AI lowered the barrier to software development, but technical debt hasn't gone anywhere

AI has made programming accessible to people without a technical background. But along with the barrier to entry, the safeguards that used to filter out bad architectural decisions at the very start have disappeared too.

How AI changed the way into development

Just two years ago, a web developer's minimal stack looked like this: knowing a programming language, understanding at least one framework, SQL for working with data, the ability to deploy an application and to read tracebacks when errors occur. Learning this stack took months. That threshold put off most people who wanted to "just make something", and in a sense that was useful.

Today the same result is reachable through a conversation with a language model: describe the task, get the code, paste it into the editor, run it. It works. And that really is good: product managers test hypotheses in an evening, designers build interactive prototypes without involving developers, founders build an MVP without hiring a team. The barrier has fallen, and there is nothing wrong with that.

But the barrier also served another function. It forced the author of the code to understand what exactly they were building and why it works one way and not another.

Where technical debt hides

A language model does not know your system. It knows patterns from millions of public repositories and applies the most common one, not the one best suited to a specific project and context. With the direct "task → code → result" approach, this creates a predictable set of accumulating problems:

• Duplicated logic: the same functionality is implemented in several places because every AI request was isolated from all the previous ones
• No validation: data is not checked at the API and database level, which opens the door to SQL injection and inconsistent states
• Secrets in code: API keys and passwords are hardcoded directly in files because in a prototype it is "simpler"
• Happy path only: error handling is minimal or entirely absent
• Irreversible migrations: database schema changes with no way to roll back

Each of these items looks insignificant in a prototype. Together, in production with real users, they are a technical fire that flares up after 6–12 months of operation. It is invisible in a demo, but very visible when the first customer reports a data leak.

How to develop with AI differently

The difference between using AI as a replacement for a developer and as a developer's tool is fundamental. In the first case, AI makes architectural decisions on its own: this is simple, fast, and produces the problems above. In the second, AI implements decisions already made by a human who understands the context.

"AI does not know your system.

It knows patterns from millions of repositories, and applies the most common one, not the most suitable one", is how senior engineers put the problem when they review AI-written code.

In practice this means giving the model the context of the system, not just an isolated task. Before each request, describe the existing architecture and state the accepted patterns and constraints. Ask the model to explain the proposed solution rather than just taking finished code. Review the result by asking "how does this fit into the architecture", not only "does it work right now".

With this approach, AI becomes a productivity multiplier for an experienced developer. With the opposite approach, it is a technical debt generator running at very high speed.

What this means

Lowering the barrier to entry in development is a positive and irreversible shift. The question is not whether to use AI: the answer is already obvious. The question is who makes the architectural decisions, the model or the human. Technical debt, vulnerabilities and architectural problems have not gone anywhere: they have simply stopped blocking the start and begun accumulating unnoticed.

Deliberate use of the tool is the only way to get development speed without paying for it later.