Rather than letting players port weapons or powers between games, non-fungible tokens will more likely serve as building blocks for new games and virtual worlds. This piece is part of CoinDesk’s Metaverse Week.
One of the most enduring legends in the cryptocurrency industry is that Vitalik Buterin started Ethereum because his warlock got nerfed.
“I happily played World of Warcraft during 2007-2010,” Vitalik wrote in one version of the story. “But one day Blizzard removed the damage component from my beloved warlock’s Siphon Life spell. I cried myself to sleep, and on that day I realized what horrors centralized services can bring. I soon decided to quit.”
The tale is a broad allegory for the power of decentralization. If a game skill or item was an immutable blockchain token – what we would now call a non-fungible token (NFT) – a company like Blizzard Entertainment couldn’t nerf, or weaken, your Siphon Life even if it wanted to. This suggests a further possibility: Because non-fungible tokens live on public blockchains, they can be read by any game’s software. If Siphon Life was an NFT that lived on a public blockchain, there was a possible future in which you could use it not just in World of Warcraft but in Assassin’s Creed or Uncharted or, who knows, Tetris.
Vitalik would have been about 16 at the time this happened, and the story is more of a winking goof (“I cried myself to sleep”?) than a serious design proposal. But it has been taken quite literally by some as a call for games built using immutable tokens to represent skills or equipment. Several rough World of Warcraft equivalents proposed to do exactly that during the 2017-2018 initial coin offering bubble, selling things like virtual swords and armor before even building the game.
Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.
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