00:27:31 · George Bonaci, Guillaume Moubeche
And I actually think that's probably like more accretive to the business because it means then that like there's lower risk. That person doesn't need to go learn like a completely new skill set or go try something that is incredibly high risk. It means that they can actually become a deeper subject matter expert and actually get like insanely higher alpha from like this channel that the average company or the average person running that channel would not have time to do because they're still clicking buttons in a platform. So So I think it probably makes subject matter experts... Deeper subject matter experts, and that's probably where you distinguish great talent from okay talent. So again, I think I think AI becomes like an amplifier. It's a big lever in every way. [00:28:06] S1: Yeah. I agree. The only question I have for you is on... To build up on that topic is whenever you look at entry level jobs or like people, you know, who are just getting starts, because I feel like not domain experts can really like dig in a lot more on what they know and they can become, you know, like the 10x profile are becoming 100x. However, for a 10x profile to exist, you know, they had to start eventually at a job and become a 10x profile, and it takes time, anything. And I feel like the entry level job, to be honest, most of the time can be done by an AI or an agent. So how do you think that over time, the... We're we're gonna be able to actually, like, keep having the entry level job, train them, and not just replace all the the task with the agents?
00:28:54 · George Bonaci
[00:28:54] S0: Yeah. The answer is like, I don't really know. I've flip flopped on this quite a bit. I'll I'll give you both points of view, but, like, I have flip flopped. And the the only thing I really can say is is Ramp published some really interesting research maybe a couple months ago that showed that the top adopters of of AI in terms of companies hire more junior people than companies that adopted AI less. And and that's very counterintuitive. It's probably a different flavor of Jevan's paradox, to be honest, but I kind of buy it. Like, you want for... And and it's probably for a couple reasons. It's probably because number one, they're more open minded, and they're more able to adapt as the technology is changing so so quickly. I think we've all felt like we're falling behind because things change, like, weekly. So I think if you're early in career, you have, like, fewer frameworks that you need to break or or or fewer priors that you need to overcome. And so, like, you're probably, like, able to adapt to these changes more easily. So maybe that's one reason why these companies are hiring more early in career folks. I think number two is you're probably more likely to be AI native if you're relatively early in career versus like late in your career. It's just like you grew up with it. So maybe that's number two. Now having said all of that, I I... I've kind of always had the philosophy that like you really shouldn't fully automate with AI or otherwise. Like you shouldn't automate something that is a path for a junior person to become like a mid level person and a senior person. Like you need to have build those talent pipelines and those learning pipelines. I shouldn't even call it talent pipelines. There's learning pipelines. Otherwise, like, you're really just screwing yourself over as a business in the future. And that is both specific to like these subject matters. Like, I'm not going to fully automate Google Ads because I need a Google Ads expert in the future. And we need to understand how the systems work and we need to understand like where they break and what their limitations are. But I'll automate the things that are not a competitive advantage and do not need to be learned. Like, there's still a role for mental math even though we all have calculators.
00:30:36 · Guillaume Moubeche
[00:30:36] S1: Yeah. Yeah. I agree. I agree 100%. Something I I wanted to to know also because you've looked a lot at, you know, like, where people are spending time and money and how exactly it splits and evolves. A few months ago, like, mean, earlier this year, a lot of people started talking about Saaspocalypse, like, the end of software as we know it. We've seen, you know, like all these public companies stock price, like, going down, getting, like, shrank, etcetera. So from your perspective and from what you're seeing at Ramp, like, do you see companies actually spending less on software, or do you see them still spending the exact same amount of money?
00:31:12 · George Bonaci
[00:31:12] S0: That's a great question for Ara, our economist, actually. He should he should write a post on this if he hasn't already. But the best example is is I remember when Claude Design first launched and the markets, I think, massively overreacted against, like, Figma as a stock. Yeah. And I was like, this is crazy. Anyone that's used Figma knows that, like, the, like, the overlap between these two concepts is very low. So I bought a bunch of Figma stock. I was like, this is... This doesn't make any sense to me. And I think that's true in general. I think there's a lot of hype around, like, how AI is going to destroy jobs, destroy industries, destroy companies, whatever it might be. But the reality is is like number one, I think the technology diffuses much more slowly than we expect. We are all like... The fact we're having this conversation demonstrates how early we actually are to this is I think number one. Then number two, I think like, especially with these really big paradigm shifts, you really don't know how it's going to play out. Like the Cloud Design Figma, like interact... Like micro example is is a great one because like the market was wrong. And I think the markets are generally very right when there is like a steady state of historical data that you can look at and like make some informed decisions on. And that that is not the case right now because the technology is changing so quickly and this is not something that most of us have been through in our lifetimes. And so humans are bad at forecasting is maybe what I would say. They're bad at predicting the future. I So wanna try to predict the future, but I do not think that things will go the way we think they will go.
00:32:28 · Guillaume Moubeche
[00:32:28] S1: And do you feel like internally you've replaced a lot of the tools you were like buying to have someone like, I don't know, like rebuild a tool from scratch and vibe code it and maintain it? Or are you more on the on the camp of people who just like, you know what, we're paying a 100 or $200 per month, having a software engineer to build a copy of this that's not gonna work the same and it's gonna be hard to maintain is not worth it.
00:32:53 · George Bonaci
[00:32:53] S0: Yeah. We've used a lot of AI to replace tools that we haven't bought is what I would say. That's like my cheeky answer. It's like it doesn't actually make that much sense to me to use AI and put effort into saving money when you could be using that to like like drive growth. And again, this is maybe just my growth bias, but it's like, why would I spend... If I had like, I don't know, a human hour and I can save myself a $100 a month by recreating this software. But to your point, there's maintenance costs, there's etcetera etcetera. Or I can take that human hour and I can use it to like drive like some small incremental growth. I'll take the incremental growth. And so I personally have not spent a lot of time or my team has not spent a lot of time using AI to replace things we're already paying for. Like, we're paying for them for the reason. They're creating some value. I would rather they use that AI to build something that like where the perfect solution does not exist or something that is actually like fully tailored to a problem that maybe only we have. I think that's the magic. Like Ramp has generally always had a bias for building versus buying because we hire a lot of builders. And as a result, we have a lot of very unique processes and tools. And being able to allow anyone to build their own processes and tools, like for them as an individual, for their specific company, like that's not taking money away from a from a SaaS business because the SaaS business never existed.