Tech Leaderism

AI and the Cognitive Load

A modern Senior Engineer is expected to know the syntax of four languages, the quirks of three cloud providers, and the history of a decade-old codebase. This is not sustainable. The human brain has a hard limit on working memory, and when we exceed it, we don't just get slower; we make architectural mistakes.

The true value of AI in engineering isn't writing code, it is semantic compression. For years, we relied on "Just-in-Case" learning-memorizing API specs and library internals because we might need them. AI allows us to shift to a "Just-in-Time" knowledge model. Instead of reading fifty pages of documentation to find one configuration parameter, we can simply retrieve the pattern. This frees up limited cognitive capacity for high-value work like system design and edge-case analysis.

This is most visible when dealing with legacy systems. The highest cognitive load often comes from deciphering code written by developers who left years ago. Rather than spending hours building a mental model of a complex file, we can now use AI to explain data flow and highlight side effects instantly. It turns a forensic investigation into a quick confirmation.

However, this power comes with a strict caveat. AI reduces the burden of writing and finding, but it increases the burden of reviewing. If you use tools to generate code you do not fundamentally understand, you haven't reduced your cognitive load, you have merely deferred it to the debugging phase where it is exponentially more expensive.