Most mistakes with technology don’t come from bad tools.
They come from skipping the boring parts.

Every generation believes its tools are different enough to exempt it from fundamentals. We say this politely now, with phrases like AI-native or exponential leverage, but the belief underneath is old: that sophistication can substitute for understanding.

It never does.

Technology doesn’t eliminate first principles. It exposes whether you know them.

The reason this is easy to forget is that modern systems are mostly abstractions stacked on abstractions. You can build impressive things without touching the ground. Frameworks hide physics. Platforms hide economics. Models hide causality. For a while, this works.

Then something breaks. And when it does, the only people who can fix it are the ones who understand what’s actually happening underneath.

First principles aren’t a method. They’re a refusal.

A refusal to accept inherited assumptions.
A refusal to confuse precedent with truth.
A refusal to treat tools as thinking.

Most people reason by analogy. This is efficient and usually good enough. If something worked before, try it again. The problem is that analogy fails precisely where technology is most powerful: in new territory. The moment you are doing something genuinely novel, there is nothing left to copy.

At that point, you either reduce the problem to what must be true – or you guess.

Advanced tools are often sold as shortcuts around this step. They promise leverage without understanding. But leverage amplifies whatever you apply it to. If your thinking is clear, technology makes it formidable. If your thinking is sloppy, technology just helps you fail faster and at scale.

This is why AI feels magical to people who already understand their domain and dangerous to those who don’t. The model isn’t the intelligence. The intelligence is in the questions, the constraints, the evaluation. Without those, the output is noise that looks convincing.

The same pattern shows up everywhere.

In business, people chase growth before they understand value.
In strategy, they design structures before incentives.
In software, they optimise systems they don’t fully understand.

In each case, the failure isn’t technical. It’s foundational.

What’s interesting is that first principles don’t change much. Physics hasn’t been updated. Human incentives are stubbornly stable. Value still comes from solving real problems. What changes is how easy it is to forget these things.

Abstractions make us productive, but they also make us careless. The further you are from the underlying reality, the easier it is to mistake motion for progress. Dashboards, roadmaps, and models all create a comforting sense that someone, somewhere, understands what’s going on.

Often, no one does.

The people who consistently build resilient systems tend to share a habit: they reduce. They ask questions that feel almost naive. What is actually happening? What must be true for this to work? Where does the value come from? What breaks first?

These questions aren’t clever. That’s the point.

First-principles thinking is uncomfortable because it strips away status. You can’t hide behind jargon when you’re explaining something to yourself from scratch. You either understand it or you don’t.

This is also why it’s rare. It’s slower. It doesn’t signal sophistication. And it doesn’t fit neatly into slide decks.

But there’s a paradox here. The more advanced the system, the more it depends on simple truths. Complex systems fail in simple ways. Robust systems are usually built on a small number of well-understood ideas, applied consistently.

The future will have better tools, not fewer fundamentals. If anything, the gap will widen between people who can operate abstractions and people who can reason beneath them.

The quiet advantage belongs to the latter.

Technology will keep changing what’s possible.
First principles will keep determining what works.