1. research talent. There's not actually that many people in the world that can adequately fine-tune a large cutting-edge model, and far fewer that can explore less mainstream paths to produce value from models. Only way to get good researchers is to have name-brand leaders, like a top ML professor.

2. data. Can't do anything custom without good training data! How to get this varies widely across industry. Partnerships with established non-tech companies are a common path, which tend to rely on the network and background of founders.

Even with both those things it's not easy to outcompete a large, motivated company in the same space, like a FAANG. They have the researchers, they have the data and partnerships, so the way to beat them is to move quickly and hope their A- and B-teams are working on something else.