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

25:22I think Obama, Ashton Kutcher, and like Oberwynn for all went on Twitter like in a summer, and we just got our askets, you know, it's like, and it was a great example of you. I think 11 of those 12 people were engineers and we were just making product. And I think it was biz -stown. 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 social service that's oriented towards following people, put some people on their worth following, you know?

25:56And instead, we were exclusively focused on polishing the product. And we actually think, you know, at our sort of peak of popularity, we were very confident just, you know, I think it was a time when like Twitter had the fail whale, and it was down half the time, and people couldn't even use it. And, you know, we, our product, we were innovating faster, we had more features, people liked it, we could, and we were up 100 % of the time, and we totally lost for no reason related to products at all. And it was an example of, I think, somewhat famously, not of a lot of great entrepreneurs have come out of Google.

26:31Because once Google was so successful, I think it's hard as a product manager to see distribution and product design and even business model when you have out -of -words and money's raining from the sky. It's hard to, you know, there wasn't as much scrutiny. And I think like it's folks like the PayPal Mafia, I think learned a lot more about entrepreneurism than like a typical PM at Google. So we're just getting punched in the face. Yeah, I'm learning this the hard way. And so that was probably the most prominent example that 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.

27:05There's a lot of reasons. I think there was a lot of flaws of the product, but it was a lot of other stuff. And so I've learned, like accumulated these skills over time. When I say the hard part of that question is answering it correctly is it's hard when you're not experiencing something to have intuition in it. So I think if there's probably a structural flaw, I don't know if I could have figured I had to reach out to Ashton Kutcher, but I wanted to do it, right? Yeah, I was like, he's on my, you know, on my Rola Dex. But I probably wasn't soliciting advice from the right people. You know, I think that what's great about the technology industry is there's a lot of advice choosing whom you listen to is actually quite difficult.

27:43But I think we're somewhat myopic. You know, we're kind of in our own little world creating this product. And we weren't asking people to like from the outside end to say like what what are you seeing that could go wrong? What do you see that could go right? What are you seeing in the industry that we're not doing that you think we might want to do? And this is why boards are important. This is why you know finding the right advisors the advisors who actually tell you what you not seem want to hear but what 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 could have learned that that was a shortcoming.

28:20And I think that was a deep lesson. I took from that I'm a huge believer in boards and I'm getting good advice. Any kind of heuristics or advice for people to know whose advice to listen to? What do you pay attention to? You know, you're not this person, but listen to this person. Yeah, that one's tough. It does come down to good judgment and being judge of people's character. One thing that is particularly hard is there's not a strong correlation between the confidence with which someone expresses an opinion and to the quality of that opinion. I don't want to say it's inversely correlated, but it's funny with all the podcasts out now.

28:58If there's topics I know a lot about, sometimes the most eloquent, confident statements about things I know a lot about are the least accurate and it sounds extremely persuasive. and the, so it does require very good judgment. One thing is I think not just asking for advice, but asking people who should I talk to to get good advice, and you'll find some common answers there, and that's often a really strong signal of good judgment. And then one thing I found is when you ask for advice, don't just ask me what to do, but why be it like an obnoxious two year old kid, you know, why, why, why, why, why, and really try to understand the framework that someone is using to give you advice.

29:41The interesting thing about advice is people are often extrapolating from relatively few experiences. So, you know, they'll say, never do this or always do that. And it's because they had one experience where that something backfired or something could have gone better if they had done it. So it's a useful anecdote, but if you don't ask why and understand they had one experience and here's what happened, it can come across as a rule. when in fact it's its anecdata. And if you ask advice of three people and they all have very similar interactions, you can create kind of like a first principles framework from which that advice emerges.

30:18And when you start applying it, you're applying it with a degree of nuance that you couldn't if you're just following a rule. So I think one is it does come down to good judgment. I think, you know, I don't know how to teach that. I think it is probably a very, I'm a huge believer in good stuff. It's one of the things I hire for. I just think that that's something that probably comes from a mix of self -reflection. Like you really need to hold yourselves accountable as an entrepreneur as a product manager. Like if you made a bad decision, spend time reflecting on it, like number one. And really try to understand why and try to always improve your judgment.

30:54I think at the end of the day, that is why you are a good entrepreneur, a good product manager. And number two, when you get advice, really understand where it's coming from and why, so that you can create sort of your own independent view of where that advice came from and recognize that No one's advice is statistically significant or very rarely is it? I mean if you're getting like advice, I'm investing, you know, for more and buff it. Yeah, okay It's statistically significant, but that's not most advice is like something happened to you once and you have regrets I love that you're like I don't I don't know if I have a great answer and then you just give us an incredible answer to this question.

31:30I want to go in a kind of a different direction. You mentioned that you described yourself as an engineer. I know I heard you code to relax still. Let me just ask you this question. Something a lot of people in college are thinking about. Do you think it still makes sense to learn to code? Do you think this will significantly change in the next few years? I do still think it's studying computer science. It's a different answer than learning to code, but I would say I still think it's extremely valuable to study computer science. I say that because I think computer science is more than coding. If you understand things like big O notation or complexity theory or study algorithms and why a randomized algorithm works and Y to algorithms with the same sort of big O complexity one can in practice perform better than others and why a cashmiss matters.

32:23And just all these little, there's a lot more to coding than writing the code. The reason I think that is I do think the act of creating software is going to transform from typing into a terminal or typing into Visual Studio code to operating a code generating machine. I think that is the future of creating software, but I think operating a code generating machine requires systems thinking. And I think that computer science, there are other disciplines as well, but computer science is a wonderful major to learn systems thinking and At the end of the day, AI will facilitate You know creating this software We may do a lot more in the next years.

33:10We can't even imagine But your job as the operator of that code manager generating machine is to make a product or to solve a problem and you really need to have great systems thinking and you're going to be managing this machine that's doing a lot of the tedious work of making the button or connecting to the network. But as you're thinking of the intersection of a technology and a business problem, you're trying to affect a system that will solve that problem at scale for your customers. And that system's thinking is all the hardest part of creating products. I'll just give you a like, it's just cheesy, simple example that I think it's representative.

33:44of at Facebook we would all, you know, we spent a lot of time designing the newsfeed. And if you ever had like a really, really good designer and they showed you at the time a Photoshop mock up of the newsfeed, it was just always beautiful. The photos, the family was happy and the photo was like a perfect photo and the posts were like all perfectly grammatically correct and of a completely normal length and the comments and the, you know, there was the light but everything was just perfect. And then you'd like implement that design and you 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 purse for all these different lengths the comments were like, you know You suck and like all that stuff and then all of a sudden you realize it like Design in a newsfeed like Photoshop is the easy part you need to actually design a system that produces a a both in content and visual design, like a delightful experience given input you don't control.

34:47That's a system. That's not, I mean, sort of a design. It's a system what we did practically. I'm sure it's changed a lot since I left in 2012, but we made a system. So designers had to show their new suite designs with real new suite data that was messy, rather than anything artificial, because I think it forced the process to be more realistic. But I say that because I think that like whether AI is writing code or doing the design or doing all these other things, like you need to learn how to have a system in your head, you need to understand the basics of what's hard and what's easy and what's possible and what's impossible.

35:24And AI can help you do that too, by the way. But I do think that's a really useful skill. I think in general with the advent of AI agents and AI approaching super intelligence in certain domains, I think the tools with which we do our job will change a lot. I think it's very important to have a very loose attachment to the way we do our jobs. And you know, that story that we won't talk about when I like reroute Google Maps, like everyone talks about that story because of like, and it's, I think it's because of Paul who told it on some podcasts and it all sort of made the rounds. I think that's going to end up sort of this vestige of the past.

36:04I almost like the human calculators at NASA before the computers were invented. Like, oh, a person was a calculator? Well, that's fun. Like, tell me that story. I think just like what I was good at will no longer be useful in the future, or certainly not like valuable in the future. And that's okay. So I think we need to have a really loose view of it. But the idea that you shouldn't study these disciplines, it's sort of like people say, I don't want to study math, because I'm not going to use it in my career for X. Well, study math is quite important. Like it teaches you how to think, it teaches you, like how the world works, physics, math.

36:37And I think computer science, especially at least sort of the foundations of it, will continue to be the foundations of how we build software and understanding that when you're interacting, particularly something that's smarter than you, producing code you might not completely understand, and how you can strain it and how you get it to produce these outcomes, I think it will require a lot of sophistication actually. It's such a great answer. There's always sense of this binary should I learn to code or not? In your point here is learn to understand how engineering works and how systems work and how what your code does and how to all interconnect.

37:10But 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 should be programming language that is more designed for LLMs versus humans. Can you just talk about that because I think a lot of people aren't thinking about that. I don't know it's a language. I'll call it a programming system because I think language might be too limited. My reductive version of the past, you know, a lot of 40 years of computers made me more is, you know, we created the hardware of computers, then we created punch cards, which is the way, you know, in like the late 70s, you know, you would tell a computer what to do, or maybe mid -delayed 70s.

37:54Then we into, you know, invented early operating systems and time sharing systems and from the invention of things like Unix at Bell Labs and Berkeley, you ended up with the C program language 4Tran and a lot of higher level programming languages, I think 4Tran and then C. And we've sort of moved up the layers of abstraction, so no one does punch cards anymore, obviously, a few people write assembly language, some people write C, some people write Rust, but a lot of people write Python and TypeScript and things like that. And as we've invented more and more abstractions, we've made it easier to do high leverage things.

38:39So, you know, I always 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 dragable map now, and I think a lot of people could do it. That was true R &D, you know, back in the day. When Salesforce was created in 1998, just putting a database in the cloud was hard. And, yeah, that was just like that alone was a technical mode that is now trivial with Amazon Web Services. And that technical mode is comically narrow, but the product mode is quite large. I think that if the act of writing code is going from something that is very costly to like the marginal cost of that going to zero, how many of the abstractions that we've built are based on human program or productivity.

39:30I think a ton. You know, like I always laugh that I assume Python is probably the most common generated code just because how much it's in the training data and data scientists love Python and I love Python too. It's such a comically bad thing for AI to generate just because it's one of the most inefficient programming languages of all time. If you know the global interpreter lock and just slow. And I've written a lot of high -scope web services and it's just quite slow. And it's very hard to verify. Like it's not as bad as Pearl, but like, you know, if you have a big Python program, how many errors will you find at runtime versus, you know, before releasing it?

40:10So 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 Scientist love it so much. So as we move to a world where like, like, let's just postulate and I'm not sure this will be completed, sure that like, we're not gonna write a lot of code as people, we're gonna be operating this code to generate a machine. 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 wanted it to do? And if it doesn't, do we want it to do?

40:43Can we change it easily? I think there's a lot of insights in programming languages that could serve this. So, you know, Rust, I think, is interesting because if I asked you to look at a C program and say, does it leak memory, you probably couldn't do it that well just because it's really hard. And if it's a very like a million line C program, that's very, very hard. If I asked you to verify that a REST program doesn't leak memory, you would just have to compile it. And because it has compile time, memory safety, just the act of compiling successfully tells you that's true. I think we need more things like that Because if a AI is generating this code by definition, 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.

41:35And so the question is, how do you enable humans to have as much leverage as possible, which means using computers to do the work on your behalf? You could have obviously the simplest form of this is AI supervising AI and doing code reviews, and that's great. Certainly, self -reflection is a really effective way of improving the robustness of an AI system. But I do think of you, if it doesn't matter how tedious it is to write the code, you could probably layer on some techniques that are out of fashion, like formal verification, unit testing, other things. And if you layer all these on, I'm sort of thinking about it as AI as a, it's like the guy in the matrix with the green letters coming down.

42:14I'm like, how can I make something so I as a operator with the code generated machine can produce like incredibly complex scale software, incredibly quickly, and know that it works. And if you start with that as your design center, I think you probably change the languages, you probably change the systems, you probably change all these things, you're probably gonna bring to barrel a lot of things. And what's really fun about is you can loosen a lot of constraints, like coding is free. Okay, so that's neat. What would that in mind, what do you wanna do? What would be best suited for the language, the compiler, for testing, for self -reflection, for supervisor models, all these things?

42:50I think that's more of a programming system than a language. But I think when we create something like that, it can really enable creators, builders, to create incredibly robust, incredibly complex systems. And I'm super excited about vibe coding, but I don't know, like, generating a prototype has been the limiting factor in software ever. It's actually like building increasingly complex systems and actually changing them with agility You know if you look at the famous like net scape one to net scape to rewrite They so like Someone a lot of people attribute that to part of their failure against Internet Explorer It's like making these things is not hard like maintaining them as hard and ensuring the robust is hard And and I think we've just started working the very early phases of defining what this new system for developing software looks like I'm very excited to see what emerges.

43:42I 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 gonna be potentially the future of coding and building. I can't wait for that. It feels like a great opportunity and a fun project. This episode is brought to you by Vanta and I am very excited to have Christina Cassiopo, CEO and co -founder Vanta, joining me for this very short conversation. Great to be here, big fan of the podcast and the newsletter. Vanta is a long time sponsor of the show, but for some of our newer listeners, what is Vanta do and who is it for?

44:16Sure. So we started Vanta in 2018, focused on founders, helping them start to build out their security programs and get credit for all of that hard security work with compliance certifications like SOC2 or ISO2701. Today, we currently help over 9 ,000 companies, including some startup household names like get Lassian, Ramp and Lange Chain, start and scale their security programs and ultimately build trust by automating compliance, centralizing GRC and accelerating security reviews. That is awesome. I never experienced that these things take a lot of time and a lot of resources and nobody wants to spend time doing this.

44:54That is a very much our experience but before the company in some extent during it but the idea is with automation with AI with software, we are helping customers build trust with prospects and customers in an efficient way. And you know, our joke, we started this compliance company so you don't have to. We appreciate you for doing that. And you have a special discount for listeners. They can get a thousand dollars off Vanta at vanta .com slash Lenny. That's V -A -N -T -A .com slash Lenny for one thousand dollars off Vanta. Thanks for that, Christina. Thank you. Okay, one more question along these lines and then I want to zoom out on just kind of where AI is and something I love to ask folks like you that are at the cutting edge of AI is what you're teaching your kids.

45:38I know you have kids. I feel like the world is gonna be very different when they grow up. What do you encouraging them to learn 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 part of their lives. I was reflecting actually when I took the AP calculus exams in 97, 98, AB and BC, I could use a graphing calculator. I haven't done this research. I mean to plug into chat to you before our conversation, we'll do it after. Did the calculus exam change before and after they allowed the calculated exam.

46:25I assume it did. But essentially, to when you allow the calculated exam, you need to make sure that none of the questions benefit people for having a calculator or not, and which actually forces you to rethink the problems to test calculus knowledge that don't benefit from, like, road or arithmetic or the other things you can do on a graphing calculator. I think that a lot of education is sort of doesn't presume you have a super intelligence in your pocket. And so, you know, if you ask someone to write an essay on a book that they read, you could probably hallucinate one pretty easily from one of the big, you know, writers like ChatGPT and maybe if you are skilled enough that prompting maybe even your teacher won't know it's written by an AI.

47:12So what do you do? Like, how do you teach kids differently? It's really hard for teachers right now because I think we haven't gone through the transition of adding calculators to the exam. So I think a lot of the mechanisms we have to evaluate students are broken by the existence of chat GPT and the like. So I think we're in a very awkward phase. But I think we can still both teach kids how to think and teach kids how to learn. And I think our education system can catch up. And I actually think these models can be one of the most effective educational tools in history. I don't know if you're a visual learner or a reader.

47:48I like to read. I didn't love going to lectures. I don't learn that well from them. I like to read the book. And if you have a teacher who doesn't teach in your style, you can now go home and ask ChatGPT to teach you in another mechanism. My kids use ChatGPT to quiz them before a test. You can use audio mode or ChatMode. It's better than QCards. You, my daughter took home a Shakespeare book. She took a picture of page. She didn't understand. and chat GPT explained it to our way better than I would have as well. I think every child in this world has a personalized tutor that can teach them in the way that they best learn visually over audio, reading.

48:32We have a platform that can test you, that can quiz you. I think it's really an amplifier of agency. I think the folks who like kids who have agency, who I have aspirations to learn something, I think, you have what is the best combination of every teacher you've ever had and these models and you can use it. So with my kids, you know, my older started to learn how to code and she was making a website and every time she had a question for me, I would just make use chat GPT. Not because I was trying to be an obnoxious father of I'm like, she's to learn that like to use this tool because it's amazing.

49:12And I, so I really am trying to have them learn how to use it constructively in their lives. But that all of that's that I just feel a ton of empathy for public school teachers right now. It's very hard because we're just with the technologies moving faster than our educational system. And I think particularly as it relates to evaluation, it's just really challenging for teachers right now. and I worry because these technologies amplify agency, the opposite can also be true of you, if you are a student trying to not learn something. And I think these tools probably provide a lot of mechanisms to avoid it as well.

49:47And so I think there's a challenge for parents and teachers. And I think we're gonna end up with kind of like a bumpy handful of years here. But I brought up the calculus AP exam because obviously, on graphing calculators, not chat GPT, don't get me wrong. But I think we've been able to figure out a way to conform homework and in class learning and tests around the technologies available to us fairly successfully to date. And I'm fairly confident we'll figure it out. And I think it's going to, and I, on the much more positive side, and I went to public schools, I don't know if you did too, like, you ended up with some pretty bad teachers at times, and now you have an outlet.

50:27You don't need to be the rich kid who can afford a tutor anymore to get tutoring. If you are a kid who excels in math and your school doesn't have advanced statistics classes, well now you do. So I think this is just an incredibly democratizing force with kids who have agency and I think that's very exciting. I'm hopeful that there's a 11 year old right now who's going to start a really amazing company 10 years from now, who's like Chatchy PT is going to be their primary tutor that to like lead to that outcome. And I think that's pretty cool. I have a two year old day. And it feels like there's like a new milestone of there's like when to give them a phone, when to give them, I don't know, snapchat, whatever it could use these days.

51:09And then it's like when to give them their first Chatchee BT account. Oh no, I wonder how soon that's supposed to happen. I think Chatchee BT, my personal take is a step number one or two. I don't think mobile phones are great in school or great for kids. And I personally advocate for waiting a long time. But I think that chat GBT is more like Google search. And it's one thing to have a device in your pocket that's addictive and has push notifications. It was another thing to use AI to learn. And so I think the two are different. And I really think of AI fundamentally as a utility. And I don't think a lot of parents before chat GBT said, when should I let my kid use Google search?

51:50That's like a different type of tool. And I think they get it like that is the way I think about these technologies. And so is the form factor for your kids like an iPad or a laptop or some. Yeah, they use like the computer on the desk. Got it. All right, get tips. This is good for me to learn all these things as my kids. Okay, I'm going to zoom out and let's talk about business strategy AI. One of the biggest questions a lot of founders think about these days is just where should I build? What will foundational model companies not squash and do themselves? Being someone building a very successful AI business and also being on the board of OpenAI, I feel like you have a really unique perspective on what is probably a good idea and it's probably not a good idea.

52:29Why do you think that AI market is going to play out and where do you think founders should focus and also just try to avoid? I think there's three segments of the AI market that wind up fairly many full markets and then I'll end with how I think it's going to play out. So, first is the frontier model market or foundation model market. I think this will end up the small handful of hyperscalers and really big labs. just like the cloud infrastructure is a service market. So, and the reason for that is that creating a frontier model is entirely a function of CAPEX, and you need a company with huge amounts of CAPEX capacity to build on these models.

53:08All of the companies that were startups that tried to do this have already been consolidated, or almost all of them inflection, adapt, character, and others. And I think it's just not, 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 fund raising runway to get to escape velocity. And also the models deteriorate and value fairly quickly as an asset class. And so you need just a lot of scale to make a return on the investment for a model that deteriorates and value so quickly. So I think that's going to end up probably no entrepreneur should build the frontier model.

53:46That's my take. And let's hear Elon. Yeah. Yeah. He's different, right? And he has the capacity to raise billions in capital. And my guess is most of the growth of listeners are just down. And then he's the greatest of all time for a reason. And he's different. You don't compare yourself to him. The other part of the market is the tooling. And I think there's a lot of folks selling pickaxes in the Gold Rush. This is data labeling services. This is data platforms. It's e -vowl tools, more specialized models, like 11 labs has a great set of voice models that a lot of companies use that are really high quality.

54:25And 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 pretty close to the sun. So if you look at the infrastructures of service market and the cloud tooling market, like the confluent and Databricks and stuff like a lot of the Amazon and Azure and others have competing products in those areas because they're very adjacent to the infrastructure itself. And every infrastructure provider is trying to differentiate by maybe not the stack and you're right there. And so there's some real meaningful companies, as I mentioned, like Snowflake, Databricks, Confluent, others, but there's a lot of others that were sort of obviated by technology from the infrastructure providers themselves.

55:10So those companies probably are the most at risk for a developer day from one of these big foundation model companies releasing exactly what they do. So you have to, there's probably a lot of people who need your tool, but the question will be if or when is probably the right way to think about it. One of these large infrastructure providers introduces a competitor, why will people continue to choose you? So it's a good market, but it's a little bit close to this end, as I said. And then there's the apply day I market. I think this will play out for companies who build agents. I think agent is the new app.

55:46And so I think that's going to be sort of the product form factor. So there's companies like Sierra. We help companies build agents to answer the phone or answer the chat for customer experience and customer service. Those companies like Harvey that make agents for both illegal, parallel legal profession, antitrust reviews, reviewing contracts, et cetera, et cetera. There's companies that do content marketing, there's companies that do supply chain analysis. I think this is sort of like this offers a service market. They'll probably be higher emerging companies because you're selling something that achieves a business outcome as opposed to being a byproduct of the models themselves.

56:23They will almost certainly pay taxes down to the model providers, which is why those model providers that has wound up extremely large scale, but probably slightly larger margin. And I think the market for them will be probably less technical. I mean, if you think about the purest form of software as a service, it's not like you ask what database do you use, right? It's really about the feature and function. I think that's where agents will go. I think it's going to be more about product than it is about technology over time. Just going back to my metaphor, for, you know, in 1998 when Mark and Parker started sales force, just getting that database running in the cloud was like a technical achievement.

57:02You know, nowadays, like, you know, no one asks about that because you can just spin up a database in AWS or Azure and it's like no problem. I think today, you know, getting an orchestrating an agentic process on top of the models is like, sounds really fancy and it's really hard and all that stuff. You know, I'm pretty sure that's gonna be easy in three or four years. It's just like just as the technology improves and so over time you say like what is an agent company? Well it looks a little bit like software service You're gonna talk a little bit less about how you deal with the models in the same way Modern SaaS few people ask what database you use But you'll probably ask a lot about the workflows and what you know business outcomes that you're driving Are you generating leads for a sales team?

57:44Are you you know minimizing your procurement spend whatever value? You're providing it's going to sort of slowly evolve towards that I I'm very excited. I don't think startups are probably build foundation models. I think, but I just, I mean, you can shoot your shot. You know, if you have a vision for the future, go for it. But I think it's probably a challenging market that's already sort of consolidated. I'm very excited about the other two markets. I'm particularly excited as building agents becomes easier to see a lot of long tail agent companies come out. I was looking at a website for the top 50 software companies in the stock market.

58:21And obviously the top five or the big, big one ones, like Microsoft, Amazon, Google, all that. But the next 50 are all SaaS companies. And some of them are very exciting. Some of them are super boring. But this is how to software markets evolve. I think we're to see something similar with agents. It's not just going to be these huge markets. We're in customer service and software engineering. It's going to be a lot of things where people are spending a lot of time and resources that an agent can just solve. but it requires an entrepreneur to actually understand that business problem, like in deeply.

58:55And I think that's where a lot of the value is gonna be unlocked in the AI market. That is incredibly helpful. So it makes me think about it. I had Mark Beniap on the podcast, you guys were co -seos. And he was extremely agent -pilled. All you wanted to talk about was agent force. Clearly you are also very agent -pilled. What is it that you can't be sure of? I never heard the term agent tell them when you get those. 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 isn't going to work with what are people not seeing?

59:31If you talk to an economist like Larry Summers who's on the OpenAI board with me, they'll talk about like what is the value of technology? Well, it helps drive productivity and economy. And if you look at the one of the big jumps in productivity and economy was in the 90s, and I think a lot of folks I talked to think it was actually that very first way of computing where people made like ERP systems and just like put a county into computers and databases, even like mainframes when I was talking like the PC era, because it was such a huge step up, like, you know, just imagine like the ledgers of, you know, numbers that you'd have for like a large multinational company before and it truly just transformed departments.

1:00:14I'll give you a little toy example. My dad just retired. He was a mechanical engineer and he was talking about when he first started his career in the late 70s and you went into a mechanical engineering firm, the majority of the firm were drafts people. So basically you take an engineer and you need to do all the different vantage points and for all the different floors and to give the contractor to do the thing. Now there are zero drafts people at his company. You just make the design in first AutoCAD and now rev it, and it's a 3D model. And the drafting has actually been eliminated. It's just not a thing one needs to do anymore.

1:00:49The actual design and drafting is not a thing that exists. It's just like you can, it's just a design. That's true productivity gains, right? It's like the job of the mechanical engineering firm was to do a design. The drafting was like sort of this necessary output put for the contractor, but it wasn't really adding value. It's just sort of like the supply chain change. If you look at the history of the software industry from the PC on, there's been meaningful productivity gains, but just not nearly as meaningful as that first huge jump. And I'm not smart enough to know exactly why, but it is interesting.

1:01:29The promise of productivity gains from technology hasn't been as realized, I think, as some people thought. I think agents will truly start to bend the curve again, like we did in the very early days of computing, because software is going from helping an individual be slightly more productive, to actually accomplishing a job autonomously. And as a consequence, just like you don't need drafts, people in a mechanical engineering firm, you just won't need someone doing that thing anymore. It means they can do something else that's higher leverage and more productive. You can actually, you know, a smaller group of people can accomplish more and, you know, truly drive productivity gains in the economy.

1:02:15And, you know, I think if you've ever sold enterprise software, you end up in these discussions as a vendor with the customer, where you'll have like a value discussion and you'll do like somewhat convoluted, you know, things like, okay, it's like you're selling a sales thing. Okay, well, every salesperson sells, you know, 5 % more, data to done, you should pay us a million dollars. Like, you know, it's roughly that conversation. And it's so unattributable, you know, especially, and it's why it's so hard to sell productivity software, which I learned in our ways. You know, it's just hard to know, you know, So what's the value of making everyone 10 % more productive?

1:02:56Did you actually make them 10 % more productive or did something else change? You don't really know all these things, but now with an agent actually accomplishing a job not only is it actually truly driving productivity in a very real way, but it's measurable as well. So all those things combined means I think this is actually like a step change in how we think about software because it does a job autonomously, which is more self -evident, a productivity driver. It's measurable, so people value it differently as well, which is why I also believe in outcomes -based pricing for software. And all of that combined to me, it feels like as significant as the cloud, or I think more technologically, but just in terms of how it transforms the business model to software industry, where there's going to be a before and after.

1:03:45I don't know how many people still sell perpetually licensed on -premises software, but it's determinous at this point. I think we're going to go through a similar transition. The whole market is going to go towards agents. I think the whole market is going to go towards outcomes based pricing, not because it's the only way, but it's going to be like the market is going to pull everyone there because it's just so obviously the correct way to build and sell software. When we pull on our last thread, so we had Motivon on the podcast recently, pricing expert legend, monetizing innovation author. and he talked about pricing strategy for AI companies.

1:04:17And he was very much in your camp of, if you can, you need to price your product as an outcome -based product. And the access he uses exactly what you shared, which is you can do that if you can attribute the impact and it's autonomous, it's running on its own. Maybe just, and he actually used Sierra as one of the shining examples of this being successful. Can you just briefly just explain a little bit what is outcome -based pricing for people that haven't heard this term before? and then just how does it work for Sierra to give an example? Yeah, I'll start with the example and then I'll broaden it.

1:04:47I'm so at zero, we help companies make customer -facing AI agents primarily for customer service, but more broadly for customer experience. So if you have a problem with your Series XM radio, you'll call or chat with Harmony who's our AI agent. If you have ADT home security and your alarm doesn't work, you can chat with their AI agent, Sonos speakers, a lot of different consumer brands. And, you know, if you think about running a call center, there's a cost for every phone call that you take. Most of it is labor costs, but if you have, let's just say, a typical phone call is in the order between $10 and $20 US dollars.

1:05:26Most of it, some of it's software, some of it's telephony, but a lot of it is just like the hourly wage of the person answering the phone. So if an AI agent can take that call and solve it, that is in the industry often called the call deflection or a containment, and that essentially means you saved, call it $15 because you didn't have to have someone pick up the phone. So in our industry, basically we say, hey, if the AI agent solves the customer's problem, they're happy with it and you didn't have to pick up the phone. and there's a pre -negotiated rate for that. And we call it like resolution -based.

1:06:06There are other outcomes as well. We have some sales agents being sale to pay to sales commission, believe it or not. We do. We really think of our agents as truly customer experience, like the concierge for your brand. And we wanna make sure that our business model is aligned with our customers business model. As you said, these agents need to be autonomous and the outcome has to be measurable. That's not always possible, but I think it's broadly possible. And what's really neat about it is if you talk to any CFO or head of procurement, you know, with their big vendors, they look at the bill of materials and it's like overwhelming and it's impossible to know if you're getting the value that you hoped from that contract.

1:06:45I think consumption based, which was popular, particularly in infrastructure space, is closer to it. But I'm not sure like a token is actually a good measure of value for AI either. I always use the analogy, like right now most of the coding agents are price -pertokin or per utilization, but there's this famous story of Apple engineer who had a bad manager who was like, had you report how many lines of code you wrote every day, which every engineer in the world knows is an idiotic way to measure productivity. He famously went in with a report that had a negative number, so I think he did a big refactor and deleted a bunch of noses away, saying fuck you to the man.

1:07:22And I think tokens are similar. Like, yeah, you used a lot of tokens like good for you. Did it produce a poll request that was good? And I think that's the whole point of all this. I don't think, I think there's a huge difference between outcomes based pricing and usage based pricing because especially in AI, they're not necessarily even correlated. And you could have a long phone call, not solve the customer's problem, and they give you a negative review online and call the call center again, all that effort was for nothing. In fact, you might have added negative value. And so I have a huge believer in this.

1:07:58And what's fun about it is, it really just aligns, I think every technology company aspires to be a partner, not a vendor. And I think at Sierra, we are truly a partner to every single one of our customers, because we're all aligned on what we want to achieve. And I think that is really where software, the software industry should go. It requires a lot of different shape of a company. You just have to have, you have to be able to help your customers achieve those outcomes. You can't just throw this off of the wall because you'll never get paid. If it doesn't, you have to, you know, really just your orientation becomes so extremely customer -centric when you do this the right way.

1:08:35I think it's just a better version of this offer industry. So I think it's right from first principles, it's right for procurement partners, and I think it's right for the world. We've been chatting a little bit about productivity gains. There's a lot of skepticism in the headlines these days of just like, what is AI actually doing? Like, is it actually helping people be more productive? If there was a recent study actually, I don't know if you saw where they showed engineers were less productive with AI because it was just putting them in different directions. They had to research all what's going wrong here.

1:09:02And so I think CX is a really good example where you clearly are seeing gains. Are you seeing actual gains at your company or any other company work with outside of CX in terms of productivity that is like clearly yes, this is working and a huge deal. I'm extremely bullish on the productivity gains for me, but I do think the tools and products right now are somewhat immature and it's quite counterintuitive. So for example, I almost every software engineering firm I know uses something like cursor to help their software engineers. Most people use cursor right now is a kind of coding auto -complete that they have a lot of agentic solutions, and there's a lot of opening on his codecs, and there's Cloud as I can't remember the anthropic products.

1:09:46There's lots of agentic agents coming as well. One of the interesting things because the technology is sort of immature, the code it produces often has problems. So there's a lot of people sort of approaching this to actually realize those productivity gains, because as any engineer who's written a lot of code will tell you, it's pretty easy to look at and edit it and fix code U -Route, 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 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.

1:10:31And in fact, if you end up producing lots of issues with your customers, you could end up producing I'm producing a lot of features, but actually it's like, mucking up the machine a little bit and having something that's not ideal. There's a couple of techniques I think are interesting. First, I think there's a lot of AI starts now working on things like code reviews. I think this idea of self -reflection in agents is really important. Having AI supervise the AI is actually very effective. Just think about it this way. If you produce an AI agent that's right 90 % of the time, that's not that great.

1:11:03But how hard would it be to make another AI agent to find the errors the other 10 % of the time. That might be a tractable problem. And if that thing's right 90 % of the time, just for arguments sake, you can wire those things together and have something that's right 99 % of the time. So it's just a math problem. Like, you know, and it turns out that you can make something to generate code, you can make something to review code, and you're essentially using compute for cognitive capacity. And you can layer on more layers of cognition and thinking and reasoning and produce things increasingly robust.

1:11:34So I'm very excited about that. The other thing though is root cause analysis. So we have an engineer at CIRA who exclusively focuses on the model context protocol server, serving our cursor instance, and our whole philosophy is rather than if cursor generated something incorrect, rather than just fixing it, try to root cause it. Try to get it so the next time cursor will produce the correct code. So essentially, it's context engineering. Like, what context did cursor not have that would have been necessary to produce the right outcome? So I think people who are trying to get productivity gains in departments like software engineering need to stop sort of waiting for the models to magically work if they want to see that gains now.

1:12:20And you really have to create like root cause analysis and systems and say like, how do we sort of go root cause every bad line of code and actually give the right context and produce the right systems so the models can do it today. The over time, that probably less necessary and you'll have less context engineering necessary to do it. But you really have to think of this as a system. And I think people are sort of like waiting for the models to just magically get better. And I'm like, well, that'll happen eventually, but you want the gains now, you gotta put it in the work. I mean, that's essentially why apply to AI companies exist.

1:12:53And the work is non -trivial, but it's you can do it. And so, you know, for customers using platforms like Sierra, Yeah, yeah, agents are perfect, but we're creating a system that lets customers create a virtuous cycle of improvement. If you want to go from a 65 % automated resolution rate to 75%, we have a billion tools to let AI help you do that. Identify opportunities for improvement, figure out why people are frustrated, what new capabilities can we add to our agent to improve the resolution rate. And you're sort of let AI put the needles at the top of the haystack on your behalf, and I think that's the really the way to optimize these systems.

1:13:27I'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 actual approach that? I'm probably out of my depth here, but it's essentially MCP, but it's essentially, you know, because that's how you provide context to cursor. And I think that almost always when you have a model making a poor decision of it's a good model, it's lack of context. And so you really want to like find the intersection of your particular product and code base with the context available to these coding agents and systems and fix it at the root is sort of the principle here.

1:14:06Got it. That is very cool. And heard people do that model, model context vertical makes sense. We've talked about productivity gains outside TX just to give you a chance to share how amazing what you built is. What are some of the gains you see for people using Sierra? Yeah, we have our customers see anywhere between 50 and 90 % of their customer service interactions completely automated, which I think is really exciting. And we serve just a really, really broad range of customers. We serve the health insurance industry, the healthcare provider space, banks. You can actually refinance your home using an agent, one of our customers, There's a built on our platform to telecommunications industry, direct TV, serious XM, to a lot of retailers as well, which is really fun.

1:14:52Everyone from Wayfair to clothing retailers like Olokaya and Chubby Shorts, what's really neat about is a pretty diverse range of use cases. And it's everything from helping you sign up for a, we have an agent that helps with customer support in one of the big dating applications to, you know, helping you upgrade or downgrade your, your serious XM plan. Actually, it's really funny. We do technical support from everything from home alarm systems to center speakers to more recently cat scan machines, which I think is amazing. So technicians going in and fixing the cat scan machine can chat with an AI agent, help them guide them through that process.

1:15:35We're the leader in the space. We're trying to enable every company in the world to create their agent with their brand at the top that I think will become as meaningful of a digital touch point as a website or their mobile app. In the short term, it can really transform the costs of running a customer service team. And what's remarkable is do so with really high customer satisfaction scores. That way Watchers agent, I believe, has a customer satisfaction score of 4 .6 out of 5, which is pretty amazing. And what's interesting about service too, it's often people having a problem. And so when you have a clear, I don't know if you use them in the airport, I think that Asian has a C -Sat score of 4 .7 out You know, people are coming in with a problem in Indian delighted and I think that's really the opportunity here and Yara whole vision is that we're going to move towards a world where every single one of the interactions with your customers can be Instant, it can be multilingual, it can be over audio, it can be over chat, it can be digital, it can be over the phone, and it can be very personalized.

1:16:39And I think that's really, really exciting. And if you think about all the best moments you've had with a brand, it's like that store associate who you know. And it's like for me, it's like the butcher at the grocery store, I love to cook. You know me, we talk. But can you actually produce that at scale for a company with 100 million customers? And can you do it in a really personal way? And I think we're really on the cusp of enabling that. I mean, let's give one more question before we get to very exciting lighting around. There's a lot of founders struggling with GoToMarket in AI with their AI apps.

1:17:12There's so many apps these days, so many products, so many things coming at buyers at large B2B companies. Clearly, you guys have figured something out. Imagine your name helps, investors help, but what have you learned about just how to successfully do GoToMarket with an AI product, say an agent specific product that you think would be helpful for folks trying to do this better? I think there's a small handful of go -to -market models that have been proven to work. And I think it's important to choose the right one for the product category you're going after. One category I would say is developer lead.

1:17:49This is some of famously striped and Twilio were probably like two of the original that did this exceptionally. And essentially the go -to -market motion there is to appeal to an individual engineer often with them the department of the CTO. who have accountability and fair amount of latitude to choose a solution. This works if your product is sort of a platform product. It doesn't work, for example, if your product is trying to help a line of business because lines of business typically don't have dedicated engineering teams or let alone the latitude to just go download a new library or start using a web service like that.

1:18:32It particularly works well if you sell the startups. Just because startups tend to have engineering teams with quite a bit of latitude to choose services to help them solve the problem given by the founder. Then there's product led growth. It's a broad term. Obviously, every company's product matters, but product led growth, more specifically, means users can sign up from the website, often get put on a 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. And so it works for small business software, almost always, because sole proprietors do everything.

1:19:07And so you're selling small business software, like you know, Shopify in the early days, and there's a lot of other products like that, where you're trying to sell to small merchants. You know, that's great. It doesn't work well when you're buyer and the user of the software are different. So I always use the example of something like expense reporting software. The user of that software is an individual employee, but the buyer's often a finance department. And so, you know, having to sign up and buy with your credit card doesn't make sense because the person using is not the person with the credit card and it just doesn't work.

1:19:39And then there's direct sales. And direct sales had gone, I don't say out of fashion, but if I think of like the best direct sales companies, I probably there's a lot of lineage from Oracle, but you think SAP Oracle Service Now Sales Force Adobe perhaps. And there's others as well. and these were companies that sold into large lines of business in a relatively traditional sales motion. I think because product -led growth became very popular, I think a lot of companies use that, which is great. That motion produces great products. But if PLG means that you aren't actually engaging with the buyer of your software, like you're not going to grow.

1:20:21And so I've actually seen more recently with a lot of AI companies, direct sales, come a little bit more back into fashion because I think so many of the opportunities in AI are actually meet that qualification where the buyer and the user are not necessarily the same person and it really requires that good market motion. Where I see entrepreneurs stumble is they'll sort of choose a good market motion without thinking through the, what is the process of purchasing the software, what is the process of evaluating the value of the software. And I think people just need to be much more, like first principles about it and much more thoughtful about it.

1:21:01And candidly, I think like a lot of companies should leverage direct sales more than they do. And even though it like because of the, you know, sometimes justified reputation of the quality of products of some of these direct sales companies, a lot of it sort of had gotten a bad name. and I think I think a lot, I'm sort of thankful to see it coming back in a lot of the eye market. I feel like this message is something a lot of founders need to hear, especially founders that aren't from a business background of that, you know, sales turns them off. They don't think they're going to be great at sales, just this push of this might be what you have to get really good at.

1:21:33And this is how you win. And you can't just rely on product like Ruth. Yeah. Brett, is there anything else that you wanted to share? Any last nugget of wisdom? Anything you I want to double click on before we get to our very exciting lightning round. No, go ahead. Okay, let's do it. Here we go. Welcome to our very exciting lightning round. I've got five questions for you. Are you ready? Yeah, go ahead. What are two or three books that you find yourself recommending most to other people? I read a lot of nonfiction, but probably I had to pick one sort of in the area of the top to talked about competing against luck, which was the book that produced jobs to be done, which is a framework I really believe in.

1:22:15Not only critique, because I think most of these sort of like business books should be like an article, so maybe buy the book and punch into chat to beauty and get the summary. But buy the book, it's Clayton Christensen talked about it, but it's a really good framework for thinking about delivering value with products and I think it's a I definitely influenced me. On the actually one book I do recommend is Endurance which is the story of Shackleton's trip to go to the South Pole. Like half the book is him starving to death and he even sealed me with his crew of people frozen and they're about I've never seen a better story of grit in my entire life.

1:22:56It's like kind of remarkable that it's a true story. And you know, if you want to like, if you're an entrepreneur going through a hard time, read that, you're like, okay, it could be worse. That's a great book, too. It's just a remarkable, that's a true story. And one thing he did a great job at is setting expectations for folks that joined that are that famous, that's a great thing. Rather that's true, it's like a remarkable, that's true. Oh, it might not be true. I don't know, I, yeah. Yeah, it's good. Damn, deep fix, even back then. Okay, do you have a favorite recent movie or TV show that you really enjoyed?

1:23:26You haven't gotten any new TV shows recently. We just watched Inception with the kids and they loved it and made me appreciate Christopher Nolan. So I and what a cool movie cool con it's a type of movie when you watch your filming you have Conversations for two days afterwards about it. So just a great film. I saw someone using I think Vio3 to create their own Inception videos. Yeah, yeah, yeah, yeah, oh man. Okay. Do you have a favorite product that you have recently discovered that you love or when you love for a long time? I'm really a big fan of cursor. I think it's like change. I love creating software and I'm excited though for agents.

1:24:07I've been really excited. I was very excited to see codecs from open and other. I think cursor will be in its current form as a transition product and I know they're working on agents as well. But I really enjoyed taking something I love and I'm like, been my life's passion and really diving into this AI tool and like seeing how it transforms, how it creates software. So I've just been like spending a lot of time with the product just because it's so core to my like what I love to do and it's a really well well crafted product. And the first time someone's actually mentioned cursor in this answer so might be the beginning of a trend.

1:24:44Michael 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 going to be this additional pseudocode layer on top of code. Yeah. Very aligned with your thinking. Do you have a favorite life motto that you often come back to and find useful in work or in life? The best way to predict the future is to invent it, which I think I attribute to Alan Kay of Zerox Park. He invented a lot of the core abstractions that we're using computing today. It's why I love, I'm an entrepreneur, it's why I love to build things.

1:25:24So it's definitely like a life motto for me. I feel like many people like say this, I feel like you've actually done this so many times. You're living this motto. Final question, we talked about you inventing the like button at Friend Feed, were there other thoughts of what they would call it other than like? Was it just like obviously like? Was there other thinking there? The context of this was before emoji. So there if you read the comments on FriendFeed posts, at least 70 % of them are cool or wow or yeah or neat and one of the principal like uses of FriendFeed was to have discussions about things.

1:26:07You'd have a post and then a pretty full some discussion underneath, and it was a very compared to Twitter and others. It was a great place to have those discussions. So the product problem we were trying to solve is get all the one word answers out so that the discussion was actually like actual comments as opposed to acknowledgments that you read the thing. So the original framing was one click comment. That was how we thought about it. And so we, the first version that I made had a heart. And there she denies remembering this, but there's an Anna Yang, now Anna Moller, who has worked with the cover she hated it.

1:26:48She said like, if I look at a heart, like hearts on every post, I'm gonna vomit. Like it's just two, it's like two, too much. You know, and it also was interesting, like we were simulating and it was like an article about a tragedy or something. A heart was just not the right thing. like, which actually turned out to be really hard to translate, was just a much more neutral sentiment, and that's what I was hard to translate, because it was subtle. And we, so that's how we ended up with this. We started with the heart, and I don't know if we ever had the word love, but we definitely start off with an iconography.

1:27:22And then like, which just felt like this positive yet as neutral as possible within the realm of positives so that it could work for like a more complex story. But it was all because we needed a one -click comment. That's where the concept came from. Wow. I've never heard of this story before. It makes me think about LinkedIn now. They're basically trying to solve that same problem. They have all these auto reply kind of pill -tag things. I don't think people like this much. They have a lot of features. So many AI features. Brett, this was incredible. This was an honor. I so appreciate you coming on this podcast.

1:27:53Two final questions. Working folks finding online. If they want to reach out, maybe go see if they want to work and how can listeners be useful to you? If you want an AI agent to help the customer service go to seared .ai. If you want to apply here, seared .ai slash careers, where we have offices in San Francisco, New York, Atlanta, and London and our hiring pretty aggressively in every department. So please reach out to be interested. And how can listeners be useful to you? Is it tryout sear, anything else there? Yeah, tryout sear, I'm a single issue butter. There you go. Stay on the message, I love it.

1:28:26Yeah. Yeah, right. Thank you so much for being here. Yeah, thanks for having me. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or a leaving review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'sPodcast .com. See you in the next episode.