00:14:04 · Guillaume Moubeche
[00:14:04] S1: Do you struggle sometimes like to allocate the right resources to the right project? Because as you mentioned, you know, like people need to get excited to join like a specific project. Sometimes it feels, you know, like internally you have like a lot of freedom to come up with your own ideas, come up with your own project, and it also feels like tons of mini startups within ramp. So how do you make sure that competition stays healthy and that your best resources are actually like working on the best project or where you have actually the best conviction.
00:14:37 · George Bonaci
[00:14:37] S0: That is ultimately, I think, like the leader's job. So it's ultimately, I think, my my job. Like resource allocate... It's like, what is the job of like a leader? It's like, okay, it's to have some general direction you want people to go in, to it's have the right people on and then it's to make sure that they're resourced. So I think the resourcing aspect is is ultimately on me. Now, if there's like a project or an initiative that people are just not... No one's excited to be on, or there's a critical function to your point that is needed but is not excited to be on that, then then that is ultimately a failure of the leader of that pod or of me to clearly articulate why this is important to the business. And I think there will be incredibly rare cases where the individual like understands why it's important to the business, still just doesn't wanna do it. And I think that probably means we may have hired the wrong person. Because ideally, you're hiring people for these types of roles where they have really strong business acumen, and they would be excited by working on something that is of strategic importance to the business. If you're not excited by that, then you shouldn't be at a startup. And we're still trying to run as... Run ourselves as a startup. So anyway, it's like, I think putting thought into those types of, like, self reinforcing loops is is very, very important. Whether it's like very tactically how you run a channel, but also just how you you build an organization and hire the right people and have the right operating cadences.
00:15:45 · Guillaume Moubeche
[00:15:45] S1: Nice. I wanna talk about, you know, like, models war and also like AI expenses. Because obviously, like, you can see which models companies are using and also how much you're spending across different providers. So what are you learning that the rest of the markets can't see yet?
00:16:03 · George Bonaci
[00:16:03] S0: I would say probably three things. And to be honest, these are all pretty obvious, but I think it's nice to have data to validate sometimes the obvious. So in this case, I think number one is there is no, like, model loyalty. I think maybe there was, like, earlier on when when AI was becoming a a a thing. Like, I remember I was, a huge Cloud Code loyalist. And then you realize how quickly, like, the technology is adapting just, like, how fierce the competition is between all of the Frontier Labs and even some of the the the open source models. And the data really does show that there is no model loyalty is like number one. I think number two, which again is very obvious and we've talked about a lot is is people just default to the highest intelligence that wastes money. To be honest, I think the user experience of a lot of the harnesses and a lot of the model... Like the products in the Frontier Labs where you choose the model and then you choose the effort and there's like six gradients of effort. It's like the average person has no idea what to pay. What's the difference between medium and high and high and extra high? So they just choose the best because they're not paying for it. They don't actually even really know what it costs. So they're just gonna choose whatever the quote unquote the best is. And I actually think that's why it's really, really important to have some sort of gateway and router that is not forcing that decision on people that have more important things to worry about. But that's that's very a separate conversation and tangent. And then I think like the third that the data is really showing that we've published some research about is you just have to be part of the race. You have to be part of the game. You have to be using AI. You have to be forcing your employees to adopt it if you haven't already. And you see that the companies that are adopting AI are are they're growing more quickly. They're hiring more people and it's like very counterintuitive. But it's just a great example of of Jevan's paradox. So I don't know. The data backs that, but I think if you sat down and thought about what was going on from first principles, you come to very similar conclusions.
00:17:39 · Guillaume Moubeche, George Bonaci
[00:17:39] S1: Can you explain what Jevan's paradox is? [00:17:41] S0: Yes. Basically, if something becomes very, very cheap, it doesn't get used less, it gets used more. Like the best example, think the classic example, it's like, oh, you add more lanes to a freeway, that doesn't decrease traffic. That that increases it because all of a sudden more people are gonna start driving. And I think that's very true obviously like with work. Like work used to be a commodity that could only be executed by like human beings or maybe like pre industrial revolution like by animals. And now work can be done by like by agents. And and not not just like manual work, I should say, but like actual, like, intellectual intelligence can now be done by machines. And so when that gets commoditized, it's not like people lose their jobs. It's actually probably like, we probably need more people. We need to hire more people. It's very similar to the the freeway example.
00:18:21 · Guillaume Moubeche
[00:18:21] S1: What's your feeling like personally, you know, because to me, when we started to see, you know, what we could do with, I don't know, like OpenAI, first we chat GPT, then Cload, etcetera, like early on, you know, the thing was, okay, everything's gonna be automated. We're gonna ultimately like work less. However, you can do a lot more, and you kind of feel like you have superpowers, and eventually, you work on so many more stuff yourself that you start like changing context and topic, like very quickly. Like typically like myself, when I work, I always have like at least, I don't know, four or five, like a chat GPT conversation at the same time. So when you think about something, I'm doing something else, or, like, having agents running in the background. So... And eventually, like, you feel like you're switching context all the time and your brain feels like it's working, like, 10 times harder than back in the days. Like, what's your feeling like personally? And do you also see like a similar impact?