From Gut Decisions to Causal Scenarios in Research with Jason Cohen of Simulacra Synthetic Data Studio

On this episode, host Sima Vasa talks to Jason Cohen, Founder and CEO of Simulacra Synthetic Data Studio, about the limits of traditional research, the evolution of synthetic data, and why causal modeling matters more than larger sample sizes. Drawing on his experience building and exiting Gastrograph AI, Jason explains how real-world data gaps undermine decision-making and how synthetic data can support scenario-based predictions when applied responsibly.

Key Takeaways:

00:00 Introduction.
05:24 Most brands can’t know in advance if research data is “correct.”​
09:44 Generic LLM personas rarely represent any real population.​
13:08 Cross‑coverage lets AI infer missing audience segments.​
16:04 Synthetic data is real when it’s actually used.​
19:36 Diversity in base samples drives credible synthetic expansion.​
23:28 Sample boosting alone doesn’t fix bad research outcomes.​
25:08 Synthetic data scales insights for hard‑to‑reach cohorts.​
26:08 Misused synthetic personas can drive completely wrong decisions.​

Resources Mentioned:

Simulacra Synthetic Data Studio | Website

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