The counterfeit economy is enormous, ruthless, and extraordinarily good at hiding in plain sight. Every day, shoppers scrolling through popular online marketplaces click ‘buy’ without knowing whether the product arriving at their door is the real thing or a convincing fake. The consequences range from the mildly annoying, a lipstick that doesn’t quite match its label, to the genuinely dangerous, like counterfeit electrical goods that skip critical safety testing. As of late 2026, the problem has grown big enough that dedicated technology is now being built specifically to hunt fakes down before they reach your basket.
BBC journalist Ammie Sekhon has been looking closely at exactly what that technology looks like in practice, as part of a wider exploration of how the tech industry is responding to some of the most pressing challenges of our digital moment. The picture that emerges is one where artificial intelligence, image recognition, and sophisticated pattern analysis are being layered together to catch sellers who have spent years outsmarting more basic detection systems.
The Scale of the Fake Goods Problem Online
To understand why this technology matters, you need to grasp just how large the counterfeit market has become. Luxury goods, sports equipment, pharmaceuticals, electronics, all of it moves through online platforms at a pace that human moderators simply cannot match. A single seller can list thousands of products in hours. Manual review teams, no matter how well-trained, are fighting a numbers battle they cannot win alone.
That is where machine learning steps in. Systems trained on enormous datasets of both legitimate and fake product listings can flag anomalies in imagery, pricing patterns, seller behaviour, and even the subtle linguistic quirks that tend to appear in fraudulent product descriptions. What once took a team of reviewers days can now happen in seconds, at scale, across millions of listings simultaneously.
The challenge, of course, is that counterfeiters adapt. The moment a detection method becomes known, sellers begin working around it. It is a constant cycle of innovation on both sides, and the technology has to evolve just as quickly as the fraud it is designed to catch.
Why Removing Fakes Is Harder Than It Looks
Detection is only half the battle. Once a suspicious listing is flagged, platforms face a genuinely complex decision: remove it and risk taking down a legitimate seller by mistake, or leave it and risk facilitating fraud. Getting that balance wrong in either direction has real consequences, commercially and reputationally.
Legal frameworks add another layer of complexity. Intellectual property laws vary significantly between countries, which means a product that constitutes a clear infringement in one jurisdiction might occupy a legal grey zone somewhere else. For global platforms operating across dozens of markets, building a single coherent enforcement system is a substantial undertaking.
There is also the question of consumer awareness. Many people buying counterfeit goods, particularly at the lower end of the market, do so knowingly, drawn by the price. The technology being built to remove fakes is therefore working against both sophisticated criminal networks and a segment of consumer demand that, frankly, prefers the fake.
A Different Kind of Chip, Built in Britain
Away from the counterfeit problem, another thread of the same BBC investigation pulls focus to something happening on British soil that deserves considerably more attention than it typically gets. Journalist Shiona McCallum visited a UK-based company that is manufacturing a type of computer chip distinct from the processors that dominate most conversations about the semiconductor industry.
The global chip conversation has, for years, been dominated by a handful of names and an even smaller handful of countries. Taiwan’s foundries, America’s design houses, and the Netherlands’ lithography specialists have commanded most of the headlines, particularly since supply chain disruptions during the early 2020s exposed just how concentrated chip production had become.
Britain, in that narrative, often appears as a consumer of chip technology rather than a producer. Which is what makes the existence of UK companies quietly doing genuinely different work in this space so worth noting. Different does not mean inferior. In many cases it means purpose-built: chips designed for specific applications where general-purpose processors are either too expensive, too power-hungry, or simply not well-suited to the task.
Why Specialised Chips Matter Right Now
The push toward AI-driven applications across virtually every industry has created enormous appetite for chips that can handle specific computational workloads efficiently, rather than doing everything adequately. Training large language models, running computer vision systems, processing sensor data from autonomous vehicles, each of these tasks has a different computational profile, and a chip optimised for one can wildly outperform a general processor doing the same job.
For the UK tech sector, companies working in this space represent both an economic opportunity and a strategic one. Reducing reliance on a small number of international chip suppliers has become a matter of national interest for governments across Europe and North America. A domestic capability, even if niche and specialised, is worth cultivating.
Two Stories, One Bigger Picture
What connects the counterfeit-hunting AI and the UK chip maker is a broader truth about where technology is heading: toward specificity, toward depth, and toward solving problems that are genuinely hard rather than simply iterating on solutions that already exist. The era of one-size-fits-all tech is giving way to something more targeted, more expert, and in many cases more interesting.
Neither story is particularly glamorous on the surface. Removing fake listings from a marketplace and manufacturing specialised silicon in a British facility do not carry the obvious drama of, say, a rocket launch or a breakthrough medical device. But the infrastructure-level work, the unglamorous scaffolding of modern digital life, is where some of the most consequential innovation is happening right now.
As platforms, regulators, and consumers all grow more sophisticated about what they demand from technology, the companies building these quieter, more specialised tools may well end up shaping the next decade of the industry more than the ones grabbing the loudest headlines.
So here is the question worth sitting with: if the technology already exists to identify and remove counterfeit products at scale, what is stopping online marketplaces from making this the standard everywhere, and who exactly benefits when they don’t?
