Developers using GitHub Copilot complete tasks an average of 55% faster, according to research presented by Antyweb. The paradox is that consciously using AI to write better code requires deliberately slowing down. The speed of generating lines of code does not equal the quality of the architecture.
TL;DR: AI can generate working code in seconds, but thoughtfully integrating an assistant takes time. Studies confirm production cost reductions of 10-100x, yet achieving that reduction depends on a methodical approach. The key is treating AI as a tool for deliberate design, not merely a line generator.
Why Does AI Sometimes Generate Worse Code Than a Developer Would?
Code assistants like GitHub Copilot and Cursor are trained on millions of public repositories. This means the models replicate popular patterns, regardless of their actual quality and business context. As a result, the generated code is often syntactically correct but architecturally suboptimal.
A developer asking for a function implementation receives a solution that works here and now. The model does not analyze long-term maintenance costs. The generated logic rarely accounts for the specifics of the entire system. The lack of full context is the main weakness of current language models.
As the Antyweb analysis points out, AI works best as an assistant, and worst as an independent co-author. The tool suggests fragments, but the decision to accept them belongs to the developer. It is precisely the rush to accept suggestions that leads to problems down the road.
What Techniques for Working with AI Force Slower but Better Coding?
A patient approach to AI requires changing habits. Instead of accepting the first suggestion, it is worth applying specific techniques that slow down the process but improve the quality of the final code.
Iterative context feeding: Instead of one general prompt, provide the model with information about the project architecture in several steps.
Manual test verification: Generated unit tests require manual inspection. They often test only the happy path, ignoring edge cases.
The small steps rule: Ask for one function to be generated, not an entire module. Smaller fragments are easier to verify for errors.
Forcing explanations: Ask the model to thoroughly explain why it proposed a specific solution before you accept it.
Comparative analysis: Generate a solution to the same problem using different models and compare the proposed approaches.
Dependency verification: Check every suggested library for maintenance status, licensing, and security.
Mutation testing: Modify fragments of the generated code to check whether the tests actually catch the introduced errors.
AI-free code review: Conduct a traditional review of the generated code with another developer, ignoring the fact that it came from a model.
| Technique | Implementation Time | Code Quality | Implementation Difficulty |
|---|---|---|---|
| Accepting the first suggestion | Very short | Low | Minimal |
| Iterative context feeding | Medium | High | Moderate |
| The small steps rule | Longer | Very high | Low |
| Forcing explanations | Medium | High | Moderate |
Above all, it is worth evaluating proposed solutions from the perspective of long-term system maintenance. I recommend applying the small steps rule in every project.
When Does AI Lower Project Quality Through Excessive Speed?
Generating code too quickly leads to a phenomenon known as “technical debt on steroids.” The developer accepts suggestion after suggestion, building layers of functionality on untested, generated foundations. As a result, a minor bug in a basic function propagates throughout the entire system.
Y Combinator points out that AI reduces production costs by 10-100x, as confirmed by MamStartup research. However, this reduction applies exclusively to well-managed projects, where every line of code undergoes rigorous verification. Speed without control is a straight path to burning through the budget on later fixes.
In my view, the biggest threat is a false sense of security. I have seen this in many projects where initial writing speed turned into weeks of debugging. The developer sees a working prototype and assumes the code quality is sufficient. This is a misjudgment.
So instead of blindly trusting generated code, treat AI as a very fast junior developer. A junior writes a lot of code, but every line requires code review.
What Mistakes Do Developers Who Over-Trust AI Make?
The most common mistake is skipping the analysis phase of the generated code. The developer sees green tests and moves on, without checking what exactly those tests verify. For example, unit tests generated by AI often only check whether a function does not throw an exception, rather than verifying the correctness of the business logic.
Another mistake is the lack of security verification. Language models are trained on public repositories that contain vulnerabilities. As a result, AI may propose code susceptible to attacks, such as SQL injection. A security review is absolutely essential.
The third common problem is ignoring performance. The model chooses the most popular solution, which is not always optimal for a given dataset. A simple sorting algorithm may work well on a hundred elements but fail on a million. I have verified this in my own projects.
The fourth mistake is blindly copying dependencies. AI often suggests adding new libraries to the project, which expands the attack surface and increases maintenance costs. It is worth limiting the number of dependencies to the bare minimum.
Why Does AI Code Review Take More Time Than Traditional Review?
Verifying code generated by AI is more difficult than reviewing code written by a human. A human developer has a defined style, known habits, and limitations that a reviewer can predict. A language model, on the other hand, generates code in an unpredictable manner.
The reviewer must check every line for correctness, performance, security, and architectural consistency. That is four areas of verification instead of the standard two. Moreover, generated code often contains hidden assumptions that the model does not communicate explicitly.
As Maciej Orlos notes on his blog, AI verification skills are becoming crucial for IT professionals in 2025. The role of the developer is evolving toward that of an architect and reviewer. This shift requires different skills than traditional coding.
It is worth spending extra time thoroughly understanding the generated code. I recommend adopting a “zero trust” policy for AI suggestions, verifying every fragment independently. This approach slows down the work but guarantees higher quality. An AI coding agent must reduce maintenance costs, not generate new problems.
Which AI Tools Work Best in Slow Writing Mode?
GitHub Copilot, Cursor, and Claude Code offer different approaches to code generation. Choosing the right tool depends on whether the developer prefers a background assistant or one that requires explicitly invoking suggestions. Research confirms that consciously using these assistants yields better results than blindly accepting proposals.
According to the MamStartup analysis, AI reduces production costs by 10-100x, but only in projects with rigorous verification. Tools like Cursor allow for iterative context refinement, which naturally slows down the process but increases quality control. Choosing the right workflow mode is more important than choosing the tool itself.
Furthermore, different tools excel in different phases of a project. Claude Code works well during architecture design. Copilot, on the other hand, shines at writing repetitive fragments. It is therefore worth matching the assistant to the task.
- GitHub Copilot – runs in the editor background, suggesting line completions and entire functions in real time
- Cursor – a code editor with built-in AI, allowing for multiple rounds of query refinement in a chat window
- Claude Code – a terminal-based assistant requiring explicit invocation and context provision
- ChatGPT / Claude – general-purpose models useful for architecture design and problem analysis before coding
- Aider – a CLI tool connecting language models with a local Git repository
| Tool | Workflow Mode | Control Level | Best Use Case |
|---|---|---|---|
| GitHub Copilot | Automatic suggestions | Low | Coding repetitive patterns |
| Cursor | Interactive chat | Medium | Iterative feature building |
| Claude Code | Explicit terminal queries | High | Architecture and refactoring |
What Strategies for Testing AI-Generated Code Are Effective?
Testing code generated by language models requires an approach that differs from verifying human-written code. AI often creates tests that only confirm the syntactic correctness of the generated function, skipping edge cases and business logic. Effective verification requires manually designing test scenarios.
As Maciej Orlos points out, AI verification skills are becoming crucial for IT professionals in 2025. The developer must design tests that verify system behavior, not just the absence of exceptions. This includes performance testing, security testing, and integration with existing components.
Therefore, every generated function requires at least three categories of tests: happy path, edge cases, and error scenarios. Moreover, tests generated by AI should be treated as a starting point, not complete documentation.
How to Build Project Architecture with AI Without Rushing?
Designing architecture with AI requires reversing the typical workflow. Instead of asking the model for implementation, start with a discussion about structure, dependencies, and trade-offs. This approach naturally slows down the coding process but produces more thoughtful system foundations that are easier to maintain.
Y Combinator notes that the 10-100x cost reduction applies only to well-managed projects, as confirmed by MamStartup research. Architecture designed with AI in slow mode requires multiple iterations. The developer describes the problem, the model proposes a solution, and then a joint analysis of pros and cons follows.
Although this process takes longer than directly generating code, it helps avoid costly rewrites. It is worth investing the time at the design stage.
- Start by describing the business problem, omitting implementation details
- Ask the model to propose an architecture with a breakdown of trade-offs
- Analyze each abstraction layer separately before implementation
- Define module boundaries and interfaces before writing the first line of code
- Establish an error handling and logging strategy at the architecture level
- Design integration tests that verify communication between modules
- Create documentation of architectural decisions with justification for each choice
- Run a load simulation of the proposed structure before implementation
What Are the Real Costs of Over-Trusting AI in Commercial Projects?
Over-trusting generated code leads to hidden costs that emerge in later phases of the project lifecycle. Architectural errors introduced by AI at an early stage can multiply the savings from rapid prototyping many times over. Debugging code that no one on the team fully understands takes far more time.
As Antyweb observes, AI works best as an assistant, and worst as an independent co-author. A developer who accepts suggestions without verification creates code that is difficult to maintain. While the initial speed is high, technical debt accumulates at an accelerating rate.
Every hour saved at the writing stage can cost ten hours at the debugging stage.
Frequently Asked Questions
Can AI completely replace a developer in writing code?
No. As MamStartup research indicates, AI reduces production costs by 10-100x, but only under the condition of rigorous human verification. Language models do not understand business context and do not analyze long-term system maintenance costs.
How much time should be spent verifying AI-generated code?
Verification should take at least as much time as generation. Every generated function requires checking the correctness of the logic, performance, security, and architectural consistency.
Which AI tools work best for slow coding mode?
Cursor and Claude Code offer the highest level of control, allowing for iterative context refinement. The choice of tool depends on the preferred interaction mode — chat in the editor or explicit queries in the terminal.
Are AI-generated tests sufficient for code verification?
No. AI often creates tests that verify only the happy path, skipping edge cases. Automatically generated tests should be treated as a starting point and supplemented with edge-case scenarios and performance tests.
Summary
- Consciously using AI for writing code requires deliberately slowing down — the speed of generating lines does not equal architectural quality.
- Every AI suggestion requires verification for correctness, performance, security, and consistency with the system architecture.
- Tests generated by AI are a starting point, not complete documentation — they must be supplemented with edge cases.
- The costs of over-trusting AI appear in later phases of the project lifecycle — debugging incomprehensible code takes far more time.
Before you start using AI in your next project, try applying the small steps rule described in this article. Pick one function, generate it with AI assistance, and then spend an equal amount of time on thorough verification. Compare the code quality and debugging time with the traditional approach. The result will show whether a slower pace of working with AI delivers tangible benefits in your specific case.