Generative AI creates delicious, sustainable, and nutritious burgers
Research shows generative AI can learn the structure of the human palate from recipe data to design novel burgers. It rediscovered the Big Mac and optimized for taste, sustainability, and nutrition. In a blind test with 101 participants, AI burgers matched or beat Big Mac in liking, flavor, and texture; its mushroom burger had an order of magnitude lower environmental impact, and its bean burger nearly doubled the nutritional score.
-->
[Submitted on 3 Feb 2026]
Title:Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers
View a PDF of the paper titled Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers, by Vahidullah Tac and 2 other authors
View PDF HTML (experimental)
Abstract:Food choices shape both human and planetary health; yet, designing foods that are delicious, nutritious, and sustainable remains challenging. Here we show that generative artificial intelligence can learn the structure of the human palate directly from large-scale, human-generated recipe data to create novel foods within a structured design space. Using burgers as a model system, the generative AI rediscovers the classic Big Mac without explicit supervision and generates novel burgers optimized for deliciousness, sustainability, or nutrition. Compared to the Big Mac, its delicious burgers score the same or better in overall liking, flavor, and texture in a blinded sensory evaluation conducted in a restaurant setting with 101 participants; its mushroom burger achieves an environmental impact score more than an order of magnitude lower; and its bean burger attains nearly twice the nutritional score. Together, these results establish generative AI as a quantitative framework for learning human taste and navigating complex trade-offs in principled food design.
Comments: 13 pages, 4 figures
Subjects:
Computational Engineering, Finance, and Science (cs.CE)
Cite as: arXiv:2602.03092 [cs.CE]
(or arXiv:2602.03092v1 [cs.CE] for this version)
https://doi.org/10.48550/arXiv.2602.03092
arXiv-issued DOI via DataCite
Submission history
From: Ellen Kuhl [view email] [v1] Tue, 3 Feb 2026 04:34:44 UTC (38,834 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers, by Vahidullah Tac and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CE
new | recent | 2026-02
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)