Uncapped #42 | Bret Taylor from Sierra

[00:00] 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 in real time. And like my hypothesis is the companies that figure it out first will move the fastest. It's fascinating to me. Brett, thanks a bunch for doing this with me. I'm super excited for it. Thanks for having me. [00:30] Apocalypse, if we can call it that. Sasmageddon. Sasmageddon. So basically, it's like, you know, in public markets, all of these companies are trading way down. You know, you go on X and everybody's talking about how, like, you know, software can now be written in two seconds. And so there's no moats anymore in software. And so it's leading a lot of people to ask, like, where does durability come from? And so I just wanted to sort of start with this topic because, you know, you've built your own companies. You've been the co-CEO at Salesforce. [01:00] AI startups there is, you're on the board of OpenAI. How do you see software like in this moment in February 26? [01:07] So first, I think the market isn't necessarily reflecting an indictment of individual companies. I think it's more of a... [01:15] uh broad view of like the the bigger questions you were saying i.e 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 um 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

[01:45] of famously powered in the early days of software. And then you end up with all the software as a service companies, SAP, Forkday, Salesforce, ServiceNow. 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 in a web browser, filling out forms. If you had to like synthesize pre-AI, like 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? [02:15] the system of the integrations, like what do you attribute the success of systems of record to? 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 know, if you wanted to, you know, 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, you know, the companies that sort of owned those [02:45] third-party company, they would often be a part of the ecosystem like Salesforce's AppExchange or whatever the marketplace equivalent is for SAP. And so you ended up with a lot of value in those 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

[03:15] So it sort of became the sun and the solar system, you know, 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, you know, so the larger you grow, the more salespeople you have. You can reach more and more people. Then there's the proverb, no one gets fired for buying IBM, which, you know, obviously a somewhat dated expression. [03:45] chose SAP, right? If you choose something new and it doesn't work perfectly, big trouble. Then you're the CI. So all those things sort of accrue. But then the question is now that all of a sudden that a lot of those start getting chipped away with AI agents. First, could you just vibe code it 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 the role of that system of record if AI agents are [04:15] around on an ERP system to onboard a vendor, if you just delegate to an AI agent to do it, [04:21] 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. [04:26] 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 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 how important is that gravity versus the gravity of the agents running around it?

[04:56] the database of leads versus the agent that generates the leads. And along 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 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 pretty well. And if you look at ITSM, which is like the place where it's [05:26] that all these questions start 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 to 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 foresee them becoming as powerful and strong as they are today. But it was good leadership, good technology. [05:56] But I don't think the market knows who is Siebel Systems and who is Microsoft in this landscape of software companies. Probably no one knows what Siebel Systems was. That was the company that Salesforce beat to become the cloud serum. So can you actually develop this ecosystem of agents around your platform? And will it become more valuable than the platform you had? And then on top of it, the existential risk of, 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,

[06:26] the sidelines. So you're going to let 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 build a [06:56] And 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. In the same way we talked about why a system of record is powerful. I think you could say the same logic for all the agents right 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. [07:26] So first, if you, there's this thing in enterprise software, there's a phrase called best of breed and best of platform. Best of platform means, hey, we're a Microsoft shop. We just buy Microsoft stuff. And it sounds silly, but actually there's a lot of logic to it. Like, A, you get sort of good procurement leverage. B, everything works together.

[07:56] the new platform, so when the web browser came out. It's much easier to get a 10x experience. A hundred percent. And also just think of the like pre and post web browser enterprise software, like you're running in like client server windows software and like it's a completely different skill set to make a web application as you and I know. 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. [08:26] the incumbents figure out the technology. And that's what was 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? 'Cause like I see these companies that have [08:44] let's call infinite resources, roughly speaking. They ought to be able to hire who they want. They ought to know what [08:50] 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 at a big company. Is it cultural? Is it systems? Is it? I like the phrase strategy tack. I don't remember who to attribute that to. We could pull up chat,

[09:20] what were your strengths can become weaknesses. So let's just take Siebel Systems and the birth of the web browser. They have a 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 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, [09:50] 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 like, you know, sound really clever because you're playing to your strengths. And in practice, if the technology wave is bigger than the category, which I think the web was, as an example, you end up sort of basically chipping away at sort of doing a pure play value proposition. It can also happen with business models though. [10:20] time you'd have perpetual license software and moving to software as a service, that's a huge change for a business to make. For your customers, it goes from being capex to opex. For you as a company, it changes routable revenue. I mean, Adobe, Chanteneau did this at Adobe. Very few companies could make that transition. [10:39] 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 overnight. So you can't just, it's so easy for a clever or Silicon Valley, like just pivot. I'm like, yeah, if you're a public company, you have to, you know, go in front of your investors every single quarter and be like, hey guys, I know our revenue just went off

[11:09] 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, [11:16] person, company succeed. Well, they have none of those, all of the advantages that you had all of a sudden become anchors that are holding you back from actually doing the right thing. And that's why... [11:28] I always like to remind our company, Sierra, that 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 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'd probably like search as a category, e-commerce as a category, digital payments, that's definitely going to happen. [11:58] I guess Amazon probably had around then. PayPal, probably not founded yet. The categories are obvious. The categories are like whether or not any of those founders existed, all of the would be winners. And it's the same now. It is 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, [12:20] remarkably powerful, the strengths of the incumbents start to wither in the face of the technical change. And that's why you tend to get new great companies. The companies that are enduring tend to be created in platform shifts more than any other time. I'd actually be curious on this topic 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. It looks like it works.

[12:50] you know, to a place, you know, at the 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, you know, wide fields, like, you know, the incumbents are sort of like categorically different. And so like, it seems like inevitable that we're going to have agents doing support. And so there's that. And then on the other side, you know, a lot of other companies see the same thing. A lot of other people have been building it. So before getting into the specifics, I'm just curious, like experientially day to day, [13:20] as [13:21] 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. [13:51] 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, you know, there will be a culling, you know, just as the market progresses. But it does feel, you know, quite competitive. I'll just sort of give you maybe like a quick glimpse of the past couple of years. So we've had a remarkable growth at CERA. We closed 100 million in seven quarters, 150 million in eight quarters, which has exceeded my expectations. [14:19] But this past year has felt like an inflection 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. You know, how can we trust this technology directly engaging with our customers or consumers? What are the risks?

[14:49] - The first question is, [14:50] Clearly, we need this yesterday. I mentioned this to you earlier, but over a quarter of our companies have 10 billion or more in revenues. 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 and saying, we've evaluated it. We know what we want. We've heard of you. We've done all this evaluation. Here's an RFP. Let's go. [15:20] and then it's a question of like, why Sierra? You know, why Sierra? And, you know, I'm happy to talk more about that. I mean, obviously love to, as Dr. Rekete, all the reasons we're 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. Well, I mean, so like, you know, they're like, yeah, I'm bought in on an agent. So like, why is it Sierra? Like, what have you found is like the most important thing that, [15:42] makes you win. So one thing we really did uniquely, the reason why over [15:46] quarter of our customers have over 10 billion revenue is we've tried to serve [15:51] more complex, more regulated industries. You know, we want, we serve most of the U.S. healthcare insurance market, as an example, and we serve U.S. banks, Spanish banks, U.K. banks, like, and these are companies that, you know, as you, if you know the industry, they're regulated by everybody. It's easy to make a demo on AI. It's why, like, you can go on X and just see a thousand demos and, like, demos are cheap, but making an agent sort of industrial grade is hard. And we've really uniquely been able to make agents that can actually have complex conversations. The other

[16:21] that we do really uniquely is in addition to i think having a really easy to use product is we help companies move faster um uh we went uh live with cigna in in two months that's crazy which is remarkable yeah i mean how big is cigna it's a fortune 20 um healthcare company and i was on stage with suchin who runs their ai practice there at the health conference and he was talking about this and part of that is like how can you show up at a like we're really great at ai cigna's [16:51] How do you bring those two together to move extremely fast? And so for a lot of our clients, like the reason they bring us on is like, can you help us move quick, move quickly? And that requires knowledge of AI, knowledge of business. And I think we sort of show up with a greater sense of maturity there. You mentioned like, you know, that the pricing scheme was one of the difficult things, you know, in the past. Yeah. You know, we don't have to like belabor it. But obviously, you know, going from, you know, just buying a license to a cloud subscription and now usage based is like the future. [17:21] What are you feeling as important in, you know, as you have created and probably continue to iterate on pricing? Like, what are the important levers for agent companies? [17:30] We do something specific at Sierra that I'm sort of an evangelist for, which is outcomes-based pricing. So it turns out in our industry, the outcome is usually well-defined. 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, your company has paid your salespeople commissions, right? Like that's where you can measure the outcome. You want to incentivize the outcome.

[18:00] is measurable and trackable. What an interesting opportunity to actually [18:04] charge for that. And if you look at the history of software, like let's take advertising, [18:09] 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. [18:17] And then you had enterprise software, you went 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... [18:30] What an interesting and accountable business model. And I think there's some challenges to it because, you know, you obviously put some revenue at risk, but I don't think most advertising tech people would say CPC ads put revenue at risk. It's 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. [18:51] And so my view is [18:53] 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 like, you know, if those are like... [19:08] you know, 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? [19:22] You care about the number and quality of the leads, right? And so you really don't care how many tokens the model uses.

[19:30] It's not obvious to me that 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 the cost to serve and the quality of the product. You could have a really good engineer write it or a really bad engineer write it, but it really covers 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. [20:00] And I think this is actually... [20:03] You know, I'm a huge believer in applied AI, but I actually define applied AI as can you describe your value proposition 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 CSAT score of 4.8 out of 5. You don't mention models. I mean, models are an input to that, but on output. [20:33] 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 applied AI company? Yeah. If you can, 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?

[21:03] or maybe the other format for it would be if you can't describe the outcome cleanly. Like, for example, code coding, which we probably think is super important. Obviously, it's like a little harder to say, [21:15] 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 at Apple that made people thought of for him saying, how many lines of code did you write? And this engineer infamously wrote a negative number because he just like refactored a bunch of stuff. It's my it's like the good analog historical analog for why tokens don't matter, because it was he was it was his way of saying, you know, [21:45] 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 essentially it is like in the world of software engineering, people [21:57] 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 [22:05] the customer product market fit. So it's a nuanced point, but I'll say where I see it might happen. So right now, [22:13] If you're evaluating a 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 effect as AI becomes prevalent is the reference point for its value will change. The thing I would say is that's true where you're thinking about a cost center.

[22:43] 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, a lead is a 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 costs. And there's a big part of it. But the other side of it is outcomes. And so could [23:13] 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 cause? I think probably you'll start looking for value is my guess. Will they all be the same? I don't know. You know, it's like, well, 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 of software engineering, it's been the opposite. Like every new model comes out and you're like, oh my gosh, it can write [23:43] complex software. My theory of that is it depends on what you're testing. So if you're using chat GPT for trip planning, you probably haven't seen a material change over the past year and a half because you reached sort of sufficient intelligence for trip planning a long time ago. If you're [24:01] Codex is like, [24:02] mind blowing right now. So I think one of the interesting things when I think about second, third order effects, and the progress of AI is [24:11] 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. 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? Like, are there still edge cases, last mile things like?

[24:28] that AI can't do still. [24:30] Yeah, we are, though. I imagine a lot of the [24:33] 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. [25:03] 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, if you are in a noisy environment, it constantly thinks it's being 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, OK, that's probably going to be a commodity two years from now, one year from now. I mean, who knows? [25:33] to do it because you need to be the best at every stage of your company's existence. And I think then the way we think about the world is we have a product, which is called Agent Studio or Agent OS. And we're going to make, in three years, we'll judge us by our product. And right now, we're probably, our clients don't really put it this way, they're judged by the technology. But if you go back to 1996, I remember when Netscape had a web server and Apache was new and done it. Like, no one cares how you serve web pages now. Like, it's a commodity. But at the time,

[26:03] And now you have increasingly higher order website building like Shopify. So I just think the AI agent market is going to take that progression. We're going from a tech-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. I mean, for that to be true, you just have to be in the middle of an insane rate of change. [26:33] everybody knows is just for two years, but it still matters nonetheless. It's crazy. I mean, you look at traditional, I'll just say enterprise software, consumer's a little different. But you think about you're building up this asset, your intellectual property is a fancy name for it. It's like, look at this platform that we're building. And like, we took so many years to build it. It's got all these features. [26:51] And now you're like, I'm building this and I'm 100% certain we'll throw it away in the next 14 months. But I have to build it because if I don't, I can't serve the bank that has a big business in Hong Kong or whatever it might be where we need Cantonese support. So... [27:06] That is the reality right now. And so I actually think... [27:10] I've been thinking a lot about this actually, just because I think it was Toby Lukey who sort of said something provocative around, you know, when generating the code is easy, it's almost like the system and the prompts. [27:22] that are actually the durable asset. Put another way, could you sort of terraform your software from scratch? It's the prompts that led to it. I do think that is sort of the software of 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

[27:52] are 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'm so excited to be a part of it. I mean, I think it's really fascinating. I think there's something... [28:06] really interesting about AI impacting the software engineering industry almost first and most. Because... [28:14] Like we're disrupting the craft of making what we're building in real time. And it's fascinating. It's like a fascinating time. I think there's a prevailing idea in tech that [28:24] AI is moving so fast that young founders have this massive advantage. And I mean this with no offense. [28:30] - You're telling me I'm old. I got it. - No, you're not old. You're not the youngest 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, 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 to have the advantage? You know, I'm always... [28:59] big believer. I don't know if it's a real quote, but some VC said, why was this founder 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 principled first principles thinking

[29:29] you know, dominates the market. You think there's a better, faster, cheaper way to do it. And because you don't have any of the hard-win lessons that can end up, you know, oversimplified analogies, [29:41] 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, WebVans Monzo or whatever it was like. I can't remember all the dot-com bubble companies. But I do think, especially in enterprise software, the experience that some of our team members bring, including the old man me and Clay, bring to it really does matter. [30:11] We can go into a bank or a healthcare payer, a healthcare provider, a revenue cycle management firm, or a big telecommunications company and understand their business. [30:22] We're working with one large medical device companies consolidating 40 of their call centers into one. And we can have a discussion about like the change management of doing that. And that's [30:33] not really a tech problem, but it does require, you know, understanding business. And I think there's like, we always joke at Sierra, it's like the Venn diagram, like there's a circle people understand like next generation of AI 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. Like we can actually help you go live. And I think the

[31:03] I'm curious if you can point to [31:06] what has created the lead you have so far. And obviously, I know you're just getting started, but at the moment you do, you know, you've you've 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 like foundational decisions that you've made or strategic approaches that, you know, 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. [31:31] um areas of investment not not they're not independent but they're like uh very different one is the product and one is our sort of our go-to-market and partnership model and they're both really intentionally built on the product side we've tried to balance ease of use and extensibility because when you serve really large companies with very comp that have been around for 200 years you know you need to work with mainframes you need to work with a thousand different systems [32:02] And so that's why you tend to have most, I'll say, enterprise software that's designed 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, one of the things I've just spent a lot of time thinking about is we're trying to have our cake and eat it too. Can you go live in two months and still be maximally extensible? And I'm really proud of the product that we've built. And some of that is born from experience of what does extensibility mean? [32:31] of what it means and have been able to accommodate

[32:35] like some fairly exotic deployment requests and still do it fast. That's really unique. The second thing is our go-to-market and partnership model. 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 the 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 deployed engineering in Silicon Valley. [33:05] technology. Like most of our clients build and maintain their agents themselves. It's 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 [33:26] Change management. It's a mix of both. You know, I don't know if it's 50/50. Do you know it as 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 [33:42] we want to be like 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, yeah, exactly. It's crazy what we're doing. And so 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 at Sierra. I mean, you can go in and like we're working with most of the health care insurance companies. Like you want to change health care costs and, you know, like what a cool vantage point to do.

[34:12] to. You said that it's not just support [34:14] agents now. Yeah. It's like, what else are you finding shoots in? I'll give you one of my favorite relationships with the rocket. So based in Detroit, remarkable story. Uh, you know, their founder has done more for Detroit than I think any one 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, uh, [34:44] 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've done 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 yeah to originating the mortgage to servicing that mortgage i think it's pretty cool and like they have an amazing cto named sean mohotra like pretty visionary uh and i love their ceo of roon too but it's like everything from finding a house [35:14] all the way through servicing. It's kind of what we believe a lot of businesses will do, is like look at their entire customer lifecycle, [35:21] from uh you know i'll say purchase consideration which is a fancy way of saying like browsing and i think homes are probably one of the more considered purchases that you could do through executing the purchase they're having issues with it all the way through you know 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 you know

[35:51] from satellite radio subscriptions to cable television subscriptions. It's pretty cool. I mean, it's like really over a billion dollars of mortgage folders a month. Basically just like all transactional communications eventually. The way I think about it is, [36:07] Your website is a technology, but your .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 interesting as agents go. It's often interacting with other agents, right? [36:37] you know, that's quite complicated. So our agent that's having the phone conversation when you're on the fender bender will interact with that. But it is almost the intersection of all of the that technology because it's sort of your front door. And our whole hypothesis is every company needed a website in 1997. Every company needs an agent in 2027. And like we want to be that that company. What's the nuance about like agent builders, though? Because I know you have like [37:07] - Yeah, I mean, [37:08] I've been surprised how many large-income enterprise software companies, their first foray into AI was... [37:16] you can an agent building tool it just feels inevitably to be a commodity in my mind because you may be making a website 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 brucelle which i love but it's not like there's a huge market for this stuff um and and in practice i think the same will happen with agent building um i think open ai will have a great tool probably all the foundation model

[37:46] like LangTain and LangGraph. [37:50] The idea that you have the right to win there, I don't know if anyone has the right to win there, just because it's just a technology. It's a horizontal technology, and I just believe in open source, and it's just going to become a commodity. So my belief where there's value is really going to be in agents 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. [38:20] have an agent that will do an antitrust review. You know, I think there'll be a finance agent that audits your financials. There'll be one that helps you onboard a, you know, supply chain vendor. There'll be one that, you know, if you just think about onboarding a new vendor, it's like there's a procurement process, there's a legal process, there's 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 improves the technology. [38:50] Aside from being the founder of Sierra, you're also [38:52] on the board of OpenAI. You're the chairman there. I wanted to ask you specifically about Codex. Like over the last, you know, couple of weeks, it's been unbelievable. It's like, you know, a curtain just came down. Did you expect this? Like, did you think that what has happened here was going to happen? Or like, when did you start to have an inkling that like code was going to go vertical like this? I'll say yes, I expected it just because, you know, being on the board of OpenAI, we talk a lot about it and all the labs, Anthropic and OpenAI in particular, talk a lot

[39:22] coding agents to help build AI. And certainly, building an AI researcher is an important part of building an AGI lab. The weird part about, for me, as someone who is a software engineer, [39:35] 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 and it turns out really good and not slop, but really good. It's an emotional experience, I think. I mean, for me, it was. It was just sort of like, holy shit. [39:55] Like, this is real. Yeah. [39:57] As you said, it's really over the past. [39:59] 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 an engineering team that had 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 [40:29] 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 CI, like true continuous delivery, because there's so

[40:59] 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 if we were talking, we could talk about what are the best practices to set up a software team that's optimized for this technology. And we'll know what those... [41:17] best practices are. And right now we're just figuring them out in real time. And like my hypothesis is the companies that figure it out first will move the fastest. And the other part of that is the companies that don't will move much more slowly. It's fascinating to me. And Andre Carpathia, he had a really interesting post about this too. Like I think a lot of folks who are sort of like in deep here have been thinking about it and it's fun to see the industry you [41:47] like software engineers on one end, and then say somebody who's like, [41:52] in some part of the country where AI has not yet gotten its tendons fully extended. There's a wide gap in people's current... [42:00] 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 posts going on right now 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 is solved. 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 strongman question there would be like, you know, people have been

[42:30] that there's going to be my brother, you know, these 10 person billion dollar companies. You know, is that are we at the precipice 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 dollar company, but I don't necessarily think will 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, T-Mobile. [42:55] 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. [43:13] All of them have access to AI. [43:15] Every single one. So the idea that you could deploy AI and, you know, not have to do things you're 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 against 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 teller machines were introduced to banks, the teller job went away. [43:45] And it's because, I don't know if 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 effect of the efficiencies of AI will be investment to compete, lower prices or customer acquisition or whatever it might be.

[44:15] they'll be way more productive. And so you just end up with way better software. Or you might have fewer engineers and more of something else. Or you might have more engineers. You know, I don't, I'm not sure. But it's the idea that like, it will be what it is today, but just more efficient, I think, is like a lack of imagination, in my opinion. The interesting thing though, is the other part of this, [44:34] Software engineering does feel special. And I think people extrapolating too much from software engineering are... It's a bit simplistic. You're 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... [45:04] digital, like exclusively. So, you know, 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, like that, that cargo ship still needs to be in the water. [45:17] And I always bring this up, you know, like just imagine you run a pharmaceutical company, you know, you can think about, you know, how to make a therapy. You probably need a wet lab. So, OK, well, that's intersects the real world. Maybe you could do robotics, but then you need a clinical trial and then, you know, so just a lot of the economy is like real. And so it definitely will change the way companies are built. But I think when people say everything will be 10 people, it's like maybe just the stuff that lives in bits. [45:43] Yeah, that's right. Which is a lot of the economy, but not the economy. Yeah, I mean, you know, it's easy to talk about this, but you're right. Like if you just like move around the physical world and you get off of, you know, this podcast and, you know, this computer I'm sitting in front of all this stuff and you got into the world and there's like, you know, trucks moving dirt around and people who need a building that has lights in it and all.

[46:13] I think you're probably right. [46:17] And so, you know, like robotics will have a big impact as well. But I think [46:21] people are thinking about this a bit simplistically, is 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 [46:34] perfectly exponentially. And so I just think people are a little bit simplistic. Do you think there's any cognitive things that are [46:41] immune from intelligence. So 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 you need 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, [47:09] somebody who's very deep with open AI. Like, do you think that these other parts of intelligence also [47:15] you know, go the way of AI. [47:17] I don't know if... [47:19] 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 going to wear to school, I don't think they will consider chat GPT's opinion. They care more about what the person in class next to them is wearing. Yeah. [47:41] Similarly, if you go to the most elite, competitive, college preparatory...

[47:48] school or the worst school in 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. And like, it's all relative and it's all very local and 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, you all see these things that go around online where people are sort of [48:18] like the bicycle and, you know, we've been weaker than machines for my entire life. And I don't, [48:25] I don't think it like it doesn't make me feel like weak as a person. And I think we 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 codecs writing code that was high quality was an experience because, you know, I might have some of my identity tied up in that task. Yeah. And the next day I woke up and I'm using it as a tool and I can make better software. I'm like, this is great. [48:55] code. 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 your identity. And so I think I actually am quite optimistic that we will be human. We will all be status-seeking animals. We 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,

[49:25] That's my view on it. And 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 it will be largely good for humanity. I have a friend who believes that like as this kind of progress, you know, we're already, everybody's already completely addicted to their phones and it's a disaster and whatever. Now you have all this AI 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. [49:55] and like [49:56] 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 like parents were revolting on social media, like about social media for their kids and like a bunch of schools and all the parents like nobody take a phone, like everybody agree 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, [50:17] gets sharpened i hope so actually one of the things you know i i love the iphone is one of the greatest inventions of uh 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 yeah you know as a consequence of this and that is the [50:42] a purpose of technology. So, you know, just like the Industrial Revolution had Luddites and globalization led to job loss in the Rust Belt of the United States, but certain goods got less expensive in 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

[51:12] for me, and I think for like, if you're thinking about how does 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 imagining being an accountant before Microsoft Excel and after Microsoft Excel. Yeah, so much of the act of an 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 you provided didn't change. But actually, [51:42] 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 anxiety. Yeah, it makes sense. My last question about AI, there was a shot 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 [52:12] business model? What are the trade-offs of all of this? You've obviously, like, you know, you have experience with social networks and a lot of different pricing, you know, models, you know, open AI 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. [52:33] 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 did they even make money? And I laughed just because I was like, I think I listened to the Acquired podcast. It's 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

[53:03] or 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 you, which was sort of the backhanded implication, but I just think it's not true. And so I actually think if ads are clearly labeled and not getting the experience, [53:25] I think it's really aligned with the OpenAI 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 the Hippocratic Oath, first do no harm, the job of a doctor to cure you. So then after you say, okay, it's safe, [53:40] 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 PBC board, like... [53:51] Our mission matters and being able to offer it for free widely is a huge part of that. And we need to be able to afford that. [53:58] I think it's not only... I just... I find it... [54:02] 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 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 what we're in 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

[54:32] hours a month on stuff, but they really want these services. Like, you know, if the whole world had to pay for Google, like that'd be a worse world. Like it's really good that everybody has access. I just think it's important we do it well. Yeah. Yeah. People want good ads. Like I like good ads. Like I would actually, if people bring me the right product, I'm like, that's really nice. This is the other part of it. It's 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, I find the discussion not, not particularly authentic. Yeah. Yeah. The last thing I wanted to ask you about was [55:02] 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 or capitalizing and you know i'm asking just because this is a podcast has a lot of you seeing it so i gotta have a little flourish yeah totally um we have uh three members of our board which are sort of represent kind of [55:32] Peter Fenton from Benchmark, Ravi Gupta, who just left Sequoia, though he's still a venture partner there, and Neil Mehta from Green Oaks. Just a fantastic group of people. And chose them all, both for the firm and the person. But notably, like Peter, I've worked with both my previous companies. So, you know, our first round of financing, I didn't talk to anyone else. And

[56:02] 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 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 could sort of start on third base just because we've worked together a lot before. And so you [56:32] the funny business in the boardroom, it was 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. But 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 great board. Like, it's just like people we seek out advice from as opposed to, [56:51] people we report to, you know, every quarter. So it's amazing. How do you think about, because you're both like, no, like, you know, when OpenAI, we won't go back to the story, but like, you know, when OpenAI had its 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, [57:12] 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 in the boardroom. I've done this for the last two companies I've started, and it's just been amazing.

[57:39] Great to send out a, you know, a board document. Sometimes people will comment ahead of the meeting, but I actually think the main thing is it's been read and it's been read ahead of time. And then you end up with a meeting about the actual meat and 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. 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 that is really interesting is like, don't write it with AI. It's so funny to have to say that now. [58:09] - The process of the writing? - The process of the writing 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 it, you talk about it, but to 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've written a shorter letter, like spend the time. 'Cause that's actually how you can show respect to your stakeholders that you're thinking about the strategic issues going on in your business. [58:38] And the last thing I'd say is board members aren't sort of single issue voters, but everyone has their strengths. And, you know, at OpenAI, we've recruited a pretty diverse set of skills. Zico Coulter is a professor at CMU who specializes in, among other things, jailbreaking. 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.

[59:08] 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 a really unique relationship. But you really want folks like who's your head of sales going to go talk to? 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 surround your management team? Well, and I think a functional board really has those relationships. And then when you're in a board [59:38] 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? [59:51] I think the real exciting part is going to be [59:55] adoption and regulated industries. I think we are moving beyond the early adopters to everyone. And so I think if we talk a year from now-- - You're gonna be doing the hard stuff. - It's gonna be like the really hard stuff. - That's awesome. [1:00:09] If you want a hot take, I think, [1:00:11] My intuition is regulators will start asking for agents. 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 if it'll happen this year, but I think that will happen. All right, well, I'll call you in a year and we'll do take two of this. That sounds great. All right, thanks so much for doing this, Brett. This was great. Thanks for having me.