The Coded Cost of Coding: Can Software Engineers Trust AI?
AI can write code in seconds. But AI-generated pull requests contain 1.7x more issues than human-written code. The real bottleneck isn't writing — it's verification.
Two years ago, GitHub Copilot was a smart autocomplete tool that helped developers save time and effort. Today, autonomous agents such as Devin and Cline can browse project files, run terminal commands, and correct their own code errors. Many articles praise these tools for improving productivity, but increased speed is not the only story. The software industry is not only becoming faster but also changing in structure and value. AI is transforming which skills are most important for developers. Writing syntax is now a low-cost task that no longer defines technical expertise. The true value lies in the ability to design, manage, and verify complex systems.
This change is affecting career paths, reducing opportunities for entry-level developers, and adding to technical debt. In the following sections, this article explores how these shifts are driving companies to reconsider the economics of software development.
How AI Speed Is Creating a Verification Crisis
A new approach called “Vibe Coding” is changing the way software is developed. This method focuses less on writing each line of code and more on achieving the overall goal of the program. Developers describe what they want the software to do, and AI systems turn those ideas into working code. The human developer provides direction while the AI fills in the technical details. In this approach, creativity, problem-solving, and design thinking become more important than syntax and structure.
This shift has also created new challenges. The growing use of AI-generated code has led to what people call the “Review Fatigue” crisis. It is often harder to read and understand code produced by machines than to write it manually. When AI can produce hundreds of lines of code within seconds, human reviewers must spend extra time checking its accuracy. This verification process is mentally demanding and slows development. The code may be completed instantly, but it can remain stuck in review because developers do not fully trust or understand it. Teams are beginning to realize that the real challenge is no longer writing code quickly, but ensuring that AI-written code is correct, safe, and reliable.
The Hollowing Out: How AI Is Breaking the Career Ladder
The software industry is experiencing a growing talent crisis known as the “hollowing out” of the developer workforce. AI now handles boilerplate code, unit tests, and documentation, leaving few entry-level tasks for junior engineers. These roles once built the foundation for technical growth and skill advancement.
Research from the Stanford Digital Economy Lab reports a 13% drop in demand for young workers aged 22–25 in AI-exposed occupations. By automating foundational work, companies are removing the path that once allowed juniors to gain experience and evolve into experts. This imbalance threatens the future talent pipeline and risks creating a generation without senior architects.
Seniority itself is being redefined on the basis of AI literacy. Expertise is no longer measured by knowledge of complex programming syntax. It now depends on AI literacy — the ability to decompose problems and guide AI systems effectively. The modern “10x developer” is the one who manages and orchestrates multiple AI agents to deliver reliable outcomes.
The Hidden Costs of Speed
Fast code often hides serious quality problems. Many AI tools are built to produce code that looks correct instead of code that is carefully verified, which makes fragile implementations more common. A pull request can appear clean and professional while still containing logic errors, performance issues, or security flaws that are costly to detect and fix later.
Recent analysis from CodeRabbit reports that AI-generated pull requests contain around 1.7 times more issues on average than pull requests written by humans, including problems with logic, maintainability, security, performance, and readability. As a result, teams release features more quickly but also take on more defects and review work. Because generating code is so easy, developers often accept long AI-generated snippets where a much shorter human solution would be enough, leading to bloated and difficult-to-maintain codebases.
AI systems sometimes hallucinate imports of packages that do not exist, and attackers can register these package names and distribute malicious code through the software supply chain — a technique known as slopsquatting or package hallucination.
Conclusion
The software industry is undergoing a major economic transformation. The traditional billing model, where clients pay for hours worked, is no longer viable. AI drastically reduces the time needed to write and deliver code, making hourly rates unsustainable. Companies must now shift to outcome-based pricing, charging for the value of the solution rather than the time spent developing it.
AI is not replacing human engineers. Instead, it acts as a force multiplier, enhancing their capabilities. The true asset in software development is no longer the code itself, but the understanding of the code.
About the Author
Ethan Seow is a Centre for AI Leadership Co-Founder and Cybersecurity Expert. He’s ISACA Singapore’s 2023 Infosec Leader, ISC2 2023 APAC Rising Star Professional in Cybersecurity, TEDx and Black Hat Asia speaker, educator, culture hacker and entrepreneur with over 13 years in entrepreneurship, training and education.