The Challenges of Using AI coding Tools to Build a Scalable Product
MVPs are being built faster than ever using AI-powered tools. The concept of vibe coding and prompting your way up to your first prototype has changed the software development game for good. These tools assist with code generation, debugging, and automation, making them an asset for founders and small teams.
While AI coding tools can speed up the initial development phase, long-term sustainability remains a challenge. Many developers and startups have reported encountering significant roadblocks when scaling AI-generated codebases. Common issues include unstructured code, technical debt, and security vulnerabilities.
The question is: Can AI-generated code stand the test of time and build sustainable products without a tech team behind?
The Rise of AI Coding Tools
AI-powered coding tools are in everyone’s mouth today thanks to their versatility and how easy they are to use, even if you don’t know how to code. However, the first iterations were supposed to be code assistants for software engineers.
Today, some of the most common and used AI coding tools are:
- GitHub Copilot – Uses OpenAI’s Codex to suggest code snippets and functions.
- Cursor – AI-assisted development environment for debugging and code completion.
- Bolt – An AI pair programmer that helps write and refactor code.
These tools allow founders to prompt fully functional prototypes into existence. While developers may use them to improve their daily output and code production, technically inexperienced founders can have a go at creating whole codebases from scratch.
Challenges of Relying on AI for Code Generation
Code Quality and Maintainability
One of the biggest concerns with AI-generated code is its lack of structure and maintainability. AI coding assistants often generate redundant or inefficient code. While it may not affect the operability of the prototype in the early stages, it can severely impact the product in the long run, especially when adding more functionalities.
This lack of structure ultimately makes it harder for teams to manage and scale applications. While whipping out a prototype with prompts is definitely an advantage, it doesn’t guarantee the long-term reliability of the product.
According to a report from LeadDev, AI-generated code has led to a significant rise in duplicate lines of code, violating the DRY (Don’t Repeat Yourself) principle. The report states:
“In 2024, we saw an eightfold increase in code blocks with five or more duplicated lines. This redundancy leads to bloated codebases that are difficult to maintain.”
Technical Debt Accumulation
While AI helps create functional MVPs quickly, it often accelerates technical debt. Many AI-generated solutions prioritize immediate functionality over long-term efficiency. Technical debt is not necessarily a bad thing. Experienced teams can make informed decisions and prioritize tasks and actions in favor of results.
However, these decisions aren’t taken lightly. The technical leaders of the projects know there’s a tradeoff, and they consciously accept it. The other problem is that AI technical debt is, to a large extent, unknown. AI is making decisions in order to favor velocity and functionality without necessarily informing the prompter of the hidden costs they are paying for it.
Software architect Kin Lane highlights this problem:
“I don’t think I have ever seen so much technical debt being created in such a short period of time.” (LeadDev)
As a result, companies that rely heavily on AI-generated code often find themselves refactoring large portions of their applications later, increasing costs and slowing down product development.
Security Vulnerabilities
AI-generated code is not always optimized for security, and in many cases, it introduces vulnerabilities that can be exploited. A 2023 study by Stanford University found that AI-assisted code often lacks proper input validation, making applications more susceptible to attacks like SQL injection and cross-site scripting (XSS).
Cybersecurity expert Jeff Wilcox warns:
“Developers using AI-powered tools must rigorously test and review AI-generated code, as it frequently overlooks critical security best practices.”
Scalability Issues
Building a scalable product requires well-architected software with modular and reusable code components. AI-generated code often lacks foresight in terms of architecture, leading to scalability issues down the line.
While vibe coding and AI coding tools are everywhere for their incredible benefits, a quick online search can lead you to the other side of the story. Many founders report having to rework on their products in order to make it production ready after building most of it with AI tools.
A Reddit user shared their experience:
“We built our MVP with Copilot, and while it worked fine initially, scaling it became a nightmare. We had to rewrite entire modules because the AI-generated code was not designed for extensibility.”
This highlights a recurring theme: AI tools can help get a project off the ground but may not provide the best foundation for long-term growth.
When you build a software product with a team, there are scope and reach considerations in place. You are building a product with a deadline for release, but you also know that the product doesn’t end there.
A successful product needs to have multiple iterations well into the future. AI code might not account for these needs, making it shortsighted and ultimately, a liability for the long-term viability of the product.
Case Studies: When AI-Generated MVPs Fail
GitHub Copilot’s Impact on Developer Productivity
A study conducted by Harness examined the impact of GitHub Copilot on developer productivity over several months. In the initial two months, 50 developers worked without Copilot’s assistance. Subsequently, they integrated Copilot into their workflow, aiming to measure changes in the number of pull requests and cycle time.
While the integration of Copilot led to an increase in code generation speed, it also introduced challenges related to code quality and maintainability. Developers noted that while Copilot provided quick solutions, it sometimes suggested code that was not optimal or required significant refactoring, leading to concerns about long-term scalability.
AI-Powered Debugging Tools Introducing New Issues
AI-driven debugging tools are designed to automate the identification and resolution of code errors. However, while these tools can expedite the debugging process, they may also introduce new issues if developers accept AI-generated suggestions without thorough scrutiny. It is key to critically evaluate AI-provided fixes to ensure they do not inadvertently introduce additional bugs or security vulnerabilities.
Additional Research on AI-Generated Code Challenges
A 2024 study by Google’s DORA (DevOps Research and Assessment) found that teams relying on AI-generated code reported a 7.2% decrease in delivery stability, indicating that while AI accelerates development, it may introduce instability that affects overall software quality.
Furthermore, the State of Software Delivery 2025 report by Harness found that developers are spending more time debugging AI-generated code and resolving security vulnerabilities, highlighting the trade-offs between speed and long-term reliability.
Balancing AI Assistance with Human Oversight
Despite these challenges, AI coding tools remain valuable when used appropriately. The key is balancing AI assistance with human oversight. Here’s how:
- Implement Rigorous Code Reviews – Always have experienced developers review AI-generated code.
- Follow Best Practices – Enforce coding standards to ensure quality and maintainability.
- Prioritize Security Audits – Regularly test AI-generated code for vulnerabilities.
- Refactor Early – Address technical debt before it becomes unmanageable.
While AI prototypes can be a great way to validate ideas and test your product with real users, they aren’t suitable for the long run. The experience of a software development team cannot be replaced with some prompts. At least not yet.
At Bixlabs, we have a set of true and tested coding best practices that allow us to build scalable products and set them up for success. Our 10 years in the industry allow us to find creative solutions and integrate AI-powered tools to combine the best of innovation with experience.
AI Tools Are a Starting Point
If you’ve built an MVP using AI tools and are now facing scalability issues, Bixlabs can help. Our expert software development team specializes in refining, scaling, and optimizing AI-assisted projects to ensure they stand the test of time.
Ready to take your AI-powered MVP to the next level? Contact Bixlabs today and let’s build something sustainable.