AI has changed how quickly a website or application can be built. A task that once took several hours can sometimes be completed in minutes with an AI coding tool. That sounds like an obvious productivity win, and often it is, but there is another side to that speed that is easy to overlook. AI can make it much faster to create technical debt too. The problem is not necessarily that AI writes bad code. It can produce code that works perfectly well. The problem is that working code can still be duplicated, unnecessarily complicated, poorly structured, or inconsistent with the rest of a project.
I experienced this while building GamesMom. I'm a digital marketer rather than a professional developer, but building a real product forced me to work much more closely with frontend code, AI coding tools, testing, deployment, and maintenance. One of the biggest lessons I learned was that getting a feature to work is only the beginning. The more important question is whether the solution fits the rest of the project and whether someone will be able to understand and maintain it later.
AI is very good at solving the problem you put in front of it. If you ask it to add a scoring system, it can create one. Ask it to add a timer, and it can create one. Ask it to build a new component, and it can produce one almost immediately. The problem starts when the project already contains similar functionality. Unless the AI has enough context about the existing codebase, it may create another implementation rather than finding and reusing what already exists. The new code works, the feature works, and everything appears fine. A few months later, however, you may discover that three different parts of the application contain slightly different versions of the same logic.
That is where technical debt begins to accumulate. A developer may know that duplication is undesirable, but when an AI tool can produce a working solution in seconds, the temptation to accept it and move on becomes much stronger. The immediate problem is solved, so there is little reason to stop and investigate whether the project already has a better solution. The cost doesn't disappear. It simply moves into the future.
This is also why working code should not automatically be considered good code. AI can generate a solution that produces exactly the expected result while still introducing unnecessary abstractions, additional dependencies, duplicated functions, inconsistent naming, or logic that is difficult to understand. If the code is being reviewed by someone who understands the project, these problems can often be caught quickly. If generated code is accepted without review, they can quietly become part of the architecture.
Context is one of the biggest challenges. A project can contain hundreds of files, shared components, configuration rules, dependencies, conventions, and decisions that were made months earlier. An AI tool might understand the file you are currently editing very well while having limited understanding of why another part of the application was implemented differently. Software is rarely a collection of independent files. Components depend on each other, shared utilities affect multiple pages, and small changes can have consequences somewhere else.
AI can also encourage overengineering. When you ask for a solution, it may suggest patterns that are technically valid but unnecessary for the problem you are actually trying to solve. A simple feature can suddenly involve another abstraction, another utility, another package, and several additional files. None of those decisions may be wrong individually, but together they increase the amount of software that needs to be understood and maintained. I have found it useful to ask a much simpler question before accepting a generated solution: can this be solved with less code?
The cost of technical debt is particularly difficult to notice when working with AI because the initial implementation is so cheap. You ask for a feature, review the result, test it, and move on to the next task. The real cost appears later when something needs to change. You discover that similar logic exists in several places, that different components follow different conventions, or that an old dependency is being used for something that could now be handled more simply. A feature that originally took fifteen minutes to create can then require several hours to understand and refactor.
This is why I think AI makes refactoring more important rather than less important. When development becomes faster, it becomes easier to keep adding features without stopping to clean up the existing system. Eventually the project becomes harder to navigate. Functions become difficult to trace, similar code appears in multiple places, and making a small change becomes risky. AI can help with refactoring, but I would rather give it a specific problem to investigate than ask it to rewrite an entire project. Understanding what needs to change should come before changing it.
Testing becomes important for the same reason. If AI allows you to produce more code in less time, you also have more code that needs to be tested. That does not necessarily mean every small project needs a massive automated testing framework. It means checking the important user flows, considering edge cases, testing different devices and browsers, and making sure a change hasn't broken something that was already working. For a website, that might mean checking mobile layouts, keyboard interaction, touch input, loading behavior, forms, navigation, and unusual user input.
My own workflow has changed as a result. Before asking AI to build something, I try to understand whether the project already has a similar implementation. When I need to modify existing code, I often ask the AI to explain the current approach before suggesting a replacement. If it generates a new utility or component, I check whether something similar already exists. I also try to keep common functionality genuinely shared rather than allowing several slightly different versions to develop across the project.
The important thing is that AI is not really the cause of technical debt. Developers have been creating technical debt long before AI coding tools existed. The difference is that AI dramatically reduces the cost and time required to produce code. That is useful when the process is disciplined, but it also means poor decisions can be multiplied much faster. AI can accelerate good engineering practices, but it can just as easily accelerate bad ones.
The biggest productivity gain from AI is therefore not simply writing code faster. It is reducing repetitive work, helping explore solutions, and allowing people to spend more time on problems that require judgment. But productivity should not be measured by how quickly the first version is produced. A better measure is how quickly you can build something that remains understandable, reliable, and maintainable.
The cheapest code to write can become the most expensive code to maintain.
That is probably the part of AI assisted development that deserves more attention. As AI makes software easier to produce, deciding what software should exist becomes even more important. The goal should not be to generate more code. It should be to build the right thing with as little unnecessary complexity as possible.
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