I’m riffing a bit more on the idea of reading code which I captured in my note this morning.
I used to treat code as a mysterious incantation—something only the initiated could summon. But the longer I worked with engineers (and, increasingly, with LLMs), the clearer it became that reading code is first and foremost an exercise in logic, not syntax. When I trace a data flow on a whiteboard—login token leaves the front-end, hits the auth provider, returns with credentials, then fans out into inventory look-ups—I’m practicing the same deductive discipline geometry once drilled into us: define the givens, follow the transformations, test the conclusion. Master that narrative eye and suddenly the file tree stops looking like sorcery and starts reading like a novel with clean character arcs and the occasional unreliable narrator.
That narrative lens is strategic leverage in an AI-saturated world. Today’s models are spectacular at generating snippets that look plausible in isolation—authentication helpers, pagination utilities, CRUD endpoints—but they stumble at the seams where those snippets hand off responsibility. Reading code gives you x-ray vision for those seams: Why is auth logic co-habiting with inventory routes? Why are side effects leaking out of a “pure” function? Spotting these mismatches early prevents the nightmare where you paste a 500-line stack trace into ChatGPT and watch it chase its own tail. Fluency in reading turns you from passive prompt-monkey into director of the whole play, delegating specific rewrites to AI while safeguarding the plot.
I’ve learned the hard way that LLMs are brilliant assistants and terrible architects. They pattern-match, they don’t world-model. Without a human (you) enforcing a coherent mental model of the system, they’ll cheerfully invent agreements that were never signed—an API response shape, a database column, a redirect that skips state validation. Reading code trains the mental checksum that catches those hallucinations. It also teaches you to prompt better: “Think step-by-step, outline a test plan, then propose the fix.” That small discipline breaks the fix-and-fail loop where the model patches one error only to spawn three more.
Most people won’t become professional engineers—and that’s fine. But in 2025, almost every high-leverage role intersects with code or AI-generated code. Product leads, growth analysts, even operations chiefs need to interrogate a pull request or vet an agentic workflow. The hiring signal isn’t whether you can write perfect TypeScript; it’s whether you can read a diff, reconstruct the story, and ask one incisive question that saves a sprint. Ten minutes of deliberate code reading a day is not vitamins for some abstract future—it’s present-day career compounding. Cultivate it, and you’ll navigate the next decade of AI-augmented work with clarity instead of guesswork.
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