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.
47:54
Episode guests
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- Dylan Zhang highlights the innovative use of Graph Neural Networks (GNNs) in processing on-chain data for enhanced prediction accuracy in crypto applications.
- The discussion emphasizes the transformative potential of integrating AI with DeFi to create more dynamic structures that adapt to market conditions.
Deep dives
Dylan Zhang's Journey into Crypto AI
Dylan Zhang, co-founder of Pond AI, entered the crypto space initially through Bitcoin, discovering it in 2017 but not taking it seriously until later. His interest grew into the intersection of crypto and AI when he recognized the challenges of searching for social connections and information across different platforms like Twitter and LinkedIn. This led to the creation of a user search engine utilizing blockchain's unified data structure to address identity misalignment. By leveraging on-chain data, they developed algorithms to understand social connections within the crypto community, enabling users to easily find network connections to notable figures like Vitalik Buterin.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
