Inside Nansen's AI Trading Agent Platform

He explains how Nansen uses ClickHouse databases and a mix of algorithmic heuristics, agentic teams, and human specialists to maintain the highest industry precision. The conversation dives deep into the intersection of LLMs and blockchain, exploring how standard AI models lack domain-specific common sense and why Nansen augments them with real-time data and visual artifacts. Alex introduces Nansen Gym, a simulated historical replay environment for training trading agents and teases the upcoming release of Smart Money 2.0, which aims to predict future profitable addresses with 2-3x uplift on precision. Finally, they discuss the existential risks of AI, the striking parallels between open-source AI and early DeFi, and why Alex believes agentic trading will be the absolute default by 2028. Chapters00:00 Intro Context04:15 Nansen's Evolution Agentic Trading09:30 Harmonizing Data The Attribution Layer15:00 Deterministic vs. Inferred Labeling (Uniswap vs. Binance)21:45 Evaluating AI Agents: LLMs as Judges27:10 User Privacy Public Blockchain Realities35:20 Building a Unified Trading OS42:15 Smart Money 2.0: Predicting Which Wallets Win49:00 The Limitations of Vanilla LLMs in Crypto55:30 Nansen Gym Time-Traveling AI Agents59:45 The Open Source AI vs. DeFi Parallel LinksAlex Svanevik on X: https://x.com/ASvanevikNansen: https://www.nansen.ai/NEAR: https://near.ai/Sponsors:NEAR AI Cloud now lets developers deploy OpenClaw—the rapidly growing open-source AI agent platform—inside Trusted Execution Environments, providing hardware-level encryption with cryptographic attestations. With OpenClaw on NEAR AI Cloud, you can run agents with cloud convenience, but without traditional cloud data exposure. No hardware to manage. No trust assumptions required. Learn more at near.ai.