00:44:20 · Luca Ferrari
[00:44:20] S0: Yeah, think it's a good way of looking at it. We have businesses with lower retention, businesses with higher retention, but I can't recall a single instance where retention got worse after Bending Spoons took off. Often it's improved, it's at least stayed the same, but ultimately we win by being relatively better, we don't need to, it's not that we necessarily need to buy only businesses with perfect retention, as long as these businesses do better under us than under the previous owners. And so we're, you know, we bought businesses with mediocre retention and business with great retention and generally preserved or improved those retention rates. And yes, when it comes to our team members, we are fanatical about creating one of the best work environments on the planet. And and I I think, you know, there's a lot we could improve and no doubts about it. We've put plenty of flaws, but we've done pretty well there overall. We we have had essentially no unwanted churn of Spooners, of these people part of the core team. Last year we had point 6%, so 0.6% were most companies in tech as far as I can tell. Now 5% is considered actually pretty good, and a lot of that super high retention comes from the excitement of being able to learn and grow across all of these different challenges. It keeps it fresh and interesting. Whereas if you're working on say Evernote, maybe it was exciting the first couple of years, let's say our product manager, but then after a while, you know, refining and refining and note taking, it can grow stale. But what can you do? You like the company, but ultimately you have to look for a different employer to take another step, learn something new. At Bending Spoons, just raise your hand and say, I feel I've exhausted my creativity and excitement for Evernote, what else can I do? And we may put you on a platform team to build an internal technology, you could move to AOL and trying to improve email UX, which is a completely different and fascinating challenge. So yes, we have been able to retain people very effectively when it comes to the retention of teams that come on board through acquisitions. I was quite worried initially that a lot of people would feel disheartened because, you know, it's it's... Know, we got acquired. Sometimes this is perceived as a failure even though it's it's not...
00:46:50 · Luca Ferrari, S1
It's maybe success meaning that you actually got a nice exit, but then you may feel that you're not as important as the core central team. I think there too, there is a lot to improve and we value these teams and we're trying to do better, but overall I think things have gone a lot better than I thought they would. We have an excellent relationship with old team members, retention rates for those team members, in no case are they lower than pre municipals, sometimes they have improved substantially. So at least we're not damaging the quality of those workplaces, if anything, in many cases improving it. But those retention rates are not as high as those we have in the core team. They're more in line with what I said before, that's considered okay for most technology companies. [00:47:40] S1: What would you say is core to the culture here?
00:47:45 · Luca Ferrari
[00:47:45] S0: There are several things, but probably the one or two that truly stand out as particularly distinctive, one is something called extreme ownership. We want everybody to care deeply, tremendously about being amazing at what they do about helping their team and the company succeed. We'd rather work with slightly less intelligent people if it comes down to that, but they have to really care. We don't want to work with anybody for whom doing well here, seeing the company succeed is not our super high priority. And the second aspect of our culture that I think is unusual is that we are highly scientific in how we approach the work. Again, first principles, being logical, being rational, being enthusiastic and putting a lot of effort into probing again reality for, you know, so that we can refine that model of the truth. We do a lot of that. Last year alone, we ran more than 3,000 experiments across our products. And those are just the ones that are like super quantitative and recorded and documented. Of course, there are a lot more initiatives that maybe don't qualify as a perfectly rigorous experiment, they're motivated by a desire to learn and understand things better. And that building Bending Spoons as a learning machine has been instrumental to, in general succeeding, specifically being able to expand the competence circle so that we can successfully acquire and transform and drive returns from a broader and broader set of businesses. If you look at what we were acquiring, you know, when we started, it was very simple iOS apps, very basic, and then more complex and larger iOS apps, then Android apps, and then web products, and then we went from self serve to now substantial enterprise sales organizations, and we even did, like I mentioned earlier, hardware recently with Tractive, and I mean, it's early, but it's going really well. So we try to keep expanding the share of the world that we believe we understand and improve that understanding continuously.
00:50:22 · Molly O'Shea, S1
[00:50:22] S2: Today's episode is sponsored by VCX by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at getvcx.com. Some of you may not have heard this yet, but our sponsor Public just launched something called Generated Assets, and it brings AI into investing in a way I've honestly never seen before. Here's how it works: You type in an idea like AI powered supply chain companies with positive free cash flow or defense tech companies growing revenue over 25% year over year. Publix AI then dispatches a swarm of agents that scan every single US stock, evaluates them, and instantly builds a custom index around your thesis. What really stands out is how clearly it explains why each stock is included. And before you invest, you can even backtest your idea against the S and P 500, so you're making decisions with real context, not just guessing. And beyond generated assets, Public lets you invest in stocks, bonds, options, crypto all in one place. They'll even give you an uncapped 1% match when you transfer your investments over from another platform. If you want to build a portfolio that actually reflects your thesis, visit public.com/sorcery, paid for by Public Investing. Full disclosure is in the description. Founders scale faster on Deal. Set up payroll for any country in minutes, hire anyone anywhere, Get visas handled fast and get back to building. Visit deal.com/sorcery. That's deel.com/sorcery. [00:51:54] S1: Talking about the culture and how you've built out, where do Alt Spooners come into play?
00:51:58 · Luca Ferrari
[00:51:58] S0: Yeah. Alt Spooner as an ALT alternative or alter ego is just one of those 50 plus tools, something that we we developed this year, and it's it's it's been adopted very enthusiastically across the company. Basically, build this... It's really like an agent that lives in Slack, at least the interface with Spoons members of the core team. We'll be looking to deploy this across all of our team members, but you interact with it on Slack as if it were any other colleague, and you have one old Spooner, you can name it and give it a profile picture and all that. And automatically that agent will have exactly the same level of access you do across all of our platform. So if you have access to certain code base, it does too. If you have access to a certain customer support tool with a certain level of permissions, it does too. And I can basically task this agent to do stuff for you and it could in principle do pretty much anything, like anything you can do in principle, can do, obviously AI is not perfect, so some things it's better than other things. And it's fully integrated, so it's basically, I don't think you can really achieve this at all with third party solutions, because they, as impressive as they are, you're never gonna be able, I believe to, at least not in the foreseeable future, to integrate them as deeply across the board, and to tailor them specifically to what you want. And by the way, you do then you're, I don't think you can, but even if you could, then you'll be locked in with these vendors, which is dangerous because you'd be completely exposed to massive increases in prices potentially in the future. So partly with Allspooner, we achieved, I think, much better effectiveness because it can do a lot for you in a very efficient way. And I'll give you an example in a moment. And partly, we achieved a good separation and very low levels of dependency from any provider of AI infrastructure or AI models. Examples of what Old Spooner can do, it can do data analysis for you. I was looking last night, I was looking at the results of certain AB tests on StreamYard, is one of our products, and normally I would have had to ask someone from the team, a data scientist to pull the data, prepare the analysis, or at least do some of the work for me.
00:54:34 · Luca Ferrari
I can do my own data analysis, but I would have needed someone to do some of it, and certainly would have taken either me or this person multiple hours. And I simply chatted with this, with my old spooner and I told it, oh, please, you know, go on and do this or that. And maybe five minutes later, had the analysis. I was actually very positively impressed. It gave me plots with highlights, very detail oriented and nice. Or if you're running one of our businesses, can, and I saw this firsthand multiple times, you can go to your old Spooner and you can tell it that you saw a bug. This happened, it's a real thing that happened with Evernote. The general manager of Evernote told her old Spooner that she had encountered a bug as well, she was using the app and tasked the agent to check on our customer support platform that whether the bug was widespread among users or her report was the only one, and then to go into the code base, identify the root cause, code a fix and ping the engineering lead for that project to review the fix and push it to production. So maybe she spent, I don't know, three minutes to provide these instructions, and then I'm sure the engineering lead had to spend maybe an hour or two reviewing the code, but the bug got fixed. And this is something the same day, and this is something would have taken weeks probably between back and forth between different people and way in a lot of hours of actual human work to get it done. So thanks to this and many other technologies we've built were probably two, three times as productive easily, and the list is long, but this is roughly what to expect. Now potentially from a business perspective, even more interestingly, AltSpooner really helps us, as I said, stay independent of providers. We have built our own orchestration, so every time you task your agent with something, under the hood, we have an algorithm that will select the most appropriate AI model, or multiple AI models to get the job done, considering, say, quality effectiveness, but also cost. And it can draw from many, many different models.