Why AI-Generated Code Might Cost You More Than You Think

The AI code generation tools market is expected to reach $19 billion by 2030, growing at a CAGR of 24.4%. As these tools become increasingly popular among development teams and people with limited technical skills, many businesses are rushing to implement AI-generated solutions to cut costs and accelerate development. But is this approach actually saving money or creating hidden expenses?

At Che IT Group, we've worked with numerous clients who've tried the AI-first approach, and our findings might surprise you.

The AI Development Promise

AI tools have undeniably revolutionized certain aspects of web development, delivering impressive results in specific areas:

• Transforming screenshots and mockups into interface templates. There are several AI development tools (in particular, Uizard, Builder.io in conjunction with GPT-4 Vision, or Locofy.ai) that can automatically recognize the UI structure in an image and turn it into HTML/CSS or code for React, Next.js, Flutter, etc. This saves designers hours of work at the prototyping stage.
• Adapting templates for specific tasks. AI can quickly solve repetitive tasks, such as renaming variables, changing styles, sharpening individual interface elements, etc. – for example, GitHub Copilot and CodeWhisperer have such capabilities.
• Developing test scenarios. AI can help generate unit and integration tests. There are plugins (CodiumAI, TestGPT, Refact.ai, and others) that, based on the component's source code, create a test with correct assertions and offer edge-case scenarios (sometimes directly in the IDE).

Over the past year, many development teams have used these tools – not to replace technical experts but to save time writing code from scratch. Hence, their clients achieve cost efficiency in their projects and have the opportunity to launch them earlier than their competitors, seeing genuine benefits in speed and efficiency.

The Hidden Reality: Where Costs Actually Come From

Now, let's dig deeper and consider the insights kindly shared by our Tech Lead, Oleh Honcharuk.

Oleh Honcharuk is Tech Lead at Che IT Group with over 8 years of experience in web development, specializing in WordPress and backend technologies. 

The 70% Reality Check

"From a technical perspective, AI-generated frontend code today achieves a level sufficient for creating basic interfaces, prototypes, or MVPs", Oleh explains. "However, here's what most clients don't realize: they expect AI to reduce development budgets by up to 50%, but developers often spend up to 70% of their time refactoring AI-generated code to meet production standards".

This isn't because AI is inherently bad – it's because of the approach.

The core issue is that AI doesn't understand your project's specifics regarding code quality requirements. In addition, it doesn't build the architecture, synchronize the frontend and backend, and, ultimately, follow the code style adopted by the project team. As a result, such a frontend may look normal at first glance, but problems will still appear when integration with API or business process logic is needed.

Where AI Code Falls Short

Based on our Tech Lead's experience, here are the most common issues that drive up costs:

• AI often generates code without considering scalability, reusability, or architectural logic, resulting in either overly complex or oversimplified solutions that require extensive refactoring;
• When code is generated in multiple stages or with different prompts, you get a mix of incompatible approaches that complicate maintenance and readability;
• While output may look acceptable initially, it often lacks consideration for edge cases, various screen sizes, and adaptive design principles;
• When UI elements require interaction, the logic may be duplicated or implemented superficially without considering scaling or refactoring needs;
• "When one person generates code using AI and another must integrate it with the backend, difficulties almost always arise", Oleh explains. "Without established team standards, integration becomes a time-consuming puzzle".

The Expensive Misunderstanding

Here's where costs escalate: many clients approach AI code generation without proper technical guidance. They expect to hand off AI-generated frontend to developers and save 50% on development costs. In reality, the integration process often requires:

1. Complete code audit to understand the structure;
2. Refactoring to match team standards and architectural patterns;
3. Fixing compatibility issues with existing systems;
4. Implementing missing functionality that AI couldn't handle;
5. Adding proper error handling and security measures.

"Sometimes it's actually faster to generate everything from scratch with proper professional guidance than to understand and fix a structure that doesn't meet any standards", Oleh notes.

But Here's the Plot Twist: When AI Actually Saves Money

The issue isn't AI itself – it's the approach. When used correctly, AI can genuinely reduce costs and accelerate development. Here's the key difference:

Professional-Guided AI vs. DIY AI

DIY Approach (Often More Expensive):

• A client or non-technical person generates code;
• No architectural planning;
• Generic prompts without technical specifications;
• Requires extensive professional cleanup.

Professional-Guided Approach (Actually Cost-Effective):

• Technical experts create detailed prompts;
• AI is used for specific, well-defined tasks;
• Generated code follows team standards;
• Integrated into established workflows.

When AI Becomes Cost-Effective 

"AI's current level is comparable to a junior developer on any popular framework", Oleh observes. "It knows syntax, understands typical practices, and can generate functional code. When properly directed by experienced developers, it significantly accelerates development".

• Security considerations: contrary to common concerns, AI can generate more secure code than some developers when the prompts specify security requirements. "If you indicate validation, sanitization, and protection requirements, AI can generate code following security best practices", Oleh explains.
• Framework compatibility and standards: AI works well with React, Vue, Angular, and other frameworks when given proper context. For cross-browser compatibility, accessibility, and SEO: "Everything depends on the prompt. AI can handle these requirements when clearly specified – often better than novice developers who might not understand accessibility standards or SEO markup specifics".

‍The Strategic Approach: Maximizing AI Value

Strategic AI use can reduce frontend development costs for:

• Creating prototypes and MVPs;
• Simple landing pages;
• Basic interface templates;
• Routine coding tasks within established patterns.

The Cost-Effective Framework

For AI to actually save money, you need:

1. Technical experts writing prompts who understand architecture and requirements
2. Established team standards that AI output can follow
3. Professional oversight throughout the process
4. Clear integration workflows

"In our company, we use standard libraries for modal windows and sliders, and all developers know which classes to add and in what sequence", Oleh explains. "When AI generates code following these same patterns, integration becomes seamless".

The Real Answer: It's Not About the Cost

After working with dozens of AI-generated projects, here's what we've learned: the question isn't whether AI code costs more or less – it's whether you're using AI strategically or reactively. 

Reactive approach: "AI is cheap, let's use it to save money", → Often results in higher total costs due to rework.

Strategic approach: "AI is a powerful tool that can accelerate our established processes", → Genuinely reduces costs while maintaining quality.

Essential Pre-Implementation Checklist

Therefore, before relying on AI code generation, you need to understand:

If the vast majority of answers to these questions are “no”, most likely, you will waste your time and energy working with AI in vain, and the team you hired will have to redo everything from scratch – of course, if you want to get a good result.

Future of AI in Web Development

We cannot underestimate the benefits of AI frontend development. However, AI still cannot completely replace developers and other experts. 

It cannot understand the progress and tasks of development, isn’t responsible for the result, makes mistakes, and can fix them only with correct comments from experienced specialists.

EXPERT INSIGHT: The future lies not in choosing between AI and professional developers, but in finding the right balance where AI enhances human expertise rather than replacing it.

Conclusion: The Surprising Truth

So, does AI-generated code cost more than you think? It depends entirely on your approach.

When used as a shortcut by non-technical teams without proper planning, AI code often becomes more expensive than traditional development due to extensive rework requirements. However, when integrated strategically into professional workflows – with proper prompts, team standards, and technical oversight – AI becomes a powerful accelerator that genuinely reduces costs while maintaining quality.

AI code can either be your biggest development expense or your most valuable efficiency tool. The difference lies in having the right strategy and professional guidance from the start.

Ready to implement AI code the right way? Contact us for a free consultation, and our tech experts will show you how to maximize AI benefits while avoiding the costly pitfalls.