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 The risk of AI flattening nuance and marginalized perspectives Why human data is one of the most defensible moats in AI Enzo's own definition of what "data" really means Episode Timestamps: 00:00 Intro 00:21 What Prolific actually does 02:48 MCP vs. API vs. CLI access 04:19 How frontier labs started working with Prolific 06:40 Data volume vs. quality, and the role of RLHF 10:58 Who Prolific's biggest customers are 13:12 Why labs choose Prolific over other data vendors 16:13 Fact vs. opinion in AI training 19:02 Stereotypes and bias in AI models 21:15 Prolific's ICLR research on commercial pressure 23:36 Who's responsible: labs, governments, or data companies 27:22 How Prolific's data collection actually works 31:59 Synthetic data vs. human data 36:04 What defines "taste" in AI-generated content 39:33 Good taste vs. bad taste, and the risk of AI regression to the mean 42:36 Why Enzo joined Prolific 45:56 Blind spots most people have about training data 47:22 The "SaaSpocalypse" and data as a business moat 51:38 How Enzo visualizes "data" in his own mind 54:22 Why Enzo does what he does 57:16 Where to find Enzo and Prolific Connect with Enzo on LinkedIn: https://www.linkedin.com/in/enzoblindow/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices