In this episode, host Friederike Ernst is joined by Alex Svanevik, CEO of Nansen, to explore the platform's radical pivot from passive on-chain analytics to active, AI-driven agentic trading. Alex unpacks the technical hurdles of labeling over 500 million addresses, the transition from raw data into harmonized insights, and why true alpha now lies in attribution rather than raw data . 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.

Chapters

00:00 Intro & Context
04:15 Nansen's Evolution & Agentic Trading
09:30 Harmonizing Data & The Attribution Layer
15:00 Deterministic vs. Inferred Labeling (Uniswap vs. Binance)
21:45 Evaluating AI Agents: LLMs as Judges
27:10 User Privacy & Public Blockchain Realities
35:20 Building a Unified Trading OS
42:15 Smart Money 2.0: Predicting Which Wallets Win
49:00 The Limitations of Vanilla LLMs in Crypto
55:30 Nansen Gym & Time-Traveling AI Agents
59:45 The Open Source AI vs. DeFi Parallel

Links

Alex Svanevik on X: https://x.com/ASvanevik
Nansen: 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.