An AI-designed menu may look professional at first glance, but many users notice something unsettling about it: sandwiches that are too symmetrical, perfectly round scoops of ice cream, cheese flowing in a way that looks more like a drawing, or shrimp that seem to be eating their own tails. These details do not necessarily mean the image is obviously fake, but they give it a uniform, polished appearance that differs from real food.
The article examines this phenomenon as a problem of convergence in AI outputs, not merely a visual error in a restaurant menu’s design. Generative models learn patterns from enormous quantities of data and then produce what they consider the most likely appropriate response to a request such as creating a menu for a burger restaurant. When the materials on which they were trained are already similar, the result becomes closer to recycling a single visual style.
Training Data Pushes Toward Familiar Forms
Alex Lisle, the chief technology officer at Reality Defender, says some of these images look as though an alien entity is trying to make a pizza without understanding its basic principles. He believes the reason is connected to the data from which the models derived their capabilities. When asked to design a menu for a fast-food restaurant, models may draw on menus from well-known chains such as Wendy’s, Burger King, and McDonald’s, materials that already share a similar visual style.
The result is not an exact copy of any particular menu, but a combination that reproduces the most common features: ideal lighting, symmetrical arrangements, smooth surfaces, and colors presumed to be more enticing. Lee Rainie, director of the Imagining the Digital Future Center at Elon University, points out that data optimization focuses on what appears acceptable and inoffensive, which can turn into homogeneity that erases edges and differences.
The problem worsens if AI-generated materials enter later training sets. Lisle distinguishes this situation from Model Collapse. Model Collapse is a more severe scenario that arises when a model’s outputs are repeatedly fed back into the model itself, leading to a broad deterioration in quality. What appears in menus, according to his description, is convergence or homogenization that reduces the variety and quality of results without making the model entirely useless.
Repeated Edits May Increase the Flaws
The problem does not always come from the initial image generation. A user on the platform X, using the name Labtec, shared an experiment in which they created a menu through ChatGPT and then edited it 100 times. As the edits were repeated, the food images looked less like real food and more rounded and smooth. The article reported that TechCrunch repeated the experiment and found a similar result.
This scenario matters to restaurants that use generative tools to change item prices, names, or small details in a menu. Each individual editing process may seem limited, but their accumulation can gradually push the image away from a natural appearance. In this case, AI is not merely a time-saving tool, but part of an editing cycle that may reinforce the artificial features the designer is trying to conceal.
Why Do People Detect the Sense of Fakeness?
Rainie believes people sometimes have a difficult-to-explain ability to distinguish between a generated image and a real one, even when they cannot identify the suspicious element. The article links this discomfort to what is known as the Uncanny Valley effect. Researchers at the University of Duisburg-Essen in Germany found that food images that appear almost real may provoke more disgust and discomfort than images that are clearly fake.
In the case of food, the paradox is clearer: traditional advertising itself usually exaggerates the arrangement and lighting of food to make it look better than reality, as happens with images of a Big Mac in advertisements. But AI outputs may push this beautification to a level at which the food loses its familiar characteristics. When a customer notices that the cheese, bread, or shrimp does not behave visually as expected, the intended enhancement becomes a source of aversion.
What Changes in Practice?
The lesson for restaurants is not that every use of AI in design fails, but that relying on an ideal appearance alone may harm trust. Images that do not accurately represent the actual food may create a gap between a customer’s expectations and experience, while highlighting the need for human review capable of noticing details that the model’s standards for a “beautiful image” cannot reduce to a formula.
The implications extend beyond menus. Lisle says that the historical reliance on sight and hearing as trustworthy evidence, including in court systems that granted recordings and images a special status, is no longer equally safe. If it is sometimes difficult to distinguish between a real food image and a generated one, then verifying visual and audio content in more sensitive fields becomes a practical question, not merely an aesthetic one.
The source does not offer a definitive technical solution to this problem, nor does it establish that every AI-generated menu will provoke rejection. But it clarifies an important limitation of current models: the pursuit of outputs that are broadly acceptable may end up removing local and individual characteristics, then reproducing a single image of what food—or any other content—should look like.