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Subversive
Subversive is a podcast dedicated to sharing stories from the best consumer subscription apps in the world. We'll bring you lessons for how to grow your consumer subscription business, including insights and inflection points that led to exponential growth from leaders at category-defining companies and innovative startups.
Subversive
How Duolingo Built Duolingo Max
Natalia Castillejo is a Director of Product at Duolingo, where she has spent the last decade since graduating from Yale University in 2016. Natalia started her PM career at Duolingo working on the company’s English Language Test and other core product features. More recently, she helped lead the development and monetization of AI-powered features like “Video Call,” “Roleplay,” and “Explain My Answer” that form the core of Duolingo Max, and then transitioned from core product to monetization to help accelerate the growth of this new higher-priced subscription tier.
Key Takeaways:
- Duolingo started investing in AI product features in 2022 after getting early access to ChatGPT. It spent a year building and refining these features before packaging them into its new higher-priced Duolingo Max subscription tier in 2023.
- Duolingo Max wasn’t an overnight success. It wasn’t until the company launched video calls and repositioned Max from “pay more for AI” to “pay more for conversational language learning” that the new subscription tier really took off.
- Duolingo requires all PMs to be daily users of its app, and the company A/B tests every product change. This has led to a world-class product development culture built on both strong product intuition and rigorous experimentation.
- Duolingo always starts with the user problem that needs to be solved and works backward from there. This helps avoid “technology in search of a problem,” which is especially important right now as AI is unlocking all sorts of new technology.
- Duolingo is also encouraging its PMs to invest in AI tools to boost efficiency. This includes using tools like Replit to build prototypes without engineers, and building internal tools to predict the impact of an A/B test based on previous experiments.
Natalia Castillejo:
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