Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

Wiz's product team, trained at Microsoft, avoids building features that only solve for today's customer but break with tomorrow's enterprise giant. This 'infinite scale' mindset isn't about slowing down; it's about making conscious architectural choices that prevent time-consuming and costly refactoring later on.

As a company grows, its old operational systems and processes ('plumbing') become obsolete. True scaling is not about addition; it's about reinvention. This involves systematically removing outdated processes designed for a smaller scale and replacing them entirely.

While no-code can help validate an idea, it inevitably leads to a growth-killing stall. Founders will hit a platform limitation that forces them to stand still for 3-6 months to rewrite the entire codebase from scratch. This sacrifices critical early-stage feature velocity and market responsiveness.

AI's productivity gains mean that on a lean, early-stage team, there is little room for purely specialized roles. According to founder Drew Wilson, every team member, including designers, must be able to contribute directly to the codebase. The traditional "design artifact" workflow is too slow.

When a startup finally uncovers true customer demand, their existing product, built on assumptions, is often the wrong shape. The most common pattern is for these startups to burn down their initial codebase and rebuild from scratch to perfectly fit the newly discovered demand.

Karri Saarinen of Linear posits that design should be a "search" phase, free from coding constraints. Jumping directly into code introduces biases from the existing codebase, making designers more conservative and less idealistic, which ultimately hinders breakthrough product ideas.

Block's CTO argues that engineers mistakenly equate code quality with product success. He uses the example of early YouTube, which had a famously poor architecture but became wildly successful, while the technically superior Google Video failed. The focus should be on solving a user problem, not on perfect code.

Every change introduces a temporary performance decrease as the team adapts—an 'implementation dip.' This guaranteed loss often outweighs the uncertain potential gain from minor tweaks. Real growth comes from compounding skill through repetition of a working system, not from perpetual optimization.