# Bending Spoons Acquisition Strategy and AI Integration

**Podcast:** Prof G Markets
**Published:** 2026-10-04

## Transcript

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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, his company went public on the NASDAQ at a valuation of more than $18 billion, 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 Prof G 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 a startup in 2010, also in Copenhagen.
We were using AI clearly too early to automatically write diaries for people.
And we did that for three years, raised a million dollars and failed miserably.
I couldn't make it scale, although I'm pretty proud of it.
the product we built, but sometimes that's not enough.
And so in the ashes of that company, we were left with about $40,000.
The VC who had given us the money basically sold their shares back to us for a nominal $1 and they apparently appreciated we'd worked hard and been 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 I was a seed capital for Benning Spoons.
So that's 2013, again, Copenhagen.
Moved the company to Italy about a year later, but that's where 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, it gets 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 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, if we work on this 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 one to ten 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, awfully vast, diversified portfolio of 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 he had watched it the night prior or something, and in the morning he 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 liked the idea of making a little bit of fun.
of poking a bit of fun of ourselves, you know, 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 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 Banish Foods 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 Evernote 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, you know, 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.
more difficult to launch something that can 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 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 added in the IPO a few months ago.
We had significant Injection of equity, about a quarter of a billion dollars toward the end of 2025.
So really, up to that point in time, most of the growth was through the reinvestment of our 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?
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, or 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 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 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 serial acquirer.
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 the private equities I'm aware of or I've seen operate, they do intervene.
Obviously, they may cut costs, maybe intervene on monetization, they may change the executive team, but the interventions are relatively superficial.
And you can tell by looking at the the team at private equity firms, it's mostly small teams of financial professionals.
Benny Spoons takes a different route.
We do those things as well, but we take a different route in that we...
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, reimagine 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, customer-based, user-based 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 to the core team, forget for a second, the acquired teams, that's roughly 800 people and overwhelmingly they're software engineers, data scientists, product managers, product designers.
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 equities 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 investor some time ago told me that after due diligence in Venice, that they found that it's almost like we buy companies and then they get stripped down to a product installed on a shared operating system.
And I think it's a bit of a simplistic metaphor, but there is truth to it.
So if you look at how we do things, generally within a few months, From the moment the acquisition closes, we swap out the entire, let's say, technological foundation.
We've built dozens of proprietary technologies, whether it's AI orchestration, data processing, A-B testing, recruiting, payment management, credentials management, like basically every component of running a digital business we've built in now, 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, G&A, marketing, who we redeploy very fluidly across all of our businesses to go after R&D opportunities and when they're exhausted, we draw 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 a downside of our model.
So there are similarities with private equities.
First and foremost, the focus on acquisitions, but I think the...
The day-to-day operational approach 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 very also very similar to the private equity model and to 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 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, I 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 costs.
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 assumption some people have is that the number of people you have in a company is almost, you know, definitively gonna determine whether the company produces great product, it 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's 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 a coin of 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 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.
Demonstrably, the rate at 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 the 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 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 acquirable?
I think you're right in many ways.
I would say a big percentage of our acquisitions have been struggling companies.
Not all we have acquired, several companies that were growing nicely.
I'm thinking Komoot Airtable recently, we shared the growth rate.
But yes, we have a bigger percentage of companies in our acquisition portfolio, let's say, that were...
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, I'd rather you do it.
So it is, 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 linearly with the...
with the scale of the business.
And so we're better off acquiring fewer companies and deploying those resources toward transformations that move the needle as opposed to acquiring 100 companies, but not 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, you know, five or six years out with confidence.
And that's an entire...
whole discussion of 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, the tech, the org, monetization marketing, ultimately revenue and costs.
It's an earnings game at the end of the day.
So those are the three kind of big criteria.
Then how we get to those answers, that's 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 return so we can compound as fast as we can.
We'll be right back.
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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 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 M&A team, look for, you know, brands from a bygone era 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 unsexy 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're being rational capital allocators and operators.
When we were 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 fleshier, 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, AOL is...
it's actually a very good business.
It's not, and 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, flashy new email clients simply because they, they like AOL, you know, so we are 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, you know, 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, retaining that engineering and scientists.
engineering scientist mindset and try to embed it as much as possible in the organization and 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's generally, that 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, you know, there's 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 plenty you can read.
It's not rocket science if you try to learn from people who are smart and have done it before.
And then, of course, try not to copy-paste blindly but understand the boundary conditions they had and boundary conditions that apply to you, there's a lot of mistakes you can avoid.
So yes, we try to value not necessarily in the way that it has to be, again, a business that's beyond past its prime necessarily.
For us, it's math.
It just happens that the market values the in-their prime businesses so highly that we end up buying more of the past or prime businesses, but ultimately we 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 seem 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 small companies.
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 apt comparison?
I'm a big fan of Buffett and his colleagues, Dad Weschler.
I think they're amazing.
So I think you always want to be careful with comparing because we're talking, it's almost 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 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 been a more passive acquirer.
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.
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 Copenhagen, 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 headquartered in Milan, Italy, but most of our people, and actually more than 50% of our team is in the U.S.
and 60% of our revenue, and most of the businesses we acquire are in the U.S., so we're definitely very international with a huge U.S.
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.
Other 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 you want to call it.
Once we decided to start Benistrums in Copenhagen, we put some thought into where we...
should be building the company and chose Milan, Italy quite consciously for two reasons.
One was just a commercial selfish reason, and that was Italy, 60 million people, good education actually, lots of people with a chip on their shoulder to prove that we're not less intelligent or less capable.
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 that hopefully would be a huge company one day.
And for us, we'd be able to surround ourselves and create people, create strong teams and hopefully have better chances of succeeding.
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 hubs 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 wanted to prove that you could build a company.
of global caliber, an absolutely kind of kick-ass company in technology, which was our passion, 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 been good and bad.
Look, the good part is the Italian 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 or 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 encumbrances 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...
being able to fire a team member because you can hire someone with higher performance.
Make it easy to deploy, to launch projects and allocate resources efficiently and then use taxes to provide a 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 and 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 to 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 US, which I think I know visionably 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 helped 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 the 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 the 1,000 of the best graduates from Italian universities every year, let's actually take the top 100.
Let's really go at the top of the best of the best, or really the people who tend 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 Band of Spoons as a great opportunity not to be able to come back and yet not give up that level of ambition, high compensation, fast career growth and whatnot.
Also, we happen to be...
Generally, we hire people who are graduating or when they're 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 benefits 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 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 timeframe.
It is quite worrisome in my view.
Even, frankly, in many ways, even for the US themselves, because again, a world where people in the US are 10 times richer than everybody else.
I don't know that's a stable world, a world where 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.
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We're back with Luca Ferrari.
You were able to not let this affect your business.
And I think this is one of the big questions for founders or 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 the talk.
So they actually don't put in the time or effort into that as those words would warrant.
So at Bennis Rules, we try to remind ourselves that the jobs we offer are our most important product, more important than 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...
and satisfied and they will tell their other friends from university, it's actually fantastic, you should join, we have hired so many people that way.
So, and we can discuss how we've tried to make JobsSeer amazing.
It's actually not that difficult if you stop for a second, remember that like you are in a, as a founder or CEO, you are in an unusual position and in an awful vantage point, 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 that 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 Bend and Spones, we already have something like three or four people doing recruiting alone.
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, convincible.
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 virtuous 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, you know, go read on Glassdoor or go talk to 50 people who work here.
You can establish the truth pretty easy.
So that's a lot of effort.
And then you want to select well among anyone who 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 like 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 a 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.
which tends to be pretty good.
So that's your objective function.
And then what you do is you need to identify as many signals as you can in someone's profile, even beyond the pure application.
And then you just rigorously track them and see what combinations of signals best predict that outcome at the 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, pre-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 Benning's most.
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 went 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 others are quite amusing and yet quite predictive.
For example, we found that if someone...
was politically active when they were a teenager, but stopped being politically active when they were in their 20s, that's a very good predictor of drive, of agency entrepreneurship, which at least we definitely wanted Bending Spoons.
It would have never occurred to me, we spotted it through 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.
That's a good sign of, again, proactivity, agency, a healthy level of distance distraction.
If they're still doing it, when are 24, 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 likely 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, how do they interact with people who are not in a position of power.
We found that how people email our, let's say, our 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 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 staunch believers in the fact that most people react really well to being trusted.
Yeah.
And so we try to hire people who we 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've 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 a part of 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 it work late or you didn't like...
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 in.
very much, if anything, to be honest.
But pre-hire, you don't have, like pre-hire, you need to, you don't have any information, you need to look at in every niche and 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 spoons 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 Eventbrite, I think.
We haven't done it yet.
It's very early, but I think one of the ways AI could help make Eventbrite better is...
by greatly enhancing the recommender systems.
When you go to Eventbrite, 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, we believe, could be quite helpful in making that better.
And even marginal improvements in that recommender 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, let's say, general purpose that applies to almost any business that's using AI and technology more generally, cutting edge technology to be operationally much more effective and efficient.
For instance, again, I told you we have dozens of proprietary technologies.
Most of these...
are based on AI, especially these days where if you don't use AI, you're 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, highly selected recruiting process and people who 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 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 it 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 a 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 investigate it by going to this tool we built to 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 cause, code a fix and then message the technical lead for that project, Marco.
so that it could review the pull request and if fine, basically approve it so that that fix would go and benefit all customers.
And she did that in, I don't know, like five, 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 solved that bug because it would never probably have reached the 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 used, that's been 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 combination 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 to actually go up and wait, spend almost nothing.
We're currently, like I said, close to $4 billion in run 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 analysis, almost all of our design.
On a run rate annualized basis, we're spending $15-15 million in all AI-related spend because it's almost sold down 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 junior 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 a...
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 it 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 who made an intro.
And so I, plenty of people who, everybody I've heard say, don't worry, AI will create more jobs.
Their claim was, ultimately, AI 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 get there.
So I think that's a dumb argument.
But this economist made an argument that I think is brilliant and is like the concept of comparative advantage.
or opportunity cost or what you will.
You are probably, you know, you or anyone at the 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 in 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 UMOS will very likely be the next best option.
And in a world where the economy has grown massively because...
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 the AI pie, so to say, is willing to pay a nanny a hundred times more than they're paying her today.
And I think nurses are paid a lot more today than they were paid a hundred years ago for very similar reasons.
So I've gotten more optimistic.
But there's this scenario which could be almost heaven unheard in some ways and maybe heaven unheard is 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-heaven unheard.
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 we lose control of AI, then why are we feeding humans?
Because that's taking away energy that could be fed to giving AI that extra 0.1% to do even more.
If we're pushing out that limit, what prevents those who control it or AI itself from turning this into a massive dystopia, a bit like the paperclip typical dystopia?
I've gotten more optimistic, but also this scenario, I think, opens 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.
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.
out of time i want to just uh wrap up with um final question for you you have found yourself in extremely successful position um you are working in ai you're also working in investment you've 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 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 well and 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 AI could do to make it better or more successful.
But don't look where everybody's looking, because especially if you're looking for...
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.
Almost everybody who chooses to have children does not regret it.
Sometimes people who choose not to later regret it.
With entrepreneurship, it's quite similar.
I've rarely found an entrepreneur who's 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.
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 Chalon and Kristen O'Donoghue.
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