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Get a DemoWhat changes when a single marketer creates as much value as an entire department? Reza Khadjavi (Founder & CEO, Motion) joins Connor MacDonald (CMO, Ridge) and Cody Plofker (Advisor & fmr. CEO, Jones Road) to explain why he built Runneth, an AI agent meant to eventually replace Motion’s own product. He walks through the context engineering behind that shift and what it means for marketers. Motion scrapped its own revenue targets right after a record quarter, betting the whole company on AI agents. Reza explains how markdown files and organized repositories turn those agents into something closer to a new hire. His advice: skip the Mac Mini, move to the cloud, and put close to 10% of payroll behind AI. Powered By Motion https://9ops.co/motion-runneth Proppel https://9ops.co/weareproppel Aftersell https://9ops.co/aftersell-mops Haus https://www.haus.io/operators NeonPixel https://9ops.co/neonpixel Richpanel https://9ops.co/richpanel Operators Portal https://portal.9operators.com/dashboard Operators Newsletter https://9operators.com/ Chapters 00:00:00 Runneth Vs Motion 00:05:31 What Runneth Does 00:10:51 Teaching AI The Job 00:17:02 Why Markdown Files 00:24:23 Where Work Is Headed 00:30:50 Something Different 00:39:45 Agents Working 24/7 00:49:34 Legacy Firms Cooked? 00:58:01 Craft Beats AI Skill 01:04:47 The Judgment Edge 01:08:24 The Marketer Payday
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We got a very, very special guest today. I think back for the second time, one of the friends of the pod now, Reza from Motion. How's it going, man?
Very good.
Very good. Good to be here, guys.
Reza from Motion. At what point does it become like Reza from Motion and Runneth, or is Runneth a subset of Motion?
How are you?
Are you, are you as a holding company now? Like, how are you thinking about that?
You know, maybe, maybe this is breaking news here, but I suspect by 12 months from now, it's only Runneth.
Okay, there we go. See, I was— we were looking for the hot takes. I'm glad we got one 15 seconds into the episode.
Yeah, you went, you went straight for it.
There is no question in my mind that obviously people love and use Motion. We have, we have no, no plans to like sunset that product anytime soon, but I, I suspect within 12 months, none of our customers will use the core product and they will do everything they love to do inside of Motion. They'll do it through Runneth and they'll be 100 times better.
Is that like a terrifying feeling as a CEO to wake up and be like, hey, I think our business model, our, our main product is, is going to be obsolete and not a thing. Is that like an exciting thing? Like, cause for me and Connor, right? And I'm not that I'm at Jones Road, but like for, for a consumer brand, like I'd be terrified if like, you know, no one needed makeup or wallets in a year from now.
Yeah, so, you know, part of the raise was obviously this idea that we have to build into AI. I think the thing that it wasn't clear was like, do you just bolt AI on top of a core product? And I think many SaaS companies tried to do that and like, it just didn't work quite well. And so we knew going into the raise that, a big part of our roadmap was going to be AI, but I think very quickly it was like, no, no, no, it's not like we need to add AI features. It's, it's that we, we have to reimagine the entire company from first principles thinking about AI. And, um, and one of the things that was concerning about it was like, it wasn't clear to me how much the market itself was going to change, how much like the core principles of the problems that we set out to solve. with Motion, how much of that was going to be in flux. But thankfully, like the core thesis of Motion is like more and more true every day where, you know, there's this special type of talent, the person who is equally creative, equally data-driven and building. That's kind of who we built Motion around the first time. It's like that individual is going to be a really important part of the marketing org. And I think when AI happened, like that same person is still a really critical profile in the marketing org. And so the core premise of the business never changed. The only question was like our approach to technology. And I'm happy we raised the capital to be able to like have the time and resources to do what needed to be done. But like we basically, we looked at the entire product from scratch and basically rebuilt all of it as if we were competing with ourselves. The idea was that like if a newly minted seed company were to look at this problem with fresh eyes and a Codex subscription, like what would they do? How would they tackle this problem? And so that's what we did. And the synergy though between like Runneth and Motion is like, it's so one-to-one that I don't feel like we've taken the business in a different direction. It's just, Everything that our customers would expect us to do from an AI standpoint is like baked into how we're thinking about Runneth.
So did you raise 'Cause you raised a lot of money. Did you raise specifically for this or were you like, hey, we have this money now, let's go and use it on this?
Yeah, so if you think about, Motion was solving a problem within one part of the flywheel, which was like, the creative workflow is you launch some stuff, you learn from it, you do research, you launch more stuff, and like you do that in a flywheel. And Motion's core offering was around like understanding performance data in a way that creative people could like visualize it. So it wasn't just like pure data and numbers, but fundamentally it was around like finding insights and signal among your data, right? So like when you're doing competitor research, when you're researching TikTok, when you're like doom scrolling creators, when you're looking at your own performance data, all of that are like inputs into context that you will process as a creative and marketing team. And in some ways, like, uh, the software industry is like always a couple steps ahead of like the rest of knowledge work. But a lot of people in software are thinking about, um, like the idea of a software factory where you have a bunch of like bugs and feature requests and ideas and like things like that that come in as inputs. And then you have the throughput of the factory, which is actually like building the stuff and then quality control and then shipping those features. The way that we do knowledge work, both in like software engineering and marketing, kind of resembles like an assembly line of a factory, but obviously it takes exceptional judgment to make good decisions across that. But the fundamental job is like, how do we take inputs, process them through our own judgment, and then create outputs that we hope will do better than the last ones? And so like that, that was the problem that Motion solved. And with Runneth, it's like, it's the whole thing. It's not just like the performance data. It's like running the entire system of the way we make decisions, the way we process context. And most important, like the way we compound learnings over time. I think that's the missing piece that was very difficult for us to put into motion. I remember one of our earliest customer conversations, like 5 or 6 years ago, we talked about this idea of like, how can you memorialize your learnings? How can you make it so that every turn of this flywheel, we've processed inputs, we've made decisions, we've shipped stuff. How do we compound those learnings over time? And I think, you know, people had like spreadsheets and whatnot where they would try to like organize their learnings, but it was fundamentally an unsolved problem. And I think that's the difference with AI. You can really start to compound learnings in a way that was just not possible to do before. And so same problem, but kind of just looking at it from an AI-native standpoint and saying, do we really need to load a bunch of dashboards all the time in order to get signal? Or could we articulate the signal that we're looking for? And have AI like look out for those and then surface them. Or, you know, sometimes when people use a motion report, they're like creating a new report, then they're applying a date range filter, then they're filtering for some campaigns. Like really what they're doing is like they're asking a question and they're trying to find the response to that question through like point and click the dashboard and like visually consume the information. But why not just ask the question? What's the difference between this campaign and that campaign or this visual format versus the other one? Or like, that's like probably the first fundamental difference is instead of like point and click, build a dashboard, ask questions in natural language in Slack with your team. So I think it's just like start, of the things I think about AI is that it, it just starts to become a lot more like the way that you collaborate with a human team member and less so a software product that you have to like build workflows around.
Maybe we could take like a very quick step back because everybody's been seeing the Runneth bot on X. Everybody's reading the X posts. Um, but like fundamentally you say you built Runneth in a way that competes with the core product motion or that you guys were competing against yourselves once you built it. Maybe you could just like briefly describe like what is the work that's getting done within Runneth? How is it sort of an extrapolation of what was getting done in Motion originally? And then where does it differ?
Yeah, so if you think about, Motion was solving a problem within one part of the flywheel, which was like, the creative workflow is you launch some stuff, you learn from it, you do research, you launch more stuff, and like you do that in a flywheel. And Motion's core offering was around like understanding performance data in a way that creative people could like visualize it. So it wasn't just like pure data and numbers, but fundamentally it was around like finding insights and signal among your data, right? So like when you're doing competitor research, when you're researching TikTok, when you're like doom scrolling creators, when you're looking at your own performance data, all of that are like inputs into context that you will process as a creative and marketing team. And in some ways, like, uh, the software industry is like always a couple steps ahead of like the rest of knowledge work. But a lot of people in software are thinking about, um, like the idea of a software factory where you have a bunch of like bugs and feature requests and ideas and like things like that that come in as inputs. And then you have the throughput of the factory, which is actually like building the stuff and then quality control and then shipping those features. The way that we do knowledge work, both in like software engineering and marketing, kind of resembles like an assembly line of a factory, but obviously it takes exceptional judgment to make good decisions across that. But the fundamental job is like, how do we take inputs, process them through our own judgment, and then create outputs that we hope will do better than the last ones? And so like that, that was the problem that Motion solved. And with Runneth, it's like, it's the whole thing. It's not just like the performance data. It's like running the entire system of the way we make decisions, the way we process context. And most important, like the way we compound learnings over time. I think that's the missing piece that was very difficult for us to put into motion. I remember one of our earliest customer conversations, like 5 or 6 years ago, we talked about this idea of like, how can you memorialize your learnings? How can you make it so that every turn of this flywheel, we've processed inputs, we've made decisions, we've shipped stuff. How do we compound those learnings over time? And I think, you know, people had like spreadsheets and whatnot where they would try to like organize their learnings, but it was fundamentally an unsolved problem. And I think that's the difference with AI. You can really start to compound learnings in a way that was just not possible to do before. And so same problem, but kind of just looking at it from an AI-native standpoint and saying, do we really need to load a bunch of dashboards all the time in order to get signal? Or could we articulate the signal that we're looking for? And have AI like look out for those and then surface them. Or, you know, sometimes when people use a motion report, they're like creating a new report, then they're applying a date range filter, then they're filtering for some campaigns. Like really what they're doing is like they're asking a question and they're trying to find the response to that question through like point and click the dashboard and like visually consume the information. But why not just ask the question? What's the difference between this campaign and that campaign or this visual format versus the other one? Or like, that's like probably the first fundamental difference is instead of like point and click, build a dashboard, ask questions in natural language in Slack with your team. So I think it's just like start, of the things I think about AI is that it, it just starts to become a lot more like the way that you collaborate with a human team member and less so a software product that you have to like build workflows around. It— AI should feel like a labor force at your company.
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Yeah. And so like, you know, I remember when I was like, when a lot of the AI change was happening, like my first instinct was that like, I need to be deep, deep, deep into the weeds myself, which is kind of where the chief marketer, like doer-in-chief leader idea comes from, is that I think it's very hard to do this stuff without being like deep in the weeds. And one of the first things that I built with AI was this repo on my local machine called research. And what I wanted to do with that was that like, I felt like a lot of my job was just scouring for signal, right? I was like a context machine. I'm just looking at signal. I don't really know how I'm gonna use it yet, but I'm just collecting signal from Slack, from customer conversations, from talking to our team, from Twitter. And I'm just like consuming a lot of context. And then there's a synthesis, synthesization that happens somewhere where I'm connecting the dots between all the context that I've accumulated. And one of the first things I wanted to do with AI was that, can I like 10x or 100x the amount of context that I consume? Right? Because I felt like when I consume high-quality context, I make higher-quality decisions. And I only have a certain— it's funny how similar we are to AIs. Like, I only have a certain context window myself, right? Like, if I consume too much, I'm going to hallucinate. And so I need to be careful.
Yeah.
Yeah. And so like, you know, I remember when I was like, when a lot of the AI change was happening, like my first instinct was that like, I need to be deep, deep, deep into the weeds myself, which is kind of where the chief marketer, like doer-in-chief leader idea comes from, is that I think it's very hard to do this stuff without being like deep in the weeds. And one of the first things that I built with AI was this repo on my local machine called research. And what I wanted to do with that was that like, I felt like a lot of my job was just scouring for signal, right? I was like a context machine. I'm just looking at signal. I don't really know how I'm gonna use it yet, but I'm just collecting signal from Slack, from customer conversations, from talking to our team, from Twitter. And I'm just like consuming a lot of context. And then there's a synthesis, synthesization that happens somewhere where I'm connecting the dots between all the context that I've accumulated. And one of the first things I wanted to do with AI was that, can I like 10x or 100x the amount of context that I consume? Right? Because I felt like when I consume high-quality context, I make higher-quality decisions. And I only have a certain— it's funny how similar we are to AIs. Like, I only have a certain context window myself, right? Like, if I consume too much, I'm going to hallucinate. And so I need to be careful.
I wanna like slightly switch gears and kind of explain some of this, 'cause I agree, like I'm very deep in like right now, like context phase. Like I've been working on a new thing and just like building a lot of agents. And so for me, it's like context is everything and it's either gonna be the best thing that I've ever done or it's like the models will get so good that maybe we won't even need, but it's just like dumping as much context as I can, you know? Like I'm even in the place where, All this stuff sounds so cringy, kind of like what you were saying about treating it like an employee. That's how I'm thinking about agents. And I cringe when I see people put their agent org charts on Twitter, but you kind of do have to think about it like that of what are the roles and responsibilities? How would you onboard a person and how do you think about onboarding an agent? And so I'm taking courses that I used to watch and I'm just giving the transcripts to agents and having them ingest and analyze them. And so a lot of people are obviously talking about that. Models maybe not being the most important thing, which I want to hear your take on and get into later. But talk about that, like context layers and building a brain and how just like generally for people, right? We probably have listeners at like very different levels of, you know, like their AI journey. So like talk about that a little bit and then like how you're doing that, how you've done it at Motion and how you think people should be doing it, whether it's with Runneth or without.
Yeah. So, you know, what's interesting about this topic is that Everybody intuitively understands the value. So like at the most basic level, you could be having a conversation with ChatGPT and you just upload a bunch of files and say, hey, take these files into consideration and like, help me answer this question. Okay. That's like one level of context feeding. The next level up is you create a Claude project and now you're like, okay, all the conversations that I want to have with you from here on out, are going to be related to this context. So here's a bunch of PDF files and whatnot, and then you kind of store that in the Claude project. Then there's a leap that happens where you're like, okay, what the hell's a Markdown file? I think that's like, that's a moment where you're in a new gear of basically AI adoption. And one of our views internally, we, our lead creative strategist, Alicia, is not technical by any means, but once she figured out what a Markdown file is, she's like, Oh man, like this stuff is cool. And like, and I think that's, that's probably the first level up from, you know, building a context engine is this idea of how do Markdown files work? What is a repository? Sounds very fancy and crazy, but the general idea is if you ever look at like a software engineering project, it's just a bunch of files and folders. Even like, even the most ambitious software products in the world, like you take Google or you take Meta and you're like, okay, what is this thing? Under the hood, it's basically just a bunch of files and folders, a massive amount of them. They do a lot of interesting stuff. And these agents have been trained on mountains and mountains and mountains of code. So one of the things about them is that they know their way around a file system very well.
Yeah.
Like they can navigate files and folders as if it's like second nature to them. And so that is one of the things that makes the leap from like cramming all your stuff into a Claude project to like basically having some kind of a file system. It could be on your computer, doesn't need to be in the cloud, but I'll talk in a second about like why it's valuable for it to be in the cloud. And so imagine like, have you guys seen this new format where people will post on Twitter and then it's trying to explain a concept, but it's basically a folder tree structure. There's like a parent folder and then like subfolders.
It's like the lowest TAM meme ever.
Yeah. But there's something interesting about organizing information that way where you're like, okay, here are the things that matter to me. These are my personas. And each persona will have a Markdown file. These are my like messaging angles that I'm testing. Like each messaging angle might have a Markdown file. And so if you, if you look at your system, the way that you're doing work, you could probably organize it in some kind of a file system, even if it wasn't meant for agents. Like you might do that just to like look at it and be organized with you and your team. And so when, when you do that and you create a folder and file structure like that with markdown files that are just giving context, like rich detailed context about whatever that thing is. It turns out that agents are very good at navigating that. And, uh, this was one of the breakthroughs of last year during the holidays around like November, December. A lot of people point to like, that was a moment in time in AI that like everything really changed. And it was the combination of Opus 4.5 and OpenCLAW basically emerging at around the same time. And the pattern there, I think there was like 2 big takeaways from that. moment in time. One was that, um, if you give AI access to a computer, they will do incredible things because they've been, they've been trained on how to navigate files and folders in a computer. And the other is that coding agents were going to be the vehicle for all knowledge work. So like, you know, if you took Opus 4.5, it was a really good coding agent. But it turned out that you can take a coding agent and apply it to any part of knowledge work and like fundamentally to do it, it'll do a terrific job. Um, and so anyways, all of that to say from the journey of like uploading files and folders into ChatGPT to having some kind of repository, which just means like a folder with a bunch of like subfolders and files. of organized context that makes it easy for the AI to navigate. Because what happens when you just dump a bunch of files into Claude, it's kind of probabilistic the way that the AI might decide, like, okay, what information is relevant? How do I know what to get? But when you organize it in a bit more of like a clean way, then it knows how to navigate your folder structure and find the right things when it needs to. And so that's really valuable. And so I think a lot of people get to that point and they're like, oh man, I feel like I'm on top of the world. I have a repository on my local machine with all this context and I'll use like my Claude or GPT and they're just creating so much value. But then it's like, okay, how do I, what happens when I turn my computer off? I'm, and I'm walking away, then now like everything's kind of shut down. Or how do I collaborate with my team? And I think that becomes like the next level of challenge.
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So before we get to the, the cloud portion, 'cause I do think like all of this becoming collaborative seems inevitable. We're in like inning one of like sharing any of these files, but I just wanna touch back on this idea of like, uh, folders and files and that being the sort of the bedrock of any software we use today. And as knowledge workers, we're like not really used to that. Most knowledge work right now is getting done within Google Slides, Google Docs, like spreadsheets, things like that. Um, and I remember having this conversation recently, recently, and I would, I would love to get your perspective on it, but I asked like, oh yeah, do we really think the future of knowledge work is like Markdown files? Like, like, like, or like, like files local on our— I said at the time local on our computer so we could get to the cloud portion of it. But like, But I guess, would you agree with that? Do you think a year from now, 18 months from now, the lar— the way that we are documenting information as a knowledge organization is like is a different primitive than we're using today just because it's so much more conducive with the way that AI works?
Yeah. So I think there's 2 portions. There's like, there's raw unstructured data. So for example, you take like all your transcripts, all your reviews, like mountains of unstructured data. That's like, that's like one piece of context. I think with that, it's probably less about like really rich organized markdown files and it's more about basically being able to do semantic search or like vector search across a really large corpus of unstructured data. So like, I think that's one area even today, that's the right way to approach. So if in my like folder system, I'll have a folder called like raw corpus, and in there I'm just dumping all of the raw unstructured data in just one area of the file system. And for that, you want to be able to ask like semantic search type questions. that are like, of all the people that are frustrated, what are they frustrated about? Right. And you're not going to get the answer to that through navigating a file system. Like you do need to do like semantic search on that. But then there is like, call it like our SOPs or the things that you actually want a high degree of control over your judgment, the way you make decisions, the way you're approaching work. I do think those will happen in Markdown for some large period of time. And the thing about Markdown is that it's just a text file, right? Like the thing about it that is interesting, there's a, there's a tweet from Andrej Karpathy in 2023, which is like crazy way ahead of his time, where he tweeted that English is the hottest programming language now. And the idea is that like, What makes a Markdown file special is that it is basically like, it's a program for the AI to run. It doesn't sound like that because it's so, it's so natural language. But if you think about English as a programming language, which I think that's correct, and we are all going to be, I think the future of knowledge work is building systems more so than like individual work. A good example is like pre-industrial revolution. we were making like products by hand, like artisans, right? And then factories come along. We're still making products, but now we're making products in like a scale that was unimaginable before when we were like making them by hand. And I think when people think about the scale of work from AI, the first thing that goes into their mind is like, well, it's obviously going to be slop. It's obviously going to be terrible. But if you take the physical products example, just because we're making products in a factory, Doesn't mean they have to be low quality, right? Like we might care about the assembly line, the way it works, the quality control and so on. And so I think the future of knowledge work becomes working in large-scale systems. So you as like a leader of an org, even today, you're working in a system, right? You're like, okay, I have these teams and these channels and like this process and in a lot of the way that we do work is in some kind of a system, but the system is in our head and we are kind of following the steps of that system. I think the future of knowledge work becomes translating that system into a digital system through AI. And then you're doing quality control on that really large scale of system and whatever the mechanism of that is going to be, I think It's going to be in the English language in plain text, right? And how we organize that, I think can happen in a lot of different ways. But my bet is that a big part of the context gets abstracted away until like the models get better and you're like, okay, you know what? I'm just going to dump stuff and like, it'll organize it. It'll build these like graph databases and like join different concepts. And I don't have to like organize context. to the degree that I have to right now. But I feel like there's a difference between— there's some markdown files that are just really special, like your system prompt in inside of, inside of your system that describes your judgment, that describes the way you make decisions. Those ones I think are going to be so important. We're going to like go through them line by line as if they're like the Constitution. Because everything downstream of that gets affected. So I think there's going to be some text files that are really critical and we're constantly going to iterate on the way that we describe what we want in them. And then the models are just going to get really good at organizing themselves. And, you know, it's not just the model. I think Cody brought this up, but like the way that Even, even the model companies realize that like the model itself is a commodity. It's like the harness that you build around it that creates all the value. And so you'll have a model harness combination that is tuned toward a certain type of work, like software engineering, that is really good at helping the knowledge worker of that domain fly. Right. And I think that's what we're hoping to do for Runnith is that, is that as marketing leaders want to build AI systems for their companies, there's a lot of scaffolding that everyone's going to have to repeat. And I think that becomes the value prop of vertical-specific companies that help everybody in that vertical just fly in the same way that software engineering is getting the benefit of that right now.
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The models are so good. There's, and obviously there's all the open weight ones. So, you know, there's not necessarily that much leverage in being, you know, 4.6 or 5.0 or something like that. Like they're all so good that they can do the job. It's obviously like the context, it's the harness, which is like the context and then the skills and stuff like that. Outside of the virtual machine, like what makes Runneth unique? Like how is it different than like an OpenClaw or Hermes or something like that? Like are there vertical specific things that you've like, does it have like the right connectors? Does it have like skills or parts of the harness that are like dedicated for like your ICP?
Yeah, yeah, totally. One of the ways I think about it is that, you know, when physical product companies decide to like vertically integrate their supply chain. And it's like one part of the supply chain isn't the thing that makes the whole difference. It's the fact that we control so many parts of the supply chain so that at the end we can make a product that's exceptional. So like, I don't separate the virtual machine from the harness, from the data tools, from the UI. Like to me, all of those pieces together means that like Runneth can show up at your company as a basically a digital robot ready to go for marketers. And if you want to set up like a Hermes or an OpenClaw, one question is like, okay, how do I tune the harness to be good at marketing? Okay, that's one problem. But then you do have to think about like the virtual machine or run it on a Mac mini and like, how is it, you know, how do you make it collaborative between different team members? There's a lot of scaffolding that goes into that. The thing that's really funny to me is that In some sense, everyone is going to have to solve the virtual machine problem, maybe by going and like grabbing a virtual machine subscription and like putting their OpenCLaw or the Hermes on it, because these things need to run on the cloud. And even the Mac Mini is fun and exciting, but like our internal agent was running on a Mini. The precursor to Runnith was an agent that I built called Analyst. It was running on my Mini. It was available to everybody in Slack. It would get hammered by questions every day. It was a lot of fun because like I built it and this again, the idea of like, what is leadership these days? Leadership is leverage. And I was like, if I can build this thing that gives leverage to my team, that sounds like leadership to me, right? But it looks like IC work. Anyways, we can come back to that in a second. And this thing is running on my Mac mini. And one time I'm on vacation and the team's like hammering away, asking analysts like all kinds of questions every day. And then the power goes out in my house and then the Mac Mini's off for like 3 days until I'm back home and boom, like production-grade agent just like stops and people can't use it. And so like obviously the Mac Mini has problems from that standpoint. So these agents have to run in the cloud. And so from that to things like the data tools that come with Runneth are very fascinating. So we have a, creator index of 24,000 creators that like Runneth just consumes and like doom scrolls their content and saves it. And so when you talk to Runneth about creators, it just knows, right? Like it just understands tens of thousands of creators and all their content is one example, all the Inspo library of like what anybody else is doing. And like little things like if you wanna get your Hermes agent to be able to watch videos, Yes, yes, I know the AI people can do it and it's like, oh, I'm just gonna grab a Gemini key and then I'm gonna like make it so that when I upload a video, it can work. Yes, that does work. And then, but there's so much effort to do that and it's like, did it watch the video correctly? Did it look for the right things? Then you're like, okay, I also wanna be able to upload a video in Slack and I want it to be able to take that video. And then process it and watch it. And there's just like a bunch of unnecessary repeat scaffolding to do. Whereas for Runnith, it just like, it understands video. It's like built around video. So you say, hey, watch this video in Slack or anything like that. And it just works. And one of the things that we are hoping to do is that I get into a few of these fights on Twitter because people somehow think that what I'm saying is AI is the most important thing. And what I'm actually saying is that AI is the least important thing. The thing that matters is being a really good marketer. And what my hope is that I would love to level the playing field of the people on my team, like Alicia, for example, who aren't necessarily the most AI-pilled, I eat markdown files for breakfast type of person, but she'll run laps around most of those people on marketing. Right? And so my view is that like, we need to abstract away all of the scaffolding, all of the like technical engineering stuff and let marketers just be marketers, right? Like, that's like, that's the ultimate vision for Runneth is to actually level the playing field to make AI not be an edge for anybody. Right now, like the person who knows Markdown files and knows how to set up a Mac mini, they have an edge against the marketer who doesn't. Right? But that's not necessarily the person who's the best marketer. So to me, it's like, how do you just make this stuff work out of the box so that even the people who are good at doing this, the real leverage is just being an exceptional marketer. Like that's what the job is, right? At some point you're like, wait a second, like I was trying to do marketing work. Like here I am spending 3 days like debugging my Hermes agent on like the infrastructure that it's running on. Like, why am I doing this again? Like, why was this useful? And I think it's useful to do from like a learning standpoint. I think everyone should push themselves and learn and tinker and play with a bunch of stuff. But from like a productivity standpoint, surely we shouldn't all be like wrangling with the scaffolding and the infrastructure. Like it doesn't, it's not a good use of time in my opinion, especially because a lot of the people that we've spoken to that are really like knee deep in it and like setting up their own virtual machines and whatnot. In many, many cases, like we've gone way further than them even like, oh wait, like, yeah, screw it. Like it's going to take way too much time for me to build that level of infrastructure so that it works with my team and like permissions works and everyone has their own swim lane and like it just works out of the box. And I think that's the difference is that we're building We could have, when we built Runneth, it was such like an infrastructure bet that we could take Runneth to market as just like a cloud knowledge work agent for like general purpose stuff. That's how we use it internally for like all kinds of things. Like our sales team uses it, our CSMs use it. We are like the biggest Runneth users in the world. Uh, we spent something like $70,000 of AI cost through Runneth ourselves. But my view has always been a lot of our team was like, hey, we should pivot this company to just be like general purpose. Why limit it to marketing? And I was like, yes, technically we could do that. But I think we can build the best product in the world for marketers if we stay focused on that. And there's going to be plenty of general purpose knowledge work AI that I'd rather stick to what we love and what we know and like build the best product in the world for marketers.
So, uh, I just, I think those 2 points, it's important to make that distinction a little bit, 'cause I totally agree about like the, um, virtual machine component of it and like the, the like raw general purpose infrastructure component of it. We explored this idea a lot and it's like, yeah, should we be working in GitHub? Should we be using Google Drive? Like, do it, or like Google Drive synced down to our local computers. Like we were trying to figure out this way to like, how do we collaboratively work with the same files and compound learnings across team members, things like that. Runneth out of the box. Fantastic. And that's like the general purpose component of it. But I also, when we talk about it being a great vertically verticalized tool around marketing, I feel like it comes back to that data component that like Motion was founded on where I'm like, and I say this all the time, actually, our performance creative team is on the forefront of persona testing, messaging testing, content testing. Like we are serving billions of impressions and learning a lot at a really high rate. Nobody's better at like figuring out what those concepts are, tagging that down and building the dimensions that we care about than Motion, or I should say you guys generally as a company. And that's really what sells me on the idea that Runneth not only is a great collaborative general purpose tool, but also this like ad creative beachhead is the way that it becomes a great vertically, vertically integrated, you know, marketing tool.
Totally. Yeah. And so like, you know, there's, there's all the data that we prepare, that we doom scroll, that we watch, that like is just available for people to consume. But one of the things that's been really interesting for us is to like help people see just how far you can push Runnith, like based on like all the stuff that it has and the fact that it could run 24/7. You can get Runnith in, you know, this is one of the use cases that people do is like, oh, it'd be really great If I could build an agent that can go and watch all the latest like TikTok organic videos and then like summarize them and try to look for trends and like the scaffolding that it takes to go and build an agent like that is like, it's not rocket science, but it's not nothing either to like build that and maintain it. With Runneth, you're just like, hey, every night I want you to go watch organic TikTok videos and do this, that, and like just explain what it, what you want. And so it's both us preprocessing the data and like making it available to you, but then you being able to like have an agent that lives in the cloud that has its own machine, which gives it a lot of power and autonomy. And you can give it instructions to say like, from now on, I want you to go and watch like every video from this creator and like just give it so much work that It can just do out of the box. So that basically what our hope is that if you have a task for Runnith that's like marketing specific in the age of AI, you're like, okay, I want this agent to do this task. And then you're like, okay, I'm going to do these like 6 or 7 scaffolding pieces so that I can get it to do that task. Like watch organic TikTok videos. Our hope is to be like, no, just tell it to watch organic. It understands what you mean. It has done the prep work so that when you give it a marketing-specific task, it could just run with it because that's kind of the difference of making it focused on a specific vertical is that you can give it a task that is related to that vertical. And before you have asked that question, we have prepared Runif to basically be able to tackle questions like that really well. And obviously like There's a lot of these types of tasks. So we're constantly like building more and making it better. But just the current capability set of what Runneth can do is like, we have like, people can spend tens of thousands of dollars per month on AI costs, just like putting Runneth to work because it has the infrastructure to do that. Which is the other thing, by the way, I think that people are, there's like a mental model of what is the correct level of AI consumption. that we're all going to be doing as companies. And I think people have not priced in this idea that we are going to be 1,000x-ing, maybe 10,000x-ing the amount of AI consumption that we all process. And, um, and I think most people have not realized that yet. And I think once you get to the point where you're like, um, you know, there's this funny thing when you buy a Mac mini. you step away, you have this feeling of guilt where you're like, I should, I should come up with something for this agent to do while I'm away. Like it, otherwise I've got this machine that's unproductive. So like similar idea to running a factory. Imagine you have a factory that's not running half the time. You're like, well, this is not productive. Like we should find a way to like make this factory productive around the clock. And I think that's the The switch that flips with AI where it's really just your imagination in the way of saying like, can we get AI to be productive 24 hours a day? And if not, why not? Like what, what, what are we missing to not have this like superintelligence be productive for us around the clock? Why is it that it's just sitting there, this like superintelligence just sitting there? until I show up and ask it to do something. No way. That's like, there's no way that's the right pattern. Um, and I think once you get to that level of usage, it becomes like, oh, okay. Yeah. There's actually a lot of scaffolding that I need to do in order to have like AI be productive for me and my team 24 hours a day and have that just like work smoothly.
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Yeah, it's a good question. I, I've experienced all the, um, you know, trials and tribulations that get solved by Runneth where I'm like, I shouldn't be doing this. I should, I don't need to be setting up a mini and, uh, trying to get this remote thing. Most of the, most of the tasks that I'm running on a consistent basis, I'm just running from my computer at this point and I can ping it from Slack. And it doesn't always work. I had to go to Sonoma last week, so I was like offline for a little bit. So we're still doing a lot of our work. We're at the point where it's like collaborative in the sense that we're using, um, like my performance creative team is using Runneth, right? So we're like all working in the same workspace theoretically. Uh, I'm using Claude and then I'm using the Runneth CLI. So I'm pinging Runneth from there. And then any sort of like, yeah, recurring tasks I just have scheduled and run through my computer. So. I'm excited about somebody else solving the virtual machine problem and not me trying to plug in Mac Minis at home.
Yeah. You know, part of it is when I, when I built the Analyst agent on the Mini, one of the issues I had was that some of our team would be like, oh, could you get Analyst to do this? I asked Analyst the question and it was wrong. Could you like, can you, can you, can you now ask it to do this? And all I would do is like, I would take that screenshot and put it in my terminal and be like, hey, Analyst, like, fix this or like add this or change this. And at some point I'm like, why am I doing this? Like, why am I like triaging all of this? And when we switched to Runneth inside of Slack, because the Runneth you speak to in Slack is the same persistent agent, like anyone can program it directly from Slack to be like, you know, from now on change this or do that or like update your thinking. And then we're like, oh crap, now anybody can program this thing. from Slack and get it to do anything. And then we faced people like bumping into each other and someone overriding somebody else's routine. And so we're like, okay, now we need Runneth to understand when someone talks to it from Slack, it knows exactly who you are and it knows what you have permissions to do and what you don't have permissions to do. And so then we had it where someone was like, I built a routine or somebody else on my team built a routine and someone was like, hey, can you make this change? And Runneth would go back and forth with the person like, what do you want it to do? And so on. And then it'd be like, okay, this routine belongs to Reza. So like it would @mention me and be like, Reza, gimme the, gimme the go ahead and I'll make this change. And I'm like, yep, go ahead, do it. And so basically going from like me being the single point of failure for everyone's like feature requests to come to me, to then anybody can program this thing inside of Slack, to the chaos that comes from that, to then basically like collaborative programming of an agent, uh, uh, All together in Slack, which is completely wild. If people wanna stress test this, by the way, we launched our Slack community after the Creative Strategy Bootcamp and it now has like 10,000 members in it. I hope after this people don't blow it up, but we made Runneth inside the Slack channel. If you go to Runneth in the Slack channel and you create your own channel with just you and Runneth, we've given Runneth permission to like do crazy work for people, like no token budget, just go nuts. And it'll have the memory for you and you can add stuff to it. You could build routines, you can like do whatever you want just from a Slack channel inside our community. And it just kind of shows the level of scale that Runneth can go at, that like you have thousands of students that are like attacking it from different channels and it's able to like keep itself organized. And so yeah, I recommend for people to try it inside the Slack community if you want to get a taste of of what Runneth can do. We've made it available for people and students are hammering it right now. Just be careful. It's not fully private, obviously. So like, you know, just do work that is not sensitive, but you can like do competitive research. You can build like a dashboard of creators and like all of that stuff without needing a Motion subscription or paying anything. So feel free to try that out.
Um, so I've got a question for you. I was having this conversation over dinner recently. And what this person was proposing was that there are only 2 companies now. There are AI native companies, which he was proposing are basically exclusively new companies that you have to start from the ground floor to be AI native right now. That's, that's kind of what Cody's on right now, building agents. Cody, whatever he's doing next is going to be like a single person, 9-figure business. That, that's where we're going there.
Single person, I can guarantee that part.
Yeah, yeah, yeah.
I figure can't guarantee that.
Uh, but, but like there's that path. It feels like to be truly AI native, the people who are at, in an advantageous position are the ones that are just starting. And then everybody else is a legacy company. And what they were also proposing is that legacy companies are, it's going to be extremely difficult to sort of shoehorn AI processes into your business and become AI native. or to get the leverage that some of these more native earlier businesses have. I don't believe this. I think there's totally like, there are examples of Runneth where all of a sudden that's like the 80/20 where like, if I can get some of my marketing team or my performance creative team working in this way that I can become AI native enough where I'm getting a lot of leverage. But I'm curious where you land on that scale, Reza, for these businesses that are 50, 60, 70 people and are just trying to figure this out for the first time. Like what are some of the approaches, steps 1 and steps 2 that can just help them get the leverage of a brand new company today?
Yeah, totally. Okay, so this is where my view of the executive IC and we'll see if this works. I'm trying to rebrand your job, Connor, from CMO to chief marketer is my aspiration. And I think it's related to this question. I've thought about this a lot and I think The leaders at these companies need to behave like ICs, like these super high leverage ICs. And then I think those companies can all transform. The problem happens if, if the leadership tier is like, okay, we're going to do like manager things. And then we're going to tell our team, hey team, adopt AI. It is, it is never going to work. Like no way. But I think if those companies, no matter how big they are, actually doesn't matter their size. If the most senior leadership start to behave like super ICs where they are leading from the trenches, they're like building systems with AI. They're like, they are modeling the behavior that they want to see everybody in the company do. Then I think those companies can absolutely transform to be to be AI native, but I think it takes a reimagination of what leadership means. I think the entire job of leadership has like fundamentally changed post-AI because the job of leadership was to create leverage, right? That's the only reason we have leaders is that someone's really good at doing something. Let's say like, you know, Connor is the best marketer in the org. And then people are like, hey, this guy's a really good marketer. Like we need to like clone him or we need to, we need to If we could have like 5 more Connors, that would be terrific. Can't do that. There's no clone machine. So, okay, Connor, you run this department to try our best to like clone you, right? Ultimately some version of that. But what happens if Connor can create leverage in other ways? What matters is like, can an individual who's really good, can they create leverage? And I think because you can do that with AI, The job of the leader is like the person who can create the most leverage in the org. But I think if the leadership tier adopts that, then I think they can totally adapt to becoming AI native. And that's really all the difference that you'll see with the earlier stage companies that are becoming very AI native is that the people who are at the positions of leadership, even though it's a small team, they are all behaving like ICs. And then I have this argument a bunch of times with a few people who are like, okay, and therefore we're only ever going to have 5-person teams. My counterargument to that is that suppose for a second somebody came to you and said, I have a staff of 100 people who are all exceptional. They are all like 15 out of 10 at what they do. They are at your disposal. Make them productive. Like a really talented leader would be like, hell yeah, I can do that. Like you're telling me you're going to give me 100 people who are really good at marketing, at engineering, at like sales. Of course you can put them to good use and like create even more value than you could with 5 people. So I actually don't necessarily buy the lean team argument as a necessity, because if you can create value with 5 really talented people, Then what exactly is the limiting factor that you can't create value with like 50 incredible people? The problem is like when people grow their teams, they're not always like hiring incredible people. And I think that that's where the issue is. I think there's like, when people talk about the impact on like the job market and people losing jobs and whatnot, I actually think there's going to be more opportunity than there ever has been with AI. But unfortunately you can't just like clock it in. That's just not going to work. Like you have to take your work seriously. You have to be like good at what you do. And given that there's like an unbelievable opportunity. And I think that's, that'll be the difference where yes, you don't need to grow an org of people who are not that great, but are just kind of pushing things forward slightly. I think that that will not make a lot of sense, but I think you'll absolutely have like large orgs filled with really talented people. So yeah, I think it's possible to become AI native even if you're a larger company. It's just the leaders have to go first and get in the trenches.
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One, I love the discourse on X this week around your executive IC post. It like ruffled more feathers than I was expecting because I read it, you and Taylor Holiday are saying the same thing and I'm like, I like both you guys. I'm like, dude, we're all on the same page here. I'm locked in on executive IC. And I've just been saying, I feel like I have been so much closer to the work over the last couple of months. Like even this past Monday, just in like a few hours of work, I did like, you know, part of a Klaviyo audit. I worked with Intelligems. I did weekly reporting. Like I just got so much deep insight across the org in moments when in, in historically I would have had to spend half my day. plugging through IntelliGems, trying to figure out what this A/B test is exactly doing. And that's a sort of leverage that you're talking about. And it's also, I think, the path. That's always my answer where this person that I was speaking to is like, legacy companies are cooked. They're like, it's over for these guys. They're not gonna be able to adopt AI. They're gonna have circles ran around them by these more smaller, lean team, AI-native companies. I think it's an 80/20 thing where like you identify the, I don't even know if it's 20% of the workforce. It could be 5% of the workforce. And if you can get leverage around those individuals, they'll help empower the rest of the team without everybody being as AI-pilled as, as, you know, this core few. And I think you end up getting just as much leverage as you need to be, to continue to be competitive.
I totally agree. And I also think that the trenches work right now does involve a bit of AI-pilledness, but I genuinely think that part just becomes solved. So in the same way that in the early days of like the internet, you'd be like, hey, you need like internet skills in order to do these like new types of jobs. And, but like now we don't think about it that way anymore. It's like everyone knows how to use the internet. Everyone knows how to use like the basics of knowledge work. And then the difference becomes, are you good at whatever craft you're hired to do? Are you a great marketer? Are you a great engineer? Are you a great designer? Are you a great product person? I think those are the skills that are actually going to matter. And so if you have these legacy companies who are staffed with people who are exceptional at the craft, I actually think in the long run they beat these like AI-pilled native companies who think they can like shortcut the craft by saying, oh, I don't need to be a good like product person. I'm just going to tell Claude to make all my like product decisions, but then they're going to have— software product with just like a million widgets on it. And then what they realize, like being really good at product means like deleting things. How are you going to do that? How are you, how do you know what to strip away and what to keep? And that takes like really good judgment. So I think the best positioned people are the ones who are just really good at whatever craft they're doing because the underlying AI-pilled layer is just going to get easy. Like we are not, I remember when I set up OpenClaw, it really felt like I was defusing a bomb. I was like, this is scary. Like this thing is, I like, I sent it to our CTO and he's like, okay, listen, try it, but please be careful. Like this stuff is, this stuff is dangerous. It could really get hacked and whatever.
But there's a basic level of it though. Like I don't think that anyone really needs to be doing that, but like there are like, I feel like sometimes we forget that like, from talking to other people doing this for Jonesville. Like, there are people that like, even like a skill is a new thing. Like there, I do think there's a basic level. And this is what I told my team. And this is kind of what you just said. Like, it's kind of like no one thinks about using email or internet, right? Like that's how we're going to think about AI in a few years, maybe even now. And it's just like a basic skill. It's not less like advanced thing. It's like, this is just how work will get done. So I do think there's a basic level, maybe not not, you know, 90% of the way they're cracked, but like, you gotta at least like know the basics to be able to get the leverage.
I agree. And I would actually say the skill that is important there is the skill of being like curious and high agency, right? People who are curious and high agency, like they can't help but like go and poke around and be like, what's going on here? And like, what is happening? And I think that if they're not doing that, there's something wrong at the level of like curiosity. and high agency that is not pushing them towards this obviously interesting thing.
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One of the things, uh, Reza, when you say people that are good at their craft benefit from this, I think I'm gonna try to segue your point into my point. Uh, people that are really good at their craft are gonna benefit from it over the long term because they're all, they'll ultimately be empowered. You brought up the example of the, the someone building a piece of software. The, it feels like what a lot of people are solving right now is just the ability to write code, but the people that are gonna succeed long term are the ones that understand what problem are they solving with the software. They're thinking about it from a product perspective. And they don't end up with a bunch of random widgets. They end up with like a beautiful piece, a beautiful product that solves a problem for people. Where I kind of feel like this relates to marketing right now is people are solving an OpEx problem when at the end of the day, everybody has a CAC problem. And there are many examples of building leverage so that you can free up more time, make better decisions, and solve a CAC problem. But it feels like a little bit of what we might be doing is saying, We're shifting the goalposts and saying, how lean can I just be, do all the motions of being a brand? When in reality it's like, how are we finding fundamentally how to acquire customers at a higher rate? And that we're going to maybe make that transition. We have to make that transition again, post this like AI adoption wave.
Totally. You know, one of the things, like, I think to be a marketer is genuinely one of the, one of the rarest and most difficult skills in the world. And it is the skill that will survive post-AI. If everything gets automated, the 2 things that remain are like starting a company and marketing the company.
Podcaster too. Hopefully.
Well, I don't know. Maybe, maybe, maybe AI is just going to like consume all the information and run it. No, I think people are going to want to hear from the exceptional ones who are like doing the work. So yes, I think the talented people always have that avenue as well. But like marketing is a very special skillset because the job itself is high leverage. Like if someone is good at marketing, they create leverage on the company. Basically it means like that their work can have such massive impact in terms of value creation. And it's similar to software in that sense. Like one software engineer could write software that impacts a lot of users. One marketer could do work that like creates an incredible amount of value for the company. But I think the job of the marketer is a value creation job. It is not like a private equity. Let's look at the processes here and see what we can slash and whatnot. And like it is figuring out what can you do that is going to resonate with the world. And so it's a very, very rare skillset in my mind. And so like, yeah, the people who are not great at the craft in the same way for like software, people are not great at the craft. They're like, oh great, we have like a free code machine. Let's like point this to do, create like tons of code. Same way, like, yeah, you can get AI to like build you 100 landing pages and 1,000 ads. I'm like, great, wonderful. Like, now what? What is that? Is that going to be helpful? Is it not? So the bottleneck really does come back to the people who have really great— I think there's a backlash on the word taste, so I won't use it anymore. The word judgment seems to be landing a little bit better. And I think—
I was going to say, you're like going for like the tech bro bingo card. You got first principles in here. We talked about context layers. We talked about taste. Like, I think we're getting— we're like 3 for 5, probably.
3 for 5.
Yeah. Well, you know, it's like, it, I think memes are meme for a reason, you know, like there's, there's, there's a reason behind it. And so it's the simple idea that when execution becomes like basically unlimited, where's the bottleneck? You know, it's like, it's the judgment to decide like what's good, what's bad, what to do, what not to do. Yes. People can go and ask AI, hey, what do you think? Right? And like outsource their judgment to AI, but there's no edge there because everyone could do that. And the whole job of marketing is to, it's a competitive sport and the job is to find an edge against somebody else. And so if everybody has the same like intelligence machine, then you need the people who can maintain that layer of judgment on top of it. And like, man, that's a rare skill, which is why these individuals who are good at that, in the years ahead, I suspect the world has not priced in this idea that what does it mean for a single individual to be as potentially create as much value as like 25 people in the past? It must mean that that eventually gets reflected into the comp of that individual. And I think the comps get ridiculous over time. Um, not just from the sense of like, hey, I can automate the work of 25 people. It's like, I am creating as much value for a company as potentially 25 people. What's the salary of this individual? How do you, how do you compensate that individual? It's, it's not clear to me that the salaries will stay the same at all. Like, I think, I think this is why I say that AI is going to have unbelievable amounts of opportunity for a single individual who's good. at stuff can probably earn the salary of like 5, 6 people.
Alex Lieberman from like Morning Brew, he's got like 10x as his company.
Have you heard of that?
You're probably familiar with his like engineering comp structure.
I've not seen the comp structure. I've seen, I've seen his business, but what does it— It's pretty crazy.
I don't, I'm probably speaking out of my ass, but it's like they have like a bounty system almost. So like an engineer can just like pick what they work on and just, they like, you can make up to million millions there. Or you can make, you know, normal engineer salary by just like seeing a task and be like, yeah, I'll go and do that. And so you can kind of like choose your own value that you're creating for it. And they have like a completely like no capped upside. It's pretty unique. And I wonder if something like that in marketing would ever be, you know, helpful.
Yeah, I think marketers are gonna be among the highest paid individuals in the world very, very soon, especially as like software engineering becomes like cheaper and cheaper to like produce stuff and like just in general execution becomes easier. So like to me, the chief marketer idea is not a step down for the executive, like, oh, I have to go do IC work again. I think the amount of value that talented ICs can create, they will be compensated the same way as like teams used to be. So like we're, I think we're entering a very interesting era and it's gonna be a lot of fun. Anybody who says like doom and gloom with AI is like out of their mind. It's just gonna be a complete blast and a lot of fun.
This is great news for all Marketing Operators listeners. You heard it from Reza. We're all getting raises.
You are in the right industry because think about it. If you're in something that's like automatable, right? That has like a closed system, then like AI is really good at that. So like the mathematicians might be in trouble because, you know, there's like, it's It's a very verifiable discipline, but marketing is competitive. It's dynamic. The world changes, culture changes, and like, there's always going to be a bleeding edge. And that's why I think it's like, it is genuinely one of the best places to build a career right now. And, but it's because of the value that you can create on top of AI. So don't get pulled into the— to the AI shortcuts. Like that's not, that's not, that's not where the value is for marketing. It is like read the old school books, like read David Ogilvy, try to become an exceptional marketer without AI. And, and the leverage is just going to start like showing up in your life and it'll all just work. When it comes to AI adoption, there's a lot of like rules of thumb of like, what's, what's a good way to look at it? One of the ways that I think makes a ton of sense is thinking about AI as a percentage of payroll. And I actually made this comment to somebody where I was like, I think people should have a hiring freeze until they are spending at least 10% of payroll on AI. It's like the mental model I think is like, that starts to be the right way to think about things is that AI is a productivity on your labor force. And so like, I think we're all underspending by a massive degree on AI and the costs of AI are going to fall off a cliff. But I think costs of AI are going to fall off a cliff and we're all going to spend like 10, 20, 30% of AI on payroll. And so there's like an order of magnitude thing of, am I AI pilled? Are I doing the right things on AI? And a good way to think about it is like, what percentage of payroll are we spending on AI? And then that's a lot of money and you're like, wait, how am I even going to like spend that? And I think that's the right kind of questions to be like, yes, that is the order of magnitude change of the way that we're going to be deploying AI. Um, I'm saying 10%. Jensen from NVIDIA said that it should be around like 50%. And so like somewhere in there is your, somewhere in there is your range. Got it.
So marketers making up, you know, 60, 70, 80% of payroll, AI doing another 10%, something like that. I like that.
Yep. That's the way. That's the way.
Awesome. This was a good one. Reza, thank you so much for coming on. Where can people follow you? You had a lot of hot takes recently, so tell people where they can follow you. Where can people, you know, try Motion? Where can people try Runnith? What's going on there?
Yeah, so I'm trying to get into more fights on Twitter so you can find me there. We are also creating a lot of content, either motionapp.com or runneth.com. A lot of our content these days are going to be around like AI and marketing, so you can follow us there. And if you go to runneth.com, you can find our Slack community and join it and just start talking to Runneth and like put it to work. We have given Runneth no token limit inside of that Slack community for our students. So go and give it a try and put Runneth to work.
Awesome, man. Well, this is great. Thank you so much for coming on.
I think people are gonna love this one.