When hiring, top firms like McKinsey value a candidate's ability to articulate a deliberate, logical problem-solving process as much as their past successes. Having a structured method shows you can reliably tackle novel challenges, whereas simply pointing to past wins might suggest luck or context-specific success.

To increase the "memobility" of your ideas so they can spread without you, package them into concise frameworks, diagrams, and stories. This helps others grasp and re-transmit your concepts accurately, especially when you can connect a customer pain to a business problem.

Instead of asking an AI to directly build something, the more effective approach is to instruct it on *how* to solve the problem: gather references, identify best-in-class libraries, and create a framework before implementation. This means working one level of abstraction higher than the code itself.

Product management "range" is developed not by learning domain-specific facts, but by recognizing universal human behaviors that transcend industries—the desire for simplicity, convenience, or saving time. Working across different verticals hones this pattern-matching skill, which is more valuable than deep expertise in a world of accessible information.

The most effective way to convey complex information, even in data-heavy fields, is through compelling stories. People remember narratives far longer than they remember statistics or formulas. For author Morgan Housel, this became a survival mechanism to differentiate his writing and communicate more effectively.

Perplexity's VP of Design, Henry Modiset, states that when hiring, he values product intuition above all else. AI can generate options, but the essential, irreplaceable skill for designers is the ability to choose what to build, how it fits the market, and why users will care.

The most valuable professionals are neither pure visionaries nor pure executioners; they are "step builders." This means they can understand a high-level strategic vision and then map out the granular, sequential steps required to achieve it. This skill is critical for turning ambitious goals into reality.

The key technical skill for an AI PM is not deep knowledge of model architecture but a higher-level understanding of how to orchestrate AI components. Knowing what AI can do and how systems connect is more valuable than knowing the specifics of fine-tuning or RAG implementation.

Technical implementation is becoming easier with AI. The critical, and now more valuable, skill is the ability to deeply understand customer needs, communicate effectively, and guide a product to market fit. The focus is shifting from "how to build it" to "what to build and why."