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VANA’S DATA-BACKED TOKEN STANDARD: You might have heard of the ERC-20 token standard used for tokens on the Ethereum blockchain. Now, Vana, an EVM-compatible Layer 1 blockchain focused on enabling users to monetize their personal data by bundling it into DataDAOs for AI model training, has unveiled the VRC-20 standard for data-backed tokens. The aim is to boost trust and transparency in the market for data-backed digital assets. The VRC-20 standard criteria includes fixed supply, governance and liquidity rules while ensuring real data access by tying tokens to actual data utility. It also promotes continuous liquidity through rewards that ensure market stability. “This isn’t speculation. This is real financialization of data,” Vana noted on X. Vana launched its mainnet in December with VANA as its native cryptocurrency. The network has onboarded over 12 million data points through multiple DataDAOs, indicating strong demand for user-owned data. DataDAOs or data liquidity pools are decentralized marketplaces that bring data on-chain as transferable digital tokens. DLPs are where data is contributed, tokenized and made ready for use in applications such as AI model training. — Omkar Godbole Read more.

HASHGRAPH LINES UP Q3 PRIVATE CHAIN: Hashgraph is building a private, permissioned blockchain for enterprises in highly regulated industries, aiming for a third-quarter 2025 debut. The firm, known for the Hedera (HBAR) network, says the move is designed to bring together private and public distributed ledgers. HashSphere will offer compliance with regulations and interoperability. Hashgraph is targeting asset managers, banks and payment providers seeking low-cost cross-border transactions using stablecoins. While public blockchains like Hedera provide security and transparency, enterprises in industries such as finance and payments face KYC and AML compliance challenges. HashSphere will provide limited access to verified participants, enabling firms to develop tokenized assets, AI-powered services and other blockchain-based products while meeting regulatory standards. The network will integrate Hedera’s existing tools, including the Token Service for managing digital assets and the Consensus Service for recording transactions with trusted timestamps. The platform is compatible with the EVM, enabling developers to deploy decentralized applications using Solidity and other EVM languages. — Kris Sandor Read more.

ASICS TO BE MORE LIKE SERVERS: In the beginning, there were only CPUs, then GPUs, for bitcoin mining. Then came the ASIC in 2013, and with it, the “shoebox” form factor that has become emblematic of the bitcoin mining industry. What comes next? ASIC manufacturers are increasingly betting on a hydro-cooled server rack design to become a substantial portion of bitcoin mining fleets, leaning into the “direct-to-chip” cooling for further efficiency gains. Last September, Bitmain announced its model U3S21EXPH developed in a partnership with Hut 8. Its U3 design means that one unit takes up three spaces in a traditional server rack. MicroBT soon followed with its M63 Hydro series, as did Bitdeer’s Sealminer A2 Hydro unit. Following suit, Auradine released its server rack model, the AH3880, this March. Its U2 design, which occupies two server slots, is a bit smaller, but it packs more hashrate per unit of space at 600 TH/s (or 300 TH/s per slot) versus Bitmain’s 860 TH/s (286.66 TH/s per slot). The benefit of a server rack ASIC lies in standardization. Bitcoin miners are increasingly marching in step with the traditional datacenter industry, and that industry could see 40% adoption of direct liquid-to-chip cooling by 2026, according to Cyrus One. If miners adopt this design, then theoretically, they can optimize their supply chains by converging on server designs that are becoming best practice in the big-boy data center sector. — Colin Harper, Blockspace Read more.

GENSYN CEO BEN FIELDING: Ten years ago, when he was still a young AI researcher beginning his PhD track, Ben Fielding explored how “swarms” of AI — clusters of many different models — could talk to each other and learn from each other, which might improve the collective whole. There was just one problem: He was handcuffed by the realities of that noisy machine beneath his desk. And he knew he was outgunned by Google and other Big Tech. Compute constraints would always be an issue, he realized. The solution? Decentralized AI. Fielding

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