Why AI Food Images Look So Wrong — SkimNews
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- Restaurants and food brands are increasingly publishing AI-generated food promotional images, producing disturbing outputs including donut shrimp, wormlike noodles, trypophobic burritos, and burgers that look like masonry or cracked concrete.
- Diffusion models generate images by starting with pure noise and refining structure first, then texture — meaning structural errors get locked in early, explained Chris Russell, a professor of AI, government, and policy at the University of Oxford.
- Giovanbattista Califano, a behavioral scientist at the University of Naples Federico II, said diffusion models are notoriously weak at generating thin, continuous, terminating structures — which is why noodles, tendrils, bubbles, and clustered holes (a trypophobia trigger) bleed across the image.
- AI image generators have no actual understanding of what food is, said Roland Meyer of the University of Zurich; they reproduce the visual conventions of food photography without grasping why food should look or behave the way it does.
- Training data skews surreal, noted Simon Colton of Queen Mary University of London, because ordinary foods like red apples are rarely posted online while bizarre food content thrives as memes; AI models are also increasingly being trained on their own AI-generated outputs, a process that can cause "model collapse" and visual degeneration.
- Humans are evolutionarily primed to detect unsafe food, Califano said — AI images hit those disgust triggers simultaneously, with wormlike tendrils suggesting parasites, clustered holes suggesting infestation, and off colors suggesting spoilage.
Why it matters: Food marketing depends on appetite appeal, but brands are publishing images that activate evolved disgust responses rather than craving — a self-defeating use case given that diffusion models structurally struggle with the very textures (thin strands, contained bubbles) that define appetizing food photography.
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