In this episode of the DMDPM podcast, Stuart Winter-Tear, head of product at Genaios, explores the nuances of building AI products compared to traditional products. We discuss the transition from deterministic to probabilistic models, the importance of user experience, ethical considerations, and the challenges of pricing and business models in the AI landscape.


The podcast has 4 sections:
Intro & overview of Product Marketing
Most Asked Questions
Myth Buster
Threshold concept
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In this discussion we unpack:
Navigating the probabilistic nature of GenAI vs. the deterministic approach in traditional products.The cool trap—why chasing tech without strategy could cost you big.The shift to data-driven decision-making and the implications for your roadmap.How to balance research timelines with product delivery when the answers are not always clear-cut.How to rethink business models—traditional SaaS pricing won’t cut it in the world of AI.Key myths about GenAI—like the idea that building with AI means instant accuracy.
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Where to find Stuart Winter-Tear
  / stuart-winter-tear  
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Where to find Diana Matei
  / diana-eugenia-matei  
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Timestamps:
[00:00] Introduction to the Podcast and Guest Background
[03:48] Transitioning from Traditional AI to GenAI
[07:51] The Cool Trap: Technology vs. Customer Focus
[11:52] Building Live: Challenges in Product Development
[15:02] Risk Aversion in Product Management
[17:56] Data Quality and Maturity in AI Products
[18:52] Starting Your Journey with AI Products
[25:24] User Experience Challenges in AI Products
[30:22] Ethical Considerations in AI Development
[36:13] Business Models and Pricing for AI Products
[43:36] Common Misconceptions in Building AI Products
[47:08] Threshold Concepts: Essentials for Building AI Products