How Human Data Shapes Every AI Model | Enzo Blindow, VP of Data & AI, Prolific

The volume problem in AI is solved. Now it's all about data quality, and who gets to define it.

Enzo Blindow is VP of Data & AI at Prolific, a platform that connects hundreds of thousands of people worldwide to the frontier labs and enterprises training and evaluating AI models. In this conversation with Liam, Enzo breaks down what actually goes into building high-quality training data, why models lean too hard into stereotypes, and the research Prolific published showing how easily AI can be nudged toward commercially motivated, and sometimes harmful, suggestions.

They discuss why synthetic data hits a ceiling that only human data can break through, how a single mistranslated instruction can quietly corrupt an entire dataset, and why "good taste" might be one of the hardest things for AI to ever replicate.

Key Topics Covered:

Why data volume is a solved problem and quality is everything now

How RLHF actually shaped early versions of ChatGPT

Why AI models lean too heavily into stereotypes

The asymmetry and hidden bias baked into internet-sourced training data

Prolific's ICLR research on commercial pressure in AI models

Who's responsible when AI models cause harm: labs vs. data providers

Synthetic data's ceiling, and why humans still have to validate it

What actually defines "taste" and why it's nearly impossible to model