What GetCited Found
GetCited's scan of 106,758 citations across ChatGPT, Google AI Overviews, Perplexity, and other AI answer engines found that roughly one in four product-category citations links to a page earning money from its picks. That is a 25% share for affiliate-monetized or paid-placement review content, across queries where someone is trying to decide what to buy.
This was not a small sample. 106,758 citations is large enough to treat as a baseline signal, not a curiosity. The pattern holds across multiple AI engines, which suggests this is a structural feature of how AI engines select sources for product queries, not a quirk of one platform.
Why AI Engines Are Citing These Pages
The finding runs against an assumption many AEO practitioners hold: that commercial intent signals would hurt a page's citation odds. That assumption came from traditional SEO intuitions about E-E-A-T and editorial independence. AI engines are not applying the same filter.
Several factors likely explain why affiliate-heavy review pages earn citations at this rate:
Domain authority and age. Sites like Wirecutter, PCMag, and the major vertical review publishers have high domain authority built over years. AI engines pulling from web data inherit those trust signals.
Content format. Affiliate review pages tend to follow a consistent structure: comparison tables, clear winner picks, pros and cons, and frequent update stamps. That structure is easy to extract and summarize. AI engines process and cite content they can parse cleanly.
Update frequency. Affiliate publishers update product pages aggressively because stale picks hurt conversion. A page updated three weeks ago beats an editorial piece last touched in 2022, all else equal.
Explicit comparison framing. When someone asks an AI "what is the best CRM for small teams," a page titled "Best CRMs for Small Teams: Tested and Ranked" maps directly to the query intent. Affiliate publishers optimize hard for those exact title patterns.
None of this means AI engines are rewarding monetization. They appear to be rewarding format signals and authority signals that affiliate sites have concentrated through years of optimizing for human readers.
What This Means for Brand Strategy on Product Queries
If you are a brand trying to appear in AI answers for product-category queries, you are competing against a cohort of sites that have been training for this match for a decade. Direct citations to your product page are unlikely unless someone is asking specifically about your product by name.
The more practical target is getting cited on the affiliate and review pages that AI engines already trust. A placement on a top-10 list that is getting cited in ChatGPT and Perplexity is more valuable now than it was two years ago, because that citation path is now two steps: human links to review page, AI engine cites review page, user gets your product name in the answer.
That is a different PR and content strategy than building your own citation-worthy pages, though both matter.
Signals That Correlate With Cited Affiliate Pages
The GetCited data points toward a cluster of observable signals on pages that get cited:
| Signal | Pattern in cited pages |
|---|---|
| Domain authority | High DA publishers dominate; thin affiliate sites do not |
| Title match to query | Explicit "best X for Y" framing |
| Content freshness | Updated within 90 days in most cases |
| Comparison tables | Present on the majority of cited pages |
| Word count | Long-form, not thin listicles |
The implication for publishers: if you are building product-category content meant to earn AI citations, the review-site format is not something to avoid for fear of looking commercial. It is what works.
Practical AEO Takeaways
Mirror the format, not just the structure. If affiliate review pages are getting cited, the format signals matter. Comparison tables, update dates, explicit winner picks, and clear criteria sections all appear in the pages AI engines are pulling from. These are signals you can replicate without running an affiliate program.
Prioritize third-party review placements. For brands, getting mentioned on already-cited review pages is a faster path to AI answer inclusion than building new content from scratch. Track which review pages are being cited for your product category, then pursue placement or coverage on those specific pages.
Identify where the bias is lower. The 25% affiliate figure applies to product queries. Informational and how-to queries likely have a different citation distribution. If affiliate competition is too steep for your product category, shifting content investment toward adjacent informational queries may yield better citation rates.
Monitor citation types in your niche. Knowing that affiliate pages dominate category-wide is useful, but you need to know the breakdown for your specific product queries. Tools like Otterly and PromptWatch let you track which URLs appear in AI answers for target queries, so you can see whether affiliate pages or brand pages are being cited in your space specifically. Peec and OmniSEO offer citation-level tracking that can surface competitor URL patterns over time.
If you want citation-type breakdowns at scale, Profound and Evertune AI both provide enterprise-level monitoring that can segment citation sources by domain type, which is closer to what you need to operationalize the GetCited finding for your own category.
The Operational Reality
The GetCited data changes the cost-benefit math on a few decisions. Investing in affiliate-format content is more defensible now that there is evidence AI engines cite it at scale. Pursuing placements on high-authority review sites is a legitimate AEO tactic, not just traditional PR. And expecting brand product pages to compete directly with established review publishers on category queries, without a format or authority match, is a harder case to make.
Track which review pages are winning citations in your category. Then decide whether to build toward that format, place within those pages, or focus energy on query types where the field is less concentrated.