Bret Taylor: A Vision for AI’s Next Frontier

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.

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 to the next level, consider joining our membership program at fs.blog.com. 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 truce 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 into 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? 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, you know, seeing a generated image of an avocado chair, I don't think I extrapolated to what, you know, where we are now, but it.

uh, for me, shook me and realized I need to pay more attention to this space and open AI specifically than I had been. Um, I think I had, that was when I realized like I clearly have been not paying attention to something significant. And then it was, you know, six months later, coincidentally, like the month after I left Salesforce, the chat GBD came out and, uh, before it became a phenomenon that it did so quickly, but I was already, you know, plugged into it. And, and, uh, and I was from then on, you know, I could not stop thinking about it. Um, but that avocado chair, I don't know why I think it was the.

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, it just blew my mind at the time. 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. 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. Let's 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. If you take everything very personally, from the product to the customers to the press to your competitors, the both inner and outer measures of success. And I think when you go to being acquired, there's a business aspect to it. And can you operate within a larger company? But that's intertwined with a sense of identity. You go from being a...

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. And 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 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 try to embrace it. And I think it's really hard for some founders to do. Some founders don't want to, honestly. They maybe cash the check and it's more of a transactional relationship. I really actually...

I'm 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. 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 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? I'll talk abstract about talking about some specific acquisitions, too.

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, and corporate speak could be synergies, but like, 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 via 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 a 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 like true third rails that you didn't find because you were having these storytelling discussions rather than getting down to brass tacks about how things work and what's important. The other thing I think is really important is being really clear what success looks like. And, you know, 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 like 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 ask them, like, what does success look like two years from now? My guess is like 80% of the time you get different answers. And I think it goes back to this sort of storytelling thing where you're talking about the benefits, the acquisition, what does success look like? So I really try to approach it. I try to pull forward some harder conversations when I'm doing acquisitions or even when I'm being acquired since it's happened to be not twice.

So that when you're approaching it, you not only get the, hey, why is 1 plus 1 equal to 2? 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...

uh, naivete, you know, it's like you're, you're, you're not your company anymore. You're part of something larger. And I think, you know, successful ones work when everyone embraces, um, embraces that. What point in the acquisition process is that conversation? Is that after we've signed our, our binding, you know, sort of commitment or is it, we should have that conversation before. So I know what I'm walking into.

My personal take is it's not something you have. You have to get to the point where the two parties want to merge, you know, 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. And in those cases, there's often a qualitative threshold where someone's like, yeah, let's do this. We kind of have the high level terms, sometimes a term sheet, you know, formally. I think it's right after that. So 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 them 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 on 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?

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 I've really, I think, become a better leader having learned to do that. I have really only joined boards that were led by founders because typically...

I think 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... 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, not the greatest CEO of our generation as a professional manager. But you look at everyone from Toby Lukey to Mark Benioff to Mark Zuckerberg to Sam at OpenAI. And I think when you all 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.

um, for that reason. Uh, but I love working with founders. Um, and I, I love working with people like Toby and Sam because they're so different than me yet. Um, uh, and I can see how they operate their businesses and I am inspired by it. I learned from it. And obviously working for, for market Salesforce, you, I'm like, wow, that's really interesting. Like I'm almost like an anthropologist. Like, why did you do that? You know, I want to learn more. And so I love working with founders that inspire me because I just learned so much from them. 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 of the Brian Chesky, you know, the founder mode. Do you think, talk to me about that.

I have such an honest 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, right? It's like process over outcomes. And then similarly,

You look at the disruption in all industries right now because of AI, you know, the companies that will recognize where things are clearly going to change, like 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 sort of interpret that and sort of execute it as a caricature of what I think it means. You know, there was a, I remember after Steve Jobs passed away and, you know, I don't know, I've met Steve a couple of times. I haven't never worked with him in any meaningful way, you know, but he was sort of, if you believe the story, he's like.

kind of pretty hard on his employees and very exacting. And I think a lot of founders were like mimicking that, you know, down to wearing a black turtleneck and yelling at their employees. I'm like, I'm not sure that was the cause. You know, I think Steve Jobs' taste and judgment through, you know, executed through that, you know, packaging was the cause of their success. And somehow, and then similarly, I think founder mode can be weaponized as an excuse for just like 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.

companies that are like solely executing through the judgment of individual probably aren't going to be able to scale to be truly great companies. So 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, you know, founders have license to go in and all the way to a small decision and fix it, the infamous question mark emails from Jeff Bezos, you know, that type of thing. 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, you know, people trying to decide, make business decisions because of what will please our dear leader, you know, which is like the caricature of this. And so, you know.

After that came out, I could sort of see it all happening, which is like some people 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. Totally. Thank you for the detail and nuance there. I love that too. Do you think engineers make good leaders? 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 all parts of their business.

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 in public policy becomes extremely important. And I think as you see... founders grow from doing one thing to growing to be in 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. But I also think that, you know...

when we were speaking earlier about identity, I think one of the main transitions founders need to make, especially engineers, is you're not like the product manager for the company or the CEO. And at 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. And 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 sort of plateaus in companies growth. So 100%, I think engineers make great leaders. And it's not a coincidence, I think, that most of the Silicon Valley, 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. To what extent are all business problems engineering problems? 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 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, you know, like forming personal relationships, you know, and the importance of those to building trust with a partner. It's not all just, you know, product and technology. And so I would say I think a lot of things coming with an engineer mindset really benefit. But I do think that taking that to its like 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. 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 to use that at work? Yeah, it's particularly important right now because the market of AI is changing so rapidly. So if you rewind two years, you know, 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.

And 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, CiraWorks, is being transformed. Software engineering is being transformed. And 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.

And so 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 has been most transformed is software engineering. And, you know, 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. So 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 privileges of doing 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. And the logic there is in a world where making software is easier than it ever is before and you're delivering outcomes to your customer, the delivery model of software probably should change as well. And 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. 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? How geeky can I get? As geeky as you want to. 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, you know, 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 check-in. Similarly, there's 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. And 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. And 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. The irony now that I've taken things that are code 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 AIs 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 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, you know, in a world where AI is generating a lot of code.

should we be investing more in formal verification so that the operator of that code-generated machine can more easily verify that it does, in fact, do what they intended it is 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-generated machine 20 times more productive, but more importantly, make the robustness of their output 20 times greater? And then similarly, things go in and out of fashion, but like test-driven development. You write your unit test first or your integration test first and then write code until it fulfills the test. Most programmers I know 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, you know, so writing tests is free, you know, 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, like the elephant in the room for me is the anchor tenant of most of these code generating systems are an IDE right now, you know, 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. It's so interesting that if software AI is enough to write the code, should we get enough to check the code?

That's a great, great question. But actually, it's still funny to me that we'd be generating Python, just because for anyone who's 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. And, you know, 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, you know, 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. Smalltalk came out of the development of the graphical user interface at Xerox PARC, and, you know, there was a sort of a confluence of message passing as a metaphor and the graphical user interface. And then there's a lot of really interesting principles that came out of networking, you know, and sort of distributed systems, distributed locking, sequencing. I think we should recognize we're in this brand new era as significant as the GUI. You know, 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 and with the same processes we have today, I think we'll feel native to the 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. Let's dive into AI a little bit. How would you define AGI to the layman? 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 better. I'm not sure that'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 and AGI is about generalization. So can you have a system that is intelligent 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, you know, at or better than a person is certainly a good standard there. And that's sort of the definition of super intelligence. 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, you know, the 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 sort of absorb intelligence more quickly than others. And so 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 might not apply with equal velocity, even once we reach that point of a system being able to have that level of intelligence. 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 compute, there's energy, there's data, and there's LLMs. Am I missing anything? So you're saying the ingredients to AGI? 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? 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, you know, one of the things after the Transformers 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 LLF, just they're large. And the scaling laws, you know, a couple of years ago, you know, indicated like the larger you make the model, the more intelligent it would be and at a degree of efficiency that was tolerable. So 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 there 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. Is that synthetic data? 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 is 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, you know, because that's not possible with physics. So by constraining a simulation with a set of...

real-world constraints, the data has more efficacy, you know, and so, and there's sort of a, 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, you know... 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 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.

something that's why there's so many data center investments. It's why NVIDIA as a company has grown so much. The probably 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 it's really interesting, but it shows you how you can run up against walls and find new opportunities to use it. And then finally, algorithms.

The biggest breakthrough was obviously the Transformers model. Attention is all you need. That paper from Google that sort of 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 into what at OpenAI, what we did with the O1 model, which is to do some reinforcement learning those chains of thought to really reach new levels of intelligence. And so 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. You know, 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 in 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 sort of 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. 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 AGIs, 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 of years. At what point does AI start making AI better than we can make it or making it better while we're sleeping? We can't be too far from that.

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. So 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 and... But this is sort of the difference between, you know, the assisted driving and Tesla versus like self-driving. Yeah. Right. Like at what point do we leap from I'm a co-pilot in this to I don't have to do anything? I mean, it's a question that there's so much nuance to answer. I'm not sure to answer because I'm not sure you'd want to necessarily. Like I think for some software applications, that's important. But when we brought up, you know, we're talking about the active software development.

people have to be accountable for the software that they produce. And 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 a part, 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 like 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 every engineer. And that's a principle that should not change. What does that mean? Like when we say safety and AI?

That seems so vague in general that everybody interprets it quite differently. Like, 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? I'll answer it 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, though. Obviously, the mission was created prior to my arrival because it's both about safety, kind of hypocritical, 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. And 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 who created it and the intentions of the people operating it so that 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? And 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's really interesting from an intellectual and ethical standpoint as well. What was the bridge in Canada that fell down where it motivated the ring that a lot of engineers... Oh, yeah, I forget the name of it. Just like whether it's the Tacoma Narrows Bridge in Washington or Three Mile Island or these intersections where we've engineered these, what at the time people hoped would 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 super intelligence 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, like, who gets to decide who's accountable? And it's a perennial question. I mean, it's whether you're deciding, you know, should you use your smartphone in school? You know, 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, you know, the folks working on frontier models are ensuring those models do benefit humanity. And then there's the sort of geopolitical landscape, which is, you know, 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, you know, the technology organizations creating AGI are, in fact, focusing enough on that infinity and humanity, all the other stakeholders who's...

interests that they're accountable for and ensuring that the West remains competitive. And I think that's a really nuanced thing. And I think, you know, my view is it's very important that the West leads in AI. And I'm very proud of the fact that, you know, 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, you know, having sort of seen inside, I think we're really focused on benefiting humanities. I tend to think that, you know, 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...

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, Europe, China, elsewhere. I want to come back to compute and the dollars involved. So, I mean, on one hand, you have, if I could start an AI company today by putting my credit card down and 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 and 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 has spent meta. So meta, I'm like aging myself here. So meta comes along and, you know, possibly for the good of humanity, but like, I tend to think Zuck is like incredibly smart. So I don't think, I don't think he's spending, you know, a hundred billion dollars 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?

It's a really complicated business to be on just given the capex required to build a frontier model. But let me just start with a couple definitions of terms I think are useful. I think most large language models I would call foundation models. And 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. And 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. And I think that leads in the same way that data center builders having a lot of data centers enables 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 credit to Reid Hoffman, but I may be mistaken on that. That's where I heard it from. And these are the models that are usually like the one or two that are clearly the leading edge, O3, as an example from OpenAI. And these frontier models are being built by labs who are trying to build AGI that benefits humanity.

And 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, then 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.

the return on that investment is probably fairly questionable because why use your fourth best large language model versus a frontier model or an open source one for meta? And 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 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. And so you can't really, 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. And 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. But this is just it though, right? 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 in the 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, you know, an endless amount, presumably in sight or like 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. I'm just trying to work through it from Zuck's point of view. I actually will take Mark at his word. That post he wrote about open source, I think, was very well written and encouraged people to read it. I think that's a strategy. If you look at Facebook...

You know, you got me saying Facebook, too. You know, the company has always really embraced open source. And if I look at really popular things from React to, you know, now the llama models, it's always been a big part of their strategy to court developers around sort of their ecosystem. And Mark articulated some of the strategy there. And I'm sure there's elements of commoditizing your complement. But I also think that, you know, if you can attract developers towards models, there's a strength. I, you know, I'm not really on the inside there. 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-40 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. So 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. I have no idea where things are going. But, you know, you can 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 this is the fact, like, 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, you know, H100s, now all of a sudden you're built servers like this, you know, asset that's worthless. So I think with a lot of these models, you know, the presence of one source is incredibly important and I really appreciate it. I also think, like, 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. And 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 talent, you'll have a different answer, and that's appropriate. And some people use open source Kafka. Some people work with Confluent.

Great. That's just the way these things work. 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? 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 like how do you use that like what do you what is uh what is the packaging um and some of uh intelligence will be simply the outcomes of it like a discovery of a new drug which would be you know remarkable and hopefully we can cure some illnesses uh but others will be just how you as an individual access it and you know i most of the people i know like if they're signing an apartment lease we'll put it in chat gpt you get a legal opinion uh if you get you know lab results from your doctor you can get a second opinion on chat gpt um Clay and I use the O1 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. Like, what a kick-ass way to benefit humanity. So I really think that will be central to what we come as a society to define as AGI. 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. People think that, you know, because I'm affiliated with it. You're not going, like, here's my strategy. What do you think? What are you putting in there? I often, with the reasoning models, which are slower, will use a faster model first, GPT-4.0, 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've written enough that it's easy for me. So I decided to see if I could have O1 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, you know, run it all. And I would spend a lot of time iterating on 4.0 to sort of like refine and make more complete and specific what I was looking for. And then I would put it into one promo, go get my coffee, and, you know, 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 like you're like 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 chip. At some point, there's probably someone from OpenAI listening who's going to roll their eyes, but that's just I. Who can I talk to at OpenAI that's like the prompt? 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.

prompt it with, I'm prompting an AI. Here are the key things I want 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? It's a good technique. I mean, there's lots of different techniques like that. Like self-reflection is a technique where you have a model, observe and critique, you know, a decision like a chain of thought. So in general, you know, that mechanism of self-reflection is, I think, a really effective technique. You know, at Sierra, we...

help companies build customer-facing AI agents. So if you're setting up a Sona 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 sort of figuring out how do you exact the robustness and precision out of these models. So it's really fun, but changing rapidly. 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, right? So now all of a sudden you're competing. It's not the United States. How do you sort of set up a country from first principles all the way back to what does that look like? What are the key variables?

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 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 sort of operate the data center. And again, there's really immense power requirements for these data centers as well. And then it's attracting sort of 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.

how your place in the geopolitical landscape is quite important. Like, will research labs and will the U.S. 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. You know, 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, you know, attracting talent and all that. But as I said, you know, 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.

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 like look at these questions through.

How do you see the next generation of education? Like if you were setting up a school today from scratch and again, hypothetical, not your domain of expertise, but like using your lens on AI, how do you think about this? So like 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? 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. Oh, totally. Have you seen Synthesis Tutor, by the way? No, I have not. 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 and replaced their teachers. And like, 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 five or six right now. That's amazing. And the results are like off the charts. Well, it doesn't surprise me. And I don't actually view it as like 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, you know, I can really think of, say, an English teacher, a history teacher, orchestrating their learning journeys through topics, 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. I think it's just incredibly powerful. And so one of the things I think is neat about AI is it's...

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, a 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. It's the most incredible thing for humanity. It's the most incredible thing, humanity. And so I...

I just think education will change for the positive in so many ways because I actually, with my kids walking around when they ask, you know, if you have little kids, they ask, why, why, why? And, you know, there's some point a parent just starts making up the answer and being dismissive. And like, we have ChatGPTL. It's like the best when you're traveling. Put on advanced voice mode and be like, ask away. 100%. And I'm listening too, you know? 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 I was, she asked me a question. I was like, I felt this is like.

total inadequacy. I was like, I was very bad at this the first time. And then we put it into chat GPT and it was the most thoughtful answer. And she could ask follow-up questions. And I actually was, you know, 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's really, I think it would 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 of 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 in 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 be disrupted. But I think if we can empower people 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.

uncomfortable, not really in education, or it's just earlier in most people's lives. It's just, I think the pace of change exceeds that of most technology transitions. And I think it's unreasonable to expect most people to change their way to work that quickly. And so I think the next five years, I think will be, you know, 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 it's the change will require...

from society, from companies, and from individuals, like an open-mindedness about reskelling and reimagining their job through the lens of this like dramatically different new technology. 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 at 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, you know, based on this 1972 study that was fabricated, but that one study had all these derivative studies and like, I'm telling you it's false, you know, because I can look at it through an objective lens. and get rid of these 30 billion? You're smiling soon. Oh, no, I just, I hope soon. I mean, I hope, I mean, I, there was a lot of, there's a, one of the models I can't remember, which one introduced a very long context window, and there's a lot of people on X over the weekend putting in their thesis, you know, like grad school thesis in there. And it was actually critiquing them with like surprising levels of fidelity. And I think we're sort of there perhaps with the right tools, but certainly over the next few years, I, you know, we talked about how, what does it mean to generalize AI? Certainly in the areas of...

science that are largely represented through text and digital technological 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.

think i think we'll get there soon and actually i um what's really neat about is most of the scientists i know people who are actually like doing science like they're the most excited about these technologies and i they're using them already and i think that's really neat 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 like a lot as a amateur about is just The slowdown in scientific breakthroughs over the past, you know, a few decades and some theories that it's because of the degree of specialization that we demand of grad students and things like that. And I hope with, you know, 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 or breakthroughs. 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. How important is the context window? 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 attention 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, you know, most, 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 closely 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. You know, there's a, I can't remember someone.

some researcher said, you put all of human knowledge in the context window and you ask it to invent the next thing. And it's obviously a reductive thought, but interesting. But I actually, I'm equally excited about sort of the industrial applications of large language models, sort of like my company, Sierra. And if you're returning a pair of shoes at a retailer and it's a process that's fairly complicated and, you know, is it within the return window? You know, do you want to return it in store? Do you want to send it? Do you want to print to your 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, you know, there's going to be a lot of things where computers are a part of the experience, but it's not like manifesting itself as a computer. So I'm actually equally excited about those. And I think context window is slightly less important in those applications. Do you think that the output from AI should be copyrightable or patentable? Or let me just take an example. If I go to the US patent office, I download a patent for, let's say, the AeroPress, and I upload it to O1 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. And 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. 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...

in general, like the sort of marginal cost of intelligence will go down a lot. So a lot of the, you know, I think in general, like 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, you know, the marginal value of that insight that you had might be lower than it was, you know, and years ago. What I was hoping you would say is that, you know, that's going to 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 like there's legitimate patents that people infringe on and there should be legal recourse. But if I could just go and patent like a hundred things a day. It seems like that should not.

be allowed. This is what I'm saying. In general, I think that companies, I think... Patents make sense if it's protecting something that's an active use that you, you know, 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 that seems destructive to the value of it. 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? Yeah. So I was like, what if I just set off like an instance and just publish it on a website? Nobody has to read that website. Here's a billion ideas. Exactly. But it's like basically patenting like anything, 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.

fun um tell me about the google map story this is like now legend and i want to hear it from you uh this is my weekend coding is that what you want to hear about yeah um yeah so I'll start with just 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. You probably most listeners don't even know what Yellow Pages are, but it was a thing back then. And 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 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 like four people in it, if I'm remembering correctly, started by Lars and Jens Rasmussen called Where2 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 hair of them and took together the Google local team and Lars and Jens' team and said, OK, let's take the good ideas from this Windows app and the good ideas from Google local. And 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 Young's 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 like, it's hard to describe the like early 2000s people who live it, but like XML was like really in fashion. So like most things in Windows and other places like 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. So similarly, when we were taking some of these ideas and putting them in a web browser, we kind of like went into autopilot and used like a ton of XML.

And it made everything just really, really tedious. And so Google Maps launched with some really great ideas, like the draggable maps. And we did a bunch of stuff for the local search technologies. 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.

One of the engineers implemented, like, a full XSLT transform engine in JavaScript to get it to work. And it was just, like, shit on top of shit on top of shit. And so what was a really elegant, like, fast web application had sort of quickly become something, you know, there's a lot of dial-up modems at the time. So, like, you'd show up to Maps, and it just was slow. And, like, it just bothered me as, like, someone who takes a lot of pride in their craft. And so I got really energized and, like, over a weekend and a lot of coffee, like, rewrote it. Rewrote the whole thing, though. Rewrote, yeah, more or less the whole thing. And it took probably another week of, like, you know.

working through the bugs. But yeah, I sent it out to the, you know, the team after that weekend. And it was, it was nice. The reason I was able to do it, yeah, I'm like a decent programmer, but you know, you'd also like lived with every bad decision up to that point too. So I knew exactly the output I was going to, like I had simulated in my head, like if I could do it over again, this is the way I do it. So by the time I like put my hands on the keyboard on like, you know, Friday night, I, it wasn't like I was designing a product. Like I knew I had been in every detail of that product since the beginning and including me, 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 who worked on a big system, you have the whole system mapped out in your head. So I knew everything. And I also knew that, you know...

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 like, you know, protective of the code they had written a few months ago. And so I really wanted the prototype to go out. And so I did it. And then I didn't, it's funny, I never talked about it again, but I think Paul Buhite, who was a co-creator of Gmail, and I worked and 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 was a true story. And XML is just the worst.

Did you get a lot of flack from the people who had built the system you effectively replaced? Like you were part of that team, but everybody else had so much invested in it, even though it was like shit on top of shit on top of shit. You know, 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, you know, I think, you know, the engineer had written that XSLT transform, I think was like, you know, a little bit, that's a lot of work. So you have to throw out a lot of work, which feels bad, but particularly, you know.

Lars and Jens and I, like, we want to make great products. And so I don't think there was a, at the end of the day, everyone's like, wow, that's great. You know, we went from a bundle size of 200K to a bundle size of 20K and it was a lot faster and better. So, you know, broadly speaking, I think good engineering culture is you don't want a culture of, you know, 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. And 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. That's awesome.

I'm 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. I literally don't know how to answer that question. How would you think about it without giving me an answer? Yeah, that's a good question. First of all, I'll give you how I think about it, but having not been a public company CEO for a couple of years, I blissfully don't pay attention as much to the public markets. And in particular right now, 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 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, like, NVIDIA's stock is so high right now because if you want to sort of get downstream, you know, NVIDIA will probably benefit from all of the investments in AI.

I'm not sure I would do that for 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. I love that. Where do you think, like 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, like what areas of the economy do you think are limited by intelligence or workers, like smart workers, in which case like...

That's another limit of intelligence. Yeah, I mean, two that are, I think, probably going to benefit a lot are technology and finance, you know, where you're, you know, if you can make better financial decisions than competitors, you'll generate outsized returns. And that's why over the past, you know, 30 years, you know, of machine learning, you know, hedge funds and financial service institutions, everything from fraud prevention to true investment strategies, it's already been an area of domain, 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, if 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. 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 like processing information are areas that will really benefit quite a bit here. And so that and I think the thing that I would think about over a 20 year period is like 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 one pro a little bit to help me. But, you know, because you can end up generating a bunch of growth in the short term. But then, you know, if everyone does it, it commoditizes the whole industry, you know, type of thing. So, you know, there used to be, you know, before the introduction of the freezer, ice was like a really expensive thing. And now it's free, you know, and so.

I think it is really important to actually think through those if you're talking in a timeframe of like 20 years. And that's why having not thought about this question ahead of time, you could be quite simplistic elsewhere. But I would say software and finance areas that I think stand to reason should benefit quite a bit. I love that response. How do you balance having a young family with also running a startup again?

I work a lot. I don't, I really care and love, 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 like jump out of bed in the morning and work out, go to work, and then spend time with my family. Broadly, probably, you know, being honest.

First, I'm not perfect at it, but second, I don't have a ton of hobbies. You know, I basically work and spend time with my family. The first time we talked, you saw a couple of guitars in my background. I haven't picked one of those up in a while. I mean, I literally pick it up occasionally, but I, you know, do not devote any time into it. And I don't regret that either. Like, I'm so passionate about what we're building at Sierra. I'm so passionate about opening. 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.

Having sold the companies you started twice, does that influence what you think of Sierra? Like, are you thinking like, oh, I'm building this in order to sell it? Or do you think differently? Like, this is my life's work. I'm building this. That's not going to happen. I absolutely intend Sierra to be an enduring company and an independent company. But to be honest, every entrepreneur, every company starts that way. And so, you know, I'm really grateful for both Facebook and Salesforce for having acquired my previous companies and hopefully 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 CIRA, we have just a ton of traction in the marketplace. I really do think CIRA 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 CIRA don't listen to us anymore. I want this to be something that not only is enduring, but outlives me. And I think, just actually, 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 when we moved, it was actually quite awkward. We moved into like 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, we moved out of the second building. We were in Palo Alto, a slightly larger building. I think 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 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 for whom that amplifies their existing value proposition. And it will really hurt others where it will essentially the seat-based kind of model of legacy software will wane very quickly and then really harm them. 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. 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, you know, 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 company. You mentioned AI agents. How would you define that? What's an agent?

I'll define it more broadly, and 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 CIRA 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 know, you're flying back to Edmonton and help you arrange your travel. Two more complex things like, you know, I'm going to go ask my boss for promotion and like help me role play. And, you know, I'm setting up my resume for this job. Help me do that too. 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 us. 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're really commercially valuable because most companies hire people or consultants that do those things already, like analyze the contracts of the supply chain, right? 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 the 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. And by the way, it changes the total addressable market of these models too. Like, I don't know what the total addressable market of legal software was three years ago, but it couldn't have been that big. I couldn't tell you like 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. And so 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 bundle your home in auto. You can add a member of your family to your premium. There's a million things you can do. 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 the digital instantiation of the company. And that's what we're helping our customers do at Ceres, 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. And 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, right? That's not something that, can you make a web page that does that? No, but that's a great conversation. And so we really aspire that when you encounter a branded agent in the wild, we want Sierra to be the platform that powers it. 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. Yeah, I mean, it turns out... And it was an AI agent. There was no human involved in the whole thing. Well, 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. And so it's harder, but I 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. Like, what are the universe of options that you can do? When you have an AI to represent 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 to put on your website to whatever your customers ask. And then you can decide how to fulfill those requests or whether you want to. But I think it would really change the dynamic to be really empowering to consumers. As you said, 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. It can be you can call you on the phone. It can be all of those things. And as new models and new technology comes out, our platform just gets better. You're not like re-implementing your customer experience. And I think that's really important because

You know, 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 like, how can you maintain your AI agent in the face of all 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. 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 like 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? 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 it. 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. There's definitely been stories of executives coming in and ready, fire, aim, 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 its internal narrative can be stronger than

the truth from customers. I remember one time when the 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, you know, 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, like you were definitely uncool if you were using anything else. And from my perspective at the time, like the war had already been lost.

Like it was definitely a two horse race between Apple and Google and 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, like it wasn't a confrontation, but I made a comment to someone who was like, you know, something along the lines, are you required to use Windows phones? And I just was like curious. And then I got a really...

bold answer, which is like, yeah, we're going to win. Like, we're taking over the smartphone market. And I was like, I didn't say anything because it was like a little socially awkward. I was like, no, you're not. Like, you lost like four years ago. There's a process. There's something that's happening that's preventing you from getting reality. Well, that's the thing is, if you think about it, if anyone, if you've ever worked for like a large company, you know, you're, 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, you know, junior vice president of whatever and you're, you know, eight levels.

below your you know ceo and you have a set of objectives and results uh you might be focused as 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 you know the social dynamics of their high school not the world outside of it and it's probably rational by the way because like you know probably their social life is more determined by those you know 1 000 kids in their high school and it's like all the things outside but this is that's the life of a person inside of these big places and so you end up where uh you know 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, 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, you know, 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 like, you know, 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, you know, the Blackberries of the world or the TiVos or the, you know, you can really... you know, as the plane is crashing, like tell the story that you're not. And, and, and then similar, as I said, like culturally, you can still have like the person in the back of that crashing plane being like, when am I going to get promoted to SVP? And then you're like, you know, and that's, I mean, this is like, I've seen it a hundred times. And so I think it really comes down to leadership, you know, and I think that one of the things that most great companies have is they are obsessed with their customers. And I think the, the free market doesn't lie. And so I think the, one of the most important things I think for any like enduring culture, particularly an industry that changes as rapidly as software is.

how close are your employees to customers? And how much can the direct voice of your customers be a part of your decision-making? And that is something that 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. Is it direct? Is it filtered? How many filters are there? That's exactly right. And then I think the other part on leadership is, we talk about bureaucracy, is process is there to serve the needs of the business.

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. Well, 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, like 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, 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 when you feel it. That's a great answer. We always end these interviews with the exact same question, which is what is success for you? Success for me, we talked about how I spend my time with my family at work is, you know, having a.

happy, healthy family, and being able to work with my co-founder Clay for the rest of my life and making Sarah into an enduring company. That would be success for me. Thanks for listening and learning with us. The Farnham Street blog is where you can learn more about my new book, Clear Thinking, 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.com. Until next time.