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/
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