Prof G Markets - This EU Firm Made Billions Buying Up Forgotten Tech

Episode Date: October 4, 2026

Ed Elson sits down with Luca Ferrari, co-founder and CEO of Bending Spoons, to discuss the company’s origin story, what sets its acquisition model apart from private equity, and how it decides which companies to buy. They also discuss the role of headcount reduction in Bending Spoons’ strategy, what it’s like to build a company in Europe, and how the company uses AI to transform the businesses it acquires. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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join them and get $500 when your business signs up and starts traveling at engine.com slash founders. Hey guys, it's Ed. Just so you know, we recorded this conversation live on Substack last week. So if you would like to catch the next live stream and tune in in real time, make sure to head over to profgmedia.com where you can subscribe and I'll see you on the next live stream. Welcome to the ProfiMarkets founder series. What do you do with a software company that was once a household name but has fallen on hard times?

Our next guest has built a business around answering that question. His company acquires struggling businesses like Vimeo, like AOL, AirTable, and Eventbrite with the goal of overhauling how they operate, cutting costs and making them profitable again. And over the summer, this company went public on the NASDAQ at evaluation of more than 18,000, billion dollars, with the stock jumping 40% on its first day of trading. That IPO turned what was once a relatively obscure Italian company into a public market story that investors around the world were suddenly paying attention to. So we wanted to understand how this model actually works, what the company does to turn struggling software businesses around, and what it's like to take this strategy from a private company to a public one. This is my conversation with

Luca Ferrari, the co-founder and CEO of Bending Spoons. Luca, thank you so much for joining us on this episode of the Proftery Markets Founders Series. I'd like to start at the very beginning in 2013, where you're not actually in Italy, you're actually in Copenhagen, you're in Denmark, and that's where Bending Spoons begins. Tell us the origin story of this company. Very few people know this, but with my co-founders, almost the same people. We launched startup in 2010, also in Copenhagen. We were using AI clearly too early to write to automatically write diaries for people. And we did that for three

years, raise a million dollars and failed miserably. I couldn't make it scale. Although I'm pretty proud of the product we built, but sometimes that's not enough. And so on the ashes of that company we were left with about $40,000. The BC who had given us the money, basically sold their shares back to us for a nominal $1 and they apparently appreciated. We've worked hard and being honest and they didn't want to go through the liquidation process and we ended up liquidating the company ourselves and basically got those $40,000 out of it almost completely. And that was a seat capital for Benisbonne's. So that's 2013 in Copenhagen. moved the company to Italy about a year later, but that's what we were at the time.

And the strategy we're still running these days, and the key insights behind it, for the most part, had originated from that failure. Obviously, you get smarter in time, but most of it was in place back then. So you had a tech company, you say an AI company, I would assume that they're calling it machine learning back then. I assume that there was not a lot of hype around that sector. That's obviously changed. It doesn't work. Then you have $40,000. left over, which you take out of that, to start this other company called Bending Spoons, what actually was the idea behind Bending Spoons, and also, I think people will probably want to know what's with this name, Bending Spoons?

So over those first three years of that failed startup, we saw our skills in engineering, marketing, product design improved tremendously. Obviously, we didn't feel we were anywhere near world class, but certainly much better. And so we thought, okay, we, if we work, on the skill set and other relevant skills and build our own tools, we should be able to get to world-class levels at some point. And at the same time, we saw that finding product market fit, going from zero to one, so to say, seemed to require a lot of stars to online

because we met so many startup teams where people worked really hard and we thought they were brilliant and still couldn't make it work. And sometimes in other cases, it did work. And so we figured we should be able to find people who are maybe tired of running a business or maybe they don't have the skills to go from 1 to 10, who will sell to us. And if you're really, really good at doing this,

we should be able to pay a price that's appealing to them and still be able to create enough value to generate strong returns and be able to compound capital efficiently. So those were the relatively simplistic ideas we had back then. And because we knew we were going to grow a vast, hopefully vast portfolio, diversified portfolio, different products, we had to find a name for the company that wasn't related to the particular product.

and so one of my co-founders, Matteo is a big fan of the movie The Matrix, and I think you had watched it the night prior or something, and in the morning it came all excited and said, we should call the company bending spoons inspired by there is this scene in the movie of a little guy bending a spoon with his mind, supposedly. And we liked it because it reminds us of,

you know, the mind is powerful. If you set your mind to something, you know, maybe you can do the seemingly impossible, So we just found it kind of inspirational. We like the idea of making a little bit of fun, of poking a bit of fun of ourselves and not taking ourselves too seriously. So that was also a cool name in our view and it stuck. So we'll get to what you guys do today, which is you are essentially buying a lot of these different software companies and we'll get to that. But in the early days, my understanding is you guys are building tools, you're building software products,

you're kind of like an app studio in the early days. Tell us a little bit about how what you were actually building back then and how it eventually led to this seemingly slightly different business model where you're actually going out and buying companies with money that now you have, but back then I assume $40,000 is not much you can buy with that. We've always acquired companies. The first acquisition happened very quickly, I think. So we started Beninjsprum in the summer of 2013.

The first acquisition was in 2014. I don't think from that point onward there's ever been one year where less than 90% of our revenue came from businesses we had acquired. So I'm not sure where the studio thing comes from. But I think it's just that the first acquisition we did that people recognized was ever north in 2023. So people just assumed that what we were doing, before was launched from scratch, but nearly all we have done over the past 13 years was acquire

existing businesses, and I'd like to think make them better, more successful. We have launched, occasionally we have launched products from scratch, more so in the early days because we had no money, and so it was both the only thing we could do, but also it was more likely to move the needle. At this point, you know, we're at a run rate in terms of revenue of almost $4 billion. It's more difficult to launch something that can, you know, reasonably move the needle. But the first acquisition happened, like I said, very early, and we paid $10,000 for it, so very small. But, you know, the power of compounding is such that it turned 10 into 20 and then 20 into 40.

Fast forward, 13 years, we are where we are. We actually had very modest injections of equity, mostly, you know, other than the IPO a few months ago, we had a significant injection of equity about a quarter of billion dollars in the end of, toward the end of 2025. So really up to that point in time, most of the growth was through the reinvestment or free cash flows and then debt, which we have used for almost 10 years at this point. So tell us a little bit about that first acquisition. How did you identify this company that was worth $10,000, same amount of money that a lot of people

would pay for a car? And what did you do with this company to turn it around? What were you bringing to the table? That one was a lot easier than things today because it wasn't even a company, really. This was a mobile app for iOS. I still remember quite distinctly that it was an app to personalize your iPhone's keyboard.

I don't even know if such apps exist these days. But in 2014, I think, around that time, Apple launched opened up APIs to be able to do that and a bunch of apps came out and we saw this one and we thought it had a bunch of users and we thought it could be both the product was not that good, maybe it had users because it was a first mover

and had achieved a good positioning on the app store. We believe we could improve the user experience. Monetization was almost non-existent and so we bought it. We rewrote the software completely redesigned the UI and introduced

monetization. And it was built, the app had been developed by an individual, like a single developer. So kind of an amateurish operation. Obviously these days we buy sizable companies with professional management teams and institutional investors. But the key principles trying to be superior at the key functional expertise necessary to run that business, superior relative to whoever is selling it to you, stood. Obviously, the bar to be stronger today is much higher, but the idea is pretty much the same. So this gets to the heart of what bending spoons actually is.

And I think there's a lot of confusion as to what this company is, because on the one hand, there's this software element where you guys are using AI. You have this software background. You kind of come from this not Silicon Valley culture, but certainly a startup culture. But at the same time, you're doing what many other asset management firms and private equity firms have done for decades, which is you're finding companies, you're buying them, you're acquiring them, you're taking up large positions, and then you're taking up a strategic stake in the company and then turning those companies around. This is what investors have been doing for decades. This exists. This is an entire industry, which I think I would call it. private equity. So in your mind, what actually is bending spoons? Because to me, it seems like it is, at the end of the day, it's an asset management firm. The estate investment part of what we do is absolutely important. Clearly, we grow primarily through acquisitions for a serial acquire. The part that would distinguish us from any private equity I've seen, I would say there are three

very important differences. And one is that we are not a fund. We don't buy to sell. We've never sold a material business. So we buy to hold and operate forever. The second, perhaps more important difference is that when we acquire a company,

at least deep private equities I'm aware of or I've seen operate. They do intervene. Obviously, they may cut cost, maybe intervene on monetization. They may change the executive team. But the interventions are relatively, relatively superficial. And you can tell by looking at the team at private equity firms.

It's mostly small teams of financial professionals. Benispoons takes a different route. We do those things as well. But we take a different route in that we rebuild the org often completely or in large part. We may re-architect the cloud infrastructure completely, rewrite big chunks of the code base, accelerate product development, reimagining monetization from the ground up marketing. So it's a much, much deeper transformation to the point that sometimes it's almost like

we buy brand, custom-based, user base, and try to overhaul everything else very deeply. And again, that's represented in the team we have, which is, even if you look just at a core team, forget for a second, the acquire teams, that's roughly 800 people. And overwhelmingly, there are software engineers. It's a scientist's product, manager, product designer. So naturally, most of the work we do is that sort of work. We also have our own team of financial professionals because we need that too, but there's this additional piece. And the third big difference is that while pretty much all private securities buy businesses

and they keep them separate to sell them down the line, sometimes they put a couple of synergistic businesses together, but that's the extent to which they put things together. We integrate everything super deeply onto the same platform. An investors some time ago told me that after due diligence in Benin Spruins that they found that we, it's almost like we buy companies and then they get stripped down to a product installed on a shared operating system. I think it's a bit of a simplistic metaphor, but there's truth of it. So if you look at all we do things, we generally within a few months from the moment the acquisition closes, we swap out to the entire, to say, technological foundation. We have built dozens of proprietary technologies, whether it's AI orchestration, data process. in AB testing, recruiting, payment management, credentials management.

Like basically every component of running a digital business, we've built in house, natively integrated, and that's swapped out for whatever third-party vendor things the business is using. We have this core centralized team for R&D, GNA, marketing, who we redeploy very fluidly across all of our businesses to go after R&D opportunities and when they're exhausted, we throw them so we keep cost efficient. So to the point that it would be almost impossible to sell one of our businesses, even if we wanted to. So that's the downside of our model.

So there are similarities with private equities, first and foremost, to focus on acquisitions. But I think the day-to-day operational approach is in integration and transformation is quite different. So it's a bit of a hybrid, I'd say. One of the cliches, but it's also true about the private equity strategy, is that you buy a company and then you dramatically cut the costs of the company, which usually comes in the form of layoffs. And that is something that you guys have demonstrated as well. This is a pattern that is pretty consistent across the companies that you acquire. It's also something I think that some people criticize you guys for, rightly or wrongly. But when you look at some of the

strategic decisions you've made, like we transfer staff, for example, two months after you bought it, 75% of the employees had been cut. I mean, how central is headcount reduction to the strategy? And what do you make of, one, the fact that that does sound also very similar to the private equity model, and two, the criticisms that people might have that, well, you're cutting headcount. If it's a moral judgment, if someone thinks that trying to make a business as successful as possible is bad. I don't have any defense against like it's a completely valid point of view. I disagree, obviously, or I wouldn't do it. I think in general, the world tends to work better. The economic pie tends to grow larger if businesses are run at an excellent level. But, you know, it's a perfectly

respect the fact that someone would disagree with me on that point. We definitely, we have done it almost every single time. So it's definitely been a pattern. I would say that in general, cost efficiencies have been approximately 50% of our value creation of all. And within that 50%, probably the org part, it's probably roughly a little over half. So maybe I would say of 100% of our value creation, I'd say maybe 30, 35% has been about optimizing organizations. One thing we do that's, I believe, misunderstood is people tend to think that we end up reducing headcount simply because we want to cut cost. And while optimizing cost is obviously a significant driver, we actually try, I'd say just as important for us is that we make that org far better

position to kick ass for a long time. The, in my view, flawed assumptions some people have is that the number of people you have in a company is almost definitively going to determine whether the company produces great product, innovates quickly. We are of the opinion that if there is a correlation between headcount and say innovation or the ability to optimize customer experience

monetization, that's a weak correlation at best. And that what drives those virtuous outcomes for the most part is talent density, whether people use powerful tools, whether the culture is one of rationality, high performance, whether or not you have a lot of red tape

or you actually give people playing a space. So What we try to do is bring these companies back to what I would call startup mode, small teams of supremely talented people. We get out of the way, give them plenty of, you know, very wide mandate, very powerful technologies I mentioned them quickly earlier. And what we see consistently is that magic happens, despite going down in headcount maybe 80%, sometimes more demonstrably. and we publish a lot of this stuff on the various blogs or brands, but demonstrably the rate of which we fix bugs, reduce, say, technical incidents, or launch new features, goes up sometimes dramatically.

So, yes, we have absolutely consistently cut headcount, no doubt about it, sometimes dramatically. So we do so also, no doubt about it, for cost efficiency. But equally, we have been able, through the same means to make these businesses a lot more competitive, from the point of view of product quality and at a pace of innovation. So for us, it's really a win-win, but I understand that it's painful for those involved. We try to mitigate the unavoidable, I think, pain of being laid off, which sucks.

I mean, it's a very tough thing to go through by being transparent, by offering top-of-market separation packages, by being empathetic and flexible, but that doesn't make that transition painless. It makes it a little bit less painful. And if we could do what we do without that objectively bad element, we will certainly be very happy to avoid it. None of us enjoys that part of our jobs for sure. My understanding is also that you are specifically finding companies that are struggling, struggling on basically every metric,

struggling with their profitability, struggling with their business model. And these are companies that otherwise would have been stagnating and perhaps would have done the layoffs anyway. I guess this gets to the next question, which is, how do you identify what company is the right company for your business model, for integration with your software products and your software team? What makes a company acquireable? I think you're right in many ways.

I would say a big percentage of our acquisitions have been struggling companies. Not only have acquired several companies that were growing nicely. I'm thinking, Camuth Airtable, recently we shared the growth rate. But yes, we have a bigger percentage of companies in our acquisition portfolio, let's say, that we're struggling than, say, most acquires. That's, I think, broadly speaking, a valid characterization. And sometimes, yes, the team, the executive teams themselves tell us that, look, if I say I need to take difficult decisions, you know, in a way, let's rather you do it. So it's happened. I wouldn't say it's happened often, but sometimes it's happened.

So what we look for is on a qualitative level, three things. We like to acquire large businesses relative to our current size. And the reason is I described how we conduct these extremely deep transformations and integrations. Those take a massive amount of time and effort. The good news is we found that we don't need, like the number of people, the amount of time we need to complete those transformations does not seem to scale really, at least not leading. with the scale of the business. And so we're better off acquiring fewer companies and deploying those resources toward transformations than move the needle as opposed to acquiring 100 companies,

but then not having the time to do anything really well. So we like to buy, say, five companies a year, give or take that are large. The second thing is we want to feel confident we can predict their performance, at least five or six years out with confidence. And that's an entire whole discussion in science, but on a high level, that's the answer we need before we decide to pull the trigger. And the third big criterion is we need to believe we can greatly improve those businesses, hopefully the product, detect the org.

Monestrian marketing, ultimately revenue costs. You know, it's an earnings game at the end of the day. So those are the three, you know, kind of big criteria than how we get to those answers. That's, you know, a lot of many steps and it's quite complex, well, other than the scale. The scale is very obvious. And once those are, you know, three checks, then it boils down to the math of returns. You know, among the companies that check all three boxes, then we look for investments that will yield the highest returns so we can compound as fast as we can.

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Founders. EngineX visa commercial cards are issued by Fifth Third Bank, N.A. member FDIC, earn up to 10% back in points on eligible engine travel purchases. Actual reward rates vary by purchase category and may change points of no cash value and are redeemable for rewards through our program. See full rewards terms for details. Go to engine.com slash X slash rewards dash terms. We're back with Luca Ferrari. When we look at some of the names of the companies you've acquired, especially like AOL and Vimeo, these are sort of, once great companies or once, you know, companies that were previously known to everyone, sort of darlings of the tech industry, that have gone out of fashion, I would say. Is that part of the strategy is taking these once big name brands and transitioning them, or is that just a coincidence? I think it's mostly a byproduct. We don't actually have internally guidelines for our MNA team, look for, you know, brands from a bygone area,

or anything like that, it's more that I think, at least historically, maybe, at least in the last five or ten years, maybe the future will be different. We found that the market tended to undervalue the unsexy things. And in our view, overvalue the new and sexy sometimes to a fault in a major way. And so as we try to be rational operators, you know, you can never prove it. I'd like to think we've been rational capital allocators and operators. When we're comparing, after having done the qualitative screening and done the math, we were comparing the returns we would get from the AOLs of the world, or I'm not going to name names, but let's say, flash-year, newer, sexier things, we found that the latter would consistently give us much worse returns,

and we saw no reason to do it, simply because people liked it. You know, so, you know, AOL is actually a very good business. It's not, obviously, it's not going places, but 30 million people use it as their, for many of these as their main email and they use it all the time and these are perfectly, you know, they're not, they're not less, no less valuable as human beings or as customers as people using the small, fleshy new email clients simply because they like AOL. So we're happy to serve those people, just as we'd be happy to serve, you know, the cooler, early adopter. And it seems that many investors prefer to go toward cool and sexy and we're happy

to take the less sexy things. This is one of the reasons I'm fascinated by your business, and it's a big theme on our show, which is that the best investments are the unsexy investments. You want to capitalize when everyone has, for various stupid reasons, written some company or some stock or some investment off. It seems like you are capitalizing on that dynamic. That is a very investment-minded strategy,

and you are someone who comes from, you were building companies, you were building software products. What is your background in investment, in financial valuation? Because it does seem like a very, in a lot of ways, it's what classic value investors have done and you seem to be in that camp.

What got you to this point, given the fact that you kind of had more of a software background. Michael Funders and I studied very similar things, engineering, physics. I think that's really our background and maybe driving passion is technology and science. We just felt that applying that to business would be fascinating because it's actually business offers a lot of challenging intellectual problems. It's also rewarding financially. So we decided to go down that route, but we have, I believe, retain that engineering and scientists, engineering and scientists mindset,

and I try to embed it as much as possible in the organization, in everything we do, even talent. I mean, the way we recruit is completely scientific at this point. And then I'd like to think we're curious people. We have studied, you know, there's plenty of, I think it's more difficult, let me put it that way, it's more difficult to learn quantum physics than, you know, how to calculate an IRR.

So I think it's better to have studied STEM, and that generally tests your intellectual capacity and gives you better problem-solving tools. You can then learn other things if you put in the effort. So we have plenty of books on finance and economics, and, you know, we're big fans, people like Warren Buffett or Tom Murphy or Harry Singleton, Benjamin Graham. There's funny you can read. It's not rocket science if you try to learn from people who are. smart and I've done it before and then of course try not to copy paste blindly but

understand the boundary conditions they had and the boundary conditions that apply to you, there's a lot you cannot, a lot of mistakes you can avoid. So yes, we try to be value, not necessarily in the way that it has to be again a business that's beyond as past its prime necessarily. For us, it's math. It just happens that the market values in their prime businesses so highly that we end up buying more of the past their prime businesses, but ultimately, just try to compound efficiently. That's all we're trying to do.

You mentioned Warren Buffett, and that's exactly who I was going to bring up, because there seemed to be a lot of parallels here. You talk about you're not flipping companies, you're not trading companies, you're buying companies, and the plan is to hold them forever. And I have to say, I mean, the first company that comes to mind when I think about this business model is Berkshire Hathaway. And I look at what Warren Buffett did, starting with very, very, very small companies. It sounds like I think he took a less strategic position in a lot of those companies, but he still took some strategic positions as well. But it sounds like that's quite similar to what you're doing, but you're doing it in a tech and software-enabled world.

Is that kind of the vision for bending spoons? Or do you think that is an app comparison? I'm a big fan of Buffett and his colleagues, Dad Weschler. I think they're amazing. So I think you all just want to be careful with comparing because it's almost like, like you're asking an aspiring. Comparing to Michael Jordan. Exactly. It's like, sure. Obviously, yes, it'd be great.

But at the same time, we need to stay humble and acknowledge that we have done, you know, 1% of what they did. But broadly speaking, I think it's an app comparison that forever ownership mindset, rational investing, trying not to let the trends of the moment sway you unless there's a good reason for it. I think a huge difference would be that, you know, Berkshire has historically being a more passive acquire, like they try to be very good at selecting the right companies and then letting them operate doing more of the same. Where on that particular dimension, we're almost at

the exact opposite end of the spectrum where we like companies where our ability to make big changes and the very integration into our technological and people platform will make a difference. But yes, it would be a dream to be able to build a company that's almost an institution of that scale. We'll see. We're certainly here to try. So you start in Cobra Hagen, then you move to Milan, you are in Italy. I think that this IPO put Italy on the map in terms of business news. We rarely hear about Italian companies unless we're talking about luxury cars or storied brand names. To what extent does being in Europe play a role in your business? There are a lot of European companies that have decided we're going to

move to the US because there's more opportunity here. I had the CEO of Salonis on recently who tried to move a lot of his operations to America. Tell us a little bit about operating in Europe and the extent that it has impacted your business and your strategy. First of all, currently, yes, we're headquarters to Milan, Italy, but most of our people and actually more than 50% of our team is in the US and 60% of our revenue. And most of the businesses require are in the US. so are definitely very international with a huge US footprint,

but we continue to be a European company, let's say at least formally and certainly a big chunk of our R&D and our headquarters. We decided to build a company in Italy specifically, quite consciously. All startups, in my experience, are built out of inertia, like we happened to graduate here, let's launch a startup. That's what we did with the failed AI startup

or machine learning or whatever want to call it. Once we decided to start Benisprinus in Copenhagen, We put some thought into where we should be building a company and chose Milan, Italy, quite consciously. For two reasons. One, it was just a, let's say, commercial selfish reason. And that was Italy, 60 million people, good education, actually. Lots of people are cheap on their shoulder to prove, you know, that we're not less intelligent or less capable. We thought we, but very few great job opportunities.

we thought we could find a lot of capable talent. And it would be a win-win for them to join an ambitious project. Hopefully would be a huge company one day. And for us, we'd be able to surround ourselves and great people, create strong teams and hopefully have better chances of success. So that was one reason. The other reason was we just had this idea that, and I still have it, the world is a better place if knowledge, opportunity, wealth is better distributed.

I think it's totally fine that you have one or two habits. or those are off the charts, you know, Silicon Valley, you're never going to have a uniform distribution, and that's okay. But I think if you have just a gigantic gap and a growing gap, I don't think that's a stable world, a world where ultimately anybody wins. And we want to prove it you could build a company of global caliber, an absolutely kind of key cast company in technology, which was our passion, from, with roots in an unlikely place like Italy, but it could have been Portugal or Greece, it just happened to be. Italian, it was easier for us, plus the other thing I just told you. So we moved there and built it there, and I think it's being good and bad. Look, the good part is the talent part, I just described. The bad part is Europe has truly incredibly high levels of oppressing regulation.

I'm a big fan of a, I'm not a libertarian, anything like that. I want to be clear. I'm actually a fan of an extremely generous, supportive welfare state, a society. that helps people. I just don't think that a good way to do it is by imposing massive encamprances on companies. I would say, you know, have us pay lots of taxes and help people but don't make,

getting a permit for building something impossible, getting, you know, being able to fire a team member because you can hire someone higher performance impossible. Like make it easy to deploy, to launch projects and allocate resources efficiently and then use taxes to provide us, general safety net for those who are left behind. I'm actually in favor of wealth taxes and more taxes for rich people. I just think we're doing it the wrong way. But Europe is good, but it could be a lot

better if I think changed in this regard. I was going to ask you what you think the gap is between U.S. entrepreneurship and European entrepreneurship, and there is a gigantic gap. And there is a gigantic gap in productivity, in GDP, and overall wealth creation. I assume you believe it is a result of overly burdensome regulation in Europe, or is there anything else? No, there's more. That would be reductive. I think that's a very big reason. One of the main reasons, I'd say another big reason is culture. I think Europe has historically, and this changes by country, Europe is quite, people who have traveled Europe and spent a lot of time in Europe, it's quite a heterogeneous country. Like, the people can be quite different. But I think on average, compared to my understanding of the U.S., which I think I know reasonably well at this point, there's a greater tendency to fear and criticize failure. People can be stigmatized for failing.

And when they succeed, they're often criticized for being too ambitious. And maybe there's a sort of the default is to be suspicious. Maybe someone, you know, help them too much or they got lucky, which obviously doesn't breed. you can still be a successful entrepreneur, but it doesn't help. And then I think another factor is the market is, especially historically, being more fragmented, whereas the U.S. has done a fantastic job at basically unifying the market. And so winners got bigger, faster. And in a global world, that's a huge advantage.

None of these things is huge in and of itself. They're all marginal frictions. But the way, in my opinion, the economy works is that when a certain location in this case is even 20% more appealing, you know, resources start trickling from the less appealing location to the other location, whether it's human resources, capital resources. And over time, that builds an advantage in the more appealing location that can be massive. And I think the U.S. today, you know, in my estimation, if I look at, if I take 1,000 of the best broadways from Italian universities every year, let's actually take the top 100.

Let's really go at the top of the best, or really the people who take. to have breakthroughs in R&D or launch successful companies. My estimation is that probably a good three out of four end up in the US. And early, often even before graduating, and that's difficult to capture in the statistics, but down the line for our 10 years, that compounds to a major disadvantage to Italy or Europe. And that's been going on for decades at this point.

So how do you deal with that issue? If the talent in Italy and in Europe is being sucked out of Europe, and they're all going sent over to the U.S. and they're working at AI companies in Silicon Valley and San Francisco. I mean, how do you deal with that? Is that a problem for your business? Not so much for us because we, I mean, we're,

first of all, we attract, we bring people back quite a bit. There are a lot of people who are originally European and they like the U.S., but maybe their family, their dear ones are in Europe and they've been looking forward to coming back and they haven't had that many opportunities, so they see Benin Spoon's as a great opportunity, to be able to come back and yet not give up that level of ambition, high, high compensation, fast career growth and whatnot.

Also, we happen to be, generally we hire people who are graduating or when their students who would have gone abroad and they stay and they have a fantastic career. And, you know, we'll be opening offices in the States next year. So it's not really a benefit from this problem per se, but it's a massive problem for society here in Europe, I think. How to solve it, frankly, I don't know. I don't have a silver bullet. I think you need fundamental reform, certainly massive intervention on regulation,

but frankly, if you did that, it wouldn't solve it overnight. It's something that would have to build up over a long period of time. I don't know that you can reverse the compounding that's going on for decades, to be honest, not in any reasonable time frame. It is quite worrisome in my view. Even, frankly, in many ways, even for the U.S. themselves, because, again, a world where people in the U.S. are 10 times richer than everybody else. I don't know that's a stable world, a world where, you know, people can travel

safely and be generally happy. I think we need a level, you can have a country that's dominant and substantially better off, but if the gap becomes, and it's not today's situation, but with AI potentially accelerating the divide, if the divide becomes just massive, it's a difficult earth to inhabit, I think, for all, really, even the ultra-rich and ultra-successful, in my view, at least. We'll be right back. The more for the show comes from Engine. The companies winning right now aren't cutting travel. They're booking smarter. Here's the reality. The average business traveler spends 45 minutes booking a single trip on a legacy platform. With Engine, that booking time drops to as little as two and a half minutes. And its AI-powered personalization gets faster the more you use it. Multiply that across your team every trip, every year. That's not a perk. That's a competitive advantage. Last year, Engine customers saved more than $300 million on travel. And with the Engine X card, get up to 10% back on any hotel booking.

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terms. We're back with Luca Ferrari. You were able to not let this affect your business. I think this is one of the big questions for founders with the big problems to solve is how do I recruit talented people to my business? And especially it's difficult if you're starting from scratch. What did you learn about how to build an excellent brand that world class students, and engineers want to come to and want to come to work for. Because to me, I think that when you're building a business, the people are almost everything, and you need to convince them that they're going to succeed

and that they're going to win. How did you do that? A lot of people, in my experience, tell you that they believe people are important, but then they don't walk to talk. So they actually don't put in the time or effort into that as those words would warrant. So at Benisodes, we try to remind ourselves that the jobs we offer are our most important product. More important to any customer-facing product we have.

So we try to make those jobs as great as possible. Then word of mouth will help you a lot. You know, you hire two people. They are absolutely happy and satisfied. And they will tell their other friends from university. It's actually fantastic here you should join. We have hired so many people that way. And we can discuss how we've tried to make jobs here.

It's actually not that difficult if you stop, for a second, remember that you are in a, as a founder or CEO, you are in an unusual position and in an awful vantage boy and actually trying to stand in the shoes of team members. And, you know, it's not that difficult to find what the type of person you want to hire would appreciate, you know, whether it's, of course, good pay, but then autonomy, trust, respect. a lot of times management teams are a little bit arrogant in expecting the people should love it to work with them simply because they want it to be that way. So you try to work hard to make it a great product and then you need to put in an effort to promote that product. So when we were 50 people at Benin Spones, we already have something like three or four people doing recruiting alone.

Like a massive investment, just going to recruiting events. posting job ads, calling people, emailing people. At the beginning, you need to persuade people. I spent so much time persuading people to join us. And there, a good founder needs to provide a vision that's credible and the energy and passion that would convince those who are, you know, convinceable. So you punch above your weight.

Basically, you hire slightly better people than you should be able to. And then if you do that right, hopefully a virtual cycle is ignited, where you offer better and better jobs. You can pay more and more as the company becomes more successful. And the track record speaks for itself. At the beginning, it's more like, you have to believe me. But now, go read on Glassdoor or go talk to 50 people who work here. You can establish the truth pretty easy.

So that's, you know, a lot of effort. And then, you know, you want to select well among anyone applies. And I believe that's a question of science. And we have tried to make it that. We were data scientists and AI researchers. we put in a lot of effort to detect signals in someone's application that predict performance and we keep iterating and improving. Not unlike, I think, a quantitative trader with algorithms trying to find signals in the market

to tell them which stock is likely to go up and which stock to go down. We try to do the same thing with people and find people who will be amazing if provided with enough opportunity and coaching. What does that look like, that sort of more scientific approach to hiring? You need to first establish an objective function, what you want to optimize for. We found that in our case, at this stage, that's performance as rated by one's colleagues after 12 months on the job. The more people you hire, the deeper you can go into the funnel while still retaining

statistical significance. We are currently hiring several hundred people a year, so we can go fairly deep. one year, it tends to be pretty good. And then we, so that's the, your objective function. And then what, what you do is you need to, to identify as many signals as you can in someone's profile, even beyond the pure application. And then you just, you rigorously track them and see where, you know, how, what combinations of signals best predict that outcome at the objective, objective function level.

and that's a, that's not a difficult problem that's pretty well established in statistics. The problem is, of course, capturing that outcome at one year in a way that's reliable, always in the same way, pretty structured. And those signals, the signals are really the key. Some are pretty obvious, for example, GPA. It's not a perfect predictor. It's only a mild predictor, but if someone has done really well in university,

they're more likely than not to do well, at least at any spoons. Most people would believe that helps. If someone has gone to a more competitive, selective university, that's a, it's a predictor too. A weak one, surprisingly. People think, oh, someone to MIT, they must be a genius. Actually, it's much less predictive than you would think, but it is predictive a little bit. Some other are quite amusing and yet quite predictive. For example, we found that if someone was politically active during when they were a team,

teenager, but stopped being politically active when they were in their 20s as a very good predictor of drive of agency, entrepreneurship, which at least we definitely wanted Benin Spoons. It would have never occurred to me. We spotted it through with AI, basically. But then you can rationalize it by saying if someone is 16 and they're not content enough with being a good student, they're looking for ways to change the world around them. a good sign of, again, a productivity, agency, a healthy level of dissatisfaction. If they're still doing 1024, it's great, but it's probably more of a life mission, and their job is probably, you know,

takes a backseat to it, and so they're unlikely to do super well relative to people who prioritize their job. So, you know, hundreds of signals like that, we keep finding new ones. Many we generate through tests. We have people go through practical tests, but we try to, we look at, you know,

what people write in their emails, what tone they use, use, how do they interact with people who are not in a position of power? We found that how people email are, let's say, are support agents who help them just with the logistics of their application and clearly are not the person deciding whether they get hired is a very good predictor of whether they are a collaborative, humble team member as opposed to because they're always nice to people late in the funnel when they know that the offer depends on that interview.

But those who are assholes in life, they will show up as assholes more often than not with people who are not perceived to be in a position of power. So hundreds of those, none is very predictive, but when you put them all together, we're pretty predictive at this point. It sounds quite similar to the strategy

that Ray Dalio used at Bridgewater for many years, where there was a lot of, he wanted to track everything. He wanted to track every meeting, every interaction, sort of put everyone on a social, graph, which I think in a lot of ways probably worked, but also he got a lot of criticism for it, and a lot of people did not like it because it feels like, it felt like to a lot of the

workers in that culture that they were being surveilled and that every interaction was a test. And it sounds like in a lot of ways with the level of tracking that you find that you are employing, that you might run into potentially similar issues, I guess, how do you think about that issue and do you think about it? Do you think of it as a potential issue? I didn't work at Bridgewater. I've read a few things about it, so I'm just slightly familiar with it. What I just described only applies before you get hired. On the job, we actually don't do any. There's just a review at the end of the year, which is based on input from your colleagues. We don't do any tracking of working hours, of anything really. So precisely for what you mentioned,

We are stanched believers in the fact that most people react really well to being trusted. Yeah. And so we try to hire people who believe are amazing or can be amazing given enough time. And then we give them uncomfortably high levels of trust and freedom to the point that sometimes we've been criticized quite the opposite. Because sometimes we have had these situations where we acquired a business and then we establish as a general manager someone who's like 27 years old. and sometimes acquire team members tell us, are you crazy? Like, you know, this person,

how can you trust she can do the job? And historically, she has, by the way. But I think if we were to monitor or surveil people, that would really destroy that trust. That's such a motivating factor for people to come to work and do their best. I guess it makes sense. It's like you're extremely discerning before they're hired

and then extremely trusting once they're hired. which I think makes a lot of sense. It's probably the right strategy. Over time, you assess people's outcomes over a range of scenarios. You don't need to surveil how they got there. If someone is consistently delivering, delivering quickly, high-quality work,

I don't care to know really how you got it done. You know, like if you use AI or didn't use AI, did you work later? Ultimately, we want to work with people who are effective. And so we can just measure how we contribute to projects over time. And I don't think surveilling people would add it very much, if anything, to be honest. But pre-hire, you don't have, like, pre-hire,

you don't have any information to look at in every niche in cranny to be able to figure out whether extending an offer makes sense. One of the big themes with your business is sort of using AI and using modern computer science and infusing it into these businesses. What does that look like?

What does it look like to take a company like Eventbrite and then give it whatever the AI-bending Spoon's Magic actually is. What are you attaching onto these businesses? So I think we try to use AI, broadly speaking, for two things. One is there are sometimes features you can build for customers that are only possible, say, today, thanks to AI that were maybe not possible in the past. You mentioned Event Bright, I think. We haven't done it yet. It's very early, but I think one of the ways AI could help make Event Bright better is by greatly enhancing the recommender systems. When you go to

event bright, there's roughly 80 million people who go there monthly to find interesting events near them. What you show them as a recommendation makes a huge difference on whether they are satisfied and whether organizers sell tickets. So it's really like a win-win. And that's a difficult problem, especially because you don't have the data that meta does. I mean, you have very limited data clearly because the people only go there for the events. So AI would believe could be quite helpful in making that better and even marginal improvements

in that recommend your system can be quite valuable for both parties of the marketplace. And the other big ways, and that's completely bespoke to the product, obviously. And then there is something much more, to say general purpose that applies to almost any business that's using AI and technology more generally, cutting out of technology to be

operationally much more effective and efficient. For instance, we again, I told you we have dozens of proprietary technologies. Most of these are based in AI, especially these days where if you don't use AI are probably giving a lot of value on the table. But some are really AI-centric. For instance, we've got something called alternative spooner, old ALT spooner.

Each one of us, and Spooners, by the way, are basically the members of this core team, you know, highly selected recruiting process and people will move around their businesses fluidly. But we're working now to extend this technology, by the way, to all team members. It takes a little bit more work. But you have this personal assistant that lives in Slack. We use Slack for communication. And it automatically has access as exactly the same level of access you do across all. of our toolkit.

And you just talk to this agent and you ask you to do pretty much anything. For example, you could say, I actually saw this live as I was in a channel on Slack where we post feedback on Evernote ideas to improve the product. I was there to, I was just writing about the bug I found. And I saw our general manager for Evernote. She wrote that she had experienced the bug

and asked her alternatives to, first of all, investigated by going to this tool we built to collect and collect. and categorize customer support tickets and check whether that bug was prevalent among customers or was just something. She was a corner case. And then go into the code base,

identify the root code, a fix, and then message the technical lead for that project, Marco, so that it could review the pool request and then fine, basically approve it so that that fix would go and benefit all customers. And she did that in, I don't know, like less than five minutes. and the agent went off and did it. The bug was fixed the day after because Marco apparently didn't have time to review it the same day,

but it probably took, I don't know, 30 minutes of a person's time to do something that would have taken optimistically 10 hours even just two years ago. But actually we would probably never have sold that bug because it would never probably have reached a level of priority that we would go after it with the cost. So this is just one example. And interestingly, that technology, you don't even get to choose the model that's being used. So there is a gateway that the harness is built in-house of open-source components. And then the gateway is also built-in-house. So then when a task comes in, we have an algorithm that will determine the best model or culmination of models to implement the task, trying to optimize for quality and cost.

So we have, of course, access to the APIs for the frontier models, but also we have self-hosted over probably 15 or something, different open-weight models and combinations thereof. And for 99% of the tasks, we actually go up and wait, spent almost nothing. We're currently, like I said, close to $4 billion in round rate revenue. We are, as far as I can tell, one of the most aggressive users of AI of any company writing 94% of our code

with AI, almost all of our data knowledge, This is almost all of our design. And on a run rate annualized basis, we're spending $15, $15 million in all AI-related spend. Because it's almost all done with super efficient open-weight stuff. And the frontier models are used only for supervision, which is super cheap. It's like if you have a senior engineer, just overseeing 10 engineer engineers and just taking a look but not doing the work themselves, that's way cheaper. And then for some super critical tasks, but those are quite rare. So when you bring this to an acquired team, it can make a massive difference in the efficiency

with which things get done. And it's not just you cut costs, but also you just launch more features. You can run more experiments in monetization. It's all a virtuous cycle of compounding over time, which is quite powerful, especially if you can deploy across new acquisitions, because then you can invest more in building these tools because you amortize across a growing base of revenue. It is a really good endorsement of how, AI can make businesses more efficient.

Also an endorsement of the value of the open source models, which is that they're cheaper and you're not losing that much so long as you're strategic about when and how to use them, which I think is going to be a huge shift in the AI landscape over the next few years. But also, it is kind of an endorsement of the AI will take your job story. Because you're using AI, it's making the business more efficient, and you're also reducing headcount pretty dramatically in a lot of cases. And it seems like AI is doing a lot of the business work that humans would have been doing in a, yes, less efficient system. But I think if I were worried about AI taking my job, I would point to bending spoons as an example. Are you

concerned about that? What do you think about this issue? Yeah, very much. So I agree with what you're saying. I also think that I don't believe the answer is telling companies, Well, try to be worse than you could be because we, like, I think ultimately that's not a lasting solution. But I think societally this could be a big problem. Up until a few months ago, I was quite pessimistic about this. I really thought I couldn't see a way for people to be employed even in 10 years, at least the bulk of the population, I guess. I've gotten a little bit more positive about it. As I read an article by an economist, I forget his name now, but we made an interesting.

And so I point of people who everybody have heard say, don't worry, I will create more jobs. Their claim was, ultimately, I cannot do everything humans can, better than humans can. And I think that's a dumb argument. I think it's true today. But our brains are ultimately molecules and electromagnetic signals. Look, we're going to get there. You know, maybe next year, maybe in 20 years. It's a difficult but definitely not insurmountable technological problem.

And the upside for whoever solves it is so massive that someone is going to solve it. Maybe it's not LLMs. Maybe we need some breakthroughs architecturally that we, you know, but we'll gather. So I think that's a dumb argument. But this economy has made an argument that I think is brilliant and is like the concept of comparative advantage or opportunity costs or what you will. You are probably, you know, you or anyone at a company is probably much better at a range of tasks than some colleagues who are doing those very tasks, but your time is limited.

And so it's ultimately more efficient for you to focus on certain things, other things you're better at or they're more value-adding, and other people will do the other things. And AI, ultimately, although the capacity it can have can be orders of magnitude greater than that of a human, ultimately it's limited by something, whether it's energy or chips or, you know, it's never going to be infinite. And so there will be things that AI cannot do because it would be stupid to use those limited resources for AI to do those things. And you must will very likely be the next best option. And in a world, where the economy has grown massively because of productivity gains through AI,

maybe we get paid so much more for those things, simply because there's so much more money to go around, that whoever is enjoying part of that share of that the AI pie, so to say, is willing to pay a nanny 100 times more than they're paying her today. And I think nurses are paid a lot more today than they were paid 100 years ago for very similar reasons. So I've gotten more optimistic, but there's this scenario which could be almost, you know, having on hurt in some ways. And maybe having an unhurtis, we wouldn't even need to work because we found a way to distribute that wealth and we can all pursue our hobbies and whatever.

But this is a semi-same having unhurt. I quite like it. But I also got pretty worried that if the point AI is so powerful and there's this limiting factor, say, energy, it's a slippery slope where if, if, the point AI is so powerful and there's this limiting factor, say, energy, it's a slippery slope where, if, we lose control of AI, then why are we feeding humans? You know, because that's taking away energy that could be fed to giving AI that extra 0.1% to do even more. Like if we're pushing out to that limit, what prevents those who control it or AI itself from turning this into a massive dystopia, a bit like the paperclip typical distop.

So I've gotten more optimistic, but also this scenario, I think, opens some. up even more concerns on some truly extraordinarily bad scenarios a lot worse than people are unemployed kind of scenarios. So I'm excited about AI because ultimately I'm a technologist and I think it's fascinating, but I'm also extremely scared about some of the implications it could bring to society. It seems that so much rests on the transition period. How do we transition into that future and what decisions do we need to make? And it seems like we do need to make some pretty important decisions. We are out of time. I want to just wrap up with final question for you, you have found yourself in extremely successful position.

You are working in AI, you're also working in investment. You've seen a lot of things. As a founder, what advice would you give to anyone who's listening to this podcast, who's interested in being entrepreneurial, maybe thinking about living in a more AI-enabled world and how to succeed in that world? What advice would you give? Try to look for for ways to be valuable, create value where few people are looking. I think if you're building the harness number 28, simply because someone like a VC

was not being able to finance the bigger brands as willing to give you $5 million. I think you're probably wasting your time. So try to find a place that's a little bit concealed, unseen, unsexy. And by all means, ask yourself a question what I could do to make it better or more successful. But don't look where everybody is looking because especially if you're looking at me to give you the answer, you're probably not Elon Musk. And so you have no chance of winning, I think, where everybody else is already pouring billions and trying hard. That's one thing. But then go for it.

I guess I've heard very few people, it's a bit like having children. I've almost everybody who chooses to have children does not regret it. Sometimes people who choose not to later regret it. With entrepreneurship, I've rarely found entrepreneurs later like, oh, I wish I never tried, even if they fail. They almost all cherish the experience. So, you know, give it a shot.

Don't be stubborn. You know, if you fail time and again at some point, get a job, but give it a shot. Luca Ferrari is the co-founder and CEO of Bending Spoons. Luca, we really appreciate it. Thank you for joining us. and to our audience. Thank you for tuning in live. We will see you next time. Thank you for having me. This episode was produced by Alison Weiss and engineered by Benjamin Spencer. Our research

associates are Dan Shalon and Kristen O'Donoghue, and our senior producer is Claire Miller. Thank you for listening to the Prof GMarkets Founder Series. We'll see you next month with another founder's story. Support for the show comes from Engine. Here's the reality. The average business traveler spends 45 minutes booking a single trip on a legacy platform. But the companies winning right now aren't cutting travel. They're booking smarter.

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