Generative Vision Interview Questions, issue 9, Jun 17, 2026
The Spatial Addressing Paradox
Senior AI Engineer interview at Midjourney, and the interviewer asks:
“Your text-to-image model nails single subjects, but prompt it with ‘a brown teddy bear next to a white wall’ and the colors bleed across the entire frame. An intern on your team says ‘add more training data.’ Why is that wrong, and where is this actually breaking?”
Don’t say: “The model needs more examples of multi-object scenes.”
Why a perfectly trained model will still paint a white wall brown — and how swapping a global conditioning knob for joint attention saves your multi-object generation.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.