Lenny's Podcast

AI and product management | Marily Nika (Meta, Google)

Marily is a computer scientist and an AI Product Leader currently working for Meta’s reality labs, and previously at Google for 8 years. In 2014 she completed a PhD in Machine Learning. She is also an Executive Fellow at Harvard Business School and she has taught numerous courses, actively teaching AI Product Management on Maven and at Harvard. Marily joins us in today's episode to shed light on the role of AI in product management. She shares her insights on how AI is empowering her work, and why she believes that every Product Manager will be an AI Product Manager in the future. We also discuss why PM’s should learn a bit of coding, where they can learn it, and best practices for working with data scientists. Marily shares some insight into building her AI Product Management course and also why she full-heartedly believes you should also create your own course. — Brought to you by Amplitude—Build better products: https://amplitude.com/ | Eppo—Run reliable, impactful experiments: https://www.geteppo.com/ | Pando—Always-on employee progression: https://www.pando.com/lenny Find the full transcript here: https://www.lennysnewsletter.com/p/ai-and-product-management-marily Where to find Marily Nika: • Instagram: http://www.instagram.com/marilynika • LinkedIn: https://www.linkedin.com/in/marilynika/ • YouTube: https://www.youtube.com/c/MarilyNikaPM • Website: https://bio.link/marilynika Where to find Lenny: • Newsletter: https://www.lennysnewsletter.com • Twitter: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ Referenced: • The Download newsletter: https://www.technologyreview.com/topic/download-newsletter/ • TLDR newsletter: https://tldr.tech/ • ChatGPT: https://chat.openai.com/auth/login • MidJourney: https://discord.com/invite/midjourney • Whisper: https://whisper.ai/ • Machine Learning Specialization course: https://www.coursera.org/specializations/machine-learning-introduction • Career Foundry: https://careerfoundry.com • Coding Dojo: https://www.codingdojo.com/ • Building AI Products—For Current & Aspiring Product Managers course on Maven: https://maven.com/marily-nika/technical-product-management • arXiv: https://arxiv.org/ • Marginal Revolution blog: https://marginalrevolution.com/ • Automl: https://cloud.google.com/automl • Inspired: How to Create Tech Products Customers Love: https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507 • You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place: https://www.amazon.com/You-Look-Like-Thing-Love/dp/0316525227 • The Adventures of Women in Tech Workbook: A Life-Tested Guide to Building Your Career: https://www.amazon.com/Adventures-Women-Tech-Workbook/dp/1646871022 • Boz to the Future podcast: https://podcasts.apple.com/us/podcast/boz-to-the-future/id1574002430 • The White Lotus on HBO: https://www.hbo.com/the-white-lotus • Lensa: https://apps.apple.com/us/app/lensa-ai-photo-video-editor/id1436732536 In this episode, we cover: (00:00) Marily’s background (03:20) How Marily stays informed about the latest developments in AI (04:46) What is overhyped and underhyped in AI right now (05:59) How Marily uses ChatGPT for work (08:25) Why product managers will be AI product managers in the future (11:16) How to get started using AI (14:12) When not to use AI (15:47) How much data do you need for AI to work properly? (17:01) When should companies develop their own AI tools? (18:35) What an AI model is and how it is trained (21:25) How Google demonstrated the ability of AI to translate a conversation in real time (23:02) Why AI will not replace PMs (23:48) A case for learning to code (26:21) Where to learn to code (27:40) How to become a strong AI PM (29:25) Challenges that AI PMs face (31:16) Getting leadership on board with investing in AI (33:10) How PMs will work with data scientists and AI (35:29) Marily’s AI course (39:12) AutoML and how a renewable-energy company used it to improve its turbine maintenance procedure (40:31) How Marily built her course and the modifications she has made (42:53) Why you should create your own course (44:08) Lightning round Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Marily NikaguestLenny Rachitskyhost

Feb 4, 202348mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

How AI Transforms Product Management Without Replacing Product Managers Themselves

Marily Nika, an AI-focused product leader at Meta and former Google PM, explains how AI is reshaping product management and why every PM will eventually be an AI PM. She argues that PMs shouldn’t chase AI for its own sake, but instead start from real user problems and use AI as a smart tool to solve them. The conversation covers practical ways PMs can use tools like ChatGPT today, how to work with research scientists and data, and when not to invest in machine learning. Marily also shares her framework for building AI products, teaching AI PM skills, and upskilling through coding, courses, and hands-on experimentation.

IDEAS WORTH REMEMBERING

5 ideas

Avoid the “shiny object trap” of adding AI without a real problem.

Marily emphasizes that AI should never be used just because it’s trendy; start by identifying a real user pain point, validate that an intelligent solution is appropriate, and only then bring in data scientists or models.

Use AI tools to enhance, not replace, core PM responsibilities.

Tools like ChatGPT can significantly improve mission statements, user personas, and idea generation, freeing PMs to focus on vision and strategy rather than tedious writing or segmentation work.

Every PM will increasingly be an AI PM by default.

Because personalization, recommendation, and automation are becoming table stakes in products, PMs must grow comfortable collaborating with research scientists, navigating uncertainty, and scoping AI-driven features.

Do not use AI for your MVP—fake it first.

For early validation, Marily advises using prototypes and simulated AI behavior (e.g., Figma mocks or manual workflows) instead of investing months in model training before you know if users even want the feature.

Data quantity and quality fundamentally constrain AI projects.

The feasibility and impact of AI depend heavily on having sufficient, relevant, and well-labeled data; for complex tasks you may need thousands of examples, and sometimes even synthetic data or creative collection tactics.

WORDS WORTH SAVING

5 quotes

Don't do AI for the sake of doing AI. Make sure there is a problem there, make sure there is a pain point that needs to be solved in a smart way.

— Marily Nika

I believe that all product managers will be AI product managers in the future.

— Marily Nika

A generalist PM helps their team and their company build and ship the right product. But the AI PM helps their team and company solve the right problem.

— Marily Nika

There is nothing I can write that's gonna be as good as what ChatGPT would write [for a mission statement].

— Marily Nika

Do not use AI for your MVP… If you want to prove that there is a market, fake it.

— Marily Nika

Practical uses of AI tools (e.g., ChatGPT) in day-to-day product managementWhy every future PM will need AI literacy and how AI changes the PM roleIdentifying real problems vs. falling into the “shiny object” AI trapWhen and how to build AI/ML into products, including data and model basicsWorking effectively with research scientists and managing AI product uncertaintyLearning paths and resources for PMs to become strong AI product managersDesigning and teaching an AI product management course and its outcomes

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