AI Product Descriptions: What Marketplaces Now Require

In 2023 ZeroGPT rated the US Constitution 92.15% AI-generated. That gets retold as a joke about a bad tool, but it is really a story about genre: formal, repetitive, heavily imitated writing scores high regardless of who produced it. Product copy is the most formulaic prose on the internet, which should tell you something uncomfortable before you scan your own catalog.

As of mid-2026 marketplaces broadly do not ban AI product descriptions outright. What they enforce is accuracy, originality and volume: unverifiable claims, listings duplicated across sellers, and mass-generated pages that add nothing. Detection scores barely enter into it, which is why sellers optimizing for a low AI percentage are usually solving the wrong problem.

What marketplaces actually enforce as of mid-2026

Read the seller policies of the major platforms and you find a consistent shape, even though the specific wording differs everywhere. None of them are structured around how the words were produced. They are structured around four things.

Accuracy comes first. A listing must describe the item you will actually ship, with the right dimensions, materials, compatibility and quantity. This is where AI-generated copy causes genuine harm, because a model asked to describe a product it has never seen will happily invent a warranty length or a certification.

Originality comes second. Copying a competitor's description was a violation long before generative models existed, and bulk generation quietly recreates the problem from a different direction: feed the same category prompt to the same model and thousands of sellers converge on near-identical text.

Prohibited and regulated claims come third, and they carry the most legal weight. Health, safety, efficacy and environmental claims are governed by advertising law rather than by platform preference. In the US, the FTC's guidance on advertising has long required that objective claims be substantiated before they run, and nothing about a model drafting the sentence changes who is responsible for it.

Volume and quality come fourth. Platforms have become blunt about thin, mass-published pages that exist to occupy search results rather than to help a shopper choose.

Where AI product descriptions are genuinely fine

Take the strongest objection seriously, because it is correct as far as it goes: in a spec-driven category there is exactly one way to write "18/10 stainless steel, dishwasher safe, 1.5 quart capacity." There is no voice to add. Demanding hand-written prose for that sentence is theater.

Agreed, and that is precisely why detector scores are the wrong instrument here. Work with a risk map instead.

Listing element	AI risk	What to check before publishing
Spec table and dimensions	Low	Values match your source data
Care and compatibility notes	Low	Matches manufacturer documentation
Feature bullets	Medium	Every bullet traceable to a real feature
Benefit and lifestyle copy	Medium	Not duplicated across your own SKUs
Health or safety claims	High	Substantiated, and reviewed by a human
Comparison to competitors	High	Verified, current, and defensible
Product imagery	High	Provenance known and disclosed

The pattern in that table is worth stating outright. AI is safe where the text is a rendering of data you already hold, and risky wherever the text asserts something about the world that nobody checked. Drafting is a formatting job on the top rows and an editorial job on the bottom ones.

The duplicate content problem bulk generation creates

Here is the failure mode nobody plans for. Three sellers list the same white-label bluetooth speaker. All three ask a mainstream model for a 120-word description using the same supplier spec sheet. The outputs are not identical, but they are close enough that a search engine deduplicating near-matches sees one page with three URLs.

The consequence is not a penalty in the punitive sense. It is invisibility, which costs the same and is harder to diagnose. One listing gets treated as canonical and the other two are filtered out of results, and the sellers who lost never receive a notification explaining why.

Put a person in it. Devi runs a two-person homewares brand with 340 SKUs and a fourth quarter that pays for the whole year. In August she regenerates every description to freshen the catalog, using one prompt template for the entire run. Product page traffic slides through September, and she spends six weeks auditing ad spend, page speed and pricing before anyone lines up two listings and notices that her ceramic mug and her ceramic serving bowl now open with the same sentence. Nothing was penalized. Her catalog simply stopped distinguishing itself from itself, and the fix, once identified, took an afternoon.

That is the version of this problem that actually costs money, and no detector percentage would have surfaced it. What surfaces it is comparing your own listings against each other, which almost nobody does after a bulk run.

Scale changes the arithmetic in an interesting direction, though. Suppose you run 2,000 SKUs at roughly 120 words each. That is 240,000 words, which at 1 credit per 100 words comes to 2,400 credits to scan the entire catalog. On AI Detector 360 that fits inside a Starter plan month at $9.99 for 4,000 credits, and a Pro plan at $24.99 for 15,000 credits covers a catalog several times larger. Full pricing is on our pricing page. The point of scanning is not to hunt for a magic percentage. It is to find the clusters where your own copy has collapsed into the same three sentences, which a sentence-level heatmap surfaces immediately.

What Google actually penalizes

The most repeated myth in e-commerce SEO is that Google penalizes AI-written copy. It does not, and it has said so plainly: the standard is helpful content, judged regardless of how it was produced. What Google targets is scaled content abuse, meaning large volumes of pages generated primarily to manipulate rankings rather than to help anyone.

Those two rules are perfectly compatible, and the distinction is about purpose and value rather than authorship. A generated spec paragraph that accurately describes a product a shopper is trying to evaluate is helpful content. Ten thousand near-identical category pages targeting keyword permutations are scaled content abuse whether a person, a script or a model produced them. Our full treatment of whether Google penalizes AI content unpacks the guidance line by line, and the practical detection workflow for SEO teams covers how to build review into a publishing pipeline.

A useful internal test: could a knowledgeable buyer read this listing and make a decision they could not have made from the spec table alone? If yes, it earns its place. If no, adding words will not save it and removing the page might.

Product images have a separate, harder problem

Text is the easy half. Generated and heavily edited product imagery raises questions with real consumer-protection weight, because an image that shows a product feature the item does not have is a misrepresentation no disclaimer fixes.

Provenance signals exist but are fragile. C2PA Content Credentials are embedded by OpenAI's image tools since February 2024, by Adobe Firefly, by Microsoft's Bing and Designer products, and by Google's Nano Banana image models in 2026. The catch is that platforms routinely strip that metadata on upload, so its absence proves nothing at all. Google's SynthID watermarks Gemini-generated images, but there is no public third-party API to verify a SynthID mark, which means only Google can check.

Pixel-level detection degrades under exactly the conditions e-commerce imposes. Bellingcat found in September 2023 that a leading image detector missed 7 of 10 AI images after social-media-level compression, and every marketplace re-encodes and resizes what you upload. AI Detector 360's AI image detector inspects C2PA and EXIF provenance alongside pixel analysis and reports a likely generator when the signals support one, at 5 credits per image. We are equally clear about the limits: a clean result on a compressed thumbnail is weak evidence, and we say so in the report rather than burying it.

The workable policy is internal rather than forensic. Keep a provenance record for every catalog image at the point of creation, note which were AI-generated or AI-edited, and never let a generated image depict a feature, finish or included accessory that the shipped product lacks.

A pre-publish workflow that scales

Six steps, arranged so that the expensive human attention lands only where it changes an outcome.

1. Generate from structured data, never from the product name alone. Your spec fields go into the prompt.
2. Diff against your own catalog before publishing. Near-duplicates within your store are the problem you can actually fix today.
3. Route claims to a human. Anything about health, safety, efficacy, certification, warranty or environmental impact gets read by someone who can substantiate it.
4. Scan in batches for sentence-level repetition rather than for a headline percentage. AI Detector 360's free AI detector covers 5,000 characters with no sign-up if you want to sanity-check a sample before committing to a full pass.
5. Log image provenance at upload, including which files were generated or edited.
6. Spot-check quarterly, because supplier data drifts and copy written against last year's spec becomes an inaccuracy without anyone touching it.

Notice that only one of those six steps involves a detector, and it is the least decisive one. That is deliberate. If your listings are flagged, the useful question is never "how do we lower the score." It is "is anything in here untrue, duplicated, or useless to a shopper." Our explainer on why honest writing gets flagged is worth sharing with anyone on your team who panics at a percentage.

What nobody can verify yet

Three genuine unknowns, stated plainly because guessing at them is how sellers waste quarters.

Whether marketplaces will ever gate listings on detection is unresolved. There is no public evidence of platforms rejecting listings on a detector score as of mid-2026, and the economics argue against it: false positives on spec copy would be enormous, and duplication checks are cheaper and more reliable.

How the EU AI Act lands on catalog copy is genuinely open. Article 50's transparency obligations become applicable on August 2, 2026, requiring AI-generated content to be marked in machine-readable form and deepfakes to be disclosed. Whether routine product descriptions fall inside that framing, and what a compliant marking looks like inside a marketplace that strips metadata, are questions the guidance has not answered in practice.

And nobody can tell you a detector score that proves a description was human-written. That is not modesty about our own tool. Scores are probability estimates with published error rates, and on the single most formulaic genre in commercial writing those estimates are at their weakest. Use detection to find duplication and drift across a catalog. Use humans to decide whether a claim is true.