The Exchanges

Q is that a concern that I hate this argument that a lot of VCs have, which is like, oh, the biggest customers scale out of your solutions because they very rarely do. Shopify and Stripe's a great example, I think. But to what extent is that a concern that the biggest companies in the world do build their own and it works better on their own data in that way?

A I would say from our experience so far, very few people will build it themselves. Robinhood is, I mean, they have an exceptional engineering team, and they also have the different philosophies, and so I think there will be a couple pockets of players that are more of that profile that are happy to build it themselves, where they're super tech heavy and generally on the sort of like the lower enterprise side of things. What we've seen in just the enterprise at large is that folks may have done some work internally. Some people experiment with GPT or somewhat, and They quickly realized that to actually get something production level, you need a lot more than that. You need essentially a whole framework for non-technical users to come in and build and adjust things. You need a learning, monitoring, observability, the ability to run experiments, A-B testing, and stuff like that. Why build that internally? It's just, it will take you way too long, and it's not really what your engineer should be doing, and so that's generally what people come to, and that's when they decide to partner. So I would say it's not very common that we see people doing the full in-house approach, but sometimes it'll be a hybrid, right? It's like, hey, there are certain things we want to own in-house, and so then it's, it's our job to really flex around that and make sure that we can provide the support the…

AI assessment note: “very few people will build it themselves”

Q Jesse, what do you think you believe that most around you disagree with you on?

A I'll give a sort of work related one and then more of a out there one. So the, the work related one is, I don't know how hot of a take this is nowadays. I feel like more people are buying into this, but, uh, technically we really value just like clock speed really, really highly. And clock speed, I just mean like how fast your brain works and how fast you can learn. And this is across all functions, right? Obviously it's important for engineering, but for Sales and for marketing and so on, like a bunch of our top sales reps, they're just like really smart people. And they're just like figuring stuff out from first principles. Like they're just go, go, go really aggressive. So clock speed matters a lot more to us. That matters a lot more than things like experience and necessarily being able to sell to this industry and so on.

AI assessment note: “clock speed matters a lot more to us. That matters a lot more than things like experience”

Q I love the visceral response to that as well. You're like, oh, hell no. That is like not even a thing. Final one for you. If you do a reflection on your own leadership, where do you need to improve that you haven't yet worked on?

A My style is just naturally very intense. Same with Ashwin's. And, um, what that leads to is I think we have very much of this culture of it's really geared towards winning for what we talked about before. And so far we have been winning. And so the team is quite happy and so on. I would say that there is, I think the best leaders have a bit of nuance there, whereas you kind of combine intensity with a bit more of the softness, I would say. And that's something I've observed from some of the top founders out there. Right now, we're just so in the weeds, like every day is like so packed that we definitely lean much more on the intensity side. But I think over time we would like to, or I would like to be a bit more just wise about that sort of dynamic.

AI assessment note: “combine intensity with a bit more of the softness”

Q What does starting from scratch AI native fundamentally allow you that you are not afforded or allowed if you're having to integrate into existing platforms?

A Sure. Yeah. I'll give a very tangible example in our space. So if you just think about Automation in customer experiences in the past. There's been a long history of it, right? You have these phone trees that no one likes, and then there's chatbots as well. And generally the way you build them is the classic SaaS approach, which is you give people like a framework, and sometimes it's like a configuration language, like what Salesforce does, where you just have technical people just building stuff out. There's a couple of issues with that. One is that as things get really, really complex, the rate at which you can iterate Becomes slower. And the other thing is that it often gets bottlenecked through engineers. So you need technical people doing stuff. The main thing that AI unlocks is that you're able to democratize that a lot and empower a lot of the non-technical business users to use natural language as the medium to build things and iterate and so on. So what we're seeing in this new generation is that, you know, one of the, I would say, biggest innovations that we brought that people really liked was we have this concept that we call AOPs, Agent Operating Procedures. That are mostly natural language. And that's how you build the AI systems. And if you compare that with the previous solutions, right? Like none of the previous work you would do to kind of create all these con…

AI assessment note: “empower a lot of the non-technical business users to use natural language”

Q Brilliant range there, 6070, 80. It's like, there's a very, very large chasm there. What do you think that is in three to five years?

A I think there's a couple of nuances to that question. I think this space, the reason why it's exciting is not just that that number goes up. Like, of course that number should go up. I think in three to five years, it'll probably creep towards like just consistently 80 to 90, right? Because you're able to just so easily allow the end users to capture more logic and teach you new things. And I mean, that's the sole purpose our company exists. But at the same time, the sort of scope of AI agent will expand. I think that's the exciting part. And that's why a lot of senior leaders are excited is that When you talk about 70%, right, it's like 70% of all the support inquiries. Like, hey, I need to reset my password, or I lost my item, like I need a new one, or I have questions about my loyalty points or whatever, right? Solving those is obviously great, but if you had a really, really good AI agent, it becomes almost like a new conversational UI of your brand. It's like a concierge almost, where you just talk to a lot more about all sorts of different things. Like, maybe you start talking to it before you become a customer, or it's helping you kind of learn about the product and That leads to more revenue because you're opting into a new product line or it starts to be more proactive instead of reactive. And so that's where the excitement is. And so I would say in three to five years…

AI assessment note: “in three to five years, it'll probably creep towards like just consistently 80 to 90”

Q someone like Harvey in particular, no disrespect to them, but they sell ahead of time. And then, bluntly, they build for the promises they've made before, but they win the space by selling ahead of time and by over-promising, and momentum begets momentum in this space. Do you think he's right, or do you think the alternative, which is all the AISDRs have fucked themselves because they've oversold and under-delivered?

A I think it depends on the market. I have a huge respect for the Harvey team. They are almost exactly a year older than us. And so they've been very gracious about, you know, teaching some of their learnings. I think it just depends on the market. So I think their market is one where it's a lot more, um, self-contained in the sense that like, if you get the big players, there's only so many law firms out there. Like people will see that and like, oh, that's great. And like, kind of you join in. And so in that sense, I think speed matters quite a bit in kind of selling the vision and so on. Our market, for example, is just a lot broader. And so you have to balance it a little bit. You can't just go in and sell the vision because no one needs to buy into the vision. I think everyone already believes the vision. And so then it's more about showing the results quickly and being able to demonstrate that, hey, there is ROI here and that, you know, we're not selling vaporware. You can actually deploy it and see results.

AI assessment note: “I think it depends on the market.”

Q And so what does it create that you didn't want for founders listening who have options like you did and maybe aren't aware of them?

A So there's a couple of things just mentally. It just creates like, okay, well now the goalpost is here. So all the wins you do have just feel a little bit diluted and you're like, okay, well that was cool. But you know, the goalpost is way over here. So And the whole team just feels a little bit demotivated. It's like, okay, we're working so hard and we are making progress, but everything's benchmark so high. The second thing is that it makes hiring a little bit weirder because people will come in and like, they're getting their equity packages, but it's like, okay, well, how long will it take you to get to this valuation? Whereas I think that our current valuation hopefully is not going to be that long. And so that's the dynamic that you see. And then lastly, I would say it just really limits your optionality in the future. Like, When you think about people that raised that huge valuations, I, I, I've known folks that have done this. You get to a point where you maybe are still doing well as a business, but the markets change and you're just not able to raise at that price with some healthy multiple on it. And so then it just feels like your business has lost momentum, even though maybe internally you feel like, well, you know, we've been growing steadily. It's just maybe not as fast as we could hope, but still at a fast rate. And then you just feel like a zombie company. So I…

AI assessment note: “it just really limits your optionality in the future”

Q that you're willing to impose versus not willing to impose? And is it like a slider on preference for customers in terms of the freedom that agents have? Say I come and I start asking questions that are inappropriate. To what extent you can choose to respond slash completely not slash say a joke that kind of puts it away. How does one think about the guardrails that one has?

A Yeah, those, those have to be customized from customer to customers. You essentially have to give them the option to enforce these guardrails, and then our job is if the guardrail is set, then we need our infrastructure to be one where we're heavily monitoring them and making sure it doesn't happen. There's like a bunch of techniques you can use for this. You can have essentially a supervisor model that's there when the core agent is running, and its sole job is to check the guardrails and make sure that they're all passing, and if not, then it will instantly rewrite the response. There's stuff like that that you can do, but it really depends on the company, right? Like if we work with financial services clients, they might say, we never want the AI to give financial advice, which is a very reasonable thing. And so that will be a guardrail that we have to put in. And, you know, if you're in a retail space, probably you don't care about that guardrail. And so you might have other ones and it has to be really customized.

AI assessment note: “those, those have to be customized from customer to customers.”

Q The single biggest problem in AI B to B will be hiring for the next year, 18 months. Do you agree? And how do you think about the challenge respectfully of like Decadon's a hot as fuck company, but no offense against Anthropic, Meta, and Cursor, where you're just against, you know, Massa Sun styles of cash. How do you do that?

A Well, actually, typically I find we don't compete with Them too much. Like the, the large companies, I think a few people who want to work there versus people that want to work at companies our stage are usually not the same folks. And so I don't think there's too much direct competition with those folks, but to your point, there's a lot of companies hiring, right? So it is a big challenge to make sure that you, you land the best folks. And so you just got to be clear about, you know, what, what is the unique advantage of working at a place like us, right? And I think we have a pretty unique culture. I think everyone here is, is one where they're opting into like, We're in the office all the time, and we're working hard, and that's going to lead to somewhere. That's going to lead to me leapfrogging my career or, you know, financial outcomes in the future, so.

AI assessment note: “typically I find we don't compete with Them too much.”

Q someone like Harvey in particular, no disrespect to them, but they sell ahead of time. And then, bluntly, they build for the promises they've made before, but they win the space by selling ahead of time and by over-promising, and momentum begets momentum in this space. Do you think he's right, or do you think the alternative, which is all the AISDRs have fucked themselves because they've oversold and under-delivered?

A I think it depends on the market. I have a huge respect for the Harvey team. They are almost exactly a year older than us. And so they've been very gracious about, you know, teaching some of their learnings. I think it just depends on the market. So I think their market is one where it's a lot more, um, self-contained in the sense that like, if you get the big players, there's only so many law firms out there. Like people will see that and like, oh, that's great. And like, kind of you join in. And so in that sense, I think speed matters quite a bit in kind of selling the vision and so on. Our market, for example, is just a lot broader. And so you have to balance it a little bit. You can't just go in and sell the vision because no one needs to buy into the vision. I think everyone already believes the vision. And so then it's more about showing the results quickly and being able to demonstrate that, hey, there is ROI here and that, you know, we're not selling vaporware. You can actually deploy it and see results.

AI assessment note: “I think it depends on the market. I think their market is one where”

Q When I'm investing, I always ask the question, which is like, how long is the road to the start line? And what I mean by that is how much work needs to be done to reach feature parity with existing players, Zendesk, Salesforce, Intercom, or is it an entirely new paradigm where everyone starts afresh? Which one is it?

A I would say for AI, mostly it's starting fresh. And the reason is that having the older way of doing things actually serves as baggage because you have so many customers already locked into that approach. And because you have a lot of customers, anything you build has to be compatible with all of them. And so it just always inherently gives startups an advantage. And that's why when you have these like big technology shifts, it just opens up a big area of opportunity because it's not like the old players don't know that AI is here of Of course they are investing in AI and of course they want to be AI friendly as well, but it's just tough for them to compete head to head against more agile things that are AI native.

AI assessment note: “I would say for AI, mostly it's starting fresh.”

Q In terms of unlocking higher value, how much more revenue does one unlock when moving from software spend to human labor? You said you're already seeing it. Is this like a three X-ing of spend, a five X-ing, a 10 X-ing? What does that look like?

A It's tough to say just as a rule of thumb, but if I had to give a rule of thumb, human labor is generally like an order of magnitude larger than software spend, like 10 X or more. Of course, you're not gonna capture all of that, but when you're kind of bringing in software, if you can give them like a three X to five X ROI, that's very compelling. And so what that translates to is like, you know, roughly three X multiple on the value you're able to capture there. And so if you, if you look at a lot of our customers, for example, they're spending more on the AI agent Than the previous CRM software for support. And that's because the, the value is just a lot higher. You're able to just like solve the issue completely instead of, you know, just giving software for humans to use.

AI assessment note: “roughly three X multiple on the value you're able to capture there.”

Q Brilliant range there, 6070, 80. It's like, there's a very, very large chasm there. What do you think that is in three to five years?

A I think there's a couple of nuances to that question. I think this space, the reason why it's exciting is not just that that number goes up. Like, of course that number should go up. I think in three to five years, it'll probably creep towards like just consistently 80 to 90, right? Because you're able to just so easily allow the end users to capture more logic and teach you new things. And I mean, that's the sole purpose our company exists. But at the same time, the sort of scope of AI agent will expand. I think that's the exciting part. And that's why a lot of senior leaders are excited is that When you talk about 70%, right, it's like 70% of all the support inquiries. Like, hey, I need to reset my password, or I lost my item, like I need a new one, or I have questions about my loyalty points or whatever, right? Solving those is obviously great, but if you had a really, really good AI agent, it becomes almost like a new conversational UI of your brand. It's like a concierge almost, where you just talk to a lot more about all sorts of different things. Like, maybe you start talking to it before you become a customer, or it's helping you kind of learn about the product and That leads to more revenue because you're opting into a new product line or it starts to be more proactive instead of reactive. And so that's where the excitement is. And so I would say in three to five years…

AI assessment note: “in three to five years, it'll probably creep towards like just consistently 80 to 90”

Q To get, like, a 20, thirty billion dollar outcome, I think one has to believe that you can make the transition from, like, post-sale customer support to that conversational agent across different fragments of the consumer life cycle. What do you have to achieve to make the transition from post-sale customer support to conversational brand interface for large companies?

A The way I would describe it is it's all about finding the right level of abstraction. So if you're being super customized, right, you would basically go to a customer and you're like, all right, what is the conversation or the types of conversations you have? And you would just like code it from scratch and be like, all right, here's an agent for you, right? That's not going to be a huge business because it only matters to that company. So if you zoom out one layer and you're like, okay, maybe now we're solving all customer support inquiries, but for your industry, then you might be able to build some level of abstractions so that, you know, they can go into your product and fill around with some things and And build their agent and, and now they're able to do all the common workflows like refunds or chargebacks or whatever. And then we zoom out one more and that's where we're at is, okay, now we have this abstraction layer AOPs that allows you to build essentially any sort of conversational support inquiry. And when I say support, it's the dynamic of the user is coming inbound to you, right? And you have a procedure that you're executing. Now, I think we have a pretty good abstraction layer for that of how you can just use that framework and customize it To any sort of use case that is, you know, support specific. And so to make the jump to what you're saying, you have to go f…

AI assessment note: “you have to go from that to one level of abstraction even higher”

Q And so what does it create that you didn't want for founders listening who have options like you did and maybe aren't aware of them?

A So there's a couple of things just mentally. It just creates like, okay, well now the goalpost is here. So all the wins you do have just feel a little bit diluted and you're like, okay, well that was cool. But you know, the goalpost is way over here. So And the whole team just feels a little bit demotivated. It's like, okay, we're working so hard and we are making progress, but everything's benchmark so high. The second thing is that it makes hiring a little bit weirder because people will come in and like, they're getting their equity packages, but it's like, okay, well, how long will it take you to get to this valuation? Whereas I think that our current valuation hopefully is not going to be that long. And so that's the dynamic that you see. And then lastly, I would say it just really limits your optionality in the future. Like, When you think about people that raised that huge valuations, I, I, I've known folks that have done this. You get to a point where you maybe are still doing well as a business, but the markets change and you're just not able to raise at that price with some healthy multiple on it. And so then it just feels like your business has lost momentum, even though maybe internally you feel like, well, you know, we've been growing steadily. It's just maybe not as fast as we could hope, but still at a fast rate. And then you just feel like a zombie company. So I…

AI assessment note: “So there's a couple of things just mentally... The second thing... And then lastly”

Q Jesse, what do you do that you know is bad, but continue to do?

A So I have a tendency to, um, basically it's very difficult for me to escape the low level details. It's definitely not a very responsible use of my time at this point, because like to lead the org, you generally have to make sure you're spending enough time thinking about the high level strategic things, but I cannot avoid just like going each deal that is coming up, like I care a lot about this deal. I have to go like super in the weeds and the refs sometimes don't like that either because it's like, oh man, I'm getting scrutinized so much. That is probably the main thing and you know, trying to, to be a little bit more balanced there, but it's just hard.

AI assessment note: “basically it's very difficult for me to escape the low level details.”

Q Okay, so we have that as, like, number one talent attraction. What else does it matter, do you think?

A The other piece is just with customers, right? Like, customers do care, especially in the early days. It gives them a little bit of validation that, hey, these people are not just gonna be frauds and completely just waste my time. That's helpful. I think the thing that is overrated, and I don't know if this is a hot take, but early on, the brand name VCs will sell their, like, platform, and hey, we have, like, a great platform as well that'll help accelerate. I think it's basically impossible for any VC to help accelerate the process of getting to PMF. I think the issue is that VCs will sell that they can, but that's not possible. On the flip side, though, I would say we've really found the platform teams that are Investor is quite useful, but that was like after we started accelerating.

AI assessment note: “The other piece is just with customers, right? Like, customers do care”

Q Did you face resistance from CFOs buying and top-down enterprises when having to get their arms around a new form of pricing and bluntly a new bandwidth of pricing?

A Not really, actually. So I would say the benefit in our space is that C-suites already want to adopt AI. Like, no one needs to be convinced that, like, hey, we need to use AI. And oftentimes when they look internally in the org, okay, where can we adopt AI? The obvious ones tend to be, you know, customer service and then maybe like coding, like you said. Right. So those are the two obvious ones. That is helpful for us in that we don't have to go in and like try to pitch that, hey, you should be investing in this, like people know. And so then it's more, it's on us to really demonstrate the business case, right? It's like, hey, we have shown that we're able to resolve this many conversations, and we've shown the rate at which you can improve it and add new workflows and iterate on it is really fast. And so in our first year, we think that this will happen, and we've proven it in this pilot, and the customer satisfaction has also gone up. So it should be a no-brainer. That makes it easier.

AI assessment note: “Not really, actually. So I would say the benefit in our space”

Q is that a concern that I hate this argument that a lot of VCs have, which is like, oh, the biggest customers scale out of your solutions because they very rarely do. Shopify and Stripe's a great example, I think. But to what extent is that a concern that the biggest companies in the world do build their own and it works better on their own data in that way?

A I would say from our experience so far, very few people will build it themselves. Robinhood is, I mean, they have an exceptional engineering team, and they also have the different philosophies, and so I think there will be a couple pockets of players that are more of that profile that are happy to build it themselves, where they're super tech heavy and generally on the sort of like the lower enterprise side of things. What we've seen in just the enterprise at large is that folks may have done some work internally. Some people experiment with GPT or somewhat, and They quickly realized that to actually get something production level, you need a lot more than that. You need essentially a whole framework for non-technical users to come in and build and adjust things. You need a learning, monitoring, observability, the ability to run experiments, A-B testing, and stuff like that. Why build that internally? It's just, it will take you way too long, and it's not really what your engineer should be doing, and so that's generally what people come to, and that's when they decide to partner. So I would say it's not very common that we see people doing the full in-house approach, but sometimes it'll be a hybrid, right? It's like, hey, there are certain things we want to own in-house, and so then it's, it's our job to really flex around that and make sure that we can provide the support the…

AI assessment note: “from our experience so far, very few people will build it themselves.”

Q Jesse, what do you think you believe that most around you disagree with you on?

A I'll give a sort of work related one and then more of a out there one. So the, the work related one is, I don't know how hot of a take this is nowadays. I feel like more people are buying into this, but, uh, technically we really value just like clock speed really, really highly. And clock speed, I just mean like how fast your brain works and how fast you can learn. And this is across all functions, right? Obviously it's important for engineering, but for Sales and for marketing and so on, like a bunch of our top sales reps, they're just like really smart people. And they're just like figuring stuff out from first principles. Like they're just go, go, go really aggressive. So clock speed matters a lot more to us. That matters a lot more than things like experience and necessarily being able to sell to this industry and so on.

AI assessment note: “technically we really value just like clock speed really, really highly.”

Q Okay, so we have that as, like, number one talent attraction. What else does it matter, do you think?

A The other piece is just with customers, right? Like, customers do care, especially in the early days. It gives them a little bit of validation that, hey, these people are not just gonna be frauds and completely just waste my time. That's helpful. I think the thing that is overrated, and I don't know if this is a hot take, but early on, the brand name VCs will sell their, like, platform, and hey, we have, like, a great platform as well that'll help accelerate. I think it's basically impossible for any VC to help accelerate the process of getting to PMF. I think the issue is that VCs will sell that they can, but that's not possible. On the flip side, though, I would say we've really found the platform teams that are Investor is quite useful, but that was like after we started accelerating.

AI assessment note: “The other piece is just with customers, right? Like, customers do care”

Q In terms of unlocking higher value, how much more revenue does one unlock when moving from software spend to human labor? You said you're already seeing it. Is this like a three X-ing of spend, a five X-ing, a 10 X-ing? What does that look like?

A It's tough to say just as a rule of thumb, but if I had to give a rule of thumb, human labor is generally like an order of magnitude larger than software spend, like 10 X or more. Of course, you're not gonna capture all of that, but when you're kind of bringing in software, if you can give them like a three X to five X ROI, that's very compelling. And so what that translates to is like, you know, roughly three X multiple on the value you're able to capture there. And so if you, if you look at a lot of our customers, for example, they're spending more on the AI agent Than the previous CRM software for support. And that's because the, the value is just a lot higher. You're able to just like solve the issue completely instead of, you know, just giving software for humans to use.

AI assessment note: “roughly three X multiple on the value you're able to capture there”

Q Dude, you left me with like a cliffhanger there in terms of not the right way to build companies and there being a right way to build companies. How do you think about the right way to build companies in, in 20, 25?

A I think what I've noticed is that generally when you try new things, where you learn new things, the rate at which you're able to discern good versus bad improves a lot faster than your actual skill level. A classic example of this is like if you're learning piano or something, right? Like the rate at which you can tell what is Good music versus bad playing is it can actually grow pretty quickly, but your own abilities will be a lot slower. And so what that leads to is generally people quitting early or just not seeing it through or just getting demoralized. I think there's a bit of that with startups as well, where because you're able to read all the articles about, you know, great founders that have, you know, great stories, or you're listening to the podcasts, you feel like you can discern good from bad really quickly. And then when you're doing the work, obviously it's a lot messier. And so you're in the situation where It's really easy to not pursue things. It's really easy to also just like follow hypey trends and stuff like that. I think the number one thing that people should do is just ignore all of that. It's obviously useful signal, but it's just way too dangerous to read too much into that. I think the right thing to do is you just have to dive into it and you learn through just reps. And for us this time around, the reason why we were able to get off the ground a l…

AI assessment note: “the right thing to do is you just have to dive into it”

Q Did you face resistance from CFOs buying and top-down enterprises when having to get their arms around a new form of pricing and bluntly a new bandwidth of pricing?

A Not really, actually. So I would say the benefit in our space is that C-suites already want to adopt AI. Like, no one needs to be convinced that, like, hey, we need to use AI. And oftentimes when they look internally in the org, okay, where can we adopt AI? The obvious ones tend to be, you know, customer service and then maybe like coding, like you said. Right. So those are the two obvious ones. That is helpful for us in that we don't have to go in and like try to pitch that, hey, you should be investing in this, like people know. And so then it's more, it's on us to really demonstrate the business case, right? It's like, hey, we have shown that we're able to resolve this many conversations, and we've shown the rate at which you can improve it and add new workflows and iterate on it is really fast. And so in our first year, we think that this will happen, and we've proven it in this pilot, and the customer satisfaction has also gone up. So it should be a no-brainer. That makes it easier.

AI assessment note: “Not really, actually. So I would say the benefit in our space is that C-suites”

Q Can I ask what specific function within the company do you think will be most changed in how it operates in the next few years?

A For us is probably sales because go to market is such a big part of organizations like ours. But I do think that three to five years, people will figure out some things that where AI can be really useful. I think we, we're still not using AI as much as we could on, on the sales side. And I don't think fundamentally there should be a reason why like, you know, companies like us are making such a big impact on the, you know, CX side, but sales for whatever reason is just like a constant thing that cannot, AI cannot help with. I do think people will find more creative ways there and that'll really change the way that work is done. Uh, even now, well, our sales team relies a lot on JATGPT to do like research and stuff like that. So that's like one simple use case, of course, but that's like not that impactful in the grand scheme of things. So I do think in three to five years, there'll be some big changes there.

AI assessment note: “For us is probably sales because go to market is such a big part”

Q Dude, what's the biggest internal debate you have at Dakagon today?

A There's a constant push pull of, we have so many customers coming in. Okay. We have to like service those customers and maybe push out work that is more like longer lasting. But then it's like, okay, yeah, but we have to do that work too. So there's, I think that's like the constant, like, okay, at what point do we invest? And that's all kind of under the umbrella of, all right, we hire, hire, hire, hire a lot of people. I think it's a constant dynamic. It's a, it's one obviously we're fortunate to have because we're growing really fast, but I think most companies in our position, there's this constant tug of war between, all right, optimizing for the near-term growth of getting these folks super successful, and then more of the long-term, making sure the product continues to improve and so on. And we're lucky to have a lot of Really great customers. And so we just naturally tend towards like, okay, we're all in. We need to make sure all of our customers are really successful and they get up and running. And then we have our, you know, our product folks are like, oh, but we need, you know, we need engineering resources to do this other stuff.

AI assessment note: “constant tug of war between, all right, optimizing for the near-term growth”

Q How does the crystallization of clock speed transpire in job interviews? What do you do ask to determine clock speed ability?

A I would say that when I interview people, that's the only thing I look for, because the rest of the team does like the actual interview. My job is to, I just have a conversation with them. I talk about the projects they've worked on in the past. I talk about how they make decisions. I ask them to explain stuff to me, like, You know, maybe something that I don't really know much about and it was something that they worked on. I mean, one part of it is like, how good are you articulating stuff? But it's just like, how fast can you think, right? Like I've changed the topic and like ask about this other thing and that's helpful. Yeah. It's more of a general feeling. I wouldn't claim that we have some perfect measure of clock speed. It's just, I think it's become just a view that we hold pretty tightly.

AI assessment note: “I ask them to explain stuff to me”

Q I want to start with Olympiad mathematician, and I think there's a correlation and you've noticed the connection before as well. How do you think being the Olympiad mathematician and the mindset that comes from that helps make you a better founder?

A Yeah, I think it's, it's just this concept we were talking briefly earlier around, you know, math Olympiads is just a, it's a type of math contests and There's nothing really fancy about it other than it kind of collects a lot of really smart, talented, younger people together. And what I've noticed is that a lot of the folks in my generation have, you know, gone on to start companies and done very well. And I'm now in touch with a lot of the younger generation as well, who are just really smart and really ambitious. And it just turns out that these folks do very well in startups. And it's maybe not a surprise on the surface level, but historically, a lot of You know, the people who have performed the best at, you know, math contests and things like that have generally gone on to do research and quant trading and things like that. But I think we're, we're seeing that if you can really unlock that level of, you know, reasoning capability and just like smartness and combine it with folks that can, you know, teach them how to sell or how to build a company, I think that's a pretty good combination. So that's something I think is a little bit underrated right now. I'm really excited to spend as much time as I can with the younger generation and see if I can, you know, push them into Doing more startups, doing more sales. I think that's, that'll be really, really fun.

AI assessment note: “unlock that level of, you know, reasoning capability and just like smartness”

Q Dude, you left me with like a cliffhanger there in terms of not the right way to build companies and there being a right way to build companies. How do you think about the right way to build companies in, in 20, 25?

A I think what I've noticed is that generally when you try new things, where you learn new things, the rate at which you're able to discern good versus bad improves a lot faster than your actual skill level. A classic example of this is like if you're learning piano or something, right? Like the rate at which you can tell what is Good music versus bad playing is it can actually grow pretty quickly, but your own abilities will be a lot slower. And so what that leads to is generally people quitting early or just not seeing it through or just getting demoralized. I think there's a bit of that with startups as well, where because you're able to read all the articles about, you know, great founders that have, you know, great stories, or you're listening to the podcasts, you feel like you can discern good from bad really quickly. And then when you're doing the work, obviously it's a lot messier. And so you're in the situation where It's really easy to not pursue things. It's really easy to also just like follow hypey trends and stuff like that. I think the number one thing that people should do is just ignore all of that. It's obviously useful signal, but it's just way too dangerous to read too much into that. I think the right thing to do is you just have to dive into it and you learn through just reps. And for us this time around, the reason why we were able to get off the ground a l…

AI assessment note: “the right thing to do is you just have to dive into it and you learn”

Q I want to start with Olympiad mathematician, and I think there's a correlation and you've noticed the connection before as well. How do you think being the Olympiad mathematician and the mindset that comes from that helps make you a better founder?

A Yeah, I think it's, it's just this concept we were talking briefly earlier around, you know, math Olympiads is just a, it's a type of math contests and There's nothing really fancy about it other than it kind of collects a lot of really smart, talented, younger people together. And what I've noticed is that a lot of the folks in my generation have, you know, gone on to start companies and done very well. And I'm now in touch with a lot of the younger generation as well, who are just really smart and really ambitious. And it just turns out that these folks do very well in startups. And it's maybe not a surprise on the surface level, but historically, a lot of You know, the people who have performed the best at, you know, math contests and things like that have generally gone on to do research and quant trading and things like that. But I think we're, we're seeing that if you can really unlock that level of, you know, reasoning capability and just like smartness and combine it with folks that can, you know, teach them how to sell or how to build a company, I think that's a pretty good combination. So that's something I think is a little bit underrated right now. I'm really excited to spend as much time as I can with the younger generation and see if I can, you know, push them into Doing more startups, doing more sales. I think that's, that'll be really, really fun.

AI assessment note: “unlock that level of, you know, reasoning capability and just like smartness”