AI-generated recipes are flooding the web, raising concerns about authenticity, ad placement risks, and brand safety in the booming digital food space.
As the internet continues to serve as the go-to destination for home cooks, a quiet revolution is reshaping the online recipe ecosystem—one driven not by chefs but by machines. According to DoubleVerify’s (DV) inaugural DV Deep Dive, low-quality AI-generated content is increasingly saturating cooking and recipe websites, raising essential questions for advertisers about authenticity, quality, and brand suitability.
The Rise of AI in the Recipe World
The digital recipe category is booming, growing more than 15% annually. Nearly 90% of consumers now rely on the internet as their primary source for recipes. This surge in demand, with the low barriers to entry and strong monetization potential, has made recipe websites a magnet for creators and, increasingly, for generative AI.
With tools like ChatGPT and Claude, anyone can spin up a website with hundreds of AI-written recipes and realistic images in hours. These platforms often blend affiliate links, sponsored posts, and ad placements into content optimized for search engines but rarely tested in real kitchens.
The question is: does it matter if a lemon poppy seed bread recipe was never baked? For consumers and advertisers alike, the answer appears to be yes—especially if you really get a sloppy, misleading recipe website that a human being would find hard to digest.
Consumers Crave Authenticity—and So Should Advertisers
DV’s report reveals that 87% of consumers believe image authenticity is essential, and 83% support mandatory labeling of AI-generated content. This reflects a deeper expectation for honesty and transparency, directly affecting brand perception.
When advertisers unknowingly place their ads alongside AI-generated “slop” content, like unvetted recipes, AI stock images, and even fake author bios, they risk damaging consumer trust. Questions arise: Are these sites designed to engage users genuinely or to churn out ad-friendly content? Do they reflect the values your brand stands for? And, crucially, can you even tell the difference?
Examples of AI-Heavy Recipe Sites
DV analysis spotlights several high-traffic sites that illustrate this new frontier, including:
- Insanely Good Recipes attracts over 3 million visits monthly. DV’s proprietary GenAI detection tools, along with manual reviews, indicate that both its text and images are likely AI-generated. The site’s author, “Kim,” appears to use an AI-generated headshot, although the site itself is reportedly managed by a real person with SEO expertise who has openly discussed using GenAI tools.
- Quick Recipes, which showcases obvious signs of automation including AI-generated food photos, inconsistent editorial design, broken links, and fictitious authors such as “Emily” and “Sophia,” whose photos and bios appear to be fabricated. Even the site’s contact information references a different website altogether.
- MarketGrow, once a financial news outlet, is now a recipe site pulling in nearly 4 million visits a month. It exemplifies a strategic pivot: evergreen food content allows for higher ad density and doesn’t require frequent updates. Like the others, it’s populated with likely AI-generated content and images.
Why This Matters to Advertisers: The Risk of Cookie-Cutter Content
AI-generated content isn’t inherently problematic, but the combination of synthetic recipes, fictional personas, and excessive ad clutter is a red flag. These characteristics don’t necessarily classify a site as “Made-for-Advertising/Arbitrage” (MFA), but they signal a poor user experience that could tarnish a brand’s image. DV’s categories for MFA, Clutter and GenAI Low-Quality, allow brands to select their own preferred flavors of brand suitability, while protecting their media spend from slop.
To navigate this evolving landscape, advertisers need to ask tougher questions of their DSP and SSP partners:
- How do you detect and evaluate AI-generated content?
- What controls are in place to prevent ads from running on low-quality content farms? ● Do you differentiate between AI-assisted editorial processes and fully automated sites? ● Can we opt out of placements on sites that heavily rely on AI?
- How often do you refresh and audit your inventory for media quality?
These questions can help advertisers gain transparency into how platforms manage AI-generated inventory and ensure their ad spend supports high-quality, human-vetted content aligned with their brand and performance goals.
As AI-driven cooking and recipe content continues to attract advertising dollars, advertisers must stay informed about these evolving trends and risks to ensure their media investments align with brand standards and transparency goals.
