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- [00:00]
A
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 and 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.
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B
Brett, thanks a bunch for doing this with me. I'm super excited for it.
- [00:23]
A
Thanks for having me.
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B
So you're one of the best people to ask this following question, which is what is your view on the SAS apocalypse, if we can call it that.
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A
Sas, Magedon, Sasmageddon.
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B
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, you're now building like one of the fastest growing AI startups there is. You're on the board of OpenAI. How do you see software like in this moment in February 26th?
- [01:08]
A
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, 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 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 databases that Oracle sort of famously powered in the early days of software. And then you end up with all the software as a service companies. SAP Workday, 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.
- [02:05]
B
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 to?
- [02:20]
A
So I think the reason why a system of record has always been the most valuable is the anchor tenant of your technology deployments. If you wanted to create a workflow for quote to cache or something like that, you had to integrate with your ERP system and your CRM system. So as a consequence, the companies that owned 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 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 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, 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 is somewhat dated, you know, expression, but it sort of was like, hey, if you're going to put in a new ERP systems, no one's going to blame you for choosing SAP, because everyone chose SAP.
- [03:47]
B
If you choose something new and it doesn't work perfectly, big trouble.
- [03:49]
A
Then you're, you're this. Yeah. So all those things sort of accrue. But then the question is now that all of a sudden 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 doing most of the work? So rather than people clicking around on an ERP system to onboard a vendor, if you just delegate to an AI agent to do it, yeah, 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.
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B
In other words, like, does a. Does a system of record have a place in the world if nobody logs into it?
- [04:40]
A
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 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 and tracked 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. I just know CRM systems pretty well. And look at itsm, which is like the Play Source now Plays or ERP systems, which is workday, SAP, Oracle, et cetera. 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. No, I didn't foresee them becoming as powerful and strong as they are today. But it was good leadership, good technology. 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. Siebel Systems was that was the company that Salesforce beat to become the cloud CRM. 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, I'm going to sit on the sidelines on this. I'm going to let the market play out a little bit. And I Think that's sort of what's going on.
- [06:32]
B
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 of or is it easier for a modern agent to go say, well I'm going to go build a database at some point because I could do that and I've got the customer relationship. And how do you think about what creates the rights to expand?
- [07:03]
A
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 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 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, you
- [07:45]
B
don't have to deal with a ton of people.
- [07:47]
A
There's probably some benefits, all sorts of things.
- [07:49]
B
Yeah.
- [07:49]
A
What ends up happening when new technologies come out as you the pendulum swing from best of platform best to best of breed. Because when the new lister when the web browser came out, it's much easier
- [07:59]
B
to get a 10x experience, 100%.
- [08:01]
A
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 incumbent 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's 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.
- [08:39]
B
You know, I don't Understand why is that? Like what's the real reason for it? Because like I see these companies that have 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. Why is it so hard for let's say legacy companies to catch up quickly versus an AI startup with 50 engineers seems to outperform the teams that are 10 or whatever times bigger at a big company. Is it cultural? Is it systems? Is it.
- [09:11]
A
I like the phrase strategy tack. I don't remember who to attribute that to. We could pull up ChatGPT 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 Siebel Systems. In 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 two and that's our strength, we should play to that strength. And you start basically making all these decisions that sound really clever because you're playing 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. So you know, in that 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 ratable revenue. I mean Adobe Shantanu did 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 incentives of salespeople. There's a strategy tax because you don't want to just have your business collapse overnight. It's so easy for clever or 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 our 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 had 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 you, our company Cira, 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 probably like search as a category, E commerce is a category. Oh, digital payments, that's definitely going to happen. I don't know which. Google hadn't been found yet. 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.
- [12:10]
B
And it's the same now.
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A
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 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 like the companies that are enduring tend to be created in platforms, shifts more than any other time.
- [12:35]
B
I'd actually be curious on this topic of sort of, you know, there's these obvious things and within AI, I would say, you know, not to discredit your insight, but support I would count as an obvious thing. Like in a good way, like it looks like it works and you did it, you know, early enough that you were able to get, 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 and a lot of other people have been Building it. So before getting into the specifics, I'm just curious, like experientially, day to day, does, does your sort of operation of the company feel competitive or like wide open?
- [13:26]
A
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.
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B
So you feel like there's lots of demand and there's like a contest with each sort of situation.
- [13:43]
A
Yeah, that's right. The way it feels, it feels like there's sort of too much capital available. Put it another way, there's always going to be competition in 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 culling just as the market progresses. But it does feel quite competitive. I'll just give you maybe a quick glimpse of the past couple years. So we've had a remarkable growth rate at net zero. We closed 100 million in seven quarters, 150 million in eight quarters, which has exceeded my expectations. 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. How can we trust this technology directly engaging with our customers or consumers, and what are the risks? Now the conversation is 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 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. 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? You know, why Sarah? And you know, I'm happy to talk more about that. I mean, obviously love to. As an artist, I could tell you all the reasons are 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.
- [15:36]
B
Well, what I mean, so like, you know, they're like, yeah, I'm bought in on an agent. So like, why is it Sierra? What have you found is like the most important thing that makes you win?
- [15:43]
A
So one thing we really did uniquely at Sierra, the reason why over quarter of our customers have over 10 billion revenue, is we've tried to serve more complex, more regulated industries. We serve most of the U.S. healthcare insurance market as an example, and we serve U.S. 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 X and just see 1000 demos and 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 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 Cigna in two months.
- [16:32]
B
That's crazy.
- [16:33]
A
Which is incredible.
- [16:35]
B
Yeah, I mean, how big is Cigna?
- [16:36]
A
It's a Fortune 20 healthcare company. And I was on stage with Sachin, who runs our 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 is really great at healthcare. 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 and knowledge of business. And I think we sort of show up with a greater sense of maturity there.
- [17:05]
B
You mentioned like, you know, that the pricing scheme was one of the difficult things, you know, in the past year. We don't have to belabor it, but obviously going from just buying a license to a cloud subscription and now usage based is 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?
- [17:31]
A
We do something specific at Sierra that I'm sort of an evangelist for, which is outcomes based pricing. So it turns out our industry, the outcome is usually well defined. So in a service context is, could the AI 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's paid your salespeople commissions, right? Like that's. If you can measure the outcome, you want to incentivize the outcome. The interesting thing about agents is they're 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 like let's take advertising, we went from impression based ads to cost per click ads to now for mobile ads you can do pay per install, at least that's my understanding. 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, 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. It's like the opposite, right? Because the closer you get to the outcome, the more valuable it is for the company, so they're 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.
- [19:03]
B
Why is it better than token based? So like, you know, if those are like, 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?
- [19:16]
A
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 the cost to serve and the quality of the product. You know, you can have a really good engineer write it or a really bad engineer write it, so you really cause 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, 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 not output. If you have to mention token utilization, it's 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 applied AI company? If you can. If you don't have to talk about
- [20:54]
B
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 cleanly, like for example, code coding, which we 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?
- [21:23]
A
Yeah, so I mean, there was 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 fill out a form saying how many lines of code did you write? And this engineer infamously wrote a negative number because he had just like refactored a bunch of stuff. 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. You're 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 the customer product market fit. So it's a nuanced point, but I'll say like where I see it might happen. So right now 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. 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, a lead is a leader 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 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 costs? I think probably you'll start looking for value, is my guess. Will they all be the same? I don't know. It's like the. Well, I was just reflecting on. Over the past year there has been all these articles about has AI progress slowed down? And then in our world of software engineering, it's been the opposite. Every new model comes out and 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 ChatGPT 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 using AI to write Rust code, Codex is like mind blowing right now. So I think one of the interesting things when I think about like second, third order effects and like the progress of AI is will you pass the horizon where every model is sufficient in that task? And then there'll be some things where the frontier continues to move. And it's hard to imagine, but it's just like we're in a crazy time.
- [24:22]
B
Where are we at with support agents right now? Are there still edge cases, last mile, things that AI can't do still?
- [24:31]
A
Yeah, we are, though I imagine a lot of the technical problems as opposed to production problems will become easier. But there's a lot of them still. So we at Sierra support most spoken languages in the world. And if you want to support Cantonese and Tagalog, most of the good voice models don't come from the traditional Western model companies. Similarly, one of our clients is Safelight Autoglass. It's like roadside assistance. And it turns out that car horns, background noise, kids talk in the background 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, 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, okay, that's probably going to be a commodity two years from now, one year from now. I mean, who knows? 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 os and we're going to make in three years, you'll judge us by our product. And right now we're probably our clients, don't really put it this way, judged by the technology. But if you go back to 1996, I remember when Netscape had a web server and Apache was new and da da, da, like no one cares how you serve web pages now, it's a commodity. But the time that was what you sold. And now you have increasingly higher order website built in like Shopify. So I just think the AI agent market's going to take that progression. We're going from a tech centric sales cycle to a product centric sales cycle.
- [26:15]
B
It's interesting that you're obviously having to be the best at something that you know is going to get commoditized.
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A
Yeah.
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B
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 entire 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.
- [26:37]
A
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. There's 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 and it's got all these features and now you're like, I'm building this and I 100% certain we'll throw it away in the next 40 days, same month. 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 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 Lucky who sort of said something provocative around. When generating the code is easy, it's almost like the system and the prompt that are actually the durable asset. Put it 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, if you think about a product requirements document versus the code, what percentage of the emergent product that comes out of is encoded? Almost like 90%. Like a lot of the little details. Yeah. 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 think it's really fascinating. I think there's something really interesting about AI impacting the software engineering industry almost first and most, because we're disrupting the craft of making what we're building in real time. And it's fascinating. It's a fascinating time.
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B
I think there's a prevailing idea in tech that AI is moving so fast that young founders have this massive advantage. And I mean this with no offense, you're not old, but you're also,
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A
you're
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B
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, do you think by and large young founders have the advantage? What does it take for more experienced founders to have the advantage?
- [28:58]
A
I'm always a 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 that a lot of young founders have. You just don't know why this messy bad product 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 won lessons that can end up oversimplified analogies keeping you from actually taking that leap, you can end up with Tony made doordash and didn't care about say Webvan's Monza or whatever. I can't remember all the 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. 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 or healthcare provider or revenue cycle management firm or a big telecommunications company and understand their business under we're working with one large medical device company is consolidating 40 of their call centers into 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 there's like we always joke at Sira, 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 experience has benefited us.
- [31:03]
B
Yeah, I'm curious if you can point to 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 pulled away in a big way and I'm sure there's a lot of just like daily blocking and tackling but. 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.
- [31:28]
A
I think there's two almost independent areas of investment. They're not independent, but they're very different. One is the product and one is our go to market and partnership model. And they're both really intentionally built on the product side. But we've tried to balance ease of use and Extensibility because when you serve really large companies that have been around for 200 years, you need to work with mainframes, you need to work with 1,000 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 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, 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 that we've built. And some of that is born from experience of what does extensibility mean? And I think we have an opinionated view of what it means and have been able to accommodate 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 want to be able to work with the largest companies in the world and have 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. We don't to call it that. And it's a very unique model because it's not all about technology. 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. We're not going to let you fail. 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.
- [33:24]
B
How much of that is technical versus change management?
- [33:27]
A
It's a mix of both. I don't know if it's 50 50,
- [33:31]
B
but do you as two people or it's one person who does both?
- [33:33]
A
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,
- [33:51]
B
like a forward deployed change management engineer.
- [33:54]
A
Yeah, yeah, exactly.
- [33:55]
B
It's crazy what we're doing.
- [33:56]
A
And what's really neat about it is if you're like a really talented technical person who wants to transform an industry, you can do it at Sierra. I mean, you can go in and like, we're working with most of the healthcare insurance companies. Like, you want to change healthcare costs and, you know, like, what a cool vantage point to do. It's. We've been able to attract some really remarkable people too.
- [34:13]
B
You said that it's not just support agents now. Yeah, it's like, what else are you finding shoots in?
- [34:18]
A
I'll give you one of my favorite relationships with the Rocket. So based in Detroit. Remarkable story. You know, their founders 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 bought a mortgage servicing 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 did of this mortgage servicing 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. And like, they have an amazing CTO named Sean Mohotro, like, pretty visionary. And I love their CEO of Rune 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 like, look at their entire customer life cycle from, you know, I'll say purchase consideration, which is a fancy way of saying like, like browsing. And I think homes are probably one of the more considered purchases that you could do through executing the purchase, through having issues with it, all the way through retention. And for example, a lot of our telecommunications customers, their AI agent is actually doing negotiations. So you've probably negotiated your cable bill at some point, probably. And so our AI agents are doing billions of dollars of negotiations for everything from satellite radio subscriptions to cable television subscriptions. It's pretty cool. I mean, it's like really over a billion dollars in mortgage flow, basically, just
- [36:03]
B
like all transactional communications eventually.
- [36:06]
A
The way I think about it is 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, it's interesting as agents go, it's often interacting with other agents. Right. If you think about a home and auto insurance company, you may have a claim adjudication agent 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 that technology because it's 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.
- [37:01]
B
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.
- [37:08]
A
Yeah, I mean I've been surprised how many enterprise, like large income enterprise software companies, like their first foray into AI was you can, an agent building tool. It just feels inevitably to be a commodity in my mind because maybe making a website was hard in 1995, but today there's a million ways to make a website. Most of them are open source. And you have cool companies like Vercel, 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 OpenAI will have a great tool. Probably all the foundation model companies will, there'll be open source packages like LangChain and Langgraph. 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. And you know, they have an agent that will do an antitrust review. You know, I think there'll be a finance agent that audits your financials. There will 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, the 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. Yeah. Agent building is not a product. Agent building is a technology. Yep.
- [38:48]
B
Speaking of the platforms, aside from being the founder of CR, you're also on the board of OpenAI. You're the chairman there. I wanted to ask you specifically about Codex. Like, over the last 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?
- [39:11]
A
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, anthropic 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 AGI lab. The weird part about, for me, as someone who is a software engineer, I didn't feel it until I used it. So you, like, you can talk about it all the time and then like the first time you one shot something and it turns out like really good and not like slop, but like really good. It's an emotional experience, I think. I mean, for me it was, it was just sort of like, holy shit, like, this is real.
- [39:56]
B
Yeah.
- [39:57]
A
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 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 worked in 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 CI like true continuous delivery, because there's so many implied processes that are incompatible with that. It's 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 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 Andrej Carpathia, he had a really interesting post about this too. 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 love sort of flipped on its head in real time.
- [41:46]
B
Well, it's interesting because I think people 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 its 10 points fully 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 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, salt. People, you know, use the word software as solved. I don't know if it's that, but I'm curious if you have a view on like if codec and club code and sort of like the latest in coding, is that going to change the way companies are built? One easy strawman question. There would be like people have been claiming that there's going to be, my brother, these 10 person, billion dollar companies. Are we at the precipice of that? Does that make sense? Are there other changes? What's going to happen now?
- [42:41]
A
There probably will be a 10 person, billion dollar company, but I don't necessarily think it'll be the norm. And the reason for that is competition. If you imagine the mobile phone market in the United States, there's three main competitors, Verizon, AT&T 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.
- [43:04]
B
They can't make more of us they
- [43:05]
A
can't make more of us. They can build up their network. They can do other pricing, 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 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 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 we spoke about this earlier. But it's the reason why when automated teller machines 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 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 onto 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.
- [44:14]
B
So we want to have fewer engineers per company. They'll be way more productive. And so you just end up with way better software.
- [44:18]
A
Or you 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 interesting thing, though, is the other part of this. Software engineering does feel special. And I think people extrapolating too much from software engineering are. It's a bit simplistic.
- [44:42]
B
You're like, the same thing might not happen to every other function.
- [44:44]
A
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 manipulating sort of digital things. And you could imagine AI automating that. Most of the economy isn't digital 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, like that, that cargo ship still needs to be in the water. 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, okay, 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, 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
- [45:42]
B
just the stuff that lives in bits.
- [45:43]
A
Yeah, that's right. Which is a lot of the economy, but not the economy.
- [45:46]
B
Yeah, I mean, you know, it's easy like 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. There's like a lot of physical things. And I, 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.
- [46:14]
A
I think you're probably right. And robotics will have a big impact as well. But I think 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 that can't be absorbed by the economy perfectly exponentially. And so I just think people are a little bit simplistic.
- [46:38]
B
Do you think there's any cognitive things that are immune from intelligence? Dylan Field, when he was on this podcast, gave an example of Brad Summer as something where he was just like, that would have been such an insanely hard call for an AI to make. And you needed so much context and taste and opinion. Where my head was going is, okay, so coding is whatever's happening there is happening there. But what about brand or storytelling? And I'm kind of asking you this both as an operator and as somebody who's very deep with OpenAI, do you think that these other parts of intelligence also go the way of AI?
- [47:17]
A
I don't know if taste is necessarily related to intelligence. 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 would consider ChatGPT's opinion. They care more about what the person in class next to them is wearing. Similarly, if you go to the most elite, competitive college preparatory 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, I was. You all see these things that go around online where people are sort of lamenting older technology like the bicycle. And you know, we've been weaker than machines for my entire life. And I don't think it like, it doesn't make me feel like weak as a person. And I think this for the first time we have computers that are going to be more intelligent than us. I think there will. The emotions I had about Codex writing code that was high quality was an experience because 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 now I can make better software. I'm like, this is great.
- [48:51]
B
Probably actually a good self actualization anyway to go through that and be like, oh, not my ability to code.
- [48:56]
A
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 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, I think, think humanity will be just fine. 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 will be largely good for humanity.
- [49:35]
B
I have a friend who believes that 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's, that might be an interesting call. I do think that people will kind of hit a tipping point with a lot of this stuff where all of it will happen. Intelligence will get so good. And then people will sort of just be like enough of all of this. And hopefully there's a big screen time reduction. And it's like parents were revolting on social media about social media for their kids and a bunch of schools and all the parents like nobody take a phone, everybody agree to it. So I think that'll be an interesting thing of does humanity, is there an essential humanity that gets sharpened?
- [50:19]
A
I hope so. Actually one of the things 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 now that AI can talk to you and human computer interfaces. So this is my point. I actually think hopefully humanity can become more self actualized as a consequence of this. And that is the a purpose of technology. So 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. 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 does this impact me is like have a more flexible view of your own identity. How you do it every day doesn't define you. I always like the metaphor because it was so obvious before and after imagining being an accountant before Microsoft Excel and after Microsoft Excel. So much of the act of being an accountant was like adding up numbers and things. 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 the act of doing it is completely different. Like the skill set is 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 anxiety ridden.
- [51:49]
B
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 was they were good ads, they were funny. But then it 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 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 have experience with social networks and A lot of different pricing models, you know, OpenAI well, you know, how to consume AI. So I'm just curious how you think about this and what is the right thing when you consider a lot of these dimensions.
- [52:31]
A
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 and 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 laugh just because I was. 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 and has created an economy around it for demand fulfillment advertising. I think there's reasonable criticisms of advertising 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. I actually think if ads are clearly labeled and not in the experience, 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, how do we widely distribute it? And I think we have an obligation be an emission driven, you know, I'm the chair of the foundation and on the PBC board. Yeah. Like 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. Yeah, I think it's not only I just, 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 taint the experience.
- [54:10]
B
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 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, 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.
- [54:39]
A
I just think it's important we do it well.
- [54:41]
B
Yeah.
- [54:41]
A
Yeah, we will.
- [54:42]
B
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.
- [54:47]
A
This is the other part of it is, 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.
- [55:00]
B
The last thing I wanted to ask you about was how you've chosen to sort of finance the company. And I guess I'm curious about three parts, which are how you got started and working with Peter Fenton, and then what you've done since then to date, and what's been important for you. And then I'm curious, just as you think about the future, what's important to you as you think about other partners or capitalizing? And. And I'm asking just because this is a podcast that has a lot of VC in it, so I got to have a little flourish.
- [55:25]
A
Yeah, totally. We have three members of our board which represent kind of like our three rounds of investment. So Peter Fenton from Benchmark, Ravi Gupta, who just left Sequoia there, 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 Peter. I've worked with both my previous companies, so our first round of financing, I didn't talk to anyone else, and introduced him to Clay, my co founder, who 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. 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 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'd worked together a lot before. And so you just don't end up with a lot of the. There was no funny business in the fundraising process, no funny business in the boardroom. It was just like, let's get to work. And it's fun. It was fun to 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 people we report to, you know, every quarter. It's so. It's amazing.
- [56:55]
B
How do you think about. Because you're both, like, known. Like, you know, when. When opening, I. We won't go back through the story, but, like, you know, when OpenAI had. It's like, oh, my God moment. Like, Sam was like, you know, Brett, you gotta, like, you're like the board member. And then you've also got a board that you're.
- [57:08]
A
So you're.
- [57:08]
B
You're in both roles at once. How do you, like, make the most out of a board? Like, you know, obviously you've got these particular relationships, but, like, what do you expect that relationship to look like first?
- [57:18]
A
I really like written documents for boards over presentations, both as a board member and as 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 great to send out a board document. Sometimes people will comment. Both had at 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 staring at a bunch of sales numbers for the first time.
- [57:56]
B
You're not running through slides.
- [57:57]
A
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 don't write it with AI. It's so funny to have to say that now, but I find that the
- [58:10]
B
process of the writing.
- [58:11]
A
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 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 in your business. And the last thing I'd say is Board members aren't sort of single issue voters, but everyone has their Strengths. And 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 she's an expert in a lot of legal issues. And, and what's really nice is when you grow out a board beyond sort of your 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 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 kind of been there, 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 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. The whole should be greater than the sum of his parts.
- [59:48]
B
Anything this year you're particularly excited about that you can share.
- [59:52]
A
I think the real exciting part is going to be adoption in regulated industries. I think we, we are moving beyond the early adopters to everyone. And so I think if we talk a year from now, you're going to
- [60:06]
B
be doing the hard stuff.
- [60:07]
A
It's going to be the really hard stuff.
- [60:08]
B
That's awesome.
- [60:09]
A
And if you want a hot take, I think 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. I don't know if it'll happen this year, but I think that will happen.
- [60:29]
B
Well, I'll call you in a year and we'll do take two of this.
- [60:31]
A
That sounds great.
- [60:32]
B
All right. Thanks so much for doing this, Brett. This is great.
- [60:33]
A
Thanks for having me.