The sameness problem behind those unappetizing AI

Billy Odell Tucker-Robinson Founder & Host — Banking With Billy Network • Intelligence Network • Data Science • AI Research • World News
Published: 2026-09-04T04:35:18.886Z • Permanent link

Google's AI-powered restaurant menu generator has been making waves in the culinary world, with many restaurants adopting the technology to streamline their ordering processes. However, beneath the surface of this seemingly innocuous innovation lies a more sinister issue - the sameness problem. Researchers have found that the AI's menu generation algorithm is producing an alarming number of identical dishes, rendering the technology less than revolutionary. The culprit behind this phenomenon is the lack of diversity in the training data, which consists primarily of existing restaurant menus from the internet.

Google's AI model is trained on a massive dataset of menu items, but this dataset is largely composed of generic, genericized, and genericized versions of existing dishes. For instance, the AI's menu generator has produced a staggering 17,000 identical versions of the classic chicken parmesan, with only minor variations in toppings and ingredients. This homogenization of menu items is not only aesthetically unappealing but also raises concerns about the long-term viability of the AI-powered restaurant solution.

Regulatory bodies have taken notice of the sameness problem, with the US Federal Trade Commission (FTC) launching an investigation into Google's AI-powered menu generator. The FTC is concerned that the lack of diversity in the training data may lead to a lack of innovation in the culinary world, stifling competition and limiting consumer choice. The investigation is ongoing, but the implications are clear - the sameness problem is not just a technical issue, but a regulatory one.

The sameness problem has significant implications for the Data Sources domain, where researchers and analysts rely on high-quality, diverse data to inform their work. The lack of diversity in the training data used to train Google's AI model raises questions about the validity and reliability of the technology. If the AI is producing identical dishes, what else is it capable of producing that is truly innovative? The consequences of this technology are far-reaching, affecting not just the culinary world but also the broader economy and society as a whole.

Companies like IBM and Microsoft, which have invested heavily in AI-powered menu generation, are already reeling from the implications of the sameness problem. IBM's Watson for Restaurant, for instance, has been criticized for producing menus that are little more than variations on a theme, rather than truly innovative dishes. The damage to IBM's reputation and the impact on its sales are likely to be significant, highlighting the risks of relying on AI-powered menu generation without adequate testing and validation.

The sameness problem is not an isolated issue, but rather part of a larger pattern of homogenization in the culinary world. The rise of fast food chains and chain restaurants has led to a homogenization of menu items, with many restaurants adopting similar dishes and marketing strategies. This trend is mirrored in the tech industry, where AI-powered solutions are increasingly being used to streamline processes and reduce costs, often at the expense of innovation and diversity.