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

Transcript from https://podmenti.com/t/97627c30870c455d

You're CTO of Meta, you're co CO of Salesforce, you're chairman of the board at Open AI. How do you think the AI market is gonna play out? The whole market is gonna go towards agents. I think the whole market is going to go towards outcomes based pricing. It's just so obviously the correct way to build and sell software. So makes me think about I had Mark Benioff on the podcast. You guys were co COs. He was extremely
It's so hard to sell productivity software, which I learned our way. And to not do that well with a link from the Google homepage is like kind of embarrassing.
They sort of gave me another shot to do a V two of it that resulted in Google Maps. We got about ten million people using it on the first day.
Today, my guest is Brett Taylor. Bret is an absolute legendary builder and founder. He co created Google Maps at Google.
He co-founded the social network Friend Feed, which invented the like button and the real time 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 seven hundred fifty million dollars.
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.
While we're all still sleeping on the impact that agents are gonna have on the business world,
How coding is going to change in the coming years, where the biggest opportunities remain for startups, lessons on pricing and go to market in AI, the story behind the like button.
And so much more, this is a truly epic conversation with a legendary builder.
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With that, I bring you Brett.
Taylor.
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Brett, thank you so much for being here. Welcome to the podcast.
Thanks me.
My pleasure. There's so much that I wanna talk about. You've done so many incredible things over the course of your career. It just boggles the mind the things that you've done. And we're gonna talk about a lot of that sort of stuff.
But I wanna actually start with the opposite. I wanna talk about a time that you messed up.
A time that you screwed up in a big way. We have this recurring segment on the podcast I call Fail Corner.
And so I thought it'd be fun to just start there.
Before we get into all the great stuff you've done.
What's a story that comes to mind when you think about maybe your biggest mistake in building a product?
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, uh, for me as a a product designer. So
I joined Google in uh late two thousand two, early two thousand three.
Yeah.
I was one of the earliest associate product managers at the company and first was
working on the search system, uh essentially expanding our index from one billion web pages to ten 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 Mercermeyer um gave me the opportunity to lead a new product initiative, which was a big bet on me. And I was you know, it was both an opportunity to do something for Google, but I was also being pretty
scrutinized just uh as a young new
Product manager.
And the premise given to me was work on local search. Uh at the time the yellow pages was still dominant.
And while Google was really good at searching the web. It wasn't really good for finding a plumber or a restaurant just because
It wasn't really a huge part of the internet at the time, so this content wasn't necessarily on the internet.
And even if it was, it was you really needed a different
Uh you didn't really want to find
you know, plumbers in Manhattan, you want to find plumbers in San Francisco if you're me. And so it was a kind of a
But a technical problem and a product problem and a content problem.
Uh
We
Launched a the first version of that product that uh I was the product manager for was called Google Local.
And it was
You know, with the I'll be a little bit more critical now than I might have been at the time, but it was um a little bit of a
a Me Too version of Yahoo Yellow pages, you know sort of uh um
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, made a standalone
uh site at local dot google dot 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 billion. I mean you could put almost any link on the Google homepage and get a lot of traffic to it. And despite that
It didn't do that well. And to not do that well with a link from the Google homepage is like
kind of embarrassing. You know, it's it's 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 that 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 since than at the time that I had some of the time, but
Why use this instead of Yahoo Yellow Pages, but more than anything else, like why use this instead of yellow pages? You know, it was sort of a digital version of 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 V two 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 I I certainly was feeling a little dejected from going from sort of a hot shot.
New PM to a new thing.
She spent a lot of time.
Thinking about
How can you make something that's just much more compelling and and not just sort of a digital version of of
uh the yellow pages and not just uh so so similar to some of the other products out there.
And that's ended up being the threat that we pulled uh that that resulted in Google Maps. Um we had
uh licensed
From map quest the ability to put like this little map.
Next to the search results, it was always the ugliest part of the product. And we always
Yeah, made sort of these like
backhanded comments about it internally.
And we spent a lot of time saying, like, what have we sort of inverted the hierarchy here and made the map the canvas? We ended up finding Larsen Jens, uh Rasmussen, who had been working on this.
Windows mapping product and we sort of uh got them into the company and
started exploring this space and um
Uh it ended up where
Through that exploration we ended up integrating a lot of different products. We ended up integrating
mapping, local search, driving directions like all of these products at the time were actually
separate product categories and ended up with something that kind of
Uh
redefine the industry and 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 ten million people using it on the first day, which at that scale of the internet at the time was huge.
And then in August of twenty
Two thousand five.
We integrated satellite imagery from a recent acquisition called Keyhole, which became Google Earth.
And we got ninety million people using it on the same day. Everyone wanted to look at the top of their house, you know, when the imagery came out.
Mm-hmm. And it was really interesting because there's so many subtle product lessons in there. Um, you know, first is
I think as you have these new technologies
Rather than literally digitizing what came before, if you can
create an entirely new experience. It it creates it sort of answers the question for a new customer, like why should I give this the time of day? You know? And so
Really
disassembling the Lego set and reassembling it to something new rather than just digitizing what was there before.
Certainly that was the lesson I think in Google Maps. It really was native to the platform in a way that like a paper map couldn't be, you know, and and that was like a a really meaningful breakthrough.
Um, and then with satellite imagery, it honestly wasn't the most important part of Google Maps, but it was sort of the sizzle to the stake. And it created uh you know, I don't think the term viral was a thing people said back then, but it created a viral moment. We ran Saturday Night Live, which is the coolest thing, Andy Sandberg and I think it was called Lazy Sunday.
you know, wrapped about Google Maps and Lars and I were texting each other, We did it. We're very alive.
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 s 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 um that I work on.
That is that is an awesome story. One I I think it's really empowering for people to hear.
Uh even you, Brett, who I'm gonna share all the successes you've had have had a
massive failure with like the CO of Google versus Ma Meyer just like
Brett, you screwed up. This is
And it was like such a big bet. So
One just yeah.
Like it's possible to succeed as you have succeeded in spite of a massive failure like this.
And then some of the product lessons you share, just to highlight a few of these things, because I think this is great, is just
Uh you will
often not win if you just make something
That's kind of a better copy of something else.
Would you want to look for something that is an entirely new experience, something that's differentiated, something that's a lot more compelling? Um let's flip to talk about what you've learned from actually being very successful at a lot of things.
So I was looking at your resume and you basically have been
Very successful at every level of the career ladder.
And in
Such a huge variety of roles.
So let me just read a few of these things for folks that aren't super
Familiar with your background.
You were CTO of Meta, you were co CO of Salesforce, you're also
C P O F C O Ot 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 gonna talk about that. You're chairman of the board at OpenAI.
You were chairman of the board at Twitter.
You've also founded three different companies, one social network, one productivity docs company called Quip.
And now Sierra
Fun fact at
Friend feed, you invented the like button. I don't know if people know that, and also just the news feed. I'll just throw that out there to give you some credit.
So you're basically an associate product manager, an I C product manager, an engineer.
CPO, CO, CTO.
See 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 mindsets or habits or just ways of working have you
worked on building in yourself that you think of most
contributed to you being successful in such a variety of roles and levels.
Yeah, it's actually something I am proud of. I I like the fact I've worn different hats. It's actually amusing.
When I meet colleagues that I've known from one of those jobs, they'll often think of me through the lens of that job, you know? And so
Uh you know
go to meet folks from Facebook and they think of me largely as an engineer. They'll meet folks from Google, they think of me largely as a product, you know, person
At Salesforce, you know, a lot of the folks there interact with me as like a
For lack of a better word to suit, you know, like the boss. And I I'm not sure they think of me as a as an engineer at all, even though, you know, still probably coded on the weekends for fun.
And
One of the things that is a principle for me is to have a really
flexible view of my own identity.
I really think of myself
I probably would self describe as an engineer, but more broadly I think of myself as a builder and I like to build
Products and and I think companies are one of the most effective ways to build products. There's also things like open source, but I think
Uh, I'm a huge believer in the confluence of technology and capitalism to produce
You know, just incredible outcomes for customers.
And as a consequence, I think to to really build something of significance, uh, you know, I think to be a great founder, you really need to be able to
uh not have such a ossified view of your identity that you can't transform into it.
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.
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 uh uh good engineering. I mean, if you're building a technology company, the technology comes first. It's you know, why this industry is so transformative.
I probably credit
And I've told this story before, but I'm I'm very grateful for her, but I probably credit Cheryl Sandberg for um
really changing
the way I approach new jobs. Um
The
Um so I had
uh just become the chief technology officer of Facebook.
Yeah.
When I first got the job it was uh
sort of the flavor of CTO where I had relatively small group reporting into me, but
uh uh contributed almost as like a very senior
kind of architect, you know, on on a number of projects.
And then at some point, uh, Mark Zuckerberg reorganized the company and kind of split it into a bunch of different groups. So I ended up with a
Very large group.
Uh you know, it's essentially running our platform and mobile groups, uh, products, design, engineering.
So I went from, you know, a handful of reports to like over a thousand or something. It was a it was a big group.
And it was the largest, you know, management job. I had I become a manager at Google, but a modest uh modest team and so
Uh, and I was doing f 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'cause the the presentation I got didn't make my quality bar and I was
editing it and sort of griping about, she sort of pulled me into a room and
um kinda gave me a talking to like a little bit about
Holding my team to as high of a standard as I have.
Uh, if someone wasn't, you know, meeting my expectations, you know
what was my plan to like manage them out of the company and or, you know, just like kind of
Giving me management one oh one.
Uh and
And she uh she's a remarkable mentor in the sense she can kinda give you feedback that's very direct and like often a bit uncomfortable and it but you know she cares about you, you know, and so it's the type of feedback you listen to.
I sort of went home that night and I was kinda stewing on it and like not very happy. I was like, you know, you get sort of naturally a little defensive in those moments like Is that really true? Am I really fucking it up or is it you know, she overreacting?
And then I woke up the next day and I was like, No, she's right and I had realized sort of this subconscious
like limiter that I or 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. I think in
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 at Facebook. What's the most important thing to do today to make our mobile plat mobile and and developer platform successful?
And
When I
reframed the job that way. I did different things. And the thing that was the biggest pleasant surprise to me
was I liked it. Uh you know, I thought I
Liked engineering and product, but in fact when I
you know, changed an organization and it turned out to be more successful. I derived a great deal of joy from seeing that success.
uh, you know, our developer platform had a lot of partners and you know, when there was an issue there and I'd spend time on partnerships and it worked and
you know, our platform became healthier, the partner became more successful. I was
took pride in that success.
And then I just started being better at my job and
I realize that um
the actual act of engineering or product design or all the things I thought I liked. What I really liked is impact. And and uh
And so that conversation led to my sort of waking up every morning, uh, sometimes literally, but certainly in the broadest sense of the word saying
What is the most impactful thing I can do today? And really think in a
Almost like a if you had an external board of advisors, you know, telling you like where are the
What are the things where if you focus on them you can maximize the likelihood that
what you're trying to achieve will happen.
And sometimes it's recruiting.
Sometimes it's product, sometimes it's engineering, sometimes it's sales.
And I've become much more self reflective just about what is important to work on.
And
I have become much more receptive to doing things that I previously would have said aren't my favorite things to do because I derive so much joy from having an impact that I enjoy a lot more things now. And uh so I really credit show. I'm so grateful. And actually it's interesting, I think a lot about this when I give feedback to people now, just like
Uh, those moments that can kinda like change the trajectory of your career. Uh I mean, I give her all the credit for it.
There's so many people that share stories of Cheryl Stanber giving them advice and that changing their life.
What a what a mench.
Yeah. My biggest takeaway from this, uh, which is this question of what is the most impactful thing I could do today.
Such a powerful heuristic just to kinda keep in mind. To your point, you may realize you don't want to be doing sales or hiring, but
If that's the most impactful thing.
And you end up doing it, you may realize I like this and then I'm good at this and
Can I double click on that though for a second absolutely I think it's really hard.
Um
One of the dangers for founders and product managers, uh, but I think particularly for founders is
Incorrect storytelling.
Uh 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.
Um, well, you better hope you're right.
Because if you orient your strategy around
Fixing that problem, uh
And you're wrong.
Your company's gonna fail.
Um, so you know, why did you lose a deal?
Uh, you know, you could talk to the
salesperson who was on the account, or perhaps maybe a product manager was involved in the conversation.
It's very important to have intellectual honesty in those moments because you could say something like Oh
Uh they didn't buy it because it the platform cost too much.
Um that and that's something a salesperson might say.
Maybe the real reason is they didn't actually see much value in your platform. So it was communicated to the salesperson as it was too expensive. But in fact
It the problem was product differentiation.
And you could end up going into a discussion on pricing.
When in fact there was a much deeper, much harder problem to solve there.
But it's not, you know, just like when you break up with someone, you don't say, It's because I don't like you anymore. You say it's not you, it's me. You know, you say all these sort of pleasantries because we're all social uh animals and you and you want to be pleasant with the people that you around you.
So, you know, literally taking what a customer says or what a user says in like a focus group or a usability study is rarely
Uh
Correct. Um it often is uh 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
Yeah, you the the proverbial redesign I joke's like the dead cat balance of a consumer product. Like a re this next redesign will fix all of our problems. I I don't know if it's ever ever worked.
Um and then you if you I met a lot of entrepreneurs who are like
come from sort of a business development background, they're always thinking about partnerships and and you know
Oh, we just get this partnership done for this distribution channel. Everything's gonna change.
And
I think it's really important when you're a founder
to be self aware that you will naturally, subconsciously
pick the thing that is your strength, your superpower.
as a solution to our problems and in fact
If that you think that's a solution to your problem, it may be right, but you probably by default should question it. Like if you think
the thing that you've been doing your whole career is the way to fix your problem.
It's at least
thirty percent likely that you've chosen that because of comfort and familiarity, uh, not truth.
And so I think it's like one of those skills I think is
uh it really goes around to like do you have a good co founder, do you have a good, you know, leadership team, uh if you're a product manager, like your partner in engineering, your partner in marketing.
You really want to have very real conversations, um, to ensure that you're actually working on the right
the actual correct thing. And I think it's easy to say what's the most impactful thing.
To do today.
My guess if a lot of people try that, they'll lie to themselves more often than not. And it's a very challenging question to answer. The question's 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, where you actually end up
Oh yeah.
Friendly was my first company. Um
At our peak, we had twelve employees, um, twelve of the best people I've ever worked with. Um started the company with
Jim Norris, who's an engineer I've known since Stanford, and Paul Buhite and Sanjeev Singh who. Um, Paul started Gmail. Sanjeev was the first engineer on Gmail. So we had the
Google Maps people and like Gmail people was like
Pretty awesome uh founding team.
We made a social network, as you said, we sort of invented a lot of concepts that um be became popular in in the newsfeed. We invented the like button. It was really neat. It was a fun time.
We were only really popular in Turkey, Italy, and Iran. And at one point we were blocked in Iran, so we're only popular in Turkey and Italy and Silicon Valley. Um
Tuesday actually a lot of folks that look on Valley are like I love love friend feed. I'm like, that's awesome.

successful business.
There was a we were a follower oriented social network, um, not a friendship oriented social network, which meant a lot of our content was more like
uh X or Twitter than it is Facebook in that respect. And a lot of showing newspaper articles, interests, scientific communities, things like that.
And uh there was a period when um
Twitter, uh which was one of our competitors at the time, though there was a lot more social networks at the time.
Uh I probably screened us a little bit. I think Obama, Ashton Kutcher, and like Oprah Winfrey all went on Twitter like in a in a summer and
We just got our ass kicked. You know, it's like
And
It was a great example of you. I think
Eleven of those twelve people were engineers and we're just making product and
Uh, I think it was Biz Stone. I mean, if you talk to the Twitter folks, they could give you the history on this, but I think Biz was really focused on like getting
celebrities and public figures onto Twitter.
Which is totally obvious. Like if you have a uh social service that's oriented towards following people, put some people in there worth following, you know, like
And instead, you know, we were exclusively focused on polishing the product and
We actually I think
Yeah, at our uh
uh sort of peak of popularity. We were very confident, just, you know, I think it was a time when like Twitter had the fail whale and was down half the time and people couldn't even use it.
And you know, we our product, we were innovating faster, we had more features, people liked it, we could
And and we were up a hundred percent of the time.
And we totally lost for no reason related to product at all. And uh and it was an example of
And I think uh
Somewhat famously, not of like a lot of great entrepreneurs have come out of Google because once you like Google is so successful, I think it's hard as a product manager to sort of see like
distribution and all product design and even business model when you have AdWords and
you know, money's raining from the sky. It's hard to you know, uh y 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 of Google. So I we're just getting punched in the face, you know, and learning this the hard way.
And so that was probably the most prominent example of it. You know, and I think we probably did have a I can tell you all the flaws of that product, but I don't think that was like
the reason why we lost. There's a lot of reasons. I think there was a lot of flaws with the product.
But it was a lot of other stuff. And so um
I've learned like accumulated these skills over time, but I
Say the hard part of that question is answering it correctly is
It's hard when you don't have experience 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 uh Ashton Kutcherman wanted to, right? Yeah, I was not like he's on my you know, um on my Rolladex.
But I probably wasn't soliciting advice from the right people. You know, I think that's what's great about the technology industry is there's a lot of advice.
choosing whom you listen to is actually quite difficult.
But I think we're somewhat myopic. You know, we're kind of in our own little world, uh uh creating this product and we weren't asking
people to like from the outside in to say like what what are you seeing that could go wrong, what are you seeing that could go right? What are you seeing in the industry that we're not doing
that you think we might want to do. And this is why
Boards are important. This is why you know, finding the right advisors. The advisors who actually
Tell you what you uh 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 market at the time.
But if I had solicited the right advice, I w you know, uh could have learned that that was a shortcoming. Um and and I think that was uh a deep lesson I took from that. I'm a huge believer in in boards and and getting good advice.
Any kind of heuristics or advice for people to know whose advice to listen to? What do you pay attention to when you're like, okay, ignore this person, but listen to this person?
Yeah.
That one's tough. It is definitely
It does come down to good judgment and being judge of people's character.
One thing that is particularly hard is there's not a strong correlation between the confidence
Uh with which someone expresses an opinion and the quality of that opinion.
No, I don't want to say it's inversely correlated, but you know
That's funny with all the podcasts out now.
If there's topics I know a lot about, you know, sometimes the most elegant eloquent, confident
statements about things I know a lot about are are the least accurate and it sounds extremely persuasive and and the
Uh so
It does require very good judgment. Um
One thing is I think not just asking for advice, but asking people who should I talk to to get good advice and you'll find some common answers there. And that's often a really um strong signal um of of good judgment.
And then one thing I found is um
when you ask for advice, don't just ask what to do, but why, like be it like an obnoxious two year old kid. You know, why, why, why, why, why
And really tried to understand the framework.
that someone is using to give you advice.
The
Interesting thing about advice is people are often
extrapolating from relatively few experiences. 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 done it.
So it's it's a useful anecdote, but if you don't ask why and understand they had one experience and here's what happened.
Uh it can come across as a rule when in fact it's it's an data. Um
And if you ask advice for three people and they all have very similar interactions, you can create kind of like a first principles framework from which
That advice emerges.
And when you start applying it, you're applying it with a degree of nuance that you couldn't if you're just following a rule.
Um so I think
One is it does come down to good judgment, I think.
No, uh I don't know how to
teach that. I think it is probably a very
I'm a huge believer in good judgment. It's one of the things I hire for. I just think if that's something that
Uh you know.
probably comes from a mix of self-reflection, you know, like you really need to hold yourselac accountable, like as a as an entrepreneur or as a product manager, like if you made a bad decision,
Spend time reflecting on it, like number one.
And really try to understand why and try to like always improve your judgment. I think at the end of the day that is why
you are a good entrepreneur, a good good product manager.
And number two, when you get advice, really understand where it's coming from and why. So that you can create sort of your own independent
The view of
Uh where that advice came from and recognize that
No one's advice is statistically significant. Or very rarely is it. I mean, if you're getting like advice on investing, you know, for more and buffet, yeah, okay, it's statistically significant. But that's not most advice is like
Something happened to you once and uh 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 wanna 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.
Um let me just ask you this question something a lot of people in college are thinking about. Do you think it
Still makes sense to learn to code.
Do you think this will significantly change in the next few years?
I do still think it's studying computer sci uh w is a different answer than learning to code, but I would say I still think it's extremely valuable to study computer science. Um
I say that
because I think computer science is more than coding. Um if you understand things like, you know, big O notation or complexity theory or
Uh, you know.
uh study algorithms and you know why
But
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 code in than than
Write in the code.
The reason I think that is I do think
The act of
Uh
Creating software is going to transform from
Type in into a terminal or typing into Visual Studio Code.
to operating a code generated machine.
Um, 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
uh major to learn systems thinking. Um and
At the end of the day, AI will facilitate uh you know creating the software.
We may do a lot more in the next years we can't even imagine.
But your job as the operator of that code manager generated machine is to make a product or to solve a problem. And you really need to have great systems thinking. And
You're gonna be managing this machine that's doing a lot of the tedious work of making the button or
You know, connecting to the network.
Um, but as you're thinking of the intersection of a technology and a business problem, you're trying to affect a system that will solve that problem at scale for your customers and
That systems thinking is always the hardest part of of creating products. I'll I'll just give you like it's
It's this cheesy simple example, but I think it's representative.
At Facebook we would always uh you know, y we spent a lot of time design the newsfeed.
And have you ever had like a really, really good designer and they showed you at the time a photoshop mock up of of the newsfeed, it was just all as beautiful. The photos, the family was
Happy and
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 uh the light button, everything was just perfect.
And then
You'd like implement that design and you'd look at your own newsfeed and it looked like shit because it turns out like
Not everyone's photos were made by like a professional photographer.
The posts were all these different lengths, the comments were like, you know, the you suck and like all that stuff. And then all of a sudden you realize that like
Design in a newsfeed, like Photoshop is the easy part.
You need to actually design a system that produces
a like uh 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 I'm sure it's changed a lot since you know I left in two thousand twelve, but
We um made a a system so you know designers had to show their newsfeed designs with real newsfeed data that was messy, uh, 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 f have a system in your head.
You need to understand the basics of what's hard and what's easy and what's possible and what's impossible. And AI can help you do that too, by the way.
But I I do think that's a really useful skill.
I think in general with the advent of AI agents
And you know, uh 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 re wrote Google Maps, like everyone talks about that story because of like and it's I think it's because of Paul Kuhay, who told it on some podcasts and sort of made the rounds.
I think that's gonna end up sort of this vestige of the past. Like I uh almost like the human calculators at NASA before the computers were invented, like wow, a person was a calculator? Wow, that's fun. Like tell me that story.
I think just like what I was good at will no longer be useful, uh, in the future, or certainly not like valuable in the future, and that's okay.
Um, so I think we need to have a really loose view of it. But the idea that you shouldn't study these disciplines is sort of like people say, I don't want to study math because I'm not gonna use it in my career for X.
Well study math's quite important. Like it teaches you how to think.
It teaches you like how the world works, physics, math.
And I think computer science, uh especially at least sort of the the foundations of it
will continue to be the foundations of how we build software and
understanding that when you're interacting, particularly with something that's smarter than you.
producing code you may not completely understand how you constrain it and how you get it to produce these outcomes, I think it will require a lot of sophistication, actually.
It's such an great answer. There's this always sense of this binary should I learn T code or not.
And your point here is learn the to understand how engineering works and how systems work and how
what your code does and how it all interconnects, but the way you actually do the coding at your desk will change significantly.
This reminds me of something you mentioned on a podcast recently, this idea that you think there's gonna or there should be a new programming language that is more designed for
L O Ms versus humans. Can you just talk about that? Because I think
A lot of people aren't thinking about that.
I don't know if 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 forty 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 seventies, you know, uh you would
Tell a computer what to do.
Um, or maybe mid to late seventies.
Uh then we into uh you know invented early operating systems and uh time sharing systems and from the invention of things like uh Unix at Bell Labs.
And and Berkeley, you ended up with
Um the C programming language for Tram.
uh and a in a lot of sort of higher level programming languages, I think Fortran and then C.
And
You
We've sort of moved up the low layers of abstraction. So no one does punch cards anymore, obviously. A few people write assembly language.
Some people right.
C, some people write Rust, but a lot of people write Python and and TypeScript and things like that.
And as we've invented more and more abstractions
Um we've made it easier to do high leverage things. Um, so uh, you know
I always look if you look at how remarkable Google was back in the day, or Google Maps, like you could probably give a lot of React programmers the task of make a draggable map now, and I think a lot of people could do it. That was true R D, you know, back in the day.
when Salesforce was created in nineteen ninety eight, just putting a database in the cloud was hard.
And yeah, that was just like that alone was a technical moat.
that is now trivial with Amazon web services and and that technical mode is is comically narrow, but the product mode is is quite
quite large.
I think that
Yeah, the
Act of writing code.
is um 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, uh human program or productivity. I think a ton.
Um, you know, like I always laugh that I I assume Python is probably the most common generated code just because of how much it's in the training data and
Data scientists love Python and I love Python too.
It's such a
Comically.
bad thing for AI to generate just because it's h 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. Um like it it's
It's not as bad as Pearl, but like, you know, if you have a big
Python program.
How many errors will you find at runtime versus, you know, before releasing it?
So it was Python was designed to be very ergonomic, almost look like pseudocode for humans for me to write code in a delightful way. That's why data scientists love it so much.
So as we move to a world where like let's just
Uh
Postulate, and I'm not sure this will be completely true, that like we're not gonna write a lot of code as people. We're gonna be operating as code generator machines.
W we probably don't care how ergonomic the programming language is. What we care about is when this machine generates code.
Do we know that it did what we wanted it to do? And if it doesn't do we want it to do, can we change it easily?
I think there's a lot of insights in programming languages that could serve this. So, you know, Rust, I think, is interesting because if I
If I asked you to look at a C program and say
Uh does it leak memory?
You probably couldn't do it that well just'cause it's really hard. And if it's a very like a million line C program, that's be very, very hard.
If I asked you to verify that a Rust program doesn't leak memory, you would just have to compile it. And you know, because it has compile time.
memory safety, just the act of compiling successfully tells you that's true.
I think we need more things like that because
If a
AI is generating this code, by definition, um, if you have to read every line, that is gonna be the limiting factor for producing the code. Or
worse, you're just not gonna read every line and you're gonna 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. Um certainly
Self reflection is a really effective way of improving the robustness of an AI system.
But I do think if you you know, if it doesn't matter how tedious it is to write the code, you could probably layer on
uh some techniques that are sort of out of fashion like formal verification, uh unit testing.
other things. And if you layer all these on, I'm sort of thinking about it as I as a it's like the guy in the Matrix with the, you know, green letters coming down like
How can I make something so I as 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, and you're probably gonna bring to bear a lot of things.
And what's really fun about it is you can loosen a lot of constraints. Like
Coding is free. Okay, so that's neat.
What w with that in mind, what do you want to do?
What would be best suited for the language, the compiler, for testing, for self reflection, you know, for supervisor models, all these things.
I think that's more of a programming system than a language.
But I I think when we create something like that, it can really enable um 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. Um it's actually like
building increasingly complex systems and actually changing them with agility.
Uh, you know, if you look at the famous like
Netscape one to Netscape Two rewrite. They sort of like uh 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 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 w gonna be potentially the future of coding and
And building.
Mm.
I can't wait for that. 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 Cassiopo, 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?
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And nobody wants to spend time doing this.
That is very much our experience, but before the company to some extent during it. But the idea is with automation, with AI, with software, we are helping customers build trust with prospects and customers in an efficient way.
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Thanks for that, Christina.
Thank you.
Okay, one more question along these lines and then I wanna zoom out on just kind of where AI is heading and
Uh
Something I love to ask folks like you that are at the cutting edge of
AI is
Is what you're teaching your kids.
And I have kids.
I feel like the world is gonna be very different when they grow up.
What are you encouraging them to learn uh that you think might is different maybe from previous generations to help them be successful in a world of AI abundance?
I don't know if I'm teaching them differently.
But I'm really trying to encourage them to make AI a part of their lives.
Uh
I
I was reflecting actually, um
When I took the AP calculus exams
Uh um
Uh, ninety seven, ninety eight, maybe in B C
I could use a graphing calculator.
And uh
I haven't done this research. I I was meaning to plug this into chat G V T 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.
Um but essentially to
when you allow the calculator in the exam, you need to make sure that none of the questions, you know, benefit people for having a calculator or not.
And which actually forces you to sort of rethink the problems to test calculus knowledge that don't benefit from like road
arithmetic or you know the other things you can do on a graphing calculator.
I think that
a lot of education um 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 that prompting, maybe even your teacher won't know it's written by an AI.
Uh
So what do you do? Like how do you teach kids differently?
It's really hard for teachers right now because I think we haven't gone through the transition of adding calculators to the exams. I think a lot of the mechanisms we have to evaluate students are
broken uh by the existence of chat G PT and the like. So I think we're in a very awkward phase.
But I
I think we can still both
Teach kids how to think.
And teach kids how to learn.
And I think our education system can catch up.
And I actually think these models can be one of the most effective educational tools uh in history.
I don't know if you're a visual learner or reading learner. I like to read. I didn't love going to lectures. I don't learn that well from them. I like to like read the book.
Um
And uh
You know, if you have a teacher who doesn't teach in your style, you can now go home and ask Chat GPT to teach you another mechanism. I
Uh my kids use chat GPT to quiz them before a test. Um you can use audio mode or chat mode. It's like better than cue cards.
You, uh my daughter took home a Shakespeare book.
She took a picture of a page she didn't understand and Chat GPT explained it to her way better than I would have.
Uh yeah 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.
Um we have uh a platform that can test you, that can quiz you.
Um, I think it's really an amplifier of agency. I think, you know, the folks who ha like
kids who have agency, who uh have aspirations to to learn something, I think.
You have what is the best combination of every teacher you've ever had and these these models and you can use it.
So with my kids, um, you know, my oldest daughter learned how to code and
She was making a website and every time she had a question for me, I would just make her use chat GPT. Not because I was trying to be an obnoxious
father but I'm like she needs to learn that like to 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's moving faster than our educational system. And I think particularly as it relates to
uh 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
uh or 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
Uh obviously a graphing calculator is not chat GPT, don't get me wrong, but
I think we've been able to conf
figure 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 uh and I on the much more positive side
I know I went to public schools. I don't know if you did too. Like you ended up with some pretty bad teachers to d you know, at times and
Now you have an outlet. You don't need to be the, you know, rich kid who can afford to tutor anymore to get tutoring.
Uh, you know, if you are a kid who excels in math and your school doesn't have advanced
statistics classes. Well now you do.
So I think this is just an incredibly democratizing force with kids who have agency and I think that's very exciting. I'm hopeful that this
There's a
eleven year old right now who's gonna start a really amazing company.
You know, uh ten years from now.
Who's like chat GPT is gonna be like their primary tutor that like led to that that outcome. And I think that's pretty pretty cool.
I have a two year old eh and it feels like there's like a new milestone of there's like when to give him a phone.
when to give'em, I don't know, Snapchat, whatever kids use these days, and then it's like when to give them their first chat GPT account.
Uh no. I wonder I wonder how soon that's supposed to happen. I think Chad should be my personal take is it's definitely number one or two. I I
I don't think
Mobile phones are great in school or great for kids and I I I personally advocate for waiting a long time.
But I think that chat GPT 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 was 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 Chat GBJ said, When should I let my kid use Google search? You know, that's like a different type of tool. And I think thinking of like that is the way I think about these technologies.
And so is the form factor for your kids like an iPad or a laptop or some?
Yeah, they use like the computer on the desk. All right. Good tips. This is good for me to learn all these things as my k ages. Okay, I'm gonna zoom out and let's talk about
Business strategy, AI.
One of the biggest questions, a lot of founders.
think about these days is just where should I build what will
foundational model companies not squash and do themselves.
uh being someone building a very successful AI business and also being on the board of open AI.
feel like you have a really unique perspective on
What is
Probably a good idea and it's probably not a good idea.
Why do you think the AI market is gonna play out? And where do you think founders should focus and
Also just try to avoid
I think there's
Three segments of the AI market that will end up
fairly meaningful markets and then I'll I'll end with how I think it's gonna play out. So
Uh first is the frontier model um market or foundation model market.
I think this will end up
the small handful of hyperscalers and really big labs, uh just like the cloud infrastructure as a service market. So
Uh and the reason for that is that creating a frontier model is entirely a function of CapEx.
And you need a company with huge amounts of CapEx capacity to build one of these models.
all of the companies that were startups that tried to do this have already been consolidated, or almost all of them Inflection, Adept, Character, and others. And I think it's just not
There doesn't appear to be a viable business model for a startup because of the amount of capex required and
There's just not enough runway. You can you're 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
uh a model that deteriorates in value so quickly.
Um so I think that's gonna end up probably no entrepreneurs should build a frontier model. That's my my unless you're Elon.
Yeah, yeah, yeah. He's he's not he's he's different, right? And he has the capacity to raise billions in capital and
My guess is most of your other listeners don't. And then he's he's the greatest of all time for a reason and he's different. You don't compare yourself to him, you know.
The other part of the market is the tooling.
Uh yeah, I think there's, you know, a lot of folks sell in pickaxes in the gold rush. This is data labeling services. This is
uh you know data platforms, it's uh eval tools, um
more specialized models, like Eleven Labs has a great set of voice models that a lot of companies use that are really high quality.
And 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 Databricks and stuff like
A lot of the um
Amazon and Azure and others have competing products in those c areas because they're very adjacent to the
the to the infrastructure itself. And
Every infrastructure provider is trying to differentiate by moving up the stock and and you're right there. And so
There's some real meaningful companies, as I mentioned, like Snowflake, Databricks, Consul, and others.
But there's a lot of others that were sort of obviated by um uh technology from the the f infrastructure providers themselves. So
those companies probably are the most at risk.
For
you know, a developer day from one of these big
Foundation model company is releasing exactly what they do. So you have to there's probably a lot of people who need your your tool, but the question will be.
If or when is probably the right way to think about it. One of these large
infrastructure providers introduces a competitor, why will people continue to choose you? Um so it's it's a good market, but it's a little bit
Close to the sun, as I said.
Then there's the applied AI market. I think this will play out for companies who build agents. Uh I think Agent is the new app. Uh and so
I think that's gonna be sort of the product form factor. So there's companies like Sierra, we help companies build agents to answer the phone or answer the chat for customer experience and customer service.
There's companies like Harvey that make agents for
uh both a legal paralegal profession, antitrust reviews, reviewing contracts, et cetera, et cetera.
There's companies that do content marketing, there's companies that do supply chain analysis.
Uh, I think this is sort of like this offers a service market. Um, 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, so which is why those
model providers will end up extremely large scale, but probably slightly lower margin.
And I think, you know, you the market for them will be
probably less technical. I mean if if you know, if you think about the purest form of software as a service, it's not like you ask like
What database do you use, right? It's it's really about the feat and function. I think that's where agents will go. I think it's gonna be
More about
uh product than it is about technology, uh over time. Just
Yeah, and uh just going back to my metaphor, you know, in nineteen ninety eight when
Mark and Parker started Salesforce, just getting that database running in the cloud was like a technical achievement.
You know, nowadays like, you know, no one asked ask about that'cause you can just spin up a 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.
Yeah, I'm pretty sure that's gonna be easy in in three or four years. It's just like just as the technology uh improves.
And so over time you say, like what is an agent company? Well, it looks a little bit more soft as a service, you know, talk a little bit less about
how you deal with the models, uh in the same way modern SAS, few people ask what database you use.
But you'll probably ha 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 to sort of slowly evolve uh 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 it you can shoot your shot. You know, if you have a a vision for the future, go for it. But I think it's probably a a challenging market that's already sort of consolidated.
I'm very excited about the other two markets. I'm particularly excited as
building agents becomes easier to see a lot of um long tail agent companies come out. Um
I was looking at a website for the top
fifty software companies in the stock market, and obviously like the top
five or the big big one ones like Microsoft, Amazon, Google, all that.
Like the next fifty.
Are all SaaS companies. And they're like some of them are very exciting. Some of them are like super boring. But this is like how the software market has evolved. I think we're gonna see something kind of.
Similar with agents. Like it's not just gonna be like these huge
markets like we're in like customer service and software engineering uh
It's gonna be like a lot of like things where people are spending a lot of time and resources that an agent can just solve.
But it requires an entrepreneur who actually understands that business problem. Like and
Deep deeply.
Uh, and I think that's where like a lot of the value is gonna be unlocked uh in the AI market.
That is incredibly.
So makes me think about I had Mark Benioff on the podcast. You guys were co COs.
And he was uh extremely agent pilled. All you wanted to talk about was agent force.
Uh clearly you are also very agent pilled.
I never heard the term agent, although I'm gonna use that one.
Uh, clearly you guys saw something that was just like, Okay, we need to go all in on agents. This is the future. What is it you think people are missing about just like why this is such a critical change in the way software is gonna work? What are what's
What are people not seeing?
If you talk to an economist like Larry Summers who are on the open AI board with me, it'll talk about like what is the value of technology, will it helps drive productivity in the economy. And
If you look at the
Uh, one of the big jumps in productivity in the economy was in the nineties. And I think a lot of folks I talk to think
It was actually that very first wave of computing where people made like ERP systems and just like put
Account in
into computers, even like mainframes. We're talking like the PC era
Because it's such a huge step up. Like, you know, just imagine like the ledgers of
you know, uh numbers that you'd have for like a large multinational company before
And it truly just transformed departments. I'll I'll give you a little
Toy example. My dad just retired. He was a mechanical engineer.
And he was talking about when he first started his career in the late seventies.
And you went into a mechanical engineering firm, the majority of the firm were draftspeople. So basically you take an engineering design, but you needed to do
all the different vantage points and for all the different floors and to give to the contractor to do the thing.
Now there are zero draftspeople at his company. You just
make the the design and first AutoCAD and now Revit.
And it, you know, it's a three D model and you know, the drafting has actually been eliminated. It's just not a thing one needs to do anymore. The the actual design and drafting drafting is not a thing that exists. It's just like you can it's just a design.
I I that's true productivity gains, right? It's like you know the job of the mechanical engineering firm was to do a design. The drafting was like uh sort of this
necessary output for the contractor, but it wasn't really adding value. It was just sort of like the the supply chain change.
If you
look at the history of the software industry from the PC on, um there's been meaningful
Productivity gains, but just not nearly as meaningful as that first huge jump. Uh and
I'm not smart enough to know exactly why, um, but it is
Interesting. Like there has the promise of productivity against from from technology
Um hasn't been as realized, I think, as some people thought.
I think agents will truly
like start to bend the curve again, like we did in the very early days of computing.
Because software is going from helping a individual be slightly more productive, um, you know
to actually accomplishing a job.
Autonomously.
And as a consequence, just like you don't need draftspeople in a mechanical engineering firm, you just won't need someone doing that thing anymore.
It means they can do something else that's higher leverage and and more productive and you can actually
You know
uh a smaller group of people can accomplish more and
uh you know, truly drive productivity gains in the economy.
And you know, I think
If you've ever sold enterprise software, you end up in these discussions as a vendor with the customer, where you'll have like a uh
value discussion and you'll do these like somewhat convoluted, you know
Okay, it's like
You're selling a sales thing. Okay, well if every salesperson sells, you know
Five percent more
Da da da da da and yeah, you should pay us a million dollars. Like, you know, and it's roughly that conversation.
And it's so unattributable, you know, especially and it's why it's so hard to sell productivity software, which I learned the hard way is.
You know, it's just hard to know, you know, what's the value of making everyone ten percent more productive. Did you actually make them ten percent 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.
uh for software.
And all of that combined, to me, it feels like
as significant as the cloud or uh I think more technologically, but just in terms of like how it like transforms the business model of the software industry where there's gonna be like a before and after. Like
I don't know how many people still sell perpetually licensed on premises software, but it's de minimis at this point. I think we're gonna go through a similar transition. Like the whole market is gonna go towards agents. I think the whole market
is going to go towards outcomes based pricing.
Uh not because the only way, but it's gonna be like the market is gonna pull everyone there because it's just so
obviously the correct way to build and sell software.
Let me pull on that last thread. So we had Modavan on the podcast recently, pricing expert, legend, monetizing innovation author.
And he uh talked about pricing strategy for AI companies and
He was very much in your camp of if you
Can you
You need to
price your product as an outcome based
Product and
Uh 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 ch he actually used Sierra as one of the shining examples of of this six being successful.
Can you just briefly just explain a little bit what is outcome based pricing for people that haven't heard this term before? And then just how does it work for Sierra to give an example?
Yeah, I'll start with the example and then I'll broaden it. Um
Uh at Sierra, we help companies make customer facing AI agents, primarily for customer service, but more broadly for customer experience. So
Um, if you have a problem with your serious XM radio, you'll call or chat with Harmony, who's our AI agent. If you have ADT home security
And your alarm doesn't work, you can chat with their AI agent, sono speakers, a lot of different consumer brands.
And
You know, if you think about running a call center.
Um, the there's a cost for every phone call um that you take.
Um most of it is labor costs. Um but
If you have let's just say a typical phone call is anywhere between ten and twenty dollars, uh US dollars.
Most of it some of it's software, some of it's telephony, but a lot of it is just like the hourly wage of the person answering the phone.
So if an AI agent can take that call and uh solve it.
You know, that
uh is in the industry often called a call deflection or a containment. Um and that essentially means you saved, you know, call it
fifteen dollars, uh, because you didn't have to have someone pick up the phone.
Um so at uh in our industry, basically we say, Hey, if the AI agent, you know
solves the customer's problem and they're happy with it and you didn't have to pick up the phone.
There's a pre negotiated rate for that.
Uh, and that's uh we call it like resolution based. There are other outcomes as well. We have some sales agents being sal paid a sales commission, believe it or not. No, we do. We we really think of our agents as truly customer experience, like the concierge for your brand. And we wanna make sure that, you know
uh our business model is aligned with our customers' business model. As you said, these agents need to be autonomous and the outcome has to be measurable. That's not always possible, but I think it's broadly possible.
And what's really neat about it is
If you talk to any CFO or head of procurement
You know, with their big vendors, they look at the bill of materials and they the it's like
overwhelming and it's impossible to know if you're getting the the value that you hoped uh from that contract.
I think
consumption based, uh, which was popular pr 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. Um I always use the analogy, like right now
most of the coding agents are priced per token or or per utilization, but
There's this famous story of a Apple engineer who had a bad manager who's like, How'd you report how many lines of code you wrote every day?
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 refactoring and deleted a bunch, and it was his way of saying like fuck you to the man.
I think tokens are similar. Yeah, like yeah, you used a lot of tokens, like good for you. Did the you know, did it produce a pull request, you know, that was good.
And
And I think that's the whole point of all this. I don't think I think there's huge difference between outcomes based pricing and usage based pricing because
uh 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 stuff over the wall because you'll never get paid. If it doesn't, you have to, you know, uh really just your orientation becomes so extremely customer centric when you do this the right way.
I think it's just a a better version of the software industry. So I think it's right from first principles, it's right for procurement partners, and I think it's right for the world.
We've been chatting a little bit about productivity gains.
There's a lot of skepticism.
in in the headlines these days of just like what is AI actually doing? Like is it actually helping people be more productive? There's a recent study actually, I don't know if you saw where they showed engineers were less productive.
uh 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 thinking
actual gains at your company or any other company you work with outside of CX in terms of productivity, that is like clear, yes, this is working and a huge deal.
I'm extremely
Bullish on the productivity games from AI.
But I do
think the tools and products right now are somewhat immature and it 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
uh kind of coding autocomplete, though they have a lot of agentic solutions and there's a lot of
Uh like OpenAI has Codex and there's, you know, Claude has I can't remember the the Anthropic product. There's lots of agentics.
you know, agents come in as well.
One of the interesting things because the technology is sort of immature, the code it produces
Often has problems.
Um so
There's a lot of people sort of approaching this to sort of actually realize those productivity gains because as any engineer who's written a lot of code will tell you.
It's pretty easy to like look at and edit and fix code you wrote.
Reviewing.
other people's code or particularly finding a subtle logical error in that someone else's code is actually really hard. It's actually much harder than
you know, uh editing code that you wrote yourself.
So if
the code produced by a coding agent is often incorrect. It actually can take a lot of like cognitive load and time.
to fix it. And in fact if you end up producing lots of
You know, uh
Issues with your customers. You could end up, you know
Uh producing a lot of features, but actually like, you know, mucking up the machine a little bit and having something that's not ideal.
There's a couple of techniques I think are interesting. Like first, I think there's a lot of
Uh AI starts now working on things like code reviews. I think this idea of self self-reflection and agents is really important. Having AI supervise the AI is actually very effective. Just think about it this way.
If you produce an AI agent that's right ninety percent 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 ten percent of the time? That might be a tractable problem.
And if that thing's right ninety percent of the time, just for argument's sake.
You can wire those things together and have something that's right ninety nine percent 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 compu for cognitive capacity, and you can layer on
more layers of of cognition and thinking and and reasoning
And produce things increasingly robust. So I'm very excited about that.
The other thing though is root cause analysis. So
We have an engineer at Sierra who exclusively focuses on the model context protocol server serving our cursor.
Uh instance.
And our whole philosophy is
Rather than if it if cursor generated something incorrect, rather than just fixing it.
Try to f root cause it. Um try to get it so like the next time Kirchhoel produce
The correct code. So like
Uh and essentially is 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 in systems and say, like
You know, how do we sort of go root cause every bad line of code?
and actually give the right context and produce the right system so the models can do it today. Over time that probably less necessary and 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, I mean 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 Syrah, yeah, AI agents aren't perfect, but we're creating a system that lets customers create a virtuous cycle of improvement. If you want to go from a sixty five percent automated resolution rate to seventy five percent, 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?
Uh and you're sort of let AI put the needles of the ha of the ha at the top of the haystack on your behalf. And I think that's really the way to optimize these systems.
I've never heard of this technique of improving cursor.
By adding additional context. What's the actual way of doing that? You build an MCP server that
Everything runs through or is it like you had cursor rules? What's the actual approach that
I'm probably out of my depth here, but it's essentially M C P but it's essentially you know,'cause that's how you provide uh context to Cursor and I think that
Almost always when you have a model making a poor decision, if it's a good model, it's lack of context. And so you really want to like, you know, find the intersection of your particular product and code base with
the context available to these coding agents and and systems and
Fix it at the root is sort of the principle here.
Got it. That is very cool. And heard people doing that. Model c model context protocol. Makes sense. We've talked about productivity gains outside CX, just to give you a chance to share how amazing what you've built is. What are some of the gains you see from people using Sierra?
Yeah, we have uh our customers
See anywhere between fifty and ninety percent.
of their customer service interactions completely automated, which I think is really exciting. Um
And we serve just a really, really broad range of customers. Um, we serve
The health insurance industry, the healthcare provider space, uh banks.
You can actually refinance your home using an agent one of our customers uh built on our platform to
Uh the telecommunications industry, direct T V, serious X um
to a lot of retailers um as well, which is really fun. Everyone from
Wayfarer to uh clothing retailers like Olakai and Chubby's shorts.
What's really neat about it is a pretty diverse range of use cases and it's everything from
Helping you um, you know.
sign up for a we have a uh an agent that helps with customer support in one of the big dating applications to uh, you know, uh uh helping you uh upgrade or downgrade your your Sirius XM plan.
Actually it's really funny. We do technical support from everything from home alarm systems to sono speakers to more recently CAT scan machines. Um which I think is amazing. So
technicians going in and fixing the CAT scan machine can chat with an AI agent to help them guide them through that process.
We're we're the leader in the space. Um we're trying to enable every company in the world to create their agent with their brand at the top, um, that I think will become
as meaningful of a digital touch point as their website or their mobile app.
Um, in the short term, it can really transform the costs of running a customer service team. Uh, you know, and
And what's remarkable is do so with really high customer satisfaction scores. Um you know.
That Weight Watchers agent, I believe, has a s customer satisfaction score of four point six out of five, which is pretty amazing. You know, that's and
And what's interesting about service too, it's often people have having a problem. And so you know, when you have uh our a clear I don't know if you use them in the airport, I think that agent has a C Sat score of four point seven out of five.
You know, people are coming in with a problem and and being delighted. And I think that's uh really the opportunity here. And
Your whole vision is that
We're gonna move towards a world where every single one of the interactions with your customers can be instant, it can be multilingual, it can be uh over audio, it can be over chat, it can be digital, it can be over the phone.
I think the uh
Very personalized. And I think that's really, really exciting. And if you think about all the best moments you've had with a brand, it's like that store associate who you know.
Uh and you know, it's like for me, it's like the butcher at the grocery store. I love to cook, he knows me, we talk.
Like, can you actually
produce that at scale for a company with a hundred million customers.
Um and can you do it in a in a really personal way? And I think we're really at the on the cusp of enabling that.
Let me ask you one more question before we get to our very exciting lighting around.
There's a lot of um
Found struggling with go to market in
A I 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 you 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.
Um
Category I would say is developer led. Uh this is somewhere famously Stripe and Twilio were probably like
Two of the original that did this exceptionally.
Uh and essentially the go to market motion there is to appeal to an individual engineer, often within the department of the CTO.
Uh who have
Accountability and a fair latter um amount of latitude to choose a solution.
Um
This works if your product is uh sort of a platform product. Um, it doesn't work, for example, if your product is
Uh trying to help a line of business because lines of business typically
don't have dedicated engineering teams or uh let alone the latitude to just go, you know.
download a uh a new library or or start using a web service like that.
It particularly works well if you sell the startups, uh, just because startups tend to have engineering teams with quite a bit of latitude to
uh choose services to help them solve the problem given by the founder.
Then there's product led growth. Um 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.
Um often get put on trial. Often you can buy a couple seats with a credit card.
And those work where your user and your buyer are the same person. Um so
It works for small business software almost always because sole proprietors do everything. And so you're selling small business software like you know, Shopify in the early days, and there's a lot of other products like that.
uh where you're trying to sell to to small merchants
Yeah, that's great.
Uh it doesn't work well when your buyer and the user of the software are different. So I'll use the example of something like expense reporting software. The user of that software is an individual employee, but the buyer is often a finance department. Um and so, you know, 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 and it just doesn't work.
And then there's direct sales.
Uh and
direct sales had gone I don't say out of fashion, but if I think of like the best direct sales companies, I probably there's a lot of lineage from Oracle, but you think SAP, Oracle service now Salesforce.
uh Adobe perhaps.
And there's others as well. And these were companies that
sold into, you know, large lines of business um in a relatively traditional sales motion.
I think because product led growth became very popular, I think a lot of companies
use that which is great. It could it it that motion produces great products.
Um but
Yeah.
PLG means that you aren't actually engaging with the buyer of your software, like you're not gonna grow. And so
Uh 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 motion without thinking through the uh what is the process of purchasing the software, what is the process of evaluating the the value of the software. And I think people just need to be much more like first principles about it and much more thoughtful about it.
And candidly, I think like a lot of companies should leverage direct sales more than they do. Uh and even though it like
Because of the
uh you know, sometimes justified reputation of the quality of products of some of these direct sales companies. A lot of it sort of had gotten a bad name and I think I think a lot uh I th I'm sort of thankful to see it coming back in 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 uh that you know, sales turns them off. They don't think they're gonna be great at sales, just this push of
uh 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 Roth.
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 read a lot of nonfiction. Uh
But probably we had to pick one sort of in the area of the topics we talked about, um, competing against luck, which was the
The
Uh
book that produced jobs to be done, which is a framework I really believe in.
My only critique is I think most of these
sort of like business books should be like an article. So maybe buy the book and punch into chat GPT and get the summary.
Um but by the book it's uh uh
Clayton Christensen talked about it, but it's a really good framework for thinking about
delivering value of their products. Um and I I think it's a I I it definitely influenced me.
I may um
Actually one book I do recommend was 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 a true story.
Um and you know, if you wanna like uh if you're an entrepreneur going through a hard time read that, you're like, Okay, it could be worse. It's a great book, too. It's just remarkable that's a true story.
And uh one thing he did a great job at is setting expectations for folks that joined that are that famous. I don't know if that's true. It's like remarkable. That's true. Oh, it might not be true. I don't know. Deep fakes, uh even back then.
Okay. Uh do you have a favorite recent movie or T V show that you've really enjoyed?
Yeah, I haven't gone to any new T V shows recently. We just watched um Inception with the kids and they loved it and uh made me um appreciate Christopher Nolan. So
Uh and what a cool movie. Cool convic when you watch your film and you have a
conversations for two days afterwards about it. So um just a great film.
I saw someone using I think VO three to create their own inception videos where the world's wrapping on each other.
Oh man. Okay. Do you have a favorite product that you have
recently discovered that you love
Or one you've loved for a long time.
I'm really a big fan of 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 OpenA and other. So
I think Cursor will be
in its current form is a transition product. Uh and I know that 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 this is the first time someone's actually mentioned cursor in this answer, so
Might be the beginning of a trend. Uh Michael Trell was on the podcast and he actually had a very similar message as you had at the beginning of this chat about the future of code, what comes after code.
And this concept that there's gonna be this additional pseudocode layer on top of code. Yeah. Uh
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.
Uh which I think I attribute to Alan Kay of Xerox Park, uh he f invented a lot of
the core abstractions that we use in computing today.
It's why I I love uh I I uh
I'm an entrepreneur. It's why I love to build things. Um, so it's definitely like a life model for me.
I feel like many people
Like say this? I feel like you've actually done this so many times. You're living this motto.
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? Was 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 seventy percent of them were 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're trying to solve is get all the one word answers out so that
The discussion was actually like
C 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 there she denies remembering this, but there's a uh Anna Yang now Anna Muller who has worked at the Cup and 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.
Yeah, is 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 uh just a much more neutral
sentiment. Uh and and that's why it was hard to translate,'cause it was subtle.
Um, and we
So that's how we ended up with this. We started with a heart and and I I don't know if we ever heard the word love, but we definitely started off with the iconography 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 the story before. Makes me think about LinkedIn now. They have they're basically trying to solve that same problem. They have all these auto-reply kind of pill tag things.
I don't think people like those that much. So many so many AI features. Brett, this was incredible. This was an honor. I so appreciate you coming on this podcast. Two final questions. Where can folks find you online if they wanna 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 Cra.ai. Um if you want to uh apply here, Cyr.ai slash careers. Um we're
We have offices in San Francisco, New York, Atlanta, and London and are hiring um pretty aggressively in every department. So uh please reach out if you're interested.
And how can listeners be useful to you? Is it tryouts Sierra? Anything else there? Yeah. Tryouts Sierra I'm a single voter. Stay in the message. I love it. Yeah. Brett, Brett, thank you so much for being here.
Yeah, thanks for having me.
Hi everyone.
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