AI was built on open source. Now it’s starting to return the favour: finding bugs, writing fixes, and optimizing code faster than humans can review the diffs.

At Web Summit, I sat down with the CEO of Cal.com to talk about what happens when AI agents and open-source communities start improving each other. The bottleneck may soon be code review, which is at least a problem we recognize. But there is a fix! You guessed it. It's AI.

If you'd rather read than watch, the full transcript is below.

Transcript

0:05 [music] Hi everyone. Welcome back from lunch. We're here to talk about open source and I really wanted to launch it with you Avery just because Tailscale is like you guys have embraced open source and and and you sort of have

0:28 come to sort of represent the adoption of open source for a major company like yourself. I was just if you could describe the moment right now in this sort of a genetic AI everyone going crazy staying up all night. How that affects and how you feel about that in your company? In my company Tailscale maybe ironically has always been a little bit of a late adopter and

0:50 we're intentionally a late adopter of AI. It doesn't mean we don't use AI but we use it really carefully and that's because Tailscale is a is a network security infrastructure project. It's used by Fortune 500 Fortune 50 companies and we can't like it would be violating their trust in us to take too many risks too quickly when their entire network security depends on our company, right? So we're being careful and I think there's a careful way to adopt AI that's really

1:13 really productive that I think people are overlooking. Now in a slight sort of plot twist is that at your company Tailscale is actually you were open source and then very recently two months ago to keep things a little bit spicy you did a major pivot. If you could describe that for us. Yeah, recently we went kind of viral when we went from being

1:35 historically an extremely open source company. We've always been huge like open source evangelists. We actually decided to go close source due to sort of security concerns because you know this stuff rapidly changes as we all know as all we sit here and discuss and for us we thought that the environment was a little bit turbulent to be able to safely operate in and much like Avery said, for us, we also have a duty to

1:59 protect our customers. And that led us to kind of perform a bit of a risk assessment of that. Maybe if I can just drill down a little bit on that. Like how did this how did this change happen? Because maybe it can help us sort of explore the open source experience right now. Like what happened? Was it Was it Was your customers are coming to you and saying say hey, wait a minute. We're giving you all this data. What's going on with it? What what what was the story behind

2:23 behind the change? familiar with like the changes recently you know, AI has become a lot more useful. It can now build software a lot more effectively than it could before. Like vibe coding has been around for years, but only in the last like 18 24 months did it become something which we can actually use for production ready cases. And just as AI gets better at building software, it gets better at breaking it. Yet the

2:47 problem is is AI isn't developed enough to the point that it produces the most consistent answers. You know, you get different answers to how many hours are in strawberry. You get different reviews to the same PR whether you plug it into Claude or um you know, a GPT model. And the problem for us is that with AI able to break software and find vulnerabilities,

3:10 we can't seem to find a single source of truth which can determine if an application is secure or not, which leads us to that kind of uncertain and turbulent um atmosphere. Every what I'm tempted to say how do you respond to that? I mean what what cuz he is describing a world that we all know, you know, hallucinations, bugs in the code and all that kind of stuff, but maybe it doesn't need to be that simple. It's not

3:33 a It's not a just sort of all in open open source or not. Yeah. Well, I mean, it is let's be honest with ourselves. Absolutely true that AI does a bunch of weird stuff. It's super gullible. I've described in the past as like, you know, hiring a really smart but not very worldly intern, right? That can code very fast, but it makes mistakes, right? It makes really scary mistakes. And then

3:56 you can clear its context and ask it to review its own code, and it'll it'll find those mistakes and say, "Wow, whoever wrote this, I don't know what they were what they were thinking." Right? And so, you you need to build systems around around these things to make them not not you know, to make them functional, right? And the open-source world is experiencing that in particular, right? Where, you know, a whole bunch of projects suddenly appearing cuz somebody vibe coded

4:19 something they thought was cool overnight. A whole bunch of people are like, "Oh, I can fix my favorite project by modifying it to do something. I'm going to send in a patch." But they haven't actually reviewed it, or they don't even really they've never coded before. They don't even understand what this patch does, and they're the maintainers of the project are just like, "Look, there's there's a hundred of these. I don't have time." So, I mean, some people talk about like the the the hundreds of these things,

4:41 thousands of these things. GitHub is just exploding with what, you know, some people say is like AI slop, you know? What There's just so much out there, and then so therefore, you know, engineers and tech you know, technical people are just stuck going through it all, through it all. And then then you're stuck in this moment of sort of like what what was this all good for? I mean, how do you address that kind of that that that sort of issue of this sort of like having just this just a massive surplus

5:04 of of of product out there? Yeah. I think I mean, one of the things I observed that I think people don't necessarily realize is that AI is at least as good at reviewing code as it is at producing code, right? And there's kind of in the open-source world, with this AI slop, there's this interesting like it's a you can think of it as a giant distributed system of people producing the slop. Anybody can do it

5:27 and upload it to your GitHub repository and make your life as a maintainer miserable, right? But you can also get an AI to review those incoming PRs and tell you whether they're slop or not, and sort of automatically disqualify them if they're slop. But that would require the maintainers to set up some stuff, and And like expensive time that hasn't really happened very consistently yet. People are only just getting

5:49 started with this kind of automated review process, right? But you can do very good code reviews. In in the the like extended version of this, you can have automatic code reviews of PRs coming into your project, and then the person who's bought uploaded the the code can actually read the code review and then fix the code, and it can do this in a few cycles until eventually it gives up because it's always going to be slop or it's actually come up with a pretty good change, right? And once

6:13 you've got a pretty good change, then you should bring the human in to say like, "Hey, this is a pretty good change. Do you want it or not based on the vision for your project, based on your design principles, based on these other things?" And that's the opposite of the kind of exhaustion that we've been creating today, right? So it's all about the processes that you put stuff in. Um Bailey, we had this moment of the the Mythos moment very recently.

6:35 Um I just wanted to for you to sort of like in in in the work that you do, this has there really been this uh we've heard about the Mythos and and about, you know, Firefox like where you said they discovered like these these these um these these fallibilities. I was just wondering if you could just sort of are you in agreement that this is definitely a moment where there are new sort of security dangers out there um

7:01 that there weren't before? Yeah, I mean Mythos scared us all a little bit when they found vulnerabilities across Firefox, FreeBSD, and you know, a ton of other things. And you know, as AI models improve, they're going to find better vulnerabilities. There are some extremely complex and intric- uh intricate like exploits that can be done. And some which are so hard that, you know, humans

7:24 may not be able to do that. And while Mythos like they created a monster, really. Um and they decided like let's gatekeep this and let's try and like roll it out in the the most cautious uh way. But like this is AI. Anybody can innovate in it. Um Um, know, we have frontier models coming from American companies and then suddenly China comes out with DeepSeek and it's literally

7:46 open source. You can just download a state-of-the-art model. Now, what happens when China comes out with a Mythos alternative? Does that mean that now every single person that can, you know, self-host an open-source AI model now has the ability to hack into almost any system on the planet including some of the most secure and open-source um projects that exist?

8:10 That's a very good question which I'm going to put to [laughter] Well, uh I think I'm actually more optimistic than that. I think the timing is really important, right? There's the attackers, the red teams, there's the defenders, the blue teams, and there's what I would call the arms dealers who are selling tools to both sides, right? And I maybe they don't want to think of themselves as that way, but they they have to, you know, they built this incredibly

8:31 powerful tool that can be used for good or it can be used for evil. And I respect Anthropic a lot with Mythos in particular. Like they've been holding back on availability of this thing probably for multiple reasons probably including for marketing reasons, but it it it finds real serious security holes in real critical infrastructure software. And if they'd given that to the bad guys first, those bad guys would be exploiting that software before we

8:53 have a chance to fix it and we would be in really big trouble, right? The fact that they didn't do that, the fact that they're giving us a chance to like, hey, we can fix our stuff first is like is a gift. And then the question is like, well, okay, as the models keep getting better and better, are they just going to find more and more obscure security holes and it's never going to end in this like sort of downward spiral into doom? Or are we actually just going to like is stuff just going to get more

9:15 secure and not really hard to attack? And I think it's actually more like the latter, right? Like most software in the world today has never had a security review ever, right? And even if software that's been security reviewed gets reviewed once or twice a year, gets pen tested occasionally, some of the PRs get security reviewed cuz they're considered to be sensitive and some Some don't, right? So like most code has just never

9:37 been looked at. And now you have these tools that can look at it constantly. Every single change you make can look investigate and and try to find one of these security holes. And so, I believe that if we all get on board with caring about security, which is which is a stretch. But, if we do, we can secure our software. Uh getting people on board to to to worry about security may involve

10:00 regulation or may involve governments. Is that sort of something are you um Baylor, are you reluctant to see the government taking too much of a a role in this or is that something that you think there might be a way for them to to there there is a place for for regulation here? There might be a way. I think, you know, in answer to what you said, I don't have access to me those. Something exists out there that may or may not be able to completely break my

10:23 code base. I don't have a good opportunity to defend against it. And I think much like how, you know, the government has created structured and regulated markets which enable, you know, like each party in the the stock market to be able to to trade in a fair way. Um there might be opportunity for, as long as they don't overreach, um to try and like equalize this stuff.

10:46 That might be through export controls, that might be through um you know, laws and acts. But, uh yeah, I think there's definitely a little bit of uh equalizing that needs to happen. Um at the opening of the of the Web Summit um uh Paddy Cosgrave sort of talked about like these two worlds that, you know, there's the open source, you know, there's those who say that open source is already won and it

11:09 is the way. And then there's the others who saying that, you know, the the the US frontier models have have have already won and it's the way. Um the reality is somewhere in between there probably. But, I was just sort of wondering um on the sort of big sort of big picture level, every where do you sort of see things right now? Well, I think I mean, I think all the models have their place just as in any economic system. There's going to be the premium product and there's going to be

11:32 like all the levels down to the mass-produced product, right? And I think the most expensive tokens from the most expensive models are going to get more and more expensive. That's what I think will happen, cuz there's a shortage of GPUs, and like if you want this good stuff, you're going to have to bid for it with everybody else, and the price is going to get eye-wateringly high. You think it's high right now? I think it's going to get even more high. But at the same time, the price of like the tokens that are like a year behind

11:55 that are going to are going to crater, right? And that's that's going to be really exciting. Like pick the tokens we want. I think from a security point of view, that's going to be like if you're building infrastructure software that everybody depends on, the Firefoxes, the FreeBSDs, the Linux, the Chrome of the world, and I guess the Tailscales of the world, you're going to have to pay a lot of money for like the best security defense, right? Uh what I think about open source software, like, you know, a

12:19 lot of the stuff that we're all going to vibe code for ourselves or for the five people on our team, right? You're never going to be able to afford to run the best frontier security software, for example, to review that. You have to do something else, right? My my proposal is don't put it on the public internet. It doesn't need to be there, and if it's not on the public internet, nobody can attack it, right? That that's the best approach unless you're building these

12:42 fundamental front-end-facing public products. Um Bailey, token maximization, you know, we hear these stories about, you know, like at the big corporations or people are told to spend as much as possible, you know, what is it spend? To use as many tokens as possible, which ultimately means spending. I'm I was just sort of wondering, how does a company like yours approach like, you know, token you know, just using up of tokens? Is it like

13:05 go crazy, or like how do you try I mean, how do you try to how do you navigate between like showing that you're utilizing these tools to their fullest potential, but at the same time, you know, staying afloat, you know? I mean Yeah, I'm a little bit against the whole like token maxing hype train. I think, you know, a lot of things are hype trains in this industry. Even to some degree, people want to be like open source just because it's the popular

13:27 thing or not because it isn't. And it's really about like using what's sensible for you. Now, I do completely agree with Avery on the whole thing about being a little bit sensible and a little bit cautious about AI adoption. Um, do I think that like everybody should be spending as many tokens writing as many lines of code as possible? No. We've known for years that like lines of code does not equal output. Um,

13:51 and so I think really you want people to be AI-enabled. I can do a better job with AI. Everybody on my team can do a better job with AI. But, you know, otherwise like let's keep it sensible. Right. Uh, Avery, you know, to people in the audience, um, you know, people that are, you know, coding, who are coding for a living or thinking about coding for a living, um, what is sort of your advice to them in terms of, you know, being in

14:17 this world where, you know, some people say AI and they think it's a magic wand and then something magically just appears and it's fit for purpose. Uh, there's still a a major role for, you know, the good old human being in the story, isn't there? Yeah. Well, rather than a magic wand, maybe I I can compare it to a genie, where you make a wish and you literally get what you ask for. Uh, and it turns out not to be what you wanted, right? Cuz that that's what

14:39 happens over and over again. It's like it creates that it is magical. I get it's magical in the most literal sense of like we don't even know how it works. Even the people making it don't know how it works. And it and it grants wishes, but it grants them in like strange ways that you might regret, right? Uh, and I but I think, you know, it's a tool just like just like a human is a tool. Humans are magical, right? You ask them to do something and you don't always get exactly what you wanted. And sometimes

15:01 it's good and sometimes it's bad. And we've got tens of thousands of years of building human society around the fact that humans are magical, right? But I think, you know, society exists to serve humans, right? It doesn't exist to serve computers in a like running mathematically calculations that simulate humans, right? And so it it's up to us to do what we want with the tools that we we

15:25 have, right? And these these tools can be used for anything, right? They can be used for attacking, they can be used for defending, but they can be used for like writing a bunch of slop code, but they can be used for defending and fixing slop code without humans having to be involved. So then we have to up-level ourselves to this like I'm going to think about the abstract. Like okay, I actually have this you know, this this patch came in and maybe a bot wrote it, maybe a human

15:48 wrote it, who knows, right? But like it works now. It's I've verified that it does what it's supposed to. I've even verified it doesn't have security holes. Is it what I want to exist in the world? That's what an open source maintainer will have to decide, right? And that's the that's the fun part of being an open source maintainer. That's why we get into it, right? When once we start, we realize it's like even before AI, it's like 90% like jerks writing

16:12 angry posts in your issue tracker or like triaging stuff or like slop that might or might not have been AI generated PRs, right? But like the 10% that was fun is like working with people, building something cool, and having someone send in something that makes your cool thing even more cool, right? And that can be the whole job because we can eliminate the tedious part using these tools. But it's the human's job to decide what they want to exist in the world. That's what we all

16:35 work and be are going to be able to do with these more advanced tools. Billy, do you have I mean what what is your perspective on that too? I mean in terms of like, you know, you may not be like the most senior, most most experienced like, you know, programmer, but suddenly these vibe coding tools like allow that person to to to explore ways that they they couldn't have done before. Yeah, I know for us we said we're never going to fire anybody

16:57 because of AI. I mean we never over hired the team in the first place, but now we just expect people to be able to to produce more. Um you know, like I said, this doesn't have to be going crazy at it and you know, uh overusing tokens just for the hell of it, but you know, it's it's a very exciting time to be in open source, to be in anything, really. The world is rapidly changing each week

17:21 to the next. We have more and more capability. And, you know, overall, this should be able to be a positive that we can all use just to build more things and, you know, invent. All right, cool. I think we have to we have to leave it there. Thank you very much, guys. That was really cool. Thanks a lot. Thank you. Thank you.