00:04:42 · Molly O'Shea, Gil Feig
[00:04:42] S1: Did you also notice when you're doing that the vulnerability of your business before? [00:04:47] S0: I mean, I think absolutely. I think we realized in that moment, it was the classic innovator's dilemma where we had, you know, this product, it was growing really fast, it continues to grow really fast. But we also knew where the space was going, and we wanted to put as many resources as possible in our newer products. And so during that month, since he was talking about, we also realized, like, we do not have this ability to just put unlimited resources on this new thing. How can we get more with less? And that was why we we knew we had to invest in AI. We knew we had to be able to build new products and and, you know, adapt our existing products with just a couple of people. So honestly, actually building our our next product agent handler, we had one engineer and me and Shenzi, and we were helping on nights, weekends, we did whatever we could half because we needed to contribute, we needed to help. And the other half was if we are the leaders of this company, we have to know everything there is to know about how AI works, how you build with AI so that one, we are more effective, and two, we're building for where the puck is going.
00:04:47 · Gil Feig, Shensi Ding
[00:04:47] S0: I mean, I think absolutely. I think we realized in that moment, it was the classic innovator's dilemma where we had, you know, this product, it was growing really fast, it continues to grow really fast. But we also knew where the space was going, and we wanted to put as many resources as possible in our newer products. And so during that month, since he was talking about, we also realized, like, we do not have this ability to just put unlimited resources on this new thing. How can we get more with less? And that was why we we knew we had to invest in AI. We knew we had to be able to build new products and and, you know, adapt our existing products with just a couple of people. So honestly, actually building our our next product agent handler, we had one engineer and me and Shenzi, and we were helping on nights, weekends, we did whatever we could half because we needed to contribute, we needed to help. And the other half was if we are the leaders of this company, we have to know everything there is to know about how AI works, how you build with AI so that one, we are more effective, and two, we're building for where the puck is going. [00:05:43] S2: Yeah. You know, back then, like, mostly our engineers were like, oh, like, I'm gonna input this code snippet into ChatGPT and, like, ask a few questions about, like, how to fix something. But after that, it was like, saw what was possible. Everything was different. We were like, you need to be building completely zero to one with AI.
00:05:43 · Shensi Ding, Molly O'Shea
[00:05:43] S2: Yeah. You know, back then, like, mostly our engineers were like, oh, like, I'm gonna input this code snippet into ChatGPT and, like, ask a few questions about, like, how to fix something. But after that, it was like, saw what was possible. Everything was different. We were like, you need to be building completely zero to one with AI. [00:05:58] S1: How do you keep your team up to date on everything? Obviously, like, that is an example. But, some founders have really pushed their teams to actually use and embrace AI. And then also with your new hires, do you test them? What is the experience? [00:06:14] S2: It's cultural. Like our whole team is really encouraged to do it. If you don't do it, it does it is a part of your performance. So everyone will share tricks that they learned. Also feature people who are really AI forward, it's not just R and D. So our accounting team is really AI forward, same with our recruiting and finance teams and our marketing team too. So everyone is in Cloud Code. Everyone is generating things and making it automated as much as possible. And we just really try to highlight it as like a positive part of like the merge experience.
00:06:14 · Shensi Ding, Gil Feig
[00:06:14] S2: It's cultural. Like our whole team is really encouraged to do it. If you don't do it, it does it is a part of your performance. So everyone will share tricks that they learned. Also feature people who are really AI forward, it's not just R and D. So our accounting team is really AI forward, same with our recruiting and finance teams and our marketing team too. So everyone is in Cloud Code. Everyone is generating things and making it automated as much as possible. And we just really try to highlight it as like a positive part of like the merge experience. [00:06:42] S0: We also have, yeah, we have brown bag lunches, training sessions. We just started a recurring session on merge skills. So just skills that you've built and how to use them. We ask about it in the interview process. So what we're not looking for is I use the latest cutting edge, but we're looking for, you know, hey, I use it to code sometimes, my company doesn't let me use it to do all these things, but I really want to. That's what we're looking for is just the desire. And I think we've brought in the right people for it now. [00:07:08] S2: Yeah, but I think it has to just be cultural.
00:07:08 · Shensi Ding, Molly O'Shea
[00:07:08] S2: Yeah, but I think it has to just be cultural. [00:07:11] S1: Yeah, it's so interesting. More of the conversations I've had recently when I'm talking with either public companies, CEOs, or really high growth companies, when they're hiring out, they're trying to hire for founders or previous founders, people that went through YC, like that kind of thing that like high agency self, what's it called? Self development. It's an autodidact type of DNA within talent. And I've just been seeing this over and over again where there is a clear bifurcation in people who just, like, think you can still just, like, apply to get a job. By the way, the the bar is always very low or high. I don't know what it is. It's one of it's one of those types where it's as you've taken on and you've used adoption with AI more and more internally, and the models are developing faster and they're more they're just more efficient. Have you noticed the company build faster? Are you seeing the direct correlation there? [00:08:09] S2: Yeah, I mean, last year, we didn't increase headcount that much, but our revenue accelerated pretty significantly. And so it's had like meaningful leverage on our business.
00:08:09 · Shensi Ding, Gil Feig, Molly O'Shea
[00:08:09] S2: Yeah, I mean, last year, we didn't increase headcount that much, but our revenue accelerated pretty significantly. And so it's had like meaningful leverage on our business. [00:08:17] S0: And yeah, think one thing Shuncie actually brings up a lot of the company is Keith Rabois barrels versus ammunition, you know, sort of sort of view where you have some employees that are ammunition, they're really good at specific tasks and just knocking that out. And then you have barrels who basically just knock down doors and will do anything to get things done. And it used to be that, you know, a team that had a barrel of a PM or a manager and a bunch of ammunition could get a lot done. But now that you kind of can have one person just go use, you know, Codex, Cloud Code, whatever, to go build something, you really just need a lot of barrels to just go get things done. So that also has shifted how we're hiring. [00:08:52] S1: How do you do you like Codex? How's that going? [00:08:54] S0: We we love it. Yeah. We we like we like Codex and Cloud Code. We use both. We're we're big fans of them. [00:08:59] S2: Yeah. We we allow just like a budget, so you can choose whatever tools you prefer. Some of our team members prefer Codex, some of our team members prefer Clot. It's really just dependent on like, some people prefer like other things. It's really just up to their preference. [00:09:09] S0: Yeah, the only, we just have security reviews, obviously. Can have people going wild on every tool, but otherwise, yeah.