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Dr. Marily Nika, longtime AI PM at Google and Meta, shares a simple weekly ritual that rapidly builds AI product sense – the ability to translate probabilistic model behavior into products people can trust. In this episode, Marily walks through the framework for uncovering failure modes before users do.

Listen now: YouTube | Apple | Spotify

In this episode, you’ll learn

• Why Meta added “Product Sense with AI” to its PM interview loop

• The rituals that surface hidden failure modes

• Why generative models confidently invent structure when confronted with mess

• What minimum viable quality (MVQ) means and how to define three critical thresholds

• Five strategic context factors that raise or lower your quality bar

• Why you need to estimate your AI feature’s cost envelope early

• How to design guardrails that protect users from model shortcomings

• Four patterns that cover most real-world failure cases

Referenced

• AI PM Bootcamp & Certification

• AI Product Sense & AI PM Interview prep

• Dr. Marily Nika: https://www.linkedin.com/in/marilynika/

• How to build AI Product sense (Part 1)

• Marily’s AI Product Academy Newsletter

• Thriving as a senior IC PM in the AI era

About

Welcome to Lenny's Reads, where every week you’ll find a fresh audio version of my newsletter about building product, driving growth, and accelerating your career, read to you by the soothing voice of Lennybot.

To hear more, visit www.lennysnewsletter.com

Building AI product sense, part 2

Lenny's Reads

Building AI product sense, part 2