Decentralized AI cost Target enterprises and research labs seeking cost-efficient AI training and inference by highlighting DeepBrain Chain's blockchain-enabled distributed computing model that claims substantially lower computation costs (around 30% of self-built servers and under 50% versus traditional centralized cloud).

Data privacy edge Position to organizations with sensitive or proprietary data by emphasizing smart contract driven separation of data providers and training parties, addressing data privacy concerns and enabling trusted collaboration for model development.

Ecosystem participation Leverage the universal currency model to attract developers, universities, SMEs, and computing power providers; pitch opportunities for ecosystems, incentives, and partnerships to expand the DBC network and increase data and compute availability.

Strategic partnerships Explore collaboration with cloud platforms, metaverse initiatives, and research consortia, as evidenced by Huawei Cloud partnerships and the ongoing December 2024–February 2025 ecosystem conference, to co-create AI workloads and broaden go-to-market channels.

Growth signals Capitalize on revenue potential in a growing market by noting reported revenue range and active event cadence; target early-stage AI teams and mid-market firms looking for scalable, private, and cost-effective AI computing solutions.