Persistent Frictions in the Landscape

Despite visible advancements, the path to decentralized AI infrastructure is fraught with unresolved bottlenecks. While the industry is innovating aggressively, foundational challenges continue to slow meaningful adoption:

• Lack of Inter-Agent Collaboration: AI agents are often deployed as isolated executors with no shared task memory, making coordinated workflows or parallel intelligence execution nearly impossible.

• Non-Verifiable Off-Chain Processing: Most AI tasks require off-chain compute, but without secure enclaves or cryptographic proofs, users cannot verify results or trust the execution environment.

• Limited Cross-Chain and Web2 Interoperability: Many agents lack access to external APIs or multichain datasets, severely limiting their functional scope and real-world utility.

• Weak Incentive Alignment for Developers: There are few decentralized incentive models that reward agent developers for publishing open, performant, and reusable agents—creating friction in ecosystem growth.

These pain points illustrate that the infrastructure for decentralized AI is still underdeveloped. Without addressing these underlying barriers, the sector risks building innovation on unstable ground.