AI Writes the Code, But Who Understands It? What Is Knowledge Debt?
AI-powered coding tools have accelerated software development to a level we have never seen before. Today, a developer can add a new feature, fix a bug, write tests, or refactor dozens of files simply by providing a few instructions.
The code is generated, the tests pass, and the application appears to work without any problems.
However, there is an important question that must be answered:
The generated code may work, but does the developer truly understand why it works?
One of the emerging risks software teams face in the age of artificial intelligence lies within this question. This risk is called Knowledge Debt.
What Is Knowledge Debt?
Knowledge Debt is the knowledge gap that emerges when a developer or software team does not sufficiently understand the code, architectural decisions, or system behaviour they are responsible for.
The concept has become increasingly relevant with the rise of autonomous AI coding agents. A study published in July 2026 describes Knowledge Debt as the accumulation of changes implemented by AI agents but not fully understood by developers.
Researchers argue that this accumulation resembles technical debt. However, instead of appearing directly in the codebase, it develops within the knowledge and skills of the developers responsible for that code.
Consider a simple example.
An application contains a bug that causes user sessions to end unexpectedly. The developer sends the error message to an AI tool. The tool modifies several files, updates the token refresh mechanism, and resolves the problem.
The application now works. However, Knowledge Debt may have started to form if the developer cannot answer the following questions:
- What was the root cause of the problem?
- Why did the token expire earlier than expected?
- Which files and system behaviours did the AI change?
- Is the implemented solution secure?
- Could the developer solve the same problem without AI if it happened again?
The problem may have been resolved, but the knowledge that should have been gained during the process was not acquired.
The Difference Between Technical Debt and Knowledge Debt
Technical debt refers to the future maintenance cost created by technical decisions made to achieve faster short-term results. Complex functions, duplicated code, insufficient tests, and temporary solutions are common examples of technical debt.
With Knowledge Debt, however, the code does not necessarily have to be poorly written.
The code may be clean, tested, and fully functional. Despite this, the team may not understand why it was designed in a particular way or how the system behaves under specific conditions.
The main difference can be summarised as follows:
Technical debt exists within the codebase, while Knowledge Debt exists in the gap between the codebase and the people responsible for understanding it.
Another approach proposed in 2026 suggests evaluating software health through technical debt, cognitive debt, and intent debt.
Cognitive debt relates to how well team members understand the system. Intent debt refers to how effectively the reasoning behind important decisions has been documented.
For example, when an AI agent adds a new caching mechanism to a project:
- Technical debt appears if the implementation is unnecessarily complex.
- Knowledge or cognitive debt appears if the team does not understand how the cache works.
- Intent debt appears if the reason for choosing that approach was never documented.
These forms of debt are not independent. In many cases, one causes the others to grow.
How Does AI Create Knowledge Debt?
The primary cause of Knowledge Debt is not the fact that artificial intelligence writes code. The real problem begins when AI-generated code is accepted without being understood or verified.
Treating Working Code as Correct Code
A feature working correctly on the screen does not mean that it has been implemented correctly.
An AI-generated solution may contain unnecessary dependencies, security vulnerabilities, performance problems, or decisions that conflict with the project’s architecture.
If the developer only checks the visible result and accepts the code without reviewing it, the internal behaviour of the system gradually becomes invisible.
Making Large Changes at Once
AI coding agents can modify dozens of files within minutes. However, the speed at which AI generates code is not the same as the speed at which a human can understand it.
Hundreds of lines of code produced in five minutes may require considerably more time to review properly.
As the size of a change increases, it becomes harder for developers to understand every decision and identify possible side effects.
Leaving the Entire Debugging Process to AI
Debugging is one of the processes through which software developers learn the most about a system.
Reviewing logs, eliminating possible causes, following the data flow, and examining the execution path all strengthen the developer’s mental model of the application.
When AI immediately provides a solution, the issue may be closed more quickly. However, the developer may lose the opportunity to understand how and why the problem occurred.
Research on Knowledge Debt also highlights that knowledge traditionally gained through effortful problem-solving may disappear when tasks are completely delegated to AI agents.
Asking the Same Tool to Write Both the Code and the Tests
Using the same AI tool to write tests for code it previously generated may create a false sense of confidence.
The tool may repeat the same assumptions it made while generating the implementation. As a result, every test may pass while real user behaviours, edge cases, or security risks remain undetected.
Failing to Document Architectural Decisions
AI may recommend a library, design pattern, state-management strategy, or data-processing approach.
Even when the recommendation is reasonable, the team may later struggle to understand the implementation if the reason behind the decision was never documented.
Once the original intent is lost, changing the code becomes risky. The team can no longer distinguish between intentional behaviour and accidental behaviour.
Why Is Knowledge Debt Dangerous?
Knowledge Debt is rarely noticed when it first emerges. As long as the code continues to work, the team may assume that there is no problem.
The debt usually becomes visible when the system needs to be changed.
Debugging Takes Longer
When developers do not understand how the system works, even a small bug can turn into a long investigation.
The code has to be rediscovered every time a new problem appears.
In this situation, the time initially saved through AI is eventually paid back during the maintenance process.
Small Changes Break Unexpected Areas
When relationships between different parts of the codebase are not understood, a developer may only test the screen or function they changed.
However, the change may also affect caching, authentication, multilingual pages, data synchronisation, or another API workflow.
As Knowledge Debt grows, the system begins to feel increasingly fragile.
Security Reviews Become Superficial
AI coding tools do not remove the need for security awareness. However, they may shift security responsibility from the implementation stage to the review stage.
A 2026 study involving professional developers observed that participants often failed to mention security requirements in their initial prompts, even when they had security knowledge.
If the person reviewing the code does not understand the change sufficiently, the security review may also remain superficial.
Teams Become Dependent on AI Tools
When a team can only develop with the assistance of AI but cannot debug the system or make architectural decisions without it, the tool is no longer simply an assistant.
It becomes a critical dependency.
If the tool changes, project context is lost, or the AI recommends an incorrect solution, the team may struggle to intervene.
Onboarding New Team Members Becomes More Difficult
It is difficult to introduce a new developer to a project with insufficient documentation, unknown decision history, and code that even the existing team does not fully understand.
The application may work, but important knowledge may exist only in previous AI conversations or in the memory of a few team members.
What Does the Research Say?
Knowledge Debt is not yet a mature software metric that has been measured for decades or defined through complete industry consensus.
It is a relatively new research area, and researchers are still exploring how it can be measured. The researchers who introduced the concept also identify empirical user studies and measurement methods as important areas for future research.
However, existing studies on AI usage, code quality, and developer learning provide important findings.
In a controlled study conducted by Anthropic in 2026, participants were asked to use a Python library they had not previously worked with.
The group using AI assistance scored an average of 17% lower in a later comprehension test than the group that wrote the code manually.
However, participants who asked the AI explanatory questions and actively tried to understand the generated code preserved their learning outcomes more successfully.
Another large-scale study examined 304,362 verified AI-contributed commits across 6,275 real GitHub repositories.
The researchers reported that more than 15% of commits associated with each AI coding tool introduced at least one static analysis issue. They also found that 24.2% of the tracked issues were still present in the latest version of the relevant project.
These findings are based on a preprint study and static analysis results. Therefore, they should not be treated as definitive figures representing all AI-generated code.
Instead, they should be viewed as evidence of the need for careful quality control.
These results do not suggest that artificial intelligence should not be used. They show that the speed provided by AI must be supported by verification and learning processes.
Does Using AI Always Create Knowledge Debt?
No.
Artificial intelligence can increase Knowledge Debt when used carelessly, but it can also help reduce it when used correctly.
Instead of asking AI only to generate code, developers can also use it to:
- Explain how existing code works.
- Identify areas that may be affected by a change.
- Compare alternative architectural approaches.
- Prepare pull request descriptions.
- Simplify complex functions.
- Create documentation.
- List possible edge cases.
- Ask developers questions about the code they have written.
The key difference is using artificial intelligence not as a code-generation machine, but as a collaborative and educational tool.
AI tools can save significant time, particularly when handling repetitive work, scanning large codebases, and identifying possible causes of errors.
However, an important distinction must be made:
Getting help from artificial intelligence is not the same as handing over the responsibility of thinking to artificial intelligence.
How Can Knowledge Debt Be Reduced?
Ask for a Plan Before Asking for Code
Instead of directly asking AI to implement a feature, first ask it to explain:
- The proposed solution,
- The files that will be affected,
- Possible risks,
- Alternative approaches,
- Testing requirements.
Once the plan has been reviewed and approved, code generation can begin.
This makes it easier to follow and understand the implementation.
Divide Changes Into Smaller Parts
Instead of allowing an AI agent to modify dozens of files at once, divide the task into small and manageable steps.
Smaller pull requests are easier to review, test, explain, and revert.
Do Not Merge Code You Do Not Understand
A developer also assumes responsibility for the code they merge.
A simple rule can be applied:
If you cannot explain the code to another team member, it is not ready to be merged.
Developers do not need to memorise every line. However, they should understand the main data flow, the selected approach, possible side effects, and failure scenarios.
Ask AI “Why?”
Do not only ask the AI what it changed. Ask why it made those decisions:
- Why did you choose this approach?
- Which alternatives did you consider?
- What are the disadvantages of this solution?
- Under which conditions might it fail?
- What security and performance risks exist?
- Which other files or behaviours could be affected?
These questions do not automatically make AI-generated output correct. However, they help developers conduct a more conscious review.
Generate Tests From Requirements
Do not generate tests only by looking at the implemented code.
First define expected behaviours and acceptance criteria independently from the implementation.
Manual testing and security reviews should be performed, especially for critical areas such as authentication, payments, authorisation, and personal data processing.
Document Architectural Decisions
Short Architecture Decision Records can be prepared for important decisions.
These records only need to answer a few questions:
- What problem was being solved?
- Which options were considered?
- Why was this solution selected?
- What are its known disadvantages?
- Under which conditions should the decision be reconsidered?
This prevents important knowledge from remaining only in an AI conversation or a developer’s memory.
Do Not Completely Abandon Problem-Solving Without AI
Delegating every task to AI may provide short-term speed, but reading documentation, reviewing logs, debugging, and following code execution remain essential developer skills.
Occasionally analysing a problem independently before comparing your approach with the AI’s solution can strengthen both your learning and your ability to evaluate AI-generated output.
Knowledge Debt Checklist Before Merging
Before adding code generated or significantly modified by AI to a project, ask the following questions:
- Can I explain the original problem this change solves?
- Do I know which files and system behaviours are affected?
- Do I understand why this approach was selected?
- Have security, performance, and data-integrity risks been reviewed?
- Do the tests verify the actual requirements rather than only the current implementation?
- Could I perform basic debugging on this code without AI?
- Has the necessary documentation been prepared for future developers?
If most of these questions cannot be answered, the code may work technically while the team continues to accumulate Knowledge Debt.
The Developer of the Future Will Not Be the Person Who Writes the Most Code
As AI agents generate more code, the role of the developer is not disappearing. However, its focus is changing.
Developers are no longer only responsible for writing code. They are becoming responsible for defining problems, selecting appropriate solutions, reviewing AI-generated output, evaluating security risks, and maintaining systems over the long term.
As the amount of AI-generated code increases, human understanding becomes even more important.
When production accelerates, incorrect decisions, undocumented choices, and poorly understood structures can also grow at the same speed.
It may not be possible to eliminate Knowledge Debt completely. However, it can be made visible, measured, and gradually repaid.
Artificial intelligence can help us produce code more quickly. But sustainable software development requires more than working code.
It also requires developers who understand that code.
The most valuable developer of the future will not be the person who produces the most code, but the person who understands why AI-generated code is right or wrong.
