00:26:22 · Luca Ferrari
So mostly software or digital products do not succeed because it's very expensive to replicate them. They're now SpaceX. They mostly succeed because of other factors. First mover advantage, sometimes you start, you build a brand simply because people know you and nothing else. And then there is a lot of habits people don't, you know, the actual switching costs but just also just psychological switching costs people don't want to bother switching if it means you know our company making a billion in revenue you're saving $3,000 for that such who wants to even bother taking the time and risk and it's rational not to do so. Two, it could be network effects. We own Eventbrite. People don't go to Eventbrite because you know nobody among all homo sapiens has managed to replicate a ticketing platform but it's because on Eventbrite you have 90,000,000 people who go monthly look tickets, and so you sell more basically, you know, right? And so AI can replicate that a million times, but they don't have the audience. And people go there because everybody posts events there, right? Or something like Komoot, you have proprietary data. With Komoot because of the history with all these users, we know that that particular path is muddy and you know it rained and there is a dog barking like and we have photos of those corners and trees and whatnot and turns that are not available, they're not available on satellite, they're nowhere to be found. And so again, it's not... It's actually... Komoot is quite sophisticated actually the routing, but still, know, smart people can build that over time, but they don't have the data or scale advantages, you know, Airtable, our most recent acquisition. Most customers pay Airtable less than $20,000 a year. Mostly there are large enterprises. Even if you get to a point where you can ask your agent by AI agent to press a button and it replicates the code for air table pretty much perfectly, which we're very far from. But we'll get there I think, you know, soon enough probably within the next few years. Supposedly, you'll still want to have someone at the company who's responsible to making sure that that code seems legit, that if something breaks goes to the agent and asks the agent to fix it and whatnot.
00:28:33 · Luca Ferrari, Anish Acharya
I think there's a far future where AIs run our lives and companies nobody works any longer, which is interesting, but you know, it's not... I think most people would agree it's not in the next five That's right. As long as you have someone being responsible for things, even just a 0.2 FTE of a software engineer would still cost you more than paying air table for the same thing. So, sure it's an interesting philosophical point but ultimately you're not gonna do it rationally. It's dumb. So there are all sorts of reasons why I think most not all but most digital products are reasonably well protected. Again they will suffer on the margin new customers you know maybe people search for products through their agents and agents are have a different set of criteria they follow to choose and so before they people tended to choose product A now it's more product B that will certainly hurt product A not to the point that they melt away overnight, you know, because of this. [00:29:29] S1: And those are natural market evolutions, right? Like web to mobile and preferences change, maybe it's agents instead of humans that are searching, but, you know, our data shows that the enterprise spends eight to 10% of total spend on software. So as you think about where you wanna point your AI and your token budget, it's probably not in replicating payroll. It just seems like a high downside, low upside proposition. [00:29:51] S2: I... Sounds so great.
00:29:51 · Luca Ferrari, Anish Acharya
[00:29:51] S2: I... Sounds so great. [00:29:52] S1: Yeah. Excellent. Hey. So you mentioned proprietary data, which I think is actually under discussed these days with everything happening in models. You know, there's a quip that many companies are sort of trading for less than the value of their tokens, which is the value that they would have if they were simply acquired for training data by models. Do you think that's true? Do you think proprietary data has changed?
00:30:13 · Luca Ferrari, Anish Acharya
[00:30:13] S2: So I I I haven't thought enough about this and I don't know enough about it, but I will say that claim strikes me as potentially a little bit misleading. Meaning, the value of data within new pools declines pretty rapidly. It's like if today I write a new blog post on the internet, nobody's gonna give me money for that. It's private and I'll go to open... I'm about to, you know, I may publish it, I may not. Do you want to give me it on top? You know, the first blog post they ever got access to or could ever access, they would probably have paid a thousand dollars for it, but now zero because incrementally it doesn't add anything to the corpus of knowledge that you can train AI models. So yes, maybe there's now a need for plenty of data on how people do project management or whatnot. The first companies selling that maybe make a bunch of money, but I think over time that trends to zero as the, you know, the quality of incremental training you know, returns a decelerate for incremental training. But I'm I'm in no position to determine whether, you know, companies could sell data for more of their enterprise value. I'm not I'm not certain about that. Interesting. Let... [00:31:24] S1: Let's double click on the kind of operating system you use at... Within Bending Spoons to help improve and optimize all of the companies. Can you talk about some of the key components of it? And which of the components do you think would have been most useful to you as a single product founder versus are very, you know, specific to what you do with the portfolio?
00:31:24 · Anish Acharya, Luca Ferrari
[00:31:24] S1: Let's double click on the kind of operating system you use at... Within Bending Spoons to help improve and optimize all of the companies. Can you talk about some of the key components of it? And which of the components do you think would have been most useful to you as a single product founder versus are very, you know, specific to what you do with the portfolio? [00:31:42] S2: So there's pretty much everything that you need to run a digital business. We built in house with the exception of highly, I think universal tools like Gmail or Slack and these sort of things or Google Drive, we're happy to use those that others build. We don't have highly specific or personalized needs. But everything else from data processing, the AB testing platform, all the marketing automations, user lifetime prediction, credentials management, recruiting tools, it's... The list goes on and on depending on how you count, it's over 50 different in house tools. They... We build them ourselves and and the reason why this has been valuable for us is, well, the obvious way is we we save on vendor costs quite a lot actually, but but it's not something I'd say is a game changer. [00:32:36] S1: Mhmm.
00:32:37 · Luca Ferrari
[00:32:37] S2: The two game changers are one, we make them super fit for purpose and very specific to our needs and these tools are natively integrated with one another, so the sum of the parts is much greater than what you could possibly accomplish by buying these different tools from different vendors, because each is better for what we exactly need to do and they also work in a more harmonious way together. Also because we can... There are this big monolithic foundation, we can swap out the foundation at an acquired business quite rapidly. It's actually much faster to change the entire thing in one or more or less the entire thing in one shot than removing one piece and then you place the alternative and you need to plug it to everything else, then remove another piece, plug it to everything else. So actually swapping the entire thing out tends to be faster. Now of course this is not particularly relevant to a single product company because they they don't need to buy new companies so that's that's not what they need. But they could probably benefit from a bunch of these tools. A lot of the value is unlocked by having the right culture and the right talent because like every tool ultimately the user makes a ton of difference and so depending on what company we're talking about they could be able to take advantage of it or not but some are more let's say autonomous and they don't require a lot of effort and quite powerful for example we built something called ALT ALT, like alternative spooner, that's a... It's basically an agent that lives in Slack and you talk to an ass, automatically has access to all the same tools and assets as you do. It's the same GitHub repos, same folders drive and whatnot, and you can task it to do things for you. Anything you could do in theory could do, and increasingly it can do and does. So you can tell it, for example, to run a data analysis for you and it will go fetch the data. If it doesn't have access, it will escalate a request to our credentials management team and potentially our IT security team if relevant.