Restaurants and food service companies deploying AI tools to generate digital menus are encountering a familiar complaint from early testers: the results look, sound, and feel remarkably similar.
What Happened
According to reporting by TechCrunch published September 3, 2026, restaurants using generative AI systems to create or supplement their menu descriptions have found that outputs tend toward generic, interchangeable wording. The phenomenon—dubbed the "sameness problem" by some in the industry—involves multiple establishments receiving nearly identical dish descriptions when using similar prompting approaches with commercial AI tools.
Why It Matters
For restaurants, differentiation is a core business function; menus communicate brand identity, culinary philosophy, and competitive positioning. When AI-generated text converges on similar adjectives, phrasing, and structure across unrelated businesses, the result can undermine the very differentiation these tools were meant to help create. The issue highlights a broader technical limitation: current generative models trained on large text corpora tend toward statistically common phrasings, which may not serve niche or distinctive culinary offerings well.
The Bottom Line
The incident illustrates that deploying AI in brand-sensitive applications requires careful evaluation of where generic outputs are acceptable and where human-crafted language remains valuable for standing out in competitive markets.