CIQ automates 90% of its code but struggles to recruit experts

At CIQ, AI now generates over 90% of the code, according to management. The company claims to be lacking experienced engineers and hires very few juniors. An example of developing a Linux-compatible kernel in 11 days illustrates the reported gains, while the career progression of beginners is highlighted as a point of concern.
The Junior Pipeline is Stalling and Progression is Questioned
CIQ states that it hires almost no junior developers and has not recruited anyone with less than five to ten years of experience, according to Bjorn Hovland. The company also reports that it is struggling to recruit enough experienced engineers. As hiring for beginners becomes scarce, the progression to experienced profiles becomes more challenging. Bjorn Hovland recommends that young people take on multiple software projects and learn how systems work to acquire these skills.
Accelerated Developments and a Warning on AI
Gregory Kurtzer indicates that he used artificial intelligence to develop a Linux-compatible operating system kernel in just 11 days. According to him, this result may raise concerns, and he believes that seasoned engineers who rely on AI could see their productivity significantly increase. He also warns that programmers who prefer to write code themselves and resist AI risk falling behind.
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At CIQ, AI Produces the Majority of Code but Human Needs Remain High
According to Bjorn Hovland, AI now generates over 90% of the code at CIQ. He mentions that two or three years ago, he thought the company might not need to hire more engineers, but now sees the opposite. CIQ claims to be short-staffed and is primarily looking for experienced developers rather than beginners.
Expected Skills and Context of the Debate
The automation of certain programming tasks is leading to an increased demand for individuals with a deep understanding of how software works. More than just writing code, the added value now lies in the ability to define what needs to be designed and to assess the quality of the results obtained, rather than personally executing every step. It remains essential to oversee the code produced by AI, especially when the recipients are large companies or government agencies. These issues were discussed at a recent event focused on the future of software engineering.
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