Ask an AI assistant to recommend a good pair of running shoes or a reliable air purifier, and there's a strong chance Amazon shows up somewhere in the answer, even if your brand makes a better product and sells it directly. It feels like the deck is stacked, and in some ways it is. Amazon holds an estimated 37.8% of the U.S. e-commerce market (Demandsage, "Amazon Statistics & Market Share 2026," https://www.demandsage.com/amazon-statistics/), and that scale gives marketplaces an enormous head start in sheer content volume and review density.
But the deck is not as stacked as it looks, and the data on how AI assistants actually build recommendations tells a more useful story than "marketplaces always win." This post looks at why AI defaults to marketplaces in the first place, where that default actually breaks down, and what independent D2C brands can do to compete for the same recommendation.
Why AI Assistants Default to Marketplaces
AI models favor sources they can trust and verify quickly, and marketplaces make that easy. A single Amazon listing often carries thousands of reviews, structured pricing and availability data, and a consistent format the AI has seen millions of times before. That familiarity and density of evidence make marketplace listings a low-effort, high-confidence citation for an AI model trying to answer a shopping question quickly.
Marketplaces have also invested heavily in their own AI shopping layers. Amazon's assistant, Rufus, reportedly processes more than 13% of Amazon's search queries as of early 2026 and is estimated to have driven around $12 billion in incremental sales in 2025 (10xCrew, "Global Ecommerce 2026: Where Amazon Stands," https://10xcrew.com/global-ecommerce-2026-where-amazon-stands/). That's a scale of investment most independent D2C brands cannot match head-on.
Where the Marketplace Default Actually Breaks Down
Here's the part that gets missed in most conversations about marketplace dominance: AI assistants recommending "a product on Amazon" is not the same as AI assistants recommending Amazon's own algorithmic top picks, and it's often not even close.
A large-scale study by Marketplace Pulse examined nearly 2,000 non-branded queries and over 12,000 recommendations from Amazon's own Alexa for Shopping assistant. It found that 63.9% of the AI's picks fell outside the organic top-10 search results for the matching query, and 40.9% never appeared on the visible results page at all (Marketplace Pulse, "Amazon's AI Doesn't Read the Rankings," https://www.marketplacepulse.com/articles/amazons-ai-doesnt-read-the-rankings/). Only 14.3% of the AI's picks were sponsored listings on that search page.
In other words, even inside Amazon's own ecosystem, its AI assistant is not simply reading the sales-ranked top of the page. It's evaluating products on different signals entirely, largely product data quality, review substance, and content clarity, the same signals an independent brand can compete on directly, on its own site, without needing marketplace scale to win.
What D2C Brands Can Actually Compete On
Depth of Product Story a Marketplace Listing Can't Hold
A marketplace listing is built for speed and comparison, not depth. It rarely has room for the full story of why a product is made the way it is, what problem it solves in detail, or how it compares to alternatives in a nuanced way. Your own product and content pages can carry that depth, and AI models reward well-structured, detailed content when a shopper's question calls for more than a spec comparison.
Fresher, More Specific Content
Marketplace listings often go stale, especially older ones that haven't been updated in years. A D2C brand that keeps its product pages, comparison content, and supporting blog posts current has an advantage AI models notice, particularly for fast-moving categories.
Direct Trust Signals a Marketplace Doesn't Fully Capture
Reviews on your own site, third-party press mentions, and category authority built through consistent content are all trust signals that live outside the marketplace ecosystem entirely. AI models increasingly cite brand websites as the largest single source type in shopping-related answers, which means a well-optimized brand site is not automatically at a disadvantage against a marketplace listing.
Category Specificity
A marketplace has to serve every category at once. A D2C brand can build the single most thorough, well-structured resource on its specific niche, something a general marketplace listing structure is not designed to do. AI assistants often favor the most specific, authoritative source available for a narrow query, even over a much larger general marketplace.
A Practical Approach to Competing
Audit where you're currently losing the recommendation. Run the actual comparison and recommendation queries your shoppers would ask, and see whether marketplaces, competitors, or nobody is winning the citation instead of you.
Build comparison content that names the marketplace directly. A page that honestly compares buying direct versus buying through a marketplace, covering things like authenticity, support, and exclusive product lines, gives AI models a reason to cite your independent perspective.
Double down on structured, detailed product data on your own site, since this is exactly the signal that let AI recommendations bypass Amazon's own top-ranked listings in the Marketplace Pulse study.
Strengthen your own review and trust signals rather than relying only on marketplace reviews, so your independent site carries the same evidence an AI model looks for.
Track your citation rate against marketplace listings specifically, not just against other D2C competitors, so you know where the real competition for the recommendation is coming from.
Where Blazly Fits In
Competing with marketplace scale is not about matching their size. It's about being clearly the better, more specific, more current source for the exact question a shopper is asking, and knowing whether that's actually happening.
Blazly's AI Competitor Research and AI Prompt Ranking tools let you see exactly how often marketplaces show up ahead of your brand for the comparison and recommendation queries that matter most to your category, so you're working from real data instead of a general sense that Amazon always wins. AI Citation Flow traces where those marketplace citations are actually pulling their evidence from, which often reveals gaps in your own content that are easier to close than they first appear.
On the content side, the GEO Content Writer and GEO Landing Page Generator help build the direct comparison content and detailed product pages that give AI models a specific, well-structured, independent source to cite instead of defaulting to a general marketplace listing. And because this is an ongoing competition rather than a one-time fix, AI Visibility Tracking keeps monitoring your standing against marketplace and competitor citations across ChatGPT, Gemini, Claude, and Perplexity as the landscape shifts.
Marketplaces have scale. They don't automatically have the most specific, current, or trustworthy answer for every question a shopper asks, and that gap is exactly where an independent D2C brand can still win the recommendation.
FAQ
Do AI assistants always recommend marketplaces like Amazon over D2C brands?
No. While marketplaces have scale advantages, research on Amazon's own AI shopping assistant found the majority of its picks fell outside the platform's own top-ranked organic results, showing AI recommendations are based on product data quality and evidence, not just marketplace presence or sales rank.
What gives a D2C brand an advantage over a marketplace listing in AI search?
D2C brands can offer deeper product storytelling, fresher and more specific content, direct trust signals like on-site reviews and press mentions, and category-specific authority that a general marketplace listing structure isn't built to provide.
Should D2C brands still sell on marketplaces if they want AI visibility?
Many brands sell on both channels, but building strong, structured, independent content on your own site is what allows you to be cited as a distinct source rather than blending into a marketplace listing's information.
How can a brand tell if marketplaces are beating it in AI search results?
Running the actual comparison and recommendation queries shoppers would ask across AI assistants, and tracking whether marketplaces, competitors, or your brand gets cited, shows exactly where the competition for the recommendation is happening.
Is content depth really more important than marketplace review volume for AI citations?
Both matter, but AI models weigh clarity, structure, and specificity heavily. A marketplace listing with high review volume can still lose an AI citation to a D2C page with clearer, more detailed, and better-structured product information.