Cloudflare CEO: The Internet's Business Model Is Dead

27:34banks, which is how they came after this. And the same guy to this day is running all of Iranian cyber attacks. And so when you see like all of the things where they are creating like the sort of Lego style movies, it's this one guy who's who started out as the as as attacking, you know, Turkish escorts. And and again, that's I think shows kind of the power of Cloudflare serving everyone helps us learn from even these sort of weird corners of the Internet. And as a result today, you know, almost every major financial institution in the world, almost every government, you know, relies on us in order to in order to stay stay safe online.

28:13It's fascinating and hilarious. And Michelle will be mortified when she's she's like, you need to stop. I am I Allen and Company, the big investment bank, invited me to one of their conferences they do in in Arizona. and I told this story and I haven't been invited back since.

28:37So, anyway, there you go. Okay, well, thanks for sharing. You know, the fact that you found your initial customer sort of segments in like Hacker Kids and human rights organizations, you know, when you think about like VC slides and raising money, that's not exactly your first obvious Silicon Valley kind of playbook. No, no, and it was interesting. I remember a VC early on said, okay, you guys have gotten really popular, but how are you going to expand outside of the Bay Area? And I remember sort of saying, and the person was just completely incredulous. I was like, we're really big in Tunisia, but we don't have anyone in the Bay Area.

29:25And so it turned out that wherever the internet was slow, wherever sort of, you know, it was more lawless. Those were the places that we initially got, you know, most of our traffic. And it actually took a really long time for us to win sort of the tech natives, the YC crowd, you know, the folks that were that would be sort of what you would think would be our natural audience. whereas you know I mean we were again if you went to Congo you know 50 % of the top 100 local websites in Congo you know were using Cloudflare because it was a because the further you were actually away from sort of the tech epicenters the more valuable what we provided was yeah goes to show that's not one way of doing things no one playbook and again I think you know it's and this I say this to people and it sometimes kind of frustrates them, but it wasn't like we wrote grand strategy memos on why we built a developer platform.

30:27We built it because we needed it. But that's then turned out to be incredibly valuable kind of to defining what Cloudflare is going forward. And so at some level, the story of Cloudflare has always been, you know, create these really thorny, hard technical problems, solve them for ourselves. And then in the process, that builds what it is that really drives the business going forward. Yeah. And speaking of VCs, there was some like fun back and forth on X maybe a couple of weeks ago now. I guess without necessarily re-litigating the specifics, precisely because you had a little bit of a different kind of business.

31:09What was your path and what were some of the, I guess, lessons learned in terms of like fundraising? You know, I think, first of all, I mean, we ended up getting to work with just some incredible investors over time. You know, Carl Ledbetter, Scott Sandell, Brad Burnham at USV. you know, they really matched kind of our style. And what our style tended to be was we thought of venture capitalists primarily as, you know, sources of money. And, you know, mostly sort of we wanted, when we needed money, we wanted to be able to access capital. No thought leadership. But we didn't, yeah, we really just weren't, like, we didn't need help hiring.

31:54We didn't really want a buddy. We sort of wanted people who would push us from time to time, would sort of say, hey, have you thought about what's over the horizon? But weren't in our business being like, well, I think you should price it at$22.99, not$21.99. We were sort of like, we know our business better than anyone else. And so if you believe in us, do that and get out of the way. We saw we saw lots of other varieties of that, you know, people who really wanted to be kind of operators. And and again, I think there's not one way of doing this for some founders. That's that's that's incredibly valuable to them.

32:40And they and they want that. And and so, you know, we had we talked to almost everyone that was that was out there. And there were lots of people that just thought, you know, we were complete idiots. A bunch of people at Emergence Capital like Santi and Gordon, who I love. There was one guy who was just like, you're an idiot and we're never going to invest in you. And again, I get it. We kind of were. Um, but, but, but that's okay. Cause we found people who, who did believe in us and, and, um, and have, and have, you know, done incredibly well coming along the journey with us. Uh, and it made sense, but, but we were an odd, you know, company.

33:24Um, you know, I think that if you look at the three of us that founded it, most companies get founded by, by people who are friends with each other. Michelle Lee and I, we were not friends. It wasn't that we were enemies. I think we had a ton of respect for each other, but we were just radically different people. To this day, Michelle, she has a giant pink piano in her living room. And her idea of a good time is at night to sit down with her kids, and she plays piano, her husband plays guitar, and the kids sing along. That sounds like the lowest circle of hell to me. but again we've obviously we've become friends over time because we built this together but we were radically different as people and I think that some investors when they looked at us they were like how does this motley crew fit together but what was powerful was because we were so different and the way Michelle describes it she's like when you're starting a company you want it to be like a Venn diagram where you've got big circles and they have just enough overlap that you can like still communicate with the other people, but that your lanes are incredibly clearly defined.

34:36And, and so, you know, in the, you know, 16 now, almost 17 years that we've been at this, like Michelle and I have never really had a fight because, and because fundamentally like what I do and what she does are just in, you know, we each have our own lanes and we can, we can, we can succeed in those, those lanes. Um, and even though, you know, even though I, I am, I am good at some things, um, she is good at a completely different set of things. And then that just really drives a lot of, um, of, of what I think has, has caused us to be, be successful over time. And, you know, we, we now get, uh, founders that are coming to us for advice all the time.

35:15And one of the most common questions we get is like, how did you guys split up responsibilities? And, you know, I, it depends on how snarky a mood I'm in. But if you're asking that question, it probably means you have the wrong co-founders because fundamentally, like what you want is, is to cover as much surface area as possible and for each person to have a very distinct lane. And so in our case, I, you know, Lee was the technical genius that was, that just was heads down writing code. It didn't want to be on the board, didn't want to meet with investors, was sort of, you know, but he, but, but an absolute technical genius.

35:53Michelle was sort of the operations person who, you know, like would take a thing that worked once and figure out how to scale it to, you know, a million times. And so, you know, ended up running a lot of like our go-to-market organization, those things that you need to have real process and scale. And then I, I'm, I'm a good storyteller. And so, So, and so like in this, you know, in the case of the VC thing, like, you know, Vinod Khosla, we is thinking of investing in us. I am doing most of the talking because I'm the good storyteller. Michelle's, you know, thinking about, I mean, she was a, she literally was a Six Sigma black belt.

36:30Like she's, she's a process person and she's incredibly good at, I'm terrible at that. But like she was doing that and Lee's like, why am I at this dinner? Like, seriously, I could be, I have so many more productive things going on if I was just back writing code. And so, again, I don't blame people for saying, like, you know, if you're pattern matching off, you know, everyone's got to be a good storyteller. That just wasn't what we were. But I actually think there's real strength in that because you want different people with different perspectives and different skills. And I think that's, you know, part of why we were such a strong founding team.

37:06Okay, awesome. Let's talk about the AI stuff at Cloudflare from a product standpoint. because you guys have built so much. And I think the world is starting to realize, but I was struck. I'm like, I knew some of the things you built, but as I was prepping for this, like, it's just like such a list. So walk us through the general strategy and maybe timeline of like when you started building for AI and what you built. Yeah, so I think that the real kind of beginning of it starts back in, well, I mean, at some level, cloudflare has always been an ai company i remember pitching benchmark actually and um and i said you know cloudflare is an ai company and like matt kohler rolled his eyes so like that i was like i'm never gonna say that ever again um and that was back in like 2010 or something and um but at some level that's always been true again the turkish escorts like that's a machine learning system that's recognizing patterns and then and and that whole thesis was if you get enough traffic going through our system and you can run machine learning systems against it, you can do interesting things.

38:16So, so like that's always been kind of in our DNA. And if you, if you actually kind of look at where the real start of Cloudflare was, um, it, it came from Paul Graham before Y Combinator used to throw a, um, used to throw a conference at MIT called the MIT anti-spam conference. And, um, and it was, it was really a machine learning conference because spam was this perfect machine learning place. And, you know, like, like John Graham coming, who's now on our board, but was our CTO for a really long time. Like he was a machine learning guy. And so like, it was always kind of in our, in our core, but, but we would, but we would not have really described ourselves.

38:53I think that the modern era of kind of Cloudflare and AI started really in 2017. And what had happened was we were deploying software in the same way that everyone was deploying software, you know, and, and, um, but, but our network kept growing and then the, the size and importance of the customers that we had, uh, kept growing. And it, it was terrifying because if you pushed, when you push code out, it, um, you know, there was always a risk. You'd make a mistake and take things down. And we had, we had done that. And I remember getting a call from the CEO of FedEx at the time. And we would have, we were in the midst of an outage and he He was like, when are you going to be back online?

39:33And I was like, any second now, we're so sorry, but you're not even a customer. And he's like, no, no, but we rely on a bunch of your customers. And some of our planes can't land if you're offline. And they could technically land, but they had a sort of checklist of procedures. And one of these things was to check against some systems that were on us. And that was just terrifying to us. And so we thought we've got to build a new way of developing and deploying software, which can be extremely lightweight. It can scale, you know, automatically to whatever size you need. It can be, you know, rolled out to a single customer or a group of customers or to all of our customers in, you know, a really fine grain controlled way.

40:21You can pull it back very quickly and it needs to be cost effective because we're still running and trying to build this as efficiently as possible. And that gave rise to the Cloudflare developer platform, which we call Cloudflare Workers. I wish I could say that we saw what was coming in AI. And we did in some cases back in either late 21 or early 22. I can't remember. Um, we, uh, did a partnership with this, um, you know, graphics processor company to put, um, GPUs at the edge of our network in order, we said, to be able to do a bunch of interesting things, including inference. It issued a press release, did, did a whole thing.

41:07It was crickets. No one cared. Um, and, and we're like, huh, that's surprising. and that graphics chip company was NVIDIA. And hilariously, like two or three years later in 2024, and you can look this up, we actually issued the exact same press release, just changed the date and our stock doubled. And so I think we've always kind of been in this space thinking about it. And I think because we are so close to users around the world and we are so connected to everything else, it makes us just a very natural place to take some of these AI tasks. So, you know, inference, we're really, really good at.

41:58And, you know, almost all of the models, you can now use Cloudflare to directly access and you get better performance, you get better responsiveness. We can deliver it at a much lower cost than most anyone else. And those are open source models that CloudFront runs? But even the big, like, you know, OpenAI is a big customer of ours. You know, all their mobile app is built, you know, on us. We have, you know, direct access to them and we can get you better performance than you can if you oftentimes just go directly to them. Anthropic, again, another big customer. All of Cloud.AI sits on top of us.

42:37But if I'm a developer and I'm looking to run Kimi, Mistral, whatever, I can do this. Totally run on us. And in that case, those will be processed on the GPUs that are sitting in our network distributed around the world in hundreds of cities. And that's where in France, the edge network doesn't lend itself to training necessarily. Not today. You know, I think that if you think about what you need for training, you need lots and lots and lots of machines with very powerful GPUs. We have both of those things. But what we don't have is a way of like we don't have an InfiniBand network fabric that's connecting all of those machines.

43:22And so where, you know, an AWS or, you know, even in, you know, SpaceX, they're building giant data centers with lots of machines very close together, hyper networked. We're we're we have just as many machines, but they're spread out all around the world. And as a result, most training tasks were not the right place to do that. We're always testing and experimenting. And, you know, I don't know that that will necessarily be be the case forever. But what we are really good at is anything that you need kind of a beefy machine where like your phone or your laptop isn't enough and you want it to be either fast or lower cost or you have some sort of regulatory jurisdictional reason why you want it to run in a particular region and not necessarily get sent back to Ashburn, Virginia.

44:20And I think that's driven a huge amount of the adoption of these AI workloads across our network. And that's by far the fastest growing part of our business. So that's workers. Another product that seems incredibly timely given the current conversation is the gateway. Do you want to talk about that, what that does? Yeah. So, I mean, again, it's all driven by us. And so we needed to be able to use AI models. But we're a security company. We have various regulatory requirements based on the customers that we have. And so we needed some way to be able to understand how our team was using these models.

45:01And so we built something that we called the AI gateway. and it allows you to do a couple of things. One is it allows you to audit as if you're the CIO or CTO, audit all of the sort of the prompts that have gone off to the various AI systems and then all the responses that you get back. And we actually have an AI system that allows you to very quickly surface things based on what you might think of as problematic. If you've got a customer support agent and it starts acting like a Nazi, like you want to be able to see that, you know, incredibly quickly. And so we give you that auditing. We also allow you to do basically, you know, prompt injection the way that if you're in the way that Claude or the way that Anthropic sort of put guardrails around around Fable in order to take what was mythos and and turn it into something that they thought they could release broadly is they put a whole bunch of prompts.

46:01to say, do not do the following things. You as a business, like if you're Cloudflare, there are certain things that we don't want AI systems to do. And the AI gateway allows you to inject that into it. And then the third thing, which again, we built for ourselves, was you need to control costs. The cost of all these tokens can be just overwhelming. And it's really easy for you to lose control of that. gateway allows you to route things intelligently where you don't necessarily have, I don't know, somebody who wants, um, you know, an AI agent to summarize their email. Like you don't need the latest frontier model in order to do that.

46:44You can run it on something, which is a lot simpler. And so for, for our team, oftentimes they have no idea which model they're hitting. Gateway is just deciding that, um, for them. And again, we built that for ourselves and we, and, and people are always asking us like, what are you guys doing around AI? And we showed it off and people are like, I need that. And so now again, that's turned into a product. So continuing out Tor, drawable objects, agencies, DK, workflows, browser runs, sandboxes. It's all just stuff we needed. And then it turns out other developers needed it as well. And so if you think about how did AWS develop, it was Amazon's way of building essentially systems where their own team could build things very, very quickly.

47:33And that was, I think the story of Cloudflare has very much been the same thing where we needed all of these things to build what we were doing. And then that's turning out to be incredibly valuable to our customers and to anyone who's trying to do anything in AI today. We are the best place to build any kind of agentic workload. An example of this, if you take every knowledge worker on earth, and you imagine that they're each going to have one agent that is working on their behalf. It turns out that the CPU utilization of that is more, it's 40 times the current annual CPU production that's out there.

48:20If you're doing it in a traditional sort of AWS containers based way, It's just too expensive to do that. And I think the idea of every knowledge worker having just one agent is insane. You're going to have hundreds of different agents that are working on your behalf, in which case we just don't have the capacity to be able to provide that. And so what we've done is said there's got to be a better way of doing that. And so, again, what workers did is it said, you know, if once upon a time we had, you know, physical servers and then we had VMs, then we had containers, workers said, what's the next step beyond that that's even more efficient?

49:01And we built all of workers around a concept, which everyone's familiar with because it's the tabs in your browser, which are isolates. And that is a, you know, safe environment that is sandboxed from the other tabs that are running. but it doesn't need to have a whole new copy of the operating system. It doesn't need to have a whole bunch of tools. And as a result, you can stand it up much more quickly. You can tear it down much more quickly. And for agentic workloads where your agent is effectively going to be right, whatever you're asking it to do, it's effectively going to be writing code for you all the time.

49:38That is turning out to be kind of the perfect environment to be able to build these things. And again, I wish it was because we were so clever and saw the future, but I think a lot of it is, it was what we needed to build for ourselves and it's turning out to be just a very powerful platform for anyone who's doing anything kind of on the modern internet. That looming CPU shortage crisis, by the way, it seems to be an somewhat under-discussed topic. I mean, these agents, you need a lot of GPUs, We all know how, but like CPU utilization is, is going through the roof and, and that's going to be, you know, in, in a, in a bunch of different, in a bunch of different directions.

50:20And so we're constantly trying to think about, you know, how can we improve the underlying kind of efficiency of, of the network? So, you know, like a while back, three or four months now, we rewrote WordPress in Rust in order to deliver it. And we called it M - because we used, it was a play on the fact that we used AI to do it. And people are like, you know, why'd you do that? And we did it because there's a huge community that supports WordPress. WordPress should be successful going forward, but there's no way that WordPress is going to be able to scale for what's coming going forward. It's just, you know, it's a PHP piece of software.

51:07Again, amazing for what it was, but it costs like$3 or$4 at current traffic levels in order to run. If traffic explodes a thousand times, like people aren't going to be putting WordPress sites up because it's going to cost$3 or$4 ,000 in order to be able to run these things. So we've got to reinvent a bunch of these fundamental technologies that are out there. Not because we're like, oh, we want it, you know, we need to open source. We give it away. But because we believe that our mission is to help build a better internet. and what the tidal wave that is coming, if we don't get in front of it, if we don't upgrade some of these fundamental systems, you know, it's going to make, you know, a lot of the internet just break because we have to have an infrastructure that can actually scale.

51:58And that's going to be, you know, how do we take the user interfaces and the things that people know and are familiar with and have an ecosystem around them, but then port them into ways that they're just significantly more cost-effective and efficient to run. In terms of breaking, what does an agentic internet mean in terms of security? Yeah, I mean, it's, security is going to be, I think for the next two years, we're going to see just a series of really scary things happen online. You know, probably the worst kind of bug that was out there for a long time was this thing called Log4j. where you could send a very simple command and compromise almost any server running anywhere.

52:49I think for the next 104 weeks, so two years, you're going to see a log4j-like vulnerability every single week. because, and again, we were part of Glasswing and got to use Mythos to look at software. We get early releases of various open AI models. We've built our own security models. These models are incredible at finding vulnerabilities, and they're going to find them like crazy. And I think security is going to be, it's going to seem really scary for the next little bit. There's going to be a bunch of VCs that invest a bunch of money because you're going to see just an explosion in cybersecurity companies that are stopping this.

53:40But what's interesting is I think it's going to be a little bit of a head fake because I think that two years from now, what's going to happen is software is going to get just massively better as a result. Um, so internally at Cloudflare, you know, we, um, we, we, like last year we had an, we had an outage that was incredibly embarrassing and, and, and awful. And we said, we have to get better. And so we actually built an agent that not only reviews every code release that we, we send out, but every configuration change. So if you go to the Cloudflow dashboard, you push a button that generates a file that then gets pushed out to the rest of our network.

54:19And we now have an agent that's checking all of those files, every code release. And it's trained on 10 years of incidents. We have big public incidents that make the news. But there's sort of a background noise of small incidents over time. and like one of those sort of just audible gasp moments that we had was a guy who runs our infrastructure engineering team who was presenting at our all hands meeting and he said, okay, here's sort of our background noise of incidents and then there's a cliff and then it goes like this. He's like, what do you think happened there? And that was the moment that we released this agent that's checking in.

54:57And I think one of the things that people don't appreciate and don't talk about enough with AI is, yeah, AI is good at replacing a bunch of tasks that humans do and it doesn't tire and it doesn't need breaks and it doesn't have to sleep. But the really interesting thing is it's also uncorrelated to all the other biases that are within an organization. A team of people working together will tend to develop a set of biases. And that doesn't mean that AI doesn't have biases, but they're uncorrelated with the rest of the team. And so it's actually incredibly good at measuring things. And what I think you're going to see is that if it's going to seem like software is incredibly scary for the next two years, and then it'll kind of just fade away.

55:42And we're going to all be releasing software, which is so much better than it ever has been before. And so like our uptime and our reliability and our performance has gone up, you know, an order of magnitude over the course of the last year. And it's all because we're using agents in order to more securely build our systems. And I think everyone is going to be doing that going forward. Since we are on that topic, let's go into how you guys use AI internally. You have some interesting stats that you published just a few weeks ago in April where you said the company has 93 % of R &D employees using AI coding tools, 3 ,683 internal users, whatever, 241 billion tokens.

56:28How did you get there? How did you create that culture for a company that was started in 2009? And two, how do you measure success? We were selling picks and shovels and sort of the AI gold rush. But we were actually pretty cautious users ourselves. We'd run experiments, but we were sort of not kind of all in on it. And we had a bunch of people internally that were skeptical. A guy named Kenton Varda who built the workers platform is an incredible engineer. We're not supposed to talk about 10X engineers, but Kenton is clearly a 10X engineer. He's just a genius. And he, you know, last in the spring of 2025, he's like, this AI stuff is BS.

57:15I'm going to go play with all of these tools and show you just how much of a mess they are and why we shouldn't be relying on them. And he did. He went away. Super senior guy on our team. Went away for a month, played with them, came back, and he was like, oh, my God. I am now 100 times more productive than I was before. Which, if that's right, and he's a 10X engineer, means that he alone was more productive than our entire team was back in, like, 2019. Right? And so, and, and, and, and like, I kind of believe that's true. Like it's, it, it was, it was remarkable. And so internally on the engineering side, I think it spread pretty quickly.

57:54And I think the real turning point for us was probably November of 2025, where it felt like, and it was the release of Claude Opus and, and, and a bunch of things, but it felt like it, it tipped where we all of a sudden on our engineering side, we're getting, you know, much, much, much more productive. I think that's the story everyone, that's sort of where everyone is today. And everyone is seeing some version of that. I will say that, you know, for us, you know, we've, there, there, there have been the, the, the people that I continue to worry about are the ones that have been very hesitant to embrace these tools.

58:32And it tends to be not the super senior folks, not the super junior folks. It tends to be the people kind of in the middle of their career. and it makes some sense where they, um, they, they've been doing well in the world that existed. They've been told this is the game to play. And if you play this game, you're going to succeed. And so they, they've played the game and now someone comes along and says, we're changing the rules of the game on you. And, um, and that feels deeply, deeply, deeply unfair. Um, and, and yet again, you kind of have to adjust to what's out there, but I, I really do worry that we're going to have this sort of lost generation of, of sort of either, you know, young millennials or, or older Gen Z folks that just, they, they're, they were sort of, they're going to have incentives to, to say we shouldn't use these tools because again, it, it, if they do, they, they don't feel like they have any advantage of the people who are coming, coming from behind them.

59:29And so a lot of what I've been trying to do is how do you bring those groups, groups along. The other piece was, you know, I think engineering teams are very natural users of these products and coding has been a place where you've gotten really clear value from them. But like, that's just a small part of Cloudflare. We've got, you know, legal, finance, you know, marketing, sales, all of these other organizations that are part of the team that were like, How can we make them significantly more productive as well? And so we came up with this idea that, you know, could we create a sort of a user friendly system that allowed, you know, someone on the finance team to be able to, you know, do their job incredibly, incredibly efficiently.

1:00:26and um and we called it cloudflare os and it's it's it's it's basically it's a harness where we've taken a bunch of things that have to be true and gotten them into it we've taken a bunch of things that are sort of just informative based on our experience and then we've taught it skills where it can do various things that the there's nothing that's that magical about what we did except that we had a pretty significant advantage because we had all the security tooling where we could hook into all the different systems of record we have, the ERP system we have, the sales systems we have, the workplace management systems that we have, and do it in a way that was secure and where an agent working on behalf of a user could inherit the specific kind of access that that user had.

1:01:17And I think that's where a lot of these things fall down is where people don't have like that security model in place. We did. And so we were able to build it pretty quickly. The other thing that was where I think people fall down is if you ask someone to describe what they do in their job, they'll describe it, but they'll leave like half of their job out. And so the question is, how do you capture really what are the jobs to be done within an organization? And so the clever thing, there's a guy named Sam Ray on our team. And he was like, listen, we're going to set up an email address. And we're going to tell everyone that it's a magic AI agent.

1:01:58So for us, it was auto at cloudflow.com. And we told everyone, it's a magic AI agent. We've set it up. You can ask it anything. Now, in reality, behind the scenes, it was a team of about 20 people that staffed this 24-7. And yes, they were using AI tools, but what they were really doing was gathering kind of the information about what are the real problems that people have in their jobs. And then they were building that in and turning it into a set of skill files and information to be done. And so an example of this is there's a woman named Heather who's on our investor relations team. And so we're a public company.

1:02:38And so every time we have earnings, it's about two weeks of prep ahead of time to generate all the IR documents and everything that needs to be done. And it's kind of arduous, kind of drudgerous work to put this all together. And Heather's like, I think I can automate it all. And so we went from what used to be a two-week process down to what is now a three-minute process. It is more accurate. We actually found errors in in in things that we had put up before. Nothing, nothing significant, but, you know, little little things here and there. And now we're able to take that. And that means that then our team can spend more time meeting with investors, talking with our shareholders, doing all the things that are actually the really productive parts of their job and much less time actually generating those documents.

1:03:27And so I think that every company is going to be going through this. And it's interesting. We get questions like, hey, how are you guys doing this? And we keep showing off this Cloudflare OS thing. I'm on the organizing part of the group that's organizing the 2034 Olympics in Utah. and I was talking to the guy named Elario, who's the CTO of the IOC. And he's like, listen, we need this, we need a system to do this. And I was like, well, let me show you what we're doing at Cloudflare. And so I showed it to him. We never thought this was gonna be a product. And he's like, I need you in Lusanne, you know, next week to, and bring your team because we need to implement this.

1:04:12And so we all flew up and now they're actually using this to, help better organized Olympic Games. And I think that if you get, every organization is going to have to build something like this. And if you do it in a way which is secure, that gives users access, again, I think it's going to just massively empower teams in order to be successful. I'm not sort of in the, I guess, the Dario camp of AI is going to replace all jobs. I think it is going to replace a certain set of jobs. Like I think we're going to, like if I have a developer and I can now give them AI tools and they're 10 times as productive, I'm going to hire as many developers as I can.

1:04:53If I have a salesperson and I can give them tools that eliminate the parts of their job that they don't like, which is coming up with account plans and doing all of those things. What they want is to be in front of customers and out there selling. I'm not as many salespeople as I can. Where I think there's going to be a big change is the parts of the organization that aren't building and they aren't selling. They're basically measuring. And it turns out that AI systems are much better measures than any humans, again, in part because they're uncorrelated. And so I think a lot of those things are going to be the case.

1:05:30Another example internally, internal audit, which is, again, one of these things that you do when you're a public company. You probably don't do it when you're a small startup, but you do it eventually. We have 105 or something risk areas and every quarter, historically, the internal audit team has picked six to 10 of them. And then they do sort of these deep dive audits to make sure that we're not screwing up procurement or we're doing revenue recognition correctly. And they're measuring effectively the organization. We're now getting to a place with these tools where we can take all 105 of those risk areas and continuously audit them and find any issues that are there.

1:06:13And I think that that's making us a better organization. It's freeing up people to be able to work on either building or selling, which are really where value is created within organizations. And it's just incredible to see how much more productive the teams are. And do you think that there is a painful transition period from the current world to the next world? So do you think everybody's going to have to go through this as organizations recalibrate? We laid off more than 20 % of our team. And not because the business was struggling. And not because they weren't great team members. But we just need fewer middle managers.

1:06:56We need fewer measurers. and those roles were going away. And as I talked to peers at other companies, they're all seeing the same thing and they're all saying, yeah, we're gonna have to do the same thing at some point. And you're seeing these sort of trickle out, but there's a real fear in leaders out there that they don't wanna be the first ones to do that. And pardon the vernacular, but I think that's chicken shit because the cruelest thing that you can do is wait. I do think in the next six to 12 months, almost every company is going to go through some exercise like this where they're going to cut a bunch of their team.

1:07:41And I think a lot of people are waiting, a lot of CEOs are waiting around to do it because they're afraid that they're going to look bad in the process. what really struck me was the realization that once you know that that change is going to happen the kindest thing that you can do for your your team is to do it as soon as possible because it's way easier to get a job today than it's going to be in six to 12 months because the market's going to get flooded and so we put together an incredibly i mean the the most generous severance package in In tech that we've seen, we let stock continue to invest, which nobody does, but we did.

1:08:22And we're working incredibly hard to place these people because they're incredible people. And somebody who is a middle manager at Cloudflare can be a senior manager at a startup and do an extraordinary job. But once you're as an organization, you've realized you're going to do it. Like the kindest thing that you can do for your team is to do it as soon as possible. because what the worst thing that can happen is that there's just a bloodbath six to 12 months from now where everyone does it at the same time. And the, and the market gets flooded with a bunch of people who are all of a sudden looking, looking for jobs.

1:08:55And so that was the cattle, that was sort of the, the rationale that went through us. And, and again, I, there were, there were people who were really close friends of mine, uh, who, who we let go because the roles that they had just didn't make sense anymore. And we wanted to do it in a significant enough way that we would never have to do it again. And so I feel comfortable saying that for the foreseeable future, we're not going to have to do anything like that again, because we've reorganized the organization. And I think that one of the metrics that we look at is span of control. And so how many direct reports.

1:09:32What's the average number of direct reports in an organization? And the historic number that like the Harvard Business School did a study and they came up that the right number for that is about six. So for every manager, they have six direct reports. And that was exactly what we were at. It's so clear that with these new tools, a manager can manage a lot more people. You know, Meta is trying to get to 50 to 1. I think that seems like too many to me. But I think 12 to 1 is right. And the benefit of expanding the average number of direct reports is it inherently flattens the organization. The fewer number of direct reports on average a manager has, inherently the organization is going to be much more hierarchical.

1:10:19The more direct reports they have, it's going to be flatter. And I think one of the things that, you know, we're seeing is that the flatter the organization is, the easier it is for us to move quickly, to be able to deliver things. And again, I think that's what's driven a lot of us. But yeah, I think this is coming across the industry. And again, it's not a statement that any person is a bad person or a bad individual. in fact they're great but there are roles that you're just not going to need anymore and some people you're going to be able to sort of re-home and re-skill inside your organization but like if you're going to go from 6 to 1 to 12 to 1 that means a whole bunch of middle managers are going to go away.

1:11:09So what do you do if you're one of those people? If you're a middle manager and I think earlier you mentioned old Gen Z or young millennial and you find yourself in that situation like mid-career, do you just go down a complete rabbit hole of like just making yourself as AI-peeled as possible? I think yes. And recognize that it's natural for that to feel scary and that your instinct is going to be to fight against it. But that the more that you can embrace these tools, the better. I have never seen, you know, and again, running this now for 17 years, like I've never seen more senior managers on our team say, raise their hand and say, hey, I want to go back to being an individual contributor.

1:11:56And so we're rethinking like even how compensation works and everything else, because I think that the power of an individual contributor and the amount of work that they can do is extraordinary. And so I think that for us, you know, our most senior members of our team are embracing this because it's a way for them to reinvent themselves. And they're senior enough that they're not scared about it. Our most junior folks, so we hired 1 ,111. That number is meaningful to us. But interns this summer, you know, and I mean, it's like they're the most extraordinary humans because everybody stopped hiring interns, which I think is just total mistake.

1:12:37And so we got sort of the pick of the litter. and they're all totally AI native. And what we're trying to do is get both of those groups to get the folks that have been more hesitant because the fact is 92 % of engineering organizations using AI tools, that should be 100 % for sure. But you've got to get people who especially are sort of mid-career to be brave enough to learn these new tricks. And I think that that's really important. And again, you can't have one person who is 10 or 100 times as productive in the same role as somebody else, making the same amount of money as somebody else who's sort of doing it the old way.

1:13:26Like one of those two is going to leave. And the worst case is if the person who's 100 times as productive leaves. And so we're just, we are continuously trying to get every, you know, lift everyone up. And again, part of why we did the layoff was in order to be able to, you know, really, really free up the resources and catalyze the entire organization to be able to do this. But it's not just your developers. It has to be finance, legal, marketing. everything has got to be embracing these new tools. So almost a year ago now, you declared, I think that was content. Independence Day, July 1st. July 1st.

1:14:04Coming up on the second anniversary, or I guess first anniversary. Yes. What happened in the last year? How is the whole debate evolving? Yeah, so. And what was it in the first place? Yeah, so again, I think that it is, you've just seen an enormous rise in the amount of sort of automated traffic that's running across the internet. I think in five years, the bots and agents will outnumber humans online a thousand to one. At the same time, like the interface through which we access the internet is changing. And we've gone through other sort of platform changes before. You know, we went from browsing the internet on a browser on your desktop or laptop to social, to mobile.

1:14:52But throughout all of those things, the underlying business model of the internet for the last 28 years has remained basically the same, which is it's largely advertising and then to a lesser extent subscription driven. The problem is that first, there's just gonna be a lot more cost to run on the internet because you're gonna have a thousand times as much traffic, which means you're gonna have to have servers and CPUs and network and all that stuff. And someone needs to pay for it. And the business model of the last 28 years, which is largely advertising-based, doesn't work because bots don't click on ads.

1:15:35And so something has to change. And so a part of that is how do we make the internet more efficient? Like how do we take legacy software and port it to things that can continue to scale in a cost-effective way? But another big part of that is how do we figure out what the future business model of the internet is going to look like? And, you know, we have great relations with all the different AI labs and companies and they get it. and they actually, you know, they say, listen, as long as it's fair, as long as everybody has to do the same thing, we get that we've got to pay for the content that we're using in order to build our systems and our models, but we've got to build a system to do that.

1:16:19And so like we're experimenting in a bunch of different ways. The first thing that we did was we said, in order for a market to exist, you know, you always think you have to have supply and demand. It's not exactly right. What you actually need to have is scarcity of supply and demand. If everything is free, then no one pays for it. Like we're sitting in this room, we're breathing the air, doesn't cost us anything. On the other hand, if we go scuba diving, air is scarce. And so we have to pay for it. And so, you know, that's that. So step one was we said we need to create controls that allow the people who have content online to be able to say who can access it and who can't.

1:16:58And again, it's everyone's going to make different decisions. In our case, we want all the bots to be consuming our information. We want that to be out there. And that's probably half of our customers are in that camp where they actually want to make it as easy as possible. And so we provide the tools to as cost effectively, as efficiently as possible, get information and take HTML, which has all this cruft around it, converted into Markdown, which is a much more streamlined system. And then as a result, like you can get more information in these AI systems without blowing up their context windows and all kinds of things.

1:17:32It makes sense. On the other hand, if you're an ad supported business, agents aren't good for you because they're taking your content and then people are consuming it through their AI system and you're not getting eyeballs. So you're the ad systems that work. So we said, like, let's give everyone the tools to be able to stop it. Let's create some scarcity in the supply that's out there. And what we've seen is if you go across the biggest publishers in the world, Condé Nast,.ash Meredith, you know, they are they because we've now given them these tools, they're actually signing much better deals with the AI companies.

1:18:08And so that's I think that's sort of the first step. But we've got to go beyond that, because if you're a small blog or, you know, an influencer, a content creator, like you don't have the ability to go strike the same deal that a Condé Nast does. There should be some basically a micropayment. Every time we access a site and get information, we're happy to pay a fraction of a penny in order to do that. That's harder than it sounds. So we process in any given second about half a billion requests through our network. We estimate that somewhere between 1 % and 10 % of those would be sort of monetizable through some micro payment transaction.

1:18:47And so that means that you have to be able to support day one, call it 10 million financial transactions per second with a clear ramp to be able to get to 100 million transactions per second. Visa, which is the largest payments network in the world, does fewer than 100 ,000 transactions per second. So we need to be, you know, two orders of magnitude bigger than Visa, but at much, much smaller volumes. And so we're working on that. And we think that in partnership with folks like Coinbase and Stripe and protocols like X402, which is interesting, the original internet had something, a response code, the 402, we all know 404, which is not found, and 500, which is an error.

1:19:37I mean, there are different things, but 402 was payment required. And we just never have really built anything around that. I think that's going to happen going forward. And so, you know, Google really is responsible for having built the business model that's powered the last 28 years of the internet. And I think we are trying to figure out, you know, what is that business model that's going to power the next 28 years of the internet? But something has to pay content creators because they deserve to eat. Something has to pay for the infrastructure that all of these bots are taxing effectively.

1:20:12and I think we're working a lot around figuring out exactly what that looks like. Great. It's been an absolutely fascinating conversation. The overarching thought as I was listening to you is to which extent Cloudflare is becoming even more relevant today than it's been for the last 17 years, which you build the network and now the internet is changing and you are absolutely at the sort of central point for it. Yeah. Is that how you guys think about it? Like you have another 17 years? People are like, why do you keep doing this? Because there are days that aren't fun. I mean, laying off a bunch of people, I mean, that wasn't fun.

1:20:58And it's, and you know, I've made plenty of money. So it's not about any of that. What it is about is really our mission. And we, and we believe in that mission fundamentally. And we, we do things that, that cost us money or, or that certainly don't make us any money because we believe in helping build a better internet. And like, I, I, I can't imagine anything that I could possibly be doing that is more rewarding than, than figuring out how do we create some sort of sustainable business for the future. And I think there's a lot to be incredibly optimistic about. The business model of the internet for the last 28 years wasn't the most healthy.

1:21:44Traffic has always been a terrible proxy for value. And, um, and, and, and, and, and like so much of like the media landscape was how do you sort of define your narrow audience? And then with a headline, piss them off enough that they'll click on a link in order to, you know, drive eyeballs to, to your, to your site. And, you know, so much of this, so much of what is dividing the world today is actually the underlying business model of the Internet, which has been traffic based. How do you drive as much traffic as possible? That's going away. And I think that, you know, if we think about what can replace it going forward, it can be something which is much healthier and much, much better.

1:22:31I was, I, uh, I flipped a Stockholm, um, to, to meet with Daniel at who started, um, Spotify and, um, and cause I mean, like nobody has compensated creators at scale like Spotify. It's just, it's just, it's remarkable when we can quibble about are the right people getting the money or not, but, but there's more money going into music creation today on, on per capita per GDP than anytime in human history by far. and and spotify gets a lot of credit for that and and you forget that like 22 years ago the music industry was dying you know because on napster and everything everyone was just normalizing everyone's stealing stealing content um and then you know steve job steps on stage announces itunes 99 cents a song that's not the business model that won but it at least planted a flag that said that there's there's something that's there and then i mean spotify last year sent something like 12 billion dollars out to music creators that's more than the that's more than the entire music industry's market cap was 22 years ago, which is, which is extraordinary.

1:23:31And, and so Daniel was telling the story. I said, one of the things that they, that they do is if you search on Spotify for like Taylor Swift, shake it off, like they know they return results and they know that those results are what you were actually looking for. On the other hand, if you search for like, I want a song to a disco beat about how much fun it is to dance with my cat, they also return results, but they know that they probably don't have that. And what they do is they actually publish those queries that are sort of bad results back to music creators. And there are hundreds of music creators that are making a living off just doing this.

1:24:08One in particular is based in Denmark. Daniel shared is making something like 40 million euros a year just writing songs for unfulfilled Spotify queries. And I think there's something that's actually quite beautiful about that. Because if you search for like something on Spotify, What you're searching for is how do I kind of bring an emotion out? And so going forward, like we, for the first time in human history, have essentially a mathematical model of all of human knowledge. That's what the LLMs are, right? It's not perfect, but it gives you that. It also shows you where the gaps are, where there are the holes.

1:24:40I picture like a giant block of Swiss cheese. There's lots of cheese, but there's a lot of holes in the cheese. And when you talk to the AI companies, what they want is highly reputable sources that are filling in the holes in the cheese, that are contributing net new knowledge. They don't want yet another story about what happened at 1500 Pennsylvania Avenue. Right. They want something where people are actually telling and creating new information that moves the world forward. And I think that's what I want. I think that's what you want. I think that's what all of us are craving is how do we get back to sort of a media environment, which isn't just trying to provoke outrage in order to drive traffic, but is actually trying to find the truth and fill that in.

1:25:21And if we can create a system where the people who, again, are highly reputable and are creating sort of net new knowledge, that those are the people that get rewarded, then I think that's incredibly, incredibly beautiful. What I keep pushing the AI labs to do is I'm like, we should create the equivalent of, I don't know if it's the Academy Awards and Nobel Prize, where we measure across a whole bunch of different segments, who has contributed the most to human knowledge in the last year in, you know, I don't know, cancer research and, you know, foreign policy news. I mean, you can create a bunch of these things and they can actually measure and say, you know, this particular year, the best podcast was, you know, the first mark podcast that was out there or whatever it is.

1:26:03And we should be rewarding and celebrating both in terms of honoring these people, but then also paying them for doing that. And I think if we do that, you're going to get a lot more interesting information. My wife and I bought the local newspaper in our hometown, Park City, Utah. I think we will make more this year off AI licensing deals than we do off display ads. Why? Because if you're planning a vacation to go skiing in Park City, Utah, you want to know what's the best hotel to stay at, what's the best restaurant, who's going to be performing, you know, what are the snow conditions like? And the only place that has that is the local newspaper.

1:26:40And so while the last 28 years of the internet kind of killed local, quirky, unique, interesting information, what I'm hopeful is that as we figure out what the business model is for the next 28 years, it actually brings a lot of those things back. And And it actually gets back to let's talk about facts. Let's talk about reality. And like the New York Times be amazing if they were covering like in the Marriott Marquis hotel in Times Square. Room 1313 is better than room 1314. Or if you know, if you're if you want to sleep in late room, you know, 1314 faces north. And so it's less likely to get sun in the morning.

1:27:22Like those are the sorts of here's the best bodega in New York. The very interesting, quirky local stories. I think that that is both a media future and an Internet future that we all crave and want. And I think it's one that we we actually have an opportunity to build because we get to reinvent what the business is going to be. Fascinating. Well, Matthew, thank you so much. This was an absolutely wonderful conversation. We appreciate it. Thank you for having me. Hi, it's Matt Turk again. Thanks for listening to this episode of The Mad Podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from.

1:28:02This really helps us build a podcast and get great guests. Thanks and see you on the next episode.