Artificial intelligence is becoming established in software development, but confidence in its results remains limited: 46% of developers surveyed acknowledge that they do not trust the accuracy of the code generated by these tools. A paradox that becomes especially relevant on the occasion of World Programming Day, which is celebrated on October 29. While producing code becomes easier and faster, understanding how it works is emerging as one of the most valuable skills for developers.
A line of code generated in seconds can hide a bug, a security vulnerability or a design problem. The initial time savings thus risk becoming an additional burden of verification, debugging and correction. The challenge is not so much to allow AI to program as to prevent the speed of execution from being confused with the quality of development. Without a solid knowledge base, developers will hardly be able to critically evaluate the responses of these tools, detect their limitations, and ensure the reliability of the software they produce.
The challenge now is to train professionals capable of taking advantage of AI without depending on it. To achieve this, it is essential to reinforce the fundamentals of programming and encourage critical thinking from the early stages of learning, while expanding access to these skills. Along these lines, Acer supports the STEM4ALL association in France, which promotes women’s access to training in engineering and research through its STEM AI and cybersecurity program.
However, theoretical training is not enough. To learn to identify the limits of artificial intelligence, developers need to experiment, test their results and correct possible errors in real environments. Development tools that allow you to work directly with AI models offer an opportunity to translate theoretical knowledge into practice.
One example is the Acer Veriton GN100 workstation, used during the Spark Hack Series event in New York to locally experiment with artificial intelligence models. This solution enables developers, researchers, data scientists, and students to work with common frameworks and tools, such as PyTorch, Jupyter, and Ollama, to prototype, tune, test, and deploy large language models (LLMs) locally, including those with up to 405 billion parameters. In this way, developers can explore the possibilities of AI with greater control over the work environment and better understand how the models they use work.
Although AI speeds up work, responsibility for the code and its quality will continue to fall to the professionals who develop it
«AI should not lead to training developers who know how to obtain code without understanding the mechanisms. On the contrary, its rise makes the fundamentals of programming even more important. The objective is not to compete with the speed of the machines, but to develop the necessary skills to exploit their capabilities while keeping a critical eye on their results,” explains Karim Ouahioune, Director of Marketing and Communication at Acer EMEA.
46% of developers do not trust the accuracy of results generated by AI tools
In the era of artificial intelligence, learning to program can no longer be limited to mastering a language or the ability to generate code using instructions. Developers must also understand the logic of programs, evaluate the quality of proposed solutions, and detect potential errors before they reach production.
The real gap will not be between those who use AI and those who do without it, but between those who know how to monitor, verify and control their results and those who depend on them without questioning them. Therefore, learning programming must place technical criteria, understanding of the fundamentals and the ability to make informed decisions at the center. Because, although AI speeds up work, responsibility for the code and its quality will continue to fall on the professionals who develop it.
