CARD · Card record
AI food images: what changes their appeal?
Visual oddness and an AI label are separate questions. Two food-image studies show why imperfect pictures can unsettle viewers without establishing that every AI menu reduces appetite.
01
THE STORY · The original
Read the explanation and its limits.
A 2025 Appetite study asked 95 people to rate 38 selected images: 32 generated pictures spanning different levels of realism, plus six rotten-food photos. Imperfect AI food was more unsettling than highly realistic or stylized images. The realistic group also used generated images; this was not simply real dishes versus AI dishes.
A study published in 2024 asked 297 UK participants to rate real photos and DALL-E 3 reconstructions, with absent, correct or reversed origin labels. AI pictures scored higher without labels; with correct labels, the overall difference was not statistically significant, although real ultra-processed foods scored higher. Data came from November 2023, not current models.
- Originalpress reporthttps://techcrunch.com/2026/09/03/the-sameness-problem-behind-those-unappetizing-ai-generated-menus/
Why it is worth understanding
Our suggestion: check pictured ingredients and portions against the actual dish. For a small evaluation with your own dishes and customers, distinguish visual appeal, accurate representation and willingness to order.
Conditions and limitations
These studies measured responses to images, not restaurant orders, dining or sales. Their samples and selected images do not represent every dish, culture or current model. BitShovel read the papers but did not reproduce the experiments.
Important additions and corrections
Dates below mark changes to our coverage.
- Addition ·
Added two original studies: image distortion and AI labels have different effects; these findings do not establish that all AI menus reduce appetite.
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02
THE THREAD · Full timeline
1 event, every row traceable.
A card accumulates records as its story develops: new corroborators, market contrasts, material changes at the original — each dated and linked to its archive day.
- Added to BitShovelEntered the BitShovel radar: first-party on TechCrunch AI (19 entries visible at collection).
03
SOURCES · Sources and evidence
Check each link's purpose and limits.
Sources and evidence · 4 links
Each link has a purpose and scope. Link counts do not establish independent confirmation; direct support applies only to the statements specified below.
- Reporting and primary researchDirect support
The reporting behind this topic. Experimental findings come from the papers.
Limits: Reporting, not a restaurant experiment.
- TechCrunch AI feedScope not documented
A per-link statement of support has not been attached.
- Diel et al. (2025): paper, pp. 3–5, 7Direct support
Supports the relation between realism, distortion and unease in selected images; methods and results were read.
Limits: Mainly German-speaking participants in Germany; pilot-selected stimuli cannot estimate the average effect of all generated images.
- Califano & Spence (2024): paper, pp. 2, 6–8Direct support
Supports image ratings under different origin-label conditions; Study 2 was read.
Limits: UK sample, 2023 imagery and a limited food set; no actual orders or sales data.
04
EDITORIAL REVIEW
Review of this card's wording and evidence.
Editorial review · Evidence reviewed
· Beijing time (UTC+8)
Read two original papers, separating image realism from origin labels and adding counterevidence, sample and date limits. This is a literature review, not a restaurant trial.
This dates our review of the card's wording and evidence, not a project release or product update.