Artificial intelligence is now a part of every software development team’s workflow. Developers claim it improves their productivity, but at what cost?
GitClear’s latest research on AI-generated code quality showed that coding with AI is increasing code duplication and reducing the quality of development. The report also emphasized that it increases the rate at which technical issues are built up. And if these lapses are left unchecked for a long time, it could snowball into an unmanageable technical debt, which erodes performance and delivery timelines.
Now you’re thinking of auditing your AI workflow to ascertain your risk level. But before then, let’s uncover more about technical debt and its implications for your business.
In this article, you’ll learn the following:
- What is Technical Debt in relation to AI
- Examples of Technical Debt
- The Consequences of Prolonged Unpaid Technical Debt
- How to Manage it
- GAP’s Solution to Avoid Technical Debt in Your AI Workflow
What is Technical Debt in AI?
Debt in finance is often incurred when there’s a need to meet a particular financial goal in the absence of funds. At that point, it becomes a lifesaver, but without a payback commitment, it starts building up to a financial crisis caused by compounding interest until one day, when you face legal actions.
That’s what technical debt looks like in AI projects.
AI adoption in software development isn’t a bad practice, but when continuous monitoring and code quality review processes are left to chance, technical debt compounds over time, escalating to more problems.
Technical debt (code debt or design debt) is a broad term in the IT field, which means the future implications of choosing an easy and faster means of code generation or design rather than a more robust one.
Ward Cunningham coined the term “technical debt” based on his observation. He said that writing and delivering software is like going into debt, with every error unchecked counting as the interest. In Ward Cunningham’s words in a YouTube Video, when explaining how this metaphor relates to software,
Shipping first-time code is like going into debt. A little debt speeds development so long as it is paid back promptly with refactoring. The danger occurs when the debt is not repaid. Every minute spent on code that is not quite right for the programming task of the moment counts as interest on that debt. Entire engineering organizations can be brought to a stand-still under the debt load of an unfactored implementation, object-oriented or otherwise.
See GitClear’s result on how code duplication has skyrocketed as AI adoption evolves.
Source: GitClear
Common Examples of Technical Debt in AI
Generally, poor IT and AI development best practices are the gateway to technical debt. A missed quality assurance or best practice step can lead to unexpected technical issues. But what does technical debt in AI look like?
Below are examples of technical debt in AI:
- Duplicate code snippets
- Inconsistent code quality
- Insufficient data
- Legacy system incompatibility
- Lack of adherence to coding standards
- More code than necessary
- Hidden security vulnerabilities introduced by AI-generated code
The “Interest Rates” You’re Paying on AI Technical Debt
Financial debt often imposes financial stress; likewise, technical debt. Dr. James Stanger of CompTIA emphasized the implications of unpaid technical debt.
“If you don’t reduce technical debt, you end up with serious problems, and if cowboy IT is allowed to run roughshod, it leads to a toxic tech and business environment”. “When you accept a risk but never redress it properly, you end up with problems and implications that keep companies from making strategic use of their technology.”
Let’s uncover the interest rates you pay as your AI-generated code incurs more technical debt.
Maintenance overhead and slower development cycles
GitClear’s report showed how code rewriting in 2024 exceeds the number of moves. This means that developers are investing more time into generating new code (because AI makes code generation faster) than improving existing code. Now the question: what happens when code improvement becomes an afterthought?
Source: GitClear
As this practice progresses, developers will need more time to maintain code quality. In the long run, developers’ time will increasingly be consumed by “defect remediation” (fixing bugs and cleaning up messy AI-generated code) instead of working on new features. For businesses, this slows innovation and makes it harder to stay competitive.
Increased cost of risk mitigation
Poor code quality makes it harder to detect vulnerabilities. With every new code generated, the codebase becomes messier. This is ironic regarding the productivity benefits of using AI for code generation. Interestingly, Google’s DORA research found that while AI improves processes like documentation and code review speed, it can harm delivery performance and system stability.
Source: Google DORA’s 2024 research
Therefore, if not carefully reviewed, AI-generated code can incur more technical debt, leading to paying more later in risk mitigation and recovery costs.
Compliance fines
Every unpaid debt attracts a higher interest rate as it lingers. Likewise, an unpaid technical debt could escalate to compliance issues. In the absence of a strict governing system to evaluate code for context and industry regulation compliance, poor documentation is inevitable.
It’s important to note that AI operates on statistical patterns and often misses the context of your specific workflows or industry requirements. This gap can result in software built on biased data, insecure processes, and weak privacy controls. Over time, these flaws may lead to violations of regulations like GDPR or HIPAA, resulting in data breaches, legal penalties, and reputational damage.
Cloud infrastructure inefficiencies that increase your bill
Code duplication can contribute to the inefficient use of cloud resources. As technical debt is accumulated from shortcuts taken in development, it manifests in areas like poor code quality, inefficient algorithms, and inadequate infrastructure, leading to higher resource consumption and costs.
Unused or redundant code increases cloud storage costs, extends testing cycles, and adds complexity during debugging. AI tools don’t account for these long-term consequences; they simply generate more code without optimizing for sustainability or cost-efficiency.
Productivity illusion
According to the Stack Overflow Developer Survey, 81% of developers say AI tools have improved their productivity. This is exactly where the actual issue stems from. Many teams are falling into the trap of measuring developers’ productivity by surface-level metrics like commit count or lines of code, metrics that don’t reflect actual software quality.
Developers’ over-reliance on “what gets measured gets done,” a principle better suited for operational tasks than coding, can lead to shortcuts, overlooked design flaws, and mounting technical debt. It creates an illusion of progress while the codebase suffers from instability and poor maintainability.
How to Manage Technical Debt
As Ward Cunningham puts it, “some problems with code are like financial debt. It’s OK to borrow against the future, as long as you pay it off.”
The question now is how to balance quality and avoid paying higher interest rates or fines.
We’ll explain critical measures you should take to manage technical debt in your AI development processes.
Establish rigorous code review processes for AI-generated code
Speed doesn’t equate to quality. Therefore, it’s expedient to put strict code review measures in place, especially for AI-generated code. This is critical because AI is a tool and is prone to hallucination and going out of context. However, it’s important for tech team leads to establish strong code review processes that limit the implications of future damage. To do this,
- Treat every AI-generated code as a draft
- Understand the AI’s training data
- Identify edge cases
- Implement automated testing, linting, and static analysis
- Frequently communicate with team members during the review to establish improvement in processes
Define coding standards and enforce them with AI tools
It’s time for senior developers/team leads to be very specific with coding standards. Help every team member understand that “more lines of code added” doesn’t translate to team productivity. You may emphasize the DRY (Don’t Repeat Yourself), modular approach to software developers. With this approach, everyone focuses more on coding that avoids logic duplication, which may bloat the system.
Additionally, team leads should rethink metrics to measure and focus on metrics or standards that facilitate long-term maintenance costs to tighten up the coding standards.
Invest in continuous monitoring and risk assessment
It’s poor practice to leave AI-generated codes unvetted. There needs to be a routine monitoring system in place to catch any drift and decay in outcome quality. This can be achieved by setting aside a section of each sprint to analyse and review AI-generated code. You can also set up automated alerts and dashboards to track model performance, data input changes, latency, and security risks.
For a long-term reinforcement of this culture, the team lead may consider celebrating developers who meet the quality metrics.
Continuous team training
Technology evolves, and so should your team adapt. If your team is using outdated methods or tools, technical debt is bound to grow. As workflows shift, it’s essential to have a clear education plan to keep your team aligned and informed. Document mistakes generated by AI and share them regularly. This keeps everyone on the same page when interpreting AI inputs and outputs across projects.
Additionally, external industry-standard courses for upskilling and in-house training are great ways to ensure continuous training in your team. A well-informed team makes better decisions, writes smarter code, and builds AI systems that are easier to maintain and improve.
How GAP’s Modernization Service Helps You Pay Down AI Technical Debt
Without careful monitoring and reviewing systems, AI can turn your system into a technical debt machine.
More often than not, many teams lack the expertise required to monitor and manage technical debt religiously. This isn’t enough as an excuse; you need an expert in AI development. This is what the GAP modernization service solves for tech teams who want to adopt AI into their workflow without incurring technical debt.
Here’s how we ensure you pay no interest by cutting down every technical debt.
- Detecting hidden “interest” in your codebase, cloud, and infrastructure (security, performance, compliance)
- Refinancing your technical debt into clean, modular, future-proof architecture
- Using AI-powered auto-refactoring like Azure AI Copilot to maintain code quality while controlling cloud and AI costs
Partner With Experts Like GAP for Modernization and Debt Consolidation
Technical debt during AI development is becoming unavoidable. What matters is how you manage it to minimize risk and cost.
Don’t know where to start yet?
We’ve got you covered at GAP. Whether you need an AI tech stack audit or a monitoring system integration, our experts provide the guidance and hands-on support you need.
With our modernization services, you’ll have access to the skills and strategies needed to use AI as a true competitive advantage, without accumulating hidden debt.
Talk to our experts today to audit your AI systems and keep technical debt under control.
