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

Everyone will have many agents and companies will build their own agents. Linar becomes kind of like a system for guiding the agents and like building this context. This is the perfect business for this era because it's still SaaS. 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 information. But you don't have to pay for any of the actual tokens. Kari, 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 use 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 and have always admired the level of taste and craft that you bring to what you build. And also, I think 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, and be patient and willing to build something quality over the long term. And I think that that also has something to do with how you approached AI. Like, You guys are really in AI right now. 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. OpenAI came out with Symfony the other day, and the main thing that it hooks into is Linear. And you've successfully transitioned the product to be really agent-native. And so, but like when GPT-3 first came out, I didn't see anything about that on Linear. So I'm sort of curious about... 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 this was going to be the thing and then eventually be like, this is the thing we need to rebuild the product or change how the product works in a significant way? Talk to me about that. Yeah. Yeah. Well, first of all, thanks for being an early user. And I think 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 helping companies move work forward and often build software products. And it's like, in some ways, this new AI stuff, it doesn't really change that mission. It kind of maybe even improves it. And our goal was always that, can Linear take more of the burden of running... these product teams are like figuring out things to do or like figuring out when to do them and, and let the product teams on the individuals actually build the things. And now like, it's also like they build it with AI or the AI builds it. 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. 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 have like, I personally always have this problem, 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 jump into that, like, oh, I can do this, so I'll do it now. But did you think, should you do it? Or does it actually help you? So that was kind of our thinking with the early AI and the chatbots. Every company is 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 would actually need this or use this? So we have spent all this, like, a couple of years now, like, trying to understand these workflows. Like, how do people actually want to use these things? And, like, we did... A couple of things. Well, though, like I think that we released this like agent platform. So it's kind of like an open platform. It has very good docs and agents can build the integration themselves using the docs. And because of that, we now have most of the coding agents or agents out there integrated with Linear. And this is like OpenAI brought their codex cloud agent in there because we just had this available. So I think... We kind of saw this world that like, I don't think there's going to 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 our customers. And they built their own homegrown coding agents, which then will integrate with Linear. So Linear becomes kind of like a system for... guiding the agents and building this context. But it doesn't like, we don't try to own everything in this world or in this market. We can play with 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 and like what people could use these tools for versus just jumping into like, well, everyone else is doing 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, 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, and context to the organization, which they can then use as part of the AI building workflows. So because I think once the AI builds more and executes more, the problem becomes how do you productively harness this in a good way? You can task a million agents doing something, but what are those things they should be working on? Yeah. Probably not all of those, if you don't think about it, probably a lot of those work is not necessarily that useful. You need to have some kind of decision-making process of, is this actually important? Should we do this? And linear is a way to do that and build that intent and build that context and then go to build it with the agents. There's an 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. Um, 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 market is starting to, the public markets are now starting to look at that and like require that. And I, I imagine that. 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. And it seems like you waited until you had the fat pitch. And I'm curious if that is true, what that was like. and what you think it means for 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 we really need to launch an agent platform or whatever. Yeah. Yeah. I mean, I think we don't really have a pressure from the investors. That's one benefit of picking the right investors. And also, they trust us to make the right calls. And then also, we obviously did talk about this, but then we also had that discussion. It's like, we just don't see the value right now doing it this way. We need to find the actual real value here that actually helps these companies. And so, I think it wasn't that bad. Yeah, there's definitely internal pressure. It's like... And now I think that the speed of the market has picked up a lot, like every month or something, like a couple of weeks, there's something changing. And we are tracking those changes and kind of try to see where all of this is going. But there's also like this, it creates a lot of noise in the market that, There's this like, oh, now this week someone is doing these loops. And then a couple of weeks later people are like, no, the loops are a bad idea. And then like, we kind of 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 a lot of times people are also testing these things. They're not testing it in some large organizational context where things actually matter if they work or not. And so I think there's that we haven't tested all these things, so we can't make these predictions of how things are exactly going to change. I think on the SaaS narrative... I do think it's probably directionally correct that with SaaS companies, you probably have to, as an investor, there's more uncertainty of the future cash flows. Because if the landscape is changing, you can't expect that everything will stay the same. But I think the narrative is simplistic. Oh, people will white coat 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 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 the public companies probably get hit the hardest here because their modes are kind of disappearing in a way. I think even for us, we consider now we need to live in this day one world again where we can't rely on our previous decisions anymore. We have to look at these problems in a fresh way that... Like what happens when these things change? What happens when the agent come into this product development process? What are the new problems that come out of it? And how do we help that? So we shouldn't be tied into the past experience, like the past product we have, but see what the future product should be. And I think this is harder for large companies and companies that have existed for decades. So I don't think it's like an easy task. And I think their growth companies or startups can do it a lot better. How big is the team now? About 120 total. I think I would say like about half of them, like 60 people are on the product team. 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 going to eliminate programming as a job? And then how has that change cycle been to actually go change your workflow, figure out what the new programming workflow is like? How did you get the team in shape to do that? And what did you learn in that process? Yeah, I think there was definitely a time in a company to like, we had to encourage people to use these tools more. I think there's always that. There can be habits where you've always done stuff this way, so you're less and less interested in trying new tools. But I think now, let's say, probably all of the engineering and sometimes our design and VMs also are now using agent coding or coding tools. We don't track any kind of specific... To me, I joke about this sometimes on Twitter, people... Now it's like the biggest vanity metric is how much of your code is agent written or how many BRs are you merging? And I think that's not the right metric. It measures output, but what does that output do? Does it actually generate value? Is it improving the product? You need to have, 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 we have large companies that are token sellers. And then when you have a lot of incentives, your business model is to spend more tokens and things like our revenue will be higher and 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 just think about things and like spend it well. So I think there's... Again, like I think people are maybe 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 building products. Like, yeah, there's some value in 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. 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 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? I mean, I think it's still the classic measures of like profits or revenue or user like love or some of these things are like what you should be aiming for. Those seem like lagging indicators. Yeah, they are. But yeah, there isn't like, I think like you should still measure like. some of these things like token usage per person or by different teams or something. But you shouldn't take it to the extreme of this is the only metric that matters now. You should use it as a signal that are we doing something? And then think like, well... is our product actually improving? Do we have any indication of this product is actually improving? Do we get comments on the new features? Are there less bugs? I think bugs is actually a measurable metric if you run an honest bug tracking process where you actually track bugs. And then I think now I almost feel like with the agents and AI, it's almost like, Why do you even have bugs in your product? There's no excuse for it anymore. And internally, we have the zero bugs policy, which is we have a linear team triage, and any bugs go there. then there's a one-week SLA that every bug needs to be fixed. And now I think with the coding agents, the coding agents actually can do the first pass on it. And then once it's done the fix, it will kind of attack the engineer on it. And the engineer maybe... doesn't like it or there's some like changes that they want to make, 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 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 quickly as we can. And that's like a priority to everyone. So I think it's still like, it's still a choice if you like care about the quality of the output or you are just wanting like more of the output. What are the ways that these tools have changed your product building workflow, both personally and as an org? And what are the most effective ones that might be surprising? Yeah, I think on the product side, I think it's definitely a lot better. I think with Linear and LL, I have this skill where I fed some of our internal docs and blog posts about how we think about product development and made this a linear way skill. And then it writes to the side, I tell it, okay, look at this. help me understand this feature request. We collect this feature request in InsightLinear. For example, there's a request like multiple assignees per issue. It's requested by lots of people, like hundreds of people. And so I can tell it to go synthesize. Help me understand what are the different reasons people want it. So it kind of starts with 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 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 helping me understand the problem. And in a very quick way, I don't have to go ask around or find people to do it for me. On the design front, I actually don't personally use it much. I actually like the manual design process. I still have Figma open. And then when I have a problem or idea, I just draw it in there. And to me, it's like... 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 doing, like, why am I drawing it this way? Or like, should I draw it different way? Yeah. But then the broader team, the design team, when they work on problems, I think now they are building a lot more prototypes. And we have this quite robust build system. So you can actually build it into that. You can make a 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 helps the testing it or the prototyping stage of it but i still tell the designers so i kind of explore more freely in figma first or wherever and 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 the engineering side is probably similar to a lot of other ones that where 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 tackle in there and they're saying like, hey, can you create the issues out of this conversation and then we'll do it. And So it helps us come back to it later and 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 assigning people. So I think there's like... I think it's kind of like, 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 very little effort to do it right away. Which is interestingly, sometimes it seems like you're the exact opposite of your preferred 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 interact with that outlook? Yeah, I think it's a good point. I think it's more like, I think we shouldn't go fast in deciding things or just kind of speed running the decisions or not even doing decisions. Some people do it now where they just have an idea, then they build it. And now we're all looking at this idea that no one really knows why it exists and should we even do it? And it's like, 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. It's like how useful this is versus other things. Like should we spend the time actually like now committing on this idea? Because we already have like kind of decided on this some of the other ideas. So I think there's this like danger of like, You don't have some kind of decision-making way. We don't have a lot of processes in linear, but it's more like we want to commit on this. Once we commit on the thing or the fix or the project, then I want it to improve fast. 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. Here's a simple test for whether your AI is actually ready for production. Would you stake a business decision on what it just told you? If the answer is not yet, you're not alone. The gap is in capability because AI can do a lot. It's really about trust. You can't verify the output of the AI, you can't trace its reasoning, and nobody with real domain expertise has touched it. Dialect is a new system from ScaleAI that captures how enterprises make decisions and closes that gap. It puts your actual experts in the loop, AKA the people with years of institutional knowledge, and encodes their judgment into your AI systems. Every correction, every override comes with full context. It's actually really interesting. So the next time your AI makes 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 slash dialect. That's scl.ai slash dialect to learn more. While I'm doing that, back to the episode. So 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? Maybe it's different, but I can see that workflow. I feel like that workflow is kind of like understanding. You're trying to understand what you're doing. Yeah, it's building. It's like making things as understanding. Yeah. 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, uh, like we just shouldn't deliver this necessarily, but this is like, uh, 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. What's an example? Cause 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? Well, I think in the past, in large companies, I've used the concept term not to scare people. So usually it's like rethinking some area completely. And that's like a concept. 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... Sometimes people... I don't know, this is partly a large company thing, but I think it can happen in small companies too, is that once you see something very different, your fears might start coming up. Like, well, if we change this, what else is going to happen? What's going to break? But the point is not right now to decide that. We just decide, does this concept, this new idea have merit? And do we think it's important enough for something to take it further and then deal with the problems later? So it's kind of like you're trying to divide... the decision, like which decisions you're making now. And like, I've used it. Yeah. Like in, in our company and our companies, 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, if one work for this and that line, I'm like, okay, yeah, that's fine. 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 think there's, like, even with all this tooling, like, the output shouldn't always be, like, we ship something. Like, sometimes the output can be something internal that, like, hey, we 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. Right. 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. Yeah. And something like I did have to share with the company recently was that We always care about the quality bar a lot, but I think this thinking process of are we doing the right thing is what we're trying to decide. Sometimes now with AI, it's actually hard to tell. It's kind of like... if the tooling changes all the time, like people, like the, the LLMs 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 that, so I think like there's definitely nuance to this right now that, you know, There are situations where it's always with product building. There's a limit how much you can 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, it's more like every stage you kind of have some kind of goal in mind. Yeah. Now, if we put it to beta, the goal should be understand the workflows and how people use it and how they want it to be better, not to something else, not to try to ship it as fast as we can or something. We should be honest about what is the actual goal for this stage. 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 into the core feature? Should you have both? What should they be able to do? Yeah. How does it affect your product strategy and your vision for what a good product is? Yeah. I mean, I would say we are now adding agent, like a linear agent that has context of their work and the context of the organization and the products you build that you can use in different ways and the PM workflows. You can also, as a designer, use it the way to understand the problems. And then we will also do a coding agent where you can actually start writing code with the agent. Interesting. You can see the diffs online. So it's kind of like a cloud thing. conductor 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, 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 issue tracking is this kind of like, it's like a ticketing system for the kitchen, but engineering. So it's like order comes in, like someone orders fish. So now that fish goes 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 a 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 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, they can also complete them. But like, we think like there's still value of like, like collecting that context and like that, they make the shaping that works something actionable and providing agents like good context from the, from the environment. But the one lesson we learned with the, uh, with the agents is that it's tough when we are not ourselves in control of it. Like it's like, we do wanna 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 on them to do it. 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 tasks in linear. It's like 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 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, one of the problems I see, like when I use this like, like Claude or ChatTDB or some of these tools or Codex, is that I have to really explicitly tell the agent, the tool always what context to bring. And then I think the value with linear is the context lives there. And then if we kind of inject it smartly part of the work stream, it's much more like... natural, we can design the flow that makes sense and we don't spam the context windows or something. And I think we see this feature as you probably have this linear as kind of like the multiplayer or the organizational context of what's happening in the product and what is potential future state of it. 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 the 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. That's really interesting. I think from a product strategy perspective, I'm really curious about the decision to integrate your own agents. Because 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. 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 information. but you don't have to pay for any of the actual tokens. And it sounds like you're adding a layer where you will have to pay for the tokens. And 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 costs a month, but I assume there's a lot of interesting discussion there about how adding in token costs change the business model. 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 do have to offer like usage based billing because it's gonna get very expensive. On the basic like linear agent functionality that's like answers questions for you. It's like, that should be more like included 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 linear still is going to be like this, like for fairly focused platform for like certain kind of things. Like you shouldn't be running random things here. Like, I think it should be still like pretty like clear what you should be doing inside linear and like what kind of like workflows are you running there or workloads. So we're not trying to like build this like very generic like agent platform. It's just like 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 just like a way to like work around your product and it's kind of like an api into the product thinking versus like um 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 can we see a demo i'd love to Is the screen sharing okay? Yes. All right. Yeah. So what we have coming up, like this is actually my like real linear instance. And like what do we have come up is like, we do have like now, if you do a new tab inside linear, that 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. 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, um, I fed some materials from our blog and it's like, act like a linear product teammate. 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. And so I made this to kind of like, help my workflow, like something just quickly and trying to understand sometimes this feature request. So I can like do it like, well, let's do the multiple workspaces. So we have this collection of stuff about multiple workplaces, and then it can kind of go through there. There's probably many different requests, and it will try to start thinking through it. It will look into the customer activities. It will look through the different things. What model is it under the hood? I did... I think we'll eventually apply multiple models, but now we use Claude for this. Sonnet or Opus? I don't actually know. So it starts going through. It's like, okay, there's a real need, but it's more complicated than it sounds. So companies maybe want this multiple workspaces for different reasons. And I think my understanding generally is that they want one place to have this billing and governance, but then they might have multiple different divisions in the company. And so they would want to, like, divide the workspace more, but they might still have some kind of, like, overarching control. So it goes through the kind of, like, trying to, like, explain, like, what is missing and what is good about it. It also, like, makes this, like... few recommendations of the product direction. So I do this or that on that. So it helps to make this something that is quite complicated into some kind of actionable thing. And we can talk about this as a team or something. But similarly, more like a micro example is that if there is like, I want to make a new theme, like new dark theme. What's a theme? Themes are just like in our app. So you can have like a theme. Oh, okay. Got it. Yeah, yeah. Like the way it looks. Yeah. So maybe I want to create a new version of like a dark theme, like make it just black. So I can now task a coding agent on it. And it can look into the code base and it can try to understand the code base, obviously. And then at first it's turning it into an issue and then delegating it into... uh, into an issue. So, so I created this issue it's in progress, it's still located to linear. And then now like linear starts working on it. There's a, it's spinning up the, um, the sandbox for it. And I think one of the benefits of the issue is now people know I'm doing this. So the team knows I'm doing this. And I can say, hey, no, I'm like, FYI, I'm doing this. So he can also come here to look at this, what is happening. And then the thing is this... Agent session is visible to everyone. So it's visible to me and to him. So I can call once it's, it will take a while, but once it's like, does it, we can both jump into this chat and tweak it together if we want to, or just kind of see what happened here. So it's similar to what you do on your computer, but then now it's happening in a shared context. And there's more understanding, where did this come from? Okay, it came from me. This could come from a customer discussion. The shared context is interesting. So two people can be in the same chat? Yeah. Yeah. So I know that's really cool. I don't have NON 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 it, NON who is our head of product and like Connor who is our head of design, they both like, We're working on some tweaks on the inbox, so they could go back and forth. Like a PM and a designer could go back and forth. It's like, no, it's not quite right. Like, let's fix this thing. And then they could both see the preview link. Let's see if I have. something here. Okay, so there's one like my previous pull request. So we will have pull request here. You can kind of see the activity, but you can also see the code. So you see the code divs and then if you want to comment on it or you want to work on this code with the agents, like, no, this is not right. And then I can work on it. And similar workflow works for code reviews where engineer might come in and say like, this is not right. They could just task the agent to fix it versus telling the other engineer to fix it. So I think it kind of collapses the cooperation loop a lot more and allows multiple people use the agents to work on one thing. And then here, this is only a backend change, so there's no... client review I can do. But 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. That's interesting. But yeah, those are a few things we're like adding. I'm curious about this... 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 exist to some degree somewhere else. And obviously there are things that you can do differently. So you can have multiple people in the chat. You can, it's, it's more plugged into linear more generally, but it's, 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 going as hard and as fast as they can to build AI coding agents? Yeah, I think there's definitely the question we 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... What we see the value is just kind of like sitting upstream where that 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 bugs 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 and so it doesn't like maybe work for all kinds of situations it's not 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 um that's really interesting but then like yeah generally we've been thinking about the problems like we don't want to be a kitchen sink product like we do everything for everyone and like sometimes companies end up in the state because you have the enterprise buyers and you have the checklist and You just need to get the checkmark into the right spot on the checklist. And we don't think it's that those things like don't create like a good product experience. So the way we always thought about it and... build the products, it's like we try to feel like what is like a natural next step in this workflow. So if we go from an issue, like it's like, yeah, someone likes a natural next step is like someone needs to fix this. And so like, how do we help people to fix it faster? So one option is like we do this cloud agents and then they can fix it. But now the cloud agents does this stuff. Like, how do you know it's like good? So then you need to see the code. Like you need to see the diffs and like you need to run the builds and like whatever. So 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 kind of 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 are kind of like trying to like find this optimized workflow for people to do certain kind of like product things. So we're almost out of time. My last question for you is... If you had to project how product development will change over the next five years, let's say, what will be different? And also what will be the same? I think that the one difference, I think there's going to 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 So you could have a common workflow. What we do is we have projects going on. It's like a project is often a feature, a part of the interface, or part of the product. And then we have a lot of feedback and requests and things coming in. I think there's opportunities to turn that into more like the product or that feature is kind of like an agent itself. And it kind of like tries to make decisions based on the input it creates. And then 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 the solution seems to be like potentially something that works for people. And I built a made up. build, I send it to some customers and they say the feedback is good. So it's kind of like it does things on its own based on some kind of context and like a rule-based system or some kind of guidance. And then I do think like... the thing I'm still like, I think people should still think even in this world where 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 to, like what is worth doing and like, what are the areas we should be doing and, I think it's... So there, I think a lot of this still... Humans having meetings or discussions or writing issues or writing documents. I think it's reading documents. There's still going to be a place where humans need to understand this stuff too. You can't just outsource... the thinking purely to the AI agents. 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 thinking into actual like these autonomous things. So 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. Maybe I don't want to believe in it, but I think it's, I think things will change like that. The roles will change. Maybe there's some like, movement around exactly what does engineering do, how many engineers we will need, and what is the job in the future. But I still don't see how the agents, how the AI actually does all the thinking and the choices or decisions. I think product building is still kind of like a craft or an art. A lot of times we... We talk about intuition. We just decide things based on 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 AP testing and data-driven product development, which I think could work well for agents, but I think it doesn't work for all kinds of products. 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 i love it kari thanks so much for joining yeah thanks for having me this was great Oh my gosh, folks, you absolutely positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. 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