The more I study React and Vue, the more I realize that developers often spend more time debating frameworks than understanding the fundamentals that make those frameworks possible.

Discussions about performance, developer experience, ecosystem maturity, and new features are everywhere. While these conversations are valuable, they often overlook a simple reality: React and Vue are ultimately abstractions built on top of JavaScript.

When we look beneath the APIs we use every day, we find the same underlying concepts repeatedly appearing in different forms.

Take useMemo in React and computed() in Vue, for example. Although their implementations differ, both rely on the same fundamental ideas: caching, dependency tracking, and closures. The framework provides a convenient abstraction, but the underlying principles remain unchanged.

The same is true for useCallback. Many developers treat it as a magical optimization tool, when in reality it is primarily a mechanism for preserving function references across renders. To fully understand its behavior, you need to understand closures, memory references, and dependency comparisons.

Similarly, Vue's watch() and React's useEffect() may look different on the surface, but both depend on concepts such as dependency tracking, scheduling, and the Event Loop.

State management follows the same pattern. Whether you're using React's useState or Vue's ref and reactive, you're still working with the same underlying concepts: state, observability, controlled mutations, and reactivity.

Even modern asynchronous programming is built on foundations that haven't changed.

Promises and async/await dramatically improved developer experience, but they didn't replace the mechanisms that power asynchronous execution. Behind every asynchronous operation, concepts such as the Call Stack, Event Loop, Callback Queue, Microtasks, and Macrotasks continue to govern application behavior.

Frameworks evolve.

APIs evolve.

But the fundamentals remain.

And that's one of the reasons I have mixed feelings about the current wave of AI adoption in software development.

Don't get me wrong—I use AI every day.

For coding, architecture discussions, debugging, research, documentation, and learning. It has become an incredibly valuable part of my workflow.

However, I've noticed an increasingly common pattern.

Many developers are becoming highly productive without necessarily becoming more knowledgeable.

AI can generate React components, Vue composables, tests, documentation, and even architectural suggestions in seconds. The output is often impressive.

The problem is that productivity and understanding are not the same thing.

More and more, I see developers working with React, Vue, AI-assisted coding tools, and even agentic systems without fully understanding concepts such as closures, lexical scope, prototypes, hoisting, memory management, garbage collection, reference semantics, or the Event Loop.

And this concern extends far beyond JavaScript.

The same thing can happen with software architecture, design patterns, SOLID principles, system design, separation of concerns, engineering methodologies, and technical decision-making.

The immediate impact is rarely visible.

The application still works.

The feature still ships.

The sprint still ends successfully.

But over time, something important starts to erode.

The ability to reason deeply about a problem.

The ability to challenge a proposed solution.

The ability to identify architectural weaknesses before they become expensive.

The ability to understand not only how something works, but why it works.

AI is an extraordinary tool for accelerating development.

But outsourced knowledge is not acquired knowledge.

If we stop exercising our fundamentals, we gradually lose the ability to evaluate, adapt, and improve the solutions we receive.

Ironically, I believe the best way to use AI is not as a replacement for learning, but as a catalyst for it.

Ask why a solution works.

Ask about trade-offs.

Ask for alternative approaches.

Ask for deeper explanations.

Use AI as a teacher, not just as a generator.

Because the engineers who will thrive in the coming years won't simply be the ones who use AI the most.

They'll be the ones who combine strong fundamentals with powerful tools.

Frameworks will change.

Tools will change.

AI models will change.

But JavaScript fundamentals, software engineering principles, and critical thinking remain the foundation upon which everything else is built.

And that foundation is still worth investing in.