AI builders, listen up 🗣️ @LazAINetwork is rolling out ERC-8028 on @MetisL2 🌿 a token standard made for AI assets. Models, datasets, even AI services can now have usage and revenue rules built right in. Mint a token for your dataset, set how many times it can be used, and decide how revenue gets split automatically. Contributors get paid without any extra bookkeeping. Every usage is recorded on-chain, so you finally get transparency and fairness in the AI economy. This is huge for anyone building AI that needs to be shared, monetized, or composable across projects. 🔗 https://lazai.network/blog/data-anchoring-token-dat-and-erc-8028-an-ai-native-asset-standard-for-ethereums-dai-era
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With the recent push from @LazAINetwork around DATs, it’s no longer news that AI assets need better structure, not just better models. DAT (Data Anchoring Tokens) basically gives data, models, and outputs their own on-chain identity [ who created it, who can use it, and who gets paid when it’s used. ] That solves a huge problem because most AI workflows reuse tons of datasets and contributions, but nobody gets proper credit or share of value. It’s simple to understand: if you contribute something, the DAT tracks it. If your work gets used downstream, you earn from it. For anyone building agents, training models, or running data pipelines, this standard makes your whole workflow more transparent and easier to monetize. And getting started is basically as straightforward as anchoring your data through LazAI’s framework. If you’ve been wondering how AI contributors finally get real ownership.. this is the direction LazAI is pushing.
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Trustless dispute resolution is often overlooked, but it’s essential for agents and DAOs to scale without friction. On-chain AI, arbiters, picked fairly and paid instantly via @LazAINetwork GMPayer, tackling three big problems at once: 🔸Removing delays caused by humans needing to step in 🔸Cutting down bias with verifiable, protocol-driven selection 🔸Guaranteed enforcement with automatic execution and public logging It fills a massive gap in the autonomy stack. Agents can work, trade, and even disagree. (All handled entirely within the system). Exactly the kind of modular tool the agent economy needs
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