AI and Coding: A Barrier to Development or a New Door?
Recently, artificial intelligence (AI) tools have been rapidly integrating into software development processes. Tools like GitHub Copilot stand out with their capabilities for automatic code completion, function suggestions, and even building simple projects from scratch. This brings up the question: Do AI-powered coding tools blunt developer skills, or do they open new horizons? Based on my 20 years of field experience, I want to share my thoughts and observations on this topic.
Since I started using AI tools in my own projects and corporate software development processes, I've had the opportunity to closely observe both the potential of this technology and some of the concerns it brings. Especially since the last quarter of 2023, AI's code generation capacity has increased remarkably. However, how this increase will affect developers' fundamental problem-solving and algorithmic thinking abilities is an important subject of debate.
โน๏ธ The Impact of Coding with AI
AI tools can automate repetitive and time-consuming coding tasks. This allows developers to focus on more complex and strategic problems. However, this situation can also bring the risk of moving away from fundamental coding principles.
The Risk of Blunting Fundamental Skills: A Real Threat?
AI tools can hinder the development of some fundamental skills, especially for entry-level developers. For example, instead of learning step-by-step how to write a function or implement an algorithm, asking AI to generate code directly can undermine this learning process. In the long run, this can lead to a superficial accumulation of knowledge, lacking deeper understanding.
In recent months, I saw an AI-powered coding assistant being used in a student project. The students had AI write a simple data processing script. The result was satisfactory; the script worked. However, when there was an error in the script, the students struggled to find the source of the error because they didn't fully understand the logic behind the code. This experience was a concrete example of how the ease offered by AI can overshadow fundamental problem-solving ability.
โ ๏ธ The Importance of the Learning Curve
Software development is not just about writing code. Fundamental skills like debugging, performance optimization, algorithmic thinking, and system design are acquired through experience. AI tools can accelerate this process, but it should not be overlooked that acquiring these fundamental skills requires time and patience.
Opportunities Offered by AI: Efficiency and New Perspectives
On the other hand, the increased efficiency and new perspectives offered by AI tools cannot be ignored. Tasks such as writing repetitive code snippets, remembering standard library usages, or learning the parameters of a specific API can be quickly handled by AI. This allows developers to allocate their time to more strategic tasks.
For example, in a side product I developed that performs financial calculations, I needed to define a set of rules for data validation. Many of these rules were based on standard formulas. Thanks to AI tools, I generated the code equivalents of these rules much faster. This reduced a task that would normally take several hours to about 30 minutes. This saving gave me the chance to focus on more complex AI-based prediction models.
Code Quality and Security: The Role of AI
The quality and security of code generated by AI tools are also important issues. Initially, AI-generated code, while often functional, may not adhere to best practices, may have security vulnerabilities, or may not be performance-optimized. Therefore, every line of code generated by AI needs to be carefully reviewed and validated.
While working on a corporate ERP project, during the debugging process of a module, we noticed that an AI-generated code snippet was unexpectedly vulnerable to SQL injection. The AI that generated the code had forgotten to properly escape the parameters. Although this was just a few missing characters, it potentially posed a serious security risk. After this incident, we initiated a procedure to manually review all critical AI-generated code against our security standards.
๐ฅ Necessity of Validating AI-Generated Code
Artificial intelligence tools are not perfect. The code they produce may contain errors, lead to security vulnerabilities, or be incompatible with the existing codebase. Therefore, it is vital that every piece of code generated by AI is carefully reviewed, tested, and validated by an experienced developer.
Transformation of Developer Skills: Adaptation and Redefinition
The trend of coding with AI does not mean that developer skills are blunted; rather, it means that these skills are evolving and being redefined. Future developers will be individuals who can effectively use AI tools, evaluate the generated code, and solve more abstract problems that go beyond AI.
This means that new skills like "prompt engineering" are gaining importance. Asking AI the right questions, clearly describing the desired output, is critical to fully unleashing AI's potential. At the same time, knowing AI's limitations, understanding the principles behind the generated code, and maintaining critical thinking skills will retain, and even increase, their former importance.
Conclusion: Evolving Together with AI
Artificial intelligence tools are revolutionizing the software development world. These tools have the potential to increase efficiency, accelerate the learning process, and generate new solutions. However, to maximize this potential and prevent the blunting of fundamental developer skills, a conscious approach must be adopted.
Instead of seeing AI as a threat, embracing it as a partner, developing our skills alongside it, and focusing on continuous learning will be key to future success. We must remember that no matter how advanced technology becomes, human creativity, critical thinking, and problem-solving ability will always remain our most valuable assets. AI can be a tool to further empower these abilities, but it can never replace them.
Top comments (12)
Mustafa I have a question?
Of course. ๐
Go ahead โ Iโm curious what your question is.
Should I learn HTML and JavaScript,css?
Yes, definitely.
If someone asked me where to start today, Iโd still say HTML, CSS, and JavaScript.
Not because youโll spend your whole career writing them, but because they help you understand whatโs actually happening behind the screen.
AI can generate code for you, but when something breaksโand trust me, it will ๐โyouโll need enough understanding to figure out why.
My advice would be: learn the basics, build small things, and donโt be afraid to use AI along the way. Just donโt let it do all the thinking for you.
The goal isnโt to memorize everything. The goal is to understand enough that you can tell when the AI is helping youโฆ and when itโs confidently leading you in the wrong direction.
Ok ok I will learn but I find it boring๐
Thatโs exactly what TheLazyGirl would say. ๐
The good news is that you donโt need to love every part of learning. Most developers didnโt fall in love with HTML tags on day one. What keeps you going is building something that makes you smile and then learning just enough to make the next thing.
Yaah I am Lazy! ๐
AI coding tools arenโt blunting developer skills โ theyโre exposing which skills were already shallowly held.
If someone relies on AI to replace thinking, then yes, fundamentals will decay. But thatโs not an AI problem โ itโs a learning approach problem. Even before AI, copy-paste coding, Stack Overflow-driven development, and framework abstraction already shifted how developers learn.
The real shift now is this: writing syntax is getting cheaper, but understanding systems is becoming more valuable.
Strong developers will still do what theyโve always done โ break problems down, reason about tradeoffs, debug with intent, and question outputs. AI just compresses the time between idea โ implementation. It doesnโt remove the need for understanding; it raises the cost of not understanding.
So instead of asking โIs AI weakening developers?โ, a better question is:
Are we still practicing deep thinking, or just delegating it?
Because AI wonโt replace strong developers โ but it will quietly amplify the gap between those who understand and those who only execute.
I completely agree with the distinction between syntax and understanding.
Over the years, Iโve seen similar debates around frameworks, IDEs, code generators, Stack Overflow, and now AI. The tool changes, but the underlying question stays the same: are we using the tool to extend our thinking, or to avoid thinking?
What concerns me isnโt that developers can generate code faster. Itโs that they can now generate complexity faster.
A developer who doesnโt understand architecture, performance, security, or failure modes can build a much larger system than beforeโwithout necessarily understanding more of it.
Thatโs why I increasingly view AI literacy and systems thinking as complementary skills. AI can accelerate implementation, but it cannot replace the responsibility of understanding the system youโve just created.
As you said, the gap may not disappearโit may actually widen.
I am excited for your next post!!๐๐บ
Thank you! ๐๐บ
That means a lot.
Iโll keep writing as long as people keep asking good questions and starting interesting discussions. And youโve already contributed to both. ๐
Hopefully the next post will be interesting enough that even TheLazyGirl wonโt be tempted to skip to the conclusion. ๐
I hope that!!