If SaaS Is Dead, Linear Didn't Get the Memo

Founded in 2019, Linear is the rare company started pre-ChatGPT to have successfully reinvented itself as an agent-native business. On this episode of AI & I, Dan Shipper sat down with Karri Saarinen, cofounder and CEO of the product management tool, to discuss building a platform where humans and agents develop software together—and why the "SaaSpocalypse" isn’t coming for all SaaS companies. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI. Timestamps: 0:00 Introduction 2:00 Why Linear waited to ship AI features instead of rushing to chatbots 5:06 Linear's agent platform and becoming the system that guides AI agents 7:42 Why "SaaS is dead" is a simplistic narrative 12:18 How Linear adopted AI coding tools 17:45 AI's impact on product building workflows—speed versus thoughtfulness 22:18 The value of conceptual work and thinking before shipping 29:30 How AI is reshaping Linear's product strategy 37:18 Demo: Linear's agent skills, shared context, and code review workflow 47:48 The future of product development and the enduring role of human judgment

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Everyone will have many agents and companies will build their own agents. Linear becomes kind of like a system for guiding the agents and like building this context.

• 0:07Dan Shipper?

  This is the perfect business for this era because it's still SaaS. You're the one who has the sort of sticky interface because it's where everyone is kicking things off from and where they're recording all the information, but you don't have to pay for any of the actual tokens.

• 0:38Dan Shipper?

  Hari, welcome to the show. Oh, thanks. Thanks for having me. Really, really great to finally meet you. You are the co-founder and CEO of Linear. Little known fact, the first time I ran into Linear, it was because we were using it in 2020 at the very beginning of every to act as our content management system for the newsletter. And at the time, it was like this very kind of like hush, hush. You couldn't get access to it, but everyone, if you knew, you knew that Linear was like amazing. And we used it for a while and really loved it, but then we realized it was made for software, not publishing articles. So we moved off of it, but it was really cool while we did it.

• 1:20Dan Shipper?

  And I've always admired the, the, the level of taste and craft that you bring to, to what you build. And also I think the, the level of thoughtfulness and patience that you build it with. And I think that's one really interesting thing is the, the way that you built the company originally was to keep it closed for a while, not raise too much money, not put too crazy expectations on the company.

• 1:50Dan Shipper?

  And, and, and be patient and willing to build something quality over the long term. And I think that that's also has something to do with how you approached AI. Like you guys are really in AI right now. You know, when I think about the companies that are successfully transitioning into this moment that were started in the pre-AI era, Linear is definitely on that list. You know, OpenAI came out with Symphony the other day and the, the main thing that it hooks into is Linear. And you've successfully transitioned the product to be really agent native.

• 2:23Dan Shipper?

  And so, but, but like when GPT-3 first came out, I didn't see anything about that on Linear. So I'm sort of curious about that transition for you. What was that like emotionally to have built this product for a particular way of working and a particular way of building software and then see the world change, but maybe not be totally sure if like this is, this was going to be the thing and then essentially eventually be like, this is the thing we need to rebuild the product or change how the product works in a significant way. Like, talk to me about that.

• 2:55Speaker 00

  Yeah. Well, first of all, thanks for being early user. And I think like the, the thinking I think has always been the same. It's like, we just want Linear to be the best product in this category and like helping companies move, move work forward and often like build software products. And it's like, in some ways, like this new AI stuff, like it doesn't really like change that mission. Like it, it kind of like maybe even like improves it. And like our goal was always that like, can Linear take more of the burden of like running these product teams or like figuring out things to do or like figuring out when to do them. And, and let the product teams or the individuals actually build the things. And, and now like, oh, it's also like they build it with AI or the AI builds it.

• 3:38Speaker 00

  So I think like, in some ways, like the mission for us didn't change. Yeah, actually, like, I think the AI is making it better because now we can automate more and like take more of that burden and let people do it, uh, kind of like use their craft or like use their like taste or thinking or something in it. But yeah, I think like we do how like, I personally always have this problem, like a, like a way of addressing problem, which is like, I come from a design background. So a lot of times my like, the way I approach things is first I'm trying to understand them. So this sounds kind of obvious, but then I think like what happens in the tech world, a lot of times it's like, people don't try to understand things. They often like jump into that, like, oh, I can do this, so I'll do it now.

• 4:23Speaker 00

  But like, did you think like, should you do it or like, does it actually help you? So that was kind of like our thinking with the early. AI and the chatbots, like every company is like rushing into this moment. It's like, hey, we are now an AI company because we have this chatbot integrated. And we tried that too internally. And then we just realized this is not really that useful. Like, how do you actually use, like, what is the workflow where you actually need this or use this? So we have spent all this like couple of years now, like trying to understand these workflows. Like, how do people actually wanna use these things? And like, we did a couple of things well, though. Like, I think that we released this like agent platform, like, so it's kind of like an open platform.

• 5:06Speaker 00

  It has very good docs and like agents can build the integration themselves using the docs. And because of that, we now have most of the, like, the coding agents or agents out there, integrated with Linear. And like, this is like OpenAI brought their Codex,

• 5:24Speaker 00

  kind of like cloud agent in there, because we just had this available. So I think like, we kind of saw this world that like, I don't think there's gonna be one agent, but everyone will have many agents. And like, companies will build their own agents, which we're now seeing with like Coinbase and Ramp, who are customers, and they, they built their own homegrown coding agents, which then integrate with Linear. So Linear becomes kind of like a system for guiding the agents and like building this context. But it doesn't like, we don't try to own everything in this, in this world or in this market. We can like play with other people, other companies too. So I think like, yeah, like the approach was much more like, how do we like, understand the workflows, like what is actually valuable?

• 6:09Speaker 00

  And like, what people could do, use these tools for versus just jumping into like, well, everyone else is doing this, this thing, so we should do it too. And by the way, now we are adding kind of like a chat interface into Linear. But it's a lot more like, we kind of like, there's tools and there's skills and there's more of like, understanding, we gather like, how you should use it. Like, you can use it to kind of synthesize customer requests, because that's like Linear can handle that. Linear is a place for customer problems or requests or other things. So now like a Linear agent can kind of like natively work through those and like see patterns or things like that. And that's kind of like the, we're trying to bring like a clarity

• 6:51Speaker 00

  and context to the organization, which they can then use as part of the like AI building workflows. So because like, I think like, once the AI builds more and executes more like they kind of like the problem really becomes like, how do you productively harness this like in a good way? Like you can, you can task like a million agents doing something, but like should, what are those things they should be working on? Like probably not all of those, like if you don't think about it like it probably like a lot of those like work, work is not necessarily that useful. You need to have some kind of like decision making progress of a process of like, is this actually important? Should we do this? And like linear is a way to like do that and like build that intent and build that context

• 7:36Speaker 00

  and then go to like build it with the agents.

• 7:39Dan Shipper?

  There's a interesting, I don't know if it's a meme or a mind virus or what going around right now, but the stock market thinks that SaaS is dead.

• 7:51Dan Shipper?

  And I think you're, you're pointing to something really interesting, which is this dynamic of a couple of years ago, a lot of companies, including a lot of SaaS companies rushing to chatbots. And I think a big part of that is, well, we know this thing is happening, so we have to at least show that we're doing something, you know? And I think that the, the market is starting to, the public markets are now starting to, to look at that and like require that. And I, I imagine when the AI stuff was coming out and you guys were maybe testing AI features, but weren't releasing them, I imagine there was some pressure maybe from investors or from yourself or maybe internally to do something.

• 8:33Dan Shipper?

  And you kind of, it seems like you waited until you've, you, you had the fat pitch.

• 8:41Dan Shipper?

  And I, I'm curious if that, if that is true, what that was like? And what you think it means for, you know, all of the public market companies, all the public market SaaS companies that are down right now and whose CEOs are like, well, I guess what you really need to launch a agent platform or whatever, you know?

• 8:59Speaker 00

  Yeah, I mean, I think that's we didn't really have a pressure from investors like that's like one benefit of picking the right investors and also like they trust us to like make the right calls and then also we we obviously did talk about this, but then we also like had that discussion. It's like we just don't see the value right now doing it this way. We need to find actual like real value here that actually helps these companies. And so I think it wasn't like that bad. There was yeah, there's definitely like internal pressure. It's like and now I think the the the speed of the market has picked up a lot like every month or some like couple of weeks, there's something changing and we are like tracking those changes.

• 9:42Speaker 00

  And kind of like try to see where all of this is going, but there's also like this, it it creates a lot of noise in the market that there's this like, oh, now this week someone is doing this loops and then a couple of weeks later, people are like, no, the loops are a bad idea. And then like, we we kind of like you shouldn't like, I think like those things are like signals that you should like read and understand, but like you also need to know that like a lot of this stuff is not tested and like a lot of times that people also testing these things are not testing it in some like large organizational contexts that where things actually matter like if if they work or not. And so I think there's that like, we haven't tested all these things, so we can't like make these predictions of like how

• 10:26Speaker 00

  things are exactly going to change. Um, I think on the on the SaaS narrative, I do think like it's it's probably directionally correct that you with SaaS companies, you probably have to like as an investor, you kind of have to, there's more uncertainty of the future cash flows that like, because if the landscape is changing, you can't expect that everything will stay the same. But I think like the the narrative is kind of simplistic, like, oh, people will wipe out their own CRM tools, and I don't think that's exactly going to happen, but I think like what might might happen is like there's new companies that come out or I think like a lot of the public companies are not the most like

• 11:08Speaker 00

  I don't know, flexible or like the most robust solutions out there. They are the big solutions that the big companies use. And there's a certain kind of like inertia in there. So like, I would say that the, yeah, I think like the public companies probably get hit the hardest here because they are like, their modes are kind of like disappearing in a way. I think even for us, like we, we consider now, it's like, we need to live in this day one world again, where like we can't rely on our previous decisions anymore. Like, we have to like, look at these problems like in a fresh way that like, what happens when, when these things change? What, what happens when the agent come into this product development process?

• 11:52Speaker 00

  What are the new problems that come out of it? And like, how do we help that? So like, we shouldn't be tied into the past experience, like the past product we have, but like, see like what the future product should be. And I think like this is harder for large companies and like companies that have existed for like decades. So I don't think it's like an easy task and I like, I think their growth companies or startups can, can do it a lot better.

• 12:16Dan Shipper?

  How, how big is the team now?

• 12:17Speaker 00

  About one hundred and twenty total. I think I would say like about half of them, like sixty people are on the product team.

• 12:25Dan Shipper?

  And what was that, what has that transition been like? I assume that over the last couple of years, there have been a lot of divided opinions on is AI coding really a thing? Is it just glorified autocomplete? Is it gonna eliminate programming as a job? And then how have you, how has that change cycle been to actually go change your workflow? Figure out what the new programming workflow is like? How, how did you get the team in shape to do that? And what did you learn in that process?

• 12:54Speaker 00

  Yeah, I think like there, there was definitely a time in a, in a company to like, we had to encourage people to use this tool smaller. I think there's always that. There can be like habits where you always like done stuff this way. So like you, you are kind of like less and less like interested in like trying new tools. But I think like now, let's say like the probably all of the engineering and, and sometimes our design and BMs also like are now using agent coding or coding tools. We don't like track any kind of specific, to me, it's like I joke about this sometimes on Twitter, like people now it's like the, the, the biggest like vanity metric is like how much of your code is agent?

• 13:35Speaker 00

  Like uh written or how many PRs are you merging? And I think like that's like not the right metric. It's, it's, it's, it's measures. Yeah, like output, but like what does that output do? Like, does it actually generate value? Is it like improving the product? Like you need to have like, if you're measuring this kind of metrics, you need some kind of counterbalance, like what is actually the quality of this work and like, is it actually meaningful? And I think like that's like, also, I think what's playing out in the market is like, we have large companies that are token sellers. And then like when, when um like you have a lot of incentives, like your business model is like to spend more tokens and like things like our revenue will be higher and like

• 14:18Speaker 00

  our market share will be higher. So I think there's a lot of incentives saying people like you should spend more tokens. And not saying like, well, you think about things and like spend it well. Um, so I think there's that again, like I think people are looking at it too, like simplistically or like kind of like, oh, there's a good thing if we just like, like spend more tokens, things will be better. But I don't think that's ever been the case in, in building products. Like, yeah, there's some, some um value and speed and like making changes, but then like you should also understand any change or addition you make, like it can also have a negative impact. So it's like, it's not always like activity is always positive. Like sometimes it can be negative too.

• 15:03Dan Shipper?

  What do you think is a more nuanced metric for, you know, if you're judging how well, how in this AI world are we? How well are we? Doing our job of figuring out these new workflows and adapting to them and using them in our own work? If you know tokens or number of PRs submitted or percentage of agent generated code is are not necessarily the right metrics, maybe even in isolation, they're not the right metrics. What do you look at or how do you think about it?

• 15:29Speaker 00

  I mean, I think it's still the classic metrics of like profits or revenue or user like love or some of these things are like what you should be aiming for.

• 15:39Dan Shipper?

  Those seem like lagging indicators, right?

• 15:41Speaker 00

  Yeah, they are. But yeah, there isn't like, oh, I think like you should still measure like some of these things like token usage per person or like by different teams or something. But you shouldn't take it as like to the extreme of like, this is the only metric that matters now. You should be like, use it as a signal that like, are we doing something? And then think like, well, i- is our product actually improving? Like, do we have any indication of this product is actually improving? Like, do we get comments on the new features? Are the bug, is there less bugs? And like, I think bugs is actually like measurable metric. If you, if you run like honest bug tracking process where you actually track bugs.

• 16:23Speaker 00

  And then I think like now, I almost feel like with the agent, agents and AI is almost like, why do you even have bugs in your product? Like, you should be like, there's no excuse for it anymore. And like internally, we have this zero bugs policy, which is like, we have a linear team triage and like any bugs go there. Then there's a one week SLA that every bug, bug needs to be fixed. And then now I think with the coding agents, the coding agents actually can do the first pass on it. And then like, once it's done the fix, it will kind of like attack the engineer on it. And the engineer maybe doesn't like it or there's some like changes that they want to make.

• 17:06Speaker 00

  They can do it also now inside linear and they can review the code in linear. So there's this like very good workflow for now, but I think like it's, it still starts from the fact that like, do we care if we are? Product is buggy or not, and like we have made the choice, like we think it's bugs are like, kind of like bad things or mistakes and like we should fix them as as quickly as we can. And that's like a priority to everyone. So I think it's still like, uh, it's still a choice if you like care about the quality of the output or you're just wanting like more of the output.

• 17:42Dan Shipper?

  What are the ways that um these tools have changed your product building workflow, both both personally and as an organ? What are the most effective ones that might be surprising?

• 17:56Speaker 00

  Yeah, I think like on on the product side, I think it's definitely a lot better. Like, I think it's, I have with with linear now, I have this like skill where it's like, I fed some of our internal docs and um blog post about like how we think about product development. I made this like a linear way skill. And then like it it writes soon as I tell it like, okay, like, look at this. Like, help me understand this like feature request. Like, we have this, we collect this feature requests and and inside linear. And then, like, for example, there's a request like multiple assignees per issue. It's like requested by lots of people, like hundreds of people. And so I can kind of like tell it to go synthesize like, help me understand like what are the different reasons people want it like I don't want.

• 18:41Speaker 00

  So it it's kind of starts with like explaining the problem, like trying to understand the core problem, which is usually what I want to know. It's like, so this helps me like when I when I see a new request, I might go into linear and say like, oh, like, do we have this kind of request already? And then help me understand it. And then like it helps me kind of like give an understanding like which then like helps me like potentially like, should we actually tackle this now? Or is this something we could do later or maybe never? So there's that like before we start building anything, it's like it's helping me kind of like understand the problem and like in a very quick way, like I don't have to like go ask around or like find people to do it for me. On the design front, I actually don't personally use it much.

• 19:26Speaker 00

  I I actually like the manual design process. Like, I I still have Figma open. And then when I have a problem or idea, I I just draw it in there. And it to me, it's like. Yeah, I'm often like my work is often more like that kind of like exploring things. So I actually don't think the speed really helps there. Like I actually like the slowness of the manual thing, like you draw things manually. Every time you draw something, you have to kind of like check on yourself. It's like, why am I do like, why am I drawing it this way? Or like, should I draw it a different way? But then like the broader team, when the design team, when they they work on problems. I think now they are building a lot more like prototypes and we have this quite robust like

• 20:11Speaker 00

  build system. So you can, you can actually build it into that, like you can make it VR and then it will run the build and you get the preview link to the build and then you can use it live in the in the product. So it it helps the testing it or the prototyping stage of it. But I I still tell the designers, so I kind of explore more freely in in Figma first or wherever. And like try to think about how you approach the problem. It's just like, let's just jump into doing it. Like there's projects like that too, where it's very clear what needs to be done. But then if it's like a bigger project, I think they should still spend that time. And then yeah, like engineering side is probably similar to a lot of other ones that where

• 20:57Speaker 00

  we can kind of like fix problems a lot faster. Once we identify them and like decide to do it. We use Slack a lot and like with our Slack agent, like we have a discussion and then we eventually decide like, yeah, we should do this. And then we just tack linear in there saying like, hey, can you create the issues out of this conversation and then we'll do it. And so it it helps us like track, come back to it later and like actually make it actionable right away versus like, oh, we need to have a meeting and then we start a project and then we start like assigning people. So I think there's like,

• 21:34Speaker 00

  I I think it's kind of like, I I would say like, kind of like the pattern in all of those things is like it's shortening the some kind of loop there and like making it faster. Like you can do the thing right away versus like waiting, waiting like, I don't know, next week or some other time to do it. Like it's it's very little effort to do it right away.

• 21:53Dan Shipper?

  Which is interestingly, sometimes it seems like you're the exact opposite of your preferred.

• 22:03Dan Shipper?

  Outlook, you know, actually, we shouldn't do things faster. Actually, we should take things a little bit slower. How does having tools that make you go much faster?

• 22:16Dan Shipper?

  Interact with that outlook?

• 22:18Speaker 00

  Yeah, I think it's a good point. I think it's, I think it's more like,

• 22:23Speaker 00

  I think like we shouldn't go fast in like deciding things or, or just like, kind of like speed running the decisions or like not even doing a decision. Like, I think there's this, some people do it now where they just like have an idea, then they build it. And now we're like, now we're all looking at this idea that no one really know why it exists. And like, should we even do it? And it's like, it's, it's a, every new prototype or idea can kind of like seem useful. But then like you now like, don't have like a good way of like framing. It's like, how useful this is versus other things. Like, should we spend the time actually like now committing on this idea? Cause we already have like, kind of decided on this, some of the other ideas.

• 23:02Speaker 00

  So I think there's this like danger of like, you don't have some kind of like decision making way. We don't have like a lot of processes in, in, in linear, but it's more like, we want to commit on this. Like, once we commit on the thing or the fix or the project, then I want it to improve fast. Like, I want the loop to be fast to actually work on the problem. But I don't want the problem finding to be fast. Like, you should take the time to find the right problem and like the right approach for the problem. And then, once you decide that, then you can go faster on it.

• 23:36Dan Shipper?

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• 24:18Dan Shipper?

  a call, there's an expert's reasoning behind it. That's how you go from a cool AI demo to an AI system you can trust. Visit scl.ai/dialect. That's scl.ai/dialect to learn more. While I'm doing that, back to the episode. One thing that what you're saying makes me feel is I totally get that approach. And also for myself as a product builder, I often don't know what I'm doing until I do it. And I can't think it through until I've done like five different things that I can't explain. And then I'm like, okay, here's the thing and I understand it. Is is what you're saying different from that? Or is it the same, just like said differently?

• 24:59Speaker 00

  Maybe it's different, but I can see that workflow. I feel like that workflow is kind of like, kind of like understanding, like you're trying to understand like what you're doing. And

• 25:09Dan Shipper?

  Yeah, it's building. It's like making things as understanding. Yeah.

• 25:13Speaker 00

  And I think that's fine. I think the problem there just becomes like sometimes it's like, you kind of like, don't know. Are you, I think like conceptual work, sometimes like in design, I consider this like a conceptual work where it's like, the output of this is a concept, like it's not like, like, we just shouldn't deliver this necessarily. But this is like, like, I made this, like, I went through this process of understanding this problem. And like, I have a concept for it, or like, I have

• 25:40Dan Shipper?

  What's an example? Because I would assume that the output of a design process would be Figma, you know, a Figma that you could export. So what's an example of a concept that comes out of a design process?

• 25:51Speaker 00

  Well, I think like, in the, in the past, like in a large companies, I've used the concept term to like, not to scare people. So usually it's like, like rethinking some area completely. And that's like a concept, like, it's all like, it's like a concept car. So it's like, this car won't go into production, but here's some ideas that could influence the next car. So it's like, you're trying to like, like, sometimes people, I don't know, it's, it, this is like, partly like a large company thing, but I think it can happen in small companies, too, is that, once you see like, something like, very different. Your like fears might start coming up like, well, if we change this, like what else gonna hap- like what is gonna happen, what's gonna break?

• 26:34Speaker 00

  But the point is like not right now to decide that, like we just decide like, does this concept, like this new idea have merit? And like, can we like, do we, do we think it's like important enough for something to like, take it further and then deal with the like, the problems later? So it's kind of like you're, you're kind of like trying to divide like, the decision, like what, which decisions you are making now? And like I, I've used it, yeah, like in, in our company, in our companies, I, I just like, completely rework a surface and say like, hey, I think the project should look like this, like which is completely different from what it's currently is. And then people like, oh, that's actually interesting. Or they're like, well, it won't work for this and that. And I'm like, okay, that's fine.

• 27:15Speaker 00

  And it's like, it's a way to like, um, and it's, it's maybe like a figment design or a prototype. So it's just like, I, I think there's, like even with all this tooling, like the output shouldn't always be like, we ship something, like it, it's sometimes the output can be something internal that like, hey, we're just, now we have like a better understanding of this problem. We can like tackle it better and like, we can actually make it into a shippable thing. But like, we first try to like, think about it before doing it.

• 27:45Dan Shipper?

  Right. And, and to you, thinking about it can include building stuff. It's just, the reason you're building stuff is not to ship it the next day. It's to understand it better. But thinking can be designing, it can be writing, it can be, you know, talking about it, that kind of stuff.

• 28:00Speaker 00

  Yeah, and, and something like I did have to share with the company recently was that, I like, we always care about the quality bar a lot, but I think like, um, and like, kind of like this thinking process of like, are we doing the right thing? It's kind of like what we're trying to like, uh, like decide. Sometimes now with AI, it's actually like hard to tell. Like it's, it's kind of like, i- if the tooling changes all the time, like people like, the, the L L Ms are not deterministic anyway. Like you don't always know like, like how useful this thing could be. And then there's a moment you just have to decide like, yeah, I think we should, obviously we can try this internally, but we also need to try it with customers. And you kind of like put it into some kind of beta or something.

• 28:44Speaker 00

  That, that, so I think like there's definitely nuance to this right now that- There are situations where it's just like, and it's always with product building, there's a limit how much you can like, think about it inside your company, until you need to actually put it somewhere to someone else to use. And then you learn from that use case. But again, like it's, it's more like every stage, you kind of have like some kind of goal in mind. Like now, if we put it to better, like the, the, the, the goal should be like, understand the workflows and how people use it and how they want it to be better, not to like something else, like not to try to ship it as fast as we can or something like we, we should be honest about like, what is the actual goal for, for this stage?

• 29:28Dan Shipper?

  So we've talked about how AI has changed your internal workflow. I'm also curious how it has changed your product strategy and how you think about building products, not like the actual work of building products, but what kind of product to build? And what, for example, should you let AI agents connect into your product, which I know you've done, versus build your own AI, like into the core feature? Should you have both? What should they be able to do? Like, yeah, how does it affect your, your product strategy and your vision for what a good product is?

• 30:02Speaker 00

  Yeah, I mean, I would say like, we are now adding agent, like linear agent, that can like has context of your work and the context of the organization and the products you build, that you can use in different ways and like the PM workflows. You can also like as a designer, use it the, the way to like, understand the problems. And, and then we will also do like a coding agent where you can actually like, start like writing code with the agent.

• 30:29Dan Shipper?

  Interesting.

• 30:30Speaker 00

  It will, you can see the diffs online. So it's kind of like a cloud conductor and environment where you can kind of like see the changes. And you can kind of, you can guide it. And we think like, the, the strategy has definitely like changed. And we are just trying to like, like understand like, what are the problems set of today? We think like, One of the things is like, what is changing is that I think historically people thought like issue tracking is this kind of like, uh, like it's like a ticketing system for the kitchen, like, uh, like but engineering. So it's like order comes in, like someone orders fish. So now that fish goes

• 31:11Speaker 00

  into the kitchen, there's a ticket, like make fish. And that's like, kind of like people think about issue tracking and like, we kind of never thought about it that way. Like for us, like, linear is more like the backbone. We rely on like collecting signals and collecting problems or collecting decisions like we should do this thing. So I think like, I think there's definitely like shift. We have to like teach people like these products is really meant to like improve your team's workflow, not to be this kind of like a weird ticketing system for your like different parts of your organization. And that's kind of like, probably like going away like with the agents. So like, you don't need that anymore. Like the agent can do those tickets and like,

• 31:50Speaker 00

  they can also like complete them. But like we, we think like there's still value of like, like collecting that context and like the, they make, make the shaping the work something actionable and providing agents like good context from the, from the environment. But the, the one lesson we learned with the, uh, with, with the agents is that it's, it's, it's tough when we are not ourselves in control of it. Like it's, it's like, we do want to like support all companies and all agents as much as we can. But then if we have ideas for it, we can't do it. Like it's, it's on them

• 32:30Speaker 00

  to do it. So, so now like one of the reasons we are doing this coding agent is like, we actually think we see this like a lot more smoother end to end workflow where you start your, you don't have to do everything, but you start some of your task in linear. It's like you, you can ask the agent like, hey, does this thing exist already? Or if not, make an issue, make like a work stream out of it. And then like start working on it and then start like writing the code and then you can like diff, see the diffs coming in, you can like review it and you can like merge it. Or like you can see the prototypes. So it's just like trying to like, you, one of the problems I see like when I use this like,

• 33:08Speaker 00

  Like Claude or ChatGPT or some of these tools or Codex is that like I have to really explicitly tell the agent or like the tool always like what like what context to bring. And then I think the value with Linear is like the context lives there. And then if we kind of inject it like smartly part of the the work stream. It's like much more like like natural or we can design the flow that like makes sense and we don't like spam the spam the context windows or something. And I think we we see this feature as like you probably have this. Linear is kind of like the the multiplayer or the the organizational context of what's happening in the product and what what is potential future state of it.

• 33:54Speaker 00

  You might still run local agents, but there's situations where like you should just automate some of the like bug fixes or you should automate. That small task and like just do it in Linear and then like kind of like let it run in the background. While in a sandbox, while you like run your own work and in your own computer or somewhere.

• 34:14Dan Shipper?

  That's really interesting.

• 34:17Dan Shipper?

  I think for a product strategy perspective, I'm really curious about the decision to integrate your own agents. Because I'm before we did this interview, I didn't know about the Linear agent. And I was sort of sitting here thinking, wow, this is the perfect business for this era because it's still SaaS. There's no AI token costs, but it is the place where you control all of the AI. So all the other companies have to deal with all the other coding agents and whatever have to deal with all the token costs, OpenAI and Anthropic and whatever. But you're the one who has this sort of sticky interface because it's where everyone is kicking things off from and where they're recording all the all the information.

• 35:01Dan Shipper?

  But you don't have to pay for any of the actual tokens. And it sounds like you're adding it adding a layer where you will have to pay for the tokens and and you you may prefer that. And I think the reason you're saying is because a tighter integration between the two means you can do more interesting, more powerful things. How did you think about that from a business perspective? You know, changing your margin profile that much, I assume. You know, I don't know, I don't, I don't know off the top of my head how much linear cost a month, but I assume there's a lot of interesting discussion there about how adding in token costs change the business model.

• 35:36Speaker 00

  Yeah, I mean, like honestly, I think it's something like we'll have to see like in the future more. We definitely thought about it and like have some like calculations or thinking on it. I think on the coding agents, like we, we do have to offer like usage based billing because it's going to get very expensive. On the basic like linear agent functionality, that's like answers questions for you. It's like that's, that should be more like included into the, into the system and like we will have a lot more like, we will have to see like how much the usage actually is, but like in, in linear is still always going to be like this like for fairly focused platform for like certain kind of things. Like you shouldn't be running

• 36:19Speaker 00

  random things here. Like I, I think like you should be still like pretty like clear what you should be doing inside linear and like what, what, what kind of like workflows are you running there or workloads? Um, so we, we're not trying to like build this like very generic like agent platform. It's, it's, it's just like a more like the product context or the product memory platform where you can integrate those agents and you can use linear agents from other tools too, or you can bring other tools into linear. So it's, it's just like a way to like work around your product and it's kind of like an API into the product thinking versus like

• 36:58Speaker 00

  using like more of the like normal tools where it's like you always have to like tell it to like go fetch this thing, go find this thing because it doesn't have any understanding like what do you generally do or like what, what kind of like context that might be existing already.

• 37:14Dan Shipper?

  Can we see a demo? I'd love to see it.

• 37:16Speaker 00

  Oh, yeah.

• 37:23Speaker 00

  Is the screen sharing okay?

• 37:26Dan Shipper?

  Uh, yes.

• 37:29Speaker 00

  All right. Yeah. So what we have coming up, like this is actually my like real linear instance. And like what we have come up is like, we do have like now, if you do a new tab inside linear, there will be like the classic box of like, what do you want to do? There's also like this other interface where you can like, if you are inside some context, like a project or something, you can kind of like do the work there. But like, for example, the one, um, we will have skills and the skills, we will have guidance, like organizational guidance and a personal guidance. And you can have skill, like personal skills or organizational skills. So for example, like what I was mentioning earlier is that sometimes I want to understand problems.

• 38:10Speaker 00

  So, so, um, I want to like, understand this problem of like multiple assignees. So I made the skill, which is like essentially like, um, I fed some materials from our blog and it's like act like a linear product teammate. And, and then it has this format of like, it starts with the underlying need. And it has this like way it goes through the problem.

• 38:40Speaker 00

  And I, so I made this to like, kind of like, um, help my workflow. Like some just quickly, I'm trying to understand sometimes this feature request. So I can like to do it like, uh,

• 38:52Speaker 00

  well, let's do the multiple workspaces. So, so we have this like collection of, of stuff about like multiple workplaces. And then, um, it can kind of like go through it there. There's like probably like many different requests and like it will try to start, um, thinking through it, they will look into the customer activities, it will look through the different things.

• 39:17Dan Shipper?

  What model is it under the hood?

• 39:19Speaker 00

  I did, I think we'll eventually have multiple models, but now we use Claude, Claude for this.

• 39:27Dan Shipper?

  Sonar or Opus?

• 39:29Speaker 00

  I don't actually know.

• 39:34Speaker 00

  So it starts going through, it's like, okay, like, there's a, it's, it's, there's a real need, but the, uh, it's like more complicated than it sounds. So, so companies maybe want this like multiple workspaces for different reasons. And I think like my understanding generally is that they, they want like it one place to have this like billing and governance, but then they might have multiple different divisions in the company. And so it's, it's not, um, they would wanna like divide the, the team, that the workspace more, but, um, the, they might still have some kind of like overarching control. So it goes through the, the kind of like trying to like explain like what is, what is missing and what is good about it.

• 40:18Speaker 00

  It also like makes this like few recommendations of the product direction. So like do this or that and that. So it's like, it helps to like, kinda like make this something that is like quite like not like, I don't know, complicated into some kind of like actionable thing and like I can, we can talk about this as a team or something.

• 40:39Speaker 00

  But similarly, like it more like a micro example is that like if, if there's like a, I, I wanna make a new theme, like, um, new dark theme.

• 40:51Dan Shipper?

  What's a theme?

• 40:52Speaker 00

  Themes are just like in our app, like, so you can have like.

• 40:55Dan Shipper?

  Oh, okay, got it. Yeah, yeah, like the way it looks.

• 40:58Speaker 00

  Yeah.

• 40:58Dan Shipper?

  For the way, yeah, yeah, okay.

• 40:59Speaker 00

  So maybe I wanna create a new version of like a dark theme, like make it just black.

• 41:09Speaker 00

  So I can now task go like a, like a coding agent on it. And, uh, it should start like looking into, it can look into the code base and it can try to understand the code base obviously. And then, um, at first it's like, it's turning it into an issue and then like delegating into,

• 41:28Speaker 00

  Into an issue. So, so I created this issue. It's in progress. It's still located to Linnaire and then now like Linnaire starts working on it. There's a, it's spinning up the, um, the sandbox for it. And like, I think the broad, like, kind of the, one of the benefit on issue is like, now people know I'm like, doing this. So the team knows I'm doing this and I can say like, hey, no, I'm like, FYI, I'm like, I'm doing this. So like, he can also come here to look at this, like, what is happening, like? And then the thing is like, this agent session is visible to everyone. So it's visible to me and to him. So I can call, like, once it's, it'll take a while, but like, once it's like, does it, like, we can both jump into this chat.

• 42:14Speaker 00

  And tweak it together if we want to, or just like, kind of see what happened here. So it's like similar to like what you do on your computer, but then now it's like, kind of like happening in a shared context. And there's more like a, an understanding, like, where did this come from? Okay, it came from me. If this could come from a customer, like, discussion or-

• 42:35Dan Shipper?

  The shared context is interesting. Like, so, so two people can be in the same chat?

• 42:39Speaker 00

  Yeah.

• 42:41Speaker 00

  So I don't-

• 42:41Dan Shipper?

  That's really cool.

• 42:42Speaker 00

  I don't have non to, ready to demo this, but like, we did have this, like, instance, kind of like, accidentally, we noticed this, like, this is actually useful sometimes is that, um, it,

• 42:53Speaker 00

  a non who was our head of product and like Connor, who was our head of design, they both like, were working on some tweaks on the inbox. So they, they could kind of go back and forth, like a PM and a designer could go back and forth. It's like, no, it's like, it's not quite right. Like, let's fix this thing. And then they could both like, see the kind of like the, the preview link. Uh, let's see if I have

• 43:15Speaker 00

  something here. Okay. So there's like one, like my previous pull request. So we will have like pull request here. You can kind of see the activity. But you can also see the code. So you, you, you see the code divs and then if you want to like comment on it, or you want to like work on this code with the agents, like, no, this is not, not right, like, and then like, and like, work on it. And similar workflow works for like code reviews where engineer might come in and say like, this is not right. They could just task the agent to like fix it versus like saying, like telling the other engineer to fix it. So I think it's like, kind of collapses the collaboration loop a lot more.

• 43:56Speaker 00

  And allows like multiple people use the agents to work on one thing. And then like here, this is only like a backend change, so there's no like client review I can do. But if I had, if this would be a client facing thing, I could kind of like open the preview link and then like actually see like, how does it look live?

• 44:18Dan Shipper?

  That's interesting.

• 44:20Speaker 00

  But yeah, those are, yeah, a few things we're like adding.

• 44:23Dan Shipper?

  I'm curious about this.

• 44:26Dan Shipper?

  One interesting thing about this is it seems to increase the surface area of the product a lot. It's about a lot of different things that already exists to some degree somewhere else. And obviously there are things that you can do differently. So you can have multiple people in a chat, you can, it's, it's more plugged into linear more generally, but you, you kind of have to recreate a lot of stuff that's already being built by a lot of other companies. So how do you think about that and the trade offs of that and doing that well? Especially entering something like AI coding where all the big companies are just like going as hard and as fast as they can to build AI coding agents.

• 45:10Speaker 00

  Yeah, I think there's definitely the question we keep, need to keep asking ourselves like, what is our advantage or like unique advantage here? And I think like we, I think like honestly, like I don't think we will solve all the different coding needs, but like we don't necessarily also have to like, I think it's, it's what we see the value is this kind of like sitting upstream where the work is coming from. There's like really like good leverage there that we can offer to companies where it's like work comes in or a box come in. They automatically get spawned into agents, like, like even delegated to the agents, like engineers never even see them. Or like if they see them, they see them like, once there's like a fix already being built.

• 45:53Speaker 00

  And so it doesn't like maybe work for all kinds of situations. It's like, it's all like, it's not the agents, like you go to like, hey, build me a new product. Like, we don't think that's like where we should be like working in. It's more like you have a large company, you have a lot of things requested from you, a lot of bugs filed in, like, how do we like, reduce that workflow for you like automatically? And then, yeah, you can use the other coding agents to do other kinds of work, but this is like where we kind of focus on.

• 46:19Dan Shipper?

  That's really interesting.

• 46:20Speaker 00

  But then like, yeah, generally we've been thinking about. The problem is like, we don't wanna be a kitchen sink product, like we do everything for everyone. And like, sometimes companies end up in this state because you have the enterprise buyers and you have the checklist. And then like, you just need to get the check mark into the right spot on the checklist. And it, it, we don't think it's that, tho- those things like don't create like a good product experience. So the way we always thought about it and built the products is like, we try to feel like, what is like a nat- natural next step in this workflow? So i- i- if we go from an issue, like it's, it's like, yeah, someone like the natural next step is like, someone needs to fix this. And so like, how do we help people to fix it faster?

• 47:05Speaker 00

  So one option is like, we do this cloud agents and then you, they can fix it. But now the cloud agent does this stuff, like, how do you know it's like, good? So then, then you need to receive the code, like you need to see the diffs and like, you need to run the builds and like, whatever. So it's, it's like, we are more always focused on the workflow and how do we like, improve it? Like, how do we make the, make the, out, kinda like, help companies to output better and faster? Versus trying to like, own every surface? So we don't have to own like, every piece of the surfaces, but like, we, we are kinda like, trying to op- like, find this optimized workflow for people to do certain kinda like, product things.

• 47:47Dan Shipper?

  So, we're almost out of time. My last question for you is,

• 47:53Dan Shipper?

  if you had to project how product development will change over the next five years, let's say, what will be, what will be different? And also, what will be the same?

• 48:06Speaker 00

  I think that the one difference, I, I think there's gonna be more of this like, self-driving aspects of like, you can set up. Some kind of rules or guidance and we even like are building something like around like a project memory or like a like so like you could have like a like a common workflow, what we do is like we have projects going on. It's like a project is often like a feature, a part of the interface or a part of the part of the product. And then like we have a lot of like fe- feedback and requests and things coming in. I think there's opportunities like turn that into like more like that the the product or that feature is it's it's kind of like an agent itself. And it kind of like tries to make decisions based on the input it's it creates.

• 48:49Speaker 00

  And then it it can still have like maybe ask a certain amount of input, but it could run automatically. It's like, hey, like these kind of I seeing these patterns and these patterns point into this solution and this solution seems to be like potentially something that works for people. And I built the made a build, I send it to some customers and they say it's the feedback is good. So it's it's kind of like it gives you like it it does things on its own based on some kind of context and like a rule based system or like some some kind of guidance. And then I I I do think like the thing I'm still like, I think people should still think even in this world where

• 49:31Speaker 00

  agents do some of the thinking and like does run automatically to some degree. I do think like it makes people have to be like a lot more explicit, like what do they want or like how like what is worth doing and like what are the areas we should be doing and I I think it's so there, I think like a lot of this like still like humans having meetings or discussions or writing issues or writing documents. I think it's like reading documents like that there's still gonna be like a place where like humans need to like understand this stuff too. Like you can't just outsource the thinking purely to to the AI or agents.

• 50:11Speaker 00

  And and but like you should like the more you can clarify your own thinking and the strategy or something, the better it's for your team, but also it's the better it is for the agents too, because then like you can codify some of those like strategies or or thinking into actual like these autonomous things. So I I I think like I personally Don't see the future in a way that we are replacing humans and I don't quite believe in it, but maybe I don't want to believe in it, but I think it's, I think things will change, like the roles will change. Maybe there's some like movement around exactly

• 50:53Speaker 00

  like what does engineering do, how many engineers we will need and like what is the job in the future. But I still don't see like how the agents like or how the AI actually like does all the thinking and like kind of like the choices or decisions. I think product building is still kind of like a craft or an art. You kind of, a lot of times like we, we, uh, we talk about intuition, like we just decide things based on what we understand, how we understand the problem. We hardly use any data as part of decision making. Sometimes we use it to look at something, but it's more like a signal. So I never personally believed in this like A P testing and, and data driven product development, which I

• 51:34Speaker 00

  think could work well for agents, but I think it doesn't work for all kinds of products. And I also think like the best products are not necessarily like being built that way. Like you still need the human kind of touch of like what is interesting or like what, what would make this good.

• 51:51Dan Shipper?

  I love it. Kari, thanks so much for joining.

• 51:54Speaker 00

  Yeah. Thanks, Doug, for having me. This was great.

• 52:04Speaker 02

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