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Nintendo is requesting a dismissal of a class action lawsuit filed against it following Nintendo's own lawsuit against the US Government, as it argues that it doesn't owe customers any money back if they paid for price-hiked products.

Back in March, it was reported that Nintendo was filing a lawsuit over the allegedly "unlawful imposition of tariffs" in the US, and requested that "all tariffs collected under the IEEPA Duties must be refunded with interest."

However, as reported by Game File in April , two individuals then went on to file a class action lawsuit against Nintendo, claiming that, "unless restrained by this Court, Nintendo stands to recover the same tariff payments twice – once from consumers through higher prices and again from the federal government through tariff refunds, including interest paid by the government on those funds."

The individuals were referring to the Nintendo price hikes we saw last year in the US – first, we saw the price of Switch 2 accessories go up before the console even launched , and a few months later, the Switch 1 family of consoles also saw price increases . The class action complaint , filed "on behalf of millions of consumers who purchased goods from Nintendo during the tariff period and who paid inflated prices," seeks "restitution of those tariff overcharges, together with appropriate declaratory, injunctive, and monetary relief."

Now, though, as Game File reports , Nintendo is asking for this proposed lawsuit to be dismissed, reportedly stating in a new filing that customers "received exactly what they bargained and paid for" when they purchased goods after the price hikes. It notes that "the common thread" in the claims against it is "that it is somehow 'unfair; that Nintendo has not retroactively adjusted its prices for completed sales in response to the outcome of the tariff litigation," but says "that is now how commercial transactions work."

(Image credit: Nintendo) It continues, noting that customers willingly paid the hiked prices – "Plaintiffs are not entitled to a rebate simply because of intervening legal developments related to tariffs," it adds.

Beyond that, Nintendo also claims it didn't "simply increase each product's price by the amount of tariffs it paid on that product or impose an across-the-board tariff surcharge," saying it instead "imposed modest and selective price adjustments" and "chose to bear the costs of tariffs on some of its most popular products of 2025, including its flagship console, the Nintendo Switch 2."

The price hikes clearly weren't the sole result of the tariffs, either, as Nintendo points to "market conditions including costs of memory, labor," and "shipping," too – making the concept of trying to work out exactly how much a given product's price had increased by as a result of the tariffs alone a more complicated affair.

Doubling down on the idea that consumers willingly paid the prices set out in front of them, Nintendo also argues that the individuals behind the lawsuit don't have grounds to sue since, well, they could have simply chosen not to pay. "If a consumer did not want to pay the advertised price, they were free to abstain from purchasing the product or seek out competing products."

Nintendo thinks that the case be dealt with via private arbitration – its call for dismissal is reportedly in case this is denied.

It's not just Nintendo that's been rolling out price hikes over the last year or so – just last month, Xbox announced yet another console price hike (the third one since 2025 ), while Sony announced PS5 price increases back in March . At the time, Sony blamed "continued pressures in the global economic landscape." As Nintendo notes in its recent court filing, the increasing price of memory is also no doubt a huge factor here, and unfortunately, Valve engineer Yazan Aldehayyat recently stated that the RAM crisis is "still getting worse."

$1,049 Steam Machine is still a "good value," Valve engineer says, even though it was supposed to be $789 like the Steam Deck.

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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.

From hours to minutes

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.

What orbital constraints teach edge architects

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 bandwidth math

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.

Architecture decisions that preview what's next

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.

The scaling problem is familiar

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.

A design principle worth carrying

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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Historian Kim Phillips-Fein’s book Country of Lords looks back at the many men who have tried to fight against egalitarianism, from John Adams to Peter Thiel

The historian Kim Phillips-Fein’s new book is published amid celebrations of the 250th anniversary of the declaration of independence and its truth “self-evident, that all men are created equal”. Country of Lords, however, is a history of Neo-Aristocrats, Social Darwinists, Tech Utopians, and the Long Fight Against Equality in America.

Concise and hard-hitting, the book arrives at a time of glaring social and economic inequality , Phillips-Fein seeing Elon Musk “ emerging as a trillionaire very much on people’s minds, with a deep level of discomfort, both fear and anger about what this portends”.

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