Poseidon Raises $15M Seed to Build Decentralized AI Data Infrastructure

San Francisco-based Poseidon has secured $15 million in seed funding led by a16z Crypto. The investment aims to support the development of a decentralized data layer specifically designed for AI model training.

The company addresses the critical shortage of high-quality, IP-cleared data in AI development. “LLMs and compute are no longer the bottlenecks; it’s high-quality data that’s missing,” explained Sandeep Chinchali, Poseidon’s Chief Scientist and Chief AI Officer at its incubator, Story Protocol.

“Poseidon delivers the IP-cleared, structured real-world data sets AI teams need to build systems that actually perform in physical, complex environments,” Chinchali added.

Placeholder Image (Adjust Alt Text) — *Server utilization rate by data center type. Source: Shanu Mathew*

**Decentralized Pipeline for Legal AI Training Data**

Poseidon’s solution leverages decentralized infrastructure for data collection and distribution. A key differentiator is its integration of Story’s on-chain licensing infrastructure, providing end-to-end traceability and monetization. This enables data contributors to be fairly compensated while shielding developers from IP infringement risks.

The company contends that centralized data sourcing struggles to meet demand for specialized, high-context data required by next-generation AI models, particularly in robotics and spatial computing.

Founding investor Chris Dixon of a16z Crypto called the project “a step toward a new economic foundation for the internet,” rewarding creators and suppliers for essential inputs for advanced AI systems.

Proceeds will accelerate the scaling of Poseidon’s infrastructure, including planned releases of contributor tools, software development kits and licensing utilities for developers and data providers. Early access is expected this summer as part of a phased rollout.

Related: Decentralized OORT AI Data Ranks Top on Google Kaggle

Poseidon to Solve AI’s Data Drought

a16z analysts Chris Dixon and Carra Wu noted that the initial wave of foundation models largely used readily available online data. However, this easily accessible pool has been largely exploited, leaving AI systems starved for fresh, high-quality, legally usable information.

They observe, “The challenge isn’t just technical — it’s a problem of coordination. Thousands of contributors must work together in a distributed way…” posing effective scale and diversity issues. “A decentralized approach can solve this,” they conclude.

Video: The Challenge of Quality AI Data