Editor's Synopsis
A product manager with 3–4 years of experience in AI products shares a candid critique of the "AI Product Manager" role as it exists today, particularly in the Indian startup ecosystem. The author argues that many positions marketed as AI PM roles are actually implementation and customer success roles disguised with an AI label. In these roles, the core AI platform is already built by engineering teams, and the PM's job reduces to configuring prompts, designing conversation flows, and deploying the system for enterprise clients — not shaping the product's roadmap or working on core AI capabilities.
The post highlights a frustrating career trap: after spending one or two years in such a role, professionals find themselves stuck. They lack the ML engineering background that companies building serious AI infrastructure demand, yet their experience is too AI-focused for generic PM roles. The author also challenges the prevailing hype, estimating that AI improves enterprise workflow efficiency by roughly 20–25 percent — far from the transformational claims commonly made. Many startups, the author notes, are essentially API wrappers built on top of OpenAI or Anthropic, yet hiring expectations still reflect an older era when AI products required deep ML research expertise.
Commenters largely validate the observations, with several noting that the implementation-vs-core-product distinction is a longstanding pattern in PM hiring that AI has merely amplified. Others draw parallels to previous hype cycles around mobile, blockchain, and growth PM titles, predicting that "AI PM" will eventually dissolve into standard product management as the technology matures. The consensus advice: before accepting an AI PM role, carefully evaluate whether the position involves building and evolving the core product or merely deploying it for clients.