The Exchanges, every show

Q Can you talk to me about what that is, how it was built, what it does? I'm just intrigued to see how companies change and how they operate.

A Yeah. It's one of the more significant developments in how we run the company of the last six or nine months. And we, we began by building what we call our MCP gateway. This is a single MCP server. That aggregates all of the main systems and services that we use to run the company. And so you can add this single gateway to your Claude instance, to your Codex instance, uh, and, uh, indeed to, to Pinecone, and basically via any one of those agents have full access with the permissions, of course, that you, as, as an individual at the company, you can't read someone else's documents, but you can read your own. You can read your own Slack messages. And it's kind of like having superpowers, right? You can, you can interrogate in essence, the entirety of the company, all information that is published, whether it's, um, slack messages or presentations or operating reviews, uh, and, and so on, and use access to all of that information to better reason, make decisions, get things done. Pinecone, uh, of course incorporates that MCP gateway, but then is a Purpose built harness for all of Sierra. So Pinecone knows how to build Pinecone. So it, there's a whole harness around the engineering of Pinecone and our engineers there are phenomenally productive. We have a whole harness around the core of our platform, our agent architecture, agent studio, where you build and deploy agents, speeding…

AI assessment note: “began by building what we call our MCP gateway. This is a single MCP server”

Q to the way that you run the company. It was so important in so many of my conversations before this. If we start with like the board meetings, I, I suppose as I said, many of the ambassadors, Every six weeks, not every quarter. Can you talk to me about your biggest lessons on how to really get the most out of your board and run the best board meetings?

A We do a couple of things. You mentioned the, uh, six week cadence. We have kind of a tick tock, a three hour meeting and a one, a one and a half hour meeting. We've done this since the beginning of the company because we could just see if you're on the AI time clock, it moves a lot faster. Things are changing, and most recently we came back from winter break, and suddenly coding agents were amazing. You had, right, Claude, four, five, Codex, five, two. There is a fundamental step change in the capabilities of these models. It changed our approach to software development. It changed our approach to the core product, and so having a cadence where you can take in information, even from the last six weeks, kind of update your priors, and then change course, I think is quite important. As for running the board meetings themselves, we don't have board decks, we have board memos. So Brett and I write a usually six to 10 page memo. There's a saying, writing is just thinking on paper, and I think it's very hard to hide from writing. And so getting our thoughts clearly out onto paper, sending that in advance, giving each of our board members some kind of soak time to, to think through the issues and come prepared. Rather than be like presented to and managed, I think is a big part of it. And then the contents of the board letters themselves, I think is notable. We've done quite well in o…

AI assessment note: “we don't have board decks, we have board memos”

Q Did you consider training owned models and what was the thought process around not?

A That's a great question. We did briefly And discarded it. Uh, if you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models. Character, inflection, adapt, great people at these companies, but the capital expense, uh, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work. Just doesn't work for any, but a small number of companies. And so our calculus was for areas that are deeply capital intensive. How do we slipstream behind the investments that the labs, that, uh, the hyperscalers are making and take as much as we can off the shelf while still being willing to, uh, engineer more deeply. So today we have a set of our own proprietary fine tune models. But these are fine tunes on top of open weights models. So we're not going, you know, all the way down to the, uh, you know, mega cluster training runs. Um, and I, I think it's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.

AI assessment note: “We did briefly And discarded it.”

Q to the way that you run the company. It was so important in so many of my conversations before this. If we start with like the board meetings, I, I suppose as I said, many of the ambassadors, Every six weeks, not every quarter. Can you talk to me about your biggest lessons on how to really get the most out of your board and run the best board meetings?

A We do a couple of things. You mentioned the, uh, six week cadence. We have kind of a tick tock, a three hour meeting and a one, a one and a half hour meeting. We've done this since the beginning of the company because we could just see if you're on the AI time clock, it moves a lot faster. Things are changing, and most recently we came back from winter break, and suddenly coding agents were amazing. You had, right, Claude, four, five, Codex, five, two. There is a fundamental step change in the capabilities of these models. It changed our approach to software development. It changed our approach to the core product, and so having a cadence where you can take in information, even from the last six weeks, kind of update your priors, and then change course, I think is quite important. As for running the board meetings themselves, we don't have board decks, we have board memos. So Brett and I write a usually six to 10 page memo. There's a saying, writing is just thinking on paper, and I think it's very hard to hide from writing. And so getting our thoughts clearly out onto paper, sending that in advance, giving each of our board members some kind of soak time to, to think through the issues and come prepared. Rather than be like presented to and managed, I think is a big part of it. And then the contents of the board letters themselves, I think is notable. We've done quite well in o…

AI assessment note: “we don't have board decks, we have board memos”

Q Did you consider training owned models and what was the thought process around not?

A That's a great question. We did briefly And discarded it. Uh, if you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models. Character, inflection, adapt, great people at these companies, but the capital expense, uh, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work. Just doesn't work for any, but a small number of companies. And so our calculus was for areas that are deeply capital intensive. How do we slipstream behind the investments that the labs, that, uh, the hyperscalers are making and take as much as we can off the shelf while still being willing to, uh, engineer more deeply. So today we have a set of our own proprietary fine tune models. But these are fine tunes on top of open weights models. So we're not going, you know, all the way down to the, uh, you know, mega cluster training runs. Um, and I, I think it's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.

AI assessment note: “We did briefly And discarded it. Uh, if you recall at the time”

Q Can you talk to me about what that is, how it was built, what it does? I'm just intrigued to see how companies change and how they operate.

A Yeah. It's one of the more significant developments in how we run the company of the last six or nine months. And we, we began by building what we call our MCP gateway. This is a single MCP server. That aggregates all of the main systems and services that we use to run the company. And so you can add this single gateway to your Claude instance, to your Codex instance, uh, and, uh, indeed to, to Pinecone, and basically via any one of those agents have full access with the permissions, of course, that you, as, as an individual at the company, you can't read someone else's documents, but you can read your own. You can read your own Slack messages. And it's kind of like having superpowers, right? You can, you can interrogate in essence, the entirety of the company, all information that is published, whether it's, um, slack messages or presentations or operating reviews, uh, and, and so on, and use access to all of that information to better reason, make decisions, get things done. Pinecone, uh, of course incorporates that MCP gateway, but then is a Purpose built harness for all of Sierra. So Pinecone knows how to build Pinecone. So it, there's a whole harness around the engineering of Pinecone and our engineers there are phenomenally productive. We have a whole harness around the core of our platform, our agent architecture, agent studio, where you build and deploy agents, speeding…

AI assessment note: “we began by building what we call our MCP gateway. This is a single MCP server”

Q Final one for you, but I, I do like it. What's the kindest thing that anyone's ever done for you?

A I feel such gratitude to my parents, and I'm sorry if it's a straight down the fairway answer. Uh, my father was a career cardiologist. My mom's a very quilt, a talented quilt maker, and neither of them were in engineering or technology. And they saw that when I got a hold of my first computer, I just lit up and not really understanding what computers were about. My mom was good with them in the eighties, but it was not at all clear where they would go, but they could see that I was obsessed with them. And They supported that interest to, you know, to, to the hilt. My, I remember going with my father and my mom and dad, uh, to buy an early power Mac. And, you know, my dad was pushing like, would you be able to do more if we had more memory in it? I think I would be. And I was like, well, we should get more. And I was like, is this real life? You know, um, my mom would take me out of school one day a year. And we would go to Ken's house of pancakes, get breakfast. She would make up a doctor's appointment or something for me. And then we'd go to Macworld and I get to spend the day at Macworld, which for me was like Nirvana. And so, uh, I feel such gratitude to them and seeing in me that interest and how I lit up about this thing that was unfamiliar to them, but that they then pushed and enabled. And of course, there's a direct line from that to Uh, wonderful 18 years at Google, s…

AI assessment note: “They supported that interest to, you know, to, to the hilt.”

Q Yeah, or even on device on phones. I don't quite understand that when we think about always on AI, 24 hours a day, that's an awful lot to run locally. Is it a pipe dream, or do we think that's actually a reality that would alleviate the server side challenge?

A Oh, it certainly wouldn't alleviate it. I think it will make some consumer applications much better. But, I mean, the reality is you need, you know, petaflops, exaflops of compute, certainly for training, and you want a whole bunch of compute quickly. At inference time and you just run into thermal limits on, on your phone. I, I do think it is shocking that we're all carrying around in our pockets, you know, hyper computers these days, and will they get better? Yes. Will you have a language model optimized hardware rolling out in, in our phones, in our computers? Yes. I can see a sort of a home appliance, which is, you know, you plug into the mains and you get, you know, on demand access to A whole bunch of compute for things in your home, and maybe that helps alleviate some of it. Certainly for frontier workloads though, it's like, there's one place you can go to for that. And, you know, it is a giant rack of TPUs or GPUs in a data center somewhere.

AI assessment note: “Oh, it certainly wouldn't alleviate it.”

Q Is the future open models fine tuned to specific company needs? And if that is the future, With the realization that frontier models are too expansive. Is that a bad case for frontier models?

A I think it's a lot more complicated than that. I think if you asked any software company, would you like to upgrade your staff level software engineers to principal or distinguished level software engineers? Yes or no? A hundred out of a hundred would say, yeah, that sounds pretty great. So I, I think we have not yet appreciated the unbounded demand for Call it frontier levels of intelligence. And now you don't need that in every domain, right? So for, for instance, in our own, right, we build AIs for companies to interact with their customers. You don't need mythos to return a pair of shoes, right? Like you're good, right? It's like, uh, you want to do that well, but you know, we, we've got some capability overhang, so to speak, uh, for doing something like that. Uh, but in, in a range of domains, Uh, coding, certainly, uh, science, material science, um, uh, uh, legal, right, where the stakes are very high, there's a high degree of complexity. I, I think we're going to see effectively unbounded demand for greater levels of intelligence, and therefore the frontier models. That said, there will be an assembly line of, cool, uh, GPT-IV, which in, you know, March, April, May of twenty-twenty-three was good enough to do some set of things is now one 300th. The, the cost for an intelligence equivalent token. And so you'll have some assembly line of taking models that were once at th…

AI assessment note: “I think you'll end up with companies using both mixing and matching them”

Q Yeah, or even on device on phones. I don't quite understand that when we think about always on AI, 24 hours a day, that's an awful lot to run locally. Is it a pipe dream, or do we think that's actually a reality that would alleviate the server side challenge?

A Oh, it certainly wouldn't alleviate it. I think it will make some consumer applications much better. But, I mean, the reality is you need, you know, petaflops, exaflops of compute, certainly for training, and you want a whole bunch of compute quickly. At inference time and you just run into thermal limits on, on your phone. I, I do think it is shocking that we're all carrying around in our pockets, you know, hyper computers these days, and will they get better? Yes. Will you have a language model optimized hardware rolling out in, in our phones, in our computers? Yes. I can see a sort of a home appliance, which is, you know, you plug into the mains and you get, you know, on demand access to A whole bunch of compute for things in your home, and maybe that helps alleviate some of it. Certainly for frontier workloads though, it's like, there's one place you can go to for that. And, you know, it is a giant rack of TPUs or GPUs in a data center somewhere.

AI assessment note: “Oh, it certainly wouldn't alleviate it.”

Q Is the future open models fine tuned to specific company needs? And if that is the future, With the realization that frontier models are too expansive. Is that a bad case for frontier models?

A I think it's a lot more complicated than that. I think if you asked any software company, would you like to upgrade your staff level software engineers to principal or distinguished level software engineers? Yes or no? A hundred out of a hundred would say, yeah, that sounds pretty great. So I, I think we have not yet appreciated the unbounded demand for Call it frontier levels of intelligence. And now you don't need that in every domain, right? So for, for instance, in our own, right, we build AIs for companies to interact with their customers. You don't need mythos to return a pair of shoes, right? Like you're good, right? It's like, uh, you want to do that well, but you know, we, we've got some capability overhang, so to speak, uh, for doing something like that. Uh, but in, in a range of domains, Uh, coding, certainly, uh, science, material science, um, uh, uh, legal, right, where the stakes are very high, there's a high degree of complexity. I, I think we're going to see effectively unbounded demand for greater levels of intelligence, and therefore the frontier models. That said, there will be an assembly line of, cool, uh, GPT-IV, which in, you know, March, April, May of twenty-twenty-three was good enough to do some set of things is now one 300th. The, the cost for an intelligence equivalent token. And so you'll have some assembly line of taking models that were once at th…

AI assessment note: “I think you'll end up with companies using both mixing and matching them”

Q Sierra, you can't do that. I mean, as you mentioned Sierra, it's a enterprise business, and you have to have a different structure of the team. When you look at the future of teams, are we seeing a world of dramatically leaner, fewer people in teams, or actually, is it still very much dependent on customers, and we will still have very large teams for companies like Sierra with enterprise?

A I think the general The general direction of travel clearly is towards smaller, higher leverage teams. We have software engineers who are completely AI-pilled and using Cloud Code, Codex, our own internal agent we call Pinecone that we used to run much of the company on, and they estimate they are between three and 20 times more productive in terms of features shipped. Now, the, the productivity gains certainly in software engineering and Uh, data science, data analysis, and other area we're seeing it in spades, but I think in time it will touch all parts of, uh, really every company. So that's the general trend. I think within a company like Sierra, where we serve in particular, the large enterprise, we work with 40% of the fortune 50. We have 50% of our customers doing over a billion in revenue. We have 30% doing over ten billion in revenue. These are some of the most complex and in cases regulated organizations in the world. And to be able to sell and implement our product and solution successfully for organizations which are snowflakes. The process of selling and more importantly, successfully implementing and deploying a solution like ours into the large enterprise is still a lot about Deeply understanding our customers' business outcomes and objectives, about understanding their technology stack, integrating with it successfully, building relationships, earning trust to s…

AI assessment note: “The general direction of travel clearly is towards smaller, higher leverage teams.”

Q Is it a unique time because there is buyer pull like never before for this specific moment?

A There is effectively unbounded demand, I, I think, in two areas. One, we've talked about coding agents. The other is the space where we're the category leader. And so one of the reasons we've grown as quickly as we have is to meet that moment and meet that demand. We, um, we're now a hundred people here in Europe. We recently acquired a company in Japan, Opera Technologies. You and I were talking about this to hit the ground running there. And to have a team that can be attuned to the cultural nuances of, of Japan and, um, you know, the concept of omotenashi, which is like extreme hospitality, like that is what is expected in Japanese service, and that's what we intend to build there.

AI assessment note: “There is effectively unbounded demand, I, I think, in two areas.”

Q Why are you so opinionated about in-person?

A In particular for a young company, I think it is, I won't say impossible, but very challenging to build a culture, a set of shared norms, Camaraderie. I think so many of the things we talked about, enterprise software as a team sport, it feels great to be part of an amazing team. And it's different when your connection to that amazing team is via a Brady bunch of Zoom squares. And so we have rituals that we've developed that we only would have developed if we were all working in person. There is, I think for younger employees, apprenticeship and mentorship that happens. So much of what I learned, and I think the initial conditions of my career were from experienced people Taking me under their wing or letting me in cases, literally look over their shoulder at how they were doing something. Uh, I think there's, um, an element of paying it forward. That's, that's important. Uh, and, and in-person has a role to play in that as well. There's a talk that I love by the renowned computer scientist, Richard Hamming, you and your research. And it's, uh, for any new graduate, probably the single best thing, uh, on a per word basis, I think you can read. And there's several interesting points in it, but one of the central theses is find great people, work with them and learn from them. And I, I mean, it, it, it sounds obvious, but there's something deeply correct. Like how do we learn as …

AI assessment note: “it is... very challenging to build a culture, a set of shared norms, Camaraderie.”

Q this as a method to learn from you and Probably when I, especially when I was 18, it was harder to meet amazing people, and so I completely agree with those two. There are a lot of young people today. You have kids, kind of picture them leaving university who are uncertain about where the world is, what to do. What would you advise them knowing all that you know?

A The obvious kind of tsunami coming is AI and Okay, what are the implications on jobs for that? I think there's been a lot of concern understandably about, okay, what, what happens to entry level jobs? How do you apprentice and so on? I think the unfair advantage that young people have coming out of university is you've just had four years to spend effectively unlimited time. You got to go to class and, you know, pass some exams and stuff, but you have Huge control over your time and disposable hours. Coming out of university as a master of these AI tools, boy, let me point you to a thousand companies that would love to have you infuse what you know into how they're doing things. And I, I can't remember a time when a young person with no work experience, but with the right mindset and experience using some of these tools has ever been so valued. Some of our most effective employees at the entire company are 22 or 23 years old and have been completely AI pilled and have a Comfort and facility with these tools that many of our more experienced folks don't.

AI assessment note: “Coming out of university as a master of these AI tools, boy, let me point”

Q Um, okay. Parenting. Four kids and a, Unbelievable operator and founder. What's your biggest advice?

A First of all, uh, having kids is the greatest gift. It is such a privilege. And I, uh, a few things I would say. First of all, you feel that way. Um, you will be a changed and different person, you know, on the other side of holding your son or daughter. It's just, it is the, in my opinion, single, uh, fastest rate of change, single biggest change that anyone experiences in their life. After they themselves are being born, right? Welcoming your, your first child. Carve out time, family dinner. We have many mornings on Sundays, maker mornings with two of my sons, where we block out an hour or two and we build something at home. And so rituals and discipline around making time and space. I think anything important in life, my view is a product of Clear goals and good habits. And so I think if you have a clear goal around how you want to be as a parent and then habits that help you build towards that, I think that's a very important ingredient. And then the other is making kids interests your own. And so I'm terrible at basketball. My eldest son is an incredible basketball player. I am so proud of him. I go and watch him play and say, he does things like, I could never do that. Not only can't I do that, I could never do that. And, and so I, I follow the playoffs. I've, I've learned about the sport. I've learned about the, the best players. I've learned about coaching so that I can…

AI assessment note: “making kids interests your own”

Q You mentioned the 40 of the fortune 50 being customers, and you use that quite a lot in a lot of your marketing materials, and it strikes me as like a very enterprise company, candidly. Is it difficult, or how do you retain a real product focus, a real closeness to customers when you're so enterprise? Is that difficult?

A I think it's a little bit of a false choice you're implying there. I, I think being, being large enterprise doesn't necessarily mean you need to be distant from your customers, in our case, our customers' customers. So Brett and I are constantly building agents ourselves. Uh, one of the more interesting things of the last six months, we released Ghostwriter. This is an agent for building agents. It's kind of agents all the way down. It's pretty cool. But we are constantly in the products ourselves, and, uh, Brett is actually still an extraordinarily capable software engineer. It's remarkable. So, you know, some of the code that is in production, right, he, he has written, um, I've, I've probably got a couple lines here or there, but, you know, pales in comparison. And, and so, uh, of course, we can't, uh, on our own simulate the, uh, Complex multi-system environments that characterize many of our largest enterprise customers so that, you know, we have to kind of simulate in our heads, but we're in the product. And then one of the, one of the things I think a lot about is we will in short order be in a way, one of the larger B to C companies. We're doing that via our customers, but you know, we'll, we'll, we're serving hundreds of millions of interactions, right? Soon billions of interactions. And, uh, so staying close to the end experience there as well, voice fluency, latency,…

AI assessment note: “Brett and I are constantly building agents ourselves. Uh, one of the more interesting”

Q Why are you so opinionated about in-person?

A In particular for a young company, I think it is, I won't say impossible, but very challenging to build a culture, a set of shared norms, Camaraderie. I think so many of the things we talked about, enterprise software as a team sport, it feels great to be part of an amazing team. And it's different when your connection to that amazing team is via a Brady bunch of Zoom squares. And so we have rituals that we've developed that we only would have developed if we were all working in person. There is, I think for younger employees, apprenticeship and mentorship that happens. So much of what I learned, and I think the initial conditions of my career were from experienced people Taking me under their wing or letting me in cases, literally look over their shoulder at how they were doing something. Uh, I think there's, um, an element of paying it forward. That's, that's important. Uh, and, and in-person has a role to play in that as well. There's a talk that I love by the renowned computer scientist, Richard Hamming, you and your research. And it's, uh, for any new graduate, probably the single best thing, uh, on a per word basis, I think you can read. And there's several interesting points in it, but one of the central theses is find great people, work with them and learn from them. And I, I mean, it, it, it sounds obvious, but there's something deeply correct. Like how do we learn as …

AI assessment note: “very challenging to build a culture, a set of shared norms, Camaraderie.”

Q Would you say that's the same for your partner? Like, do you need to have aligned interests in a partnership in a marriage? Or is it good to have different ones?

A I mentioned married to my high school sweetheart. Uh, we, we will have been together for almost 30 years. Um, and I, you know, I'm not that old. Um, I think a great, a great marriage is a partnership. It, and a, and a partnership means you are working In pursuit of and service of some shared set of goals. And, and so you asked about interest. I think having shared interest in what you are pursuing as a partnership is deeply important. Happy kids who grow into, uh, uh, adults who can enjoy their lives and contribute meaningfully to those, uh, around them. Um, Building a set of values in one's family that are aligned with, with your own. And then, and then I think, uh, ensuring as part of that partnership that the other member in it themselves, themselves thrives and fully realizes themselves. And, and there are those interests may be different, but there can be a shared interest And enabling each other to become the best that you're able to become.

AI assessment note: “having shared interest in what you are pursuing as a partnership is deeply important”

Q more and more advanced, Does that not mean the problem set for frontier models becomes more and more challenging? As you said, we've seen the progression of open so much that actually they can do the majority where it's like, I get it for like solving climate change, cancer treatments and materials, but actually like for the majority, like what percent of enterprise tasks can be done with open today?

A Well, I think if you look at what percent of enterprise tasks are completely automated today, it's a rounding error, right? It's very low. So is that, is that a model gap? Is that a diffusing the technology into the company? Is that an application layer gap? I think it's probably all of these, some combination of them. You're obviously correct that as the open weights models become more capable, the set of things they can do grows larger. The set of things where all else being equal, if they're much less expensive, that you would want to point a frontier model, uh, becomes smaller. But again, I think we're not imagining just how high the ceiling is, uh, in terms of demand for frontier intelligence, invention, discovery, building new products, building new services. I, I think it's hard to get your mind around when you have intelligence that can work around the clock and, uh, to invent, to build, to discover how you would use that and how much of you, how much of it you could use.

AI assessment note: “percent of enterprise tasks are completely automated today, it's a rounding error”

Q Will you build products that aren't uniformly applicable across customer bases? So if one customer needs specific cart abandonment product features, Is that something we build, or is it, no, that's not applicable to the platform.

A One of our approaches in building the company, and this kind of goes back to where we started, you can build a platform and hope that people come, right? The applications get developed on it, or you can build applications to inform a platform that makes building the third, fourth, and fifth that much easier. And so wherever we can, we're scanning for opportunities to strengthen our platform. So commonality is much better than something that is truly a one-off. That said, if we're working with a Fortune 50 or Fortune 20 or Fortune 10 or Fortune five company and there is some element of that company that is literally unique, of course we'll build that. Of course we'll build that. And one of the neat things is it's actually become feasible to build that because of coding agents, because of the pace at which you can move. There's a real unlock there in being able to build a solution on an already deep platform, but extend it in ways that Uh, it may apply, you know, to a, to a single customer. My hunch though is that if you build it for one, right, someone else is going to have that same problem, right? And so it's, it's less common than you would think. True one of ones.

AI assessment note: “there is some element of that company that is literally unique, of course we'll build that.”

Q Craftsmanship, intensity, and family are three that I wouldn't normally see. Can you talk to me a little bit about why those are so important?

A I'll start with craftsmanship. Both Brett and I, just because of the way we are, we care about doing things well. If you're going to do something, do it with excellence. And I think there's two ways in which doing things with excellence mean much more than just kind of Sweating the details. One is what is a great company? A great company is an aggregation of thousands and thousands of things that are themselves great. It's great people. It's processes that are well designed. It's a great product. It's a great culture. And, and so how do you build an excellent company? Well, you build everything with excellence. And, and so I think holding ourselves to the standard, like if it is worth doing, it is worth doing well. Is, is one part of that because that adds up to, to a great company. How else do you get there? The other is you think about what our customers are trusting us with back to the trust value, but I'll, I'll make the connection with craftsmanship. It is with their most precious asset. It is their customers. How will a company know, how will a set of people who are considering working with us know how we will show up with their customers? A lot of it is how we show up with them. And so, sweating the details in how we show up and interact with our customers, the level of professionalism, care, dropping everything when something matters. I'll give you an example there. Whe…

AI assessment note: “I'll start with craftsmanship. Both Brett and I... care about doing things well.”

Q this as a method to learn from you and Probably when I, especially when I was 18, it was harder to meet amazing people, and so I completely agree with those two. There are a lot of young people today. You have kids, kind of picture them leaving university who are uncertain about where the world is, what to do. What would you advise them knowing all that you know?

A The obvious kind of tsunami coming is AI and Okay, what are the implications on jobs for that? I think there's been a lot of concern understandably about, okay, what, what happens to entry level jobs? How do you apprentice and so on? I think the unfair advantage that young people have coming out of university is you've just had four years to spend effectively unlimited time. You got to go to class and, you know, pass some exams and stuff, but you have Huge control over your time and disposable hours. Coming out of university as a master of these AI tools, boy, let me point you to a thousand companies that would love to have you infuse what you know into how they're doing things. And I, I can't remember a time when a young person with no work experience, but with the right mindset and experience using some of these tools has ever been so valued. Some of our most effective employees at the entire company are 22 or 23 years old and have been completely AI pilled and have a Comfort and facility with these tools that many of our more experienced folks don't.

AI assessment note: “Coming out of university as a master of these AI tools”

Q Yeah, why after 18 years at Google were you like, uh-huh, now?

A Yeah, so Brett and I met 20 years ago. We both started our careers in the associate product management program at Google. He was class one, I was class three, and we met in the context of some kind of shared project that we were assigned to and kind of hit it off and ended up staying in touch socially through mostly a monthly poker group that, you know, in a good year might play two or three times, so not, not quite monthly, and had always wanted to work together and almost did a couple times. I think when Brett left, I can't remember if it was for friend feed or equip, tried to get me to join that. And the short answer is, you know, twofold. One, I just loved my time at Google. Culturally, it was me. I learned more than I can ever imagine having learned in, in those years. And, uh, the people were so extraordinary to work with. And, um, I had a series of managers and leaders I got to work with who took bets on me, gave me, on paper at least, more responsibility than I deserved. And got to work on just truly fascinating things. And so I was just incredibly happy and engaged and growing as a person and professional. And then in late 22, kind of the planets aligned in a way that I didn't think that they would probably align again. I'd always wanted to start a company. I started a very modest company when I was 13 years old and always thought I would start another. And if you're g…

AI assessment note: “in late 22, kind of the planets aligned in a way”

Q Um, my question to you on the back of that is, and it's a terrible question, you can chastise me for it. What are your single biggest takeaways from that experience that you took with you to Sierra, and what did you leave behind?

A It's such an interesting question. Of course, the, the scale of a, you know, two and then 10 and then hundred person enterprise software company is very different from, I think when I left Google, it was roughly a 150,000 people. Things that I've definitely brought with me, number one is a willingness to invest as far down the technology stack as you need in order to build the service and product that you want. Google, I think from the early days, famously built its own, if not data centers, cluster architectures, and they were the first really to use commodity hardware that required building novel distributed systems for, uh, serving and data storage and so on. And so we could see that language models and, uh, you know, as, as early as, you know, April of, Uh, 23, when we started the company, that agents were going to be a thing. This was before all anyone wanted to talk about was agents. And we realized, okay, this should be possible. It's not yet possible, but we're going to have to invent frameworks for building these things. Our own architecture is really from scratch. So actually our, our first, uh, founding head of research was the Princeton professor who literally wrote the paper on language model based agents, the react paper. And so we invented, we invented and, uh, went, you know, further down the stack than I think some companies at that point would have been willin…

AI assessment note: “Things that I've definitely brought with me, number one is a willingness to invest”

Q You sell, again, you sell to some of the biggest enterprises in the world. Um, I had a guest on the show the other day say you can't sell to enterprise without an FDE motion. Would you agree with that knowing all that you know now selling to 40 of the 50?

A I would like to think at least in the AI space, I would say rediscovered and borrowed this model from Palantir, and we came to it almost accidentally. So we started the company, And the first thing we did was reach out to people we trusted to understand what are the biggest unsolved problems that you were looking at and saw, oh, interesting service and support as a, a foothold into something much broader, helping support customers across the entire life cycle. We then enlisted, uh, uh, half a dozen design partners that we built the first version of our product and platform with and for. And these are, in the history of the company, legendary, legendary companies. Olokai, great flip-flops, you should buy them. SiriusXM, Sonos, Weight Watchers, and we built the first version of our platform with our engineers deeply embedded inside those companies. So much so that our founding engineer, Mihai, was actually an employee of Weight Watchers, including getting, like, It's performance review time, emails, and so on. And what we realized in that was no one has ever deployed an AI agent. No one has ever put AI in this way in front of their customers. And in order for us to build the best thing as quickly as we and our customers would like, being so close to the business, the mechanics of it, the people, their business model, That we understand it. I won't say as well as our customers, bu…

AI assessment note: “rediscovered and borrowed this model from Palantir, and we came to it almost accidentally.”

Q You mentioned the 40 of the fortune 50 being customers, and you use that quite a lot in a lot of your marketing materials, and it strikes me as like a very enterprise company, candidly. Is it difficult, or how do you retain a real product focus, a real closeness to customers when you're so enterprise? Is that difficult?

A I think it's a little bit of a false choice you're implying there. I, I think being, being large enterprise doesn't necessarily mean you need to be distant from your customers, in our case, our customers' customers. So Brett and I are constantly building agents ourselves. Uh, one of the more interesting things of the last six months, we released Ghostwriter. This is an agent for building agents. It's kind of agents all the way down. It's pretty cool. But we are constantly in the products ourselves, and, uh, Brett is actually still an extraordinarily capable software engineer. It's remarkable. So, you know, some of the code that is in production, right, he, he has written, um, I've, I've probably got a couple lines here or there, but, you know, pales in comparison. And, and so, uh, of course, we can't, uh, on our own simulate the, uh, Complex multi-system environments that characterize many of our largest enterprise customers so that, you know, we have to kind of simulate in our heads, but we're in the product. And then one of the, one of the things I think a lot about is we will in short order be in a way, one of the larger B to C companies. We're doing that via our customers, but you know, we'll, we'll, we're serving hundreds of millions of interactions, right? Soon billions of interactions. And, uh, so staying close to the end experience there as well, voice fluency, latency,…

AI assessment note: “I think it's a little bit of a false choice you're implying there.”

Q Will you build products that aren't uniformly applicable across customer bases? So if one customer needs specific cart abandonment product features, Is that something we build, or is it, no, that's not applicable to the platform.

A One of our approaches in building the company, and this kind of goes back to where we started, you can build a platform and hope that people come, right? The applications get developed on it, or you can build applications to inform a platform that makes building the third, fourth, and fifth that much easier. And so wherever we can, we're scanning for opportunities to strengthen our platform. So commonality is much better than something that is truly a one-off. That said, if we're working with a Fortune 50 or Fortune 20 or Fortune 10 or Fortune five company and there is some element of that company that is literally unique, of course we'll build that. Of course we'll build that. And one of the neat things is it's actually become feasible to build that because of coding agents, because of the pace at which you can move. There's a real unlock there in being able to build a solution on an already deep platform, but extend it in ways that Uh, it may apply, you know, to a, to a single customer. My hunch though is that if you build it for one, right, someone else is going to have that same problem, right? And so it's, it's less common than you would think. True one of ones.

AI assessment note: “there is some element of that company that is literally unique, of course we'll build that.”

Q What was the most recent disagreement you and Brett had?

A Couple weeks ago, we were trying to figure out how to get something to move much faster in one space, and it's interesting, we, we basically always converge. It's like, we're, we're highly truth-seeking. It's like, what is, we have a funny expression, like, this is correct. Okay, what, what does that mean? From, from some objective truth-seeking perspective, like, this is the right way to do it. So we, we try to get to, okay, what is the correct solution? I was on one side. It was like, I think we need better kind of process and structure around this thing. Brett was on the side of people and maybe we need different leaders or a different leader in this space. Um, the answer is with most things like turned out to be some of both, right? Turned out to be some of both. But, um, I, I think, um, we, we started from, uh, No, it can't just be solved. It's like, no, it's not just people. And, you know, we pull on those threads and this wasn't think apart, think together so much as just kind of interrogating each other again with the goal of just getting to the right and best approach to something.

AI assessment note: “I think we need better kind of process and structure... Brett was on the side of people”

Q Can you expand on those? Again, two that I don't often get.

A Intensity. So I think it's, it's back to this great thing about giant market, giant market, hard thing about giant market, giant market, and others are in it too. I think there is an inevitability to companies interacting with their customers via really sophisticated agents that capture all that they know and all they can do on behalf of their customers and get the job done on their behalf that handle the complexity as opposed to Pointing you to websites and, and so where the, the conversation is the interface, right? I, I think there's an inevitability to that. And, uh, and therefore in order to win, in order to build the best company in this space, it is about pace. It is about winning. It is about, uh, building the best product. It is about, uh, being competitive and being intense about it. And, uh, knowing that, you know, we don't have the, the luxury of patience. There's no, Nothing, nothing written in the wind, right? That, that any particular company will be the company showing up in our fifth engagement, uh, and 500th engagement, you know, as intensely as we did our first, like you have to do that. And so I think there's also, I talk about the Venn diagram of who we hire for smart, nice, intense, and it's hard actually to get all of those three in a single person. When you, when you do, it's fantastic and you can feel it in the office. And Another way of translating int…

AI assessment note: “Another way of translating intensity is doing things with excellence, doing things with pace”