Uncapped #42 | Bret Taylor from Sierra

Clearly in three years, we could talk about what are the best practices to set up a software team that's optimized for this technology. We'll know what those best practices are. And right now we're just figuring them out of real time. And like my hypothesis is the companies that figured out first will move the fastest. It's fascinating to me. Brett, thanks a bunch for joining us. This is me. I'm super excited for it. Thanks for having me. So you're one of the best people to ask this following question, which is what is your view on the SaaS apocalypse? If we can call it that. SaaS McGuettan. SaaS McGuettan. So basically it's like in on public markets, all of these companies are trading way down. You go on X and everybody's talking about how software can now be written in two seconds. And so there's no modes anymore in software. And so it's leading a lot of people to ask where does durability come from. And so I just wanted to start with this topic because you've built your own companies. You've been the CoCEO at Salesforce. You're now building like one of the fastest growing AI startups there is. You're on the board of open AI. How do you see software like in this moment in February 26th? So first I think the market isn't necessarily reflecting an indictment of individual companies. I think it's more of a broad view of like the bigger questions you were saying, IE, every software stock is down, but I don't think that means every software company is equally disadvantaged. It's just basically anxiety about the future. I think it's a few things. We can talk about sort of defensibility broadly. I think it's a really interesting question. I think if you look at the history of enterprise software, a lot of the value has gone to the big systems of record. So ERP systems, CRM systems like the core database is the Oracle, sort of famously powered in the early days of software. And then you end up with all the software of the service companies. SAP, fork day, sales for service now. If you look at what a system of record is, it's essentially a database with a bunch of workflows around it. And to date those workflows are manipulated by people clicking on buttons and a web browser filling out forms. If you had to synthesize pre-AI, why were those businesses so good? Was it the source of truth thing and that there had to be some immutable thing? And so the database row, is that what it was? Was it the ecosystem of the integrations? What do you attribute the success of systems of record too? So I think the reason why a system of record has always been the most valuable is it is the anchor tenant of your technology deployments. You wanted to create a workflow for a quote to cash or something like that. You had to integrate with your ERP system and your CRM system. So as a consequence, the companies that own those databases could either develop that functionality as an add-on like a new SKU or if it was a third party company, they would often be a part of the ecosystem like Salesforce's App Exchange or whatever the marketplace equivalent is for SAP. And so you ended up with a lot of value on the systems which meant switching costs were just really high because it was sort of this that system plus all the partners that integrated with it, sort of created gravity and high switching costs. And then similarly, you just end up accruing a lot of value either by collecting rent from your ecosystem or developing premium add-ons on top. And so it sort of became the sun and the solar system for each of the different lines of business that these systems of record were sold into. And then you'd end up where you'd get a scale. So you'd get sales capacity scale. So the larger you grow, the more sales people you have, you can reach more and more people. Then there's the proverb, no one gets fired for buying IBM, which obviously is somewhat dated expression. But it sort of was like, Hey, if you're going to put in a new ERP system, no one's going to blame you for choosing SAP because everyone chose SAP, right? It's something new and it doesn't work perfectly big trouble. Then you're the same. So all those things sort of accrue. But then the question is now that all of a sudden that a lot of those targeting chipped away with AI agents, you know, first, could you just buy a coded in a weekend? So does it change build versus buy? So that's one risk. Does it change when you come up on that renewal? Are you going to make a different decision? Secondly, I actually think the more fundamental thing is what is a role of that system of record if AI agents are doing most of the work? So rather than people clicking around on an ERP system on board a vendor, if you just delegate to an AI agent to do it, all of that is sort of invisible to you. And all of a sudden it goes from being an application to sort of a database. Right. Similarly, if you imagine a CRM system and rather than having people staring at it all day to manage their leads, contacts and opportunities, if you just say, Hey, generate me some leads. In other words, like does a system of record have a place in the world if nobody logs into it? And it does. But the real question is like, how valuable is it? How important is it? You know, when you go back to my metaphor on the solar system here, how important is that gravity versus the gravity of the agents running around it? And it's just really interesting because if you imagine you're running a sales team, how much do you value the database of leads versus the agent that generates the leads? And along with like ancient history three years ago, those are the same thing. But now you're like, gosh, I actually probably care more about the lead, the lead generation and how it's stored in track is actually maybe a more tactical part of it. So there's all sorts and that's true of every system of record. This isn't, you know, I just know CRM systems you know, pretty well. And if you look at ITSM, which is like the place where it's now plays or ERP systems, which is work day SAP Oracle, etc. All these questions are coming up. And so what's interesting though is I think every single one of those companies could transform and benefit from AI. I really do believe that. You know, you saw what Microsoft did in the cloud transformation and they went from being dependent on Windows revenue, going to active directory and Azure and all those other things. But it was really awkward, you know, I think folks like you and me back in the day used to probably dismiss Microsoft. I mean, I certainly did. I didn't know. I didn't foresee them becoming as powerful as strong as they are today, but it was good leadership, good technology. But I don't think the market knows like who is Seable Systems and who is Microsoft in this like landscape of software companies, probably known as Seable Systems was that was the company that Salesforce beat to become the cloud. So can you actually develop this ecosystem of agents around your platform and you know, will it become more valuable than the platform you had? And then on top of it, the existential risk of, you know, is the value of software just going to zero. I don't necessarily believe that. But you look at all of that if you're just an investor in public markets, you're like, I'm just going to slide. Yeah, I know that the market play out a little bit. And I think that's sort of what's going on. Totally. Yeah, I mean, you can never know for sure who's going to turn into the next Microsoft, but you can kind of try to think about like who has the structural ability to expand like who's got the right with customers to make the expansions and then which products will be easier. So like, you know, in the database question, is it easier for today's databases to build agents on top or is it easier for a modern agent to go say, well, I'm going to go out, build a database at some point because I can, I could do that and I've got the customer relationship. And how do you think about like what creates the rights to expand? I think all the incumbents sort of have a right to win in a lot of ways, you know, in the same way we talked about, you know, why a system of record is powerful. I think you could say the same logic for all the agents running on top. The dynamic that plays out though, not just with AI is when a new technology comes out, like the web browser or the smartphone rarely is the expertise on how to do exceptional things with that technology at the incumbents. So first, if you, there's this thing in enterprise software, there's a phrase called the best of breed and best of platform. Best of platform means hey, we're on Microsoft shop. We just buy Microsoft stuff. And it sounds silly, but actually there's a lot of logic to it. Like, hey, you get sort of good procurement leverage, be everything works together. You're with a ton of people. There's probably some benefits, all sorts of things. What ends up happening when new technologies come out as you the pendulum swing from best of platform, best of back to breed, because when the new, let's say when the web browser came out, it's much easier to get a 10x experience. 100%. And also just think of the like pre and post web browser enterprise software, like you're running like client server windows software. And like, it's a completely different skill set to make a web application as you and I know. Totally. And so at the time, like, there's this window of time where best of breed competitors are light years ahead of the incumbents. And it's a race. So basically, can the best of breed upstarts turn into get scale before the incumbents figure out the technology? And that's what we're just going on right now. So like, I would argue very few of the incumbents have any credible like decent AI technology, but they will. It's like inevitable, they will. You don't understand. Why is that? Like, what's the real reason for it? Because like, I see these companies that have, let's call infinite resources, roughly speaking, they ought to be able to hire who they want. They ought to know what the products could look like. They ought to be able to try them. They ought to be like, why is it so hard for like, let's say legacy companies to like catch up quickly, you know, versus like an AI startup with 50 engineers seems to, you know, outperform, you know, the teams that are 10 or whatever times bigger than a big company. Is it cultural? Is it systems? Is it? I like the phrase strategy. I don't remember who to attribute that to. We could pull up chat, GVT and ask, I think the idea is like, in these moments of big platform shifts, what were your strengths can become weaknesses? So let's just take seabull systems and the birth of the web browser, they have a, you know, on premises, CRM system. When you say, okay, like, let's compete with this cloud native CRM system in Salesforce, you start to say, well, I don't want to just start from scratch. Like, we've got all these assets. So how do we do it in a way that takes advantage of all of our assets? And so all of a sudden, you're like, okay, let's not just build a great product. Let's transition from this product to that product. And what if someone wants on premises too? And, you know, that's our strength. We should play to that strength. And you start like, basically making all these decisions that sound really clever because you're playing your strengths. And in practice of the technology wave is bigger than the category, which I think the web was. As an example, you end up basically chipping away sort of doing a pure play value proposition. It can also happen with business models though. So in that time, you'd have perpetual license software and moving to software as a service, that's a huge change for a business make. For your customers, it goes from being capex to op-ex for you as a company, it changes radical revenue. I mean, Adobe, Sean's new to this at Adobe. Very few companies can make that transition. And you have to sell it differently. You have to compensate salespeople differently. Revenue recognition is different. So you have the product strategy tax. You have the business model strategy tax. You have even like incentives of salespeople, there's a strategy tax because you know, you don't want to just have your business collapse over night. You can't just, it's so easy for a clever Silicon Valley. Just pivot. I'm like, yeah, if you're a public company, you have to go in front of your investors every single quarter and be like, Hey guys, I know revenue just went off a cliff, but trust me, it's going to turn around next quarter. Like, you don't survive that. So you just compound all those things. And all of a sudden, you're like, why does a 50 person company succeed? Well, they have none of those. All of the advantages that you have, all of a sudden become anchors that are holding you back from actually doing the right thing. And that's why, you know, I always like to remind our company, Sierra, that you know, the wave that we're riding of large language models and this next generation of AI is greater than any company riding it. And so like, don't fight AI. It's going to happen with or without us. And if you go back to the internet, if we were talking in 1995 or something and we probably like searches a category, e-commerce as a category, digital payments, that stuff is going to happen. I don't know which, I didn't Google hadn't been found yet. I guess Amazon probably had around that. PayPal probably not founded yet. The categories are obvious. The categories are like, whether or not any of those founders existed, all of them, there would be winners. And it's the same now. So like, everyone knows what's going to happen. And it's like you're competing for the privilege of winning. And so in a world where the technology is that remarkably powerful, the strengths of the incomemen start to wither in the face of the technical change. And that's why you tend to get new, new great companies, like the companies that are enduring tend to be created in platform chefs more than any other time. I actually curious on this topic of sort of, there's these obvious things. And within AI, I would say, not to discredit your insight, but support I would count as an obvious thing, like in a good way. Yeah. It looks like it works. And you did it early enough that you were able to get to a place through right time, but other people did too. And so in some ways, I'm like, you have been playing both in a very blue ocean wide fields, like the incumbents are sort of like categorically different. And so it seems like inevitable that we're going to have agents doing support. And so there's that. And then on the other side, a lot of other companies see the same thing. A lot of other people have been building. And so before getting into the specifics, I'm just curious, like, experientially day-to-day, does your sort of operation of the company feel competitive or like wide open? It feels competitive, and it feels like a really big market. So it doesn't feel particularly demand constrained, which is a really great feeling as a fellow entrepreneur. It's like, you don't get to-- So you feel like there's lots of demand and there's like a contest with each sort of situation? Yeah, that's right. The way it feels, it feels like there's sort of too much capital available, put it in the way there's obviously going to be competition and meaningful markets. It feels like there's sort of too many competitors that don't necessarily have strong differentiation. I think it's probably healthy, though. I think that there will be a colon just as the market progresses. But it does feel quite competitive. I'll just sort of give you maybe a quick glimpse of the past couple years. So we've had a remarkable growth here. We closed 100 million and 7.4 is 150 million than 8.4, which has exceeded my expectations. But this past year has felt like an infection point. So the first year of our company's history, we would often go in and be explaining to clients what an agent was. The term was novel. And it was part of our marketing explaining what an agent was. Number two, people would be talking about, hey, AI is maybe non-deterministic. They wouldn't necessarily use that word. But that would be what they would be describing. And how can we trust this technology directly engaging with our customers or consumers? What are the risks? Now the conversation is clearly we need this yesterday. I'd mentioned this to you earlier, but over a quarter of our companies have 10 billion or more in revenue. So we're talking big companies. We serve most of the Fortune 20 as an example. And so these are big companies that are coming in saying, we evaluate, we know what we want. We've heard of you. We've done all this evaluation. Here's an RFP, like let's go. And as a consequence, because the market has matured, and by the illustration of the existence of things like RFPs, you end up in more competitive conversations. And then it's a question of like, why Sierra? And I'm happy to talk more about that. I mean, obviously, love too is about-- for all the reasons of the greatest. But you end up in this world where you're not explaining what the word agent is anymore. You're saying, here's why we're the right partner for you, which is a very different conversation. So they're like, yeah, I'm bought in an agent. So why is it Sierra? What have you found is the most important thing that makes you win? So one thing we really did uniquely-- so the reason why, over 1/4 of our customers, have over 10 billion revenue is we've tried to serve more complex, more regulated industries. We want-- we serve most of the US health care insurance market as an example. We serve US banks, Spanish banks, UK banks. And these are companies that-- if you know the industry, they're regulated by everybody. It's easy to make a demo in AI. It's why you can go on to action, just see 1,000 demos. And demos are cheap. We've been making an agent's sort of industrial grade as hard. And we've really uniquely been able to make agents that can actually have complex conversations. The other thing that we do really uniquely is, in addition to, I think having a really easy to use product is we help companies move faster. We went live with SIGNA in two months, which is preferable. Yeah. I mean, how big is SIGNA? It's a Fortune 20 health care company. And I was on stage with Sitchee and is who runs our AI practice there at the health conference. And he was talking about this. And part of that is, how can you show up? But we're really great at AI. SIGNA is really great at health care. How do you bring those two together to move extremely fast? And so for a lot of our clients, the reason they bring us on is, can you help us move quickly? And that requires knowledge of AI and knowledge of business. And I think we sort of show up with a greater sense of maturity there. You mentioned the pricing scheme was one of the difficult things in the past. Yeah. We don't have to like, belabor it. But obviously, going from just buying a license to a cloud subscription and now usage based is like the future, what are you feeling as important as you have created and probably continue to iterate on pricing? What are the important levers for agent companies? We do something specific at Sierra that I'm sort of an evangelist for, which is outcome space pricing. So it turns out our industry, the outcome, is usually all the flying. So in a service context, could the agent solve the problem? In a sales context, we do a lot of sales agents as well. Could it make the sale? You probably, at your company's page, your salespeople commissions, right? That's where you can measure the outcome. You want to incentivize the outcome. The interesting about agents is autonomous or can be autonomous. And so if the outcome is measurable and trackable, what an interesting opportunity to actually charge for that. And if you look at the history of software, let's take advertising. We went from impression-based ads to cost per click ads to now for mobile ads. You can do paper installs, at least that's my understanding. And then you had enterprise software, but from on-premises licenses to subscription-based software and could outcome-based software be the next. And what's so neat about that is for a company, what an interesting and accountable business model. And I think there's some challenges to it because you obviously put some revenue at risk, but I don't think most advertising tech people would say CPC ads put revenue at risk is like the opposite, right? Because the closer you get to the outcome, the more valuable it is for the companies that are actually willing to invest in it. And so my view is to the degree agents have a measurable outcome outcome-based pricing feels like the secular business model for agents. And I think it's quite both disruptive and I think a huge step forward. Why is it better than token-based? So if those are like, I guess sort of like the two reasonable options now, why is an outcome better than token-based even over the long term? - Let's say you had an AI agent to generate leads for your sales team. What do you care about? You care about the number and quality of the leads, right? And so you really don't care how many tokens the model uses. In fact, it's not obvious to me that like there's a correlation between used tokens and leads generated. And in fact, in the same way, there's no correlation in a SaaS product between their cost to serve and the quality of the product. You can have a really good engineer write it or a really bad engineer write it. No, you really can have the quality of the product. The reason why I don't think token-based makes sense is it's charging for an input that is uncorrelated with the output that your clients actually care about. And I think this is actually, you know, I'm a huge believer in applied AI, but I actually define applied AI as can you describe your value proposition without without mentioning models. Because if you think about, hey, we can answer the phone and solve 80% of phone calls without human intervention with a C-sats score of 4.8 out of 5. That's, you don't mention models. I mean, models aren't input to that, but on output. If you have to mention token utilization is probably a tool. It's probably not an applied AI. It's not an application of AI. It's just sort of like a tool around AI. And I actually think that the closer you get to a business outcome, like it's actually you should charge for the business outcome, which is uncorrelated with tokens. And I also think it's almost a measure of, are you actually an apply AI company if you don't have to talk about tokens? Do you think that there will be markets either where things get so competitive that people have to price based off of like cost rather than value? Like could that happen or maybe the other format for it would be if you can't describe the outcome completely. Like for example, code coding, which we both probably think is super important. Obviously, it's like a little harder to say what the outcome is there versus like usage or something like that. So like what are the conditions where like tokens do make sense? Yeah. So I mean, there's this old Apple site where they had sort of like Apple folklore kind of thing. And I think there was this one boss that Apple that made people thought of forms and how many lines of code did you write? And this engineer infamously wrote a negative number because he just like refactor a bunch of stuff. It's my it's like the good analog. Historical analog for why tokens don't matter because it was his way of saying, you know, fuck the man like your lines of code has nothing to do with my value. And he was doing it to sort of like, you know, piss off a middle manager to make that point. But it's interesting is like in the world of software engineering, people truly understand like the customers of those right now are software engineers who intimately understand these models. So there's a little bit of a the customer product market fit. So it's a nuance point, but I'll say like where I see it might happen. So right now, if you're evaluating the software engineering agent, a coding agent, you're probably comparing it to the cost of a software engineer. If you fast forward five years, you probably will be comparing it to the cost of other coding agents. So I think the second order of fact as AI becomes prevalent is you, you know, you're you're just you're the reference point for its value will change. The thing I would say is that's true. We are thinking about a cost center. But if you're thinking about top line revenue growth, that doesn't necessarily apply. And if you go to my example of an AI agent generating leads for your sales team, depending on what you're selling and a lead is lead is a lead. And you probably will value quantity and quality of leads. And there's a math equation. And that probably will be remain independent of token costs is my guess. And so I think a large part of AI is productivity and reducing, you know, costs. And there's a big part of it. But the other side of it is outcomes. And so could you imagine a world in four or five years where, you know, there's one coding agent that can actually produce something of greater value for your company. Will you value that or you just look at the token cost? I think probably you'll start looking for value is my guess. Or they all be the same. I don't know. You know, it's like they will. I was just reflecting on over the past year. There have been all these articles about has like AI progress slowed down. And then in our world, the software engineering, it's been the opposite. Like every new model comes out. You're like, oh my gosh, it can write increasingly complex software. My theory of that is it depends on what you're testing. So if you're using chat GPT for trip planning, probably haven't seen a material change over the past year and a half because you reached sort of sufficient intelligence trip planning and a long time ago, if you're using an AI to make right rust code, code X is like mind blowing right now. So I think the one of the interesting things when I think about like second third order facts and like the progress of AI is, you know, where you will you pass the horizon where like every model is sufficient in that task. And then there'll be some things where like the frontier continues to move. And it's hard to imagine, but it's just like we're in a crazy time. Where are we at with support agents right now? Are there still edge cases last mile things like that AI can't do still? Yeah, we are though. I imagine a lot of the technical problems as opposed to product problems will become easier, but there's a lot of them still. So, you know, we at Sierra support most spoken languages in the world. And, you know, if you want to support Cantonese and Tagalog, most of the good voice models, you know, don't come from like the traditional Western model companies. Similarly, one of our clients is Safe Light Auto Glass. It's like roadside assistance. And it turns out that like car horns, background noise, kids talking background, you know, are actually all fairly hard problems to solve. And even in some of the advanced voice mode stuff of you are in a noisy environment. It constantly thinks it's been interrupted and things like that. So you end up having to build proprietary voice activity detection, multiple speaker detection, all these other things. We develop all this technology because we need to be the best now. And I think we are the best now. And you're like, okay, that's probably going to be a commodity to use now. One year from now, I mean, who knows? But you have to do it because you need to be the best at every stage of your company's existence. And I think then you're the way we think about the world is we have a product, which is called agent studio or agent us. And we're going to make the in three years, we'll judge us by our product. And right now we're probably they don't our clients really put this way judge by the technology. But if you go back to 1996, I remember when that scape had a web server and Apache was new and done it, like no one cares who you serve web pages now, like it's a commodity. But at the time, that was what you sold. And now you have increasingly higher order website building like Shopify. So I just think the AI agent market's going to take that progression. We're going from a tax centric sales cycle to a product centric sales cycle. It's interesting that you're obviously having to be the best at something that you know is going to get commoditized. Yeah. Which is probably not something I don't know if you ever had to experience something like that. And you're I mean, for that to be true, you just have to be in the middle of an insane rate of change. But that means you have teams who are putting like, you know, a lot of their life force for two years into something that everybody knows is just for two years, but it still matters nonetheless. It's crazy. I mean, if you look at traditional, I'll just say enterprise software consumers are a little different. But you think about your building up this asset, your intellectual property is a fancy name for it's like this platform that we're building. And like we took so many years to build it. And it's got all these features. And now you're like, I'm building this and I 100% certain will throw it away in the next three months. But have to build it because if I don't, I can't serve the bank that has a big, you know, business in Hong Kong or whatever it might be where they we need can't to be support. So that is the reality right now. And so I actually think I've been thinking a lot about this actually just because I think it was Toby Luke you sort of said something provocative around, you know, when generating the code is easy, it's almost like the system and the prompts that are actually the durable asset. You know, put it out the way, could you sort of terraform your software from scratch, you know, it's the prompts that led to it. I do think that is sort of the software the future in a lot of ways where how do you encode the infinite number of little product decisions that you made because so much of that is encoded in code today. I mean, if you think about like a product requirements document versus the code, what percentage of the emergent product that comes out of his encode almost like 90% like a lot of the little detail. Yeah, or in there. I think a little bit it's like software companies of the future and the products that they make are just going to take a really different shape in the future. And I I'm so excited to be a part of it. I mean, I think it's really fascinating. I think there's something really interesting about AI impacting the software engineering industry almost first and most because like we're disrupting the craft of making what we're building in real time and it's fascinating. Yeah, I think there's a prevailing idea in tech that AI is moving so fast that like young founders have this massive advantage. And I mean, this with no offense, you're not old, but you're also you're not. Yeah, I'm young. Yeah, I'm young. This founder and you have one of the most successful AI startups there is. And it does seem like you've brought a lot of your previous experiences to what you're doing, but I can tell from talking to you that you also are just rethinking everything. And so I'm curious to your own experience for yourself and for other founders, you look around at like, do you think by and large young founders have the advantage? What does it take for more experienced founders have the advantage? You know, I'm always a big believer. There was I don't know if it's a real quote, but I some VC said, you know, like, why was this, you know, found or able to conquer this market where so many others had failed? And they said, well, he was too naive to know it couldn't be done. And there's a certain element of that that I love because you end up with this kind of naivete that is actually sort of a form of principle, the first principles thinking that a lot of young founders have. You just don't know why this messy, bad products, you know, dominate the market. You think there's a better, faster, cheaper way to do it. And because you don't have any of the hard one lessons that can end up, you know, oversimplified analogies, keeping you from actually taking that leap, you can end up with, you know, Tony made DoorDash and didn't care about, you know, say web vans, Monzo or whatever. I can't remember all the the the.com bubble companies. But I do think especially in enterprise software, the experience that that some of our team members bring, including the old man, me and Clay, bring to it really does matter. Part of the reason we're able to serve so much of the Fortune 100 is we can go into a bank or a healthcare payer, healthcare provider, revenue cycle management firm, or a big telecommunications company, and understand their business. We're working with one large medical device companies consolidating 40 of their call centers and to one, and we can have a discussion about the change management of doing that. And that's not really a tech problem, but it does require understanding business. And I think if there's, we always joke at Sierra, it's like the Venn diagram, there's a circle people understand like next generation of IIs, and people understand business. And we're like the company right in the middle of that. Maybe the only one. And that matters because I don't know, there's that sort of infamous MIT study saying, all these AI projects fail. It's like none of ours do. And that's our value proposition. We can actually help you go live. And I think the experience has benefited us. - Yeah, I'm curious, if you can point to what has created the lead you have so far. And I was just getting started, but at the moment you do, you've pulled away in a big way. And I'm sure there's a lot of just like daily blocking and tackling, but I'm curious if there are any foundational decisions that you've made or strategic approaches that, over the last couple of years you look back at, and you're like, that was pretty essential to make this happen. - I think there's two almost independent areas of investment. They're not independent, but they're like very different. One is the product, and one is our sort of our good-of-market and partnership model. And they're both really intentionally built. On the product side, we've tried to balance ease of use and of extensibility, because when you serve really large companies with very, that have been around for 200 years, you need to work with mainframes, you need to work with a thousand different systems, you've done 10 acquisitions, there's all the enterprises are messy. And so that's why you tend to have, most, I'll say, enterprise software this design for larger companies tends to be quite extensible. Often that extensibility comes at a cost, which is, is it easy to get up and running? And so as a product designer, like one of the things I've just spent a lot of time thinking about is like, we're trying to have our cake and eat it too. Like, can you go live in two months and still be maximally extensible? And I'm really proud of the product we've built. And some of that is born from experience of what is extensibility in me. And I think we have an opinionated view of what it means and have been able to accommodate like some fairly exotic deployment or requests and still do it fast. That's really unique. The second thing is our good market and partnership model. Because we knew when we started the company, we wanted to work with the largest companies in the world, not only, but we wanted to be able to work with largest companies in the world. And I focused on that. And as a consequence, we just have a really unique partnership model. There's sort of a fashionable thing to talk about, forward to plate engineering and slow-con value. We don't call it that, and it's a very unique model. Because it's not all about technology. Like, most of our clients build and maintain their agents themselves is pretty easy to do. But we show up and we help you be successful. And so it's like, we'll just show up. Like, we're not going to let you fail. Like, and I think that is a very different-- because we have this outcomes model, outcomes based pricing model, we don't get paid unless it works. And so-- How much of that is technical versus, like, change management? It's a mix of both. I don't know if it's 50/50. Do you notice two people or it's one person who does both? We have a mix of roles. We sort of evolved that. We try to hire really technical people in all roles, though, because part of our secret is we want to be your trusted partner in AI. So you want the person who is working with you every day to be the most knowledgeable AI person, you know. Like a forward deployed change management engineer. Yeah, exactly. It's crazy what we're doing. And what's really neat about is if you're like a really talented technical person who wants to go transform an industry, you can do it it's here. I mean, you can go in. And like, we're working with most of the health care insurance companies. Like, you want to change-- Yeah. --calf-hair costs. And, you know, like, what a cool vantage point to do. It's we've been able to track some really remarkable people, too. You said that it's not just support agents now. Yeah. What else are you finding shoots in? I'll give you one of my favorite relationships with the rocket. So based on Detroit, remarkable story, you know, their founders done more for Detroit than I think anyone person's done for any city, just like remarkable company. But they own Redfin, which is a home search site, Rocket Mortgage, which is like the number one consumer mortgage originator in the country. And then they bought a mortgage travestying firm recently as well. And you can go to Redfin.com and use an AI agent to search for a house. You can go to Rocket.com and finance that house. With an AI agent. And then you can with the acquisition they do this mortgage service firm, you can then, when you're servicing your mortgage, you'll talk on the phone with an AI agent as well. So like, everything from finding a house to originating the mortgage to servicing that mortgage. I think it's pretty cool. Like, they have an amazing seat to out in Sean Mahotra, like, pretty visionary. And I love their CEO of Ruin, too. But it's like, everything from finding a house all the way through servicing. It's kind of what we believe a lot of businesses will do is look at their entire customer lifecycle from, I'll say purchase consideration, which is a fancy way of saying, browsing, I think homes are probably one of the more considered purchases that you could do. Executing the purchase, they're having issues with it all the way through retention. And for a lot of, for example, a lot of our telecommunications customers, their AI agent is actually doing negotiations. So like, you've probably negotiated your cable bill at some point. Yeah, probably. And so, AI agents are doing billions of dollars of negotiations for everything from, you know, satellite radio subscriptions to cable televisions subscriptions. It's pretty cool. I mean, it's like really, you know, over a billion dollars of mortgage folders. Basically just like all transactional communications, eventually. The way I think about it is website is a technology, but your dot com, the one with your brand at the top is your website. We're sort of doing that for agents. It's sort of like agents will do a lot of things. The one with your brand at the top that your customers go to, whether it's buying or servicing, we would like to help you make that. And I think it's an interesting, as agents go, it's often interact with other agents, right? If you think about a home and auto insurance company, you know, you may have a claim adjudication agent, you know, that's quite complicated. So our agent that's having the phone conversation when you're on the fender vendor will interact with that. But it is almost the intersection of all of that technology because it's sort of your front door. And our whole hypothesis is every company need a website in 1997. Every company needs an agent in 2027. And like we want to be that company. What's the nuance about like agent builders, though? Because I know you have like a view that like just being like a generic agent builder is not the right thing. Yeah, I mean, I've been surprised how many inner product, like large and coming enterprise offer companies, like their first foray and AI was you can an agent building tool. It just feels inevitably to be a commodity in my mind because you may be making a website. It was hard in 1995. But today there's like a million ways to make a website. Most of them are open source. So you have like cool companies like Versaul, which I love. But it's not like there's a huge market for this stuff. And in practice, I think the same will happen with agent building. I think opening AI will have a great tool, probably all the foundation model companies will. There will be open source packages like Langchaid and Langgraph. The idea that you have the right to end there-- I don't know if anyone has the right to end there-- just because it's just a technology. It's a horizontal technology. And I just believe it open source. And it's just going to become a commodity. So my belief is whether value is really going to be an agent that do things. And you'll hire those agents and purchase those agents for what they do. So I believe in companies like Sierra. I believe in companies like Harvey. I really admire what they do. And they have an agent that will do an antitrust review. I think there will be a finance agent that audits your financials. There will be one that helps you onboard a supply chain vendor. There will be one that-- if you just think about onboarding a new vendor, it's like there's a procurement process. Yeah, legal process is a contract review process. Whether or not it's completely autonomous or human in the loop, all of that could be augmented with an AI. And I'm like, that's a product. Agent building is not a product. It impregnates a technology. Yep. Speaking of the platforms, aside from being the founder of Sierra, you're also on the board of OpenAI. You're the chairman there. I wanted to ask you specifically about Codex. Over the last couple of weeks, it's been unbelievable. It's like a curtain just came down. Did you expect this? Did you think that what has happened here was going to happen? Or when did you start to have an inkling that code was going to go vertical like this? I'll say yes. I expected it just because being on the board of OpenAI, we talk a lot about it and all the labs andthropic and OpenAI in particular talk a lot about using coding agents to help build AI. And certainly building an AI researcher is an important part of building an AI lab. The weird part of that for me is someone who is a software engineer. I didn't feel it until I used it. So you can talk about it all the time. And then the first time you one shot something that turns out really good and not like slop, but really good, it's an emotional experience. I think for me it was. It was just sort of like holy shit. Like this is real. Yeah. As you said, it's really over the past three months that has felt really materially different to me. And I've been thinking about it a lot. I was thinking about the past 20 years of software engineering. I remember the first time I worked on the engineering time at a real CICD where you'd check in code and it would just automatically end up into production. And I remember how, I'll just like, if you've ever worked an engineering team that did that versus one that did manual releases, it's completely different because to have something that can safely go from commit to production, there's so many things that have to happen to make that work. You end up relying a lot on testing. So both unit testing, integration testing and canary testing because the last thing you want is someone clicking a button and taking down the service. And it's almost impossible for a team that is doing manual releases to convert into CICD like true continuous delivery because there's so many implied processes that are incompatible with that. It's like easy to start that way and very hard to work. So I've been asking myself clearly in three years, we're going to like a, we were talking, we could talk about what are the best practices to set up a software team that's optimized for this technology. Like my hypothesis is the companies that figure it out first will move the fastest. Yeah. And the other part of that, the companies that don't will move much more slowly. And Andre, I probably had a really interesting post about this too. Like I mean, a lot of folks were sort of like in deep here. I've been thinking about it and it's fun to see the industry of love sort of flipped on its head. Yeah. Yeah. Well, it's interesting because like I think people like, you know, software engineers on one end and then like say somebody who's like, you know, in, you know, some part of the country where AI has not yet kind of like gotten it's totally extended. Like there's like a wide gap in people's current sort of comprehension of like what AI is going to do. And so I think you know, it's like, it's a little bit unknown. Like, you know, there's a lot of blog post going on and out that are breathlessly saying like, it's all over. I think, you know, I'm probably more in the camp of like maybe software is like, I don't know, people, you know, use the word software as solve. I don't know if it's that. But I'm curious if you have a view on like, if codex and cloud code and sort of like the latest in coding, is that going to change the way companies are built, you know, like one easy strong man question there would be like, you know, people have been claiming that there's going to be my brother, you know, these 10, 10 person billion dollar companies, you know, is that, are we at the press base of that? Does that make sense? Are there other changes like what's going to happen now? There probably will be a 10 person billion billion dollar company, but I don't necessarily think it'll be the norm. And the reason for that is competition. If you imagine like the mobile phone market in the United States, there's three main competitors Verizon AT&T mobile. And they're all competing for a fixed pie of mobile subscribers. And it's why it's extremely competitive. There's promotions, there's ads. They can't make more of us. They can make more of us. They can build up their network. They can do other pricing and packaging. And it's a really complex business to run. All of them have access to AI, every single one. So the idea that you could deploy AI and, you know, not have to do things you were doing currently because of AI is probably true. But if any one of them figures out a way to use a person to gain market share because the other one, they're going to do it. And then as a response, their competitors will do it too. And that's how, you know, we spoke about this earlier, but it's the reason why when automated telemachines were introduced to banks, the teller job went away, but there's no fewer bank branches and no fewer people in those bank branches. And it's because, I don't know, it was JPMC or someone figured out, hey, if we put financial advisors in there and other things, we can actually make more revenue per branch. My personal take is in a competitive market. And that's the key, by the way, you need competition. So people can't just pass the cost savings on the shareholders or dividends. The second order of fact of the efficiencies of AI will be investment to compete lower prices or customer acquisition or whatever it might be. So we want a fewer engineers per company. They'll be way more productive. And so you just end up with a way that our software might have fewer engineers and more of something else or you might have more engineers. I'm not sure. But it's the idea that it will be what it is today, but just more efficient, I think, is a lack of imagination in my opinion. The engine thing though is the other part of this software engineering does feel special. I think people extrapolating too much from software engineering or it's a bit simplistic. You like the same thing might not happen to every other function. I'll just be really simple about it, which is finance and software engineering might be limited by intelligence, meaning they're largely digital. They are largely like manipulating sort of digital things to and you could imagine AI automating that. Most of the economy isn't digital like exclusively. So if you need to ship something a t-shirt from Vietnam to here, yeah, you could automate some of that stuff. But at the end of the day, that cargo ship still needs to be in the water. And I always bring this up, just imagine you're on a pharmaceutical company. You can think about how to make a therapy. You probably need a wet lab. So that intersects through a little bit. Maybe you could do robotics, but then you need a clinical trial. So just a lot of the economy is real. And so it definitely will change the way companies are built. But I think when people say everything will be 10 people, maybe just the stuff that lives in bits. Yeah, that's right. Which is a lot of the economy, but not the economy. Yeah. I mean, it's easy to talk about this, but you're right. If you just move around the physical world and you get off of this podcast and this computer I'm sitting in front of all this stuff, and you got into the world and there's trucks moving dirt around and people who need a building that has lights in it. And there's like a lot of physical things. I kind of tend to think that the value of that stuff's all going to go up until maybe robots happen. But in general, I think the value of bits goes down. The value of stuff goes up potentially. That's I think you're probably right. And some, you know, like robotics will have a big impact as well. But I think people are thinking about this a bit simplistically as my take. And I think intelligence is clearly on the cusp of going up exponentially, but it doesn't mean adoption of like that can't be absorbed by the economy perfectly exponentially. And so I just think people are a bit simplistic. Do you think there's any cognitive things that are immune from intelligence like Dylan Field when he was on this podcast gave an example of like Brat Summer as something where he was just like that would have been such an insanely hard call for an AI to make. And he had so much context and taste and opinion, you know, where my head was going is okay. So coding is, you know, whatever's happening there is happening there. But what about like brand or storytelling? Like, and I'm kind of asking you this both as an operator and as, you know, somebody who's very, you know, deep with open AI. Like, do you think that these other parts of intelligence also, you know, go the way of AI? I don't know if taste is necessarily related to intelligence, you know, it might be, but I've got three kids, including a 16 year old and a 15 year old. And when they decide what they're in a wear to school, I don't think they will, they would consider chat GBT's opinion. They care more about what the person in class next to them is wearing. Similarly, if you go to the most like elite competitive college preparatory school or the worst school of the world, there's always going to be the smart kid in class and the dumb kid in class and the strong kid and the fast kid and all these other things. I'm like, it's all relative and it's all very local. It's all very human. And so I think the idea that because AI is smart, it takes something away from us as humans. I don't necessarily subscribe to. I don't, you know, I was, you all see these things that go around online where people are sort of lamenting older technology, like the bicycle. Yeah. We've been weaker than machines for my entire life. Yeah. And I don't, I don't think it like, it doesn't make me feel like weak as a person. And I think we did this for the first time. We have computers that are going to be more intelligent than us. I think there will, you know, the emotions I had about codex, writing code that was high quality wasn't experienced because, you know, I might have some of my identity tied up in that task. And the next day I woke up and I'm using it as a tool and make better software. I'm like, this is great. Probably actually like a good like self actualization anyway to go through that. I'm not my ability to code. I think this is interesting. I think people's vocations and their identities are often very intertwined. But I think once you absorb the technology, I don't think it's actually identity. And so I think I actually am quite optimistic that we will be human. We will all be status seeking animals. We will all compete for the real estate here in San Francisco. And even though our standard of living will go way up, we will all be jealous of people still. We will all compete. And as a consequence, I think humanity will be just fine. That's my view on it. I think it's just hard to imagine. But it doesn't mean it's going to be catastrophically bad. I just think it's actually, I think we'll be largely good for human. I have a friend who believes that like as this kind of progress, you know, we're already, everybody's already completely to their phones and it's just astray and whatever. Now you have all the say I happening. A friend of mine was saying that he basically thinks that it'll actually become a status signal to become increasingly offline. And I'm like, actually that might be an interesting call. Like I do think that like people will kind of hit a tipping point out of the stuff where like all of it will happen. Like intelligence will get so good. And then people will sort of just be like enough of all of this and like, hopefully there's a big screen time reduction. You know, and it's like, so parents were revolting on social media, like about social media for their kids. And like, bunch of schools and all the parents like nobody take a phone, like everybody agreed to it. So I think that'll be an interesting thing of like does humanity like, does it, is there like an essential humanity that like gets sharpened? I hope so. I actually, one of the things, you know, I love the iPhone is one of the greatest inventions of this century. I hope we're not staring at a glowing rectangle. It can't be the right way to do it. And, and you know, now that AI can talk to you and human computer interfaces, like so, this is my point, I actually think, hopefully humanity can become more self-actualized, you know, as a consequence of this. And that is the purpose of technology. So, you know, just like the industrial revolution had let it and globalization led to job loss in the rest, both the United States, but certain goods got less expensive. And other parts like these, there's not going to be no issues. I think it would be callous and insincere to imply otherwise. But I think it will largely just really accelerate humanity in a really positive way. And I think that for me, and I think for like, if you're thinking about how's this impact me is like, have a more flexible view of your own identity. Like the what how you do it every day doesn't define you. I was like the metaphor because it was so obvious before and after. Imagine being an accountant before Microsoft Excel and after Microsoft Excel. Yeah. So much of the active account was like adding up numbers and things, you know, and now it's like building a model. And it's not like what you did, like the value provided didn't change, but actually the act of doing it is completely different. Like the skills has completely different. And so I think it was just like a lot of us are just going to go through that in a very compressed period of time. And it's okay. It's just a little incinerated. Yeah. Yeah. Yeah. But from anthropic at OpenAI around the Super Bowl commercial about the ads, which is they were good ads. They were funny. But then I think sparked like a debate around sort of like the whole topic of like, what is the role of these foundation labs and how should they sort of like bring AI to the masses or not? What's the appropriate business model? What are the trade offs of all of this? You've obviously like, you know, you've experienced with social networks and a lot of different pricing, you know, models, you know, OpenAI well. You know, you know, how to consume AI. So I'm just curious how you think about this and like, what is the right thing when you consider like a lot of these dimensions? I'm very optimistic about ads done in sort of a tasteful way. You know, I started my career at Google. I think I arrived like the day AdWords came out. So it was just interesting because when I started there, you'll laugh at this. But like everyone in my family when they found I was working there was like, how do they even make money? I'm laugh just because I think I listened to the acquired podcasts is literally the most profitable business ever created. But as a consequence, you know, Google is widely available for free for people who want to use it and has created an economy around it for demand fulfillment advertising. I think there's reasonable criticisms of advertising, you know, if it starts to get in the way of the sanctity of what the AI is recommending, which was sort of the, you know, backhanded implication. But I just think it's not true. Totally. I actually think if ads are clearly labeled and, you know, not in the experience, I think it's really aligned with the opening I mission because our mission is to ensure artificial general intelligence benefits humanity. Obviously, the most important part of that mission is safety. But after you get back to the Hippocratic first student harm, the job of a doctor to cure you. So then after you say, okay, it's safe. How do we widely distribute it? And I think we have an obligation being a mission driven, you know, I'm the chair of the foundation and on the PVC board. Yeah. Like our mission matters and being able to offer it for free widely, the huge part of that and we need to be able to, to, to afford that. Yeah. I think it's not only, I just, I find it inauthentic. Like I'm like, this is an incredible opportunity to provide this at scale to society. And I think the idea that it will somehow take the experience is strong. It's funny. You know, like I grew up in like suburb of St. Louis and, you know, so it's like a whole different world than like, you know, over and now and it's like when I think about like, you know, people, you know, that I grew up with or, you know, from just other parts of the country, 20 bucks a month is a lot. And I think, you know, it's easy to forget in our ecosystem that like not everybody wants or can spend $20 a month on stuff, but they really want these services. Like, you know, the whole world had to pay for Google. Like that'd be a worse world. Like it's absolutely good that everybody has access. I think it's important we do it well. Yeah. Yeah. People want good ads. Yeah. And actually people bring me the right product. I'm like, that's really nice. This is the other part of it. Like you want businesses to be able to grow from scratch. There's such a purpose of it. It just needs to be done in the right way. So I find the discussion not particularly authentic. Yeah. Yeah. The last thing I wanted to ask you about was how you've chosen to sort of like finance the company. And I guess I'm curious about three parts, which are how you got started and you know, working with Peter Fenton and then like what you've done since then to date and what's been important for you. And then I'm curious just like as you think about the future, like what's important to you as you think about other partners are capitalizing. And you know, I'm asking just because this is a podcast has a lot of BC and it's like I have a little flourish. Yeah, totally. We have three members of our board, which are sort of represent kind of like our kind of three rounds of investments. So Peter Fenton from benchmark, Ravi Gupta, who just left Sequoia. There's still a bunch of parts there. Yeah. And Neil Metta from Greenox. Okay. Just a fantastic group of people and chose them all both for the firm and the person. But notably like Peter, I worked with both my previous company. So you know, our first round of financing, I didn't talk to anyone else and introduced him to Clay and my co-founder. I hadn't spent time with him and we talked once. He sent me a term sheet. I signed it. No edits. And it was like a very much a trust relationship. And it is interesting like one of the things I really have appreciated about. So there's some downsides to Silicon Valley and our, you know, how insular the community is. One of the great parts though is just like the relationships you can forge over years. And for me, it meant Peter and I get sort of start on third base just because we've worked together a lot before. And so you just don't end up with a lot of the, there's no, no funny business in the fundraising process. No funny business in the board room. It's just like let's get to work. And it's fun. It was fun to, you know, sort of get the band back together there. The fun part for me is I had never worked with Ravi nor Neil before. And like, Clay and I just, it's like, it's just a, it's just a great board. Yeah. And like it's like people we seek out, advice from as opposed to people we report to, you know, every quarter. It's so it's amazing. How do you think about, because you're both like, no, like, you know, when, when opening out, we won't go back to the story. But like, you know, when opening out, I had it's like, oh my god, moment, like, Sam was like, you know, right, you got to, like, you're like the board member. And then you've also got a board that you're, so you're in both roles at once. How do you like make the most out of the board? Like, you know, obviously you've got these particular relationships. But like, what do you expect that relationship to look like? First, I really like written documents for boards over presentations, both as a board member and as like a founder of a company, because you end up letting people synthesize information ahead of the board meeting. So you end up with more substantive discussions on the boardroom. I've done this for the last two companies I've started. And it's just been great to set out a, you know, a board document. Sometimes people will comment about how to the meeting, but I actually think the main thing is it has been read and it's been read ahead of time. And then you end up with a meeting about the actual meeting potatoes of the topics. You're not like staring at a bunch of sales numbers for the first time. You're not running through slides. And I find it to be incredibly, I think most companies should be run this way. The other thing is really interesting is like, don't write it with AI. It's so funny to have to say that now, but I find that the process of writing, the process of the right is a process of clarifying your thoughts. And so for Clay and me, this is a process by which we synthesize what's been happening. And you know what you talk about it, it's actually write it and write it eloquently and concisely is incredibly important because it's essentially a way of, you know, it's like what's that famous line? If I had more time, I would have written a shorter letter like spend the time because that's actually how you can show respect to your stakeholders that you're thinking about the strategic issues going on your business. And the last thing to say is, board members aren't sort of single as you voted, but everyone has their strengths. And you know, at OpenAI, we've recruited a pretty diverse set of skills, eco-cultures, a professor at CMU, he's a specializes in among other things jailbreakings. So just like one of the experts on some of the more subtle safety aspects, Nicole Seligman was a great attorney. And you know, she's an expert in a lot of like legal issues and what's really nice is when you grow out of board, you know, beyond trivial initial investors too is find people that your management team will want to go to for advice. Obviously, the audit committee chair and your CFO have really unique relationship, but you really want folks like who's your head of sales going to go talk to you? Do you have someone who's like kind of been there or done that because you want them to have that kind of like, I always think of it as like who are the advisors you want to restaurant, your management team well. And I think a functional board really has those relationships. And then when you're in a board discussion, you have all these board members who have had lots of engagement with the company, but in a really valuable kind of targeted way. So I like to think of the board as a collection of people. Don't look at the individuals. It's a, it's a, the whole should be greater than the sum of his parts. Anything this year, you're particularly excited about that you can share? I think the real exciting part is going to be adoption and regulated industries. I think we're moving. be on like the early adopters to everyone. And so I think if we talk a year from now, you're doing the hard stuff. It's going to be like the really hard stuff. That's awesome. And if you want like a hot take, you know, I think my intuition is regulators will start asking operations. The idea that you have a human set of controls over a regulated process will start to feel like a risk rather than the risk being AI. And that's my, I don't know what happened this year, but I think that one. All right, well, I'll call you in a year and we'll do take two with us. That's that's good. All right. Thanks so much for your in this breath. This is great. Thank you for having me.