The role of an AI Product Manager is legitimate and highly compensated, as confirmed by Google's Director of AI Product. Job postings and salary data sites like Levels.fyi reflect the high demand and experience required for these positions in a competitive industry.
When pursuing breakthrough ideas ("10x thinking"), the process is inherently uncomfortable. It's crucial to distinguish this discomfort, which signals you're pushing boundaries, from the feeling of being wrong. Embracing this discomfort is key to innovation in ambiguous, early-stage product development.
When hiring for creative roles like AI Product Manager, the resume itself is evaluated as a product. A generic, plain-text resume signals a lack of creativity and product taste. The design, clarity, and cohesive narrative it tells are direct demonstrations of the candidate's core skills.
Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.
Unlike traditional product management that relies on existing user data, building next-generation AI products often lacks historical data. In this ambiguous environment, the ability to craft a compelling narrative becomes more critical for gaining buy-in and momentum than purely data-driven analysis.
While the goal is to build a platform (second-order thinking), initial single-purpose app ideas (first-order) are critical. They serve as your "golden evaluation set"—a collection of core use cases that validate your platform is solving real user problems and is truly useful.
In the rapidly evolving AI landscape where ideas are quickly commoditized, the most valuable trait for a product manager is not having one great idea, but possessing the creative skill to generate many good ideas consistently. This creative muscle is more important than being attached to a single concept.