Coding in the AI Era: Do We Still Need to Learn Algorithms?

A few years ago, anyone who wanted to build an application usually had to learn a programming language first. Today, we can simply ask an AI to build one for us using nothing more than a prompt.

With a clear prompt, AI can even build a simple application from scratch, all the way to a working version. This phenomenon is often called vibe coding.

Vibe coding allows us to describe what we want to build, and the AI translates it into lines of code. The results are remarkable: people who have never studied programming can now create applications that are genuinely usable.

However, behind this convenience, one question arises.

If AI already writes most of the code, do we still need to learn algorithms?

The answer: yes, we still need to learn algorithms. This article explains why, and it also serves as the opening of a beginner-friendly algorithm learning series.

Table of Contents

• AI Can Write Code, but We Still Need to Understand It
• Algorithms Are More Than Just a College Subject
• The Vibe Coding Trap: The Code Works, but We Don't Know Why
• Learning Algorithms Doesn't Mean Becoming a Professional Programmer
• The Learning Roadmap for This Series
• The Goal Is Not to Memorize Algorithms
• AI Is Not a Replacement for Understanding, but a Booster for Our Abilities
• So, Does a Vibe Coder Need to Learn Algorithms?

AI Can Write Code, but We Still Need to Understand It

Imagine we ask an AI to build a data search application. The AI does a good job, and the application searches for data correctly.

Then the amount of data grows from 100 records to 1 million, and suddenly the application becomes heavy and slow.

At this point, the question is no longer:

“How do I write search code?”

Instead, it becomes:

“Why is this code slow, and is there a better way?”

To answer that, we need an algorithmic way of thinking. We need to know that a single problem can be solved in many different ways. Linear search checks the data one item at a time from the beginning. Binary search narrows down the search area step by step, but it only works if the data is already sorted.

AI can explain binary search to us. However, the more important skill is recognizing that the problem we are facing is actually a search problem, and then choosing the right approach. This is exactly what we will learn in this series.

Algorithms Are More Than Just a College Subject

When they hear the word algorithm, some people immediately picture a college course full of technical terms, flowcharts, pseudocode, and formulas. In reality, the core concept is very simple.

An algorithm is a set of logical steps for solving a problem.

For example, when we want to make coffee, the process can be described like this:

1. Prepare a cup.
2. Add the coffee.
3. Heat the water.
4. Pour the water into the cup.
5. Stir.
6. The coffee is ready to drink.

That is already an algorithm. There is no programming language involved, and we don't need Python, JavaScript, Java, or any other language to understand it.

The same applies when we write a program. There is an input, a process, and an output. In the example above, the coffee and water are the input, the steps listed above are the process, and a cup of coffee is the output.

Input → Process → Output

A programming language is simply one way of translating that process into something a computer can run. That is why learning algorithms is not just about learning “how to write a program”, but about learning how to think in order to solve problems systematically.

The Vibe Coding Trap: The Code Works, but We Don't Know Why

Vibe coding makes building software much easier. However, this convenience comes with a trap. We can end up with code that:

• looks correct,
• runs successfully,
• produces the expected output,
• but is actually inefficient,
• hard to extend,
• hard to fix,
• or breaks under certain conditions.

For example, suppose we ask the AI:

“Write a function to look up user data by email.”

The AI gives us the code, we run it, and it works. Are we done? Not necessarily. We still need to ask:

• How does this code search for the data?
• Does it check the records one by one?
• What happens if there are millions of rows?
• Is the data structure being used the right one?
• What happens if the email is not found?
• Could the wrong data be returned by mistake?
• Does this solution still make sense as the application grows?

These questions cannot always be answered with prompt-writing skills alone. We need a foundation, and one of the key foundations is algorithms.

Learning Algorithms Doesn't Mean Becoming a Professional Programmer

There is another misconception worth clearing up: learning algorithms does not mean we must aspire to become a software engineer. Professional developers certainly need a much deeper understanding. Even so, people who use AI to build software also benefit greatly from understanding the fundamentals.

Think of AI as an extremely fast programmer. We are still the ones who decide:

• “What problem am I actually trying to solve?”
• “Does the proposed solution make sense?”
• “When something goes wrong, which part needs to be fixed?”

The better we understand programming fundamentals, the better we can communicate with AI. We no longer just say:

“Please fix this code.”

We can start saying:

“This search still checks the data one by one. The data is already sorted, so try using a binary search approach.”

The difference is significant. In the first example, we are merely telling the AI to do some work. In the second, we understand the problem and steer the AI toward a solution.

The Learning Roadmap for This Series

This series does not begin with frameworks such as React, Laravel, FastAPI, or Next.js, and we won't rush into building large applications either. Instead, we will take a step back and study the fundamentals that underpin many programming languages and technologies. The sequence is roughly as follows.

For the hands-on practice, we will use Python. Its syntax is relatively simple, so we can focus on concepts rather than getting bogged down in complicated writing rules.

In the early stages, we learn to express our thinking clearly before writing any code. After that, we translate it into programs, study more concrete algorithms, and finally see how simple programs are assembled into more structured software.

The Goal Is Not to Memorize Algorithms

This series does not aim to make us memorize every algorithm. We are not preparing for a programming competition, and we don't need to memorize bubble sort so well that we can write it with our eyes closed.

What matters more is understanding the thinking pattern behind them. When facing a problem, we want to get into the habit of asking:

• What is the input?
• What output do we want?
• What process is required?
• Are there any specific conditions?
• Does the process need to be repeated?
• Is there a simpler or more efficient way?
• What if the amount of data is much larger?

These simple questions are part of a programmer's mindset, and that mindset remains relevant even when the final code is written by AI.

AI Is Not a Replacement for Understanding, but a Booster for Our Abilities

A more accurate way to view AI in programming is not as a replacement for programmers, but as an extremely fast assistant. AI can help us:

• write and explain code,
• find bugs,
• create tests,
• clean up code (refactoring),
• try alternative approaches,
• read documentation,
• and speed up the development process.

Even so, AI does not remove the need to understand what we are building. In fact, as AI becomes more capable, our ability to give direction, evaluate results, and make technical decisions becomes even more important.

Vibe coding helps us move faster. Learning algorithms ensures we move in the right direction. The two do not need to be set against each other.

So, Does a Vibe Coder Need to Learn Algorithms?

We don't need to become experts, memorize dozens of algorithms, or master all of computer science theory. However, if we want to use AI to build applications that go beyond a throwaway prototype, understanding the fundamentals of algorithms and programming is an excellent investment.

We want to level up from:

“I can get AI to generate code.”

to:

“I understand the problem I want to solve, I can evaluate the code the AI produces, and I know when a solution needs to be improved.”

That is the goal of this series. We start from the most basic concepts, then move step by step toward more complex ones. There is no need to be a senior programmer first, and no need to fear technical terms. Everything will be covered one at a time, with simple examples close to everyday development work.

AI can help us write code, but we still need to learn the way of thinking behind that code. And it all starts with one thing: algorithms.

In the next article, we will begin with the basics: what an algorithm is, what its characteristics are, and what its basic structures look like. Bookmark this blog so you don't miss the upcoming articles!