Are AI-Built Apps Scalable? The Risks and Realities of Vibe Coding Platforms Like Lovable

 
AI Built apps
 

Fast to build doesn’t always mean ready to scale.

The rise of vibe coding-AI-powered platforms that translate natural language prompts into functional code has sparked excitement about democratizing app development. Tools like Lovable.dev promise to build apps “20x faster” than traditional methods, enabling non-developers to create software with minimal coding knowledge.

But as businesses consider adopting these platforms, critical questions arise: Are AI-generated apps truly scalable? And what risks lurk beneath the surface of this innovation?

Let’s explore the scalability challenges and hidden dangers of relying solely on AI for app development.

What is Vibe Coding?

Vibe coding leverages large language models (LLMs) to generate code from plain English prompts.

Platforms like Lovable allow users to describe features (e.g., “A fitness app with workout tracking, social sharing, and calorie logging”), and AI handles the technical implementation.

This approach accelerates prototyping and lowers barriers to entry, but it also shifts responsibility for code quality, security, and scalability to the AI itself.

Scalability: Can AI-Built Apps Handle Growth?

The Promise

  • Rapid prototyping: Lovable’s collaboration with Supabase demonstrated that a functional event management app could be built in just one hour, complete with real-time updates and error handling.

  • Cost-effective for simple apps: Basic applications (e.g., landing pages, CRUD apps) scale smoothly in low-complexity scenarios.

  • Agile iteration: AI suggests improvements and debugs code in real time, streamlining updates.

The Reality

Complexity bottlenecks:

  • AI struggles with custom business logic (e.g., unique pricing algorithms, multi-tiered user permissions).

  • Sclable architectures (e.g., microservices, load balancing) often require manual optimization.

  • As noted by Trickle.so, AI-generated apps are often “60-70% solutions,” needing developer intervention for production readiness.

    Performance issues:

  • AI-generated code may lack efficiency in database queries or API calls, leading to slowdowns as user traffic grows.

  • Tools like Lovable prioritize speed over optimization, risking technical debt.

    Integration challenges:

  • While Lovable integrates with platforms like Supabase, custom third-party APIs or legacy systems may require manual coding.

Hidden Risks of Vibe Coding

Security Vulnerabilities

AI-generated code inherits risks from its training data, which may include insecure patterns:

  • SQL injections: Malware.news found that AI tools often produce code without proper input sanitization.

  • Over-permissioned access: AI agents may grant excessive system privileges, creating attack surfaces.

  • Compliance gaps: GDPR, HIPAA, or PCI-DSS requirements are rarely baked into AI outputs, leaving sensitive data exposed.

Technical Debt and Maintenance

  • Black-box code: AI-generated logic can be hard to audit or modify, especially for non-developers.

  • Platform dependency: Apps built on proprietary platforms risk obsolescence if the vendor changes pricing, features, or shuts down.

  • Update challenges: AI tools may not seamlessly handle framework or library updates, forcing costly rewrites.

Over-Reliance on AI Prompts

  • “Garbage in, garbage out”: Vague prompts lead to flawed outputs. For example, requesting “user authentication” might generate insecure password storage.

  • Skill erosion: Teams may lose coding expertise, leaving them unprepared to troubleshoot or optimize.

Ethical and Legal Concerns

  • IP ownership: Who owns AI-generated code? User, platform, or model trainer?

  • Bias amplification: AI may replicate biases in training data, leading to discriminatory features.

Case Study: When Vibe Coding Works (and When It Doesn’t)

  • Success: A startup used Lovable to prototype a meal-planning app in days, validated demand, and secured funding.

  • Failure: A healthcare platform built with AI-generated code failed compliance audits due to insecure patient data handling, requiring a full rebuild.

Best Practices for Scaling AI-Built Apps

  • Start small: Use AI for MVPs and prototypes, not mission-critical systems.

  • Audit rigorously: Partner with developers to review code for security, efficiency, and compliance.

  • Plan for handoff: Budget for refactoring AI-generated code into scalable architectures.

  • Prioritize governance: Implement policies for AI tool usage, data privacy, and risk assessment.

The Verdict

Vibe coding platforms like Lovable are revolutionary for speed and accessibility, but they are not a silver bullet. While simple apps can scale with careful oversight, complex projects demand hybrid approaches-combining AI agility with human expertise.

As Jason Vanzin, CISSP and CEO of Right Hand Technology Group, warns:

“AI-generated code is a powerful accelerator, but without guardrails, it’s a recipe for long-term risk.”

In 2025, the most successful teams will treat AI as a collaborator, not a replacement, ensuring scalability and security go hand in hand.


Curious how to build AI-driven apps that scale without the risk?

Book your free session and we’ll help you prototype your first custom AI agent, live.

 
 
Software DevelopmentAlejandro ZakzukSMBs, SMEsComment