7 min. reading time

Paradigm Shift With ChatGPT

In recent years, the use of AI in software development has become more widespread, with many large companies incorporating AI technology into their development processes. The main areas where AI is used are code optimization and bug fixing. One of the most popular examples is Copilot, created by GitHub and OpenAI. It can analyze code, identify potential issues and even make fixes automatically.

While AI is already playing a significant role in software development, it is likely to continue to impact the industry in the coming years. As AI technology advances, it will become increasingly capable of performing complex tasks.

The rise of language models like ChatGPT has the potential to dramatically change the way software is developed. As AI is increasingly becoming a powerful tool that is also more and more accessible, the role of software engineers may evolve to include more collaboration with language models and less time spent on routine tasks like debugging and code reviews or actual coding. ChatGPT reduces the time spent on searching for the answer to a specific problem. A simple query will bring you a solution in many cases. Surely, the more complex the problem, the more input ChatGPT will need. Nevertheless, it will likely solve your problem or give you at least a big chunk of the solution. By having such a mighty tool, the way that we work will change.

Optimized Code

One of the biggest advantages of using AI in software development is the ability to optimize code. This means that instead of manually tweaking code to improve its performance, an AI system can analyze the code and automatically make changes that result in faster, more efficient code. For example, let's say you have a function in your code that takes a long time to run. Instead of spending hours trying to identify the bottleneck and optimizing the code manually, just parse the code snippet into ChatGPT and tell it to optimize. This can be applied to a variety of metrics like energy efficiency, runtime or memory usage. The output of ChatGPT is astonishing. There are plenty of articles, where people show that ChatGPT is already in a state where code optimization exceeds expectations.

Software Architecture

ChatGPT can help software engineers and architects choose the right software architecture by analyzing and processing large amounts of data and providing insights and recommendations based on that data. By leveraging the power of natural language processing and machine learning, ChatGPT can assist in identifying the strengths and weaknesses of different software architectures.

For example, if a software team is working on a project that requires real-time processing of large amounts of data, ChatGPT can analyze the data and recommend a software architecture that is optimized for high-performance computing and that can handle the real-time processing requirements. ChatGPT is already capable of choosing the best software architecture for a specific problem. It seems to be able to do so less biased than a human architect, who is more likely to choose an architecture based on familiarity. That is demonstrated in this video by Marco Lenzo. What that video also shows is that, by giving ChatGPT a specific use case, it can derive requirements and make a decision on what architecture to use.

By enabling architects to interact with AI-powered tools like ChatGPT, architects can explore different design options, experiment with different trade-offs, and iterate on their designs more quickly and effectively. The interactive nature of AI-powered tools can also help to promote collaboration and communication among different developers involved in the software development process, enabling them to work more closely together and to share ideas more effectively. This is simply done by incorporating ChatGPT into the whole process of creating a software architecture.

Code Quality

As AI becomes more prevalent in software development, the code generated by language models may become increasingly complex and difficult for humans to read and understand. When this happens, it becomes critical for developers to shift their focus from simply creating code to ensuring that the code behaves as intended. One way to address this challenge is through the use of testing and quality assurance techniques. By thoroughly testing the code and verifying that it produces the expected results, developers can ensure that their programs are working correctly, even if the code itself is difficult to understand.

The worst case is when the AI creates a black box system. A black box system is one in which the inner workings are not easily understood or transparent, making it difficult to determine why a particular output or decision was produced. This can be problematic because it can make it difficult to identify and fix errors or biases in the system. The level of transparency and interpretability of a software created by ChatGPT is based on the prompt that it receives. Efforts can be made to use language models in a way that provides greater transparency and interpretability, such as by visualizations or explanations of the model's outputs and decision-making processes. With the recent release of GPT-4 and future updates it will be possible to use the models output to help generate visualization. For example, ChatGPT could create activity diagrams or class diagrams for the code it generates, providing greater transparency for you so that you can ensure a certain code quality. Even today it is possible to generate such diagrams, with the use of third party programs and the official ChatGPT API. 


The integration of language models like ChatGPT into software development also has significant implications on the importance of security. As AI systems process an increasing amount of sensitive data, such as personal information, financial records, and confidential business information, the risk of data breaches and unauthorized access also increases. Code itself is sensitive data, because it holds implicit or explicit secrets and information about a company’s processes. This can result in serious consequences, including identity theft, financial loss, and damage to a company’s reputation or privacy. To address this growing concern, organizations must prioritize security in the development of software when working with AI systems such as ChatGPT. This involves implementing strong encryption and authentication protocols, regularly monitoring and testing the security of systems, and providing ongoing security training and education for employees and stakeholders.
If you are interested in this topic, my colleague Rafael dives deeper in his article, here.


Over-reliance on language models could lead to bad programmers due to several factors. Firstly, these models can create a false sense of security, causing programmers to trust generated code without thoroughly reviewing it. This could lead to the implementation of incorrect or suboptimal solutions. Secondly, as language models become more advanced, programmers may become overly dependent on them, which could hinder the development of critical thinking and problem-solving skills. This reliance may also discourage programmers from learning new programming languages, frameworks, or libraries, ultimately limiting their professional growth. Lastly, an excessive dependence on language models might result in a lack of creativity, as programmers may rely solely on the output generated by the models rather than coming up with their own innovative solutions. In turn, this could have a detrimental effect on the overall quality and diversity of software development in the industry.


While AI technology has the potential to revolutionize software development, it is important to be aware of its limitations and challenges. By carefully considering these issues and balancing the use of AI with human oversight and decision-making, companies can ensure that they are able to use the full benefits of this technology while avoiding potential risks and pitfalls. Individuals can also take advantage of AI models like ChatGPT to enhance their software development skills. By learning about and experimenting with AI technologies, they can acquire new knowledge and skills that will be in high demand in the future job market. Additionally, they can collaborate with AI models to improve the efficiency and quality of their software development projects. If you ask ChatGPT whether it could replace a software developer, it will respond with a very generic answer where it says that it could never replace a software developer. In my opinion, ChatGPT is right for the time being. But ChatGPT has started a paradigm shift in software development.