DeepNode Raises $5M to Build a Decentralized AI Network on Base

The company announced that the capital injection will support its roadmap toward a planned mainnet launch in the first quarter of 2026, while working to create a permissionless AI infrastructure designed for global, community-driven collaboration.

Funding consisted of a $2 million seed round at a valuation of roughly $25 million, followed by a $3 million strategic round that raised the valuation to around $75 million. According to the announcement, DeepNode intentionally designed the seed round to include early ecosystem participants, adding validators and infrastructure supporters such as WildSageLabs of RoundTable21, DNA validator “Rizzo,” and Gateway.FM. This approach, the team said, reflects a “community-first” philosophy aimed at giving early adopters a serious role in managing and operating the network they will help secure.

The strategic round saw the participation of various well-known backers in the Web3 and AI-infrastructure investment landscape, including Blockchain Founders Fund, Side Door Ventures, TBV, IOBC Capital, Fomo Ventures, and Nestoris. Their participation, DeepNode said, goes beyond capital: it involves strategic support, technical resourcing, and industry networks as the project ramps up toward commercial-scale deployments. The Defiant also distributed the announcement, reflecting broader industry interest in decentralized AI architectures.

DeepNode plans to develop a permissionless, Base-based decentralized AI network, which has been chosen because its low-cost execution environment means typical transaction fees are below $0.01. Instead of focusing on a primary model or narrow use case to anchor the network, DeepNode positions itself as a flexible computational layer to support a wide array of healthcare diagnostics, fraud detection, predictive analytics, trading models, and other forms of real-world AI decision-making. The idea is to offer a common infrastructure on which developers, compute providers, and validators can come together without the need for centralized technology companies.

A key component of the protocol is its planned consensus mechanism, Proof-of-Work Relevance, or PoWR, which would reward AI model contributors not just for providing computational power but, instead, for serving useful, accurate outputs relevant to the real world. In such a system, model builders retain their own intellectual property while benefiting from a permissionless marketplace in which enterprises, developers, and users can access or deploy AI models as they see fit. The network will also allow private enterprise participation along with public collaboration: an approach that blends decentralized governance with enterprise-grade optionality.

The money raised will go towards what DeepNode defines as its multi-year development roadmap that is aimed at launching the initial mainnet, onboarding validators, deploying foundation-supported "domains," and encouraging early developer participation. The team says several domains are already under development and will help showcase how the network can support diverse AI workloads upon launch.

DeepNode's announcement comes at a time of increasing debate about where AI development is headed, and what role-if any-decentralized infrastructure should play. For the time being, centralized models continue to lead the market, but concerns over data control, model transparency, and access limitation have created a niche for projects pursuing a decentralized alternative. DeepNode's attempt to anchor AI development in a shared, permissionless network reflects a broader movement within crypto toward democratizing compute power, training data, and model access.

Still, there are several uncertainties around this project. First, the success of PoWR heavily relies on adoption and whether AI workloads can cut through with significant usefulness. While Base provides an execution environment at low cost, many high-value AI use cases-especially those involving regulated industries-may require hybrid systems that combine on-chain coordination with off-chain compute and compliance-grade data protection. Governance, token incentives, and large-scale IP-rights enforcement will also require further and consistent developer and enterprise engagement.

Attention will likely shift towards early signs of traction: onboarding of developers, model submissions, validator participation, and tangible demonstrations of AI functionality operating on decentralized rails. To the extent DeepNode can demonstrate compelling real-world use cases in its lead-up to mainnet, it may go a long way in accelerating broader adoption of decentralized AI infrastructure.