In the rapidly evolving landscape of e-commerce, Google has long been the gatekeeper of consumer discovery. For years, the "Popular Products" carousel has served as the bedrock of digital retail, acting as a high-traffic billboard for merchants. However, the integration of generative AI into Google’s search ecosystem—dubbed "AI Mode"—is fundamentally altering how products are surfaced, presented, and priced.
New research from Productrise, a firm specializing in organic product tracking for Google Shopping, suggests that Google’s AI Mode is not merely a different interface for the same data, but an entirely different engine with distinct preferences. According to their latest study, which monitored over two million product listings, the overlap between traditional search results and AI-generated shopping responses is remarkably thin. When the two systems do display the same product, they frequently present different sellers and, more controversially, different price points.
The Core Discrepancies: A Tale of Two Algorithms
The fundamental takeaway from the Productrise data is one of fragmentation. Between August 9 and August 31, 2026, researchers conducted identical product-specific searches in both standard Google Search (specifically targeting the Popular Products carousel) and AI Mode across the United States and the United Kingdom.
The results paint a picture of two distinct shopping experiences. When both platforms successfully returned results for a specific query, the traditional carousel was far more expansive, displaying an average of 27.8 products. In contrast, AI Mode was highly curated, offering an average of just 3.9 products per query.
More strikingly, the overlap between these two sets of results was minimal. Only 1.28% of products found in the traditional carousel appeared in AI Mode for the same search on the same day. This suggests that Google’s AI is filtering through the vast "Shopping Graph" with a completely different set of criteria than the traditional search ranking algorithm, which prioritizes the high-visibility carousel.
Chronology of the Investigation
The study conducted by Productrise was structured to isolate the influence of AI on price discovery and merchant selection. The timeline of the investigation focused on a 23-day window in August 2026.
- August 9–31, 2026: Researchers tracked over 100,000 search results daily. Each day, the team executed identical queries in both environments.
- The Matching Process: To ensure accuracy, the company utilized Google’s own unique product IDs to match items across both interfaces. The study focused on the "first-listed" offer—the primary merchant and price presented to the consumer before they click to expand the selection.
- The Data Synthesis: By aggregating these matches, Productrise established a baseline for how often AI Mode deviates from the traditional search norm. The findings were not merely incidental; they were consistent throughout the nearly month-long observation period.
Supporting Data: When the Same Product Tells a Different Story
Perhaps the most significant finding for e-commerce stakeholders is the lack of consistency in seller and price representation. When a product did appear in both interfaces, the "first-listed" seller was different 49.6% of the time. This means that a brand’s primary competitor in a traditional search result might be entirely different from the competitor highlighted by the AI.
The price discrepancies were equally pronounced. In 38.1% of matched cases, the price listed for the same product differed between the two platforms. When these prices diverged, AI Mode was significantly more likely to favor the higher price point. Specifically, in 68.4% of instances where the prices differed, the AI presented the more expensive option.
When looking at the data in aggregate—including instances where prices were identical—AI Mode prices were, on average, 21.6% higher than those found in the traditional carousel. While the study notes that some of this gap is driven by outliers (such as the AI comparing a new product to a used item in the carousel), the trend toward higher pricing in AI-generated results is consistent enough to warrant attention from both retailers and consumer advocacy groups.
Official Responses and Industry Context
The findings have sparked a conversation regarding the transparency of "black box" AI algorithms. When approached for comment regarding the study, Google maintained a neutral stance, neither confirming nor denying the specific metrics presented by Productrise.
In a statement provided to Futurism on September 2, 2026, a Google spokesperson stated:
"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 position rests on the idea that the Shopping Graph is a singular, unified data source. However, the Productrise data suggests that the application of that data—the way it is retrieved, weighted, and prioritized—varies drastically between the traditional UI and the generative AI interface.
Implications for Merchants and Retailers
For the average e-commerce brand, these findings present a complex challenge. Merchant Center’s existing performance insights provide a bird’s-eye view of "share of voice" in AI Overviews, but they remain silent on the granular details of seller rank and pricing.
The "Price Sensitivity" Shift
Hugo Huijer, founder of Productrise, suggests that the traditional search environment is heavily optimized for price competition. In contrast, AI Mode appears to be influenced by factors beyond just the lowest dollar amount. Huijer notes that "the cheapest price is less of a factor in AI Mode," speculating that brands with more comprehensive product feeds—or those that provide more detailed metadata—might find themselves favored by the AI, even if they are not the lowest-cost provider.
The Competitive Landscape
For retailers, the implication is that their search strategy can no longer be monolithic. A brand might dominate the traditional carousel while being completely absent from—or consistently undersold in—AI Mode. This creates a "hidden" competition where merchants are losing out on potential customers without ever knowing who their primary rival in the AI space actually is.
Operational Strategy
Because the data does not track click-through rates or conversion, it remains unclear whether these AI-driven price discrepancies actually lead to lost sales. However, the fact that the first-listed merchant is different nearly half the time suggests that the "top-of-funnel" visibility is being reshuffled. Retailers must now consider their "AI presence" as a distinct metric from their "Search presence."
Looking Ahead: The Future of AI-Driven Commerce
As AI Mode becomes increasingly accessible—appearing directly in the browser address bar, the search box, and as a dedicated tab—the discrepancies between these two modes will likely become a major point of friction for advertisers.
If AI Mode is intended to act as a personal shopper, its preference for higher-priced items or specific sellers over others will inevitably draw scrutiny from regulatory bodies and consumer protection agencies. For now, the best defense for a retailer is visibility. The data provided by Productrise suggests that manual monitoring is currently the only way to audit how one’s brand is being represented in the new AI-centric search environment.
As we move further into the era of generative search, the "Shopping Graph" remains the common denominator, but the AI, it seems, is writing its own rules. Whether this shift is a temporary quirk of early-stage machine learning or a permanent pivot toward a new, more curated retail experience, the data makes one thing clear: the way the world shops is changing, and the first result you see is no longer a given.
