In response to the panic that agentic AI shortens the path to expertise, this post reframes the ten-year journey to mastery. The time required remains non-negotiable, but its focus shifts from writing code to conducting AI. The new path to expertise is defined by the deliberate practice of prompt engineering, critically evaluating AI output, and applying the deep judgment that can only be built over a decade of experience.
When faced with an ambiguous problem, the instinct is often to plan and research our way to a perfect solution. This is a trap. Clarity isn’t found in a document; it’s earned through action. The most effective strategy for navigating uncertainty is to take the smallest possible step, write the “bad” code, and create a feedback loop. Momentum is a strategy. Moving your feet is the only way to find the right path.
We mistake the messy, emergent path of discovery for a predictable blueprint, vowing to perfect a past that was never knowable. This post reframes project planning through the lens of “R&D’s Hierarchy of Needs,” arguing that the foundation—validation that we’re building the right thing—is the only layer that truly matters. Drawing on the Cynefin framework and Gall’s Law, it diagnoses the “curse of knowledge” as the root of our over-planning delusion and offers a simple antidote: start simpler, validate faster, and build complexity only after the foundation is proven.