Why Building in AI Is Nothing Like Making Conventional Software

There’s a fundamental loop underneath everything we do at Every: Write -> build -> repeat. Building exposes you to aspects of the world that were previously hidden. Writing helps you to find a precise, concise way of expressing what you know and why. This loop isn’t necessarily linear—sometimes we start with building and move to writing, sometimes we start with writing instead—but it does lead to, we think, a particularly effective way of creating new things.

That’s why, today, we’re launching Source Code, a new column where we bring you into our product studio as we tinker with what comes next. This first piece, by Every entrepreneur in residence Edmar Ferreira, is an incredible articulation of building products in AI, why the key risk of new AI products is feasibility, and how to deal with it through fast experimentation. Sign up to be the first to try our products as an Every Early Adopter.—Dan Shipper

Was this newsletter forwarded to you? Sign up to get it in your inbox.

When I started building my first AI project at Every Studio, I approached it the same way I’d built products in the past: Identify a clear problem, map out a solution, build an MVP (minimum viable product), and iterate from there. It’s a fairly straightforward, software-driven approach: Build fast, test, learn, and improve.

But it didn’t work—so I asked myself: How is building in AI different from building in conventional software?

I joined Every Studio with an ambitious goal: to build nine products in three months—one project every 10 days. My first project, Mindtune, is an AI-driven alternative to traditional adtech and social media algorithms. My hypothesis is that people are fed up with the formulaic, impersonal content in their social media feeds, and that AI offers an opportunity to deliver a more relevant, personalized experience.

I started Mindtune with demand validation because this is where traditional software projects tend to fall apart. You build landing pages, talk to potential customers, analyze competitors—and only then do you invest resources in building out the product. Founders have been following this template for so long that it’s like a reflex. We don’t necessarily stop to ask ourselves, is building this thing even possible?

Building with AI requires us to break our habits and approach building in a different way. AI products bring with them a unique set of risks, and if you don’t understand them, you’re bound to make mistakes.

As I was building Mindtune, I identified three risk profiles that helped me see exactly what kind of risk I was taking on—and, more importantly, what would determine whether it succeeded. I’ll dive in to each of the risks, how they relate to each other, and how AI disrupts the traditional startup “risk chain.” My hope is that founders and builders can save themselves a few wrong turns in the idea maze by better understanding where the risks in their idea lie—and how best to defuse them.