AI

You used an AI tool today. For example, ChatGPT may have drafted an email, or Claude helped debug a function. Perhaps you looked up something you would not have asked a colleague out loud. These tools sit in almost every workflow now — and yet, most people using them cannot explain what happens after they hit send. Tokens. Context windows. Hallucinations. Knowledge cutoffs. Agents. The terms show up in standups, headlines, and LinkedIn threads. People repeat them, but few can define them. That gap matters, because the better you understand …

Last month I interviewed someone for a senior data-engineer role: a standard sliding-window style problem, nothing exotic. They echoed the prompt, then started typing in a handful of seconds. Under three minutes later they had an optimal, polished solution—honestly better than I would have typed cold. So I asked one follow-up: Why a hash map here instead of a set? About ten seconds of silence. That is when I knew. After hundreds of interviews, on both sides of the table—the shift underway in hiring is the largest I have seen. …

Something strange keeps happening in software engineering discourse: every week another headline claims coding is dead. Comments under almost every technical video ask the same question—should I still learn to code in 2026? Some engineering skills cannot be replaced with AI. Here is a clearer picture: AI replaces tasks, not engineers. Models can draft, debug, and refactor quickly. They do not sit in roadmap meetings, carry on-call context, or grow a junior through a stuck afternoon. People said arithmetic was doomed when calculators spread; then encyclopedias when Wikipedia arrived; then …