We Analyzed 600+ Amazon Listings for AI Content… The Results Were Surprising

We got curious: How many listings on Amazon are actually written by AI?

So we ran an experiment. We took more than 500 Amazon listings across 10 different categories and analyzed the content of these listings.

We then cross-referenced the results against…

• pricing data
• review numbers
• star ratings
• search rankings

The results were different than what we expected.

Sounds interesting? Then let’s dive right in.

How We Did the Analysis

Before we get into the findings, here’s exactly how we made the analysis. We want to be transparent about methodology because AI detection is messy – and we want you to draw your own conclusions from the data.

Step 1: Picking the categories

We wanted diversity: not just electronics, not just fashion – a real cross-section of what people buy on Amazon … and those niches shouldn’t be dominated by too many major brands. We came up with these 10 niches:

• Wireless Earbuds
• Gaming Headset
• Phone Case
• Crossbody Bag
• Dog Bed
• Office Chair
• Shower Head
• Yoga Mat
• Travel Pillow
• Laptop Stand

Some of these are spec-heavy products where you’d expect technical copy (earbuds, headsets, phone cases). Others sell almost entirely on vibes and feelings – comfort, relaxation, lifestyle (dog beds, yoga mats, shower heads).

Step 2: Scraping the data

On February 23, 2026, we scraped the first ~50 organic and sponsored results for each keyword on Amazon.com. For every listing, we grabbed:

• The ASIN (Amazon’s unique product ID)
• Product title & price
• Star rating and total number of reviews
• Search result position (1 through 50)
• Whether it was a sponsored placement
• All bullet points & product description
• Any A+ / Enhanced Brand Content

Everything was saved as structured JSON – one file per category, 50 listings each. 500 total. You can download the data here and below this article.

Step 3: Running two AI detections

There’s no single AI detection tool that everyone trusts, and for good reason – they all work differently and they all have blind spots. So we used two AI models:

• ChatGPT 5.2
• Opus 4.6. 

They both analyzed each listing’s text for signals and scored each listing on a …

• 0 – 100 scale

… and assigned labels ranging from …

• “Likely human / brand copy” to “Highly AI / templated.”

Both models analyzed the exact same 500 listings independently. Neither saw the other’s results.

Step 4: Merging everything

We matched listings across both models by ASIN and created a composite score – the average of both detections. 432 out of 500 listings matched cleanly (the rest were duplicates from products appearing in multiple ad slots). We then layered on the pricing, review, rating, and position data to look for correlations.

What this approach can’t do

Let’s be honest about the limitations:

• No AI detector can tell you with certainty that a specific listing was written by ChatGPT or any other tool. What these models detect are patterns that correlate with AI-generated text. A human copywriter following a template could trigger the same signals. A seller who edits AI could fly under the radar.
• The two models disagreed: The first scored listings nearly twice as high on average (47 out of 100) compared to the second (26 out of 100). They agreed on which listings looked more or less AI-like, but not on how much. The Pearson correlation between their scores was 0.48.
• Treat the numbers as indicators, not verdicts: Where both models independently flagged a listing, we’re more confident. Where they split, it’s genuinely ambiguous.
• One more thing worth noting: this analysis was itself assisted by AI tools for data processing and scoring. Take from here what you will.

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The Findings of Our Analysis

1. About 1 in 5 listings showed strong AI patterns

• Out of 500 listings, 88 – roughly 18% – scored high enough on both models to be confidently flagged as AI-generated.
• Roughly 40% landed in a gray zone: enough to raise flags, but not so clear than the other 18%.
• Only about a third of all listings read as clearly human-written, based on both models agreeing independently.

That one-in-five number is probably conservative. Listings where a seller prompted AI and then heavily edited the output would likely land in the middle bucket. The real number of AI-touched listings is probably higher.

2. Newer brands lean more on AI

We used total review count as a rough proxy for how established an Amazon brand is. A product with 50 reviews is probably newer to market than one with 15,000.

New and unknown brands are about 50% more likely to get flagged than major brands. But even among major brands with tens of thousands of reviews, 16% of listings showed strong AI signals.

3. The sweet spot for AI copy is $20–$50

This one was counterintuitive. We assumed the cheapest products would be the most AI-heavy – low margins, low investment in copy. Nope.

Sub-$10 products scored the lowest for a reason: many of them barely have any copy at all. Short title, a couple of bullets, done. There’s nothing for a detection model to flag.

At the top end, $100+ products also scored low. Premium brands tend to invest in professional copywriting – or at least in copy that’s distinctive enough to not read like a template.

The middle is where things get interesting: The $20–$50 range is probably Amazon’s most competitive battleground. Every listing needs to work hard. And a lot of sellers in that range are probably turning to AI to produce polished, extensive copy without hiring a writer.

BTW: You can also do that with our free Product Description Generator.

4. Longer listings are more likely to be AI-written

Word count had a clear relationship with AI scores. Listings over 300 words scored nearly double the AI likelihood than those under 150.

But look at the last two columns: the shortest listings have the best ratings and the most reviews. The 500+ word listings have the worst ratings and the fewest reviews.

However, products with 14,000 reviews have been around longer and are probably just better-established regardless of their copy.

But at minimum, the data says: writing more doesn’t correlate with performing better. And in many cases, the relationship runs the other direction.

5. AI-heavy listings have 37% fewer reviews

When we compared the top 25% of AI-scoring listings against the bottom 25%:

The most AI-like listings had 37% fewer customer reviews. This doesn’t mean AI copy scares off buyers – it’s more likely that AI is used more on newer products that simply haven’t accumulated reviews yet.

6. Amazon search rankings don’t care if your copy is AI-written

We expected to find some relationship between AI scores and search position. Higher AI listings ranking better? Or worse? There’s nothing.

A 2.3-point spread across the entire results page is nothing. Amazon’s search algorithm appears completely indifferent to whether your listing copy was written by a person, a machine, or a very literary dog.

Rankings on Amazon are driven by sales velocity, conversion rates, review counts, advertising spend, and fulfillment method. The words in your bullet points? They matter for shoppers deciding whether to click “Add to Cart” – but Amazon’s algorithm doesn’t seem to reward or penalize any particular writing style.

7. Unbranded products use AI at the same rate as branded ones

We sorted all 500 Amazon listings into “branded” (title starts with a brand name, like “Razer BlackShark V2” or “Carhartt Crossbody Zip Bag”) and “unbranded” (title starts with generic product words, like “Orthopedic Dog Bed” or “Gaming Headset with Microphone”).

8. Listings with 5 bullets are more likely for AI content

Amazon’s Seller Central guidelines recommend using five bullet points per listing. Two-thirds of all listings in our dataset (66.8%) follow that advice exactly.

But here’s where it gets ironic:

Listings with exactly five bullet points are 82% more likely to be flagged as high-AI than listings with any other number.

AI writing tools are trained on Amazon’s best-practices. Sellers who write their own copy tend to deviate. They might use three bullets because the product is simple, or seven because it has a lot of features worth mentioning.

BTW:

You can also create Amazon listings with our free Product Description Generator.

So What Does This Mean?

If you’re a seller, here’s the honest truth:

Amazon doesn’t seem to care whether you’re using AI or not.

AI gets you to “good enough” faster than ever before, and Amazon’s algorithm doesn’t penalize you for using it. But it also doesn’t reward you.

The most effective thing you can do isn’t to avoid AI. It’s to avoid sounding like everyone else who’s using AI.

If you’re a shopper, the takeaway is simpler:

Don’t trust the words. Trust the reviews.

A polished listing with 87 reviews and a 4.9-star rating is a very different proposition from a bare-bones listing with 14,000 reviews and a 4.4-star rating – and in our data, the second one is almost always the better bet.

The Data We Used

This analysis is based on 500 Amazon product listings scraped on February 23, 2026, across 10 product categories. Each listing was independently scored by two AI detection models and the results were averaged for composite scores. All findings are correlational – we cannot confirm with certainty which listings were or were not written using AI tools.

You can download the dataset here