The internet made shopping faster, cheaper, and vastly more convenient. It also handed counterfeiters the largest marketplace in human history. In 2026, the scale of fake goods circulating through online platforms is staggering, and the technology being used to fight back is finally starting to match the ambition of the problem. At the same time, a lesser-known but equally urgent story is unfolding in a British lab, where engineers are quietly building a different kind of computer chip that could shift how we think about processing power altogether.
The Counterfeit Economy Is Enormous, and It Is Online
Walk through any major e-commerce platform today and you are navigating a landscape riddled with imitation goods. Fake trainers, counterfeit cosmetics, knock-off electronics, products that look plausible in a thumbnail but arrive as something else entirely. The global trade in counterfeit and pirated goods has been valued in the hundreds of billions of dollars annually, a figure that keeps climbing as online retail expands into new markets and new demographics.
What makes the modern counterfeiting problem so hard to solve is its sheer velocity. Sellers can list thousands of products within hours, disappear when flagged, and return under a new storefront before the platform has finished processing the complaint. Manual moderation cannot keep pace. The platforms know it. Regulators know it. And increasingly, technology companies are stepping into the gap with tools that combine image recognition, machine learning, and pattern analysis to spot fakes before they reach a buyer’s doorstep.
BBC journalist Ammie Sekhon has been looking closely at the technologies being deployed to identify and remove counterfeit listings at scale, exploring how artificial intelligence is being trained to catch subtle visual cues, inconsistent pricing patterns, and seller behaviour that flags a product as likely fraudulent. Her BBC Tech Now investigation puts a spotlight on an arms race that most consumers never see but that affects virtually every online purchase they consider making.
Why AI Is the Only Realistic Answer
Teaching a machine to recognise a fake sounds deceptively simple. In practice, it requires enormous datasets of both genuine and counterfeit products, constant retraining as fraudsters adapt, and a tolerance for a certain error rate that must be kept low enough to avoid penalising legitimate sellers. The leading approaches combine computer vision, which analyses product images at a granular level, with behavioural signals, how a seller prices, how they describe products, and whether their account history matches credible trading patterns.
Brand owners are increasingly central to this effort. Luxury houses, sports labels, and pharmaceutical companies have invested heavily in digital authentication teams that work alongside platform algorithms to train detection models. The result is a hybrid system: machine speed with human expertise guiding the decisions that matter most. It is imperfect, but it is improving rapidly, and the consensus among the people building these tools is that doing nothing would be catastrophically worse.
The Consumer Cost Nobody Talks About
Beyond the obvious risks, a counterfeit medication, a fake electrical device that becomes a fire hazard, there is an economic cost that rarely gets discussed plainly. Every fake sale is a lost revenue moment for a legitimate manufacturer, but it is also a trust deficit for the platforms that allowed it to happen. Consumer confidence, once eroded, takes years to rebuild. The technology being developed now is not just about catching criminals. It is about making online commerce feel safe again.
A Different Kind of Chip: Britain’s Quiet Hardware Story
Shift the lens from fraud detection to the hardware that makes all of this computation possible, and you land in a very different kind of story. Also featured in the BBC Tech Now programme, journalist Shiona McCallum visited a UK company working on a chip that deliberately departs from the architecture that has dominated computing for decades.
The semiconductor industry has long been defined by a handful of dominant designs, most of them rooted in concepts developed in the mid-twentieth century and refined continuously since. The processors inside your laptop, your phone, your car, they share foundational principles that have served well but carry inherent constraints as the demands placed on them grow more complex. Artificial intelligence workloads, in particular, stress conventional chip architectures in ways their original designers never anticipated.
What companies on the frontier of chip design are now pursuing is a more specialised approach: processors built not as general-purpose tools but as highly optimised engines for specific types of work. The UK company McCallum visited sits within a broader national conversation about Britain’s role in the global semiconductor supply chain, a conversation that grew considerably louder after the supply disruptions of the early 2020s exposed how concentrated, and therefore fragile, chip manufacturing had become.
Why the UK Chip Sector Matters Now
Britain is not a volume manufacturer of chips in the way that Taiwan, South Korea, or increasingly the United States and parts of Europe are becoming. But it has genuine intellectual capital in chip design, particularly in energy-efficient architectures and specialised processing. Companies like Arm, which remains British in origin even if its ownership history is complicated, demonstrate the kind of design expertise the country can produce. The firm McCallum visited operates in that tradition: less about fabricating silicon at industrial scale, more about rethinking what the silicon should actually do.
The timing matters. Governments across Europe and North America are now actively incentivising domestic semiconductor capability, driven by a recognition that dependence on a small number of geographically concentrated manufacturers creates serious strategic risk. A UK company with a novel chip architecture is not just a technology story. It is a geopolitical one.
Two Threads, One Bigger Picture
Counterfeit detection and chip design might seem like disconnected topics, but they share a common thread: the infrastructure of the digital economy is being rebuilt, piece by piece, at a pace that most news coverage struggles to keep up with. The tools fighting fake goods online depend on the kind of specialised processing power that new chip architectures are designed to deliver. The hardware ambitions of companies building those chips depend on a digital economy that is trustworthy enough to keep growing.
Neither problem has a clean ending. Counterfeiters will keep adapting. Chip architectures will keep being challenged by new computational demands. But the fact that journalists, engineers, and platform teams are all working on these questions simultaneously, with growing urgency and improving tools, suggests the direction of travel is at least pointed the right way.
So here is the question worth sitting with: as AI gets better at policing the internet and as new chip designs give machines more raw power to do it, who decides where the line sits between protection and surveillance, and are we paying close enough attention to those decisions?
