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

[00:00] 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 [00:09] business for this era because [00:11] It's still sass. [00:13] You're the one who has this sort of sticky... [00:16] 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 [00:37] 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, [01:01] This very kind of like hush hush, you couldn't get access to it. But everyone, if you knew, you knew. [01:07] that Linear was 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. And I've always admired the level of taste and craft that you bring to what you build. And also, I think the level of [01:29] thoughtfulness and patience that you build it with. And I think that's one really interesting thing is the

[01:36] the way that you built the company [01:38] originally was [01:40] to keep it closed for a while, not raise too much money, not put too crazy expectations on the company, um, [01:49] and be patient and willing to build some equality over the long term. And I think that that also has something to do with how you approach AI. [01:58] You guys... [01:59] 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. [02:19] You've successfully transitioned the product to be really agent-native, [02:23] 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... [02:31] that transition for you, what was that like? [02:34] emotionally, [02:35] to have built this product for a particular way of [02:39] working in a particular way of building software and then [02:43] see the world change, but maybe not be totally sure if 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." Talk to me about that. 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 [03:06] companies move work forward and often build software products. And 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

[03:25] these product teams or like figuring out things to do or like figuring out when to do them and let the product teams or 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 [03:51] kind of like use their craft or like use their like taste or thinking or something in it. But yeah, I think like we... [03:58] Do you have-- I personally always have this problem, a way of addressing problem, which is like, I come from a design background. So a lot of times, the way I approach things is first, I'm trying to understand them. So this sounds kind of obvious, but then I think what happens in the tech world a lot of times is 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? [04:27] So that was kind of like our thinking with the early AI and the chatbots. Every company is rushing into this moment as 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. How do you actually use... What is the workflow where you would actually need this or use this? So we spent all this couple of years now trying to understand these workflows. How do people

[04:57] Like we did. [04:59] A couple of things well though, I think that we released this 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. [05:11] 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... [05:30] We saw this world that I don't think there's going to be one agent, but everyone will have many agents. And companies will build their own agents, which we're now seeing with 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... [05:49] guiding the agents and building this context. But we don't try to own everything in this world or in this market. We can play [05:58] with other companies too. So I think like, yeah, like the approach was much more like, how do we like, [06:05] 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, 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, [06:24] 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.

[06:54] and then used as part of the AI building workflows. So, because I think once that, [07:00] AI builds more and executes more. [07:03] that kind of like the problem with becomes like, how do you productively harness this? [07:08] in a good way. You can task a million agents doing something, but what are those things they should be working on? [07:17] 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. [07:39] There's a... [07:40] Interesting. [07:42] 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. [07:49] um [07:51] And I think you're pointing to something really interesting, which is [07:55] this dynamic of [07:57] A couple years ago, a lot of companies, including a lot of SaaS companies, rushing to do 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 [08:07] doing something. And I think the public markets are now starting to look at that and require that. And I imagine [08:17] 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,

[08:27] from yourself or maybe internally to do something, um, [08:32] And you kind of... It seems like you... [08:36] waited until you've [08:38] you had the fat pitch. [08:40] and [08:42] I'm curious, [08:44] 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 we really need to launch an agent platform or whatever, you know? [08:59] Yeah, I mean, I think we don't really have a pressure from investors. That's one benefit of [09:05] picking the right investors and also like they trust us to like make the right calls and then also we obviously did talk about this but then we also like have that discussion it's like we just don't see [09:16] 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 was definitely like internal pressure. It's like, [09:28] And now I think that the [09:31] The speed of the market has... [09:33] picked up a lot, like every... [09:35] month or something like a couple of weeks, there's something changing and we are like tracking those changes. [09:41] and kind of like 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, [09:49] 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, you shouldn't like, I think like,

[10:02] Those things are like signals that you should read and understand. But you also need to know that... [10:08] a lot of this stuff is not tested. And a lot of times people are also testing these things, so 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 make these predictions of how things are exactly going to change. I think on the SaaS narrative, [10:31] I do think it's probably directionally correct that [10:35] 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 like 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 white coat their own CRM tools and I don't think that's exactly going to happen but I think like what might happen is like there's new companies that come out or [11:05] the public companies are not the most [11:07] Like... [11:08] I don't know, flexible or like the most robust solutions out there. They are the [11:14] 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 [11:22] Yeah, I think the public companies probably get hit the hardest here because they have... [11:28] their modes are 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

[11:46] 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 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 an easy task. I think their growth companies or startups can do it a lot better. [12:16] How big is the team now? About 120 total. I think I would say like a... [12:21] 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 [12:51] what did you learn in that process? [12:54] Yeah, I think, like, there was definitely a time in a company to, like... [12:58] We had to... [13:00] encourage people to use these tools more i think there's always that [13:04] 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, [13:16] 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. Now, 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?

[13:46] 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, [13:53] metrics, you need some kind of counterbalance, like what is actually the quality of this work, and is it actually meaningful? [13:59] And I think that's like, also, I think what's playing out in the market is like we have large companies that are [14:06] token sellers. And then like when, when, um, [14:10] You have a lot of incentives. Your business model is to spend more tokens. And our revenue will be higher. And our market share will be higher. So I think there's a lot of incentives saying people. You should spend more tokens. And not saying. Well, you should think about things. And spend it well. [14:30] So I think there's. [14:32] Again, I think people are maybe looking at it too simplistically or kind of like, oh, there's a good thing if we just like, [14:39] like spend more tokens, things will be better. But I don't think that's ever been the case in [14:44] in building products. Like, yeah, there's some [14:47] some 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. [15:03] 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?

[15:17] 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 metrics 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. [15:47] 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, [16:03] is our product actually improving? Do we have any indication of this product is actually improving? Do we get comments on the new features? Is there less bugs? And 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, [16:25] with the agents and AI, it's almost like, [16:29] 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. [16:45] 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...

[17:03] doesn't like it or there's some changes they want to make, they can do it also now inside linear. And they can review the code in linear. So there's this-- [17:12] very good workflow for now, but I think it still starts from the fact that do we [17:18] care if we are... [17:19] product is buggy or not. And we have made the choice. We think bugs are bad things or mistakes. And we should fix them as quickly as we can. And that's a priority to everyone. [17:34] So I think it's still a choice if you care about the quality of the output or you are just wanting more of the output. [17:42] 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? [17:54] Yeah, I think on the product side, I think it's definitely a lot better. I think it's, I have, with Linear and LL, I have this skill where it's like, 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, I tell it, okay, look at this. [18:19] help me understand this feature request. We collect this feature request in inside linear. And then, for example, there is a request like multiple assignees per issue. It's requested by lots of people, like hundreds of people. And so I can kind of tell it to go synthesize, help me understand what are the different...

[18:38] reasons people want it. So it kind of starts with explaining the [18:44] problem like trying to understand the core problem which is usually what i want to know it's like so this helps me like [18:51] When I see a new request, I might go into linear and say, oh, do we have this kind of request already? And then help me understand it. And then it helps me give an understanding, which then helps me potentially, should we actually tackle this now? Or is this something we could do later or maybe never? So there's that... [19:11] 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... [19:25] personally use it much. I actually like the manual design process. I still have Figma open and then when I have a [19:33] problem or idea. I just draw it in there. And to me, it's like, [19:39] Yeah. [19:39] 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 [19:55] check on yourself. It's like, why am I doing it? Why am I drawing it this way? Or should I draw it a different way? 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

[20:09] quite robust like [20:11] build system so you can actually build it into that. You can make a VR and then it will run [20:19] the build and you get the preview link to the build and then you can use it live in the [20:24] in the product. So it helps the testing it [20:29] or the prototyping stage of it. But I still tell the designers to explore more freely in Figma first or wherever, and try to think about how you approach the problem. It's like, let's just jump into doing it. There's projects like that too, where it's very clear what needs to be done. But then if it's a bigger project, I think they should still spend that time. And then the engineering side is probably similar to [20:55] A lot of other ones that were... [20:57] we can kind of like fix problems a lot faster once we identify them and like decide to do it. We use the 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 like come back to it later and like actually make it actionable right away [21:27] and then we start a project and then we start like assigning people so i think there's like [21:33] 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... [21:39] some kind of loop there.

[21:41] and making it faster, you can do the thing right away versus waiting. [21:46] waiting for 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. [21:53] Which is interestingly... [21:56] sometimes it seems like you're the exact opposite of your [22:00] preferred [22:03] outlook you know actually we shouldn't do things faster actually we should [22:08] take things a little bit slower. [22:11] How does having tools that make you go much faster... [22:15] interact with that outlook. Yeah, and I think it's a good point. I think it's more like [22:22] I think we shouldn't go fast in deciding things or just kind of speed running the decisions or not even doing decisions. I think there's this [22:33] Some people do it now where they just like have an idea, then they build it. And now we're like, [22:37] 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 a... [22:44] every new prototype or idea can kind of like seem useful. [22:49] But then like, you now like don't have like a good way of like, [22:53] framing 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 like [23:06] DC Shen, [23:07] making way. We don't have like a lot of processes in linear, but it's more like

[23:13] 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 will be fast to actually work on the problem. [23:25] but I don't want the problem [23:27] finding to be fast. You should take the time to find the right problem and the right approach for the problem. And then once you decide that, then you can go faster on it. [23:57] has touched it. Dialect is a new system from Scale.ai 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, [24:14] 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/dialect. That's scl.ai/dialect to learn more. While I'm doing that, [24:32] Back to the episode. One thing that... [24:35] what you're saying makes me feel is... [24:38] 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.

[24:46] 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 what you're saying different from that? Or is it the same, just like said differently? [24:59] Maybe it's different, but I can see that workflow. I feel like that workflow is kind of like... [25:04] kind of like understanding, like you're trying to understand like what you're doing. And 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, [25:18] You kind of like don't know. Are you... [25:21] 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 it's not like a [25:30] 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. 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? Well, I think like 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, [26:00] like rethinking some area completely and that's like a concept like it's not 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 [26:14] Like sometimes people... [26:16] I don't know. This is partly like a large company thing, but I think it can happen in small companies too, is that...

[26:23] Once you see something very different, [26:26] your fears might start. [26:29] coming up like, well, if we change this, like what else is going to happen? What's going to break? 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, [26:43] Do we think it's like important enough for something to like... [26:46] take it further and then deal with the like the problems later so it's kind of like you're kind of like [26:52] trying to divide like [26:54] the decision, which decisions you're making now. And I've used it in our company. I just completely rework the surface and say, "Hey, I think the project should look like this," which is completely different from what it currently is. And then people are 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." And it's a way to... And it's maybe like a figment design or a prototype. So, [27:20] It's just like... [27:22] I think there's [27:24] like even with all this tooling like the output shouldn't always be like we ship something like it sometimes the output can be something internal that like hey we're just [27:34] Now we have a better understanding of this problem. We can tackle it better. And we can actually make it into a shipable thing. But we first try to... [27:44] think about it before doing it. Right. [27:46] 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 talking about it, that kind of stuff. Yeah. And something I did have to share with the company recently was that

[28:04] 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 is kind of like what we're trying to like [28:14] like decide. [28:16] Sometimes now with AI, it's actually hard to tell. It's kind of like... [28:22] If the tooling changes all the time, like people, like the LLMs are not deterministic anyway, like you don't always know like, [28:31] 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. [28:41] and you put it into some kind of beta or something. So I think there's definitely nuance to this right now that [28:49] There are situations where it's just like, and it's always with product building, there's a limit how much you can like, [28:55] Think about it. [28:56] 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. Now we've 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 like, [29:18] something else, like not to try to ship it as fast as we can or something. Like we should be honest about like what is the actual... [29:25] goal for this stage. [29:27] 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...

[29:37] 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 product strategy and your vision for what a good product is? [30:01] Yeah, I mean, I would say we are now adding 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 VM 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 [30:27] code with the agent. Interesting. You can see the diffs online, so it's kind of like a cloud... [30:35] conductor environment where you can kind of like see the changes and you can kind of you can guide it and we think like [30:42] FIF. [30:43] The strategy is definitely like... [30:46] changed and we are just trying to understand what are the problems out of today. We think [30:54] One of the things is like, [30:56] What is changing is that I think historically people thought issue tracking is this [31:01] kind of like a [31:02] Like it's like a ticketing system for the kitchen, like, and like, 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.

[31:21] linear is more like the backbone. We rely on collecting signals and collecting problems or collecting decisions like we should do this thing. So I think [31:30] I think there's definitely a shift we have to teach people. These products are really meant to improve your team's workflow, not to be this weird ticketing system for different parts of your organization. And that's kind of-- [31:44] probably going away with the agent. So you don't need that anymore. The agent can do those tickets and they can also complete them. But we think there's still... [31:55] value of like [31:56] collecting that context and make the shaping that work something actionable and providing agents like, [32:04] good contacts from the environment. [32:07] But the one lesson we learned with that, [32:11] uh, [32:12] with the agents is that it's [32:16] It's tough when we are not [32:18] ourselves in control of it. Like 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, [32:28] We can't do it. It's on them to do it. So now, one of the reasons we are doing this coding agent is we actually think we see this a lot more smoother end-to-end workflow where you start your... [32:41] 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, [32:46] "Hey, does this thing exist already?" [32:48] Or if not, make an issue, make a work stream out of it, and then start working on it. And then start writing the code. And then you can see the diffs coming in. You can review it. And you can merge it. Or you can see the prototypes. So it's just trying to--

[33:04] you... [33:05] One of the problems I see when I use this [33:08] like Claude or ChatTDB or some of these tools or Codex is that I have to really explicitly tell the agent or the tool always what context to bring. And then I think the value with linear is the context lives there. And then if we inject it smartly part of the work stream, it's much more [33:31] natural, we can design the flow that makes sense and we don't spam the context windows or something. [33:39] And I think we see this feature as like, you probably have this [33:43] Linear is kind of like the... [33:45] 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 [33:58] like you should just automate some of the bug fixes, or you should automate-- [34:02] the small task and like just do it in linear and then like kind of like let it run in the background [34:07] while in a sandbox while you like [34:10] run your own work and your own [34:12] computer or somewhere. [34:14] That's really interesting. [34:16] Um... [34:17] I think from a product strategy perspective, [34:20] I'm really curious about the decision to integrate your own agents. [34:24] Because I'm [34:25] Before we did this interview, I didn't know about the linear agent. [34:28] And I was sort of sitting here thinking, wow, this is the perfect... [34:32] business for this era because it's,

[34:36] Still sass. [34:37] 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. [35:00] 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 [35:20] changing your margin profile that much. I assume [35:24] 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. [35:36] 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... [35:45] calculations or thinking on it. I think on the coding agents, we do have to offer usage-based billing because it can get very expensive. On the basic linear agent functionality, that's like [35:58] answers questions for you, that should be more included into the system. [36:05] we'll have a lot more like we'll have to see like how

[36:09] much that usage actually is. But linear is still always going to be just a fairly focused platform for certain kind of things. Like you shouldn't be running-- [36:19] random things here. I think it should be still pretty [36:23] clear what you should be doing inside linear and what kind of workflows are you running there, or workloads. So we're not trying to build this very generic agent platform. It's more like the [36:38] 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. [36:51] around your product and it's kind of like an API into the product thinking versus like, [36:58] using like [36:59] more of the normal tools where it's like you always have to tell it to go fetch this thing, go find this thing, because it doesn't have any understanding, like, what do you generally do? Or [37:11] sort of context that might be existing already. [37:14] Can we see a demo? I'd love to see it. Yeah. [37:18] Um, [37:23] Is the screen sharing okay? [37:25] Yes. [37:28] All right. Yeah. So what we have coming up, like this is actually my real linear instance. And what 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,

[37:50] do the work there. But for example, the one-- [37:54] We will have skills and the skills, we will have guidance. [37:58] 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 [38:11] So I want to like... [38:15] understand this problem of like multiple assignees. So I made the skill which is like essentially like [38:23] um, [38:25] 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 [38:36] way it goes through the problem. And so I made this to kind of like [38:42] Um, [38:43] help my workflow, like some just quickly and trying to understand sometimes this feature request. So I can like to do it like, [38:52] Well, let's do the multiple workspaces. So we have this like... [38:57] collection of stuff about like multiple workplaces and then um it can kind of like [39:03] go through it there. There's probably many different requests, and it will try to start-- [39:11] thinking through it they will look into the customer activities they will look through the different things um [39:17] What model is it under the hood? [39:19] I did...

[39:20] I think we'll eventually apply multiple models, but now we use Claude. [39:24] called for this. [39:27] Sonnet or Opus? [39:29] I don't actually know. [39:33] Um, [39:34] So it starts going through, it's like, okay, like there's a... [39:37] There's a real need, but it's more complicated than it sounds. [39:45] 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. [40:00] And so they would want to divide the workspace more [40:07] But, um, [40:09] they might still have some kind of like overarching control [40:13] So it goes through the kind of like trying to like explain like what is, what is missing and what is good about it. It's also like makes this like, [40:20] few recommendations of the product direction so i do this or that on that so it's like it helps to like kind of like [40:26] Make this something that is like [40:29] quite [40:30] 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. [40:38] But similarly, more like a micro example is that like, [40:43] If there is like, I want to make a new theme, like, [40:49] 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, I'm sorry. Yeah, yeah, like the way it looks. Yeah, yeah. So maybe I want to create a new version of like a dark theme, like make it just black.

[41:05] Thank you. [41:07] Okay. [41:09] So I can now task a coding agent on it. [41:13] And it should... [41:16] start looking into the code base and they can try to understand the code base obviously. And then at first it's turning it into an issue and then like delegating into [41:26] uh, [41:28] into an issue. [41:30] So I created this issue, it's in progress, it's delegated to linear and then now like [41:35] Linear starts working on it. It's spinning up the... [41:39] um, [41:40] the sandbox for it. And like, I think the broad, like kind of the, one of the benefits of nature 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, [41:52] FYI, I'm like, I'm doing this. So he can also come here to look at this like, [41:57] what is happening like and then the thing is like this [42:01] 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. [42:14] and tweak it together if we want to or just see what happened here. [42:19] 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 an understanding, like where did this come from? Okay, it came from me. If this could come from a customer like discussion or- The shared context is interesting. So two people can be in the same chat? Yeah. Yeah.

[42:40] So I know-- That's really cool. I don't have none ready to demo this. But we did have this instance kind of accidentally, we noticed this is actually useful sometimes, is that it [42:52] Uh, [42:53] Anon, who was our head of product, and Connor, who was our head of design, they both were working on some tweaks on the inbox, so they could go back and forth. A PM and a designer could go back and forth. It's like, "No, it's not quite right. Let's fix this thing." And then they could both see the... [43:10] the preview link. Let's see if I have [43:14] something here. Okay, so there's one like my previous pull request. So we will have a 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... [43:34] not right. Like, [43:35] And then I'm like, [43:36] work on it. And similar workflow works for code reviews where engineer might come in and say, "Oh, [43:44] this is not right. They could just task the agent to fix it versus saying... [43:49] like telling the other engineer to fix it. So I think it's like kind of collapses the [43:53] collaboration loop. [43:55] A lot more. [43:56] and allows like [43:58] Multiple people use the agents to work on one thing. [44:03] And then like here, [44:05] This is only like a backend change, so there's no like... [44:08] client review I can do but if I had if this would be a client facing thing I could kind of like

[44:14] open the preview link and then like actually see like how does it look live [44:18] That's interesting. But yeah, those are a few things we're adding. [44:23] I'm curious about this... [44:26] One interesting thing about this. [44:27] is it seems to... [44:31] increase the surface area of the product a lot. [44:34] It's about a lot of different things that... [44:38] already exists to some degree somewhere else. [44:41] And obviously there are things that you can do differently. So you can have multiple people in a chat. [44:46] It's more plugged into linear more generally, but... [44:50] Um, [44:51] 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 in, [45:00] 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. [45:08] AI coding agents. [45:09] Yeah, I think there's definitely the question we keep asking ourselves, like, what is our advantage or unique advantage here? And I think we... [45:19] I think, honestly, I don't think we will solve all the different coding needs, but we don't necessarily also have to. I think it's what we see the value is kind of like sitting upstream where the work is coming from. [45:33] There's really 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, even delegated to the agents. Engineers never even see them. Or if they see them, they see them once there's a fix already being built. And so it doesn't maybe work for all kinds of situations. It's not the agents you go to, "Hey, build me a new product." We don't think that's where we should be.

[46:03] 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. [46:10] reduce that workflow for you automatically. And then, yeah, you can use the other coding agents to do other kinds of work. But this is where we kind of focus on. That's really interesting. But then, yeah, generally, we've been thinking about [46:22] The problem is 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 then like, 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... [46:42] product experience. So the way we always thought about it and [46:46] 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, 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 you, 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, [47:16] and like need to run the builds and like whatever. So... [47:19] It's like we are more always focused on the workflow and how do we... [47:25] like improve it? Like how do we make the, kind of like help companies to output [47:32] better and faster versus trying to own every surface. So we don't have to own every piece of the surfaces, but we are trying to find this optimized workflow for people to do certain kind of product things.

[47:46] So we're almost out of time. [47:49] My last question for you is, [47:53] If you had to project how product development will change... [47:58] over the next five years, let's say? What will be different? [48:03] and also what will be the same. [48:06] I think that the one difference, I think there's going to be more of this self-driving aspects of you can set up. [48:14] 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 [48:23] A common workflow, what we do is we have projects going on. 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 the features. It's kind of like an agent itself. [48:45] 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... [48:57] these kind of, I seeing these patterns and these patterns pointing to this solution and the solution seems to be like potentially something that works for people. And I built a made up, made up, [49:07] build, I send it to some customers and they say the feedback is good. So it's kind of like it [49:14] gives you like it does things on its own based on some kind of context and like a rule based system or some kind of guidance.

[49:24] And then I do think like, [49:26] The thing I'm still like, I think people should still think even in this world where [49:31] agents do some of the thinking and like does run automatically to some degree. I do think like it makes [49:39] people have to be a lot more explicit, like what do they want? Or what is worth doing? And what are the areas we should be doing? [49:48] I think it's, [49:50] So there, I think a lot of this still [49:53] humans having meetings or discussions or writing issues or writing documents. I think it's like reading documents like that. There's still going to be like a place where humans need to like understand this stuff too. Like you can't just outsource. [50:06] the thinking purely to the AI agents. [50:10] 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 [50:25] strategies or or thinking into actual like [50:28] these autonomous things. So I think like... [50:32] I personally... [50:34] I don't see the future in a way that we are replacing humans and I don't quite... [50:41] believe in it. Maybe I don't want to believe in it, but I think it's [50:45] I think things will change, like the roles will change. Maybe there's some like... [50:51] movement around exactly what does engineering do, how many engineers we will need, and what is the job in the future.

[50:59] But... [51:00] 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... [51:13] We talk about intuition, like we just [51:17] 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. And I also think the best products are not necessarily like [51:43] being built that way. Like you still need the human kind of [51:45] bunch of like [51:47] what is interesting or what would make this good. [51:51] I love it. [51:52] Kari? [51:53] Thanks so much for joining. Yeah. Thanks for having me. This was great. [52:04] Oh my gosh, folks. You absolutely, positively have to smash that like button and subscribe to AI&I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard. But instead of gold, it's filled with pure, unadulterated knowledge bombs about chat GPT. [52:27] on the edge of your seat. [52:28] craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship.

[52:36] So do yourself a favor. Hit like, smash subscribe, and strap in for the ride of your life. [52:41] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.