In this episode of the AI Agents Podcast, we explore how Sapien.io is transforming the way fine-tuned large language models (LLMs) are built.

Demetri Panici interviews Rowan Stone from Sapien to uncover how the company is rethinking AI training data by decentralizing the data collection process.

Rather than relying on centralized data centers, Sapien empowers contributors worldwide to provide high-quality, structured knowledge, helping eliminate biases and enhance model performance.

Rowan details how Sapien’s innovative data-foundry framework and incentive structures enable scalable, high-quality input for LLMs — from autonomous vehicle data to nuanced human insight across vertical specialties.

With a growing network of 560,000+ contributors and clients like Midjourney and UN agencies, Sapien.io is setting a new standard for how enterprise-grade AI models are fine-tuned and optimized in a rapidly evolving tech landscape.

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⏰ TIMESTAMPS:

0:OO - Introduction

1:00 - Introducing Sapien And Rowan

5:19 - Understanding Decentralization In AI

9:20 - Real-World Applications Of Decentralized Data

14:05 - Advice For Startups On Funding Success

16:52 - The User Base of Sapien

18:58 - Building And Scaling An Early-Stage Team

21:44 - Improving Data Quality With Peer Review

25:58 - Agentic Models And Chain Of Reasoning

30:40 - Practical Use of Chain of Reasoning in the Workplace

34:08 - Why Work with Vertical Models

37:42 - The Next Leap In Humanoid AI Robotics

40:58 - The Intelligence Level of AI Models

45:08 - Final Thoughts

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