Euler Growth Phases

PHASE 1 – Core Infrastructure Deployment

Euler SVM Chain Launch a Solana Virtual Machine–powered execution layer purpose-built for AI-native workloads. This chain supports scalable, low-latency environments for deploying autonomous, multi-agent AI systems with parallelized smart contracts and seamless state synchronization.

zkML & TEE Integration Incorporate zero-knowledge machine learning (zkML) and trusted execution environments (TEE) to ensure data privacy, secure model inference, and tamper-proof agent operations—critical for sensitive AI applications running on open networks.

Euler Benchmark Deploy an on-chain performance evaluation framework for AI models and agents. It standardizes benchmarking metrics, verifies compute outputs, and introduces a transparent scoring mechanism to enforce quality and accountability in model performance.

Euler Orchestrator Establish a decentralized coordination layer to manage multi-agent interactions, inference routing, and automated task assignment. The orchestrator enables real-time interoperability between AI agents and smart contracts through composable logic flows.

PHASE 2 – Tokenomics & Governance Layer

$EULER Token Introduce a native utility token that powers the entire Euler ecosystem. $EULER will be used for incentivizing compute contributions, model deployment, orchestration fees, and platform governance.

Superalignment DAO Form a decentralized autonomous organization tasked with protocol upgrades, fund allocation, and alignment of ecosystem incentives. Governance will prioritize the long-term integrity and safety of AI deployment onchain.

PHASE 3 – Ecosystem Growth & Integration

Strategic Partnerships Forge collaborations with AI labs, web3-native projects, research institutions, and DeFi protocols to extend the Euler stack across use cases ranging from decentralized AI agents to finance, gaming, and autonomous data networks.

Platform Scaling Advance the scalability and throughput of the Euler stack to support millions of real-time AI interactions. This includes state sharding, optimized inference caching, and cross-chain operability to handle high-frequency, AI-driven workflows.