The Delphi Podcast Dylan Zhang: Crypto Native AI Models Behind the Future of AI Agents
Nov 18, 2024
Dylan Zhang, co-founder of Pond AI, dives into the transformative blend of AI and crypto, focusing on decentralized finance (DeFi). He explains how Graph Neural Networks (GNNs) outperform Large Language Models (LLMs) in blockchain applications. The discussion covers innovative dynamic fee structures, achieving 92% accuracy in detecting malicious behaviors, and the evolution towards decentralized AI models. Dylan emphasizes model ownership in crypto and the exciting future of AI agents, showcasing the potential for collaborative development in this space.
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Dylan's Crypto Journey
- Dylan Zhang's introduction to Bitcoin in 2017 was through a Harvard PhD involved with token trading.
- Initially skeptical due to negative press, he later recognized Bitcoin's potential, leading him to explore crypto AI.
GNNs and Blockchain Data
- Graph Neural Networks (GNNs) are best suited for blockchain data, which is graphical and behavioral.
- GNNs learn and predict behaviors by analyzing interactions between wallets and contracts (nodes and edges).
Decentralized Model Layer
- Pond AI shifted from a large GNN to a decentralized model layer for broader application.
- This approach allows developers to access various smaller, specialized models and contribute to the ecosystem.