The best training data for computer vision in 2026 isn't captured. It's generated.
By 2028, 70% of computer vision models are projected to depend on multimodal, synthetic training data, not footage from real cameras.
Why? Real-world data has a ceiling. You can't film every accident, every rare defect, every lighting condition. Some behaviors, like a subtle eye movement or a micro-gesture, are nearly impossible for humans to label accurately, even when the footage exists.
So teams now build the scene instead of waiting to capture it. Simulate the factory floor. Simulate the storm. Simulate the one-in-a-million edge case that would take years to record naturally.
This flips the old assumption. More real footage used to mean a better model. Now, better-designed synthetic footage does.
The bottleneck moved from collecting data to designing it.
Would you trust a vision model trained mostly on scenes that never happened?