Editor's Synopsis
A Reddit discussion in r/ProductManagement tackles the question of what an AI Product Manager actually is, sparked by a user returning to the field after an academic break. The community's responses range from dismissive — calling it "marketing BS" and just a PM who works on AI products — to more nuanced takes that draw meaningful distinctions. A recurring theme is that most people slapping "AI PM" on their title are simply regular PMs who integrate LLM features into existing products, not specialists with deep AI expertise.
Several commenters offer more substantive frameworks. One CEO/CPO distinguishes between PMs who merely leverage an LLM for a use case and true AI PMs who understand MLOps, RLOps, LLMOps, and work on core AI platforms or models. Another commenter separates the role into Platform AI PMs (building AI/GenAI infrastructure) and Applied AI PMs (integrating AI experiences into products), predicting all PMs will eventually shift to the latter category. A practical insight emerges that 75% of what stakeholders request as "AI" is better served by traditional ML models rather than generative AI, though this claim is contested by others who note LLMs are now state-of-the-art for NLP tasks.
The thread also surfaces frustrations about the hiring landscape, where years of ML experience are dismissed because candidates lack specific LLM experience, and seasoned PMs worry their tenure counts for nothing without AI credentials. The consensus leans toward AI PM being a legitimate specialization when it involves deep technical knowledge of AI systems, but largely a buzzword when applied to PMs simply adding chatbot features to existing products.