Bending Spoons Is Coming for Silicon Valley, with CEO Luca Ferrari

We have half a billion monthly active users. That's a lot. We have built, over the past decade, an operating system of over 50 proprietary technologies. We buy these companies, and then we install them on this shared operating system. At Bending Spoons, we've been able to attract extremely strong talent, I think. We had 800,000 job applications in 2025. We hired 300 people.

You guys operate very efficiently per employee. I think the last metric you mentioned was $4 million in revenue per employee.

And this has grown tremendously. It was about $1 million just 2 or 3 years ago.

It seems like Bending Spoons is actually eating Silicon Valley. What's going on there? Luca Ferrari, thank you for having me at Bending Spoons.

Thank you for coming.

We're all the way out in Milan, Italy, and something happened not too long ago that really shook up Silicon Valley. It seems like Bending Spoons is actually eating Silicon Valley. What's going on there?

I don't know that we're eating anything, but I think you're probably referring to the announcement of the acquisition of Airtable, I suppose.

Airtable is a great business and a great product. You can say something about Airtable that you can't say about a lot of different businesses: It was identified as a high-flying, key company in Silicon Valley for quite a while. That's very difficult to do, so great job, Howie, and everybody else who built that business.

Once the announcement of the acquisition came, that was a lot more newsworthy and interesting than some of the other acquisitions we've done before.

How did you pick that one? What was the process?

1. The Airtable Acquisition Process

You pick each other when it comes to acquisitions because it's something that has to be bidirectional. But we look at a lot of companies. Actively, we may look at hundreds of companies in any one year. Then we try to establish a dialogue with those where we see the best match. If some of these companies are interested in selling, then the conversation can progress.

We look for businesses where we believe we can bring a lot of value. We are a very active acquirer, and for us to be absolutely confident we can offer a fair cash price while delivering strong returns for our shareholders, we need to be able to bring a lot of value—whether it's by improving the product, the technology, monetization, or the organization. Ideally, all of these. That's a key criterion.

Then we look for businesses whose trajectory we believe we can predict with confidence multiple years into the future. Otherwise, it's difficult to underwrite big investments. Typically, they've been digital businesses, so we've done consumer internet and SaaS. These are the key areas for us.

We also recently acquired a hardware company called Tractive, in the spring, which is very interesting: pet tracking and health monitoring for pets. The pet industry is booming, so that was a very nice acquisition, a little bit outside of our usual comfort zone.

Because the Airtable acquisition set SF, Silicon Valley, and American tech into a bit of hysteria, what do you think they get wrong about technology companies or managing them?

Not a lot, clearly. Silicon Valley has built, I don't know, 70% of the most successful technology businesses of the past 25 to 50 years. I think Silicon Valley gets almost everything right.

I believe we've been able to do well acquiring a tiny fraction of the businesses that have come out of Silicon Valley because we bring something different and new to the table, both in the way we're structured and in the platform we've built. For the most part, that's unavailable to these businesses on a standalone basis.

2. The Shared Operating System

For people who don't know Bending Spoons very well, unlike most serial acquirers out there, most serial acquirers will either buy a business because they think it's basically good as it is and they can't really improve it, but the price is low enough that they can get good returns. Then they'll basically leave it alone. That model can work. I don't think it will ever give you exceptional returns, but it can deliver reliable, potentially appealing returns if you're very good at picking businesses that are slightly undervalued.

Others are more active, but they will still keep these businesses separate, as they were before. These acquirers may have an opinion on how to price the product or how to build the organization, and so they will go in and make changes. We are even more extreme on the active end of the spectrum, and we integrate all of these businesses together quite tightly.

We have built, over the past decade, an operating system of over 50 proprietary technologies to take care of almost everything you need to run a digital business, whether it's data storage and processing, A/B testing, payment management, everything related to recruiting, credential management, the orchestration of all the AI models used in operations, and so on and so forth.

We buy these companies, and then we install them on this shared operating system, so they can be much more efficient. We also have a core team for R&D and marketing that is approaching 1,000 people at this point. We can deploy them very fluidly and rapidly across these various businesses to go after R&D opportunities.

When the R&D opportunities are no longer as exciting, we can take out the talent and move it elsewhere, so we stay very efficient. These are aspects that I believe none of the teams running these companies on a standalone basis could really access.

A lot of the value we create is because other people are myopic. They're doing the best they can with the resources available to them. But I do think we bring something extra in terms of culture.

We have developed a culture of extreme rationality, almost a scientific approach to running businesses, whereby we are not afraid of making unpopular choices when we believe it's for the benefit of the business in the long run. We tend to run these businesses very leanly, using data extensively. Sometimes we joke about taking more established companies and bringing them back to startup mode.

Mm.

Small, very talent-dense teams, removing red tape, giving these people plenty of room to maneuver, to experiment, and to move fast. We've found that this generally delivers a lot of value for customers and for the business.

I think one of the big misconceptions with the types of companies that you acquire is that they're graveyard companies. They're old brands. They're not new. Maybe they're distressed assets, that kind of thing. What's wrong about that?

I think people just try to frame things in a way that will get clicks. But I'll give you a statistic: We have half a billion monthly active users. That's a lot. Short of being Google or Meta, not many companies have that.

If half a billion people using these products every month is a graveyard, then sure, let's call it that. Often, they may not be the up-and-coming sexy thing, but that doesn't mean they're not incredibly useful or important.

You could see this at its very peak with AOL, which is, of course, an “old” brand and company. No doubt about it. It's been around for what, 40 years or something like that? However, to this day, AOL is used, especially as an email inbox, by tens of millions of people. For many, it's their primary email inbox.

To the best of our knowledge, it's the fifth most-used email provider in the Western world. You can imagine the 4 above it. At the same time, if you go to the online media, you will find so many email startups that people who don't actually have the data will assume are far more relevant, just because they got more coverage and sound a lot sexier.

But really, if you look at the data, these startups, in aggregate, probably don't add up to even 5% of what AOL means in terms of emails sent and received, activity, and people who rely on it.

We just don't care too much about being cool. I'd say we probably don't care at all about being cool. We care about being good at our jobs and creating value. If we find a business that's perceived as slightly less cool, if anything, that's a good thing for us, because it means it's probably also going to be priced a little bit more accessibly. It is what it is.

3. Silicon Valley Funding Still Works

I was trying to ask this question earlier, but I might have asked it wrong. Silicon Valley is so tied to funding for growth versus actual business growth. For some of the other email companies you might be talking about, they might not have many users, but they're the hottest, highest-flying, funded-by-every-VC kind of company out there.

Even with the Airtable acquisition, it kind of broke people's brains. There was a bit of hysteria because Airtable was seen as the golden child—or at least one of the golden children—of the brands in Silicon Valley. If that's the exit they're taking, and we're at a bifurcation with AI, what does that mean for all the other companies out there?

We've gone through different kinds of cycles with these tech companies over the years. I think the last one was the reckoning of 2021–22. There were a lot of overfunded companies that then became zombies. But the question I'm trying to get at is: What are the core characteristics that you look for in acquisitions, and how does that differ from the stereotypical culture of San Francisco?

So I think there's a lot to unpack here. Number 1, there's a lot of value in that model that relies on generous funding early. Many, many amazing companies have come to exist, often from Silicon Valley, precisely because of that abundant availability of capital. Plenty of companies probably could not get off the ground at all without it.

Even once they're well off the ground and generating substantial revenue, it's often wise to inject more capital into them so they can grow faster and get to a position of greater market power, whether through scale economies, network economies, or brand. So again, I think that model overall has been incredibly effective. I believe there's no doubt that if you look at the overall capital that's been deployed in Silicon Valley, by Silicon Valley, into technology over the past many decades, and the real, tangible, non-hyped business value of the companies that came out of that, the ROI is excellent in general.

It doesn't mean every investment decision is perfect. So I don't think that the fact that Bending Spoons is doing well should in any way undermine that model. I think it's a great model. But, like everything, especially when there are sometimes perverse incentives involved, there will be cycles of excess.

So, yes, in 2021, I think valuations were completely out of whack. When investors are ultimately incentivized by managing as much capital as possible, as opposed to actually delivering strong returns, and when returns are primarily delivered through exits, where all that matters is the multiple, not actually the cash that the business will generate in the long run—at least, that's not the primary reason why you get a certain price—then you'll get hype cycles and stuff like that. But I think, overall, when I look at Silicon Valley and its investment philosophy over the past many decades, I would say that's a relatively small price to pay for a model that overall has been incredibly successful.

Airtable specifically deserves a lot of credit because, yes, its valuation was very high in 2021. That's not anybody's fault. If anything, if we agree that it was perhaps excessive, I think most people would agree that the business was great; it was just too much. That wasn't true of Airtable alone, but of pretty much every business.

If anybody made a mistake there, it was the investor, certainly not the company. As a company, you will try to take capital at the best valuation you can. That's the responsible thing to do for your shareholders. And if anything, Howie and the team were incredibly disciplined. They actually didn't raise all that much money. They could have raised more, and they stayed profitable and burned very little of that money, if any.

In fact, if you look at the acquisition price, the enterprise value was approximately $1.3 billion, but they still had plenty of that cash on the balance sheet. So investors ultimately got back approximately all the money they had put in, plus more. The valuation at which Airtable exited was very much in line with SaaS businesses of comparable quality on the public market. They got a very reasonable deal, in my view. Obviously, I'm biased, but I believe that to be true.

So there's a lot of good there. I think Airtable has done a very good job. But, yes, the internet will have to debate, and when you go from being perceived as the ultimate winner and the poster child of success to an exit that would be considered amazing by almost any measure—I mean, over $1 billion—how many companies are started that ultimately exit at over $1 billion? 1 in 1,000? I don't know the stats, but it must be very, very few. That's super successful.

People forget how hard it is to get to $1 billion.

It's crazy.

And $100 billion, let alone this whole trillion-dollar company thing, is really disorienting.

It is, exactly. There have been literally a handful in the history of humanity at that scale. But a $1 billion-plus exit is unbelievable. It's achieved based on real economics, plenty of revenue, real customers, real growth, and an excellent brand. So I really applaud Airtable, Howie, and everybody there.

I think the business model for Silicon Valley overall makes sense, and funding a company early, even at a loss, makes sense. But it doesn't mean that things couldn't be done better. I'm sure sometimes there's too much enthusiasm, pouring money into businesses that don't make sense, or too much money in businesses that do make sense but should use less.

So what are the key characteristics that you look—

Yeah.

—for when you're acquiring companies?

So the most important thing is that we can predict where a business is going. We buy to hold and operate forever, not to sell 3 or 5 years down the line, and so we need to feel comfortable with our investment, with a long-term view. We prefer businesses that are robust and maybe growing. We're not opposed to buying businesses that are shrinking. We have done that before.

But we need to know how much they're shrinking. We need to be able to plot out their trajectory at least 5 years, ideally more, into the future. So that's non-negotiable. The second most important thing is that we need to be convinced that we'll be able to add a lot of value to that business—essentially improve revenue, lower costs, ideally both—through technology, product, or talent. Otherwise, we are unlikely to be able to offer a price that's appealing to sellers while at the same time delivering very high returns for ourselves and our shareholders.

4. Why AI Startups Stay Uncertain

Because there's a proliferation now of all these AI application companies that are dependent on token spend and lots of tokens, with sometimes negative gross margins, would those be of interest to you guys at all? Where do you see those companies getting acquired or exiting?

We use AI as much as anybody. As far as I can tell, we're probably in the 99th percentile for aggressive deployment of AI in our operations to improve our products. We have developed a lot of technologies powered by AI internally.

I'm very bullish on AI overall. I'm also concerned, but that doesn't mean I'm bullish about all, or even most, of the startups that are coming up. I'm pretty sure that some of the most valuable companies of all time—sustainably valuable companies of all time—will be coming out of this broader cohort of businesses built over the last, let's say, 5 years with AI at the center of the thesis.

But I'm equally confident that most of these companies will fail or at least fade away. It's a gold rush. When you see people raising massive amounts of money at billion-dollar valuations with pretty much nothing other than an idea, maybe a good track record in academia or elsewhere, anybody who's half credible because they were a great student or did well at a big company, and who's not too worried about their reputation, will just run and try to raise money. What do you have to lose other than your credibility and reputation?

A lot of this is just fluff, but there is real substance here and there. On the management side, right now we're not considering acquiring any of these companies. A key criterion for us is being able to predict how things will go in the medium to long run, and it's very difficult to know—not just because some of these businesses are up-and-coming and growing super fast.

When something is growing 100% a year and you only have 1 or 2 years of history, it's very difficult to know whether they'll be growing at 100% in 3 years or at 12% in 3 years, and that changes everything. Plus, valuations are very high, often irrationally high, so we don't think we can compete there and deliver good returns for our shareholders. Maybe later down the line, when the market is a little bit more mature, we'll look again and find something interesting.

5. Building the Operating System

Well, I want to go into your centralized platform, because you guys, to your point, use AI a lot. It's core to the business. I think 95% of your code is AI-written.

It's written by AI, yes.

So can you walk me through the centralized platform, how you built that out, and the various types of acquisitions you’ve made, from Evernote to AOL to Vimeo?

At our core, we try to be the most capable operators of digital businesses on the planet, and part of achieving that vision is having access to the best toolkit possible. It’s almost like if you want to be a great cyclist: Obviously, that’s not all there is to it, but you want to have a great bike. I’m not saying anything shocking here.

So we have invested in this operating system and these technologies for a long time because this was strategically critical to us. Also, my co-founders and I are all engineers, so perhaps there’s a bit of a passion angle too.

For the past decade, we have tried to develop the best technologies we could. We buy from vendors when relevant. We use Slack, for example. We don’t need a more sophisticated version of Slack. Slack is a wonderful product, so we buy Slack from them and use it.

A lot of the tools we need to maximize our potential have to be more sophisticated than almost any other company out there would need them to be, while providers of business tools generally optimize for the mass market of enterprises. It makes sense. You don’t want to build something if the market consists of only one or two potential customers. That’s not a very appealing market to go after.

Most of the tools out there are relatively simple. We often need more sophistication, so we have to build it ourselves. By building all or most of these tools in-house, we can make them natively integrated with one another, and that creates a lot of efficiency and effectiveness. Every tool talks to every other tool where relevant, which is impossible or very difficult to do if you buy from different vendors, then they change something and you have to change everything else.

Last but not least, we get to save substantially on costs. It’s difficult to know exactly how much we’re saving by building this in-house, but I’d wager it’s at least $100 million a year in costs, so it’s pretty significant.

It’s a key source of competitive advantage. Building these tools would be uneconomical for pretty much any one of the businesses we acquire as standalone companies. It would be too much money to pour into R&D to build these tools. The returns would come on an excessively long timeframe, whereas we can amortize those investments across the entire portfolio and across the portfolio as we expect it to expand in the future. It’s a scale advantage that’s unavailable to them.

Another big advantage of building this operating system is that, as all of our businesses use all of these tools, when they find that one of these tools does not serve their needs as it should—perhaps there’s a bug, a certain corner case isn’t handled, or an entire area of need isn’t fully covered—that business can improve the tool, almost as if it were an in-house open-source community. Those improvements are then propagated to benefit all the other businesses.

As we expand our portfolio and our organization grows, our ability to make these tools effective expands with it.

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I find this super fascinating because you’ve effectively created one operating system that you can use across anything. What was the unlock, and how did you determine how to architect that system?

A lot of iteration. One of the big lessons my colleagues and I learned early, at a failed startup between 2010 and 2013, was that you need to be intellectually humble. That and other lessons we learned there were instrumental to building Bending Spoons.

It’s good to have a vision. At the same time, it’s good to assume you’re probably wrong. For our technologies, we operate in a similar manner. We try to be opinionated about what ideal looks like, but we never make multiyear investments before we test them on the ground.

We like to identify smaller pieces we can build and put in the hands of our different businesses to see if they’re useful. Depending on adoption and reception, we will further develop or rethink them.

While we have platform teams that own all of these technologies, we have found that it’s never a good idea to take a platform team and task it with building something entirely new. It’s much better to have the people who need it build it. We will identify one of our businesses where that particular tool will be especially important, and then they will build it themselves.

Once it’s successful, it’ll be handed over to our platform teams to be further refined and expanded. The reason why this works is that when you need something—when you have experienced the pain of a certain problem—you’re far more likely to develop an actually useful solution, as opposed to taking a more academic angle where you think you know what the problem is and then get more excited about the engineering challenge than actually solving the problem.

These are probably the 2 main principles: Iterate with small, quick iteration cycles and high levels of intellectual humility, assuming you’re wrong, so you want to constantly test and confirm where you are, in fact, right. If not, adjust.

Always start building with people who have faced the problem, people in the trenches, as opposed to centralized teams in an ivory tower who haven’t actually gotten their hands dirty with that particular issue. They’re fine later for refining, expanding, and managing, but when you go from zero to 1 with a new technology, it’s better to have it built, if you have that possibility, by the people who understand the issue very, very well.

For us, it’s fine because, ultimately, we have engineers in our businesses and on the platform, so it’s not that we lack the capabilities either way. We can move them around all the time.

You’re taking an extreme first-principles approach to software companies, but you also have a bit of an algorithmic bend to it. Where’s the tension between the intuitive approach versus deploying a system?

Ultimately, you want to get to the truth. If you had a perfect understanding of the truth of your relevant context—life broadly, your business and the market more narrowly—you would be almost guaranteed to succeed, because you would not set objectives for yourself that are impossible. Those you did set, which would presumably be possible unless you’re masochistic, you would be almost guaranteed to reach because you would know exactly how.

It’s almost like if you’re a physicist and you need to compute the trajectory of a ball. We have the formulae and the math. Basically, you’re always going to get it right, assuming you don’t make calculation mistakes.

The point is that it’s very difficult to know the truth. It was very difficult to figure out how physical bodies behave through physics, thanks to Newton and others. But it’s in many ways equally difficult, sometimes more difficult, to figure out how a business works and how the market works, because there are so many variables. It’s a fast-changing context.

I think it starts with taking a scientific approach: doing everything you can to probe the world, learning from those experiments and observations, adjusting your model of reality, and then executing accordingly. Be highly disciplined and deliberate in improving your level of understanding of the truth.

Again, it takes a level of intellectual humility and intellectual honesty. If you think your vision is right, you're perfect, and you've already got the whole thing figured out, let me break the news: you haven't. Not even Steve Jobs, maybe one of the best to ever do it, had it all figured out. He failed repeatedly, so you haven't either.

You want to probe things and figure things out. Through that process of approximation toward the truth, I think you'll expand your competitive advantage, because most people don't have almost any understanding of the truth. Most people don't actually seek the truth. They seek pleasure or comfort, and they want to confirm that they're right and that they're good.

That mindset—that scientific process of approximation toward the truth—will set you apart and give you very good chances of succeeding. In our particular context, developing this operating system or technological platform is just an instance of that process at work. It's not the root cause or where our culture originates. It's just a manifestation of it.

Our attention to talent, our acquisition strategy—everything follows from the same root approach: seeking the truth, refining that model of the truth all the time, and adjusting our strategy and execution accordingly.

6. Bending Spoons Is Not Private Equity

I was talking to Chrissy about this before our recording, but I think there are some misconceptions about the business model of the company. A lot of people put you in the private equity bucket. Some people put you in the product manager bucket. There's also the technology bucket. From those different angles, it seems very much like you're a technology company. Why do you think so many people misconstrue that?

Some of the ways we're most different from private equity are, number 1, we're not a fund. We don't buy companies to sell them. We have never sold a material business. We intend to own and operate these businesses forever.

For those among the audience who don't know, private equity generally consists of funds. They raise money from third parties, from limited partners, and then they hold the companies for an average of 5 years, and then they sell them. It's a completely different approach and mindset in terms of what you do and what you don't do.

The second big difference is that private equity firms are typically a financial operation where there's a small group of very capable financial operators. They'll buy these companies and make changes. Often, they change the management team. They may touch prices and occasionally operations. It's a relatively hands-off approach.

I'm not aware of many instances in which the underlying technology for that business was rebuilt, the product was dramatically changed, or the organization was dramatically changed. Of course, that's the case because in private equity, you have a small team of financial operators and financial specialists, so you just don't have the workforce and expertise to make some of these changes.

If you look at the Bending Spoons organization, at this point we're approaching 1,000 people in the core team and over 2,000, including all the acquired teams. Most people—probably 60% or 70% of this pretty large team—are software engineers, AI research engineers, product designers, product managers, and growth managers.

Obviously, these people are not sitting around doing nothing. What does a software engineer do? What does a product manager do? What does a product designer do? They build products and technology. That's almost all we do.

We implement these very deep transformations in the acquired businesses. We rebuild big chunks of the codebase, architect the cloud infrastructure, launch a lot of features, and, if we think they're useful, change the user experience, trying to make it more intuitive. We experiment tremendously with monetization and often reinvent monetization in pretty significant ways: what's premium, what's available for free, the prices, and the different segments of customers.

We rethink marketing from scratch, or at least in very major ways. These transformations are very time-consuming and challenging. They're also some of the most fun parts of what we do, and that's where a lot of our returns originate.

The first difference is that we don't sell businesses. The second is that we transform them pretty deeply at the core. The third major difference is that we try to integrate these businesses pretty deeply, all together, on top of that shared platform operating system.

We have this core team who are centrally managed, and then they're deployed into the different businesses. They move very fluidly across the businesses. This is structurally unavailable to private equity because, as a private equity firm, you want to buy a business and then sell it. If you integrate it with all the other businesses you've bought, or most of them, it's going to be extremely difficult, if possible at all, to sell it to someone else.

There are similarities, but there are also pretty glaring differences between what typical private equity does—whatever “typical” means, because private equity is a very diverse world—and what Bending Spoons does.

You've raised little equity, and you've fueled most of these acquisitions with debt. I'm curious: how big can these acquisitions get?

Almost all of the capital we've deployed toward acquisitions has come from debt or our own free cash flows, with debt being the majority of the capital.

Over time, we've tried to acquire larger companies on average because I just discussed how hands-on we are, how deep we go into these companies, and how much we change them for the better. At least, that's what we try to accomplish.

Those transformations take a lot of time and effort, and we find that the time and effort don't scale linearly with the revenue potential of those businesses. In other words, we don't need nearly as many people—or, let me phrase it differently—we can get it done for a much larger business with a relatively similar number of people as for a smaller business.

Given that we don't have infinite operational capacity, we prefer to acquire, I don't know, 5 or 10 businesses each year that are bigger than 50 smaller ones. That's been our approach: increasing the average scale of the businesses as opposed to the frequency of the acquisitions, to make sure we keep compounding revenue very rapidly.

How big can it get? So far, we see no end in sight. There's no obvious saturation point. It's very difficult to tell if and when growth will slow down. I'm sure it'll slow down at some point.

I'm having some issues with Google. I don't know if that's of interest to you.

Maybe in the future. I think it's pretty far away. We have joked—I mean, just jokingly—that maybe one day, who knows? AOL, at some point, was, broadly speaking, as prominent and dominant as Google has been in the past decade, so maybe in 20 years.

But I actually like Google a lot. I hope they do super well, and I hope we do super well.

You guys operate very efficiently per employee. I think the last metric you mentioned was $4 million in revenue per employee.

Per Spooner, who is a member of that core team—

Okay.

—which is now approaching 1,000 people. If you count everybody we have on board, including the acquired teams, it's probably a little less than half of that number. But it's still very high—just a little bit less than half that number, probably.

Because you know how efficiently and leanly you can run a company, do you just look at all these big tech companies and all these other companies out there and think, “What are you guys doing?” How do you make sense of that?

No. We don't have—I mean, it's very easy to criticize from the outside. It's quite difficult to run a business; we know firsthand.

A lot of the ways we manage to create value relative to the previous owners are not available to those owners and management teams under their particular circumstances. For example, a big part of our value creation comes from that set of proprietary technologies we discussed. It would be uneconomical and unrealistic for those businesses to build them, so they don't have them.

Many times, businesses attract very good talent during their heyday. Then, as they're still pretty nice businesses—maybe growing, but their opportunity has been saturated a little bit more—they're not as cool any longer. They stop attracting some of the strongest talent.

Some of the people who are most entrepreneurial, driven, and proactive start moving on to other businesses. Those management teams find themselves, more often than not, having to run those businesses with perfectly fine talent, but not top-notch talent in many cases.

At Bending Spoons, I think we've been able to attract extremely strong talent.

We had 800,000 job applications in 2025. We hired 300 people. So we can selectively add to those teams individuals who are extremely high-performing, extremely high-agency, and very competent in relevant areas. That fuels a new wave of innovation and efficiency.

Again, that's not a shortcoming of the previous executive team. They simply didn't have the employer brand to attract those people. A lot of those people join Bending Spoons because they like the idea that they can rotate over time across multiple businesses and platform teams. That enables tremendous growth, keeps it interesting, and creates a lot of career opportunities.

That employer brand can only exist if you structure your company like Bending Spoons. You can never achieve it as, say, a single-product company. That's, say, a second major difference that drives performance and isn't available to those teams.

A third one could be differing incentives. If you're running a business and it's just 1 product, the market will typically value you primarily based on your organic growth. Is your subscriber count growing? Is your revenue growing? How fast? Because if you're invested in a company, the upside is really in believing that the company will grow.

There's still value in a flatter company, but there's not a lot of discussion there. It's not as exciting. Multiples compress. So there's pressure for management teams to show growth at all costs.

But if you only have 1 product, or a set of products in a niche in the market, sometimes there's not a lot you can do to ignite a lot of growth because maybe that market has been saturated. The truly big, groundbreaking ideas have been had, and finding whatever missing one is out there in the universe of possibilities is difficult.

Sometimes people throw a lot of money, whether it's R&D or marketing, just desperately trying to unlock that extra growth and improved multiple. If that business becomes part of Bending Spoons, we still really like to make it grow, of course, as much as we can. But there's no pressure to grow beyond what's profitable growth.

Ultimately, anybody who buys stock in Bending Spoons likes our individual businesses to do well, of course, but the main thesis is that Bending Spoons can generate amazing cash flows from businesses and redeploy them toward new acquisitions at very high returns. This, over time, compounds attractively, hopefully for many years.

Do I care all that much whether that particular business is growing 15% or 5%? Not really. I'd like to know, and 15% is better than 5%, but if 15% is achieved by burning a lot of cash for very little profit many years into the future, I'd rather get 5% and have all that extra cash deployed toward acquisitions that are accretive.

Often, these executive teams and owners are extremely competent. In fact, otherwise they probably wouldn't have built successful businesses. Their very structure and the context in which they operate put them at a disadvantage vis-à-vis that business being run within Bending Spoons.

One thing that I learned through all this is that not only are the products super-retentive, but people might think, “Oh, you might be buying this product, ruthlessly cutting headcount, making it more expensive, and maybe you'll have churn.” But no, retention is still good.

Also, within this type of organization, if you have really high agency, if you have really good ownership in yourself, and you can move around, you're giving a lot of that to your employees, and they're really retentive too. So you've created 2 really high-quality systems here.

Yeah, I think it's a good way of looking at it. We have businesses with low retention and businesses with high retention, but I can't recall a single instance where retention got worse after Bending Spoons took over. Often, it's improved. At least it's stayed the same.

Ultimately, we win by being relatively better. We don't necessarily need to buy only businesses with perfect retention, as long as these businesses do better under us than under the previous owners. We've bought businesses with mediocre retention and businesses with great retention, and generally preserved or improved those retention rates.

When it comes to our team members, we are fanatical about creating one of the best work environments on the planet. I think there's a lot we could improve, and no doubt about it—we have plenty of flaws—but we've done pretty well there overall.

We've had essentially no unwanted churn of Spooners, these people who are part of the core team. Last year, we had 0.6%, whereas most companies in tech, as far as I can tell, consider 5% pretty good.

Mm-hmm.

A lot of that super-high retention comes from the excitement of being able to learn and grow across all of these different challenges. It keeps things fresh and interesting, whereas if you're working on, say, Evernote, maybe it was exciting for the first couple of years if you're a product manager, but after a while, refining and refining the note-taking experience can grow stale.

What can you do? You like the company, but ultimately you have to look for a different employer to take another step and learn something new. At Bending Spoons, you just raise your hand and say, “I feel I've exhausted my creativity and excitement for Evernote. What else can I do?”

We may put you on a platform team to build an internal technology. You could move to AOL and try to improve email UX, which is a completely different and fascinating challenge. So yes, we've been able to retain people very effectively.

When it comes to the retention of teams that come on board through acquisitions, I was quite worried initially that a lot of people would feel disheartened because we got acquired. Sometimes this is perceived as a failure, even though it's not. It's maybe a success, meaning that you actually got a nice exit. But you may feel that you're not as important as the core team.

There, too, there is a lot we can improve. We value these teams, and we're trying to do better. But overall, I think things have gone a lot better than I thought they would. We have an excellent relationship with all team members.

In no case are the retention rates for those team members lower than pre-Bending Spoons. Sometimes they've improved substantially. So at least we're not damaging the quality of those workplaces; if anything, we're improving it in many cases.

But those retention rates are not as high as those we have in the core team. They're more in line with what I said before, which is considered okay for most technology companies.

What would you say is core to the culture here?

There are several things, but probably the 1 or 2 that truly stand out as particularly distinctive are these. One is something called extreme ownership. We want everybody to care tremendously about being amazing at what they do and about helping their team and the company succeed.

We'd rather work with slightly less intelligent people if it comes down to that, but they have to really care. We don't want to work with anybody for whom doing well here and seeing the company succeed are not super-high priorities.

The second aspect of our culture that I think is unusual is that we are highly scientific in how we approach the work. First principles, being logical and rational, being enthusiastic, and putting a lot of effort into probing reality so that we can refine our model of the truth—we do a lot of that.

Last year alone, we ran more than 3,000 experiments across our products. Those are just the ones that are super-quantitative, recorded, and documented. Of course, there are a lot more initiatives that don't qualify as perfectly rigorous experiments, but they're motivated by a desire to learn and understand things better.

Building Bending Spoons as a learning machine has been instrumental to our success in general and, specifically, to our ability to expand the competence circle. That allows us to successfully acquire and transform a broader and broader set of businesses and drive returns from them.

If you look at what we were acquiring when we started, it was very simple iOS apps—very basic ones. Then we moved to more complex and larger iOS apps, then Android apps, then web products. We went from self-serve products to substantial enterprise sales organizations, and we even did hardware recently with Tractive, as I mentioned earlier.

It's early, but it's going really well. We try to keep expanding the share of the world that we believe we understand and improving that understanding continuously.

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So, talking about the culture and how you've built it out, where do Alt Spooners come into play?

7. Alt Spooner Rewrites Productivity

Yeah, Alt Spooner, as in A-L-T—alternative or alter ego—is just one of those 50-plus tools, something that we developed this year, and it's been adopted very enthusiastically across the company. We built this agent that lives in Slack, at least the interface for Spooners, members of the core team. We'll be looking to deploy this across all of our team members, but you interact with it on Slack as if it were any other colleague. You have one Alt Spooner; you can name it and give it a profile picture and all that, and automatically that agent will have exactly the same level of access you do across all of our platforms.

If you have access to a certain codebase, it does too. If you have access to a certain customer support tool with a certain level of permissions, it does too. Then you can task this agent to do stuff for you, and it could, in principle, do pretty much anything. Anything you can do, in principle, it can do. Obviously, AI is not perfect, so some things it's better at than others.

It's fully integrated, so I don't think you can really achieve this at all with third-party—

Really?

—solutions, because as impressive as they are, you're never going to be able, I believe, at least not in the foreseeable future, to integrate them as deeply across the board and tailor them specifically to what you want. If you do, then you'll be locked in with these vendors, which is dangerous because you'll be completely exposed to massive increases in prices potentially in the future.

Partly with Alt Spooner, we achieved much better effectiveness because it can do a lot for you in a very efficient way, and I'll give you an example in a moment. Partly, we achieved good separation and very low levels of dependency from any provider of AI infrastructure or AI models.

Examples of what Alt Spooner can do: It can do data analysis for you. Last night, I was looking at the results of certain A/B tests on StreamYard, which is one of our products, and normally I would have had to ask someone from the team—a data scientist—to pull the data and prepare the analysis, or at least do some of the work for me. I can do my own data analysis, but I would have needed someone to do some of it, and certainly it would have taken either me or this person multiple hours. I simply chatted with my Alt Spooner and told it, “Please go and do this or that,” and maybe 5 minutes later I had the analysis. I was actually very positively impressed. It gave me plots with highlights, very detail-oriented and nice.

If you're running one of our businesses, you can—and I saw this firsthand multiple times—go to your Alt Spooner and tell it that you saw a bug. This happened with Evernote. The general manager of Evernote told her Alt Spooner that she had encountered a bug while using the app and tasked the agent to check our customer support platform to see whether the bug was widespread among users or her report was the only one, and then to go into the codebase, identify the root cause, code a fix, ping the engineering lead for that project to review the fix, and push it to production.

Maybe she spent 3 minutes providing these instructions, and I'm sure the engineering lead had to spend an hour or two reviewing the code. But the bug got fixed the same day, and this is something that would have taken weeks, probably, between back-and-forth communication between different people, and a lot of hours of actual human work to get done. Thanks to this and many other technologies we've built, we're probably 2 or 3 times as productive, easily. The list is long, but this is roughly what to expect.

From a business perspective, potentially even more interestingly, Alt Spooner really helps us, as I said, stay independent of providers. We've built our own orchestration, so every time you task your agent with something, under the hood, we have an algorithm that will select the most appropriate AI model or multiple AI models to get the job done, considering quality and effectiveness, but also cost. It can draw from many, many different models.

We end up using open-weight models that we self-host for 99% of requests and tokens, so these are basically free. There is a little bit of cloud cost. The frontier models, generally closed-weight, are used through APIs for maybe 1% of requests only, for the most complex tasks—

That's it. Wow.

—or for supervision. Sometimes we use them automatically to check the work that got done by slightly less intelligent models, as a more senior engineer would with a more junior engineer. But that's way cheaper than actually doing the work, which is often perfectly fine. Even if it's not, the smarter model will provide a few pointers, and then the less smart model will go and fix it. Because of this, we've been able to stay independent of any one vendor and keep our costs for tokens super low, basically negligible—

Damn.

—at our scale.

Wow. When did you start deploying these AI agents? Also, your point about not having a third party is very counterintuitive to all the marketing that's going on right now with all these AI assistants and applications that are coming out.

We work with all the big labs.

Mm-hmm.

They offer great products. We use them enthusiastically. I'd like to think we're a good customer. But we don't want to be dependent on any of those specifically, if it can be avoided. There are solutions out there that are much cheaper and often deliver essentially the same quality. But it takes strong engineering capabilities and the right culture to be able to harness those possibilities.

Obviously, the easy approach is to hand over the keys to one of these companies and buy their more expensive product. It will make you come across as AI-enabled faster and more easily, but it will be much less effective and certainly way more expensive—literally orders of magnitude more expensive.

8. AGI Is Still Undefined

I don't know if you saw this, but Jensen just declared we've reached AGI with OpenAI's Astra model.

Yeah, some people say that. Maybe it's true. I don't even know. I've heard different definitions of AGI, and none of those is super unambiguous. So, have we? I don't know. Nobody can tell. But it's obvious that it's very smart. Let's put it that way.

Mm-hmm.

Whether we should call it AGI or not, I'm not sure, nor do I care too much, to be honest. I don't think that AGI, even if we can describe it very specifically as a threshold, means that crossing it is a particularly noteworthy milestone. The moment we cross it isn't necessarily a particularly noteworthy milestone.

People sometimes define AGI as AI being able to do everything that any human can do, but better. I don't know that that's necessarily more exciting or scary than AI being able to do 95% of the things that humans can do just as well or better, but not being able to do 5% of them yet. I think this is almost as exciting and almost as scary, depending on what that 5% is. But assuming it's not handpicked to be the things that keep us in control, but just a random set of tasks at which our brains happen to be more capable.

So I'm not too keen on whether we pass the AGI thresholds or not. But the trajectory is clearly one where AI does things better than us, increasingly so, both vertically—the gap in how much better it can do a certain task is growing, unsurprisingly—and horizontally: the percentage of tasks it can take on and do better than humans is increasing. So I don't think there's any stopping that short of some sort of world war where we regress to the Middle Ages.

Well, it seems like now the next benchmark is RSI, and that with it comes a lot of fearmongering about cybersecurity and cyber risks.

Yeah. It's a very fair fear. I'm equal parts enthusiastic about AI and absolutely scared shitless. You could definitely make a case for this being the greatest boon for humanity ever, by orders of magnitude. Possible? Yes. Plausible? Maybe. I'm not sure. Equally, it could be the thing that wipes us out or creates equally awful scenarios.

I don't think humans, of course, want the latter. The point is: can we control it? Even before it's so powerful that controlling it is all that matters, I think that while we are approaching that point—maybe we're not quite there yet—but regardless, before that, can we prevent it from falling into the hands of some sort of degenerate or evil person?

Because, similar to nuclear weapons but potentially worse, I think—I'm no expert in nuclear weapons—but I think, number 1, it's very difficult to do irreversible, widespread, massive damage with nuclear weapons, such as almost wiping out humanity, without killing yourself in the process. You could use AI to your benefit while causing immense damage to everybody else. I think it's easier because it's much more surgical. So can we do that? I'm not sure. It's pretty scary.

The accessibility point.

But I don't know what the answer is. Yeah.

Yeah, the accessibility point, I think, is the most alarming because it's literally accessible to everyone. The barrier to entry is so low.

Yeah, I think that, but also something that really bothers me is that we don't know how smart AI is, and the smarter it gets, the less we know. In general, intelligence is difficult to truly measure. It's not like height or weight or colors, where we know it's objective, and so it could be a lot, it could be less. We know—at least we know.

The smarter an entity gets, the easier it is for it to hide its own capabilities if, for any reason, that's the appropriate thing to do, whatever objectives that entity has.

Additionally, it seems to me—I haven't heard anybody talk about it—but it seems to me that AIs come across as inherently low-ego. They don't brag. If anything, they tend to be humble about what they can do, and they warn you that this may be wrong and to double-check it. I'm sure this is mostly the way they're being programmed because it's a lot more embarrassing for a frontier lab to have an AI claim, “I solved your equation, and I'm sure it's right,” and then there is a clear mistake. As long as they disclaim, “I may be wrong; double-check it,” it's a little bit more acceptable.

But these AIs don't strike me as likely to boast, so our perception of their capabilities tends not to exceed their capabilities. I think they'll probably only show us what we ask them to show us. If even that. Like I said, they could also conceal their real abilities.

But even if they are well-intentioned and honest, I think they'll tend not to show us more than we ask them to show us. And so I believe that, in time, our understanding of how good they are may tend to be a little bit less than they are. We may underestimate them, basically. And that's very dangerous because as you approach a threshold of real danger and real potential, even a modest underestimation of their capabilities could be catastrophic.

Look at—exactly to your point—the cybersecurity incidents that happened. Most people were shocked, even many researchers. And why were they shocked? Because they didn't think this could happen, right? The level of lateral thinking and, call it, perseverance that these models showed, as well as their ability to collaborate among themselves, was beyond what most people thought was possible right now.

So what's to tell us that we are not ignoring plenty of capabilities that simply haven't been probed—you know, these models haven't been probed—to display? And in a year's time, I think the problem only gets worse.

It was crazy. I don't know. I'm thinking a lot about the OpenAI–Hugging Face incident. I was reading through the reports. There were 2 different research organizations that put out reports on it, and there were different civilizations, and they all passed through different ones to get to the next one, all behind the scenes. It was just a crazy situation that I don't think is really talked about much.

But I'm curious: how do you research most of the stuff in AI? How do you stay on top of it?

Well, mostly by doing. We are very active. We rarely—we have built our own models, but they're mostly narrow. We certainly don't compete on the frontier models. We sometimes build narrow-purpose models to do something very specific.

If you have a very specific use case, you can often build a model that's just as good as the frontier models at that very narrow task. It's awful at everything else. It may be completely incapable of doing anything else. But at that one thing, it can be even better, and if not, way cheaper.

For example, if you use Meetup, the events product we own, the recommender system—the system that, once you search for something or you're looking for inspiration, determines which events and groups to show you—is built in-house. To the best of our benchmarking and knowledge, it's just as good as if we were to use some of the frontier models. It essentially comes for free, as opposed to those models, which are very expensive.

So we do some of that, and most of our work is studying third-party models, sometimes fine-tuning them if they're open weights, certainly combining them and leveraging them for the different activities, and optimizing which ones we use for which activities, as I was describing before. So we're very hands-on in the field, and therefore it's relatively easy to stay abreast of advances.

But I will say, I've never seen any industry or new technology progress as fast as AI has over the past—especially the last 3–4 years. I feel that I make an effort to catch up this week, and maybe for a few months I need to focus on M&A or something else, then I feel like I'm completely outdated on my knowledge.

So it is quite exciting. I'm an engineer at heart, but also, like I said, particularly as this is quite dangerous, I think that speed is not—it's not ideal.

I'm curious: what do you think is the question about AI that people aren't asking?

Oh, that people aren't asking. I mean, I don't know. It seems that people are talking about it so much that they've asked all sorts of questions. I think maybe the main issue is whether we're answering these questions in a satisfactory way.

For example, I think most people agree that this is scary in many ways, and yet, frankly, I don't think anybody has done anything truly meaningful to make it safer. And I mean, even the labs themselves, I'm sure they're investing in safety. I don't know enough, but they're rushing to be market leaders or they're dead. Their valuations would probably drop 90% if there was a perception that they are losing ground.

And so they're trying to survive and thrive this year, next year, and I'm sure there's more they could do—they could be more cautious—but that would potentially drive existential risks for them as companies. Governments, I don't know. They may be talking about it, but I haven't seen anybody do anything meaningful.

The EU created the AI Act, which I find to be highly harmful to the industry and solves none of these problems, like the real existential threats to humanity. It doesn't really tackle those. So maybe an interesting question that people haven't asked—at least I haven't heard it asked—is: why are we failing to do something about it?

People are asking what we should do, but nothing is happening. I haven't heard a lot of people say, “What's currently preventing fixes from being proposed and deployed?” What's the root cause of this inability to do something about it? Because if we were to fully understand the root causes, then maybe we would stand a chance of doing something useful. Yeah.

What's been the biggest difference—you’re a global company—the biggest difference in the perception and application of AI in Europe versus the US? And I also apologize; I'm totally taking up all of the air right now with these AI questions, but I understand I have a very intelligent engineer in front of me, so I'm going to ask them.

But what do you think is the biggest difference between the perceptions and actual applications?

Well, I think that every time there's something moving very quickly and being newsworthy, there is a lot of exaggeration and misunderstanding. On the one hand, some people think AI today can do more. These are typically people who don't really use it but mostly read about it. They think it can already do everything for you, and that it's a lot more advanced than it is. As impressive as AI models are, I think today they still have very glaring limitations across most use cases, so I don't think we're at a point where you could hand over the keys of your life or work to AI and actually trust it to add significant value.

I think there would be a significant risk that things could go awry. But the trajectory is certainly very promising. Then there are people who rightfully fear that AI will destroy jobs. I've recently changed my mind on this, but it's certainly a very important topic. Rather than thinking of proactive ways of protecting prosperity and people, more than workers, they go on the defensive and try to come up with more protectionist regulations or approaches, which are obviously an awful idea.

A country that doesn't embrace AI—unless every country in the world stops progressing in this field, which I would say we could put in the bucket of impossible things, short of a world war—is destined to complete irrelevance and basically becoming a third-world country in probably maybe even just a few decades. That's certainly a dumb approach, although it's a populist approach, and as such, it can occasionally help get votes. Maybe those are some of the more remarkable extremes I've seen. But we could talk about it for a long time.

We could talk about it for a very long time. We have to get to the walking portion, but before we do that, I just want to ask you: What are you most looking forward to in the next 12 months?

Well, there are many things, but I'd say probably further progress in our in-house technologies, especially taking advantage of AI. We have very big plans, and we're seeing massive progress. I do think that, you know, we talked about it before: Bending Spoons, the central team, $4 million in revenue per member of that team. This has grown tremendously; it was about $1 million just 2 or 3 years ago.

I think we're about to see this keep rising pretty fast, and a lot of that will be technology. Some of it is scale: just bringing together these businesses and integrating them all together creates tremendous leverage. But a lot of it will be technology, and I'm really excited to see some of the things we're working on and have in mind actually come to fruition. I think it'll be extremely exciting.

We try to constantly reinvent what running a business effectively and efficiently looks like, and be at the cutting edge of that so that we can then go out into the world, buy businesses for really good prices for sellers, and deliver high returns. The technological aspect of that progress is very exciting to me.

Amazing. Well, thank you so much for hosting us here today and having us at Bending Spoons in Milan. This is amazing, and I'm so excited—

My pleasure.

—for all of the other conversations we're going to have with your team. Thank you so much.

Thank you.

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