Boilerplate code—the repetitive, reusable chunks that developers use for standard functionality—was once celebrated for improving efficiency. But with AI models now churning out millions of lines of it at record speed, that convenience is becoming a liability.
As automated tools accelerate software delivery, they are also multiplying the volume of code that must be maintained, revised, and audited. What was meant to save time is now quietly generating a massive backlog of technical debt.
The numbers tell the story. In 2024, Gitclear found that “large blocks of duplicated code generated by AI tools” increased by tenfold compared to 2022, while the rate of refactoring (the essential maintenance that improves code quality) fell from 24.8% in 2021 to just 9.5% in 2024. The result: cleaner code delivery in the short term, but more brittle, harder-to-maintain codebases over time.
Another study by Pegasystems and Savanta estimated that global enterprises collectively lose over $370 million annually to technical debt, with $56 million per firm spent just on maintaining and integrating legacy systems.
Developer Tarun Singh explains in his Medium blog that boilerplate isn’t inherently bad: “It’s foundation. But it steals time, invites copy-paste errors, and hurts developer morale.” Generative AI tools thrive on such repetitive structures, producing perfectly formatted code with minimal creative effort.
Yet, this speed often disguises a problem AI doesn’t think about—long-term maintenance. As Slovenia-based developer Dayvi Schuster warns in one of his recent blogposts, “This convenience increases the soft requirement for more tooling and assistance to manage and maintain your codebase, which is a dangerous spiral best avoided altogether.”
How Boilerplate Breeds Technical Debt
The connection between boilerplate and technical debt lies in maintainability.
A senior CTO at one of the Big Tech firms’ India unit, seeking anonymity, said that many firms delay updating their AI-generated software until it begins to “choke their neck or throat,” forcing them into reactive and costly fixes.
CEO of the Free and Open Source Software Foundation (FOSS), Sai Rahul Poruri, added that boilerplate multiplies the number of places where code changes must be made. “Each bug fix has to be repeated everywhere that code block exists,” he says, “turning simple updates into tedious, error-prone tasks.”
For younger developers, boilerplate-heavy systems create a new kind of fragility. Aditi Balaji, an IT fresher, pointed out that these template-based structures can become obsolete fast. “The same template might become outdated or unsuitable later,” she said. “Teams end up starting from scratch.” Her advice was simple but often ignored: refactor consistently. Regular cleaning and optimisation, she argued, is the only safeguard against collapse.
Systemic Pressures and Skill Gaps
But in today’s delivery-driven environment, refactoring rarely gets prioritised. Poruri observed that developers face “enormous pressure to ship software as quickly as possible,” which leads them to favour short-term fixes over sustainable architecture. “This is the main reason for weak technical foundations,” he said. Aditi echoed that sentiment, noting that “many developers focus on fixing or adjusting existing code rather than learning how to build it from scratch.”
The Big Tech CTO agreed that fundamentals matter more than ever: “If you’re a fresher, you learn .NET or Java—it’s not going to be a waste. You need to understand the basics and a little bit more than that. Then, of course, these tools can augment your output.” Without that grounding, AI assistance turns into dependence.
Productivity Gains, Reliability Risks
Despite the long-term risks, there’s no denying that AI tools have transformed productivity. Tasks that used to take 60–70% of a developer’s time now take just 15–20%. AI-assisted systems can design, test, and even deploy code with minimal human input. “These systems can hallucinate,” the CTO cautions, “so humans will never go out of the loop.” Each line of AI-generated code still needs human verification to ensure it works as intended.
Balaji believes the most sustainable approach is hybrid. “There can never really be a copy-paste solution when catering to unique client requirements,” she said. “Most Indian IT firms start with AI-generated templates but then build on them with original logic.” This blend of automation and human creativity helps preserve flexibility and client relevance.
Legacy Constraints and the Shift to Re-architecture
Modernisation remains a challenge for Indian IT. AI tools struggle with old, or proprietary systems—platforms like IBM mainframes, AIX, or AS400—which aren’t part of most model training datasets.
“No LLM is completely trained on all of the boilerplate base,” the CTO claimed. Moreover, legacy-skilled personnel often can’t adapt to modern AI-driven tools. “People familiar with legacy tools possess skills that are completely stale,” he said. “The new platforms demand different capabilities, and those maintaining the old systems don’t know them.”
This mismatch is prompting a major strategic shift. Rather than gradually improving old systems through refactoring, many firms are opting to rebuild them from scratch. “Many customers have put their modernisation journey on hold,” the CTO said, “thinking that with new AI platforms, they can re-architect faster than they can refactor.” For these firms, rewriting applications for the cloud and migrating data seems more cost-effective than fixing what already exists.
Governance and Ethical Responsibility
However, this accelerated rebuilding comes with governance risks. A 2024 LiCoEval study revealed that leading AI code-generation models produced 0.9–2% of outputs nearly identical to open-source code, often without proper license attribution.
Legal firm Finnegan, in one of its articles for Bloomberg Law, explained that even a few copied lines can qualify as copyright infringement. “Even slightly altered code can still count as a derivative work,” the firm noted, meaning it remains protected by the original copyright.
Poruri warns against blindly trusting AI outputs or copying code from Stack Overflow without checking source compliance. “This isn’t just about open-source rules,” he stressed. “It’s about copyright responsibility in general.” The larger issue, he said, lies in lax compliance processes within firms and weak legal enforcement in many jurisdictions.
In India, the problem is more complex. Advocates from Saga Legal point out that Indian copyright law currently protects only computer-aided works, not computer-generated ones. This legal gap makes it unclear whether AI-assisted code even qualifies for protection, creating uncertainty for companies relying heavily on generative tools.
AI has undeniably supercharged software delivery but at a cost. By amplifying boilerplate generation, it risks burying teams under their own velocity. The solution is not to reject AI but to balance its speed with human discipline.
As Balaji puts it, “You can’t automate craftsmanship.” Developers must refactor often, mentor younger engineers, and enforce strong governance standards.
The post AI’s Boilerplate Boom: Faster Code, Deeper Debt appeared first on Analytics India Magazine.
