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- [00:01]
Brett Taylor
Technology companies aren't entitled to their future success. AI, I think, will change the landscape of software, and I think it will help some companies and it will really hurt others. And so when I think about what it means to build a company that's enduring, that is a really, really tall task in my mind right now. Because it means not only making something that's financially enduring over the next 10 years, but setting up a culture where a company can actually evolve to meet the changing demands of society and technology when it's changing at a pace that is like unprecedented in history. So I think it's one of the most fun business challenges of all time. I just get so much energy because it's incredibly hard and it's harder now than it's ever been to do something that lasts beyond you. But that, I think is the ultimate measure of a company.
- [00:58]
Shane Parrish
Welcome to the Knowledge Project podcast. I'm your host Shane Parrish. In a world where knowledge is power, this podcast is your toolkit for mastering the best what other people have already figured out. If you want to take your learning the next level, consider joining our membership program at FS Blog Membership. As a member, you'll get my personal reflections at the end of every episode, early access to episodes, no ads, including this exclusive content, hand edited transcripts, and so much more. Check out the link in the show notes for more. Six months after Brett Taylor realized AI was about to change everything, he walked away from his co CEO job at Salesforce to start from scratch. That's how massive this shift really is. The mastermind behind Google Maps, and the former Chief Technology Officer at Facebook, Brett reveals the brutal truths about leadership, AI and what it really takes to build something that endures long after you've reached the top. Brett's led some of the most influential companies in tech and seen exactly what makes businesses scale, what kills them from within, and why most founders don't survive their own success. In this conversation, you'll discover why so many companies are already on life support without realizing it. How first principles thinking separates the next wave of winners from everyone else, and the hidden reason most acquisitions fail. We'll explore why AI is bigger than anyone suspects. Plus the mindset shift that turns great engineers, exceptional CEOs. Whether you're a founder, an operator, or simply someone who wants to think sharper, this episode will change how you see your business, technology and the future. It's time to listen and learn. What was your first real aha moment with AI where you realized, holy shit, this is going to be huge.
- [02:54]
Brett Taylor
I had two Separate aha moments. One that I don't think I really appreciated how huge it would be. But it kind of reset my expectation, which was the launch of Dolly in the summer of 22. Is that right? I might be off by year, but I think summer of 22 and the avocado chair that they generated. And I had been, well, my background is in computer science and pretty technically deep. I hadn't been paying attention to large language models. I just didn't follow the progress after the Transformers paper. And I saw that and my reaction was I had no idea computers could do that. And that particular launch, seeing a generated image of an avocado chair, I don't think I extrapolated to where we are now. But it for me shook me and realized I need to pay more attention to this space and OpenAI specifically the than I had been. I think I had. That was the moment where I realized I clearly have been not paying attention to something significant. And then it was six months later, coincidentally like the month after I left Salesforce, ChatGPT came out. And before it became a phenomena, though it did so quickly, but I was already plugged into it and I was. From then on I could not stop thinking about it. But that avocado chair, I don't know why. I think there was a bit of an emotional moment where you saw a computer doing something that wasn't just rule based but creative. And the idea of a computer doing something, creating something from scratch was. Well, it doesn't seem so novel a few years later. Just blew my mind at the time.
- [04:44]
Shane Parrish
One of the unique things about you is that you've started companies, you've been acquired by Facebook and Salesforce. Inside those companies, you rose up to be the CTO at Facebook, the co CEO at Salesforce. Talk to me about founders working for founders and founders working within a company.
- [05:04]
Brett Taylor
Yeah, it's a very challenging transition for a lot of founders to make. I think there's lots of examples of acquisitions that have been really transformative from a business standpoint. I think YouTube, Instagram being two of the more prominent that have clearly changed the shape of the acquiring company. But even in those cases, the founders didn't stay around that long. That's maybe a little unfair. Stick around for a little bit. I think the interesting thing about being a founder is it's not just building a business, but it's very much your identity. And I think it's very hard for people who aren't founders to experience it. You take everything very personally from the product to the customers to the press, to your competitors, both inner and outer measures of success. And I think when you go to be an acquired, there's a business aspect to it and can you operate within a larger company. But that's intertwined with the sense of identity. You go from being the founder of a company and the CEO of a company or CTO of a company, whatever your title happens to be as one of the co founders to be in a part of a larger organization. And to fully embrace that, you actually need to change your identity. You need to go from being the head of Instagram or in my case the head of Quip, to being an employee of Salesforce or going from being the CEO of FriendFeed to being an employee of Facebook. What I've observed is that identity shift is a prerequisite for most of the other things. It's not simply your ability to handle the politics and bureaucracy of a bigger company or to navigate a new structure. I actually think most founders don't make that leap where they actually identify with that new thing. It's even harder for some of the employees too because most of the time in an acquisition, an employee of an acquired company didn't choose that path and in fact they chose to work for a different company and the acquisition determined a different outcome. And that's why integrating acquisitions is so nuanced. And I would say that having the experience of having been acquired before and having acquired some companies before, when I got to Salesforce, I really tried to be self aware about that and really tried to be a part of Salesforce and tried to shift my identity and not be a single issue voter around Quip. I'd really tried to embrace it and I think it's really hard for some founders to do and some founders don't want to. Honestly, they maybe cash the check and it's more of a transactional relationship. I really actually am so grateful for the experience. Having been at Facebook and Salesforce, I learned so much. But it really took a lot of effort on my part to just transform my perception of myself and who I am to get that value out of the company that acquired us.
- [08:00]
Shane Parrish
How did you, how did it change how you did acquisitions at Salesforce? You guys did a lot of acquisitions while you were there and you're acquiring founders and sort of startups and I think Slack was while you were there too. How did that change how you went about integrating that company into the Salesforce culture?
- [08:17]
Brett Taylor
I'll talk abstract and I'll talk about some specific acquisitions too. But first I think I tried to approach it with more empathy and more realism. You know, one of the nuanced parts about acquisitions is there's the period of doing the acquisition. There's sort of the period after you've decided to do it of doing due diligence. And then there's a period when it's done and you're integrating the company and sort of the period after. One of the things that I have observed is that companies doing acquisitions, often the part of deciding to do it is a bit of a mutual sales process. You're trying to find a fair value for the company and there's some back and forth there. But at the end of the day, there's usually some objective measure of that influenced by a lot of factors, but there's some fair value of that. But what you're trying to do is what are corporate speak would be synergies. But why do this? Why is one plus one greater than two? That's why you do an acquisition just from first principles. It's often an exercise in storytelling. You bring this product together with our product and customers will find the whole greater than the sum of its parts. This team applied to our sales channel. Or if you're a Google acquisition, imagine the traffic we can drive to this product experience. In the case of something like an Instagram, imagine our ad sales team attached to your amazing product and how quickly we can help you realize that value, whatever it might be. I find that people, because there's sort of a craft of storytelling for both sides to come to the same conclusion that they should do. This acquisition sometimes either simplifies or sugarcoats some of the realities of it. Little things like how much control will the founding team of the acquired company have over those decisions? Will it be operated as a standalone business unit or will your team be sort of broken up into functional groups within the larger company? And it's sort of those little. They're not little, but those, I'll say boring, but important things that often people don't talk enough about. And you don't need to figure out every part of an acquisition to make it successful. But often you can end up running into true third rails that you didn't find because you were having the storytelling discussions rather than getting down to brass tacks about how things will work and what's important. The other thing that I think is really important is being really clear what success looks like. I think sometimes it's a business outcome, sometimes it's a product goal. But I found that if you went to most of the larger acquisitions in the Valley. And you, two weeks after it was closed, interviewed the management team of the acquiring company and the acquired company and you asked them, what does success look like two years from now? My guess is 80% of the time, you get different answers. I think it goes back to this storytelling thing where you're talking about the benefits of the acquisition, we're talking about what does success look like. So I really tried to approach it. I tried to pull forward some harder conversations when I'm doing acquisitions or even when I'm being acquired, since it's happened to me twice. So that when you're approaching it, you not only get the hey, why is one plus one equal greater than two? Everything's going to be awesome. But no, for real, what does success look like here? And then as a founder, your job of an acquired company is to tell your team that and align your team to that. And I think founders don't take on enough accountability towards making these acquisitions successful, as I think they should. And it goes back to, again, a certain naivete. It's like you're not your company anymore, you're a part of something larger. And I think successful ones work when everyone embraces that.
- [12:24]
Shane Parrish
What point in the acquisition process is that conversation? Is that after we've signed our binding sort of commitment, or is it we should have that conversation before? So I know what I'm walking into.
- [12:37]
Brett Taylor
My personal take is it's not something you have. You have to get to the point where the two parties want to merge. And that's obviously a financial decision, particularly if it's like a public company, there's a board and shareholders. Most acquisitions in the Valley are a larger firm acquiring a private firm. That's not all of them, but I would say that's the vast majority. In those cases. There's often a qualitative threshold where someone's like, yeah, let's do this. We have the high level terms, sometimes a term sheet, formally, I think it's right after that where people have really committed to the key things, how much value, why are we doing this? The big stuff. And there's usually lots of lawyers being paid lots of money to turn those term sheets into a more complete set of documents, usually more complete due diligence, stuff like that. There's an awkward waiting period there. And that's a time, I think, where the strategic decision makers in those moments can get together and say, let's talk through what this really means. And the nice part about having then for all parties is you've kind of made the commitment to each other. So I think you have more social permission to have real conversations at that point. But you also haven't consummated the relationship. And so the power imbalance isn't totally there and you can really talk through it. And it also I think engenders trust just because by having harder conversations in those moments, you're learning how to have real conversations and learning how each other works. So that's my personal opinion when to.
- [14:14]
Shane Parrish
Have it so you mentioned the board. You've been on the board of Shopify, you're on the board of OpenAI. You're a founder. What's the role of a board and how is it different when you're on the board of a founder led company?
- [14:27]
Brett Taylor
I was never really a runner. The way I see running is a gift, especially when you have stage four cancer. I'm Ann. I'm running the Boston Marathon presented by bank of America. I run for Dana Farber Cancer Institute to give people like me a chance to thrive in life even with cancer. Join bank of America in helping Ann's cause. Give if you can@bfa.com supportann what would you like the power to to do? References to charitable organizations is not endorsement by bank of America Corporation Copyright 2025 Lowe's is the destination for EGO outdoor power equipment this spring. See what's new and exclusive like the 17 inch string trimmer with line IQ technology that auto feeds to save you time and the 22 inch select cut self propelled mowers with a multi blade system for precise cutting. Shop EGO days happening now during spring festival at Lowe's we help you save. Selection varies by location while supplies last. I really like being involved in a board and I've been involved in multiple boards because I think I am an operator through and through. I probably self identify as an engineer first more than anything else and I love to build. Learning how to be an advisor is a very different vantage point that I think you see how other companies operate and you also learn how to have an impact and add value without doing it yourself. And it's a very and I've really I think become a better leader. You know having learned to do that. I have really only joined boards that were led by founders because typically I think they you can speak to them but I think they sought me out because I'm a founder and I like working with founder led companies. I think the founders I'm sure there's lots of studies on this but I think founders drive better outcomes for companies. I think Founders tend to have permission to make bolder, more disruptive decisions about their business than a professional manager. There's exceptions like Satya, I think is one of the greatest, if not the greatest CEO of our generation. And as a professional manager, but you look at everyone from Toby Lucke to Mark Benioff to Mark Zuckerberg to Sam at OpenAI and I think when you have founded a company, all your stakeholders, employees in particular, give you the benefit of the doubt. You created this thing. And if you say, hey, we need to do a major shift in our strategy, even hard things like layoffs, founders tend to get a lot of latitude and are judged. I think differently and I think rightfully so in some ways because of the interconnection of their identity to the thing that they've created. And so I actually really believe in founder led companies. One of the real interesting challenges is going from a founder led company to not. And Amazon has gone through that transition, Microsoft has gone through that transition for that reason. But I love working with founders and I, I love working with people like Toby and Sam because they're so different than me and I can see how they operate their businesses and I am inspired by it and I learned from it. And obviously working for Mark at Salesforce, I'm like, wow, that's really interesting. Almost like an anthropologist. Why did you do that? I want to learn more. And so I love working with founders that inspire me because I just learn so much from them.
- [18:06]
Shane Parrish
It's such an interesting front row seat into what's happening. Do you think founders go astray when they start listening to too many outside voices? And this goes back to the, I'm sure you're aware, the Brian Chesky founder mode. The founder mode. Talk to me about that.
- [18:22]
Brett Taylor
I have such a nuanced point of view on this because it is decidedly not simple. So broadly speaking, I really like the spirit of founder mode, which is just having deep founder led accountability for every decision at your company. I think that that's how great companies operate. And when you proverbially make decisions by committee or you're more focused on process than outcomes, that produces all the experiences we hate as employees, as customers. That's the proverbial dmv. It's like process over outcomes. Then similarly, you look at the disruption in all industries right now because of AI, the companies that will recognize where things are clearly going to change. Everyone can see it. It's like a slow motion car wreck. Everyone knows how it ends. You need that kind of decisive breakthrough. Boundaries, layers of Management to actually make change as fast as required in business. Right now, the issue I have not with Brian's statements, Brian's amazing is how people can interpret that and execute it as a caricature of what I think it means. I remember after Steve Jobs passed away and I don't know, I've met Steve a couple times. I haven't never worked with him in any meaningful way, but he was sort of, if you believe the story, is kind of pretty hard on his employees and very exacting. And I think a lot of founders were mimicking that done to wearing a black turtleneck and yelling at their employees. I'm like, not sure that was the cause. I think Steve Jobs taste and judgment executed through that packaging was the cause of their success. And then similarly, I think founder mode can be weaponized as an excuse for just overt micromanagement. And that probably won't lead to great outcomes either. And most great companies are filled with extremely great individual contributors who make good decisions and work really hard. And companies that are solely executing through the judgment of individual probably aren't going to be able to scale to be truly great companies. I have a very nuanced point because I actually believe in founders. I believe in actually that accountability that comes from the top. I believe in cultures where founders have license to go in all the way to a small decision and fix it. The infamous question mark emails from Jeff Bezos, that type of thing, that's, that's the right way to run a company. But that doesn't mean that you don't have a culture where individuals are accountable and empowered. And you don't want people trying to make business decisions because of what will please our dual leader, which is the caricature of this. And so after that came out, I could see it all happening, which is like some people will take that and be like, you know what, you're right, I need to go down and be in the details. And some people will do it and probably make everyone who works for them miserable. And probably both will happen as a consequence.
- [21:29]
Shane Parrish
So totally thank you for the detail and nuance there. I love that too. Do you think engineers make good leaders?
- [21:36]
Brett Taylor
I do think engineers make good leaders, but one thing I've seen is that I think that I really believe that great CEOs and great founders start usually with one specialty, but become more broadly specialists in our parts of their business. I think businesses are multifaceted and rarely is a business's success due to one thing like engineering or product, which is where a lot of founders come from. Often your go to market model is important for consumer companies. How you engage with the world and public policy becomes extremely important. I think as you see founders grow from doing one thing to growing to being a real meaningful company like Airbnb or Meta or something, you can see those founders really transform from being one thing to many things. So I do think engineers make great leaders. I think the first principles thinking the system design thinking really benefits things like organization design strategy and. But I also think that when we were speaking earlier about identity, I think one of the main transitions founders need to make, especially engineers, is you're not the product manager for the company, you're the CEO. On any given day. Do you spend time recruiting an executive because you have a need? Do you spend time on sales because that will have the biggest impact? Do you spend time on public policy or regulation because if you don't, it will happen to you and could really impact your business in a negative way? I think engineers who are unwilling to elevate their identity from what they were to what it needs to be in the moment often leads to plateaus in companies growth. So 100%, I think engineers make great leaders and it's not a coincidence. I think that most of the great Silicon Valley CEOs came from engineering backgrounds. But I also don't think that's sufficient either as your company scales. And I think that making that transition as all the great ones have, is incredibly important.
- [23:55]
Shane Parrish
To what extent are all business problems engineering problems?
- [24:00]
Brett Taylor
That's a deeper philosophical question that I think I have the capacity to answer. What is engineering? What I like about approaching problems as an engineer is first principles thinking and understanding the root causes of issues rather than simply addressing the symptoms of the problem. And I do think that coming from a background in engineering, that is everything from process, like how engineers do a root cause analysis of an outage on a server is a really great way to analyze why you lost a sales deal. I love the systematic approach of engineering. One thing that I think going back to good ideas that can become caricatures of themselves. One thing I've seen though with engineers who go into other disciplines is sometimes you can overanalyze decisions in some domains. Let's just take modern communications which is driven in social media and very fast paced. Having a systematic first principles discussion about every tweet you do is probably not a great comm strategy. And then similarly there are some aspects of say enterprise software sales that aren't rational, but they're human, like forming personal relationships and the importance of Those to building trust with a a partner. It's not all just product and technology. And so I would say I think a lot of things coming with an engineer mindset could really benefit. But I do think that taking that to its logical extreme can lead to analysis paralysis, can lead to over intellectualizing some things that are fundamentally human problems. And so yeah, I think a lot can benefit from engineering, but I wouldn't say everything's an engineering problem in my experience.
- [25:55]
Shane Parrish
You brought up first principles a couple times. You're running your third startup now, Sierra, it's going really well. How do you use first principles in terms of how do you use that at work?
- [26:06]
Brett Taylor
Yeah, it's particularly important right now because the market of AI is changing so rapidly. So if you rewind two years, most people hadn't used ChatGPT yet. Most companies hadn't heard the phrase large language models or generative AI yet. And in two years you have ChatGPT becoming one of the most popular consumer services in history, faster than any service in history. You have across so many domains in the enterprise, really rapid transformation. The law is being transformed, marketing is being transformed, customer service, which is where my company SierraWorks is being transformed. Software engineering is being transformed. The amount of change in such a short period of time is I think unprecedented. And perhaps I lack the historical context, but it feels faster than anything I've experienced in my career. As a consequence, I think if you are responding to the facts in front of you and not thinking from first principles about why we're at this point and where it will probably be 12 months from now, the likelihood that you'll make the right strategic decision is almost zero. So as an example, it's really interesting to me that with modern large language models, one of the careers that is being most transformed is software engineering. One of the things I think a lot about is how many software engineers will we have at our company three years from now? What will the role of a software engineer be? As we go from being authors of code to operators of code generating machines, what does that mean for the type of people we should recruit? And if I look at the actual craft of software engineering that we're doing right now, I think it's literally a fact that it'll be completely different two years from now. Yet I think a lot of people building companies hire for the problem in front of them rather than doing that. But two years is not that long. Those people that you hire now will just be getting really productive a couple years from now. We try to think about most of Our long term business from first principles. Everything from, I'll say a couple examples. In our business, our pricing model is really unique and comes from first principles thinking. Rather than having our customers pay a license for the privilege of using our platform, we only charge our customers for the outcomes. Meaning if the AI agent they've built for their customers solves the problem, there's usually a pre negotiated rate for that. And that comes from the principle that in the age of AI software isn't just helping you be more productive, but actually completing a task. What is the right and logical business model for something that completes a task? Well, charging for a job well done rather than charging for the privilege of using the software. Similarly, with a lot of our customers, we help deliver them a fully working AI agent. We don't hand them a bunch of software and say good luck, configure it yourself. The logic there is in a world where making software is easier than it ever is before and you're delivering outcomes for your customer, the delivery model of software probably should change as well. We've really tried to reimagine what the software company of the future should look like, and trying to model that in everything that we do, that's brilliant.
- [29:45]
Shane Parrish
How do you think software engineering will change? Is it you're going to have fewer people or the people are going to be organized differently or how do you see that?
- [29:55]
Brett Taylor
How geeky can I get?
- [29:57]
Shane Parrish
As geeky as you want, man.
- [29:59]
Brett Taylor
I actually wrote a blog post right before Christmas about this. I think this is an area that deserves a lot more research. I'll describe where I think we are today, and smart people may disagree, but a lot of the modern large language models, both the traditional large language models and sort of the new reasoning models, are trained on a lot of source code, and it's an important input to all of the knowledge that they're trained on. As a consequence, even the early models were very good at generating code. So every single engineer at Cira uses Cursor, which is a great product that basically integrates with the IDE Visual Studio code to help you generate code more quickly. It feels like a local maximum in a really obvious way to me, which is you have a bunch of code written by people, written in programming languages that were designed to make it easy for people to tell a computer what to do. Probably the funniest example of this is Python. It almost looks like natural language, but it's notoriously not robust. Most Python bugs are found by running the program because there's not static type checking. Similarly, most Bugs. While you could run a fancy static analysis, like most bugs show up simply at runtime because it's just not designed. It's designed to be ergonomic to write, yet we're using AI to generate that. So we sort of designed most of our computer programming systems to make it easy for the author of code to type it quickly. And we're in a world where actually generating code is going to like, the marginal cost of doing that is going to zero. But we're still generating code in programming languages that were designed for human authors. Similarly, if you've ever looked at someone else's code, which a lot of people do professionally, it's called a code review. It's actually quite hard to do a code review. You end up interpreting. You're trying to basically put the system in your head and simulate it as you're reading the code to find errors in it. So the irony now of taking things that are programming languages that were designed for authors and now having humans do the job of essentially code reviewing code written by an AI, and yet all of the AI is being in the code generation part of it. I'm like, I'm not sure it's great, but we're generating a lot of code with similar flaws to that we've been generating before, from security holes to just functional bugs and in greater volumes. And I think what I would like to see is if you start with the premise that generating code is free or going towards free, what would be the programming systems that we would design? So, for example, Rust is an example of a programming language that was designed for safety, not for programming convenience. My understanding is that the Mozilla project, there were so many security holes in Firefox, they said, let's make a programming language that's very fast, but everything can be checked statically, including memory safety. Well, it's a really interesting direction where you weren't optimizing for authorship convenience or optimizing for correctness. Are there programming language designs that are designed so a human looking at it can very quickly evaluate, does this do what I intended it to do? There's an area of computer science I studied in college called formal verification, which at the time was turning a lot of computer programs into math proofs and finding inconsistencies, and it sort of worked well. Not as well as you'd hope. But in a world where AI is generating a lot of code, should we be investing more informal verification so that the operator of that code generator machine can more easily verify that it does in fact do what they intended us to do. And could a combination of a programming language that is more structurally correct and structurally safe and exposes more primitives for verification, plus a tool to verify, could you make an operator of a code generating machine 20 times more productive, but more importantly make the robustness of their output 20 times greater? And then similarly, there's things go in and out of fashion, but like test driven development, where you write your unit test first, or your integration test first and then write code until it fulfills the test. Most programmers I know who are really good, not despise it, but it's just like it sounds better than it is in practice. But again, writing code is free, so writing tests is free. How can you create a programming system where the combination of great programming language design, formal verification, robust tests because you didn't have to do the tedious part of writing them all, could you make something that made it possible to write increasingly complex systems that were increasingly robust? And then similarly, the elephant in the room for me is the anchor tenant of most of these code generating systems are an IDE right now. And that obviously doesn't seem as important in this world. And even with coding agents, which is sort of where the world is going, it doesn't change the fact that who's accountable for the quality of it, who's fixing it? And I think there is a world where we can make reasonable software by just automating what we as software engineers do every day. But I have a strong suspicion that if we designed these systems with the role of a software engineer in mind, being an operator of a machine rather than the author of the code, we could make the process much more robust and much more productive. And it feels like a research problem to me. It doesn't feel, and I think a lot of people for good reason, including me, are just excited about the efficiency of software development going up. And I want to see the new thing. Though I'm constructively dissatisfied with where we are.
- [36:12]
Shane Parrish
It's so interesting that if software AI is good enough to write the code, should we get enough check the code?
- [36:19]
Brett Taylor
That's a great question, but actually it's still funny to me that we'd be generating Python just because for anyone who listening right now has ever operated a web service running Python, it's CPU and intensive, really inefficient. Should we be taking most of the unsafe C code that we've written and converting it to a safer system like Rust? If authoring these things and checking it are relatively free, shouldn't all of our programs be Incredibly efficient. Should they all be formally verified? Should they all be analyzed by a great agent? I do think it can be turtles all the way down. You can use AI to solve most problems in AI. The thing that I'm trying to figure out is what is the system that a human operator is using to orchestrate all those tasks. I go back to the history of software development and most of the really interesting metaphors in software development came from breakthroughs in computing. So the C programming language came from Unix. And when these time sharing systems were really, it went from sort of punch cards to something that were a lot more agile. Small talk came out of the development of the graphical user Interface@XEROX PARC and there was a sort of a confluence of message passing as a metaphor and the graphical user interface. And then there was a lot of really interesting principles that came out of networking and sort of distributed systems, distributed locking, sequencing. I, I think we should recognize that we're in this brand new era as significant as the gui. It's like a completely new era of software development. And if you were just to say I'm going to design a programming system for this new world from first principles, what would it be? And I think when we develop it, I think it'll be really exciting because rather than automating and turning up the speed of just generating code with the same processes we have today, I think will feel native to this system and give a lot more control to the people who are orchestrating the system in a way that I think will really benefit software overall.
- [38:25]
Shane Parrish
Let's dive into AI a little bit. How would you define AGI to the layman?
- [38:31]
Brett Taylor
I think a reasonable definition of AGI might be that any task that a person can do at a computer, that system can do on par or better. I'm not sure it's a precise definition, but I'll tell you where that comes from and its flaws. But there's not a perfect definition of AGI in my opinion, or there's not a precise definition of AGI? I'm sure there's good answers. One of the things about The G&AGI is about generalization. So can you have a system that is intelligent and in domains that it wasn't explicitly trained to be intelligent on? And so I think that's one of the most important things is like given a net new domain, can this system become more competent and more intelligent than a person sort of trained in that domain? And I think that's sort of the at or better than a person is certainly a good standard there, and that's sort of the definition of superintelligence. The reason I mentioned at a computer is I do think that it is a bar that means if there's a digital interface to that system, it affords the ability for AI to interact with it, which is why that's a bar that's reasonable to hit. I say that because one of the interesting questions around AGI is how quickly it does generalize. And there are domains in the world that the progress in that domain isn't necessarily limited by intelligence, but by other social artifacts. So as an example, and I'm not an expert in this area, but if you think about the pharmaceutical industry, my understanding is one of the main bottlenecks is clinical trials. So no matter how intelligent a system would be in discovering new therapies, it may not materially change that. And so you may have something that's discovering new insights in math and that would be delightful and amazing. But the existence of that system that's super intelligent in one domain may not translate to all domains equally. I just heard at least a snippet of a talk by Tyler Cohen, the economist, and it was really interesting to hear his framing on this about which parts of the economy could absorb intelligence more quickly than others. I choose that definition of AGI, recognizing that there's not a perfect definition because it captures the ability of this intelligence to generalize, while also recognizing that the domains of society like it might not apply with equal velocity even once we reach that point of a system being able to have that level of intelligence.
- [41:38]
Shane Parrish
This episode is brought to you by Indeed. When your computer breaks, you don't wait for it to magically start working again. You fix the problem. So why wait to hire the people your company desperately needs? Use Indeed's sponsored jobs to hire top talent fast and even better, you only pay for results. There's no need to wait. Speed up your hiring with a $75 sponsored job credit@ Indeed.com podcast. Terms and conditions apply. Ryan Reynolds here from Mint Mobile. I don't know if you knew this, but anyone can get the same Premium Wireless for $15 a month plan that I've been enjoying.
- [42:16]
Brett Taylor
It's not just for celebrities. So do like I did and have.
- [42:19]
Shane Parrish
One of your assistant's assistants switch you.
- [42:21]
Brett Taylor
To Mint Mobile to today. I'm told it's super easy to do@mintmobile.com Switch upfront payment of $45 for 3 month plan equivalent to $15 per month.
- [42:31]
Shane Parrish
Required intro rate first 3 months only, then full price plan options available, taxes and fees. Extra fee, full terms@mintmobile.com when I think about what artificial intelligence is limited by or the bottlenecks, if you will, I keep coming back to a couple things. There's regulation, there's computer, there's energy, there's data and there's LLMs. Am I missing anything?
- [42:56]
Brett Taylor
So you're saying the ingredients to AGI?
- [42:58]
Shane Parrish
Yeah, there's limitations on each aspect of those things and those seem to be the main contributors to what's limiting us from even accelerating at this point. How do you think about that?
- [43:11]
Brett Taylor
Yeah, what you said is roughly how I think about it. I'll put it into my own words though. I think the three primary inputs are data, compute and algorithms. And data is probably obvious, but one of the things after the transformer model was introduced is it afforded an architecture with just much greater parallelism, which meant models could be much bigger and train more quickly on much more data. Which just led to a lot of the breakthroughs with that's the L&LLM. Just they're large and the scaling laws a couple years ago indicated the larger you make the model, the more intelligent it would be. And at a degree of efficiency that was tolerable. There's lots of stuff written about this, but in terms of just textual content to train on, the availability of new content is certainly waning. And some people would say I think there's a data wall. I'm not an expert in that domain, but it's been talked about a lot and you can read a lot about it. There's a lot of interesting opportunities though to generate data too. So there's a lot of people working on simulation. If you think about a domain like self driving cars, simulation is a really interesting way to generate.
- [44:33]
Shane Parrish
Is that synthetic data? Is that what.
- [44:36]
Brett Taylor
Yeah, I would say that's synthetic data. The synthetic data has a simulation and synthetic data are a little different. So you can generate synthetic data like generate a novel simulation. I would put at least in my head and I'm sure that academics might critique what I'm saying, but I view simulation as based on a set of principles like the laws of physics. So if you build a real world simulation for training a self driving car, you're not just generating arbitrary data like the roads don't turn into loop de loops because that's not possible with physics. So by constraining a simulation with a set of real world constraints, the data has more efficacy and it constrains the different permutations of data you can generate from it. So it's. I Think a little bit higher quality. But then along those lines, a lot of people wonder if you generate synthetic data, how much value can that add to a training process? You know, is it sort of regurgitating information it already had? What's really interesting about, you know, reasoning and reasoning models is I think I feel really optimistic. These models are generating net new ideas and so it really affords the opportunity to break through some of these, the data wall as well. So data is one thing and I think both synthetic data and simulation are really interesting opportunities to grow there. Then you have compute and this is something that's why there's so many data center investments. That's why Nvidia as a company has grown so much. Probably the more interesting kind of breakthroughs there are these reasoning models where there's not quite such a formal separation between the training process and the inference process, where you can spend more compute at the time of inference to generate more intelligence, which has really been a breakthrough in a variety of ways I think is really interesting. But it shows you how you can run up against walls and find new opportunities to use it. And then finally algorithms. And the biggest breakthrough was obviously the transformers model. Attention is all you need. That paper from Google that led to where we are now. But there's been a number of really important papers since then, from the idea of chain of thought Reasoning to at OpenAI what we did with the O model, which is to do some reinforcement learning on those chains of thought to really reach new levels of intelligence. I do think that I mentioned some anecdotes about some breakthroughs there because my view is that each one of them has their own problems. Compute, it's very capital intensive. And a lot of these models, the half life of their value is pretty short because new ones come out so frequently. And so you wonder can we afford what's the business case for investing this capex? And then you have a breakthrough like 01 and you're like, gosh, with a distilled model and moving more to inference time. It changes the economics of it. You have data, you say, gosh, we're running out of textual data to train on. Well, now we can generate reasoning, we can do simulations. Oh, that's an interesting breakthrough. And then on the algorithm side, as I mentioned, just the idea of these reasoning models is really novel itself. And each of these at any given point, if you talk to an expert in one of them, and I'm an expert in none of them, they will tell you the current plateau that they can see on the horizon. And there usually is one. I mean, you'll talk to different people about how long the scaling laws for something will continue, and you'll get slightly different opinions, but no one thinks it's going to last forever. And at each one of those, because you have so many smart people working on them, you often have people discovering a breakthrough in each of them. And so as a consequence, I really do feel optimistic about the progress towards hei, because one of those plateaus might extend a while if we just don't have the key idea that we need to break through. The idea that we will be stuck on all three of those domains feels very unlikely to me. And in fact, what we've seen because of the potential economic benefits of AGI is we're in fact seeing breakthroughs in all three of them. And as a consequence, you're just seeing just the blistering pace of progress that we've seen over the past couple years.
- [49:04]
Shane Parrish
At what point does AI start making AI better than we can make it, or making it better while we're sleeping, or we can't be too far from that?
- [49:16]
Brett Taylor
Well, it might reflect back to our software engineering discussion, but broadly, this is the area of AGI around self improvement, which is meaningful from an improvement standpoint, but also obviously from a safety standpoint as well. I don't know when that will happen, but I do think by some definition you could argue that it's happening already in the sense that every engineer in Silicon Valley is already using coding agents and platforms like Cursor to help them code. So it's contributing already. And I imagine as coding assistants go to coding agents in the future, most engineers in Silicon Valley will show up in the morning, but this is sort.
- [50:00]
Shane Parrish
Of the difference between the assisted driving in Tesla versus self driving. @ what point do we leap from I'm a co pilot in this to I don't have to do anything?
- [50:14]
Brett Taylor
It's a question that there's so much nuance to the answer. I'm not sure how to answer, because I'm not sure you'd want to necessarily. I think for some software applications that's important. But when we brought up, we were talking about the active software development. People have to be accountable for the software that they produce. That means if you're doing something simple like a software as a service application, that it's secure, that it's reliable, that the functionality works as intended for something as meaningful as an agent that is somewhat autonomous. Does it have the appropriate guardrails? Does it actually do what the operators intended? Is there appropriate safety measures. So I'm not sure there's really any system where you'd want to turn a switch and go get your coffee. But I do think to the point on these broader safety things is I think that when you think about more advanced models, we need to be developing not only more and more advanced safety measures and safety harnesses, but also using AI to supervise AI and things like that. So it's probably my colleague on the board, Zico Coulter, is probably a better person to talk through some of the technical things, but there's a lot of prerequisites to get to that point. And I'm not sure it's simply the availability of the technology just because it is that at the end of the day, we are accountable for the safety of the systems we produce, not just OpenAI, every engineer. And that's a principle that should not change.
- [51:48]
Shane Parrish
What does that mean when we say safety and AI? That seems so vague and general that everybody interprets it quite differently. How do you think about that and how do you think about that in the world where, let's say we regulate safety in the United States and another country doesn't regulate safety? How does that affect the dynamic of it?
- [52:11]
Brett Taylor
I'll answer broadly and then go into the regulatory question. So I really like OpenAI's mission, which is to ensure that AGI benefits all of humanity. That isn't only about safety, and I believe intentionally so. Obviously the mission was created prior to my arrival because it's both about safety, kind of Hippocratic oath, first do no harm. And I don't think one could credibly achieve that mission if we created something unsafe. So I would say that's the most important part of the mission. But there's also a lot of other aspects of benefiting humanity. Is it universally accessible? Is there a digital divide where some people have access to AGI and some don't? Similarly, you could argue that are we maximizing the benefits and minimizing the downsides? Clearly, AI will disrupt some job, but it also could democratize access to healthcare, education, expertise. So as I think about the mission, it starts with safety, but I actually like thinking about it more broadly because I think at the end of the day, benefiting humanity is the mission and safety is a prerequisite. But it's almost like going to my analogy of the Hippocratic Oath, a doctor's job is to cure you first, do no harm, but then to cure you. And a doctor that did no harm but didn't cure you wouldn't be Great either. So I really like to think about the holistically. Again, Zeke or Sam might have a more complete answer here, but broadly I think about does the system that represents AGI align with the intentions of the people created it and the intentions of the people operating it so that it, it does what we want and it's a tool that benefits humanity, a tool that we're actively using to affect the outcomes that we're looking for. That's kind of the way I think about safety. And it can be meaningful things like misalignment or more subtle things like unintended consequences. And I think that latter part is probably the area that is really interesting from an intellectual and ethical standpoint as well. If I look at what was the bridge in Canada that fell down where it motivated the ring that a lot of engineers.
- [54:27]
Shane Parrish
Yeah, I forget the name.
- [54:29]
Brett Taylor
Just look at the. Whether it's the Tacoma Narrows Bridge in Washington or Three Mile island or these intersections where we've engineered what at the time people hope would be positively impact humanity, but something went horribly wrong. Sometimes it's engineering, sometimes it's bureaucracy, sometimes it's a lot of things. And so I don't think, when I think about safety, I don't just look at the technical measures of it, but how does this technology manifest in society? How do we make decisions around it? And you could take, put another way, technology is rarely innately good or bad. It's sort of what we do with it. And I think those social constructs matter a lot as well. So I think it's a little early to tell because we don't have this kind of superintelligence right now. And I think it won't just be a technology company defining how it manifests in society. And you could imagine taking a very well aligned AI system and a human operator directing it towards something that would objectively hurt society. And there's a question of who gets to decide who's accountable. And it's a perennial question. I mean, it's whether you're deciding should you use your smartphone in school, who should decide that. And there's parents who will tell you, hey, it's my decision, it's my kid. And then there's principals who will tell you it's not benefiting the school. And I'm not sure that's going to be my place or our place. But there'll be a number of those conversations that are much deeper than that question that I think we'll need to answer. As it relates to regulation, there's two not Conflicting forces, but two forces that exist somewhat independently but relate to each other. One is the pace of progress in AI and ensuring that the folks working on frontier models are ensuring those models do benefit humanity. And then there's the geopolitical landscape, which is do you want AGI to be created by the freedom sort of the west by democracies, or do you want it to be created by more totalitarian governments? And so I think the inherent tension for regulators will be a sense of obligation to ensure that the technology organizations creating AGI are in fact focusing enough on beta and fit in humanity, all the other stakeholders whose interests that they're accountable for and ensuring that the west remains competitive. I think that's a really nuanced thing. And I think my view is it's very important that the west leads in AI. And I'm very proud of the fact that OpenAI is based here in the United States and we're investing a lot in the United States and I think that's very important. And I also, having seen the inside of it, I think we're really focused on benefiting humanity. So I tend to think that it needs to be a multi stakeholder dialogue. But I think there's a really big risk that some regulations could have the unintended consequence of slowing down this larger conversation. But I don't say that to be dismissive of it either. It's actually just an impossibly hard problem. And I think you're seeing it play out, as you said, in really different ways in Canada, United States, European, China, elsewhere.
- [57:59]
Shane Parrish
I want to come back to compute and the dollars involved. So I mean, on one hand you have. If I just, I could start an AI company today by going, putting my credit card down using AWS and leveraging their infrastructure, which they've built, they've spent the hundreds of billions of dollars and I get to use it on a time based model. On the other hand, you have people like OpenAI, Microsoft investing tons of money into it that may be more proprietary. How do you think about the different models competing? And then the one that really throws me for a bit of a loop is Facebook. So Facebook is meta, you know, the mother maiden. Oh God. So meta. I'm like aging myself here. So meta comes along possibly for the good of humanity, but I tend to think Zuck is incredibly smart. So I don't think he's spending $100 billion to develop a free model and give it away to society. How do you think about that in terms of return on capital and return on investment?
- [59:08]
Brett Taylor
It's a really complicated business to be in just given the capex required to build a frontier model. But let me just start with a couple definitions of terms that I think are useful. I think most large language models I would call foundation models. I like the word foundation because I think it will be foundational to most intelligent systems going forward. And most people building modern models, particularly if they involve language, image or audio, shouldn't start from building a model from scratch. They should pick a foundation model, either use it off the shelf or fine tune it so it's truly foundational in many ways. In the same way most people don't build their own servers anymore, they lease them from one of the cloud infrastructure providers. I think foundation models will be something trained by companies that have a lot of capex and leased by a broad range of customers who have a broad range of use cases. I think that leads in the same way that data center builders. Having a lot of data centers enabled you to have the capital scale to build more data centers. I think the same will largely be true of building the huge clusters to do training and things like that. Foundation models I think are somewhat distinct from frontier models and frontier models, I think it's a term credited to Reid Hoffman, but I may be mistaken on that. But that's where I heard it from. These are the models that are usually the one or two that are clearly the leading edge. O3 as an example from OpenAI, these Frontier models are being built by labs who are trying to build AGI that benefits humanity. I think if you're deciding whether you're building a foundation model and what your business model is around it, it's a very different business than I'm going to go pursue AGI. Because if you're pursuing AGI, really there's only one answer, which is to build and train and move to the next horizon. Because if you can truly build something that is AGI, the economic value is so great. I think there's a really clear business case there. If you're pre training a foundation model, that's the fourth best that's going to cost you a lot of money and the return on that investment is probably fairly questionable. Because why use your 4th best large language model versus a frontier model or an open source one from Meta? As a consequence of that, I think we probably have too many people building models right now. There's already been some consolidation actually of companies being folded into Amazon and Microsoft and others. But I do think it will play out a bit like the cloud infrastructure business where a Very small number of companies with very large capex budgets are responsible for both building and operating these data centers. And then developers and in consumers will use things like ChatGPT. As a consumer or as a developer, you'll license and rent one of these models in the cloud. How it will play out is a really great question. I heard one investor talk about these as the fastest depreciating assets of all time. On the other hand, if you look at the revenue scale of something like an OpenAI and what I've read about places like Anthropic, let alone Microsoft and Amazon, it's pretty incredible as well. If you're one of those firms, you can't afford to sit on the sidelines as the world transforms. But I would have a hard time personally funding a startup that says I'm going to do pre training. I don't really know what's your differentiation in this marketplace. I think a lot of those companies, you're already seeing them consolidate because they have the cost structure of a pharmaceutical company, but not the business model.
- [63:07]
Shane Parrish
But this is just it though, right? Like OpenAI has a revenue model around a revenue model. Microsoft has a revenue model around their AI investments. They just updated the price of teams with Copilot. You know, Amazon has a revenue model around AI. In a sense, they're getting other people to pay for it through aws and then they're getting the advantages of it at Amazon too, from a consumer point of view and all the millions of projects. Bezos was doing an interview last week. He said there's every project at Amazon basically has an AI component to it. Now Facebook on the other hand, has spent all of this money already and with an endless amount presumably in sight, or not in sight, an endless amount to go. But they don't have a revenue model specifically around AI. Where it would have been cheaper, obviously for them to use a different model, but that would have required presumably giving data away or I'm just trying to work through it from Zuck's point of view.
- [64:09]
Brett Taylor
I actually will take Mark at his word. And that post he wrote about open source I think was very well written and encouraged people to read it. I think that's his strategy. If you look at Facebook now you've got me saying Facebook too. That was what it was called. The company has always really embraced open source and if I look at really popular things from REACT to now the llama models, it's always been a big part of their strategy to court developers around their ecosystem. Mark articulated some of the strategy there and I'm sure there's elements of commoditizing your complement but I also think that if you can attract developers towards models, there's a strength. I'm not really on the inside there, so I don't really have a perspective on it other than I actually think it's really great that there's different players with different incentives all investing so much and I think it is really furthering the cause of bringing these amazing tools to society. But a lot changes. I mean if you look at the price of GPT 4.0 mini, it is so much higher quality than the highest quality model two years ago and much cheaper. I haven't done the math on it, but it's probably cheaper to use that than to self host any of the open source models. Even the existence of the open source models it's not free. I mean inference costs money and so there's a lot of complexity here and actually I have the email even being relatively close to stuff like I have no idea where things are going but you could talk to a smart engineer and they'll tell you oh yeah, if you built your own servers you'll spend less than renting them from say Amazon Web Services or Azure. That's sort of true in absolute terms but misses the fact do you want someone on your team building servers? Oh and in fact if you change the way your service works and you need a different SKU like you all of a sudden are doing training and you need Nvidia H1 hundreds, now all of a sudden you're built servers like this asset, that's worthless. I think with a lot of these models the presence of open source is incredibly important and I really appreciate it. I also think the economics of AR are pretty complex because the hardware is very unique, the cost to serve is much higher. Techniques like distillation have really changed the economics of models whether or not it's open source or hosted and leased. So I think broadly speaking for developers it's kind of an amazing time right now because you have a menu of options that's incredibly wide. I actually think of it as just like in cloud computing, you'll end up with a price, performance, quality trade off and for any given engineering challenge they'll have a different answer and that's appropriate. Some people use open source, Kafka, some people work with confluent. Great. That's just the way these things work.
- [67:19]
Shane Parrish
So you don't think AGI is going to be a winner take all. You think there's going to be multiple options that have by definition, whatever the definition is of AGI.
- [67:28]
Brett Taylor
Well, first, I think OpenAI, I believe, will play a huge part in it because there's both the technology, which I think OpenAI continues to lead on, but also ChatGPT, which has become synonymous with AI for most consumers. But more than that, it is the way most people access AI today. And so one of the interesting things, like what is AGI? We talked about opinions on what the definition might be, but the other question is, how do you use that? What is the packaging? And some of intelligence will be simply the outcomes of it, like discovery of a new drug, which would be remarkable and hopefully we can cure some illnesses, but others will be just how you as an individual access it. Most of the people I know, if they're signing an apartment lease, we'll put it into ChatGPT. You get a legal opinion, if you get lab results from your doctor, you can get a second opinion on ChatGPT. Clay and I use the 01 Pro mode for criticizing our strategy at Cira all the time. And so for me, what's so remarkable about ChatGPT, which was this quirkily named research preview that has come to be synonymous with AI, is I do think that it will be the delivery mechanism for AGI when it's produced, and not just because of the many researchers at OpenAI, but because of the amazing utility it's become from individuals. And I think that's really neat because I don't know if it would have been obvious if we were having this conversation three years ago and you were talking about artificial general intelligence. I'm not sure either of us would have envisioned something so simple as a form factor to absorb it, that you just talk to it. So I think it's great, and especially as I think about the mission of OpenAI, which is to ensure that AGI benefits humanity. What a simple accessible form factor. There's free tiers of it. What a kick ass way to benefit humanity. So I really think that will be central to what we come as society to define as AGI.
- [69:43]
Shane Parrish
You mentioned using it at Sierra to critique your business strategy. What do you know about prompting that other people miss? I mean, you must have the best prompts.
- [69:55]
Brett Taylor
People think that because I'm affiliated with.
- [69:56]
Shane Parrish
It, you're not going like, here's my strategy. What do you think? What are you putting in there?
- [70:01]
Brett Taylor
I often with the reasoning models which are slower, will use a faster model first GPT4O to refine my prompts. So over the holidays, partly because I was thinking about the Future of software engineering. I've written a lot of compilers in my time. I'm written enough that it's easy for me. So I decided to see if I could have 01 Pro mode generate end to end a compiler, front end, parsing the grammar, checking for semantic correctness, generating an intermediate representation and then using llvm, which is sort of a compiler collection that's very popular to actually do run, run it all. And I would spend a lot of time iterating on 4.0 to sort of refine and make more complete and specific what I was looking for. And then I would put it into one Pro mode, go get my coffee and come back and get it. I'm not sure if that's a viable technique, but it's really interesting because I do think in the spirit of AI being the solution to more problems in AI, having a lower latency, simpler model help refine. Essentially. I like to think of it as you're a product manager and you're asking an engineer what to do. Is your product requirements document complete and specific enough and waiting for it is sometimes slower? And so I like doing it in stages like that. So that's my trip. At some point there's probably someone from OpenAI listening who's going to roll their eyes. But that's. I've.
- [71:42]
Shane Parrish
Who can I talk to at OpenAI that's like the prompt ninja. I'm so curious about this because I've actually taken recently to getting OpenAI or ChatGPT I guess, if you want to call it that. I've been getting ChatGPT to write the prompt for me. So I'll prompt it with I'm prompting an AI. Here are the key things.
- [72:03]
Brett Taylor
So similar to my technique, I want.
- [72:05]
Shane Parrish
To accomplish what would an excellent prompt look like? And then I'll copy paste that prompt that it gives me back into the system. But I'm like, I wonder what I'm missing here, right?
- [72:17]
Brett Taylor
It's a good technique. I mean there's lots of engine techniques like that. Like self reflection is a technique where you have a model, observe and critique a decision, like a chain of thought. So in general that mechanism of self reflection is I think, a really effective technique. At Cira, we help companies build customer facing AI agents. So if you're setting up a Sonos speaker, you'll now chat with an AI. If you're a SiriusXM subscriber, you can chat with Harmony, who's their AI to manage your account. We use all these tricks, self reflection, to detect things like Hallucination or decision making, generating chains of thought for more complex tasks to ensure that you're putting as much compute and cognitive load into important tricks. So there's a whole industry around figuring out how do you exact the robustness and precision out of these models. So it's really fun, but changing rapidly.
- [73:19]
Shane Parrish
Hypothetical question. You've been hired to lead or advise a country that wants to become an AI superpower. What sort of steps would you take? What sort of policies would you think would help create that? How would you bring investment from all over the world into that country and researchers? So now all of a sudden you're competing. It's not the United States. How do you set up a country from first principles all the way back to what that look like? What are the key variables?
- [73:50]
Brett Taylor
Well, I mean, especially this is definitely outside of my domain of expertise, but I would say one of the key ingredients to modern AI is compute, which is a noun that wasn't a noun until recently, but now compute is a noun. And I do think that's one area where policymakers can, because it involves a lot of things that, that touch federal and local governments like power, land, and then similarly attracting the capital, which is immense, to finance the real estate, to purchase the compute itself and then to operate the data center. And again, there's really immense power requirements for these data centers as well. And then it's attracting the right researchers and research labs to leverage that. But in general, where there is compute, the research labs will find you. I think that's it. And then there's a lot of national security implications, too, just because these models are very sensitive, at least the frontier models are. And so how your place in the geopolitical landscape is quite important. Will research labs and will the US Government be comfortable with training happening there and export restrictions and things like that? But I think a lot of it comes down to infrastructure as it relates to policy is my intuition. I think right now, so much of AI is constrained on infrastructure that that is the input to a lot of this stuff. And then there's a lot around attracting talent and all that. But as I said, you look at the research labs, it's not that many people. Actually, it's a lot. But the compute is a limited resource right now.
- [75:46]
Shane Parrish
That's a really good way to think about it. I think about this from the lens of Canada, right? Which is like, we don't have enough going on in AI. We tend to lose most of our great people to the States, who then go to set up infrastructure here for whatever Reason and don't bring it back to Canada. And I wonder how Canada can compete better. So this is like sort of the lens I look at these questions through. How do you see the next generation of education? If you were setting up a school today from scratch, and again, hypothetical, not your domain of expertise, but using your lens on AI, how do you think about this? What skills will kids need in the future? And what skills do we probably don't need to teach them anymore that we have been teaching them?
- [76:34]
Brett Taylor
Well, I'll start with the benefits that I think are probably obvious, but I'm incredibly excited about. I think education can become much more personalized.
- [76:46]
Shane Parrish
Oh, totally. Have you seen Synthesis Tutor, by the way?
- [76:49]
Brett Taylor
No, I have not.
- [76:50]
Shane Parrish
Oh. So they developed this synthesis. This AI company developed this tutor, which actually teaches kids. And it's so good that El Salvador, the country, just recently adopted it and replaced their teachers. And it'll teach you, but it teaches you specific to what you're missing. So it's not like every lesson is the same. It's like, well, you're not understanding this foundational concept. So it's like K through 5 or 6 right now.
- [77:14]
Brett Taylor
That's amazing.
- [77:16]
Shane Parrish
And the results are like off the charts.
- [77:18]
Brett Taylor
Well, it doesn't surprise me, and I don't actually view it as necessarily replacing a teacher, but my view is if you have a teacher with 28 kids in his or her class, the likelihood that they all learn the same way or learn at the same pace is very unlikely. And I can really think of, say, an English teacher, history teacher. You're orchestrating their learning journeys through a topic, say AP European history in the United States. There's a curriculum, they need to learn it. How someone will remember something or understand the significance of Martin Luther is very different. And you can generate an audio podcast for someone who might be an auditory learner. You can create cue cards for someone who needs that kind of repetition. You can visualize key moments in history for people who just maybe want to more viscerally appreciate why this was a meaningful event rather than this dry piece of history. And all of that, as you said, can be personalized to the way you learn and how you learn. And I think it's just incredibly powerful. And so one of the things I think is neat about AI is its democratizing access to a lot of things that used to be fairly exclusive. A lot of wealthy people, if their child was having trouble in school, would pay for a tutor, math tutor, science tutor. And if you look at kids who are trying to get into big Name colleges if you have the means. You'll have someone prep you for the SATs or help you with your college essays. All of that should be democratized if we're doing our jobs well. And it means that we're not limiting people's opportunity by their means. And I think that's just the most American thing ever. Canadian as well.
- [79:09]
Shane Parrish
It's the most incredible thing to humanity.
- [79:10]
Brett Taylor
It's the most incredible thing, humanity. And so I just think education will change for the positive in so many ways because I actually with my kids walking around, when they ask if you have little kids, they ask why, why, why? There's some point a parent just starts making up the answer, being dismissive. And we have ChatGPT out. It's the best when you're traveling and.
- [79:31]
Shane Parrish
Put on advanced voice mode and be.
- [79:33]
Brett Taylor
Like, ask away 100% and I'm listening too. It's like you live through your children's curiosity. And my daughter went to high school and came home with Shakespeare for the first time and she asked me a question. I was like, I felt this is total inadequacy. I was like, I was very bad at this the first time. And then we and put it into ChatGPT and it was the most thoughtful answer and she could ask follow up questions. And I actually was with her because I was like, I forgot about that, didn't even think about that. So I just think it's incredible. And I would like to, in public school systems, I think it'll be really great when public school systems formally adopt these things so that they lean into tools like ChatGPT as mechanisms to raise the performance level of their classroom. And hopefully you'll see it in things like test scores and other things because kids can get the extra time even if the school system can't afford it for everyone. And then most importantly, kids are getting explanations according to their style of learning, which I think will be quite important as well as it relates to skills. It's really hard to predict right now. And I would say that I do think learning how to learn and learning how to think will continue to be important. So I think most of primary and secondary education is not vocational necessarily. Some of it is. I took auto shop and all of that and I'm glad I did. But I couldn't fix my electric car today with that knowledge. Things change and I don't think it needs to be purely non vocational, but the basics of learning how to think, learning writing, reading, math, physics, chemistry, biology, not because you need to memorize it, but Understand the mechanisms that create the world that we live in is quite important. I do think that there's a risk of people sort of becoming ossified in the tools that they use. Let's go back to our discussion with software engineering for a second, but I'll give other examples. If you define your role as a software engineer as how quickly you type into your ide, the next few years might leave you behind because that is no longer a differentiated part of the software engineering experience or will not be. But your judgment as a software engineer will continue to be incredibly important. Your agency and making a decision about what to build, how to build it, how to architect it, maybe using AI models as a creative foil. And so I think that just in the same way, if you're an accountant, using Excel doesn't make you less of an accountant. And just because you didn't handcraft that math equation, it doesn't make the results any less valuable to your clients. And so I think we're going to go through this transformation where I think the tools that we use to create value in the world will change dramatically. And I think some people who define their jobs by their ability to use the last generation's tools really, really effectively will, will be disrupted. But I think if we can empower people and to reskill and also broaden the aperture by which they define the value they're providing to the world, I think a lot of people can make the transition. The thing that is sort of uncomfortable, not really in education, where it's just earlier in most people's lives, is just, I think the pace of change exceeds that of most technology transitions. And I think it's unreasonable for to expect most people to change their way of work that quickly. And so I think the next five years, I think will be for some jobs, really disruptive and tumultuous. But if you take the longer view and you fast forward 25 or 50 years, I'm incredibly optimistic. I think the change will require from society, from companies and from individuals an open mindedness about reskilling and reimagining their job through the lens of this dramatically different new technology.
- [83:59]
Shane Parrish
At what point do we get to, I mean, we're probably on the cusp of it now and it's happening in pockets. But what point do we start solving problems that humans haven't been able to solve or eliminating paths that we're on? Maybe with medical research that it's like, no, this whole thing you've spent $30 billion on, based on this 1972 study that was fabricated. But that one study had all these derivative studies. And I'm telling you it's false because I can look at it through an objective lens and get rid of these 30 billion. You're smiling.
- [84:33]
Brett Taylor
Oh, no. I hope soon. There was a lot of. There's one of the models, I can't remember which one introduced a very long context window. And there was a lot of people on X over the weekend putting in their thesis, grad school thesis in there. And it was actually critiquing them with surprising levels of fidelity. And I think we're sort of there, perhaps with the right tools. But certainly over the next few years we talked about what does it mean to generalize AI? Certainly in the areas of science that are largely represented through text and digital technology, like math being probably the most applicable, there's not really anything keeping AI from getting really good at math. There's not really an interface to the real world. You don't need to do a clinical trial to verify something's correct. So I feel a ton of optimism there. It'll be really interesting in areas of theoretical physics. You'll continue to have the divide between the applied and the theoretical people. But I think there could be really interesting new ideas there and perhaps some finding logical inconsistencies with some of the fashionable theories, which has happened many times over the past few decades. I think we'll get there soon. And actually what's really neat about is most of the scientists I know, people who are actually doing science, they're the most excited about these technologies and they're using them already. And I think that's really neat. And I think we're hopefully going to be. I really hope we see more breakthroughs in science. One of the things I am not an expert in, but I've read a lot as a amateur about, is just the slowdown in scientific breakthroughs over the past few decades and some theories that it's because of the degree of specialization that we demand of grad students and things like that. I hope in general, with AI democratizing access to expertise, I have a completely personal theory that it will benefit deep generalists in a lot of ways too, because your ability to understand a fair amount in a lot of domains and leveraging AI, knowing where to prompt the AI to go explore and bringing together those domains, it will start to shift sort of the intellectual power from people who are extremely deep to people who actually can orchestrate intelligence between lots of different domains for breakthroughs. And I think that'll be really good for society because Most scientific breakthroughs aren't. They tend to be cross pollinating very important ideas from a lot of different domains, which I think will be really exciting.
- [87:25]
Shane Parrish
How important is the context window?
- [87:27]
Brett Taylor
I think it could be quite important, especially it certainly simplifies working with an AI. You can just give it everything and instruct it to do something and assuming it works, you can extend a context window and the tension can be spread fairly thin. And the robustness of the answer can be questionable. But assuming, let's just for argument's sake, perfect robustness, I think it can really simplify the interface to AI. Not all uses. I also think that we're talking about open Source models and APIs. I also think that what I'm excited about in the software industry is not necessarily a large language model with a prompt and a response being the product of AI, but actually end to end closed loose systems that use large language models as pieces of infrastructure. And I actually think that a lot of the value in software will be that. And for many of those applications the context window size can matter, but often because you have contextual awareness of the process that you're executing, context window is a little bit less important. So I think it matters a lot to intelligence. I can't remember some researcher said you put all of human knowledge in the context Wendy, and you ask it to invent the next thing. It's obviously a reductive thought, but interesting. But I'm equally excited about the industrial applications of large language models. Sort of like my company Sierra. If you're returning a pair of shoes at a retailer and it's a process that's fairly complicated and is it within the return window? Do you want to return it in store, do you want to send it, do you want to print a QR code, blah blah blah blah. The orchestration of that is as significant as the models themselves. And I actually think as we just like computers, there's going to be a lot of things where computers are a part of the experience, but it's not manifesting itself as a computer. So I'm actually equally excited about those and I think context window is slightly less important than those applications.
- [89:38]
Shane Parrish
Do you think that the output from AI should be copyrightable or patentable? Let me just take an example. If I go to the U.S. patent Office, I download a patent for, let's say the Aeropress and I upload it to 01 Pro and I say I can't upload it yet because you don't let me do the PDFs, but I upload it to four. So I say, hey, what's the next logical leap that I could patent off this? It would give me back diagrams and an output. And presumably if I look at that and I'm like, yeah, that's legit. I want to file that patent.
- [90:11]
Brett Taylor
Can I. I don't know how to answer that question. I'm not an expert in sort of intellectual property, but I think there will be an interesting question of, was that your idea because you used a tool to do it? I think the answer is probably yes, that you used the tool to do it. But I also think that the. But in general, the marginal cost of intelligence will go down a lot. So a lot of the. I think in general, we'll be in this renaissance of new ideas and intelligence being produced. And so I think that's broadly a good thing. And I think the marginal value of that insight that you had might be lower than it was n years ago.
- [90:57]
Shane Parrish
What I was hoping you would say is, is that that's gonna become less and less important because I feel like the patent trolls and all of this stuff that slows down innovation in some ways. Obviously, there's legitimate patents that people infringe on, and there should be legal recourse, but if I could just go and patent like 100 things a day, it.
- [91:17]
Brett Taylor
Seems like that should not be allowed.
- [91:19]
Shane Parrish
This is what I'm saying, though.
- [91:20]
Brett Taylor
Well, in general, I think that companies. I think patents make sense if it's protecting something that's an active use that you invented. And you're trying to, you know, like the standard, you know, legal rationale for patents just generating a bunch of ideas and patenting it seems destructive to the value of a company.
- [91:40]
Shane Parrish
So here's the idea I had last night to counter this, because I was like, I don't want somebody doing this. And I was thinking, like, what if prior art eliminates patents?
- [91:48]
Brett Taylor
Yeah.
- [91:49]
Shane Parrish
So I was like, what if I just set up, like, an instance and just publish it on a website? Nobody has to read that website.
- [91:55]
Brett Taylor
Here's a billion ideas.
- [91:56]
Shane Parrish
Exactly. But it's like basically patent patenting, like anything prior, but it's creating prior art for everything. So, like, you can't compete on that anymore. I don't know. I was, like, thinking about that. I thought it was fun. This episode is brought to you by Shopify.
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Brett Taylor
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Shane Parrish
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Brett Taylor
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Shane Parrish
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Brett Taylor
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Shane Parrish
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Brett Taylor
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- [93:04]
Shane Parrish
Tell me about the Google Maps story. This is like now Legend and I want to hear it from you.
- [93:10]
Brett Taylor
This is my weekend coding. Is that what you want to hear about? Yeah. Yeah. So I'll start with just like the story of Google Maps, the abbreviated version. We had launched a product at Google called Google Local, which was sort of a Yellow Pages search engine. Probably most listeners don't even know what Yellow Pages are, but it was a thing back then. We had licensed maps from MapQuest, which was the dominant mapping provider at the time. And it was sort of an eyesore on the experience and also always felt like it could be a more meaningful part of the kind of local search and navigation experience on Google. So Larry Page in particular was really pushing us to really invest more in maps. We found this small company with four people in it, if I'm remembering correctly. Started by Lars and Jens Rasmussen called Whereto Technologies, where they had made a Windows application called Expedition that was just a beautiful mapping product. It was running on Windows long after it was sort of out of fashion to make Windows apps. But they sort of wear the technology they're comfortable with, but they're really, their maps modeled the A to Z maps in the UK and were just beautiful and they just had a lot of passion for mapping. So we did a little aqua hire of them and took together the Google Local team and Lars and Jens team and said okay, let's take the good ideas from this Windows app and the good ideas from Google Local and like, like let's bring them together to make something completely new. And that's what became Google Maps. But there's a couple of idiosyncrasies in the integration. Because it was a Windows app, it really helped and hurt us in a number of ways. One of the ways it helped us is the reason why Google Maps we were able to drag the map and it was so much more interactive than any web application that preceded it was. The standard that we needed to hit from interactivity was set by A native Windows app not set by the legacy websites that we had used at the time. And I think that by having the goalposts so far down the field, because they had just started with this Windows app, which was sort of a quirk of Lars and Jens, just like technical choices, we made much bolder technical bets than we would have otherwise. I think we would have ended up much less interactive had we not started with that quirky technical sort of decision. But the other thing was this Windows app. There's a lot of. It's hard to describe the early 2000s when people didn't live it, but XML was really in fashion. So most things in Windows and other places, XML and xslt, which was a way of transforming XML into different XML was the basis of everything. It was like all of enterprise software was like XML this, XML that, sort. Similarly, when we were taking some of these ideas and putting them in a web browser, we kind of went into autopilot and used a ton of XML and it made everything just really, really tedious. So Google Maps launched with some really great ideas, like the draggable maps and we did a bunch of stuff with the local search technology so you could overlay restaurant listings. It was really great. It was a really successful launch. We were like the hotshots within Google afterwards. But it really started to show its craft. And we got to this point where we decided we wanted to support the Safari web browser, which was relatively new at the time. This was before mobile phones and there was much less XML support in Safari than there was in Internet Explorer and Firefox. And so one of the engineers implemented a full XSLT transform engine in JavaScript to get it to work. And it was just like, like shit on top of shit on top of shit. And so what was a really elegant fast web application had sort of quickly become something. There's a lot of dial up modems at the time and other things. So you'd show up to Maps and it just was slow and it just bothered me as someone who takes a lot of pride in their craft. And so I got really energized and over a weekend and a lot of coffee, rewrote it. But it was.
- [97:25]
Shane Parrish
Rewrote the whole thing though.
- [97:27]
Brett Taylor
Rewrote? Yeah, more or less the whole thing. And it took probably another week of working through the bugs. But yeah, I sent it out to the team after that weekend. And the reason I was able to do it, yeah, I'm a decent programmer. But you'd also lived with every bad decision up to that point too. So I knew exactly the output I was going to. I had simulated in my head. If I could do it over again, this is the way I'd do it. So by the time I put my hands on the keyboard on Friday night, it wasn't like I was designing a product. I knew I had been in every detail of that product since the beginning, including many of the bad decisions too. They're not all the bad decisions. And so it was just very clear I knew what I wanted to accomplish. And for any engineers worked on a big system, you have the whole system mapped out in your head. So I knew everything. And I. And I also knew that there's a lot of pride of authorship with engineering and code. So I sort of knew. I really wanted to finish it over the weekend so that people could use it and see how fast it was and kind of overcome anyone who was protective of the code they had written a few months ago. And so I really wanted the prototype to go out. So I did it and then I didn't. It's funny, I never talked about it again, but I think Paul Bucheit was the co creator of Gmail and I worked at started friendfeed with me. He was on an interview and mentioned this story. So now all of a sudden it's like everyone's talking about it. And I was like, well, thank you, Paul. It's a little embarrassed that people know about it, but it's a true story and XML is just the worst.
- [99:07]
Shane Parrish
Did you get a lot of flack from the people who had built the system you effectively replaced? You were part of that team, but everybody else had so much invested in it. Even though it was like shit on top of. Of shit on top of shit.
- [99:19]
Brett Taylor
I wrote a lot of it too. So yeah, I'm sure there was some around it, but actually I think good teams want to do great work. And so I think there was a lot of people constructively dissatisfied with the state of things too. And I think the engineer had written that XSLT transform I think was a little bit. That's a lot of work. So you have to throw out a lot of work, which feels bad, but. But particularly Lars and Jens and I, we want to make great products. And so I don't think there was a. At the end of the day, everyone was like, wow, that's great. We went from a bundle size of 200k to a bundle size of 20k and it was a lot faster and better. So broadly speaking, I think good engineering cultures, you don't want A culture of ready, fire, aim. But I also think you just need to be really outcomes oriented. And I think people, if they become, they start to treat their code as too precious, it can really impede forward progress. I'll just take my understanding is a lot of the early self driving car software was a lot of hand coded heuristics and rules. And a lot of smart people think that eventually it'll probably be a more monolithic model that encodes many of the same rules. You have to throw out a lot of code in that transition, but it doesn't mean it's not the right thing to do. And so I think in general, yeah, there might have been some feathers ruffled, but at the end of the day everyone's like that's faster and better, let's do it. Which is I think the right decision.
- [100:51]
Shane Parrish
That's awesome. Going to give you another hypothetical. I want you to share your inner monologue with me as you think through it. So if I told you you have to put 100% of your net worth into a public company today and you couldn't touch it for at least 20 years, what company would you invest in and walk me through your thinking.
- [101:11]
Brett Taylor
I literally don't know how to answer that question.
- [101:14]
Shane Parrish
How would you think about it without giving me an answer?
- [101:16]
Brett Taylor
Yeah, it's a good question. First of all, I'll give you how I'd think about it. But having not been a public company CEO for a couple years, I blissfully don't pay attention as much to the public markets and particularly right now at obviously valuations have gone up a lot, but because it's a long term question, maybe that doesn't matter. I think what I'd be thinking about right now is over the next 20 years, what are the parts of the economy that will most benefit from this current wave of AI? That's not the only way to invest over a 20 year period, but certainly it's a domain that I understand and in particular I mentioned that talk I heard a snippet of from Tyler Cohen, which is it will probably AI will probably benefit different parts of the economy disproportionately. There will be some parts of the economy that can essentially where intelligence is a limiting factor to its growth and where you can absorb almost arbitrary levels of intelligence and generate almost arbitrary levels of growth. Obviously there's limits to all of this. Just because you change one part of the economy, it impacts other parts of the economy. And that was what Tyler's point was in his talk. But I would probably Think about that, because I think that over a 20 year period there are certain parts of society that won't be able to change extremely rapidly, but there will be some parts that probably will. And it'll probably be domains where intelligence is the scarce resource right now. And then I would probably try to find companies that will disproportionately benefit from it. And I assume this is why Nvidia stock is so high right now. Because if you want to sort of get downstream, Nvidia will probably benefit from all of the investments in AI. I'm not sure I would do that over a 20 year period, just assuming that the infrastructure will shift. So I don't have an intelligent answer, but that's the way I would think about it if I were doing that exercise.
- [103:14]
Shane Parrish
I love that. Where do you think, what's your intuition say about what areas of the economy are limited by intelligence? And not just economy. I mean perhaps politicians might be limited by this and aid and benefit from, in which case countries could benefit enormously from AI and unlock growth and potential in their economy. But I think maybe just to scope the question, what areas of the economy do you think are limited by intelligence or workers, smart workers, in which case that's another limit of intelligence.
- [103:49]
Brett Taylor
Yeah. I mean, two that are, I think probably going to benefit a lot are technology and finance, where if you can make better financial decisions than competitors, you'll generate outsized returns. That's why over the past 30 years of machine learning, hedge funds and financial service institutions, everything from fraud prevention to true investment strategies, it's already been an area of domain of investment software, similar as we talked about. I think that at some point we will no longer be supply constrained in software, but we're not anywhere close to it right now. And you're taking something that has always been the scarce resource, which is software engineers, and you're making it not scarce. And I think as a consequence, you just think of how much can that industry grow? We don't know. But we've been so constrained on software engineering as a resource source, who knows over the next 20 years, but we'll find out where the limits are. But to me intellectually there's just a ton of growth there. And then broadly, I think areas of processing information are areas that will really benefit quite a bit here. And I think the thing that I would think about over a 20 year period is second and third order effects, which is why I don't have an intelligent answer. And if you're asking me to put all my money in something I would think about it for a while, probably use 01 Pro a little bit to help me because you can end up generating a bunch of growth in the short term, but then if everyone does it, it commoditizes the whole industry type of thing. So it used to be before the introduction of the freezer, ice was a really expensive thing and now it's free. And so I think it is really important to actually think through those if you're talking a timeframe of 20 years. And that's why, having not thought about this question ahead of time, I. You could be quite simplistic elsewhere, but I would say software and finance are areas that I think stand to reason should benefit quite a bit.
- [105:56]
Shane Parrish
I love that response. How do you balance having a young family with also running a startup?
- [106:03]
Brett Taylor
Again, I work a lot. I really care about and love working. So one thing is that I, well, there's always trade offs in life. If I didn't love working, I wouldn't do it as much as I do. But I just love to create things and love to have an impact. And so I jump out of bed in the morning and work out, go to work and then spend time with my family, broadly, probably being honest. First, I'm not perfect at it, but second, I don't have a ton of hobbies. I basically work and spend time with my family. The first time we talked, you saw a couple guitars in my background. Yeah, yeah. I haven't picked one of those up in a while. I mean, I literally pick it up occasionally but I do not devote any time into it and I don't regret that either. I am so passionate about what we're building at Cira. I'm so passionate about OpenAI. I love my family so much. I don't really have any regrets about it, but I basically just like life is all about, where do you spend your time? And mine is at work and with family and so that's how I do it. I don't know if I'm particularly balanced, but I don't strive to be either. I really take a lot of pride and I love to work.
- [107:21]
Shane Parrish
Having sold the companies you started twice, does that influence what you think of Sierra? Are you thinking, oh, I'm building this in order to sell it or do you think differently? This is my life's work. I'm building this. That's not going to happen.
- [107:37]
Brett Taylor
I absolutely intend Sierra to be an enduring company and an independent company. But to be honest, every entrepreneur with every company starts that way. I'm really grateful for both Facebook and Salesforce for having acquired my previous companies and hopefully I had an impact about those companies. But you don't start off well. At least I never start off saying, hey, I want to make a company to sell it. But I actually think with Sira, we have just a ton of traction in the marketplace. I really do think Sira is the leader in helping consumer brands build customer facing AI agents. And I'm really proud of that. So I really see a path to that. And I joke with Clay. I want to be an old man sitting on his porch complaining how the next generation of leaders at Sierra don't listen to us anymore. I want this to be something that not only is enduring, but outlives me. I don't think we've ever talked about this, but it was really interesting moment for me when Google went from its one building in Mountain View to its first corporate campus. We moved into the Silicon Graphics campus, which was right over near Shoreline Boulevard in Mountain View. And SGI had been a really successful company, enough to build a campus. And it was actually quite awkward. We moved into half the campus, they were still in half. And they're like, we're this up and coming company, they're declining. And then when Facebook, when we moved out of the second building, we were in Palo Alto, a slightly larger building with. We leased it from hp, but when we finally got a campus, it was from Sun Microsystems who had gone through an Oracle acquisition and had been sort of on the decline. And it was interesting to me because both SGI and Sun had been started and grown to prominence in my lifetime, obviously I was maybe a little younger, obviously, but in my lifetime enough to build a whole corporate campus and then decline fast enough to sell that campus, corporate campus to a new software company. And for me it was just so interesting to have done that twice to move into a used campus from the previous owners. It was a very stark reminder that technology companies aren't entitled to their future success. I think we'll see this actually now with AI. AI I think will change the landscape of software to be tools of productivity to agents that actually accomplish tasks. And I think it will help some companies who, for whom that amplifies their existing value proposition. And it will really hurt others where it will essentially the SEAT based model of legacy software will wane very quickly and really harm them. When I think about what it means to build a company that's enduring, that is a really, really tall task in my mind right now because it means not only making something that's financially enduring, over the next 10 years. But setting up a culture where a company can actually evolve to meet the changing demands of society and technology, when it's changing at a pace that is unprecedented in history. So I think it's one of the most fun business challenges of all time. And. And I think it has as much to do with culture as it has to do with technology, because every line of code in Sierra today will be completely different probably five years from now, let alone 30 years from now. And I think that's really exciting. So when I think about it, I just get so much energy because it's incredibly hard and it's harder now than it's ever been to do something that lasts beyond you. But that, I think is the ultimate measure of a combination company.
- [111:26]
Shane Parrish
You mentioned AI agents. How would you define that? What's an agent?
- [111:31]
Brett Taylor
I'll define it more broadly and then I'll tell you how we think about it at Cira, which is a more narrow view of it. The word agent comes from agency, and I think it means affording a software the opportunity to reason and make decisions autonomously. And I think that's really all it means to me. And I think there's lots of different applications of it. The three categories that I think are meaningful, and I'll end with the Sierra one just so I can talk about it a little more. But one is personal agents. So I do think that most people will have probably one, but maybe a couple AI agents that they use on a daily basis that are essentially amplifying themselves. As an individual, you can do the rote things like help you triage your email to helping you schedule a vacation, you're flying back to Edmonton, and help you arrange your travel to more complex things like I'm going to go ask my boss for promotion. Help me role play and I'm setting up my resume for this job. Help me do that to I'm applying for a new job. Help me find companies I haven't thought of that I should be applying to. And I think these agents will be really powerful. I think it might be a really hard product to build because when you think about all the different services and people you interact with every day, it's kind of everything. So it has to generalize a lot to be useful to you. And because of the personal privacy and things like that, it has to work really well for you to trust it. So I think it's going to take a while to go. I think it'll be a lot of demos. I think it'll take a While to be robust, the second category of agent is I would say really filling a Persona within a company. So a coding agent, a paralegal agent, an analyst agent. I think these already exist. I mentioned cursor. There's a company called Harvey that makes a legal agent. I'm sure there's a bunch in the analyst space. These do a job and they're more narrow, but they, they're really commercially valuable because most companies hire people or consultants that do those things already, like analyze the contracts of your supply chain. That's kind of a rote kind of law, but it's really important and AI can do it really well. So I think that's why this is the area of the economy that I think is really exciting. And I'm really excited about all the startups in this space because you're essentially taking what used to be a combination of people and software and really making something that solves a problem. And by narrowing the domain of autonomy, you can have more robust guardrails and even with current models, actually achieve something that's effective enough to be commercially viable today. By the way, it changes the total addressable market of these models too. I don't know what the total addressable market of legal software was three years ago, but it's. It couldn't have been that big. I couldn't tell you a legal software company, I probably should, I just can't think of one. But if you think about the money we spend on lawyers, that's a lot. You end up where you're broadening the addressable market quite a lot. The domain we're in, I think is somewhat special, which is a company's branded customer facing agent. And the reason why, I think it's. One could argue we're sort of helping with customer service, which is a Persona, a role, but I do think it's broader than that. Because if you think about a website like your insurance company's website, try to list all the things you can do on it. You can look up the stock quote, you can look up the management team, you can compare their insurance company to all their competitors. You can file a claim, you can buy, you can bundle your home in auto, you can add a member of your family to your premium. There's a million things you can do on it. Essentially over the past 30 years, websites, a company's website, singular, has come to be the universe of everything that you can do with that company. I like to think it was like the digital instantiation of the company. And that's what we're helping our customers do at Sira is help them build a conversational AI that does all of that. So most of our customers start with customer service and it's a great application because no one likes to wait on hold. And having something that has perfect access to information is multilingual and empathetic is just amazing. But when you put a conversational AI as your digital front door, people will say anything they want to it. We're now doing product discovery. Consider purchases. Going back to the insurance example, hey, I've got a 15 year old daughter. I really am concerned about the cost of her premium. Until she grows up, tell me which plan I should be on. Tell me why you'll be better than your competitors. That's a really complex interaction. That's not something that can you make a webpage that does that. But that's a great conversation. And so. So we really aspire that when you encounter a branded agent in the wild, we want Sierra to be the platform that powers it.
- [116:51]
Shane Parrish
And it's super important because there was a case, at least in Canada where an AI agent for Air Canada hallucinated a bereavement policy. Right. But they were found liable to hold themselves to what the agent said.
- [117:05]
Brett Taylor
Yeah, I mean, it turns out, and.
- [117:06]
Shane Parrish
It was an AI agent, there was no human involved in the whole thing.
- [117:09]
Brett Taylor
Well, look, look, it's One thing if ChatGPT hallucinates something about your brand. It's another if your AI agent hallucinates something about your brand. So the bar just gets higher. So the robustness of these agents, the guardrails, everything is more important when it's yours and it has your brand on it. It's harder. But also, I'm just so excited for it because this is a little overly intellectual, but I really like the framing. If you think about a modern website or mobile app, it's essentially you've created a directory of functionality from which you can choose. But the main person with agency in that is the creator of the website. What are the universe of options that you can do when you have an AI agent representing your brand? The agency goes to the customer. They can express their problem any way they want in a multifaceted way. And so it means that your customer experience goes from the enumerated set of functionality you've decided upon your website to whatever your customers ask and then you can decide how to fulfill those requests or whether you want to. Yeah, but I think it will really change the dynamic to be really empowering to consumers, as you said. I mean, the reason that Air Canada case is the reason we exist. Companies, if they try to build this themselves, there is a lot of ways you can shoot yourself in the foot. But in particular too, your customer experience should not be wedded to one model, let alone even this current generation of models. So with Sierra, you can define your customer experience once in a way that's abstracted from all of the technology. And it can be a chat, you can call you on the phone, it can be all of those things. And, and as new models and new technology comes out, our platform just gets better. But you're not like re implementing your customer experience. And I think that's really important because we were talking about what's happened over the past two years. Can you imagine if you're a consumer brand like ADT Home Security and thinking about how can you maintain your AI agent in the face of all of that? Right. It's just not even, it's not tenable. I mean it's not what you do as adt. So they've worked with us to build their AI agent.
- [119:24]
Shane Parrish
How do you fend off complacency? Like a lot of these companies, and maybe not in tech specifically, but they get big, they get dominant and then they take their foot off the gas and that opens the door to competitors. And there's a natural entropy almost to bureaucracy in some of these companies. And the bureaucracy sows the seeds of failure and competition. How do you fend that off? Constantly.
- [119:54]
Brett Taylor
It is a really challenging thing to do at a company. There's two things that I've observed that I think manifest as corporate complacency. One is bureaucracy. And I think the root of bureaucracy is often when something goes wrong, companies introduce a process to fix. And over the sequence of 30 years, the layered sum of all of those processes that were all created for good reason, with good intentions end up being a bureaucratic sort of machine where the reasons for many of the rules and processes are rarely even remembered by the organization. But it creates this sort of natural inertia. Sometimes that inertia can be good. It's like if you end up with, there's definitely been stories of executives coming in and ready fire aimed new strategies that backfire massively. But often it can mean in the face of a technology shift or a new competitor, you just can't move fast enough to address it. The second thing that I think is more subtle is as a company grows in size, often it's internal narrative can be stronger than the truth from customers. I remember one time when this sort of peak of the smartphone wars. And I ended up visiting a friend on Microsoft's campus. And I got off the plane in Seattle, Tacoma airport, drove into Redmond, went onto the campus, and all of a sudden everyone I saw was using Windows Phones. I assume it must have been a requirement or formal or social. You were definitely uncool if you were using anything else. And from my perspective at the time, the war had already been lost. It was definitely a two horse race between Apple and Google on iOS and Android. And I remember sitting in the lobby waiting for my friend to get me from the security check in and I made a comment, it wasn't a confrontational comment, but I made a comment to someone who's at Microsoft. I was like, something along the lines, are you required to use Windows Phones? How these other. And I just sort of curious. And then I got a really bold answer, which is like, yeah, we're going to win. We're taking over the smartphone market. I didn't say anything because it was a little socially awkward of like, no, you're not. You lost four years ago.
- [122:34]
Shane Parrish
But there's a process, there's something that's happening that's preventing you from getting reality well.
- [122:39]
Brett Taylor
And that's the thing is, if you think about it, if anyone, if you've ever worked for a large company, when you work at a small company, you care about your customers and your competitors and you feel every bump in the road. When you're a junior vice president of whatever and you're eight levels below your CEO and you have a set of objectives and results you might be focused on, I want to go from junior vice president to senior vice president. That's what success looks like for me. And you end up with this sort of myopic focus on this internal world in the same way your kids will focus on the social dynamics of their high school, not the world outside of it. And it's probably rational, by the way, because probably their social life is more determined by those 1,000 kids in their high school than it is all the things outside, but that's the life of a person inside of these big places. And so you end up where, if you have a very senior head of product who's like, this competitor says they're faster, but this next version we're so much better. And then everyone says all of a sudden that's like, like the Windows Phone is going to win. That's what everyone says. And you truly believe it because everyone you meet says the same thing. And you end up reflecting customer anecdotes. Through that lens and you end up with this sort of reality distortion field manifested from the sum of this sort of myopic storytelling that exists within companies. What's interesting about that is the ability for a culture to believe in something is actually a great strength of a culture, but it can lead to this as well. And so the combination of bureaucracy and inaccurate storytelling I think is the reason why companies sort of die. And it's really remarkable to look at the blackberries of the world or the tivos or the. You can really, as the plane is crashing, tell the story that you're not. And. And then similarly, as I said, culturally, you can still have the person in the back of that crashing plane being like, when am I going to get promoted to svp? And you're like, what the this is like. I've seen it 100 times. And so I think it really comes down to leadership. And I think that one of the things that most great companies have is they are obsessed with their customers. And I think the free market doesn't lie. And so I think one of the most important things I think for any enduring culture, particularly in an industry that changes as rapidly as software, is how close are your employees to customers and how much can customer, like the direct voice of your customers, be a part of your decision making. And that is something that I think you need to constantly work out. Because that person, employee number 30,462, how does he or she actually directly hear from customers? It's not actually a simple question to answer.
- [125:51]
Shane Parrish
Is it direct? Is it filtered? How many filters are there?
- [125:53]
Brett Taylor
That's exactly right. And then I think the other part on leadership is we talked about bureaucracy is process is there to serve the needs of the business. And often mid level managers don't get credit for removing process. They often are held accountable for things going wrong. And I think it really takes top down leadership to remove bureaucracy. And it is not always comfortable when companies remove spans of control or all the people impacted will. It's like antibodies and for good reason. It makes sense. Their lives are negatively impacted or whatever it is, but it almost has to come from the top because you need to give air cover. Almost certainly something will go wrong. By the way, I mean processes usually exist for a reason, but when they accumulate without end, you end up with bureaucracy. So those are the two things that I always. And you could smell it when you go into a really bureaucratic company, the inaccurate storytelling, the process over outcomes. And it's just. It sort of sucks the energy out of you and you feel it.
- [127:11]
Shane Parrish
That's a great answer. We always end these interviews with the exact same question, which is what is success for you?
- [127:17]
Brett Taylor
Success for me. We talked about how I spend my time with my family at work. Is having a happy, healthy family and being able to work with my co founder Clay for the rest of my life, making Sarah into an enduring company. That would be success for me.
- [127:38]
Shane Parrish
Thanks for listening and learning with us. The Farnham street blog is where you can learn more about my new book, Clear Turning Ordinary Moments into Extraordinary Results. It's a transformative guide that hands you the tools to master your fate, sharpen your decision making, and set yourself up for unparalleled success. Learn more at FS Blog Clear until next time RA.