A recent study highlighted by Search Engine Land reveals an interesting reality about how AI-powered shopping recommendations are created. While many marketers assume that AI product suggestions come from proprietary databases or entirely new discovery systems, the research shows that existing search infrastructure still plays a major role.
In fact, the study found that a large majority of the products displayed in ChatGPT shopping carousels can be traced back to listings in Google Shopping. This finding offers a glimpse into how AI tools assemble product recommendations and what it means for ecommerce brands trying to stay visible in the evolving search landscape.
The research analyzed more than 43,000 products that appeared in ChatGPT’s product carousels across ten different retail categories. When those items were compared with results from major shopping engines, over 83% closely matched products that already ranked within the top 40 organic listings on Google Shopping. By contrast, only a very small portion of the carousel products showed a meaningful match with results from Bing Shopping. These numbers suggest that Google’s shopping index is currently the primary product data source influencing which items appear in ChatGPT’s visual recommendation panels.
The mechanism behind this process involves something known as “shopping query fan-outs.” When a user asks ChatGPT for product suggestions, the system often generates additional background searches designed specifically to retrieve product data. These fan-out queries are typically shorter and more transactional than the original prompt. They are then used to pull structured product listings, including titles, pricing, and merchant information, which populate the carousel displayed alongside the conversational response.
Another interesting takeaway is how closely carousel ranking correlates with positions inside Google Shopping results. Products appearing near the beginning of a ChatGPT carousel frequently correspond to items that already rank highly within Google’s shopping listings. In fact, a large portion of matches come from the top 10 to 20 Google Shopping positions, reinforcing the idea that strong performance in Google’s product ecosystem can directly influence AI-driven product discovery.
For ecommerce brands and digital marketers, this insight reframes the conversation around AI optimization. Instead of treating AI visibility as an entirely new discipline, the study suggests that traditional ecommerce fundamentals still matter. Maintaining high-quality product feeds, optimizing listings in Google Merchant Center, and ensuring accurate titles, images, and pricing remain essential steps for gaining visibility not only in search engines but also in AI-generated shopping experiences.
Ultimately, the findings highlight a broader trend: the worlds of search, ecommerce data, and AI recommendations are becoming tightly interconnected. As conversational platforms increasingly incorporate visual product results, the underlying infrastructure often still relies on well-established search ecosystems. For marketers, that means success in AI commerce may begin with the same fundamentals that have long driven performance in traditional search and shopping platforms.