As a junior developer stepping into the tech world, I was excited to finally work on code that would drive real innovation. The thrill of coding, creating, and contributing to impactful projects felt almost tangible. But this initial excitement faded fast. Instead of building new features or diving into cutting-edge technologies, I found myself spending most days tangled in a web of legacy code, some of it dating back two decades.

The Reality Check: Legacy Code Over Innovation

In my first project, I encountered a React-based solution riddled with inefficient code, which made it painfully hard to follow and even harder to maintain. The setup, structured around outdated paradigms and patched with quick fixes, often took precedence over developing quality software. As a junior developer still gaining my footing, I struggled to understand the tangled codebase. Mistakes were inevitable—my focus at that point was simply to get things working rather than refining them for long-term sustainability. The real kicker? My company didn’t seem to mind, as long as the functionality was there. Quality, maintainability, and developer experience were secondary.

Testing and Its Brief Impact on Code Quality

At one point, I joined a different project and got a taste of what maintaining code quality can look like. I was introduced to unit testing with Jest, where writing and maintaining reliable tests suddenly made me see the difference quality code could make in real productivity. The structured approach to testing created a codebase that was both easier to understand and less prone to errors, offering a level of predictability I hadn’t experienced in my initial project. But this experience was brief. Soon, I was back to the original chaotic environment—one without any consistent project management or dedicated support.

With no project manager or scrum master to guide me, and my senior manager juggling numerous other tasks, I felt stranded in a sea of tasks, firefighting and patching things up without any guidance on proper code practices. I wasn’t alone in this situation. Across the industry, junior developers are often thrust into similar scenarios, finding themselves responsible for complex tasks without the resources, training, or structure to tackle them effectively.

Why Companies Still Focus on DSA in Hiring—And Why They Shouldn’t

Despite these challenges, many companies are still testing developers on data structures and algorithms (DSA) during recruitment, skills that are no longer as relevant to daily development as they once were. Most AI systems can solve standard DSA problems in an instant; yet the focus remains on solving contrived coding puzzles rather than real-world scenarios that test how developers prioritize clean code, handle code quality, and understand the long-term cost of neglecting maintainability.

Real engineering is about more than just coding: it’s about knowing where to look for resources, deciding how to implement sustainable solutions, and problem-solving in ways that account for long-term technical debt. While AI can speed up the writing of code snippets readily available online, true value in software engineering lies in creativity, judgment, and a problem-solving mindset. Writing code is the easy part—creating solutions that are maintainable, scalable, and well-documented requires skill and foresight.

AI Anxiety and the Pressure to Deliver Quickly

Many engineers are justifiably worried about AI replacing their jobs, especially when we’re stuck maintaining outdated systems or completing repetitive tasks. This fear may even be creating a culture of rushed development, where managers push for quick delivery without proper quality controls, expecting AI tools to handle future fixes and improvements. In reality, cutting corners on quality only adds to the technical debt developers face, ultimately increasing costs rather than reducing them. It’s a cycle that increases stress, undermines employee trust, and prevents companies from building resilient, innovative solutions.

The Cost of Technical Debt: More Than Just a Developer Problem

Technical debt isn’t an abstract concept; it’s a very real burden. Developers are reported to spend between 25% and 40% of their time addressing technical debt, according to industry surveys. The costs are substantial: it not only slows down innovation but also keeps companies from adapting to new technologies as swiftly as they should. As technical debt accumulates, teams become frustrated, burnout escalates, and turnover rates rise—a vicious cycle that hurts companies and developers alike.

The repercussions can be serious. We’ve all seen the news stories about systems outages and failures that disrupt lives and damage reputations. Consider the 2021 FAA system outage, which grounded thousands of flights and created chaos for travelers—a result of years of technical debt piling up in critical infrastructure. When systems fail, it’s not the developers who face the worst consequences—it’s the businesses and the customers who depend on these services.

The AI Fallacy: Trusting AI to Handle Technical Debt

An increasing number of companies are rushing to integrate AI, convinced it will solve all their problems. In reality, laying off developers and offloading everything onto AI simply puts more pressure on remaining employees, who are expected to perform without the resources or support to handle mounting workloads. AI can assist in certain development tasks, but it can’t replace the critical decision-making, strategic thinking, or quality standards that skilled developers bring to their roles. Reducing the workforce with the assumption that AI will “take care of everything” only sets teams up for more challenges, eroding trust and leaving projects vulnerable to failure.

Real Solutions: Investing in Quality and Sustainable Development

To address the real threats of technical debt, companies must invest in creating sustainable development practices that go beyond quick fixes or automating code generation. Here are some key steps that could make a lasting difference:

1. Reevaluate Hiring Practices: Focus on testing for real-world scenarios that prioritize clean code, maintainability, and an understanding of technical debt over abstract problem-solving. Finding candidates who understand the long-term impact of their coding decisions can be invaluable.

2. Encourage Code Quality Through Testing: Mandate unit testing and code reviews to maintain a baseline level of quality. Developers given the time and tools to test effectively can create systems that are easier to work with and require less fixing over time.

3. Proper Management Structures: Assign project managers or scrum masters to support development teams and ensure workloads are manageable. Clear leadership allows junior developers to learn best practices and become better equipped to tackle complex issues.

4. Address Technical Debt as a Priority: Just like financial debt, technical debt should be tracked, assessed, and paid down in manageable increments. Ignoring it compounds issues, creating deeper problems that become harder to fix over time.

5. Educate Teams on the Limitations of AI: Developers and management alike should understand where AI can assist—and where it cannot. AI is a tool, not a silver bullet. Setting realistic expectations for its capabilities helps prevent costly errors and maintains employee trust.

Conclusion: Facing the Real Threat to Our Industry

The fear of AI replacing jobs is valid, but technical debt poses a more immediate threat to the industry. It impacts productivity, stifles innovation, and increases costs, ultimately creating an unsustainable work environment for developers. By investing in code quality, establishing proper management practices, and shifting focus from “just shipping” to sustainable practices, companies can reduce technical debt, boost job satisfaction, and position themselves for long-term success.

Embracing these changes means valuing developers for their expertise beyond code—acknowledging the judgment, creativity, and problem-solving skills that no AI can replace. We owe it to our teams, our companies, and our customers to address the very real challenges of technical debt, creating an industry that prioritizes lasting innovation over the fear of obsolescence.

FAQs

1. Why do companies continue to focus on DSA in hiring when AI can solve these problems?

Many companies use DSA tests as a legacy measure of problem-solving skills, but this approach overlooks the skills most valuable in real-world projects, like maintaining code quality, understanding technical debt, and writing maintainable code.

2. How does technical debt affect a company’s ability to innovate?

Technical debt slows down development as teams spend more time fixing issues than building new features. Over time, it can prevent companies from adopting new technologies, stifling innovation.

3. Can AI solve the problem of technical debt?

No, AI can’t solve technical debt. While AI may assist with code generation, technical debt requires human judgment, quality standards, and strategic decision-making that AI currently cannot replicate.

4. What impact does technical debt have on developers?

It leads to frustration, burnout, and reduced job satisfaction as developers spend significant time addressing old code issues rather than focusing on new development.

5. How can companies reduce technical debt?
By investing in sustainable coding practices, such as unit testing, regular code reviews, and proper project management. Creating a company culture that values code quality can also make a significant impact.

6. Is the fear of AI replacing developers justified?

While some fear is justified, AI lacks the creative and problem-solving skills necessary for true software engineering. Technical debt and poor management are far more immediate threats to developers’ roles.