What’s The Problem With Vibe Coding? [Honest Review]

Vibe coding is fast, fun, and dangerously easy to get wrong. Before you adopt it as your default workflow, read this article to the end – you might rethink how much you trust that glowing autocomplete.

• Are you wondering if vibe coding can scale beyond toy projects?
•  Is it just a new buzzword for undisciplined development?
• What happens when AI writes code you don’t understand?
• Can you ship serious software this way?

“We need to create code that demonstrably works, can be understood by other humans, and will support continued development… Software engineering is all about trade-offs.” –  Anonymous senior engineer, shared widely in AI/dev communities.

The problem is real, and growing. As AI coding tools like GitHub Copilot and ChatGPT become mainstream, more developers are using them without fully understanding the risks. Studies have shown that AI-assisted code can introduce subtle bugs, increase security risks, and create maintenance challenges (Rice University, 2023; Stanford, 2022). What started as a productivity boost is now creating fragility in real-world codebases.

Why trust me? I have experience working with AI-generated code in production SaaS and enterprise systems, as part of a team that develops an AI-powered software generation tool. I’ve witnessed both the benefits and the technical debt firsthand.

By reading this article, you’ll understand where vibe coding shines, where it breaks down, and what guardrails you need to adopt if you want to build real software without creating a mess. No hype, no marketing – just an honest breakdown.

What Is Vibe Coding?

Vibe coding is a new programming approach where, instead of writing code manually in a programming language, a developer (or even a non-developer) interacts with an AI, typically a large language model (LLM), to generate the code​. In practice, this means describing what you want the software to do in natural language (e.g. English), and letting the AI produce the actual source code. The term “vibe coding” was introduced by AI researcher Andrej Karpathy in early 2025​. He described it as “fully giving in to the vibes, embracing exponentials, and forgetting that the code even exists”​. In other words, the programmer’s role shifts from typing out every line of code to guiding the AI with ideas, prompts, and testing/feedback cycles​.

Vibe Coding Vs. Traditional Development

In a traditional software development workflow, a programmer designs a solution, writes code in a specific language, debugs it, and maintains full understanding of the program’s logic. By contrast, vibe coding is more conversational and improvisational. The human provides high-level instructions (“the vibe”) and the AI does the “heavy lifting” of coding the details​. For example, instead of manually coding a web form, a vibe coder might say to an AI, “Create a simple web page with a form for users to sign up and validate their email address,” and then iteratively refine the AI’s output. Karpathy explained, “It’s not coding – I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works.”​

This illustrates how vibe coding feels more like having a conversation with an AI assistant about what the program should do. In contrast, traditional coding requires directly writing and reading code line by line.

Another key difference is the level of abstraction. Traditional development demands precision in syntax and deep knowledge of frameworks/APIs. Vibe coding abstracts much of that away. The “hottest new programming language is English,” one commentator quipped, since describing the program in plain English can be as effective as writing the code yourself. Modern AI coding assistants (like OpenAI’s ChatGPT, GitHub Copilot, Replit’s Ghostwriter, Cursor, etc.) are powerful enough to translate natural-language prompts into code that often works. This means people who aren’t fluent in any programming language can create software by “chatting” or even speaking to an AI about the desired functionality​. Essentially, vibe coding flips the traditional script: instead of humans telling the computer how to do something via code, they tell an AI what they want, and the AI figures out how.

Benefits of Vibe Coding (Speed, Creativity, Iteration)

Speed and Productivity

At its core, vibe coding is about compression of time, of effort, of cognitive load. When you’re not hand-crafting every function and component, you move faster. Much faster.

LLM-assisted coding operates “an order of magnitude faster” than skilled humans. This isn’t just about typing speed. It’s about skipping:

• Setup boilerplate
• Syntax and imports
• Endless Stack Overflow threads
• Googling the right way to debounce a button

You go from feature idea → implementation → test in one uninterrupted flow. What used to take two days now takes two hours.

This is especially powerful for:

• Early-stage startups: where speed = survival
• Hackathons: where hours matter
• Internal tools: where polish is secondary to function

And when AI handles the mundane, your time-to-insight shrinks. You can test 5 ideas before lunch instead of 1 per sprint.

Iterative Creativity and Real-Time Development

Vibe coding turns software development into a live conversation. You prompt, test, and revise. Rinse and repeat. This unlocks an entirely different style of engineering:

• Less architectural paralysis
• Fewer speculative over-engineering traps
• More “what if we just…” moments

Instead of drawing a full spec upfront, you can explore by doing. The loop becomes:

“Let’s try this → didn’t work → let’s nudge it → ah, better → push live.”

It’s like having a whiteboard session with a co-pilot who can code.

This fosters a creative atmosphere usually reserved for art or music:

• Try unconventional UI layouts
• Mix and match patterns
• Rapidly simulate user flows

For side projects or prototypes, this style is addictive. For production apps, it’s a superpower – if paired with proper constraints.

Accessibility

Here’s the quiet revolution: Vibe coding makes software creation accessible to non-programmers.

Designers, marketers, domain experts – they’ve always had the ideas. Now they have a path to implementation:

• No more begging devs for internal dashboards
• No need to wireframe tools just to get buy-in
• You can build the MVP yourself, and then bring in engineers

This is what “democratizing software” looks like in practice.

For example:

• A startup founder with no tech background can describe an app and get a working prototype in hours
• A logistics manager can build an internal inventory tracker without IT help
• A doctor can create a symptom-tracking tool without a dev team

It’s not about replacing developers – it’s about unlocking agency for the rest of the world.

Developer Focus

Most code isn’t genius. It’s grunt work. Scaffolding, boilerplate, validation, list rendering… rinse, repeat. Vibe coding lets you skip that. AI handles:

• CRUD operations
• Form UIs
• State handling and routing
• Repetitive API wrappers

This leaves developers free to:

• Make architectural decisions
  Focus on user experience
• Solve hard, domain-specific problems

You get the equivalent of a lightning-fast junior dev who doesn’t get tired or complain. It outputs code drafts you can quickly review, tweak, and integrate.

This is especially useful in codebases where:

• You need to add dozens of similar pages
• You’re building admin panels or dashboards
• You’re evolving fast and want to avoid tech debt

You don’t lose control – you gain leverage.

Serendipity

Sometimes, the AI surprises you.
You ask for a blog feature, and it adds pagination and search, without being prompted. That’s not magic, that’s training on massive code corpora.

This kind of unexpected value makes the AI feel like a collaborator, not just a tool.

You might see:

• Better naming conventions
• More performant solutions
• Suggestions for UX improvements
• Even security practices you forgot

It’s like brainstorming with someone who’s seen every codebase on GitHub.

The key is to stay in the loop. Review everything. Guide it. But let it surprise you. That’s where the magic happens.

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Finally, vibe coding’s high-level approach can spark creative solutions that a developer might not think of. An AI trained on millions of code examples might generate an unconventional but valid approach to a problem, or suggest features/improvements unprompted. For instance, one developer experimenting with vibe coding found that the AI not only built the requested blog functionality but even added extras like pagination and a search feature without being explicitly told​. This kind of serendipitous creativity is seen as a plus – the AI can act as a brainstorming partner, offering ideas from the “vibes” you give it. Overall, the promised benefits of vibe coding include speed, ease of use, creative freedom, rapid iteration, and broader accessibility, which together paint it as a revolutionary boost to developer productivity and innovation.

Core Problems and Risks of Vibe Coding

AI-assisted coding – often referred to as “vibe coding” – can significantly accelerate development, especially in the early phases of a project. However, this method introduces serious risks related to code quality, maintainability, and security. Below is a structured breakdown of the key issues.

Lack of Code Comprehension

One of the primary concerns is developers accepting AI-generated code without fully understanding its logic. This detachment from the underlying implementation makes it difficult to maintain or debug the system later.

In many cases, developers are left with a codebase they cannot explain, adapt, or audit effectively.

This poses obvious risks during onboarding, code reviews, and incident response. Software becomes fragile not because it doesn’t run, but because no one understands why it runs.

Technical Debt and Architectural Incoherence

AI-generated code is often functional, but not necessarily well-structured. Unless explicitly guided at every step, the AI will:

• Mix coding styles
• Skip abstraction and modularity
• Bypass performance or scalability concerns

Without ongoing review and refactoring, this leads to significant technical debt. In client-facing work, we’ve seen projects where the AI-produced scaffolding had to be largely rewritten just to enable future feature additions.

In many enterprise contexts, this accumulation of poor structure eventually leads to rewrites or painful migrations, negating the initial time savings.

Unreliable Debugging and Trial-and-Error Loops

Language models can hallucinate logic or introduce subtle bugs. When something breaks, inexperienced developers may fall into a pattern of:

• Prompting the AI repeatedly with slight variations
• Accepting unverified fixes
• Failing to trace the root cause manually

This “trial-and-error loop” is not sustainable. Unlike traditional debugging, where developers analyze call stacks, state transitions, and system logs, vibe coding often reduces the process to guesswork. This undermines reliability and reproducibility.

Inconsistency Across Sessions

AI-generated code varies depending on context, prompt phrasing, and session history. Over time, this creates:

• Divergent implementations for similar features
• Conflicting dependencies
• Inconsistent naming conventions and architecture

For example, we’ve seen projects where one API endpoint used RESTful patterns, while another (written later) used GraphQL, simply because the prompt changed slightly.

In multi-developer environments or long-running projects, these inconsistencies introduce friction and slow down delivery.

Security and Performance Risks

A critical weakness of current AI tools is their lack of domain-specific risk awareness. They do not reason about:

• Input validation and sanitization
• Rate limiting and abuse prevention
• Secure storage of secrets
• Least privilege principles in authentication/authorization

A real-world incident involved an AI suggesting hardcoded credentials in a public GitHub repository, which led to API key leakage. This was caught by a senior engineer, but in many cases, such flaws go unnoticed until exploited.

Performance also suffers. AI-generated algorithms are often unoptimized, and no profiling or load testing is done by default. Code that performs fine with 10 users may collapse under 10,000.

Skill Degradation and Over-Reliance

Long-term reliance on AI for coding and debugging can erode core engineering skills:

• Reading and reasoning about code
• Understanding systems architecture
• Thinking through failure modes and edge cases

If developers stop engaging with these aspects directly, their ability to solve complex problems independently diminishes. This creates a fragile team dynamic where productivity is artificially high in the short term but collapses when issues require human insight.

Vibe Coding vs. Structured Development Practices

While AI-assisted “vibe coding” is gaining popularity for its speed and accessibility, it stands in sharp contrast to established software engineering methodologies such as Test-Driven Development (TDD), Agile, and Domain-Driven Design (DDD). These approaches differ not just in execution but in their underlying philosophy.

Proactive vs Reactive Development

Structured development is fundamentally proactive. In methodologies like TDD, tests are written before any implementation. This ensures:

• Code correctness from the outset
• Confidence in future refactoring
• Early consideration of edge cases

In contrast, vibe coding is reactive. Developers interact with an AI to produce working features and may or may not add tests later. When tests are written after the fact – often by the AI – they tend to expose previously hidden flaws, requiring multiple cycles of regeneration and patching.

This reactive loop sacrifices predictability. Instead of preventing bugs, it focuses on identifying and fixing them after the fact, increasing the risk of regressions.

Architecture vs Feature-First Thinking

Structured methods like DDD prioritize the domain model and architecture. Teams invest in:

• Clear boundaries between components
• Shared understanding of business concepts
• Long-term maintainability

Vibe coding is typically feature-driven. Developers prompt the AI to generate isolated functionalities (e.g., “add search to the blog”), often without broader architectural context. Without manual oversight, this can result in:

• Redundant or misaligned components
• Inconsistent abstractions
• Code that does not scale or generalize

As a result, vibe-coded applications often resemble a patchwork of disconnected solutions, while structured systems are designed for cohesion and extensibility from day one.

Code Review and Shared Understanding

In professional development teams, code reviews are essential:

• They maintain quality
• Ensure consistency
• Facilitate team-wide understanding

Structured environments uphold a clear standard: developers must be able to explain what their code does and why it exists.

In contrast, vibe coding often bypasses this practice entirely. Developers might accept dozens of AI-generated changes without inspection. One developer described this trend as:

“I don’t read diffs anymore – I just accept all.”

This undermines team norms and introduces opacity into the codebase. Some industry voices have proposed a rule of thumb:
Don’t commit code you couldn’t explain to a teammate.
When followed, this rule effectively reintroduces structure into the workflow, transforming vibe coding back into standard development with AI as a tool, not a crutch.

Decision-Making and Trade-Offs

Software engineering involves constant evaluation of trade-offs:

• Performance vs readability
• Abstraction vs simplicity
• Flexibility vs speed

Vibe coding often skips this step. The AI’s output reflects general heuristics but doesn’t optimize for your specific context unless prompted with precision. Developers may unknowingly accept suboptimal patterns – such as brute-force implementations or tight coupling – because the focus is on achieving quick output rather than thoughtful design.

Structured methodologies enforce intentionality. Every decision is made with traceability, not convenience.

Terminology Matters: Vibe Coding vs AI-Assisted Development

It’s important to distinguish vibe coding from responsible AI-assisted development. The former implies a casual, unstructured approach: generating code through prompts without verification or architectural discipline.

In contrast, AI-assisted development within structured processes:

• Includes thorough review and validation
• Integrates tests and specifications
• Maintains shared design principles

In this sense, true software engineering can include AI, but vibe coding is not software engineering unless disciplined processes are layered on top.

To crystallize the comparison, the table below outlines some key differences between vibe coding and more structured, conventional development practices:

Aspect	Vibe Coding	Structured Development
Approach	Ad-hoc & prompt-driven: Code is generated on-the-fly by an AI from natural language prompts, with minimal upfront design​ . The process is conversational and exploratory.	Planned & design-driven: Code is written by developers according to a pre-thought design or methodology (e.g. TDD, DDD), often with defined stages (design, implement, test).
Speed of Development	Very fast initial output: AI can produce large chunks of code in seconds, enabling rapid prototyping​ . Iterations are quick through prompt-and-generate cycles.	Deliberate pace: Slower to start since humans must write code and tests manually. Iterations take longer, but each step is verified (e.g., tests pass at each increment).
Creativity & Flexibility	High: Easy to try new ideas, just describe a change and see results. AI might introduce novel approaches unprompted, enhancing creativity​. Little penalty for changing direction early.	Moderate: Creative solutions emerge from human insight, and pivoting mid-development is possible but costlier. Changes require refactoring code by hand and rethinking design, so there’s more inertia.
Code Quality & Structure	Variable/Unpredictable: Quality depends on the AI’s training and how well the prompt guides it. The code may be correct, but not idiomatic or optimal. Without human oversight, architecture can be haphazard, leading to technical debt​ ​ .	Emphasized from start: Developers apply best practices, design patterns, and refactoring to ensure code clarity and proper architecture. The structure is intentional, aiming for maintainability and low technical debt.
Debugging	AI-assisted trial & error: When errors occur, the typical vibe coding response is to feed the error back to the AI or tweak prompts until it “magically” works​. Debugging is reactive and may rely on the AI to find fixes, which isn’t guaranteed to succeed.	Systematic troubleshooting: Developers use debuggers, log analysis, and reasoning to locate root causes. They understand the code, which makes it easier to pinpoint issues. Bugs are fixed by human insight (often informed by tests that catch them early in TDD).
Testing	Often omitted (or added late): In pure vibe coding, one might skip writing tests initially. The AI might generate some tests if asked, but they come after code and often after bugs appear​. Test coverage depends on user prompting rather than built-in to the process.	Integral to process: In TDD, tests are front and center. Even outside TDD, structured dev includes unit/integration tests as a standard step. Code is routinely validated against test cases, ensuring each feature meets requirements and continues to work as the system evolves.
Collaboration & Knowledge Sharing	Challenging: The code’s rationale lives in an AI chat log rather than in human minds or documentation. Teammates may find AI-authored code harder to understand if the original prompter can’t explain it​. Code reviews are tricky if the author isn’t sure how it works internally.	Team-friendly: Decisions and rationale are usually documented in code comments, design docs, or via code review discussions. Each team member writes code they understand, making it easier for others to review and modify. There’s a shared understanding of the codebase fostered by consistent practices.
Maintainability	Potentially low: Without structure and human understanding, code can be brittle. Future changes may be difficult – one might have to rely on prompting an AI to modify code that no developer fully groks, which is risky. The lack of clear architecture can make extending the system a painful exercise.	High (by design): The code is crafted to be readable and modular. New features or changes can be made by following the established design. Developers ensure they can maintain the code by adhering to principles (like SOLID, modularity, etc.). Issues are easier to fix because the team understands the system.
Suitable Use Cases	Best for prototyping, small utilities, or throwaway projects where speed is paramount and longevity is not critical​. Also useful as a learning tool or “coding assistant” for non-devs to automate simple tasks.	Best for production systems, large-scale apps, long-term projects where reliability, scalability, and clarity are critical. Any situation where code will be collaboratively developed or maintained over time benefits from structured practices.
Required Skillset	Prompt-crafting and domain knowledge: You can start with minimal programming knowledge, but you need to describe requirements clearly and recognize if output is off-track. Some coding knowledge helps to verify AI output and guide it.	Software engineering expertise: Requires understanding of programming languages, algorithms, design principles, testing, etc. Developers rely on their training and experience to write and structure code properly.

Vibe coding enables rapid prototyping, but it lacks the safeguards of structured development. When used without discipline, it can lead to fragile, opaque, and unscalable systems. For serious projects, integrating AI into intentional and reviewable workflows is the only sustainable approach.

Vibe Coding in the Context of AI Agents, No-Code, and Future Trends

Vibe coding is not an isolated phenomenon. It is part of a broader shift in how software is conceived, developed, and deployed. At the intersection of AI-assisted code generation, agent-based systems, and no-code/low-code platforms, vibe coding reflects a transitional phase toward more abstract, automated programming paradigms.

AI Code Generation and AI Agents

The emergence of large language models (LLMs) such as GPT-4 has made natural language-based code generation viable at scale. Tools like GitHub Copilot and ChatGPT allow developers to describe a task in plain language and receive functional code snippets in return. Vibe coding is a practical use case of these tools – developing software interactively by directing the AI through prompts.

This paradigm is evolving further with experimental agent-based systems such as AutoGPT, Flatlogic AI Software Development Agent, and Replit’s AI agent. These systems attempt to autonomously break down high-level goals (e.g., “build a blog platform”) into sub-tasks, write the necessary code, and iterate on the output with minimal human intervention.

The direction is clear: from AI-assisted coding to AI-led development. In such a future, a developer may act more like a product manager – setting objectives and overseeing the process – while the AI executes.

However, the current limitations of AI still require human guidance, especially in debugging, validating output, and managing system architecture. Vibe coding today remains a collaborative process rather than a fully autonomous one.

Vibe Coding vs. No-Code and Low-Code Platforms

Vibe coding shares a similar objective with no-code/low-code platforms: reducing the barrier to software creation. However, their methods differ fundamentally:

• No-code tools (e.g., Wix, Bubble, Zapier) offer visual interfaces and pre-built components, enabling non-technical users to assemble applications without writing code.
• Vibe coding, in contrast, involves producing real code through natural language interactions. It is more flexible and powerful but also more exposed to complexity and error.

Where no-code tools are limited by the capabilities of their UI components, vibe coding can theoretically generate any logic that can be expressed in code. However, this flexibility comes with trade-offs: lack of architecture, unpredictable output, and higher maintenance overhead if not rigorously managed.

We’re already seeing convergence. Some no-code platforms now integrate AI to generate custom scripts when built-in features fall short. Conversely, AI-assisted coding tools are starting to surface their output in more visual, no-code-like interfaces. The result is a blurring of boundaries, creating a spectrum from full visual design to fully code-based workflows, mediated by AI.

Democratization and Accessibility

Both vibe coding and no-code platforms are part of a broader trend toward democratizing software development. The ability for solo developers or small teams to build sophisticated systems without large engineering resources is accelerating.

Just as WordPress enabled non-developers to publish websites, AI-driven development may allow non-programmers to build full-stack applications.

This opens the door to a surge in indie projects, internal tools, and niche solutions that previously required prohibitively high development costs. However, it also introduces challenges in software quality, security, and maintainability at scale, especially when code is generated without full comprehension or oversight.

Shifting Paradigms: From Code to Specifications

Vibe coding fits within a long-standing trend in software: raising the level of abstraction. From assembly to high-level languages, then to libraries and frameworks, each step has aimed to reduce the cognitive load of software creation.

The next potential shift is toward specification-driven development, where developers describe what they want (via prompts, test cases, or constraints), and AI generates how to achieve it. Concepts like “prompt-oriented programming” are being actively researched, and future programming languages or frameworks may be optimized specifically for AI interpretability.

This trajectory suggests that vibe coding is an early instance of a new programming model – one where code generation is no longer the primary cognitive effort, but rather specification and validation.

Integration, Guardrails, and Engineering Culture

Despite its promise, vibe coding introduces unpredictability. AI-generated code does not follow deterministic rules and can produce flawed, insecure, or incoherent output if not constrained.

This creates a tension with traditional software engineering, which values:

• Determinism
• Predictability
• Testability
• Team-wide understanding

Bridging this gap will require both technical safeguards and cultural adaptation. Guardrails such as automated testing, static analysis, sandboxed environments, and AI-assisted auditing will become critical. At the same time, organizations must define clear standards around how AI-generated code is reviewed, validated, and integrated.

A likely near-term model is a hybrid workflow, where:

• AI handles 70–90% of the code generation
• Human developers focus on architecture, tests, security, and documentation

In this model, vibe coding becomes one part of a broader system – not a replacement for engineering discipline, but a tool within it.

Conclusion

Vibe coding reflects a transitional stage in software development. It moves developers toward a future where programming resembles directing intelligent agents rather than writing low-level instructions. It aligns with the goals of no-code: faster, more accessible software creation. But unlike no-code tools, it exposes users directly to the power and risk of raw code.

As AI coding tools improve, the distinction between “vibe coding” and “traditional development” may blur. Eventually, we may simply call it programming, with AI deeply embedded in the workflow.

For now, vibe coding offers unprecedented speed and flexibility, but requires deliberate structure and human oversight to be sustainable. Teams that can combine the advantages of AI-driven speed with the rigor of software engineering will be best positioned to benefit from this emerging paradigm.