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Home»Cryptocurrency & Free Speech Finance»Minecraft, Candy Crush Among 11 Games in EU Virtual Currency Crackdown
Cryptocurrency & Free Speech Finance

Minecraft, Candy Crush Among 11 Games in EU Virtual Currency Crackdown

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Minecraft, Candy Crush Among 11 Games in EU Virtual Currency Crackdown
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  • EU consumer authorities have opened coordinated actions against nine games companies over how they sell in-game currencies.
  • The guidelines require real-world prices to be shown and a 14-day right of withdrawal, including on unused currency.
  • Cryptocurrencies are carved out of the principles entirely.

European consumer authorities have opened eleven coordinated actions against ten video games companies over the way they sell and price in-game virtual currencies, the European Commission said Wednesday.

The Consumer Protection Cooperation Network, which groups national enforcement bodies and is coordinated by the Commission, named Activision Blizzard UK, Crytek, InnoGames, King.com, Mojang, Plarium Europe, PLR Worldwide Sales, Riot Games, Supercell and Ubisoft EMEA in a joint statement.

The games are Diablo Immortal, Call of Duty Mobile, Hunt: Showdown 1896, Forge of Empires, Candy Crush Saga, Minecraft, Mech Arena, Gardenscapes, Valorant, Clash of Clans and For Honor, picked for their broad reach, availability across devices and range of age ratings.

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The actions follow key principles the network published in March 2025. Those say the real-world price of in-game items and currency must be shown prominently, that traders should not mix several currencies or require repeated exchanges in ways that obscure cost, and that players should not be pushed into buying more currency than an item needs. Bundles that deliberately mismatch item prices, leaving players with a stranded balance, are among the practices to avoid.

Players also keep a 14-day right of withdrawal, the principles say, including for virtual currency they have bought but not spent. Contract terms granting companies the unilateral right to change the value of in-game currency, or to close accounts without the possibility of contesting it, are flagged as unfair.

Children are treated as always vulnerable, and any game not aimed exclusively at adults should expect a significant share of under-18 players. It also classes high spenders as a vulnerable group, saying so-called whales “are likely to struggle with impulse control or gambling disorders” and that games built around them face a stricter fairness test.

Crypto sits outside all of it. A footnote excludes cryptocurrencies and similar digital currencies that work as an alternative form of payment using encryption, along with virtual currencies as defined in the EU’s fifth anti-money laundering directive. Currencies that can only be earned through play, and never bought, are excluded too. The regime is aimed squarely at currencies bought with real money inside closed game economies.

The actions also reach beyond pricing. The network said it is giving particular attention to variable reward systems such as loot boxes, especially where they are aimed at or available to children, alongside dark patterns, aggressive commercial practices and direct exhortation of children to buy, which EU law already prohibits. Misleading countdown timers and unfounded scarcity claims are named among the techniques at issue. In the Activision Blizzard case the network is also examining data collection, addictive design, default parental controls and account blocking.

The network opened a dialogue with industry bodies last year, holding workshops in June and September 2025. It has since found indications that a high number of companies made no substantive changes to their games as a result, either of the published guidance or of years of talks. Self-regulation schemes such as PEGI have brought some improvements, it said, but often fail to address the core of the harmful practices.

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Agence France-Presse (AFP) Fact Check’s investigation led them to an India-based creator, who admitted creating the video with the help of AI-generative tools. His justification was disarmingly straightforward: emotionally engaging visuals perform well online. This was just one of the many digitally-manufactured “miracles” put into social media heavy rotation during the floods. Another clip, showing a bridge collapsing into floodwaters swallowing vehicles and human lives, seemed to have been lifted straight out of a Roland Emmerich disaster film. The video racked up more than 2 million views on X before it was debunked as AI-generated by Factly. What made this social media post stand out for me was the identity of one of the people sharing it – an experienced news editor. The post, still pinned on his X profile and with more than 2 million views, suggests that even experienced newsroom decision-makers could be fooled by synthetic disaster footage – or, worse still, be untroubled about the veracity of such videos. Both videos incorporate the visual language of short films, but were not released in cinemas or on Netflix. They surfaced on Facebook, X and Instagram, even as emergency teams continued to search for the thousands of people that went missing after a glacial collapse triggered devastating floods in Nepal and Tibet on August 26. Even as survivors photographed authentic eyewitness footage, synthetic visuals emerged alongside them. The floods became one of the first major climate disasters in South Asia where documentation and fabrication coexisted in the same visual ecosystem.     Screenshot of debunked Nepal floods footage “During earlier disasters, our primary challenge was identifying old footage, establishing where a video was recorded and determining whether it was being presented in the correct context,” Saurabh Shukla told Index. Shukla is founder and editor-in-chief of NewsMobile, an India-based independent media organisation that debunks AI-related deepfakes and focuses on verified information. “The biggest difference today is the sheer speed at which fabricated and misleading visuals entered the information ecosystem,” Shukla said. “With generative AI, we now have an additional challenge – determining whether the event depicted in a video ever happened in the first place.” This has fed into a media environment where unverified visuals are disseminated faster than the verified facts reported by journalists. “Speed without verification can amplify confusion, while verification without timely reporting can leave an information vacuum,” Shukla said. “The challenge for newsrooms is to strike the right balance between being fast and being accurate.” The first images of rescue missions in secluded mountainous regions almost always come from locals, and thus forming the spine of disaster reporting. These inevitably blurry and shaky recordings do not have the polished quality of professional news reporting, but do carry something far more consequential: the credibility that comes with eyewitness documentation. These images often shape the core of the collective memory of a tragedy. But in an age of visual manipulation and trickery, that presumption of authenticity no longer holds. Factly conducted 30 fact-checks into coverage of the Nepal floods; fully one-third involved AI-generated visuals. “Beyond checking the location, source, and context of a visual, we also had to establish whether the visual itself was authentic,” Akshay Kumar Appani, lead fact checker and researcher at Factly, told Index. “During breaking disasters, old AI-generated visuals can spread rapidly while being presented as current footage. This makes it harder for journalists to establish the truth. The visual of the miracle child perfectly captured this dilemma. It was designed to evoke an emotional reaction; but it also looked like the kind of footage that journalists hope to find during crisis reporting. The video was vertically filmed. The camera movement was wobbly. The lighting was grainy. The muffled background sound felt believable. The video presented none of the glaring distortions that once made AI interventions easy to spot. Fabricated videos, today, can be produced to mimic the aesthetics of authentic citizen journalism. It was for this precise reason that the floods presented journalists with a crisis that went beyond recognising AI-generated images: they were increasingly obliged to verify whether the footage reaching them was genuine in the first place. Screenshot of video debunked by Factly Newsmobile’s verification process increasingly involves examining individual frames, conducting reverse-image searches, analysing visual inconsistencies and using AI-detection tools where appropriate. “However, detection tools are only one part of the process. We still need corroboration,” Shukla said. “We cannot rely solely on automated detection tools,” Sumit Dubey, South Asia digital verification editor for AFP’s Fact Check vertical, told Index. Fact-checking, he explained, requires going beyond automated outputs: tracing the original source, examining context, verifying metadata, looking for corroborating evidence and, where possible, speaking to relevant sources. “Automated tools are useful indicators, but fact-checking cannot be outsourced to a button,” Dubey said. The proliferation of synthetic media output in the wake of natural disasters is not new. It’s the sophistication of the output that has changed. 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It was convincing because the sludge moved convincingly, because the choreography resembled actual emergency rescues, because the emotional tonality seemed genuine. “Most post-hoc detection tools were designed for content featuring physical characteristics, and they currently struggle significantly with environmental and disaster content,” Bruna Martins dos Santos – policy and advocacy manager, Witness Technology Threats and Opportunities – told Index. “Journalists might also struggle because many tools have limited capabilities to identify synthetic content generated elsewhere,” she said. Another emerging trend is the increasingly complicated role of Big Tech itself in detecting and labelling fake generative-AI content. Some of the clearest AI verdicts came from detection tools built by the very same companies whose models created the same fakes. Three weeks after the US state of California and the EU mandated the requirement for greater transparency with respect to AI-generated content, Witness and Indicator tested thirteen major AI providers. They found that seven had no public detector at all. “The capability to identify synthetic content partly existed. The governance around it did not,” Alimardani said. When both authentic and synthetic content circulate in disaster situations, the biggest challenge that journalists face is that of establishing truth amidst widespread public uncertainty and doubt. According to Martins dos Santos, “Journalists and local fact-checkers can face immense time pressure, often wasting scarce resources re-detecting the same recycled disaster footage across multiple platforms and languages.” The greatest danger emerging from Nepal may not be that people believed the fake videos. It may be that they have begun to doubt the real ones. 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