Instead of asking "Can the model do this?", product leaders should ask four critical questions about the user experience, who builds the reliability layer, the business cost of its failure, and the cost to maintain it at scale.

While AI solves complex problems, it simultaneously creates new, subtle issues. AI product development significantly increases the number of potential edge cases and risks related to data integrity and governance, requiring deep, detail-oriented involvement from product leaders.

Ditch traditional coding interviews. To hire for the AI era, ask candidates to record a full-screen video of themselves using AI agents to build a feature. This reveals their true skill: how they prompt, manage, and debug AI, which is more valuable than their ability to write code from scratch.

With AI agents handling routine coding, the engineer's role evolves. Their most valuable contributions become testing the AI's output, identifying edge cases, and making roughly ten critical product and architectural decisions daily, rather than simply writing code from scratch.

A flashy AI demo can be created quickly, showcasing best-case performance. A real product, however, must be robust and reliable even on its worst day. The unglamorous engineering effort to bridge this gap between a demo and a production-ready product is immense and often underestimated by stakeholders.

Traditional product sense questions are being replaced. AI PM candidates should expect to solve problems live using AI tools or design complex AI-native systems. This shift assesses a candidate's hands-on "builder" capabilities and deep understanding of modern AI architecture.

To assess a product manager's AI skills, integrate AI into your standard hiring process rather than just asking theoretical questions. Expect candidates to use AI tools in take-home case studies and analytical interviews to test for practical application and raise the quality bar.

The key skill for an AI PM is knowing a model's current capabilities. This is built by intensely using the model and, crucially, asking it to introspect on its own unexpected behaviors to understand *why* it made a mistake, revealing gaps to fix.

An engineer’s ability to leverage AI tools is a critical measure of their productivity. Brex has shifted its interview process to practical building exercises that are too complex to complete without proficient use of AI, directly testing for 10x potential.