and there's a lot of like openai has codex and there's you know claude has i can't remember the anthropic product so So there's lots of agentic 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, 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 like, you know, mucking up the machine a little bit and having something that's not ideal.
There's a couple of techniques that I think are interesting.
First, I think there's a lot of AI startups now working on things like code reviews.
I think this idea of self -reflection in agents is really important.
Having AI supervise the AI is actually very effective.
Just think about it this way.
If you produce an AI agent that's right 90 % of the time, that's not that great.
But how hard would it be to make another AI agent to find the errors the other 10 % of the time?
That might be a tractable problem.
And if that thing's right 90 % of the time, just for argument's sake you can wire those things together and have something that's right 99 of the time so the it's just a math problem like you know and it turns out that you can make something to generate code you can make something to review code and you're essentially using compute for cognitive capacity and you can layer on more layers of of cognition and thinking and reasoning and produce things increasingly robust so i'm very excited about that the other thing though is root cause 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 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 want to see that gains now.
And you really have to create, like, root cause analysis and systems and say, like, you know, how do we sort of go root cause every bad line of code and actually give the right context and produce the right system so the models can do it today?
over time that probably like less necessary and you'll have less context engineering necessary to do it but you really have to think of this as a system and i think people are sort of like waiting for the models to 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 it's you can do it and so you know for customers using platforms like sierra yeah yeah agents aren't perfect but we're creating a system that lets customers create a virtuous
cycle of improvement.
If you want to go from a 65 % automated resolution rate to 75%, we have a billion tools to let AI help you do that.
Identify opportunities for improvement, figure out why people are frustrated.
What new capabilities can we add to our agent to improve the resolution rate?
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 curser 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, 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 really want to like, you know, find the intersection of your particular product and code base with the context available to these coding agents and systems and fix it at the root is sort of the principle here.
Got it. That is very cool.
I hadn't heard people doing that.
Model context protocol.
Makes sense. We've talked about productivity gains outside TX.
Just to give you a chance to share how amazing what you've built is, what are some of the gains you see from people using Sierra?
Yeah, we have our customers see anywhere between 50 and 90 percent 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, 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 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, She'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 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 touch point as a website or their mobile app.
In the short term, it can really transform the costs of running a customer service team.
And what's remarkable is do so with really high customer satisfaction scores.
That Weight Watchers agent, I believe, has a customer satisfaction score 4 .6 out of 5, which is pretty amazing.
And what's interesting about service, too, it's often people having a problem.
And so when you have a clear, I don't know if you use them in the airport, I think that agent has a CSAT score of 4 .7 out of 5.
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.
in? And if you think about all the best moments you've had with a brand, it's like that store associate who you know.
And it's like for me, it's like the butcher at the grocery store.
I love to cook. He knows me.
We talk. Can you actually produce that at scale for a company with 100 million customers?
And can you do it in a really personal way?
And I think we're really on the cusp of enabling that.
Let me ask you one more question before we get to a very exciting lightning round.
there's a lot of um founders struggling with go -to -market in ai with their ai apps there's so many apps these days so many products so many uh things coming at buyers at large b2b companies clearly you guys have figured something out i imagine your name helps uh investors help but uh 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 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 couple of 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 Shopify in the early days.
And there's a lot of other products like that where you're trying to sell to small merchants.
That's great. It doesn't work well when your buyer and the user of the software are different.
So I always 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 having sign up and buy with your credit card doesn't make sense because the person using it is not the person with the credit card, and it just doesn't work.
And then there's direct sales.
And direct sales had gone, I don't want to say out of fashion, but if I think of the best direct sales companies, probably there's a lot of lineage from Oracle.
Colby. Think SAP, Oracle, ServiceNow, Salesforce, Adobe, perhaps.
And there's others as well.
And these were companies that sold into 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 opportunities in ai are actually uh meet that qualification where the buyer and the user are not necessarily the same uh same person and it really requires that go -to -market motion where i see entrepreneurs stumble is they'll sort of choose uh a go -to -market market motion without thinking through the what is the process of purchasing the 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, it sort of had gotten a bad name.
And I think I'm sort of thankful to see it coming back in a lot of the AI market.
I feel like this message is something a lot of founders need to hear, especially founders that aren't from a business background 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. Brett, is there anything else that you wanted to share?
any last nugget of wisdom anything you want to double click on before we get to our very exciting lightning round no go ahead okay let's do it here we go uh 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 non -fiction but probably i had to pick one sort of in the area of the topics we talked about 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 business books should be like an article.
So maybe buy the book and punch into ChatGPT and get the summary.
But buy the book. Clayton Christensen talked about it, but it's a really good framework for thinking about delivering value with your products.
um and i think it's a it definitely influenced me on the um actually one book i do recommend is um endurance which is the story of shackleton's trip to go to the south pole um like half the book is him starving to death and eating seal meat with his uh crew of people frozen in their boat uh i've never seen a better story of grit in my entire life it's like kind of remarkable that it's 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, OK, it could be worse.
It's a great book, too.
It's just remarkable.
That's a true story.
And one thing he did a great job at is setting expectations for folks that joined that famous newspaper.
I don't know if that's true.
It's like remarkable.
That's oh, it might not be true.
I don't know. God damn deep fakes, even back then.
OK, do you have a favorite recent movie or TV show that you've really enjoyed?
you haven't gone to any new tv shows recently we just watched um inception with the kids and they loved it and uh made me um appreciate christopher nolan so uh i and what a cool movie cool con i love it's the type of movie when you watch their film and you can have conversations for two days afterwards about it so um just a great film i saw someone using i think vo3 to create their own inception videos where the world's wrapping around each other oh man okay do you have a favorite product that you have recently discovered that you you love or one you've loved for a long time i'm really a big fan of uh
cursor i think it's like change i'm uh 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 open and other so i think cursor will be in its current form as a transition product uh 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 I create software so I've just been like spending a lot of time with the product just because it's so core to my like what I love to do and and it's
a really well well uh crafted product I think the first time someone's actually mentioned cursor in this answer so might be the beginning of a trend uh Michael Terrell 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, who 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.
So it's definitely like 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.
Uh, final question.
We talked about you inventing the like button at friend feed.
Were there other, uh, thoughts of what they would call it other than like, was it just like, obviously like, or is there other thinking there?
The context of this was before emoji.
Uh, so, uh, there, if you read the comments on friend feed posts, at least 70 % of them are cool or wow, or yeah, or neat.
and one of the principal like uses of friend feed was to have discussions about things 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 uh and so we the first version that i made had a heart
um and though she denies remembering this but there's a uh 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 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 um was just a much more neutral sentiment uh and and that's why it was hard to translate because it was subtle um and we so that's how we
ended up with this we started with a heart and and i don't know if we ever had the word love but we definitely start off the my pornography and then like which just felt like this uh positive yet as neutral as possible within the realm of positive so that it could work for like a uh 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 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 have a lot of features so many so many ai features yeah brett this was incredible Incredible.
This was an honor. I so appreciate you coming on this podcast. Two final questions.
Where can folks find you online if they want to reach out?
Maybe go see if they want to work at Sierra?
And how can listeners be useful to you?
If you want an AI agent to help with customer service, go to Sierra .ai.
If you want to apply here, Sierra .ai slash careers.
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 useful to you is it tryout sierra anything else there yeah tryouts here i'm a single issue voter stay on the message i love it yeah right brad 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 podcast spotify or your favorite podcast app also please consider giving us a rating or leaving a review as that really helps other listeners find You can find all past episodes or learn more about the show at Lenny's Podcast .com.
See you in the next episode.
