Why Online Marketplaces Profit From Counterfeit Goods

Every major online marketplace — Amazon, Walmart, Meta’s ad network — takes a cut when a counterfeit product sells. And until the brand owner files a complaint, the platform has zero legal obligation to do anything about it.

That is not a conspiracy theory. It is how the law works. Jesse Xu, CTO and co-founder of Podqi — an AI platform that automates counterfeit detection and takedown across marketplaces and the web — has spent years inside this structural gap. The misalignment between platform incentives and brand safety is the single biggest reason online counterfeiting keeps growing.

The legal loophole nobody talks about

Most people assume Amazon or Walmart actively polices their listings for fakes. They don’t — and they don’t have to.

“By law, the platforms technically do not have a mandate to proactively take these out,” Jesse explains. “Up until the right owner reports, the platforms are making margin off of this.”

The burden falls entirely on the brand. If Nike doesn’t find and report a counterfeit listing, that listing stays live — generating revenue for the marketplace — indefinitely. The platform isn’t breaking any law by hosting it. This is the structural incentive problem: the entity with the most data, the best detection tools, and the widest visibility has the least motivation to act.

It’s not just fake handbags anymore

The counterfeiting networks Jesse’s team tracks look nothing like the stereotypical knockoff luxury goods racket. These are organized operations running pixel-perfect fake websites, paid ads on Google and Meta, phishing campaigns, and cloned Shopify stores — all at scale.

“They’re not just ripping off of this X hardware company. They’re ripping off of anything from Bed Bath Beyond to supplements companies to random things. And so it’s really like a network of counterfeiters.”

A single bad actor network might target dozens of brands across completely different categories. They spin up fake storefronts quickly, run paid traffic to them, and when one gets taken down, another replaces it. “As soon as you become a target, they can just fit these up like clockwork,” Jesse says. The speed and volume make manual brand monitoring impossible.

Why brands discover the problem too late

The typical brand finds out about counterfeits the way most people find out about termites — after significant damage. A customer complains about a defective product they bought from what looked like an official listing. A sales team notices revenue dipping in a category without obvious competitive pressure. A legal team gets forwarded a screenshot of a fake ad.

By then, the counterfeiter has been operating for weeks or months, siphoning customers and eroding trust. Jesse describes the moment brands first see the full picture: “Immediately they see it and they’re like, oh my God, I didn’t even know there was this much out there.”

The scale shocks people because they’ve been thinking about counterfeiting as a one-off problem — a single bad listing to report. In reality, it is a continuous, automated operation running across multiple platforms simultaneously.

What this means for the enforcement model

The current system puts brands in a reactive position: find violations one by one, file takedown requests through each platform’s specific process, wait for action. That works when you have five fake listings. It collapses when you have five hundred, spread across Amazon, Walmart, Meta ads, Google Shopping, and standalone phishing sites.

This is why Podqi’s approach emphasizes recall first. “We essentially cast the widest net possible, we emphasize recall, and then what we’ll do is over time, we get that whittled down,” Jesse says. The AI casts wide, classifies at scale, and matches against known bad actors — because no human team can keep pace with counterfeiting networks that operate like assembly lines.

“No human should be doing this work,” Jesse puts it bluntly. The structural incentive problem won’t change through legislation anytime soon. So the enforcement model has to change instead.

FAQ

Why don’t marketplaces remove counterfeit products on their own?

Under current law, platforms like Amazon and Walmart have no mandate to proactively police counterfeit listings. They earn margin on every sale — legitimate or not — until the brand owner files a formal takedown request. The legal burden falls entirely on the rights holder to detect and report infringements.

What types of products are most targeted by counterfeiters?

Counterfeiting networks target far beyond luxury goods. According to Podqi’s data, organized networks simultaneously counterfeit hardware products, home goods, supplements, beauty products, and consumer electronics. Any brand with meaningful online sales volume becomes a target regardless of category.

How do counterfeit networks operate across multiple platforms?

Modern counterfeit operations run coordinated campaigns across Amazon, Walmart, Google Shopping, Meta ads, and standalone Shopify-style websites simultaneously. They use pixel-perfect cloned sites, paid advertising, and phishing emails. When one storefront gets taken down, replacement listings go up quickly — often within days.

How long does it take to detect counterfeit listings?

Most brands discover counterfeits reactively — through customer complaints or unexplained revenue drops — weeks or months after fake listings go live. AI-powered monitoring can reduce detection to near-real-time by continuously scanning marketplaces, ad networks, and the open web for trademark infringements.

What is the brand owner’s legal obligation for counterfeit takedowns?

Brand owners must identify, document, and report each infringing listing to the specific platform through that platform’s takedown process. Each marketplace has different requirements and timelines. Without automated tools, this becomes a full-time manual operation at scale.

How much revenue do brands lose to online counterfeiting?

Brands that implement systematic enforcement typically see one to two percent baseline revenue growth over six to twelve months as fake competitors are cleared from marketplaces. The inverse suggests that unchecked counterfeiting represents a measurable drag on legitimate sales beyond just brand reputation damage.

What is the difference between proactive and reactive brand protection?

Reactive brand protection waits for complaints or manual discovery before filing takedowns. Proactive brand protection uses AI classifiers to continuously scan platforms and the web, detecting and flagging infringements automatically. The proactive model catches violations in days rather than months.

How does AI detect counterfeit products at scale?

AI brand protection systems use multiple classifiers to match product listings against a brand’s assets — logos, product images, trademarks, and known counterfeiter patterns. The approach prioritizes recall (catching as many violations as possible) first, then refines precision over time to reduce false positives.

Can small brands afford automated brand protection?

The economics of brand protection have shifted. Previously, only large enterprises could maintain legal teams for enforcement. AI automation reduces the cost per takedown dramatically, making systematic protection accessible to mid-market brands that previously absorbed counterfeiting losses as a cost of doing business.