AI in software development offers several advantages and disadvantages. On the positive side, AI can significantly enhance productivity by automating repetitive tasks, such as code generation, testing, and debugging, which allows developers to focus on more complex and creative aspects of their projects. Additionally, AI-driven tools can improve code quality through predictive analytics and error detection, leading to more robust software solutions. Furthermore, AI can facilitate better project management by predicting timelines and resource needs based on historical data.

However, there are also drawbacks to consider. One major concern is the potential for AI to produce code that lacks transparency, making it difficult for developers to understand how decisions were made, which can complicate debugging and maintenance. Additionally, over-reliance on AI tools may lead to skill degradation among developers, as they might become less familiar with fundamental coding practices. Lastly, ethical considerations, such as bias in AI algorithms and the implications of using AI-generated code without thorough human review, raise important questions about accountability and quality assurance in software development.

Hingfung Wong AI provides many advantages in software development, such as automation of repeated coding functions, which saves time and increase productivity. This wise can help in detecting bugs and weaknesses through intelligent code analysis, improving software quality. AI-mangoing equipment can also help with the code completion, documentation and testing, making development faster and more efficient. However, AI may introduce bias or errors if trained on poor quality data, then is leading to potentially defective suggestions. More dependence on AI tools can reduce the important thinking and problem-solving skills of the developers. Additionally, the AI ​​system can be expensive to apply and require appropriate inspection to ensure reliability and security.

Of course, I've always believed that the advent of AI will inevitably cause human brains to deteriorate

In addition to the great points you already made Farkad Adnan and Anup Mahato , I want to highlight two pressing concerns that arise specifically from AI-generated code: ethical/legal issues and bias in training data. A major ethical challenge is around intellectual property—AI models like Copilot or ChatGPT are trained on massive amounts of publicly available code, even though not all of the code on the website is free to reuse. This raises serious concerns about copyright violations, especially when AI outputs mirror snippets from licensed repositories and without attribution to the author! On top of that, accountability becomes blurry: if AI-generated code causes a bug, a vulnerability, etc. Who is accountable for this consequence? The developer who uses the LLMs, or the AI creators who trained the model?

The second concern is bias and limitations in the training data. These AI systems inherit whatever was common in their training sets, including outdated practices, insecure code, and even exclusionary naming conventions. For example, developers using AI may unknowingly adopt deprecated functions or unsafe patterns simply because they appear frequently in the data the model was trained on. If that's the case, we are creating software ecosystems with existing flaws, especially if underrepresented programming languages or coding paradigms are neglected. It’s not just a technical issue—it’s the culture of programming that is affected in some measure about forward-thinking and conscious codebases for more secure and robust solutions.

Artificial Intelligence (AI) has been transforming software development, but like any other technology, it brings benefits and also some risks.

Below are some advantages and disadvantages:

Advantages:

- Increased productivity;

- Improved code quality;

- Automation of repetitive tasks;

- Easier access to knowledge.

Disadvantages:

- Excessive dependence;

- Ethical and intellectual property issues;

- Hidden errors.

Advantages & Disadvantages of AI in Software Development 2025

Advantages

Faster Development: AI automates coding, debugging, and testing, reducing delivery time.

Higher Quality: Fewer bugs through AI driven testing and code review.

Cost Efficiency: Lowers development and maintenance costs with automation.

Personalization: Creates adaptive, user focused software solutions.

Innovation Boost: Helps design complex systems AI driven apps, predictive systems

Disadvantages

Job Displacement: Routine developer roles may shrink due to automation.

Over-Reliance: Too much dependence on AI reduces human creativity/problem solving.

Bias & Errors: AI generated code can inherit biases or hidden flaws.

Security Risks: AI driven tools may be exploited if not well-guarded.

Skill Gap: Developers need constant upskilling to work with AI.

Suggestion

V. Srinivasan

Consider LLMs as very sophisticated search engines. They have no intelligence because they do not understand the content. Their inference is based on statistical algorithms. In order to understand the content, they would need logical knowledge representation which they do not have currently. The term “AI” is misleading. It’s just a part of the global marketing campaigns.

Are LLMs useful? Yes. I am a robotic programmer using LLMs almost everyday. They can suggest approaches and templates and explain code.

But they can also mislead you, if you have an absolute trust in them. So, in this case, inexperienced developers can suffer from them. But inexperienced developers, understanding the nature of LLMs, can learn from them and improve their skills.

So, thinking of LLMs making some software developers obsolete is a mystification. Software developers, especially experienced ones, will be still in great demand in the foreseeable future.

Igor Toujilov I absolutely agree.

AI can write code in seconds—but only humans can make it meaningful. In software development, AI dramatically accelerates coding, debugging, and prototyping, enabling even small teams or individuals to create applications at a pace once limited to large companies. This democratization of development lowers costs and fosters innovation, which explains the rise of solo developers turning ideas into profitable apps. At the same time, it introduces risks: AI-generated code may carry hidden bugs, security flaws, or intellectual property concerns that require expert oversight. Over-reliance could also erode fundamental problem-solving skills among developers. Ultimately, AI’s role is best seen as an accelerator—removing routine burdens while human judgment ensures quality, creativity, and responsible use.

With "conventional programs" the saying was "garbage in - garbage out", because the internals were verified. With AI-generated content, or LLM-generated content (according to Igor Toujilov excellent remark), (any content, including programming code), this can shift to "good but incomplete data in - garbage out". LLMs can help a lot, but all its outputs must be human verified!