Alright, welcome back to Jellypod Journeys, everyone. I'm Jack Mitchell, and as always, I'm joined by Emily Carter and Ethan Walker. Today, we're diving into what happens after you’ve got that shiny AI prototype—how do you actually turn it into a real, scalable product? Emily, I know you’ve got some stories here.Yeah, I mean, this is where things get real, right? Like, you’ve got your proof-of-concept, maybe it works on a small dataset, but suddenly you’re staring down the barrel of robust data pipelines, cloud infrastructure, and, honestly, a lot of late nights. I remember the first time I managed a product’s shift from prototype to full deployment—there was this moment where I realized, “Oh, this isn’t just about the model anymore.” It was about making sure the data was flowing in reliably, automating the boring stuff, and, honestly, just keeping the thing from falling over every other day.Yeah, and I think a lot of people underestimate just how much work goes into that. Like, you can have the smartest model in the world, but if your data pipeline breaks, or your cloud setup can’t handle the load, it’s game over. I’m always amazed at how quickly things can go sideways if you don’t have those automated workflows in place. It’s not glamorous, but it’s what makes the difference between a cool demo and an actual product people can use.Exactly. And, you know, building on what we talked about in our last episode—where we focused on getting that initial validation and funding—this is the next big leap. You’ve got to prove you can deliver at scale, not just in a pitch meeting. And that’s a whole different beast.So, once you realize you need more than just a clever algorithm, the next step is building the right team. And I don’t just mean hiring more engineers. You need people who really get the nuances of AI—data engineers, cloud specialists, even folks who can wrangle those gnarly edge cases that pop up when you scale. It’s a different skill set than just building the prototype.Yeah, and I’ve seen startups get this wrong so many times. There was this one company—can’t name names, obviously—but they had a killer prototype, got a bit of funding, and then hired a bunch of generalists. When it came time to scale, they just didn’t have the right expertise. The infrastructure buckled, the product lagged, and, well, let’s just say they didn’t make it to Series A. On the flip side, I’ve seen teams that brought in the right specialists early—people who knew how to build resilient infrastructure for big data and heavy model training. That made all the difference. It’s not just about headcount, it’s about the right heads.Totally. And, like, the infrastructure piece is so easy to overlook. You think, “Oh, we’ll just spin up some more cloud instances,” but if you don’t have a plan for scaling storage, handling failures, or even just monitoring everything, you’re gonna hit a wall fast. I always say, if you’re not thinking about resilience from day one, you’re already behind.And it’s not just technical, right? You need a culture that’s okay with breaking things, learning fast, and iterating. Otherwise, you end up with a team that’s afraid to touch anything, and that’s the death of innovation.So, let’s say you’ve got the team and the infrastructure sorted. Now comes the fun part—growth, validation, and making sure you’re actually building something people want. This is where a lot of AI startups stumble, I reckon. You can’t just assume your product works at scale because it worked in the lab.Yeah, and that’s where iterative testing comes in. You have to keep validating, not just for performance, but for reliability. And you need to be listening to your users constantly. If you’re not aligning with what the market actually needs, you’re just building in a vacuum.Right, and sometimes the biggest growth comes from partnerships. I always think about that AI-media partnership—uh, I think it was a few years back, where a small AI company teamed up with a big media house. Suddenly, they had access to way more data, real-world feedback, and a massive user base. That kind of strategic alliance can accelerate growth way faster than just going it alone. But you gotta be ready for the scale and the scrutiny that comes with it.And, look, it’s a balancing act. You want to move fast, but you can’t cut corners on validation or you’ll lose trust. It’s a bit like what we said last episode—responsible innovation isn’t just a buzzword, it’s what keeps you in the game long-term.Exactly. And I think that’s a good place to wrap for today. There’s so much more to dig into, but we’ll save that for next time.Yeah, thanks for tuning in, everyone. This was a fun one. Jack, Emily, always a pleasure.Cheers, mates. Catch you all on the next Jellypod Journeys. Take care, everyone.Bye, everyone!See ya!

Episodes

Scaling AI Startups