Loading...
Loading...
This article by Miqdad Jaffer (OpenAI's Product Lead) argues that traditional product-market fit frameworks are obsolete for AI companies. It introduces the "AI PMF Paradox"—achieving fit is simultaneously easier (faster iteration, better user understanding) and harder (skyrocketing expectations, comparison to ChatGPT). The piece presents a four-phase framework: Opportunity Spotting, Building MVPs, Scaling with Strategic Frameworks, and Optimizing for Sustainable Growth.
The author emphasizes that AI products differ fundamentally from traditional software because problems evolve as users learn capabilities, the solution space is infinite, and user expectations compound exponentially. Success requires dual metrics—traditional engagement indicators alongside AI-specific measures like accuracy and hallucination rates. AI PMF is a moving target requiring constant recalibration.
Building on foundational concepts, this resource explores ai product strategy at a deeper level. It's designed for PMs who have some AI experience and want to develop more sophisticated skills.
Ready to explore this resource?
Go to productmanagement.aiThis article by Tony Beltramelli (Head of Product at Miro) argues that AI agents are rapidly becoming the primary users of software products, requirin...
This article presents the 4D Method for building AI products: Discovery, Design, Development, and Deployment. The framework emphasizes that AI product...
This article by Marty Cagan (SVPG founder) advocates for using foundation AI models (Claude, Gemini, GPT) as personal product coaches for aspiring pro...