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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.
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Go to productmanagement.aiThis article argues that companies operating with 2015-era product models face existential risk in an AI-accelerated environment. The piece introduces...
This article by Marty Cagan (SVPG founder) advocates for using foundation AI models (Claude, Gemini, GPT) as personal product coaches for aspiring pro...
This article presents a comprehensive framework for AI product strategy, arguing that success requires building defensible moats rather than simply ad...