The Sameness Problem Behind Unappetizing AI-Generated Menus

Restaurants are turning to generative AI to freshen up their menus — but customers can viscerally sense that something is off.

Published: 2026-09-04 Category: Quick Take Sources: TechCrunch

The Shortcut That Backfires

There is a temptation every restaurant owner knows: the menu feels stale, and rewriting it takes time, money, and taste that not every kitchen has. Generative AI looks like the perfect shortcut — plug in your venue, your cuisine, your price point, and out comes a refreshed menu in seconds. TechCrunch's Amanda Silberling looks at why this particular shortcut tends to backfire, and it comes down to something harder to fake than a good description: sameness.

The core problem isn't that AI menus are wrong. It's that they're uniform. Trained on the same vast pool of restaurant copy, marketing guides, and recipe sites, models reliably converge on the same adjectives, the same structure, the same predictable "elevated comfort food" hedges. One AI-polished menu starts to read like every other AI-polished menu — and customers, the story argues, can feel it. They don't articulate it as "this was written by a model." They articulate it as "something is wrong with the food." That instinctive distrust is the real cost.

Why Sameness Feels Like Spoilage

There's a reason people respond viscerally rather than analytically. A menu is not just a list of dishes; it's a promise of identity. The specific way a restaurant describes its food — the voice, the quirks, the small local touches — is part of the experience before a single bite. When that voice is replaced by a statistically average approximation of "restaurant-y prose," the establishment stops signaling care and starts signaling cost-cutting. In food, cost-cutting reads as spoilage risk. The menu may be perfectly accurate, but the emotional math says otherwise.

This is the same failure mode we see across generative AI in creative work. The models are excellent at producing the median of a category and terrible at producing the point of view that makes a category worth choosing. A menu optimized for "what menus look like" can never tell you what this kitchen cares about. The homogeneity isn't a bug the model will eventually fix — it's structural to how the technology samples distribution.

The Business Lesson

There is a genuinely useful reading here for anyone deploying AI in a customer-facing role. The problem is not that AI lacks creativity in the abstract; it's that authenticity is a differentiator, and AI compresses it away. The restaurants that will win with these tools are not the ones that paste model output onto their menus but the ones that use AI as a starting draft and then burn hours making it theirs. The sameness is only a liability when you ship it as finished.

For a solopreneur or small business, the takeaway is practical: treat generative AI as a tireless research assistant, not a voice. If the output could plausibly have come from any competitor, it isn't done — it's a draft. The final edit is where the actual business value lives.

This quick take is based on reporting by Amanda Silberling at TechCrunch.