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DeepNode raises $5M to build decentralized AI on Base
Source: CryptoNewsNet Original Title: DeepNode raises $5M to build decentralized AI on Base Original Link: DeepNode, a decentralized artificial intelligence network, announced it has raised $5 million across two funding rounds.
The funding consisted of a $2 million seed round at a $25 million valuation and a $3 million strategic round at a $75 million valuation. The company describes its platform as infrastructure for “open intelligence,” where AI developers, compute providers, and validators can collaborate and earn rewards without relying on centralized technology companies.
Funding Details
The seed round included participation from community members and network validators including WildSageLabs from RoundTable21 and Rizzo from DNA, as well as infrastructure partner Gateway.FM.
The strategic round was led by a consortium of Web3 and AI infrastructure investors, including Blockchain Founders Fund, Side Door Ventures, TBV, IOBC Capital, Fomo Ventures, and Nestoris.
Proof-of-Work Relevance Mechanism
DeepNode operates using a Proof-of-Work Relevance (PoWR) consensus mechanism that rewards AI contributions based on utility rather than computational output alone. Models compete and evolve based on real-world performance, with contributors earning emissions for their work.
The platform is designed to handle predictive and decision-making tasks across multiple industries, including healthcare diagnostics, fraud detection, and cryptocurrency trading.
Base Integration and Mainnet Launch
DeepNode is building on Base, an Ethereum Layer-2 network, to leverage Ethereum’s security while maintaining transaction costs below $0.01. The company plans to launch its mainnet by the end of the first quarter of 2026, with foundation-supported domains in development across multiple verticals.
The network aims to enable builders to retain intellectual property rights, allow contributors to earn based on performance, and provide enterprises with private participation options while leveraging shared network effects.