As artificial intelligence continues to reshape how consumers interact with the internet, search engines are undergoing a fundamental transformation. Google, the undisputed titan of global search, has increasingly integrated generative AI into its core user experience. Features like AI Overviews and the dedicated "AI Mode" for product searches are now primary entry points for millions of online shoppers.
However, a revealing new study suggests that shifting from traditional search results to Google’s AI-powered shopping interface is not merely a cosmetic change—it introduces radical discrepancies in the products recommended, the merchants highlighted, and the prices displayed to the consumer.
According to data compiled by Productrise, an organic product tracking platform for Google Shopping, Google’s AI Mode rarely mirrors the product lineups found in its standard "Popular products" carousels. Furthermore, when the exact same product does appear across both interfaces, AI Mode frequently elevates different merchants and higher price points.
This comprehensive report breaks down the methodology of the study, the core findings regarding product overlap and pricing structures, official responses from tech giant Google, and the long-term implications of these findings for e-commerce brands, digital marketers, and consumers.
1. Main Facts: The Core Findings
The data published by Productrise highlights a profound divergence between how Google structures traditional e-commerce search results and how its generative AI curates product options. The core takeaways of the study include:
- Stark Contrast in Volume: When queries triggered results in both interfaces, the traditional "Popular products" carousel displayed an average of 27.8 products, whereas AI Mode presented a much more curated, narrow selection averaging just 3.9 products.
- Minimal Daily Overlap: Only 1.28% of products featured in the standard carousel also managed to appear in AI Mode for the exact same search query on the same calendar day.
- Frequent Merchant Shuffling: Among the small percentage of products that did appear in both places, the primary seller listed first differed 49.6% of the time.
- A Preference for Higher Prices: When matched products featured differing prices across the two formats, AI Mode displayed the higher price in 68.4% of instances. Across all matched products (including those with matching prices), AI Mode prices were, on average, 21.6% higher than carousel prices.
These statistics suggest that Google’s AI Mode operates under a fundamentally different discovery and ranking algorithm than its legacy carousel system, prioritizing different merchants and price tiers even when serving the same underlying shopping queries.
2. Methodology and Chronology: How the Study Was Conducted
To understand the weight of these findings, it is essential to examine how the data was gathered, verified, and contextualized over time.
Monitoring Scope and Timeline
The research was conducted by Productrise over a 23-day period, running from August 9 to August 31, 2026. During this window, the platform monitored more than 2 million product listings extracted from over 100,000 regular search results and AI Mode responses.
The geographic scope focused on product-centric search queries originating from both the United States and the United Kingdom. To maintain parity, the platform performed identical product-specific searches in both regular Google Search and AI Mode on the exact same days.
Data Matching Process
In traditional search results, the study isolated its comparison strictly to the "Popular products" carousel, deliberately ignoring other modular search features like sponsored ads or text-based organic listings. In AI Mode, the analysis incorporated all product listings displayed directly within the AI-generated conversational response.
To accurately pair items, Productrise matched products from both views using official Google product IDs. Once matched, the platform compared the first-listed offer—specifically analyzing the merchant identity and the listed price on both sides.
It is worth noting that the queries analyzed were primarily focused on products already actively tracked by Productrise’s proprietary platform. Consequently, the study’s authors do not describe the dataset as a statistically random sample of all global Google searches, but rather as a targeted examination of heavily monitored e-commerce categories.
3. Supporting Data: Breakdown of Overlap, Pricing, and Presentation
The empirical evidence compiled by Productrise sheds light on the mechanical differences between traditional carousels and AI-driven shopping results.
The Overlap Gap
The sheer volume of results presented to a user creates the first major barrier to product continuity. While a standard carousel bombards the shopper with nearly 28 distinct options to scroll through, AI Mode acts as a hyper-curated advisor, limiting its primary presentation to roughly four items.
Because of this aggressive filtering, the daily product overlap sat at a meager 1.28%. This means that an item featured prominently in a standard search carousel has less than a 2% chance of appearing in the AI Mode response for the same query on that day.
The Price and Merchant Discrepancy
When an item miraculously crossed the divide and appeared in both places, shoppers could not rely on consistency. For nearly half of these shared products (49.6%), a completely different merchant was crowned as the "first-listed offer."
Price discrepancies closely followed merchant shifts. Prices varied on 38.1% of matched products. When those prices diverged, AI Mode displayed a higher price nearly 70% of the time (68.4%).
Looking at the broader mathematical averages:
- Across all matched products, AI Mode prices trended 21.6% higher than carousel prices.
- Where AI Mode showed a higher price, the median difference was 22.2%, with the mean skewed higher (88.5%) due to specific retail outliers—such as comparing a used or refurbished item in a carousel to a brand-new item listed in AI Mode.
- Where AI Mode prices were actually lower than the carousel, the median difference was a more modest 7.8%.
Despite these variations, both interfaces typically allow users to click into a product panel to view alternative sellers and secondary price tiers. However, the initial psychological anchor—the first-listed price and merchant—remains heavily skewed toward higher price points in AI Mode.
4. Official Responses and Analytical Interpretations
As these findings began circulating within the digital marketing and tech journalism communities, industry outlets sought clarification from the search engine giant.
Google’s Official Stance
In early September 2026, tech publication Futurism reached out to Google for comment regarding the Productrise study. Google issued the following official statement:
"While we haven’t verified the accuracy of the claims in this report, all shopping results on Google Search, including AI Mode and the search results page, are powered by the same data source: our Shopping Graph. Shoppers can easily click into a product listing to compare prices for that product across retailers and choose the best option for them."
Google’s response emphasizes structural unity: under the hood, both systems draw from the same massive repository of product data known as the Shopping Graph. However, the statement does not address why the curation algorithms surface different subsets of data, nor does it explain the persistent upward pressure on prices observed in AI Mode.
The Industry Interpretation
Hugo Huijer, the founder of Productrise, offered his own qualitative interpretation of the data in the platform’s official research write-up. According to Huijer, the data strongly implies that "the cheapest price is less of a factor in AI Mode."
Huijer posits that generative AI algorithms may rely on contextual relevance, rich structured data, or merchant authority signals rather than raw price competitiveness. Consequently, brands and retailers that cannot win a traditional "race to the bottom" on price might find unexpected pathways to visibility in AI environments—provided their product data feeds are exceptionally detailed and optimized.
5. Implications for E-Commerce, Brands, and Consumers
The structural shift toward AI-dominated search environments carries profound implications for everyone in the digital commerce ecosystem.
The Brand Visibility Dilemma
For online merchants, losing control over the "first-listed" spot is a critical concern. In e-commerce, top-of-funnel visibility heavily dictates conversion rates. If a brand’s product appears in AI Mode, but a third-party reseller or direct competitor is listed as the primary merchant at a higher price point, the brand risks losing potential direct-to-consumer sales.
Compounding this issue is a current reporting gap within Google’s ecosystem. While Merchant Center’s AI performance insights report (introduced earlier in 2026) provides metrics on a brand’s "share of voice" and impression rates within AI Overviews and AI Mode, it does not expose granular seller or pricing metrics. Brands can see that they are appearing, but they remain blind to which seller or what price Google’s AI is showcasing on their behalf.
Changing Consumer Behavior
For shoppers, AI Mode promises a streamlined, conversational shopping experience, cutting through the noise of 28+ carousel options down to a curated handful of top recommendations. However, consumers must exercise caution. Because AI Mode displays higher prices in a significant majority of price-divergent queries, relying exclusively on AI recommendations without clicking through to compare alternative offers could lead to paying unnecessary markups.
Looking Ahead: How to Adapt
As AI Mode becomes increasingly ubiquitous—accessible via a single click from standard search boxes, Chrome’s address bar, and dedicated search tabs—digital marketers and e-commerce strategists must adapt.
While the data does not yet provide a definitive playbook for gaming AI ranking factors, industry experts recommend a proactive approach:
- Audit Your Top Products: Manually search your hero products in both standard carousels and AI Mode. Document who appears first and at what price point.
- Optimize Product Feeds: Since AI models rely heavily on deep, contextual structured data from the Shopping Graph, ensure your Merchant Center data feeds are as comprehensive, rich, and accurate as possible.
- Monitor Multi-Merchant Dynamics: Keep a close eye on authorized resellers and competitors who may be hijacking your product listings in AI-generated responses.
As generative AI continues to mature, the algorithms dictating what we buy, who we buy it from, and how much we pay will remain a fiercely contested frontier in digital commerce.
