Aug 25, 2026 · 1 hr 13 min · 19 segments
What 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…
Reza KhadjaviGuest
Connor MacDonaldHostConnor RolainHost
Cody PlofkerHost
You told me over a year ago, a long time ago, you said, basically, whatever process you can, like, lay out if you're onboarding a new employee, whatever process you can lay out, you can basically replace with AI over time.
And I feel like you kind of just hit that point now where you said a lot of what knowledge work is right now is asking questions via a point-and-click workflow.
And it's like, oh yeah, all of a sudden, we just take a step back and think about the intrinsic pieces of, like, what that work is, then all of a sudden it becomes much easier to understand how an AI might replicate it.
It can select the dates and build the filters and do the comparison, then come back to you with an answer that's much clearer.
And I think that's just such a big unlock, and it's cool to see that sort of begin to get realized in products like Runneth.
When a lot of the AI change was happening, like, my, my first instinct was that, like, I need to be deep, deep, deep into the weeds myself, which is kinda where the, the chief marketer, uh, d- like do- doer and chief leader idea comes from, is that I, 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, 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 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, that I've accumulated.
You-- It's funny how similar we are to AI, is like I only have a certain context window myself, right? Like [chuckles] if I consume too much, I'm gonna hallucinate, and so I need to be careful with like how many tabs I keep open.
But, uh, my thought was that like if I can have AI scour all of this context for me, and I can give it some instructions to say like, "Here are the types of things I'm looking for.
Here's like a question that I have." Uh, really good example is that like I wanted to know early on like what's coming up in our customer conversations, right? And very simple, I like took all of our transcripts, and I had like a fleet of AIs just go and read through a thousand plus customer conversations looking for signal.
In the past, I would be like, "Okay, if I had like a, a couple hours free in the afternoon, like let me watch a couple of gong calls just to like, you know, keep up with the momentum of like what are our customers saying." Like take that as like that's useful.
I think pre-AI is still useful m- for me to s- like spend an hour watch a gong call, but if I can scale that, uh, research to like thousands of conversations, then th-th...
I, basically it's like you, you can really scale good judgment, and like I think a lot of what leaders do is, is basically that.
Like try to stay close to the trenches, collect signal, have judgment about the future, and make good decisions.
And I think what a AI allows you to do is to like really scale that process of like collecting a large amount of context, which is kind of again the difference between a product like Motion and a pro-product like Runnit, is that you-- in Motion you'd have to like go one by one, open a report, look for signal, open a report, look for signal.
In Runnit, you could be like, "I wanna go and look through like every ad we've ever done from all time based on this, that, or the other thing," and like let the agent run for two hours, and you have three weeks worth of research done in like two hours, right? So I, I think it's like a different, different order of magnitude of what we can do in terms of work.
The problem becomes like if you don't have good judgment and you point your AI at this like massive, massive mountains of context, then you're just gonna confuse yourself and the AI, right? So it, it, it doesn't mean that like it makes the work easier necessarily on the leader or the person that's doing it.
You told me over a year ago, a long time ago, you said, basically, whatever process you can, like, lay out if you're onboarding a new employee, whatever process you can lay out, you can basically replace with AI over time.
And I feel like you kind of just hit that point now where you said a lot of what knowledge work is right now is asking questions via a point-and-click workflow.
And it's like, oh yeah, all of a sudden, we just take a step back and think about the intrinsic pieces of, like, what that work is, then all of a sudden it becomes much easier to understand how an AI might replicate it.
It can select the dates and build the filters and do the comparison, then come back to you with an answer that's much clearer.
And I think that's just such a big unlock, and it's cool to see that sort of begin to get realized in products like Runneth.
When a lot of the AI change was happening, like, my, my first instinct was that, like, I need to be deep, deep, deep into the weeds myself, which is kinda where the, the chief marketer, uh, d- like do- doer and chief leader idea comes from, is that I, 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, 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 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, that I've accumulated.
You-- It's funny how similar we are to AI, is like I only have a certain context window myself, right? Like [chuckles] if I consume too much, I'm gonna hallucinate, and so I need to be careful with like how many tabs I keep open.
But, uh, my thought was that like if I can have AI scour all of this context for me, and I can give it some instructions to say like, "Here are the types of things I'm looking for.
Here's like a question that I have." Uh, really good example is that like I wanted to know early on like what's coming up in our customer conversations, right? And very simple, I like took all of our transcripts, and I had like a fleet of AIs just go and read through a thousand plus customer conversations looking for signal.
In the past, I would be like, "Okay, if I had like a, a couple hours free in the afternoon, like let me watch a couple of gong calls just to like, you know, keep up with the momentum of like what are our customers saying." Like take that as like that's useful.
I think pre-AI is still useful m- for me to s- like spend an hour watch a gong call, but if I can scale that, uh, research to like thousands of conversations, then th-th...
I, basically it's like you, you can really scale good judgment, and like I think a lot of what leaders do is, is basically that.
Like try to stay close to the trenches, collect signal, have judgment about the future, and make good decisions.
And I think what a AI allows you to do is to like really scale that process of like collecting a large amount of context, which is kind of again the difference between a product like Motion and a pro-product like Runnit, is that you-- in Motion you'd have to like go one by one, open a report, look for signal, open a report, look for signal.
In Runnit, you could be like, "I wanna go and look through like every ad we've ever done from all time based on this, that, or the other thing," and like let the agent run for two hours, and you have three weeks worth of research done in like two hours, right? So I, I think it's like a different, different order of magnitude of what we can do in terms of work.
The problem becomes like if you don't have good judgment and you point your AI at this like massive, massive mountains of context, then you're just gonna confuse yourself and the AI, right? So it, it, it doesn't mean that like it makes the work easier necessarily on the leader or the person that's doing it.
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