Why Coding Remains Crucial in the AI Era: Understanding Algorithmic Thinking and Code Verification

In our digital age dominated by artificial intelligence (AI) like popular large language models (LLMs) such as ChatGPT, a big question emerges: Is learning to code still important? When organizations like Anthropic predict that 90% of code will be written by AI, it might feel like computer science (CS) classes are no longer relevant. However, evidence suggests that learning to code is now more important than ever, especially for understanding, verifying, and directing these powerful AI tools.

Watch the discussion: Why Coding Matters in the AI Era

1. Foundation of Systematic Thinking (Algorithmic Thinking)

The main reason we still teach coding isn’t just to train programmers. Instead, it’s about teaching a fundamental way of thinking. Computer Science (CS) classes are important because they teach the concept of algorithmic thinking.

Algorithmic thinking involves:

1. Breaking down large problems into smaller chunks
2. Solving those small chunks systematically, step by step
3. Combining those solutions to solve the big problem as a whole

Learning to code provides a certain mindset, teaching about abstraction and problem-solving. The analogy used is that we teach coding so people understand systems, just like we teach physics so people understand how the physical world works.

2. AI’s Role: A Powerful Tool, Not a Replacement for Basic Skills

While AI tools have become widely available to consumers—ChatGPT is just one form of AI (called a large language model)—it’s important to remember that there are many other forms of AI working in the background (for example, checking your email for spam).

AI can indeed help, but it doesn’t eliminate the need for basic coding knowledge. If you have access to AI, you still need to know how to code because you must be able to verify and check whether the code generated by AI is correct.

A study conducted at Stanford’s computer science department shows the importance of this verification:

• One group of students was given access to AI to help them write code, while another group was not
• The group working with AI was faster, able to solve more problems, and more confident about their code
• However, the code they wrote was actually less secure than code written by the group that coded from scratch

This shows that you need to understand coding to truly verify code that comes out of AI. Without basic understanding, someone might not notice vulnerabilities or inaccuracies in machine-generated code.

3. Opening Opportunities and Understanding Ethics

Understanding technology and AI fundamentally opens up more possibilities for the types of problems you can solve or the types of jobs you can pursue. AI is a very powerful tool that affects people’s lives, including how work will change or what types of opportunities are available.

Therefore, understanding the ethical dimensions of AI implementation in various contexts is crucial. When we use AI tools, we must understand the power of that technology along with ethical implications to ensure we strive for a better world and a more just society.

4. It’s Never Too Late to Start

Many people might feel it’s too late to learn coding, especially if they feel they don’t remember much of their past math lessons. However, learning programming fundamentals is actually quite easy and straightforward, and doesn’t require complex mathematical memory. It’s similar to learning a new skill, like learning to play a new instrument or a new sport.

It’s important to note that it’s never too late to learn this.

If you want to start, Python is highly recommended because it’s:

• Easy to learn
• Quick to use
• Widely used for things like data science and artificial intelligence
• Has a large collection of existing code, allowing you to build something very quickly

Conclusion

Learning to code in the AI era is no longer about becoming an expert programmer, but about technological literacy and having algorithmic thinking. Just like understanding how machines work helps you use tools more effectively, understanding code helps you control, verify, and utilize AI safely and ethically. AI might write most of the code, but humans must remain the security auditors and ethics directors.

Closing Analogy

Having AI write code for you is like having a powerful self-driving car. The car might drive perfectly 90% of the time, but you, as a knowledgeable driver, must still know how to control the steering wheel, understand traffic rules (algorithmic logic), and take control when the system makes mistakes or faces unexpected situations.

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