While software teams across sectors are already familiar with technical debt, the rapid adoption of AI-assisted coding could introduce a new challenge known as “fundamentals debt”. This is the risk that, as developers increasingly rely on AI for routine tasks, the foundational knowledge and engineering judgement needed to scrutinise code, and take responsibility for what ultimately reaches production, begins to erode.
Recent analysis from O’Reilly’s learning platform shows how AI is beginning to rewrite the developer learning curriculum. Analysis of UK platform usage between Q1 2025 and Q1 2026 found that engagement with programming fundamentals content declined by 73%, while usage of AI and machine learning content grew by 51%. Natural language processing recorded an even sharper increase of 117%, making it the fastest-growing AI subtopic.
These findings do not necessarily suggest that developers are abandoning coding. Instead, they point to a change in mindset. As AI becomes embedded in everyday development, professionals appear to be reconsidering which skills require dedicated study and which can be learned at the point of need.
This raises an important question. If developers spend less time studying the principles that underpin software engineering, could they lose some of the knowledge needed to understand complex systems and identify errors before software enters production?
Learning is moving into the workflow
Developers have always been able to learn while solving practical problems, but what has changed is the speed, availability and sophistication of the support now available to them.
The rise of AI-assisted coding, with tools now able to explain unfamiliar code, suggest fixes, generate examples and help developers navigate new technologies without leaving their development environments is changing the way developers are learning.
This could explain why engagement with more traditional development topics is declining. However, employers should not assume that access to an AI assistant is an adequate substitute for a deep understanding of software engineering.
The risk of “fundamentals debt”
The risk of fundamentals debt may be greatest for early-career developers. Experienced engineers have typically built their judgement through years of writing, testing, reviewing and debugging code. Junior developers who begin their careers with ready access to AI-generated answers, however, may have fewer opportunities to develop those instincts. Particularly as AI increasingly takes on the routine tasks that have traditionally helped people build foundational skills.
This matters because AI-generated code can look convincing while still containing subtle errors, security vulnerabilities or unsuitable dependencies. If developers become overly reliant on AI without retaining the ability to question, test and correct its output, teams may see short-term productivity gains while gradually losing the knowledge needed to debug complex systems, make sound architectural decisions, and understand how changes affect performance, reliability and security.
The answer is not to reject AI-assisted coding, but to ensure it supports rather than replaces the development of engineering judgement. As AI takes on more routine production work, developers will need to define problems clearly, provide the right context and validate whether outputs are accurate, secure, maintainable and fit for purpose. Organisations should therefore give developers, especially those early in their careers, structured opportunities to practise core skills alongside AI. An engineer’s value will not be measured simply by how well they write code, but by how well they can defend the thinking and decision-making behind it.
The strongest developers will therefore combine AI fluency with sound engineering judgement. Skills such as testing, debugging, code review, architecture and security hygiene will become more, not less, important as the volume of AI-generated code increases. AI may accelerate production, but these capabilities will determine whether faster production results in better software.
Employers need a balanced learning strategy
Whether learning is considered by an employer developing its workforce or by an individual managing their own career, AI capabilities and technical fundamentals must be treated as complementary rather than competing priorities. AI can help free up structured learning time for emerging areas where knowledge is evolving rapidly. O’Reilly’s analysis found use of generative AI content increased by 89% year on year. However, professionals need to develop not only an understanding of how these technologies work, but also the judgement to apply them effectively and responsibly within their organisations.
Employers, engineering leaders and L&D teams should therefore combine formal training with practical experimentation, peer learning and real-world development tasks, while continuing to protect foundational skills.
AI should strengthen, not replace, expertise
AI-assisted coding will reward organisations that can increase speed without compromising engineering judgement. The role of the engineer has evolved and it’s no longer enough to simply generate code. Developers must be able to interrogate it, explain it, test it and take responsibility for it.
AI can accelerate software production, but it cannot replace the human judgement needed to determine whether an output is accurate, secure, maintainable and fit for purpose. The opportunity is to use that speed to build stronger capability, rather than bypass the skills that make reliable software possible.
The winners will not be the organisations that adopt AI-assisted coding fastest, but those that use it to build engineering expertise on solid technical foundations.
