In an age of rapid AI prototyping, it's easy to jump to solutions without deeply understanding the problem. The act of writing a spec forces product managers to clarify their thinking and structure context. Writing is how PMs "refactor their thoughts" and avoid overfitting to a partially-baked solution.

AI tools accelerate development but don't improve judgment, creating a risk of building solutions for the wrong problems more quickly. Premortems become more critical to combat this 'false confidence of faster output' and force the shift from 'can we build it?' to 'should we build it?'.

Traditional SaaS development starts with a user problem. AI development inverts this by starting with what the technology makes possible. Teams must prototype to test reliability first, because execution is uncertain. The UI and user problem validation come later in the process.

In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.

Without a strong foundation in customer problem definition, AI tools simply accelerate bad practices. Teams that habitually jump to solutions without a clear "why" will find themselves building rudderless products at an even faster pace. AI makes foundational product discipline more critical, not less.

The ease of AI development tools tempts founders to build products immediately. A more effective approach is to first use AI for deep market research and GTM strategy validation. This prevents wasting time building a product that nobody wants.

The temptation to use AI to rapidly generate, prioritize, and document features without deep customer validation poses a significant risk. This can scale the "feature factory" problem, allowing teams to build the wrong things faster than ever, making human judgment and product thinking paramount.

In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.

A common red flag in AI PM interviews is when candidates, particularly those from a machine learning background, jump directly to technical solutions. They fail by neglecting core PM craft: defining the user ('the who'), the problem ('the why'), and the metrics for success, which must come before any discussion of algorithms.