The Role of AI in Programming

It seems that the faster AI develops, the more designers, writers, translators, voice actors, and other specialists become unnecessary. It's no surprise that this discussion is also centered around the field of programming. Studying computer science is considered one of the most reliable strategies for obtaining a high-paying job: according to the Bureau of Labor Statistics (BLS), employment of software developers will increase by 25% by 2032.

Despite such a positive forecast, the development of AI is making its own adjustments. For example, according to a study by the international consulting company McKinsey & Company, AI can automate 60% to 70% of employees' time.

On one hand, this trend will increase work efficiency, but on the other hand, it will reduce the number of specialists needed to perform a single task. Processes that were conventionally performed by 10 workers will be managed by four, leading to significant cuts.

After the appearance of ChatGPT, GitHub Copilot, and other AI-based tools, programmers began to worry about their future. Some of their concerns were sparked by Nvidia CEO Jensen Huang.

In February 2024, the World Government Summit was held in Dubai. During his speech, Nvidia CEO Jensen Huang stated that the profession of programming may lose its relevance. According to him, children should not study programming, as the majority of processes will be automated with the help of AI.

Huang explained that learning programming used to be extremely important, but in today's world, it will not be crucial.

In his opinion, super-AIs will direct teams of smaller AIs to assist in coding. Thus, programmers will become something like orchestra conductors, responsible for the internal interaction of AIs.

Ways of Using AI in Programming

Using AI in programming involves automating tasks traditionally performed by humans. For example, software testing, debugging, optimization and code generation, as well as developing No-code and Low-code solutions.

With AI, programmers can optimize their workflow, reduce errors, and focus on the creative aspects of programming.

• Task automation: AI tools automate repetitive and mechanical tasks, such as executing basic SQL queries, data analysis, and optimizing Data Science projects.
• Bug fixing: AI is an ideal ally for error elimination. After scanning the code, AI suggests ways to optimize it and an algorithm for correcting inaccuracies.
• Code translation: Sometimes developers work with multiple programming languages (for example, for compatibility or transferring code bases written in an outdated language). Translation takes time and requires understanding both the source and target languages. This process becomes resource-intensive.

Why AI Won’t Replace Programmers

Programming develops critical thinking and the ability to deeply understand the internal workings of a PC. Knowing programming languages helps think like a computer and understand the processes within it.

Insufficient knowledge of programming languages means an incomplete understanding of computer functions, which creates additional inaccuracies. It’s no wonder that, according to GitClear, increased use of AI-generated code leads to a decrease in its quality.

It’s logical to compare programming to drawing. Drawing is a creative process, and AI has not yet reached the level of creativity that artists can offer.

Programming is also a creative process. Creating complex projects requires more than processing hundreds of lines of code. It requires creativity, which is inherent only to humans.

The fact that AI can create paintings has not stopped creative people from drawing. So, the use of AI in programming should not cause a decline in education in this field.

Moreover, proponents of the idea of replacing programmers with AI may have their own interests.

For example, the aforementioned statements by Huang may also have a selfish subtext. By making such claims, he draws attention to AI, promotes Nvidia Inference Microservices, and builds trust among the company’s shareholders.

Despite the buzz around AI, several non-AI-based tools continue to prove invaluable for developers, enhancing productivity and saving time. Here, we present the top 5 best non-AI tools for developers in 2024, focusing on solutions that can significantly improve your workflow.

1. JetBrains IntelliJ IDEA

JetBrains IntelliJ IDEA is a powerful integrated development environment (IDE) that provides a robust set of tools for Java development. It features smart code completion, on-the-fly code analysis, and refactoring tools that help developers write high-quality code efficiently.

• Link: IntelliJ IDEA

2. Postman

Postman is a robust API development tool that helps developers design, test, and document APIs. It provides a user-friendly interface for sending HTTP requests and viewing responses, making it easier to debug and monitor APIs. With features like automated testing, mock servers, and collaboration tools, Postman enhances the efficiency of API development and ensures seamless integration with various services.

• Link: Postman

3. GitKraken

GitKraken is a powerful Git client designed to improve the productivity and collaboration of developers using Git. It offers a visually intuitive interface that simplifies complex Git operations such as branching, merging, and conflict resolution. With features like a built-in code editor, Gitflow support, and seamless integration with GitHub, GitLab, and Bitbucket, GitKraken streamlines version control processes and enhances team collaboration.

• Link: GitKraken

4. Visual Studio Code

Visual Studio Code (VS Code) is a lightweight, yet powerful code editor from Microsoft. It supports a wide range of programming languages and comes with an extensive library of extensions that add functionality such as debugging, version control, and code linting.

• Link: Visual Studio Code

5. AppSpector

AppSpector is an advanced tool designed specifically for remote debugging of mobile applications on iOS and Android. It allows developers to monitor application internals in real-time, making it easier to identify and resolve issues quickly. AppSpector's features include live performance monitoring, crash reporting, and detailed analytics, all of which contribute to a more efficient development process.

• Link: AppSpector

Conclusion

While AI tools can significantly enhance productivity, they will not replace the creativity and deep understanding that human programmers bring. AI's rapid development shows that its role in automating tasks will continue to grow, but programmers will remain essential for complex, creative projects. By staying updated on AI developments and integrating both AI and non-AI tools into their workflows, developers can enhance their productivity and stay relevant in an evolving industry.