Don't view AI as just a feature set. Instead, treat "intelligence" as a fundamental new building block for software, on par with established primitives like databases or APIs. When conceptualizing any new product, assume this intelligence layer is a non-negotiable part of the technology stack to solve user problems effectively.

The ultimate vision for AI in product isn't just generating specs. It's creating a dynamic knowledge base where shipping a product feeds new data back into the system, continuously updating the company's strategic context and improving all future decisions.

Referencing Christopher Alexander, the discussion highlights "unself-conscious" design, where creators build and adapt a product while using it. This direct feedback loop creates a more functional and soulful product than one designed by specialized "architects" who are disconnected from the end-user's experience.

Project-based companies operate on a cash flow mindset, accepting any custom work that brings in immediate revenue. A true product company uses an investment mindset, strategically saying 'no' to short-term revenue to invest in building a scalable asset that can win a market long-term.

Engineering often defaults to a 'project mindset,' focusing on churning out features and measuring velocity. True alignment with product requires a 'product mindset,' which prioritizes understanding the customer and tracking the value being delivered, not just the output.

Unlike traditional software where PMF is a stable milestone, in the rapidly evolving AI space, it's a "treadmill." Customer expectations and technological capabilities shift weekly, forcing even nine-figure revenue companies to constantly re-validate and recapture their market fit to survive.

The traditional, linear handoff from product spec to design to code is collapsing. Roles and stages are blurring, with interactive prototypes replacing static documents and the design file itself becoming the central place for the entire team to align and collaborate.

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.

Successful AI products follow a three-stage evolution. Version 1.0 attracts 'AI tourists' who play with the tool. Version 2.0 serves early adopters who provide crucial feedback. Only version 3.0 is ready to target the mass market, which hates change and requires a truly polished, valuable product.