In this episode of Signal and Noise, hosts Brian Lamar and Andrew DeCilles sit down with Samuel Cohen, Ph.D., CEO of Fairgen, to explore how generative AI and advanced statistical modeling are reshaping the way consumer research is conducted, validated, and scaled.

Samuel shares his international academic journey, from studying mathematics and synthetic data research at Oxford to working with AI in industry labs before launching Fairgen. He explains how rising costs and declining data quality in traditional market research led him to build a platform focused on generating reliable, usable insights from limited samples.

The conversation breaks down the difference between partial simulation and full simulation, clarifying how Fairgen uses statistical models to amplify real survey data rather than replacing it outright. Samuel walks through real-world applications, including how enterprise clients use data amplification to unlock granular insights across small or hard-to-reach segments without dramatically increasing budgets or field time.

The hosts and Samuel also discuss where AI works well and where it falls short, particularly in high-stakes research, governance-driven projects, and complex quantitative methods like advanced conjoint analysis. The episode closes with a forward-looking perspective on how budgets, decision risk, and organizational governance will shape the future role of simulated data in consumer research.

Key Takeaways:

• Generative AI can amplify small samples into enterprise-level insight without multiplying cost or field time.

• There is a critical difference between simulating data and strengthening real data, and most people get it wrong.

• Fairgen’s approach shows how math and models can reveal patterns humans cannot see at scale.

• Not all research should use AI, especially when decision risk and governance are high.

• The future of insights may be fewer surveys, smarter modeling, and faster strategic confidence rather than bigger sample sizes.

• AI can improve efficiency in research, but human judgment is still essential for understanding emotion, taste, humor, and nuance.

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