In 2025, vibe coding quickly become one of the most talked-about shifts in modern software development. This approach changes not only how code is written, but who can build software and how fast ideas turn into working products.
In this article, we’ll break down what vibe coding is, how it fits into AI-assisted coding, where it shines, where it breaks down, and why it’s becoming a core pattern in AI-driven software development. We’ll also look at its real-world implications for startups, engineers, and product teams.
Vibe coding in practice
First things first, let’s start with definitions. Vibe coding is an AI-assisted programming approach where developers describe desired functionality in natural language, allowing large language models to generate most of the code, prioritizing speed, experimentation, and outcomes over long-term structure.
The term was coined by the Slovak-born AI researcher Andrej Karpathy, who describes vibe coding as “giving in to the vibes, embracing exponentials, and almost forgetting that code even exists.” At its core, vibe coding shifts the developer’s role from authoring syntax to directing intent.
Instead of:
- Designing architecture upfront
- Writing boilerplate
- Manually stitching integrations
Developers can:
- Explain what they want
- Review AI-generated output
- Iterate through feedback loops
- Move fast until something works
But don’t get me wrong: the “vibe” part isn’t about being reckless, it’s about momentum. In other words, the goal is validation, not perfection. This approach has become common alongside tools like GitHub Copilot, ChatGPT, Claude, and other LLM-based coding assistants, making AI-assisted coding a daily reality rather than an experiment.
Why vibe coding emerged
The AI technology market is already massive, valued at roughly $244 billion in 2025, and it’s still growing fast. This scale isn’t abstract either. For instance, according to Microsoft CEO Satya Nadella, AI writes up to 30% of code for some of its projects, signaling a broader industry shift toward AI-driven software development.
So why did vibe coding emerge in the first place? Not because developers suddenly got lazy, but because the pace and pressure of modern software development fundamentally changed.
- MVP timelines collapsed from months to weeks
- Startups needed proof, not polish
- AI reached a point where it could generate usable code
- Engineering teams became bottlenecks for experimentation
When timelines shrink, teams need a way to move fast without waiting on full engineering cycles. Vibe coding fills that gap by prioritizing speed and validation first, while pushing structure and polish to the next stage.
Vibe coding vs traditional development
Vibe coding and traditional software development start from different assumptions about how code should be created. One prioritizes speed, intuition, and AI assistance to get ideas into working form as quickly as possible. The other emphasizes deliberate design, manual implementation, and long-term maintainability from the outset. Understanding how these approaches differ helps teams choose the right method (or, rather, a combination of methods) at each stage of a product’s lifecycle.
Vibe coding vs traditional development
In fact, neither approach is “better” universally, so to speak. The rule is simple: consider vibe coding as a tool, not a replacement.
By the way, it’s worth clarifying the difference between AI-assisted coding and AI-driven software development. These terms are often used interchangeably, but they don’t mean the same thing, so let’s untangle them.
- AI-assisted coding refers to using AI tools to support developers during implementation, such as suggesting snippets, completing functions, or speeding up repetitive tasks.
- AI-driven software development goes further, with AI influencing not just code generation but also workflows, architectural decisions, and how software is planned, built, and delivered.
To cut a long story short, vibe coding sits at the intersection of both.
Vibe coding use cases
Task automation. With vibe coding, tedious stuff like logging, documentation, or data format tweaks can be automated. That frees developers from busywork, keeps them in the flow, and leaves more energy for important tasks.
Prototyping. Vibe coding shines when it comes to rapid prototyping and MVP creation. Put simply, this approach allows both startups and established businesses to innovate with minimal risk. Even small-scale applications can be assembled efficiently, with AI-assisted tools helping to automate workflows, organize databases, and customize solutions quickly.
Developer skill building. Vibe coding can act as a hands-on learning playground for just about anyone. Students can tackle complex concepts while immediately seeing results, and veterans can experiment with new languages without a long learning curve. The key is active engagement: users who analyze and iterate on AI-generated code gain deeper insights, whereas blindly accepting outputs leads to mistakes.
Custom software development. For agencies and freelancers, vibe coding speeds up custom projects by making iteration easy and collaboration more fluid. Teams can show features as they’re built, get feedback right away, and tweak things on the spot.
Vibe coding in software development workflows
In all fairness, vibe coding doesn’t replace common development workflows, but fits into them. This way, understanding where it fits well (and, naturally, where it clearly does not) is essential if you want to use it productively rather than painfully. Like any powerful accelerator, its value depends on context and understanding.
Where it works best
Vibe coding excels when:
- Building MVPs
- Prototyping internal tools
- Exploring new product ideas
- Automating repetitive workflows
- Generating boilerplate-heavy systems
It pairs naturally with:
- No-code / low-code platforms
- Early-stage startups software development
- Hackathons and rapid pilots
- Internal proof-of-concept work
Where it struggles
Vibe coding struggles when:
- Systems need strict architecture
- Security and compliance matter
- Long-term maintainability is required
- Multiple teams collaborate on the same codebase
- Performance constraints are tight
Key takeaway
In practice, vibe coding shines at the front of the lifecycle and falters as systems mature. The moment speed gives way to scale, structure, and shared ownership, its limits become visible. That tension is exactly why cleanup, refactoring, and governance layers are no longer optional add-ons, but structural necessities (and we’ll talk about it in our next article).
Key benefits and challenges of vibe coding
Let’s talk about pros and cons, shall we? The advantages of vibe coding include:
Speed
Vibe coding dramatically shortens the path from idea to execution. Boilerplate, integrations, and repetitive logic appear in minutes instead of days.
Accessibility
Non-technical founders, product managers, and analysts can build working applications without deep programming expertise.
Creative focus
Developers spend more time on what should exist rather than how to type it, keeping momentum high during ideation.
Of course, vibe coding isn’t without its drawbacks, and its cons include:
Still requires expertise
As of today, AI does not replace engineering judgment. Without strong fundamentals, developers risk delivering broken or unsafe systems.
Quality and security gaps
Research from Veracode shows that about 45% of AI-generated code contains security vulnerabilities, highlighting significant risks when relying on automated code output.
Over-dependence
Teams relying entirely on AI risk losing architectural discipline and institutional knowledge.
Key takeaway
In a nutshell, vibe coding offers a more accessible way to build software, allowing developers and non-technical team members to focus on ideas rather than syntax. However, it still requires solid engineering judgment, as AI-generated code can contain bugs, security vulnerabilities, or structural issues. Over-reliance on AI without proper review and cleanup can undermine code quality and maintainability.
Common vibe coding misconceptions
Before comparing vibe coding to traditional development, it’s worth clearing up a few misconceptions. Much of the confusion comes from oversimplifying what vibe coding actually involves and what it changes in day-to-day engineering work. Here are the most common misunderstandings in plain English:
- Vibe coding is reduced to prompt writing. Prompting plays a role, but the real challenge lies in understanding systems, constraints, and trade-offs well enough to guide, validate, and correct AI output.
- Vibe coding makes developers obsolete. Vibe coding automates repetitive implementation tasks, not architectural thinking, debugging, or decision-making. Human judgment remains central.
- It’s inherently unsafe. AI-generated code becomes risky only when it goes unreviewed. Anyway, unchecked output is no different from rushed or poorly reviewed human code.
With those myths out of the way, the differences between vibe coding and traditional development become much easier to evaluate on their actual merits.
Key takeaway
Speaking of practical applications, here what makes vibe coding stand out in modern software development:
- Vibe coding prioritizes speed and experimentation over structure
- It relies on AI to generate large portions of code
- It lowers the barrier to building software
- It introduces real risks around quality and security
- It works best when paired with strong cleanup and review processes
Today, vibe coding is being adopted across a variety of contexts: startups use it to quickly validate ideas, product teams leverage it to test new features, and engineers rely on it for rapid system prototyping. Even non-technical founders can build MVPs efficiently, and larger enterprises are exploring its potential for internal automation.
How Mitrix approaches vibe coding
At Mitrix, we support teams working with AI-generated code through dedicated vibe coding services designed for real production environments. A key part of our approach is how we train our specialists: we develop them as engineers who not only refactor code, but understand how AI-driven systems behave once they meet real users, traffic, and constraints. Our training emphasizes:
- established code review practices for AI-produced output
- restructuring and refactoring loosely generated architectures
- identifying recurring failure patterns typical of LLM-based code
- applying production-grade standards for security, reliability, and maintainability
Mitrix specialists work directly with real AI-built codebases, learning how to stabilize rapid prototypes, introduce testing and documentation, and guide systems toward long-term evolution rather than short-term fixes. Contact us to help you turn AI outputs into robust software!
Summing up
Gartner predicts 75% of engineers will use AI coding tools by 2028, and many industry professionals agree that vibe coding isn’t just a passing fad. Perhaps, it’s a response to how fast software now needs to move. Vibe coding enables rapid creation, lowers barriers, and fuels experimentation, but it also shifts responsibility back to humans to ensure what gets built actually works, lasts, and stays secure.
Challenges exist, sure. As one Reddit user put it: “With AI it’s sometimes 95% right, sometimes 0% right because it hallucinates the whole thing, and when you ask the same question you might get a different answer”. That unpredictability is exactly why blind trust doesn’t scale. This way, vibe coding becomes a powerful accelerator rather than a shortcut when used wisely. Teams get the most out of it when they treat AI as a collaborator and back it up with cleanup and solid engineering judgment.
