The AI Model Built On Today Might Not Exist Tomorrow

Dan Haiem is the founder and CEO of AppMakers USA, helping business leaders design, build and scale apps that deliver real-world impact.

​Founders often treat model selection like a one-time technical decision. They pick the best one available, integrate it and ship. What most don’t think about is that they’re not just choosing a feature. They’re making an architectural commitment that their product will have to live with long after the model they chose has changed, been repriced or been superseded by something that didn’t exist when they started building.

I’ve been watching this play out for five years. The model that dominates the conversation when a founder decides to build is almost never the model that dominates the conversation two years later. That gap is where a lot of expensive, unplanned rebuilds come from.

The Decision That Still Holds Up

In late 2020, a healthcare client came to my team wanting to build their app’s language layer around AlphaFold 2. It was generating enormous excitement at the time, particularly in healthcare and life sciences, and the enthusiasm made sense. It was a genuinely impressive piece of research.

I advised against it, not because it wasn’t capable, but because AlphaFold 2 was purpose-built for protein structure prediction. Anchoring a production app’s general-purpose language layer to a model designed for a specialized scientific task was an architectural risk that could compound over time. The product’s needs were going to grow in directions that model was never designed to support.

Our recommendation was GPT-3 instead, chosen specifically for reliability and the flexibility to evolve with the product rather than constrain it. That app still runs today. The foundation we recommended has since evolved significantly across the entire GPT family, and the client has been able to take advantage of those improvements without rebuilding from scratch. The decision was boring at the time. That’s usually a sign it is right.