00:17:23 · Sebastian Johnson, Eric Landau
[00:17:23] S0: Yeah. [00:17:24] S1: There are places where the AIs are just kind of continuously improving at the rate or faster than the rate than than we expected. So while, yeah, there's gonna be a lot of competition, the progress of the models is not slowing down at all. And I think that's, like, a misconception that some some people have. [00:17:38] S0: Do you have a peg for who might come out on top, or are you able to look... Looking at those metrics? Are you able to see differences in the providers?
00:17:45 · Eric Landau, Sebastian Johnson
[00:17:45] S1: I think there can be multiple winners because there's there's not just one end metric that matters. Mhmm. And, I think, like, one also misconception that people have is that the intelligence of the model is the only thing that matters. And the the analogy that I give is that, you actually don't need very high intelligence for a lot of use cases. You don't want Einstein to be bagging your groceries. Like, I don't I don't need, like, a PhD level physicist to be the one that just kind of recognizes the carrot from the milk and puts them in the in the right place in the bag. Right? I need someone that... I need, like, a a model that's, like, sufficiently good. Yeah. So you can have model providers that actually do some set of tasks, like, really, really well. And and now Anthropic is doing really well in, like, focusing on enterprise and coding, and they're really winning in that space. And OpenAI is focusing on consumer use cases and the regular chatbot and, like, being more of a personal assistant. There is a world where... Like, there's more than one winner. Mhmm. At the end of the day, if I was to be a, like, purely betting man, I would say that probably the company that's attached to the biggest cash cash machine will win, which is, Google. I mean, they're, you know, they have a huge ATM in their pocket that's constantly being fed, to to data centers. They can continuously create models, but I think it won't just come down to a singular, player at the end of the day. [00:19:03] S0: Okay. Interesting. Multimodels but focused in different niches.
00:19:03 · Sebastian Johnson, Eric Landau
[00:19:03] S0: Okay. Interesting. Multimodels but focused in different niches. [00:19:07] S1: Different niches. Yeah. Models are... They have personality. Right? They're like people. Right? And they... They're gonna be good at things, and we're gonna have different, preferential, kind of treatment and, interactions with with different types of models. So there's no reason why... Yeah. You you know, you'll want a model that's, like, super friendly and, like, this is your friend model, and this is your coworker model Yeah. And this is, like, your, you know, how... Your servant model or whatever. Those can all exist, like, in in tandem with each other. They don't have to be one, one, end model. [00:19:41] S0: How how do you think about leveraging AI in your business internally? We've seen things like, OpenClaw. You know, these are major regenerative tools be... Know, that felt like another ChatGPT moment when had this new technology come out. We're seeing a wide range of tools across marketing, go to market. How are you getting up to speed with the tools and implementing them across your own business?
00:20:00 · Eric Landau
[00:20:00] S1: Yeah. It's it's a good question, and I don't know if I have, like, a great answer. We've done things like integrate, Claude into all of our different, kind of knowledge based tools into Slack and and Notion and, get people the right answer very quickly just by tagging it, and so it can pull all the, you know, the the relevant data. We did a, like, a a cursor slash, Claude Code hackathon in our... So, like, who can build the coolest thing without actually touching a line of code, within the the engineering team. And so we have, like, these different things that we're trying to enable the the the tools, but I don't think anyone has really settled on, you know, what the equilibrium state of using AI within your, like, your company context because these things are changing rapidly as well. And, I asked, like, a bunch of... Like, my co... My cofounder and I, like, are asking different companies, like, how are you using AI? And no one really has something which is, like, a very killer, hey. This completely changed everything. It's more like, yeah. We've, we use Claude here, we're doing more coding here. I think that will just continue to, like, evolve on a, like, a more piecemeal basis. Yeah. And that... That's been
00:21:05 · Sebastian Johnson, Eric Landau
[00:21:05] S0: my experience with the AI tools, like, even, like, OpenClaw, you know, using it or, like, the experience of seeing it being used Yeah. Versus setting it up for myself. I did it, and I was like, well, I kind of actually don't know what to do with it. Right. Right. It's so cool, I can see the potential Yeah. But I'm not seeing the immediate, I'm gonna use it to do these 10 things right now. [00:21:23] S1: Yeah. I I think one of the biggest bottlenecks actually that we have in AI is just a human imagination because we don't know what we can do. It's like, you give someone from the '17 hundreds, a television. Like, they're... They they don't even know where the on button is. Yeah. They... Oh, they don't know, like, what what to do with it, or you give a tool that, we just don't have, like, the the full, yeah, the full visibility of, like, all the different use cases and and ways of... And that will take time for us to just absorb them and, like, put them into our our day to day life, and that's probably the slowest part of AI adoption. And then there's...
00:21:23 · Eric Landau, Sebastian Johnson
[00:21:23] S1: Yeah. I I think one of the biggest bottlenecks actually that we have in AI is just a human imagination because we don't know what we can do. It's like, you give someone from the '17 hundreds, a television. Like, they're... They they don't even know where the on button is. Yeah. They... Oh, they don't know, like, what what to do with it, or you give a tool that, we just don't have, like, the the full, yeah, the full visibility of, like, all the different use cases and and ways of... And that will take time for us to just absorb them and, like, put them into our our day to day life, and that's probably the slowest part of AI adoption. And then there's... [00:21:58] S0: I think there's a really interesting gap that's going on, like, whereas in in software, the the classic y c advice was, like, build something that people want. Yeah. And now we're get... We're into this place where people are not aware of the, including myself, the the abilities or the capabilities of the technology. Yeah. So when they're saying things that they want, it doesn't match up with the actual technology that's available to you. And that's what I feel like. It's like I'm talking to these agents, and I'm like Right. What can you do? Yeah. Yeah. It's like the the old Henry Ford quote, which is like, I built something... If I gave people what they want That's right. I'd give them faster horses. And we're at that moment where [00:22:27] S1: it's like,