What 43,000 Annotated Camel Images Taught Me About AI Reality

There is a myth in AI that “more data solves everything.” From experience, that is simply wrong.

We annotated tens of thousands of camel images. What became obvious very quickly is that bad data scales failure faster than good data scales success.

Annotation is not clerical work. Every bounding box is a scientific judgment. If the person labeling does not understand camel behavior, the model learns nonsense.

At one point, we deliberately reduced the dataset size—removing noisy, ambiguous frames. Performance improved.

This is a lesson I’ve seen across projects: discipline beats volume. AI rewards structure, not brute force.