Aug 5, 2026 · 24 min · 10 segments
In this episode, I was lucky enough to interview Connor Snyder, CEO and co-founder of Storyy. Connor shares how growing up around entrepreneurs shaped his outlook on business and why learning…
Connor SnyderGuest
Jay AgnerHost
What I am curious about, and it's because I'm kind of figuring out the same thing in our space, and we kind of talked about it a little bit before the show, is like where do people fit into your process as it becomes easier to make software and do things with that software, obviously with AI? What-- for you guys, as, you know, creatives and as editors and all these things, like, where kind of specifically do the humans kind of fit in for you guys, and where do you envision them fitting in, you know, kind of in the future? 'Cause that's the interesting question to me is like how are people pl- planning on keeping them around because if you plan on not having people around, I think you're gonna be kind of, you know, in a bad spot in like six to 12 months when you realize, like, there's lots of things that AI can't do.
So where are you guys keeping people in the process? And don't say human in the loop or I'm ending this podcast immediately.
The way I think about this is like at the end of the day, AI has to find some happy medium across all of its training data when it's doing any sort of subjective work, right? There's like factual information where it's like this is true or not true.
But whenever we're having a, something that's like a matter of taste And we're talking without much prompting or training or whatever, right? Like AI is going to have a point of view that is going to be palatable for the vast majority of people, right?
Now, the problem in any creative space especially, and I would-- I'd say marketing is often a creative space, though not always, is you've gotta be able to stand out.
And so in a way that's similar to fashion, if you're doing five years ago or 10 years ago's marketing strategies, you're not gonna stand out and get attention, which is ultimately what people are paying Story for.
And so th- you have to have this, this balance of like when you're going to use AI or human talent, the question is: how do we make sure that we're doing something that's outside of the norm, but not so far outside of the norm that it's not gonna be effective? And then what are things that are truly repeatable tasks that have more binary evaluations of output? And so when I look at, like AI, I first look at things like validation in QA, where it's like we can define some of the subjective things, and AI can be applying against a defined scorecard of like-
But when it comes to like producing the output, you ultimately end up doing a lot of prompting that would also be ended up just being made, like creative decisions from an editor.
And so I haven't seen the quality improvement or the time savings yet when AI is driving the actual output creative process.
Now, we have a bunch of internal QA move things around processes that AI has absolutely sped up.
What I am curious about, and it's because I'm kind of figuring out the same thing in our space, and we kind of talked about it a little bit before the show, is like where do people fit into your process as it becomes easier to make software and do things with that software, obviously with AI? What-- for you guys, as, you know, creatives and as editors and all these things, like, where kind of specifically do the humans kind of fit in for you guys, and where do you envision them fitting in, you know, kind of in the future? 'Cause that's the interesting question to me is like how are people pl- planning on keeping them around because if you plan on not having people around, I think you're gonna be kind of, you know, in a bad spot in like six to 12 months when you realize, like, there's lots of things that AI can't do.
So where are you guys keeping people in the process? And don't say human in the loop or I'm ending this podcast immediately.
The way I think about this is like at the end of the day, AI has to find some happy medium across all of its training data when it's doing any sort of subjective work, right? There's like factual information where it's like this is true or not true.
But whenever we're having a, something that's like a matter of taste And we're talking without much prompting or training or whatever, right? Like AI is going to have a point of view that is going to be palatable for the vast majority of people, right?
Now, the problem in any creative space especially, and I would-- I'd say marketing is often a creative space, though not always, is you've gotta be able to stand out.
And so in a way that's similar to fashion, if you're doing five years ago or 10 years ago's marketing strategies, you're not gonna stand out and get attention, which is ultimately what people are paying Story for.
And so th- you have to have this, this balance of like when you're going to use AI or human talent, the question is: how do we make sure that we're doing something that's outside of the norm, but not so far outside of the norm that it's not gonna be effective? And then what are things that are truly repeatable tasks that have more binary evaluations of output? And so when I look at, like AI, I first look at things like validation in QA, where it's like we can define some of the subjective things, and AI can be applying against a defined scorecard of like-
But when it comes to like producing the output, you ultimately end up doing a lot of prompting that would also be ended up just being made, like creative decisions from an editor.
And so I haven't seen the quality improvement or the time savings yet when AI is driving the actual output creative process.
Now, we have a bunch of internal QA move things around processes that AI has absolutely sped up.
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