Artificial Intelligence is Revolutionizing Coding Practices. However, Is Mounting Technical Debt the Cost?
In 2025, the rush among tech teams to leverage AI in coding leads to a new mantra: "If you build it too fast, technical debt will come-and it'll stick around longer than you'd think."
The influx of AI code generation tools has made it simpler than ever to launch novel features, launch prototypes, and ship faster. But without structure, speed comes with a price—technical debt.
First coined by Ward Cunningham, author of the Agile Manifesto, the term "technical debt" serves as a metaphor that represents the tradeoff between short-term speed and long-term sustainability in software development. In fast-paced environments, strategically taking on some technical debt can be smart, enabling companies to release products faster, validate ideas quickly, and respond to market shifts.
The peril arises when technical debt remains unmanaged, hidden, or ignored.
AI's influence amplifies the best developers—not replacing them—but it could also accelerate technical debt more swiftly than traditional development ever could, particularly when teams are not intentional about integrating AI into their engineering workflows. This is the essential shift technology leaders must address today.
Forrester predicts that by 2025, over 50% of technology decision-makers will experience moderate to severe technical debt. And the situation is worsening—it's not just the old systems at fault.
Team misuse of AI generates technical debt by producing code that overlooks architecture principles or system design patterns, duplicating logic instead of reinforcing modularity and pattern reuse, skipping documentation, meaningful naming conventions, and robust testing, and fostering a dangerous feedback loop where, "It runs," becomes the new quality standard.
In essence, AI enables you to build faster. But it can also propel you towards a deadlock.
False belief that more features equate to more progress is alluring. Yet, in the face of AI-aided rapid development, discipline becomes crucial.
As Steve Jobs once said, "Simple can be harder than complex. You have to work hard to get your thinking clean to make it simple." Faced with AI pushing swift development, discipline is no longer an option—it's non-negotiable.
A new risk is brewing: vibe architecture. When quick prompts, half-understood APIs, and fragmented microservices pile up without orchestration, systems lose cohesion. The system keeps drifting into large-scale architectural entropy, resulting in fragile integrations, silent blast radiuses, and data pipelines fraught with inconsistency and duplication.
The story of "Field of Dreams" is straightforward: Build with focus. In AI-driven development, the same rule applies. Without strong engineering principles from the get-go, even the most robust AI will exacerbate drift instead of delivering stability.
New data confirms this trend. In 2024, GitClear found an 8x increase in large blocks of duplicated code generated by AI tools. Most developers now spend more time debugging AI-generated code and resolving security vulnerabilities than before. Google's 2024 DORA report discovered that an increase in AI usage accounts for a 7.2% decrease in delivery stability.
Technical debt slows teams down in the long run. Today, technical debt fosters fragility that can be catastrophic at scale. New strategies are needed to manage and overcome technical debt with AI. To win the next chapter of software development, it's not just about integrating AI into your toolchain. It's about changing how you build. Treat AI suggestions like junior developer code. Review, refactor, and question everything. Embed technical debt tracking into your delivery pipelines—you can no longer afford to guess. Prioritize modular architectures, strict API boundaries, and domain-driven design. Build AI literacy across your senior teams. Invest in training juniors intentionally. The companies thriving with AI aren't only chasing code velocity—they're constructing teams with the right seniority. Engineers and developers who can visualize beyond the next line of code, think in terms of architecture, and hold certifications proving their deep knowledge of enterprise platforms. At Inclusion Cloud, we provide certified talent that accelerates AI initiatives. And we know how difficult it's becoming to spot who genuinely understands architecture—and who's merely copy-pasting from an AI tool. That's why we've built a strict selection process to recruit top certified talent. They don't merely generate code—they build value. If you're ready to learn more, schedule a meeting with us.
- To mitigate the risks of AI-aided rapid development, team leaders should implement a policy that emphasizes training on the appropriate use of AI tools to avoid generating technical debt.
- In the context of AI-driven development, adhering to technology best practices, such as modular architectures, strict API boundaries, and domain-driven design, can prevent the acceleration of technical debt and ensure sustainable software engineering.
