The Rise of Vibe Coding: Why Speed Shouldn't Come at the Cost of Cognitive Debt

We are in the middle of the fastest acceleration in software development that the industry has ever seen.

Thanks to highly capable models from technology leaders like Anthropic and OpenAI, we have entered the era of vibe coding—a world where developers describe what they want in natural language and get working software in return. 

While this sounds liberating and exciting, and can produce incredible efficiency in the hands of a senior developer, it introduces serious problems when scaled across an enterprise.

Cognitive debt is what happens when your team doesn't fully understand the code it produces. It's a form of knowledge debt.

When a developer codes by hand, they intuitively build a mental model of how the code works. When a developer uses AI to generate code, that mental model isn't built—at least not to the same extent.

Sometimes your developers will understand the code. Sometimes they won't. Sometimes they'll say they do, but they don't. Sometimes they will think they do, but they still don't.

AI-generated code can be beautifully written and highly efficient. But that doesn't matter if no human understands it.

We've seen versions of this problem before, like when a knowledgeable developer leaves an organization and a new team inherits an unfamiliar codebase. That was usually the exception. But with vibe coding, it becomes the rule.

Scale that knowledge gap across the enterprise and it quickly becomes a governance issue—and a security one.

You might assume that as AI models improve, this problem will disappear. In reality, better models make cognitive debt worse. 

As agentic coding models become more capable, reviews might be perceived as unnecessary. Because the code looks good from the start, developers are more tempted to simply trust the output, bypassing reviews and never building a functional understanding of the system. So the better the models get, the more discipline is required to use them responsibly.

As the code gets better out of the gate—and there is more of it—it becomes harder and more impractical for developers to slow down and review it. 

Even models that are capable of highly impressive outcomes aren’t infallible. We’re still far from that inflection point. And it will be hard to truly know when we actually get there.

The gap between an AI that makes coding mistakes two percent of the time and one that makes mistakes zero percent of the time is an ocean.

When you build enterprise software, functional requirements are only part of the story. Screens, workflows, and business logic are important. But beneath the surface is a massive body of non-functional requirements that determine whether an application is truly production-ready, like:

• Security and compliance

• Scalability

• Auditability and logging

• Safe data connectivity and backwards compatibility

AI coding tools are excellent at generating functional pieces. But by default, they do nothing for these crucial non-functional requirements. 

Without a skilled, attentive developer ensuring non-functional requirements are properly addressed, vibe coding can produce applications that give the dangerous illusion of being production-ready while being full of hidden liabilities.

For example, a user can upload a basic requirements document for an employee onboarding system directly into Composer. AI instantly analyzes the requirements and generates a collaborative plan including:

• Personas: Defined users and role-based security groups

• Processes: Orchestrated BPMN workflows and logical gates

• Screens: Dashboards, forms, and record summaries

• Business rules: Reusable expressions for important business logic

• Data models: A comprehensive specification with defined relationships for the data fabric

When you use AI development tools inside a managed platform, you don’t get raw code that nobody understands. You get standardized, maintainable platform objects with built-in governance and compliance.