Synthetic data is being pitched as the end of slow, expensive market research. And in some cases, it really can help: it’s useful for testing systems safely, generating options quickly, and reducing the cost of experimentation, especially for small teams.

But “synthetic data” is used to describe two very different things. One is synthetic datasets (fake-but-realistic data for testing and privacy). The other is synthetic respondents (AI-simulated people used for market research), and confusing the two can be a major issue.

In this episode, we break down where synthetic data works, where it breaks, and the guardrails founders should use so it accelerates learning instead of replacing it.

Key Topics Covered

• What synthetic data is: artificially generated data designed to mimic real-world patterns
• Synthetic datasets vs synthetic respondents — and why confusing them leads to bad decisions
• Directional insight vs reliable truth in AI-assisted research
• Bias in / bias out, and how synthetic data can amplify existing assumptions
• Privacy tradeoffs: when synthetic data is privacy-enhancing vs when it still carries risk
• Real-world use cases discussed:
• Testing and simulation in autonomous systems and rare edge cases
• Finance and fraud-pattern modeling under data restrictions
• Marketing measurement challenges (cookie loss, attribution gaps)
• Founder use cases: pricing ranges, messaging tests, early segmentation, objection handling

Timestamps:

00:00 Introduction and Personal Updates

04:53 What synthetic data actually is (and why it’s confusing)

09:07 Understanding Synthetic Data Definitions: datasets vs synthetic respondents

12:28 Why synthetic data is everywhere now: privacy, speed, and survey fatigue

15:03 Real World Use Cases: Where synthetic data already works outside of marketing

17:47 Synthetic Respondents: Opportunities and Challenges

18:14 How synthetic respondents simulate customer opinions

22:05 The Mark Ritson argument  and the context you shouldn’t ignore

23:16 Downsides to Synthetic Data: bias, false confidence, and missing the signal

29:45 Guardrails for using synthetic data

32:04 Practical founder use cases: pricing, messaging, and segmentation

34:47 Cultural pushback against AI: San Diego Comic Con & Bandcamp

38:25 AI gone wrong: the Kafkaesque spelling fail

41:40 Wrapping up

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