AI Eats the World: New Technical Debts in the Era of Vibe Coding

Accelerated Software Development Pace and Quality Challenges

When Marc Andreessen declared that "software is eating the world," few imagined that software would be created – and then rewritten – by AI. Today, AI accelerates the pace of software development, but not necessarily its quality. This paves the way for a new era of technical debt.

In 2024, developers produced over 256 billion lines of code using the latest AI tools, and this number is expected to double this year. Generative AI has become a fundamental component, with Microsoft recently indicating that 30% of its code is written by AI, and this number is continuously increasing. It enables developers to write, test, and refactor code at a pace unimaginable just a few years ago.

But behind this leap in productivity lies a worrying truth: AI not only fails to solve technical debt but contributes to its widespread accumulation.

"Vibe Coding" and its Hidden Risks

We have entered the era of "vibe coding" (vibe coding), where developers input requests into large language models (LLMs), then review suggestions and assemble functional solutions – often without a full understanding of the internal processes. It's a quick and smooth process, but it carries significant and unclear risks.

This new class of code may seem efficient, but it often fails in production. Fundamental engineering practices – such as architectural design, runtime standards, and comprehensive testing – are typically overlooked or delayed.

The result: a massive influx of unreliable, poorly performing code inundating corporate systems. Generative AI is not merely a productivity tool; it's a new abstraction layer that hides engineering complexities while introducing familiar risks.

The AI Paradox: Solving Old Problems and Generating New Ones

Paradoxically, AI also helps address existing technical debt: it contributes to cleaning up legacy code, identifying inefficiencies, and facilitating updates. In this sense, it's a valuable ally.

But here lies the paradox: while AI solves old problems, it generates new challenges. Many models lack enterprise context. They don't account for infrastructure, compliance, or business logic. They cannot evaluate real-world performance and rarely verify outputs unless explicitly prompted, and few developers have the time or tools to enforce this. The result? A new wave of hidden inefficiencies, massive computational resource consumption, unstable code paths, and fragile integrations – all occurring at an accelerating pace.

Viability as the New Standard: Beyond Speed to Efficiency

Delivering code quickly no longer guarantees a competitive advantage. What matters now is viability: Can the code scale, adapt, and endure over time? Much of generative AI's output focuses on getting from zero to anything. Enterprise code must operate within its context – under pressure, at scale, and without incurring hidden costs. Teams need systems that verify not just correctness but performance. This means reintroducing engineering rigor, even with accelerating generation speeds.

Viability has become the new standard. It requires a shift in mindset, from fast code to fit-for-purpose code.

AI-Powered Software Engineering: Verification and Scrutiny

This shift reinforces a quiet return to data science fundamentals. While large language models generate code from natural language, it's verification, testing, and benchmarking that determine if code is production-ready. There's a renewed focus on engineering prompts, contextual constraints, evaluation models that assess outputs, and continuous refinement. Companies realize that generative AI alone isn't enough – they need systems that subject AI outputs to real-world scrutiny quickly and at scale.

Generative AI has changed how we produce software, but it hasn't changed how we verify it. We are entering a new phase that demands more than fast code. What we need now is a way to evaluate outputs across competing objectives – performance, cost, maintainability, and scalability – and to determine what is fit for the real world, not just a test case. This isn't just about prompt refinement or a return to old data science textbooks. It's a new kind of native AI engineering – where systems integrate evaluation, benchmarking, human feedback, and statistical thinking to guide outputs toward viability.

The ability to develop, test, and refine AI outputs at scale will define the next wave of innovation in software development.

The Cost of Neglecting the Shift: Increasing Debt and Slowing Innovation

What's at stake? Ignoring this transformation comes at a cost: higher cloud bills, unstable code in production, and slower delivery due to rework and debugging. Worse still, innovation slows – not because teams lack ideas, but because they are buried under piles of inefficiencies generated by AI.

According to LeadDev (published February 19, 2025), recent reports show an increase in code duplication and a decline in quality as AI programming tool popularity grows. Growth Acceleration Partners (published July 17, 2025) also indicates that technical debt accumulated from development shortcuts leads to poor code quality, inefficient algorithms, and inadequate infrastructure, increasing resource consumption and costs. Excess or unused code can contribute to higher cloud storage costs and prolonged development cycles.

The Future of Rapid Verification: Towards Optimal AI Utilization

To fully leverage AI in software development, we must move beyond mere "good feelings" and focus on viability. The future belongs to those who can generate quickly and verify even faster. Successful teams will subject their AI-powered outputs to rigorous engineering scrutiny, balancing not just what AI can generate, but using their expert judgment to determine if it's fit for the task.