The Exchanges

Q In terms of, I'm, I'm a venture investor for my sins. I, I quite enjoy financial engineering. When you think about like the, the weight of capital, how do you finance the acquisitions? Is it on like raised capital now? Obviously you've raised, is it on debt capital that then you have like very low cost of capital on? How do you think about efficient use of cash for acquisitions?

A So that certainly is pretty, you know, Common playbook, uh, equity for sure. Most of it has been, uh, retained earnings. We have raised, I think, a remarkably little equity relative to our financials and our valuation. Um, so that has helped. But really, up until Evernote included, which is a year ago, I can, on first approximation, I can say that All of what we have done, we have done through our own earnings and debt. When we raised, we had prior to Evernote, we had raised a little bit of equity, but it was almost immaterial in the grand scheme of things. Now, more recently, we have raised more equity, uh, roughly, um, two hundred million dollars over the past 10 months. Um, but again, I think most of it is our own cash flows and debt.

AI assessment note: “All of what we have done, we have done through our own earnings and debt.”

Q I was going to suggest venture deals by Brad Feld. And so you have a lot more diversity than me, Lucas. So don't worry. Uh, uh, some people tell me I need to get out of venture. I probably agree with them. What does a day look like for you? Do you have a routine? I'm just intrigued by you. You look like a fit, dude.

A I mean, I don't think I have anything incredibly surprising. I woke up around seven, seven, 30, walk my dogs, go to the gym, you know, can run, maybe lift weights. I try to mix it up a little bit. Uh, then I have breakfast and read at the same time. That's my half an hour of, uh, um, let's say meditation, not like proper meditation, but to me reading, uh, and eating a light breakfast is really recharging. And then I have my, my work day. I try to have a lot of time to do individual work. I think you'd be surprised by our few meetings and external meetings particularly. This is what we're doing here is quite rare for me. I maybe do Two of these, three per year max. So most of the time I spend it doing individual work. We promote individual work a lot at the company at all levels. We want all of our managers to be incredibly hands-on.

AI assessment note: “I woke up around seven, seven, 30, walk my dogs, go to the gym”

Q What was the first product, and how did you acquire it, given you didn't have funding or money?

A The, the company, we funded it with around 40,000 dollars, which was the leftover, uh, capital from Evertail. And then the venture capital firm at the time preferred to sell their, the money would have been theirs because they had liquidation preferences. It would be pretty typical for a VC deal, but they, for them, it was more of the hassle of going through the liquidation process for 40,000 dollars and paying for lawyers and all that, and so they sold their shares to us for, I don't know, a euro, I think, or something, and then we liquidated Evertail, and then we founded with a different group of people, as I mentioned, the same three founders plus two employees we had at Evertail. We founded Benny Spoons. Um, the first product wasn't, wasn't an acquisition. I think the first two or three were not, were very simple apps. Uh, the first one I remember because I coded it myself with one of my co-founders, co-founders, I think it was called Fonzie. Uh, it's not, it's not, it hasn't been on the App Store for, like, probably almost 10 years. Um, Uh, but, um, it was a very basic fonts app. We built it in, like, a few days, and, you know, neither of us was an expert software engineer, so long story short, I think we made around 10,000 dollars in all-time revenues from that particular app. Uh, launched another one or two, maybe one slightly more successful, maybe made a 100,000 dollar…

AI assessment note: “the first product wasn't, wasn't an acquisition... it was called Fonzie.”

Q Luca, how would you respond to me saying it's like a PE model buying kind of distressed assets, potentially turning them around and having a roll-up play? Is that wrong?

A I would say yes. Uh, but again, it depends on the level of depth and sophistication one is looking to categorize us with. Um, and I would say the main differences are we The private equities, typically, they focus on, on, you know, finding sufficiently cheap financing, and then making some relatively high-level improvements, uh, and then make a profit a few years down the line. In our case, we, uh, are incredibly hands-on, so unlike a private equity, we generally rewrite the whole software, or at least the most critical parts of the, of the code base. We completely change the IT architecture, we Uh, redesign the user experience and the user interface. Uh, we add lots of features, remove other features, revise, uh, the marketing and monetization dramatically. So you could say we almost, it's almost like as if we built a product to launch it, but we do so on the foundation of an existing customer base or brands who are incredibly hands-on. In fact, if you look at us, you know, our private equity in their team, they will have the, almost all of the people will be, Investment managers, for lack of a better word. In our case, out of 400 people, At least 300 are software engineers, AI researchers, data analysts, data scientists, a product manager. So we are a product and technology company, operationally speaking, but we do have a second soul, and it's the capital allocation soul, wh…

AI assessment note: “I would say yes. Uh, but again, it depends on the level of depth”

Q How did you choose your investors? They're another form of partner, capital partner. How did you choose your investors?

A Oh, I mean, it was easy. Nobody else would give us money, so we, no, sorry. I'm joking. No, no, um, I mean, there have been different moments in which we welcomed new investors, and the answer would change a bit, but for the sake of brevity, I will say we, we have some advisors we, we trust. One, for example, is called Allen & Company. It's a, an equity M&A financing advisor from the US, excellent. I guess it changes from partner to partner, but I think the firm is great in general, and the partners we work with are awesome. So that, you know, they know a lot of investors, they've seen them in different situations, particularly the shitty ones where you really see what sort of principles they abide by, because it's easy to be lovely when you've been generating great returns, but it's a lot harder to be professional and fair when things aren't going that well. So they provided us with a short list Of firms they thought highly of. Uh, and then we, we had a few relationships, so we added a few names to that list, and then we went through a process of they studying us, us studying them, and talking to people who had, had them as investors for a long time, and had, uh, had difficult moments with them on board, and ultimately, you know, it was a mutual selection.

AI assessment note: “we went through a process of they studying us, us studying them”

Q What's the immovable term for you? You mentioned terms that are important. Is there one that's like, hey, no, no mass, not going to budge on that one.

A Well, it changed each time, but I would say one that's stayed the same throughout our rounds was liquidation preferences. We never, all our share owners, myself, our institutional investors, our colleagues who got equity through their working at the company, nobody has any liquidation preferences, so we're all on equal terms economically. The reason why we fought very hard to avoid that is we didn't want our Uh, colleagues who, despite our efforts to educate them financially, of course, are not as financially savvy as an investor, and unlike an investor which has maybe dozens or even hundreds of investments, uh, many of them bet their, metaphorically speaking, their hows on the company. We really didn't want to be in a situation where had things gone poorly, uh, then Everybody would be left empty ended, uh, but, uh, one or two investors. So that was something we really pushed against.

AI assessment note: “one that's stayed the same throughout our rounds was liquidation preferences.”

Q Can I just jump in and ask, I didn't know that. What did you learn from the failing of that company? I think we learn a lot from failings. What did you learn from that failing?

A Well, many things I would probably say two in particular. One is the importance of building a good team. We were, at the time we were quite naive and superficial in that regard. Um, and we got lucky with some people, but overall I think we were pretty, uh, unsophisticated. And the second one was to Be very thoughtful as to what you build, or more broadly, what you do and why. I feel we, we had this idea, and we just thought we were right, and arrogantly got into execution mode, and of course we crashed and burned. Um, and you know, you can still crash and burn, even if you're thoughtful, but your odds are better. So I would say we learned to be more, um, Uh, to do our homework a lot more and iterate more and faster.

AI assessment note: “One is the importance of building a good team.”

Q In terms of the bootstrapping nature, why did you decide to bootstrap? You were three very smart guys. I'm sure you could have raised. Why did you in those kind of in between periods, 2014 to 2019, why did you not raise?

A I don't know. We could have, I guess you can always raise, but I don't know that we could have at sufficiently appealing terms, consider that we founded the company. Well, we, we founded it in Copenhagen, Denmark, but we moved it to Italy Pretty soon after. Um, and so we were three people with a failed startup behind us, uh, building a technology company in Italy, which had a negligible VC scene and, uh, attracted absolutely no interest from international venture capital firms, um, with a strategy that was, as far as I can tell, unheard of. To this day, I don't really have a comparable company. I mean, there are, of course, you can come up with examples, but nothing that's really spot on like exactly what we do, and so we felt our likelihood of attracting capital in sufficiently appealing terms was very low. To this, you have to add, we really wanted to build this for, with a multi-decade, decade view, and we felt that it was quite dangerous to relinquish control so early. Uh, of course, we would have done it had the terms being sufficiently appealing. But, and lastly, we could afford not to. I mean, that's a big factor. If you, if you are building a business that's losing money, and you're expected to lose money for, for a while, then there's no other way, right? We, for better or worse, we had a model that, uh, you know, maybe was more, it's not the fastest growing model. We …

AI assessment note: “we felt our likelihood of attracting capital in sufficiently appealing terms was very low”

Q When you think about the deals that you've done, Without sounding bad, have you mispriced any? And in the ones that you mispriced or misjudged, what did you not see that you should have seen or would like to have seen?

A There hasn't been a single one we have priced correctly. Thankfully, we have made mistakes in both directions, so sometimes things went better than we thought, and sometimes they went less well. Um, I think the more, probably the most significant problem or error We've made, particularly from the, let's say, the downside negative point of view has been to project future rates of user acquisition too optimistically. Of all the important KPIs in determining the success of a product, an internet product at least, um, I would say rate of user acquisition is The hardest to project accurately by a huge margin, at least in our experience and based on what we know today. So at least once it's happened multiple times, at least once we were way too optimistic with that and the ultimately the returns from the acquisition turned out to be much, much worse than we had anticipated.

AI assessment note: “has been to project future rates of user acquisition too optimistically”

Q No, it's not. But what did you learn from that failing? I'm intrigued again there.

A Well, I think it reinforced the lesson we had with the every tail startup I mentioned earlier that, um, you have to, at least we, we feel we have to be intellectually humble when it comes to our ability to predict what the market will want, particularly when it comes to very new things. And if you're replicating, say, something that works in the US and you do the same in Germany, I think you can be more, ah, sure that it will work. Uh, still not certain, but, uh, it's more likely to succeed. But if it's something very new, and at the time, to my knowledge, what I just described was unique. I'm sure someone will have seen something similar and now claim, uh, I'm uninformed, but based on our research at the time, there was nothing, uh, like it. Uh, certainly not a mobile. Um, and so when it's something so new, the likelihood that you are, uh, delusional as to the chances of success is pretty high. So I always suggest Lower the odds in our equation. Assume you're being positively biased toward your idea. Uh, the truth is probably worse than you think it is.

AI assessment note: “we feel we have to be intellectually humble when it comes to our ability”

Q I do want to touch on the people around you because so many people told me about the talent density that you built and you mentioned before to me about talent and motivation density. It's quite a specific use of words actually. It's not like, oh, we have great people. What did you mean by talent and motivation density?

A Yeah, I think so. We, we built a framework internally to describe exactly these sort of things. Um, but a short of it is that Three important components are talent, which is how good you can be. In our definition, I'm not saying this is, you know, kind of a standard definition. Then there is experience, which is the, the exposure, cumulative exposure to relevant experiences, which will help you, based on your talent, uh, unlock your potential. And then there's motivation, which is kind of a multiplier factor, goes, going from zero, let's say, to, uh, to one, where Based on how much you care to be great in that particular context, you'll do better or worse within the range determined by your talent and experience. It's a trade-off when you hire people. You can say, I want to hire for experience, talent, motivation. You can, but, um, you're gonna do worse at any one of these than if you focused on just one or two. In our case, we chose to focus on talent and motivation, uh, almost entirely disregarding experience. Uh, because we experience, we can provide talent. We can't motivation. We can, we can try to create the conditions for it, but you don't change what, what, what someone wants and cares about. And, and this is the way in the long-term we have the best team we can.

AI assessment note: “In our case, we chose to focus on talent and motivation”

Q What do you know about risk and risk mitigation today that you wish you'd known 10 years ago?

A Well, again, the bigger lesson is Assume you're not as smart as you think you are. That is the more, that, that's probably the most valuable. The issue here is that most people who end up making significant capital allocation calls, generally those people tend to, they wouldn't be there if they hadn't been somewhat successful by most measures. Um, sometimes, you know, academically and then Professionally, but at least professionally, right? Otherwise it's unlikely to imagine that someone would be managing fifty billion dollars or, you know, a scale up worth billions. And so, If that's the case, uh, it's very easy to think too highly of yourself, and that is very dangerous. The moment you think you're very smart, the likelihood that you make very dumb mistakes skyrockets, in my opinion. So, now you need to retain a little bit of self-confidence, otherwise you won't make any moves, but I think a healthy level of second-guessing yourself, assuming you're biased, Uh, in favor of your ideas, um, uh, that you're lazy and don't want to look into a certain risk factor because you know it's going to take you 50 hours of grinding through benchmarks and analyzing data. You should assume that's the case and adjust your aim accordingly.

AI assessment note: “Assume you're not as smart as you think you are.”

Q How did you choose your investors? They're another form of partner, capital partner. How did you choose your investors?

A Oh, I mean, it was easy. Nobody else would give us money, so we, no, sorry. I'm joking. No, no, um, I mean, there have been different moments in which we welcomed new investors, and the answer would change a bit, but for the sake of brevity, I will say we, we have some advisors we, we trust. One, for example, is called Allen & Company. It's a, an equity M&A financing advisor from the US, excellent. I guess it changes from partner to partner, but I think the firm is great in general, and the partners we work with are awesome. So that, you know, they know a lot of investors, they've seen them in different situations, particularly the shitty ones where you really see what sort of principles they abide by, because it's easy to be lovely when you've been generating great returns, but it's a lot harder to be professional and fair when things aren't going that well. So they provided us with a short list Of firms they thought highly of. Uh, and then we, we had a few relationships, so we added a few names to that list, and then we went through a process of they studying us, us studying them, and talking to people who had, had them as investors for a long time, and had, uh, had difficult moments with them on board, and ultimately, you know, it was a mutual selection.

AI assessment note: “they provided us with a short list Of firms they thought highly of”

Q Is there anything that you'd do differently about the fundraising processes having been through them? Different things on negotiation, on price, on length, anything that you would do differently?

A Yeah, I think you want to create a certain level of competition if you can. I think we were, I'm very happy with investors we have. I will say we could have obtained better terms. I mean, I think we had good terms with better terms early on, and we could have closed faster. Have we created a little bit more competition? Because often what they tell you, and they might actually mean it, um, the initial enthusiasm. You always hear this is awesome. We're definitely gonna invest. We're gonna cover the whole round. You don't need to talk to anybody else. This would be amazing. And then once due diligence starts, there's always a tendency to then commit a little bit less capital, try to walk back on a few of the key terms. And, uh, and if you don't have any competitive tension, it's hard to avoid that from happening. But if you have a reasonable amount of competitive pressure then, and you keep it up all the way to the end, big mistake is to just base your selection on the early feedback. And then wave goodbye to all those who don't plan to take on board and proceed only with the one or two that you plan to take on board. You shouldn't do that. Just keep a broader spectrum of parties involved up to the very end when you're signing, if you can. Um, that's my, probably my most useful piece of advice on that regard.

AI assessment note: “we could have obtained better terms... and we could have closed faster.”

Q What have been the biggest mistakes you've made in talent identification and hiring?

A Yeah. I mean, many, too many to count, but I would say broadly speaking, the biggest one was to Assign too much weight to experience. In that, in that trade-off I mentioned earlier, ultimately, if you assign a lot of value to experience, you get someone who's more valuable immediately or early on, but then because you, you've had to trade, to trade talent and motivation potential off for more experience, you'll have a lower level of contribution in the long run. And so, and we, and we saw that quite clearly, um, and we hired multiple experienced people, which are not to be absolutely amazing. It's not, you know, it's just on average that I think we did that a little bit too much, but we have since adjusted our, our, our aim accordingly.

AI assessment note: “the biggest one was to Assign too much weight to experience.”

Q Can I just jump in and ask, I didn't know that. What did you learn from the failing of that company? I think we learn a lot from failings. What did you learn from that failing?

A Well, many things I would probably say two in particular. One is the importance of building a good team. We were, at the time we were quite naive and superficial in that regard. Um, and we got lucky with some people, but overall I think we were pretty, uh, unsophisticated. And the second one was to Be very thoughtful as to what you build, or more broadly, what you do and why. I feel we, we had this idea, and we just thought we were right, and arrogantly got into execution mode, and of course we crashed and burned. Um, and you know, you can still crash and burn, even if you're thoughtful, but your odds are better. So I would say we learned to be more, um, Uh, to do our homework a lot more and iterate more and faster.

AI assessment note: “two in particular. One is the importance of building a good team.”

Q In terms of the bootstrapping nature, why did you decide to bootstrap? You were three very smart guys. I'm sure you could have raised. Why did you in those kind of in between periods, 2014 to 2019, why did you not raise?

A I don't know. We could have, I guess you can always raise, but I don't know that we could have at sufficiently appealing terms, consider that we founded the company. Well, we, we founded it in Copenhagen, Denmark, but we moved it to Italy Pretty soon after. Um, and so we were three people with a failed startup behind us, uh, building a technology company in Italy, which had a negligible VC scene and, uh, attracted absolutely no interest from international venture capital firms, um, with a strategy that was, as far as I can tell, unheard of. To this day, I don't really have a comparable company. I mean, there are, of course, you can come up with examples, but nothing that's really spot on like exactly what we do, and so we felt our likelihood of attracting capital in sufficiently appealing terms was very low. To this, you have to add, we really wanted to build this for, with a multi-decade, decade view, and we felt that it was quite dangerous to relinquish control so early. Uh, of course, we would have done it had the terms being sufficiently appealing. But, and lastly, we could afford not to. I mean, that's a big factor. If you, if you are building a business that's losing money, and you're expected to lose money for, for a while, then there's no other way, right? We, for better or worse, we had a model that, uh, you know, maybe was more, it's not the fastest growing model. We …

AI assessment note: “I don't know that we could have at sufficiently appealing terms”

Q In terms of, I'm, I'm a venture investor for my sins. I, I quite enjoy financial engineering. When you think about like the, the weight of capital, how do you finance the acquisitions? Is it on like raised capital now? Obviously you've raised, is it on debt capital that then you have like very low cost of capital on? How do you think about efficient use of cash for acquisitions?

A So that certainly is pretty, you know, Common playbook, uh, equity for sure. Most of it has been, uh, retained earnings. We have raised, I think, a remarkably little equity relative to our financials and our valuation. Um, so that has helped. But really, up until Evernote included, which is a year ago, I can, on first approximation, I can say that All of what we have done, we have done through our own earnings and debt. When we raised, we had prior to Evernote, we had raised a little bit of equity, but it was almost immaterial in the grand scheme of things. Now, more recently, we have raised more equity, uh, roughly, um, two hundred million dollars over the past 10 months. Um, but again, I think most of it is our own cash flows and debt.

AI assessment note: “All of what we have done, we have done through our own earnings and debt.”

Q it, but then you switch to Evernote, which is a very well-known brand. And a very significant customer base. What was the thinking around that acquisition? Because I just called a spade a spade. People were like, that's a turnaround of brand is going down and declining, but you saw value there. Can you talk to me what you saw that others didn't and how you thought about that acquisition?

A Well, I, I don't know what others saw in it or didn't see in it, but we, we thought we could, uh, improve the product. Monetize it more efficiently and also run the company more efficiently in terms of costs. So we felt all three, uh, levers were offered a little bit of space for improvement. Um, and so very excited to, to acquire it and work on it. It's also, I won't deny it. It's, it's, uh, it's especially exciting when you get to, as a tech person, a, you know, someone who's loved, uh, Working on digital technology products for now almost 14 years. It's particularly exciting to, exciting to be able to work on something that is so relevant. Sometimes people have characterized Evernote as, as you said, kind of on a decline, and it can be true in some ways, but it's still as used or more used than a lot other Brands that people think are more successful or cooler. And I, I don't want to name names, but Evernote is very important. I mean, there are millions of people who have built their professional workflows on it and use it every single day and at thousands upon thousands of notes. Um, so it was very motivating for us to be able to at least try to make it better. And I think we, we have, we've done the team. I haven't done much, but the team has done a lot of work in that regard in just one year.

AI assessment note: “we thought we could, uh, improve the product. Monetize it more efficiently”

Q And the hard thing is, You've got to make the seller want to sell at the price. And actually, you know, it's like secondary today, which is there's a big chasm between what the buyer's willing to pay and what the seller's willing to sell at. How do you find seller response to your pricing analysis?

A So we, we don't really convince anybody to sell. I think people are, uh, with these people are always adults, uh, generally highly competent, intelligent professionals who have, uh, their own clear view of value. What we try to do is be quick. Uh, and decisive in determining our price. We try to make an offer that's not a bluff. It's an actual, you know, a very good offer and close to the maximum we are reasonably willing to offer. And then they decide, I mean, we, if, if they don't like it, we, we walk away. But historically, uh, we have never lost a process. Meaning every single time there was willingness to sell, And we had a chance to bid, because sometimes you don't get to know about the opportunity and you only hear about it after the deal is done. Every single time To my knowledge, our offer ended up being the highest. Um, and I think the reason is, uh, well, one reason could be we're terrible negotiators. Uh, another reason is we, because of our model and being so operationally involved and trying to improve the product, the operations, the marketing, the monetization, everything so deeply, it's a lot of work. It doesn't, uh, you don't scale it as quickly as Buying just from a financial point of view would, but you can unlock more efficiencies and therefore you can offer a better price.

AI assessment note: “historically, uh, we have never lost a process.”

Q Why is rate of user acquisition so tough? I'm sorry to ask, but they tend to be quite baked assets. You've got historical data, you've got very baked Customer acquisition channels. Why is it so variable and volatile to predict?

A Few factors. One is it depends on more drivers than other key, um, let's say inputs to the successful product. And these drivers tend to be outside Of your control more than, than drivers that are behind other, other key, key factors, and so the combination of more, basically, if it's an equation, you have more variables, uh, and you control them less, and so your ability to then force the future, the direction you want it to go, is lower, so when you make a mistake there, You generally didn't have a, an opportunity to, to fix it. Um, whereas in other cases, maybe you thought something, you could, you could achieve something, um, and you thought it would be easier, it turns out to be harder, but you can go the extra mile, allocate a few more resources, somehow you get there, but if, uh, the rate at which you're acquiring users isn't where it needs to be, often there isn't much you can do. Uh, I mean, you can spend more in advertising, but, uh, you're going to lose money. Uh, otherwise you wouldn't be spending it already. Right. So the problem is you can't find ways of acquiring users efficiently and that has to be the end of it. Uh, you just accept the trajectory.

AI assessment note: “you have more variables, uh, and you control them less”

Q Luca, when did you take too much risk on an asset? Too much that you shouldn't have taken?

A It's difficult in hindsight, you know, it's, it's like if you play a hand of poker, it's quite dangerous to assess whether you played it well based on the results. Um, so I'm thinking based on what I knew at the time and what I could have possibly known. Yeah, I think we, we did buy, I mean, maybe multiple times. One that's obvious to me, we did buy this, this app. I'd rather not Mention it by name, but, um, a pretty significant product. We made the mistake I mentioned earlier. So we, we were quite bullish that our projections of user acquisition rates were conservative. And I think we were a little bit lazy in Really studying the underlying factors driving user acquisition historically. We just looked at it in aggregate and a bit naively. Had we done our homework more thoroughly, I think we would have spotted that some of the underlying factors were waning and dropping quickly, and that consequently the rate of user acquisition was likely to, to shrink, uh, faster than we had anticipated. Because we had actually Projected it to decline, but not nearly fast enough. And so, you know, um, that's certainly a case where I feel I'm not very proud of the way we handle ourselves. I think we did a mediocre job there.

AI assessment note: “One that's obvious to me, we did buy this, this app.”

Q Let's just unpack those like talent. What, what does that mean? And how do you test for it? Is it raw skills and how do you test for them?

A We, we try to test people as much as possible, practically and measurably. We have come to, to be wary of interviews, at least the more traditional and structured interview where the interviewer will just ask a bunch of questions and then tell you how they feel about the candidate. We felt, we, we discovered that is not a good predictor. And so we try to have practical tests or at least tests that, uh, Simulate or require the underlying abilities that the actual job will require. And we try to go more toward general problem solving abilities than, as I said before, acquired knowledge. So we're fine to have someone who doesn't know the things as yet, as long as they show us that they have the work ethic, the, the ability to learn, the right, uh, teamwork and mentality to, to then be great at our company.

AI assessment note: “we try to have practical tests or at least tests that simulate or require the underlying abilities”

Q What have been the biggest mistakes you've made in talent identification and hiring?

A Yeah. I mean, many, too many to count, but I would say broadly speaking, the biggest one was to Assign too much weight to experience. In that, in that trade-off I mentioned earlier, ultimately, if you assign a lot of value to experience, you get someone who's more valuable immediately or early on, but then because you, you've had to trade, to trade talent and motivation potential off for more experience, you'll have a lower level of contribution in the long run. And so, and we, and we saw that quite clearly, um, and we hired multiple experienced people, which are not to be absolutely amazing. It's not, you know, it's just on average that I think we did that a little bit too much, but we have since adjusted our, our, our aim accordingly.

AI assessment note: “the biggest one was to Assign too much weight to experience.”

Q In terms of density, how important is it for you that people are together, that there's a physical co-location?

A Yeah, a huge topic. I, I don't know. I, we have seen that, um, Working together physically tends to drive, well, I won't say tends to drive, tends to correlate with the higher performance, levels of performance. It's really hard to tell whether that's because people are in the same place on site, or it's a consequence of the fact that people who are intrinsically more motivated about Working at the company or doing what they do. They also want to be with others because if you really care, you typically tend to want to be where the action is. And so it's simply that it's self-selected that sample. I don't really know, but we have incredibly high performers who work remotely all the time too. It's just on average, slightly less probable. Our decision has been to fully support remote work. Um, because we, you know, we get to attract, uh, time from broader pool of candidates. And again, we do have Some of our best performers are working almost entirely remotely or completely remotely, but we do pay a price for that. It's not clear cut. I, when a company decides to only do on-site, I don't feel I can say it's a stupid move. I don't have, I don't have enough information. I could see how that could ultimately prove to be the right move. For us, it's kind of toss of a coin. We're not sure. So we, we just keep all options open and Again, we're very happy with our remote colleagues. They…

AI assessment note: “Our decision has been to fully support remote work.”

Q And how did Bending Spoons come to be then? So this company failed and the three of you went and go, hmm, what now?

A Yeah. So connecting to your previous question, uh, so the three of us, we started it, um, we didn't have any money. Um, so we had this agreement whereby we, all three of us would look for a job and then whoever got the most lucrative job would, uh, work and pay. For food and rent, and the others would work full-time, and then once we, we get, uh, an investment or some sort of, uh, you know, ability to do without the financing from the person working, you know, that this person would join full-time. So I, I ended up getting this offer from McKinsey, which I took. I told them before accepting that, you know, or as I accepted that I meant to work and start up, uh, part-time, and then resign as soon as we hopefully got this investment, and they, I thought they would basically, Kick me in the ass and tell me to go away, but they were great. Actually, they told me, this is awesome, and we love the ambition, and sure, sure, you know, this works for us, and so I worked there for about a year, I think a year and three months, give or take, um, and then I was working, uh, nights and weekends in my vacation also on, uh, on Evertail. I, I like to joke that once I joined full-time, I made it fail very quickly. And then, uh, we immediately pretty much, uh, started Bending Spoons, uh, right there and then on the ashes of, uh, of that startup.

AI assessment note: “we immediately pretty much, uh, started Bending Spoons, uh, right there and then”

Q Do you not have direct reports that report to you, Luca?

A Yeah, I do. I do. We do one once, once, uh, every two weeks. So that doesn't take, uh, you know, maybe it takes about five hours a week. And then I have other meetings, but I would say I probably have no more than 20 hours of meeting a week. And, and I squeeze in maybe 40, 50 hours of individual work each week doing those sorts of things. Yeah, and then I try, tend to be done, uh, around maybe eight 39 in the evening. Um, I spend an hour with my, my fiancé, with, with my dogs again. Um, you know, maybe just chatting, watching part of a movie, eating dinner. Um, then walk the dogs again, read a book, fall asleep. Pretty simple.

AI assessment note: “Yeah, I do. I do. We do one once, once, uh, every two weeks.”

Q And how did Bending Spoons come to be then? So this company failed and the three of you went and go, hmm, what now?

A Yeah. So connecting to your previous question, uh, so the three of us, we started it, um, we didn't have any money. Um, so we had this agreement whereby we, all three of us would look for a job and then whoever got the most lucrative job would, uh, work and pay. For food and rent, and the others would work full-time, and then once we, we get, uh, an investment or some sort of, uh, you know, ability to do without the financing from the person working, you know, that this person would join full-time. So I, I ended up getting this offer from McKinsey, which I took. I told them before accepting that, you know, or as I accepted that I meant to work and start up, uh, part-time, and then resign as soon as we hopefully got this investment, and they, I thought they would basically, Kick me in the ass and tell me to go away, but they were great. Actually, they told me, this is awesome, and we love the ambition, and sure, sure, you know, this works for us, and so I worked there for about a year, I think a year and three months, give or take, um, and then I was working, uh, nights and weekends in my vacation also on, uh, on Evertail. I, I like to joke that once I joined full-time, I made it fail very quickly. And then, uh, we immediately pretty much, uh, started Bending Spoons, uh, right there and then on the ashes of, uh, of that startup.

AI assessment note: “we immediately pretty much, uh, started Bending Spoons, uh, right there and then”

Q Why is rate of user acquisition so tough? I'm sorry to ask, but they tend to be quite baked assets. You've got historical data, you've got very baked Customer acquisition channels. Why is it so variable and volatile to predict?

A Few factors. One is it depends on more drivers than other key, um, let's say inputs to the successful product. And these drivers tend to be outside Of your control more than, than drivers that are behind other, other key, key factors, and so the combination of more, basically, if it's an equation, you have more variables, uh, and you control them less, and so your ability to then force the future, the direction you want it to go, is lower, so when you make a mistake there, You generally didn't have a, an opportunity to, to fix it. Um, whereas in other cases, maybe you thought something, you could, you could achieve something, um, and you thought it would be easier, it turns out to be harder, but you can go the extra mile, allocate a few more resources, somehow you get there, but if, uh, the rate at which you're acquiring users isn't where it needs to be, often there isn't much you can do. Uh, I mean, you can spend more in advertising, but, uh, you're going to lose money. Uh, otherwise you wouldn't be spending it already. Right. So the problem is you can't find ways of acquiring users efficiently and that has to be the end of it. Uh, you just accept the trajectory.

AI assessment note: “you have more variables, uh, and you control them less”

Q Luca, when did you know that you had something? When did you sit down with your other two co-founders and go, hmm, this is working. We have enough signal.

A Yeah, it's a good question. I, so on the one hand, we were pretty confident early on, but it was mostly based on some observation and first principles. We didn't have a track record. Uh, on the other hand, I tend to be paranoid by nature, so I always second-guess myself and wonder if there isn't some huge risk lurking in the shadows. So I, I don't know, I never, not even today, am I massively confident. So I think our level, at least my level of confidence, I can't really say for the others, but has more or less remained between, uh, you know, decent and good, but was never super high or super low for, for the whole of this decade.

AI assessment note: “I never, not even today, am I massively confident.”