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The 2026 World Cup is already under way. Billions of eyes are on the pitch. The story that matters to anyone running a supply chain, though, is playing out in warehouses, customs terminals and distribution centers spread across three countries.

For the first time, the tournament spans three host nations: the United States, Canada and Mexico. That means millions of product lines, thousands of supplier handoffs and cross-border compliance requirements across three distinct regulatory environments, all compressed into a window with zero tolerance for error.

The operational scale of this tournament is without precedent.

Now that the group stages are live, the pressure on supply chains is real and immediate. The lessons surfacing are worth paying attention to, because they apply well beyond sport.

Official merchandise for an event of this scale moves across multiple countries, customs jurisdictions and retail channels simultaneously. Product identification has to work at every stage of that journey, from the manufacturer's floor to the stadium vendor's shelf. The label that cleared customs in Los Angeles may face entirely different requirements in Toronto.

The deeper problem is structural. Today's supply chains still largely operate as disconnected islands. Each site, supplier, co-packer and carrier maintains its own systems and repeatedly re-enters the same product and compliance data. That fragmentation creates built-in waste at every handoff: redundant setup, duplicate records and inconsistent label versions. Under normal conditions, these inefficiencies are costly but can be masked by day-to-day operations. Under the pressure of a live global tournament, they become critical.

Demand shifts are happening in real time. A host city that reaches the knockout stages sees fan merchandise demand surge overnight. Supply chains built on static, batch-processed labelling data are finding they cannot respond at that pace.

The speed of these shifts can be surprisingly tangible. In Atlanta, shortly after the Spain-Cape Verde match, I walked through the airport and was struck by how many people were wearing Cape Verde jerseys. In the space of a few hours, merchandise that had been relatively low-profile had become highly visible, underscoring how quickly demand signals can emerge and spread during a global event.

What the World Cup is making visible in real time is a shift that has been under way for several years. Product identification is no longer a print-and-forget exercise. It is a live data problem.

The industry is moving from fragmented, internal systems to connected, multi-partner ecosystems. The organizations managing the tournament's supply chain most effectively are those that have made this shift: rather than each stakeholder operating in isolation and recreating the same product and compliance data from scratch, they are working within a shared, real-time environment where information flows seamlessly across systems, suppliers, customers and geographies.

The benefits of this approach are measurable. Organizations that can operate with real-time visibility and trusted data across their extended value chain can reduce delays, prevent errors at source and respond faster when disruption hits. In sectors where production downtime can exceed $1-2 million per hour, that responsiveness is not a nice-to-have but operationally critical.

The consequences of siloed product data are well understood in theory. A tournament of this scale is making them visible in practice.

When product data does not flow seamlessly across sites and trading partners, the failure surfaces in predictable ways. Rejected shipments at customs. Compliance failures that stall distribution. Production downtime while teams manually reconcile data across systems. Against the backdrop of a global event with fixed deadlines, those failures are not recoverable.

The organizations absorbing those costs right now are those still operating inside the organization perimeter - managing product identification as an internal function rather than a network-level capability. The distinction matters. Supply chain resilience increasingly depends on the ability to coordinate accurate product data across every site, trading partner, and customer - creating a connected ecosystem in which product identity can be shared, trusted, and acted upon seamlessly.

Connected, network-driven approaches to product identification are no longer a future aspiration. The World Cup is demonstrating their value in real time, at a scale most supply chains will never encounter but from which every supply chain can learn.

The direction of travel is clear. Organizations that can rapidly adapt labelling requirements across plants and partners, share trusted product data in real time and eliminate the manual rework that comes with disconnected systems will outperform those that cannot. That is as true in retail, pharma and automotive as it is in a stadium in Los Angeles.

The World Cup will be over in a matter of weeks. The infrastructure challenges it is exposing will still be there when it ends. The organizations that use this moment to address those fundamentals will be better placed for whatever high-pressure deadline comes next.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

The conversation about AI in space keeps arriving at the same image: a floating supercomputer processing the world's information from 400 miles up. It’s a compelling narrative, but there’s a gap between what companies are hoping to build and what is being built today.

Today, satellites run on fixed power budgets measured in watts with strict constraints. Bandwidth is scarce enough that every byte reaching the ground has to earn its place.

Those limits push the field toward architecture that looks more like a nervous system, than a data center – lightweight models running onboard that interpret sensor data in near real time and convert observations into structured events. The event reaches the ground.

The raw pixel doesn’t.

In a conventional Earth observation pipeline, a satellite captures an image, downlinks it to a ground station, ground systems process the raw data, and the result reaches whoever needs it. Best case that happens in hours, but often it’s closer to a day.

For a disaster response team managing a flood in a low-lying river delta, or a conservation authority trying to locate the origin point of a wildfire in a remote national park, that day comes at a cost.

A satellite running inference onboard changes the math. Detection happens in seconds. What gets downlinked is a position, timestamp, or risk score. The bottleneck shifts from the space segment to the ground distribution, which is a comparatively manageable problem.

The use cases where this matters most are not the visible disasters people are watching. They’re the methane leak on a pipeline with no weekly inspection schedule, an oil spill beyond the reach of coastal patrols, a wildfire that began in a remote area before anyone had reported smoke. Onboard inference turns a passive imaging asset into an early warning system.

The tradeoffs being resolved in orbit are an extreme version of the same constraints facing any organization deploying AI outside a well-provisioned data center.

Cloud-native AI development often has a back up plan: when the model is too large add compute; when bandwidth is constrained, increase it; when latency is a problem, move the processing closer. In orbit, none of these options exist. You build within the envelope, or the system doesn’t function.

The result is a forcing function that enterprise architects rarely encounter at the same level. Industrial IoT deployments face intermittent connectivity. Autonomous systems can’t afford round-trip latency to a central server at decision time.

The shift from 'send everything, process centrally' to 'process locally, transmit what matters' is happening across multiple industries. Space is where that shift ran without a safety net.

The data reduction numbers transmitted in real time is not just 80-90 percent. Once processing happens on the spacecraft, the reduction for the real-time layer exceeds 99 percent. This is semantic compression. The satellite sends the meaning of what it saw, not the measurement it produced.

A conventional operator downlinking hundreds of terabytes of raw imagery daily is paying bandwidth cost for data that largely contains nothing of interest. With onboard inference, what's transmitted in real time is a structured detection event: a position, a timestamp, a risk score, and perhaps a small compressed image. That is hundreds of kilobytes, not terabytes.

An operator downlinks only what warrants examination, rather than blindly dumping the full data stream.

A model making decisions before a human is in the loop carries different requirements than one generating recommendations for human review. Ambiguity tolerance is lower. Inference behavior needs tighter scoping. This is the same conversation that medicine and finance have been having for years.

AI-assisted diagnostics and accountability distributed across platform, model, training data, and end customer rather than concentrated in a single layer. The space industry is joining a conversation other sectors have already been having for years.

In a cloud environment, model size and computational efficiency are optimization targets. Meaning they are important, but secondary to capability. In a constrained orbital environment, they are the primary design constraint from which everything else follows. A model that cannot run within the available compute envelope is not a model that gets deployed. There is no option to add a larger instance.

A maritime patrol aircraft that previously ran random vessel inspections now works from a ranked list of targets with risk scores attached. Some alerts will be false positives which is a physical reality of any probabilistic system. But the aircraft's operational effectiveness improves substantially compared to random patrolling or no monitoring at all. The AI narrows the search.

One satellite running an onboard model is a proof of concept. A constellation of hundreds running distributed inference is a different infrastructure problem as orbital AI scales.

Centralized orchestration becomes the bottleneck when constellations grow. Every decision can’t route through a ground station. Distributed inference is a requirement. Enterprise architects hit the same wall when a pilot deployment expands to thousands of edge nodes. The centralized model that worked in development becomes the thing that breaks in production.

The cloud infrastructure analogy supports this. Nobody builds a data center before launching an application. The pattern is shared infrastructure, with control at the model, mission logic, and decision layer. Those can be sovereign regardless of who owns the underlying compute.

It’s hard to develop constraint-based thinking in environments where adding compute is always on the table. The organizations that have built it tend to have faced conditions where it wasn’t.

The strategic advantage in edge AI over the next decade will not just be measured in the amount of compute available. It will also be measured in code deployed to the right place in the stack. Satellites are running that experiment first.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

As part of Best Buy's Black Friday in July sale, the price of the Apple MacBook Air 13-inch (M5) has dropped to $1199 (was $1299).

Powered by Apple's M5 chip, it combines a 10-core CPU, 16GB of memory, and a 512GB SSD in a lightweight design. For my money, with the extra RAM and storage this makes it to model to choose if you're buying a MacBook Air.

It's an excellent choice for students, professionals, and anyone looking for a premium everyday laptop that's lightweight and loaded with long-term value - especially given Apple recently announced price increases across all its laptops and tablets.

13.6-inch 2560 x 1664 display, Apple M5 processor with 10-core CPU and 8-core GPU, 16GB memory, 512GB SSD, two Thunderbolt 4 ports, backlit keyboard.View Deal

✅ Buy this deal if...

You want a lightweight premium laptop with strong everyday performance, and enough memory and storage for years of work, study, and creative tasks. Battery life impressed us during testing, lasting around 15.5 hours of web use and comfortably getting through a full working day on a single charge.

❌ Skip this deal if...

You need a larger display, more ports than Thunderbolt 4 provides, or dedicated graphics for demanding professional workloads and modern PC gaming. In that case, check out the MacBook Pro instead.

In his review, our laptop expert Lance said the latest MacBook Air was "the best ultraportable I've ever used". He noted it delivers an outstanding balance of performance, battery life, and portability, handling everything from everyday productivity to photo editing, 8K video editing, and even gaming without feeling out of its depth.

Despite its slim, fanless design, he found it surprisingly difficult to push the M5 chip to its limits, even with demanding creative applications and heavy multitasking.

The 10-core CPU, 8-core GPU, and 16GB of memory provide plenty of performance for work, creative projects, and everyday computing, while the upgraded 512GB SSD delivers faster storage than previous entry-level MacBook Air models, helping applications launch quickly and improving file transfers.

At just 2.7 pounds, it's easy to carry every day, while the premium aluminum chassis feels exceptionally solid despite its lightweight design.

Although the $100 discount isn't huge, deals on Apple's newest MacBook Air models are relatively rare - especially with Apple recently hiking prices across all its devices. With the upgraded 512GB base storage and M5 processor, Apple's latest ultraportable is a must-have.

This configuration offers excellent everyday performance, but it isn't designed for intensive 3D rendering, high-end gaming, or workloads that benefit from dedicated graphics.

Connectivity is also limited to two Thunderbolt 4 ports, so you may need a hub or dock if you regularly connect multiple wired accessories.

• Shop all MacBooks at Best Buy