Decentralized AI Data Network Dataline Closes $10.6M Seed Round, Plans Token Launch by Year-End

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The decentralized AI data network Dataline has closed a $10.6 million seed round led by Dispersion Capital and Lattice, with participation from Symbolic Capital, Coinbase Ventures, Anagram, and Robinhood co-founder Vlad Tenev. The April round, structured via a SAFE with a token warrant, will expand data sources, enhance validation, and support new use cases such as robotics and AI training data. Dataline plans a token launch event by year-end, including a public token sale on Base via Uniswap’s CCA. The project will also leverage inflation data metrics to inform its tokenomics strategy, with full details expected next week.

Huo Xing Cai Jing reports that on October 22, according to The Block, Dataline, a decentralized data network for AI agents, announced the completion of a $10.6 million seed round led jointly by Dispersion Capital, backed by Lattice and Samsung, with participation from Symbolic Capital, Coinbase Ventures, Anagram, and Vlad Tenev, co-founder and CEO of Robinhood, among others. Additional unnamed investors also participated, including former CFTC chairpersons and founders or partners from Venmo, Anchorage Digital, NEA, General Catalyst, Jump Capital, and FalconX. The round closed in April this year via a SAFE with attached token warrants; the valuation was not disclosed, and investors did not receive board seats, observer seats, or advisory roles. Dataline co-founder Nuo Shi stated that the platform is now live, covering approximately 50 data sources, with initial “AI Data Launch” partners including Hyperliquid, Polymarket, Kalshi, Base, Robinhood Chain, BNB Chain, Sui, and TON; developers on Base and Robinhood Chain can access the platform for free. The company plans to launch its token before the end of this year and intends to conduct a public token sale on Base via Uniswap’s Continuous Clearing Auction (CCA), with details expected to be announced next week. The funding will be used to expand data sources, enhance data validation, and extend use cases to include robotics and AI training data.

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