The internet made shopping gloriously convenient, but it also handed counterfeiters the largest marketplace in human history. Today, in 2026, fake products lurk inside legitimate platforms with unsettling confidence, sitting right next to the genuine article and daring you to tell the difference. The good news? Technology is catching up fast, and the people building the tools to fight back are doing some genuinely remarkable work.
BBC journalist Ammie Sekhon has been looking closely at the technologies now being deployed to identify and scrub counterfeit listings from online marketplaces, and what she found paints a picture of an arms race that is quietly reshaping e-commerce as we know it. Meanwhile, her colleague Shiona McCallum visited a UK-based company producing a distinctly different kind of computer chip, one that could matter enormously to the future of British tech.
The Scale of the Counterfeit Crisis Online
To understand why this fight matters, consider the numbers. The global trade in counterfeit and pirated goods has grown into one of the largest shadow economies on the planet, worth hundreds of billions of dollars annually. Luxury fashion, consumer electronics, pharmaceuticals, sporting goods, almost nothing is off-limits. And with the rise of third-party seller marketplaces, even the most reputable platforms can unwittingly become storefronts for fakes.
The challenge is not simply that counterfeits exist. It is that they have become extraordinarily hard to distinguish from legitimate products, both for consumers browsing on a phone screen and for the platforms themselves trying to police millions of listings in real time. A human content moderation team, no matter how large, cannot match the speed at which bad actors can upload new listings.
How AI and Machine Learning Are Changing the Game
This is where technology steps in with something approaching a genuine answer. Modern counterfeit detection systems use machine learning models trained on enormous datasets of both authentic and fraudulent product listings. These systems learn to flag anomalies, from suspicious pricing patterns and inconsistent product imagery to seller histories that do not add up.
Image recognition has become particularly powerful in this space. AI can now compare a product photograph in a listing against a brand’s authenticated image library, identifying subtle differences in logos, stitching, packaging, or serial number fonts that a human eye would almost certainly miss. Some systems go further, cross-referencing seller data with known fraud networks, flagging accounts that share characteristics with previously banned sellers even when operating under entirely new identities.
The brands themselves are also getting involved at the manufacturing stage. Digital watermarking, microscopic QR codes embedded in physical products, and blockchain-based provenance records are all being used to give genuine goods a verifiable identity that a counterfeit simply cannot replicate. When a consumer or platform scans that product, its entire journey from factory to shelf can be confirmed in seconds.
The Cat-and-Mouse Problem
None of this means the problem is solved. Counterfeiters adapt quickly. As detection tools grow sharper, the tactics used to evade them grow more creative. Listings are now sometimes seeded with authentic product images before being switched post-purchase. Fake reviews are deployed to build seller credibility. Some operations run legitimate products alongside counterfeits, using the genuine sales to build trust ratings before the fraud begins.
This is why the technology has to keep learning. Static rule-based systems are already obsolete. The most effective tools today are dynamic, updating their models continuously as new fraud patterns emerge. Think of it less like a lock and key, and more like an immune system that gets stronger each time it encounters a new threat.
A Different Kind of Chip: The UK Company Doing Things Its Own Way
Away from the fraud detection story, Shiona McCallum’s visit to a UK chipmaker adds a fascinating second dimension to the broader tech conversation. The global semiconductor industry has long been dominated by a handful of giants operating out of the United States, Taiwan, South Korea, and the Netherlands. The idea of a British company carving out a meaningful niche in chip design and manufacturing is the kind of story that does not get told often enough.
What makes this particular company notable is not that it is trying to outmuscle the behemoths at their own game. It is building something categorically different, a chip architecture suited to specific, high-value applications rather than the mass-market processors that power most consumer devices. This kind of specialist approach has historically been where smaller, agile companies can outperform industry giants, because the giants are structurally built for volume, not customisation.
Why UK Chip Development Matters Right Now
The timing is not accidental. Governments across Europe and North America have spent the past several years grappling with the strategic vulnerability exposed by chip shortages, particularly during the supply chain disruptions that followed the pandemic years. The political will to nurture domestic semiconductor capability is stronger now than it has been in decades.
The UK government has made semiconductors a stated industrial priority, and companies operating in this space are working within a policy environment that, while not as lavishly funded as US or EU counterparts, is more supportive than at any point in recent memory. For a smaller company with a distinctive product, that context can make a real difference.
Two Stories, One Bigger Picture
On the surface, fighting fake trainers online and building specialist computer chips in a British facility seem like entirely separate stories. But they share a common thread: the insistence that technology, built thoughtfully and applied precisely, can solve problems that once seemed intractable.
The counterfeit economy thrived for years because the tools to fight it simply did not exist at scale. Now they do, and the balance is shifting. Similarly, the assumption that advanced chip development was the exclusive preserve of a few well-capitalised Asian and American giants is being quietly, methodically challenged by companies working at the cutting edge of what is technically possible.
Both stories are worth watching closely. The fight against online fakes is far from over, but for the first time, the defenders have technology that is genuinely up to the task. And in a nondescript UK facility, someone is building the kind of chip that might end up inside something you rely on, without you ever knowing it was made down the road.
As authentication technology grows more sophisticated and niche chip development accelerates, the bigger question worth sitting with is this: in a digital marketplace where trust has become the scarcest commodity of all, how much of that trust should we place in algorithms, and how much still needs a human hand to guarantee it?
