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—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.Find the full transcript here: https://www.lennyspodcast.com/ai-and-product-management-marily-nika-meta-google/#transcriptWhere 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/marilynikaWhere 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://midjourney.com/home/• 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/id1436732536In 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 roundProduction and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Marily Nika, the Gen AI Product Lead at Google Assistant, sheds light on the unique challenges of managing AI products. She discusses the ethical considerations crucial in AI development and the importance of adaptability through experimentation. Marily emphasizes the necessary skills for product managers, including user empathy and technical knowledge. The conversation also delves into achieving product-market fit by balancing user desirability, business viability, and technical feasibility while navigating the evolving landscape of AI.

Marily Nika, an AI Product Lead at Google with a PhD in machine learning, shares her wealth of knowledge on breaking into AI product management. She emphasizes that anyone can become an AI PM and offers a crash course in core AI concepts. Marily discusses the essential traits of effective AI PMs and how to secure leadership buy-in for AI projects. She also highlights the importance of managing expectations in AI development and shares her experiences with AI in her personal life. Plus, a live demo of ChatGPT Voice showcases practical AI applications!

Joining in on a bonus episode, Ayushman, Nkem, Diego, and Marily delve into API and platform product management, strategies for tech industry navigation, and the impact of AI on product management. They explore customer expectations in API product development, tying KPIs to business outcomes, and the challenges and opportunities presented by AI advancements. A must-listen for product managers looking to stay ahead in the field.

Marketing professor Jeff Shulman and advisory board members welcome experts Adam Grupp, Greg Young, Sumathi Kadambi, Marily Nika, and Sumantro Das to discuss decision-making in product management. They cover different styles of decision-making, the cost of failing to make decisions, creating metrics, building an ecosystem of experts, favorite decision-making matrices, sourcing new ideas, unconventional ideas, and decision-making frameworks and strategies.