What Doesn't Change

“The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” — Alvin Toffler

Everything in tech is accelerating. New frameworks every week. AI models that make last month’s breakthrough obsolete. The half-life of specific technical skills keeps shrinking.

And yet, the faster everything changes, the more valuable the things that don’t change become.

I’m talking about fundamentals. The boring stuff. How computers manage memory. How networks move data. Why O(n²) algorithms don’t scale. What happens when two processes try to update the same resource. These principles were true in the 1970s and they’ll be true in the 2070s.

Here’s what’s counterintuitive: AI makes this more true, not less.

Everyone’s either panicking that AI will replace them or assuming they don’t need to learn anything anymore. Both miss the point entirely. AI amplifies what you already know. If you understand distributed systems, you’ll use AI to build better ones. If you don’t, you’ll use AI to create distributed disasters.

The difference? When that AI-generated code breaks in production — and it will — you need to know why. When it doesn’t scale — and it won’t — you need to understand the bottlenecks. When it creates race conditions, memory leaks, or architectural nightmares, GitHub Copilot won’t save you. Your fundamentals will.

This isn’t about being a purist or gatekeeping. It’s about recognizing that every “revolutionary” technology builds on the same core concepts. When you understand the principles, you see through the hype. You know what’s genuinely new versus what’s repackaged. You can learn any tool because you understand the problems it’s trying to solve.

The engineers who thrive through every technology shift aren’t the ones frantically keeping up with trends. They’re the ones who went deep on what doesn’t change. They read those classic papers. They understand algorithms and data structures. They know why things work the way they do.

So where do you start? Pick one fundamental area and go deep. Really deep.

Maybe it’s algorithms and data structures. Not the interview prep nonsense — understanding why quicksort behaves differently on nearly-sorted data, or when a hash table becomes a liability. Maybe it’s networking; actually learn TCP/IP, understand what happens during a TLS handshake, trace a request from browser to database and back.

Or dive into operating systems. Understand virtual memory, process scheduling, what actually happens when you fork a process. Distributed systems? Start with the CAP theorem, then consensus algorithms, then why exactly distributed transactions are such a nightmare.

Don’t try to learn everything at once. Pick one area, spend a few weeks/months going deeper than feels reasonable. Read the original papers. Write actual code to implement the concepts. Break things and understand why they broke.

Then watch what happens the next time you encounter a new technology. You’ll see right through it. You’ll understand what problems it’s solving, what trade-offs it’s making, where it’ll break. That framework everyone’s excited about? Maybe you’ll master it in days, instead of months.

The tools are temporary. The foundations are forever.

Invest accordingly.