What is Vibe Coding? The Pros, Cons, and Controversies

Vibe coding has recently gained significant traction in the software development and programming communities after Andrej Karpathy, a founding member at OpenAI and former director of AI at Tesla, introduced the term earlier this year.

There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper…

— Andrej Karpathy (@karpathy) February 2, 2025

However, like a number of other AI advancements that spark concerns about replacing humans, the idea behind vibe coding has elicited mixed reactions from coders and non-coders alike. Does vibe coding really work? Is it safe? Is learning programming languages even necessary anymore? And, most importantly, what does vibe coding mean for the future of coding?

While many recognize the potential benefits of using AI-assisted workflows, others are concerned about the new challenges this AI coding approach could pose, specifically the need for thorough human oversight to ensure that the AI-generated codes are usable, secure, and maintainable.

In this blog post, we’ll explore what vibe coding really means, how it’s reshaping the developer’s role, and why its promise of speed and creativity must be balanced with caution, context, and code-level accountability—and what we’re doing at Tanium to help organizations navigate this balance.

• What is vibe coding?
• Is vibe coding a new concept?
• History of AI use for coding
• How does vibe coding differ from traditional code development?
• What are the main benefits of using vibe coding?
• Top 5 use cases for vibe coding
• What are the potential downsides of vibe coding?
• Security challenges with AI coding
• What vibe coding should and should not be used for
• How Tanium strengthens the foundations of vibe coding

What is vibe coding?

Vibe coding refers to an approach where AI assists in generating and refining code based on natural language prompts provided by the user. Instead of writing code line-by-line, vibe coding allows you to:

• Describe the desired functionality in plain language
• Let the AI generate the code
• Test, tweak, and guide the AI as needed

Vibe coding involves using AI tools, such as GitHub Copilot, ChatGPT, Claude OpenAI, and Gemini, as well as integrated development environments (IDEs) designed to assist developers in generating code lines and refining code syntax based on natural language prompts.

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These AI agents utilize large language models (LLMs) to comprehend and interpret the complexities of natural language, transforming plain speech or text descriptions into executable code. This capability can accelerate prototyping and reduce coding barriers, making the code generation process more intuitive and accessible for individuals with varying levels of programming knowledge. Vibe coding is less about precision engineering and more about rapid ideation and experimentation.

Vibe coding places developers in a unique position where they leverage their coding knowledge to guide the AI rather than writing every line of code themselves. This approach can potentially streamline the development process, reduce the time spent on routine coding tasks, and allow developers to focus on more complex and creative aspects of software development.

However, while vibe coding offers a revolutionary approach to software development, it essentially shifts the developer’s role from writing code to guiding, testing, and refining the AI-generated output.

No longer is the developer solely responsible for crafting each line of code; instead, they become a mentor to the AI, ensuring that the generated code aligns with project requirements and best practices.

This transformation in roles means that developers who use AI must also be vigilant in carefully reviewing the generated code to ensure it meets security standards and is sustainable for long-term projects to maintain the integrity and security of their codebase—a new challenge that some feel could introduce too much potential risk that outweighs the time saving and other benefits using AI to code promises.

However, it’s important to recognize that the fundamental components of vibe coding are actually not entirely new concepts. Instead, what sets vibe coding apart is the innovative way these elements are being integrated and utilized together.

Let’s delve into how these familiar components are being combined to create a unique and powerful coding evolution.

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Is vibe coding a new concept?

While Andrej may have coined the term “vibe coding,” not only has using AI in coding been around for quite some time, but the fundamental concept behind vibe coding, which is to describe what you want the code to do to guide its creation, is also not new.
 
Vibe coding essentially combines two existing strategies: utilizing the advanced capabilities of AI to generate and vet code and streamlining the gathering and implementation of user requirements to evolve traditional code development workflows.
 
To understand how vibe coding emerged, it’s helpful to explore a brief history of AI in coding and an existing methodology that software developers use to ensure the code addresses stakeholder requirements.
 
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History of AI use for coding

By the late 20th century, machine learning had progressed enough to be integrated into coding tools, enabling intelligent code analysis and enhanced syntax validation. Then, the 2010s saw a surge in deep learning, which significantly enhanced natural language processing (NLP) and code comprehension.

[Read also: Machine learning in cybersecurity – A primer for beginners]

This evolution paved the way for LLM-powered AI coding assistants that could offer context-sensitive code suggestions based on user prompts. Fast forward to the current day, different types of AI are being widely integrated in various aspects of software development, from code generation to debugging and optimization.

The need for vibe coding ultimately arises from the increasing complexity of software projects and the demand for faster development cycles. Traditional coding methods often struggle to keep up with these demands, making AI-assisted coding a valuable tool for modern developers.

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How does vibe coding differ from traditional code development?

In traditional development workflows, non-technical stakeholders communicate their requirements to developers or software engineers, who would then interpret these requirements and write the necessary code. This process often involves multiple iterations, feedback loops, and adjustments to ensure alignment between the stakeholders’ vision and the technical implementation.

Vibe coding, on the other hand, automates part of this translation process by using AI to generate code based on natural language prompts. Essentially, it enables developers to act as intermediaries, providing high-level guidance to the AI, which then generates the initial code. This shifts some of the responsibility for code generation from the developer to the AI while still requiring the developer’s expertise to refine and validate the output.

In this sense, vibe coding represents a modern iteration of the traditional collaboration model that has long been a staple in software development. Developers have long translated ambiguous stakeholder needs into structured logic, often using whatever tools helped them move fastest from idea to implementation. What’s new is the scale and speed that AI brings to that process.

[Read also: How AI for automation will revolutionize today’s IT workflows]

By integrating AI into this process, vibe coding aims to enhance productivity and innovation while still relying on developers’ expertise to ensure the end product’s quality and security.

When used intentionally, vibe coding can unlock significant advantages—not just in speed but in creativity, accessibility, and collaboration. Let’s explore some of the key benefits that make this approach so compelling.

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What are the main benefits of using vibe coding?

The potential benefits of vibe coding are substantial. It can boost productivity, accelerate prototyping, and enhance problem-solving by lowering the barrier between ideas and implementation.
 
By blending automation with experimentation, vibe coding creates a more dynamic and creative development environment—one where developers can quickly test features, explore new concepts, and iterate freely.
 
This freedom enables them to shift their focus toward higher-level concerns, such as design, architecture, and long-term scalability. The result? Fresh insights, faster innovation, and meaningful improvements in how software gets built.
 
So, what does vibe coding actually look like in practice? When is it most effective, and who’s benefiting from it today? Let’s break down the top five use cases where vibe coding is already making a meaningful impact—and why these scenarios are proving to be an ideal ground for this hybrid approach.
 
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Top 5 use cases for vibe coding

From developers and project managers to content creators, IT teams, startups, and even large enterprises, the appeal lies in how it combines speed with creativity and automation with human insight.

Some popular use cases of vibe coding emerging today include:

1. Prototyping and MVPs
   
   Vibe coding is ideal for quickly spinning up prototypes or minimum viable products. You can describe a feature like “a dashboard with three charts and a filter” and get working code in minutes.
2. Side projects
   
   As Karpathy put it, vibe coding is “not too bad for throwaway weekend projects.” It’s great when you want to build something fast without worrying about long-term maintainability.
3. UI/UX mockups
   
   Need a quick HTML/CSS layout or a React component? Vibe coding can generate visually functional interfaces based on your description.
4. Data scripts and automation
   
   You can describe a task, such as “clean this CSV and plot a histogram,” and the AI will generate Python or R code. This is especially useful for marketers and analysts who want to automate repetitive tasks.
5. Learning and experimentation
   
   It’s a great way to learn coding concepts by seeing how the AI solves problems. You can ask it to explain what it’s doing or try variations to see different approaches.

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Vibe coding opens the door to faster workflows, more creative exploration, and broader accessibility. The use cases we’ve just covered show how it can empower teams across disciplines to move from idea to execution with unprecedented speed.

But like any powerful tool, vibe coding comes with trade-offs. Its strengths—speed, flexibility, and ease—can also become its weaknesses if not applied thoughtfully. So, it’s worth taking a closer look at where vibe coding can fall short and what risks teams should be aware of when adopting it at scale.

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What are the potential downsides of vibe coding?

While vibe coding brings exciting possibilities, it also introduces challenges that demand a thoughtful and experienced approach, which is why crafting effective prompts—and pairing them with a solid understanding of the underlying code—is essential to producing secure and reliable results.

The quality of AI-generated code is only as good as the guidance it receives. AI systems respond literally to the instructions and context they’re given. Vague or overly broad prompts can lead to fragile or even unsafe code. In contrast, clear, well-structured inputs, such as specifying the tech stack, performance expectations, or security constraints, can yield surprisingly robust output.

But even with a perfect prompt, the job isn’t done. Developers still need to review, test, and refine the output produced by the AI. This involves checking for logical gaps, validating assumptions, and ensuring the code aligns with the project’s objectives. It’s not just about whether the code runs—it’s about whether it runs right.

[Listen to our free podcast: Ethical AI – how (and how much) we can rely on it with AI philosopher and designer Josh Bachynski]

Another challenge is traceability. When a human writes code, there’s usually a rationale behind every decision—something you can ask about, document, or debug. With AI-generated code, that reasoning is often opaque. Why did it choose that pattern? Why that library? Without a clear intent, debugging becomes a matter of guesswork, and maintenance becomes riskier over time.

Performance is another concern. AI may generate code that works but isn’t optimized, resulting in slower execution, higher resource usage, or scalability issues. In high-stakes environments, that can be a dealbreaker.

And finally, there’s the risk of over-reliance. AI can be a powerful co-pilot, but it is not a replacement for engineering judgment. When teams start trusting the output without questioning it, they risk introducing subtle bugs, security flaws, or architectural debt that’s hard to unwind later.

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Security challenges with AI coding

One of the most pressing concerns with vibe coding is the potential for hidden bugs and security vulnerabilities. AI-generated code may appear clean and functional at first glance, but beneath the surface, it can harbor subtle logic errors, performance bottlenecks, or serious security flaws, such as SQL injection or cross-site scripting (XSS).
 
The core issue is that while AI models are powerful, they lack the contextual awareness and judgment that experienced developers bring. They can mimic patterns, but they don’t truly understand the intent. That means they might generate code that works but doesn’t protect—and that’s a dangerous gap.
 

[Read also: The ultimate guide to AI in cybersecurity]

Take input validation, for example. If a prompt doesn’t explicitly call for it or the AI misinterprets the context, it might skip critical safeguards. That’s how you end up with code vulnerable to SQL injection, where an attacker can manipulate database queries to gain unauthorized access. Or XSS attacks, where unsanitized user input becomes malicious scripts that compromise other users’ data or sessions.

Even more concerning, AI can unknowingly reinforce existing vulnerabilities. If your current codebase contains insecure patterns, the AI may replicate or build upon them, amplifying the risk rather than mitigating it.

That’s why human oversight isn’t optional—it’s essential. Developers must review AI-generated code with a security-first mindset, applying the same scrutiny as any third-party contribution. While AI can accelerate development, it can just as easily accelerate the spread of harmful code if we’re not paying attention.

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These security concerns aren’t just theoretical but directly result from how vibe coding is used today. While the intent was to support developers, not replace them, we’re increasingly seeing it treated as a shortcut to full-scale coding. That shift—from assistive tool to autonomous coder—is where things start to break down.

To understand why, we need to revisit what vibe coding was actually meant to do—and why expecting it to carry the full weight of software engineering is a fundamental misstep.

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What vibe coding should and should not be used for

When you take a step back and consider what vibe coding is—and what it isn’t—it becomes clear that many of its challenges stem from trying to make it do something it was never designed for: actual coding.
 
As Andrej explained in his introductory post about vibe coding, “… it’s not really coding—I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”
 
And that’s the crux of it. Real coding isn’t just about getting something to run. It’s about understanding why it runs, how it scales, how it fails, and how it evolves. Vibe coding skips over that foundation. It’s like building a house by intuition—sometimes you get a cozy cabin, but other times you get a structure that looks fine on the surface but starts to crack the moment you try to live in it.
 
Vibe coding is quickly becoming another example of when a tool meant to accelerate creativity or experimentation gets misapplied as an engineering solution. Take the rise of visual web builders in the early 2000s—tools like Dreamweaver or FrontPage. They were revolutionary for their time, letting people drag and drop their way to a website without writing a line of HTML.
 
However, when those same tools were used to build production-grade sites, the results were often bloated, brittle, and impossible to maintain. Under the hood, the code was a mess—non-semantic, redundant, and riddled with layout hacks. It looked fine on the surface, but it didn’t scale, performed poorly, and certainly wasn’t secure. We’re seeing echoes of that now with vibe coding and AI-generated code.

Much like the early 2000s rise of visual web builders like Dreamweaver and FrontPage—tools that made web creation more accessible but often produced bloated or brittle code—today’s AI-assisted development tools offer speed and simplicity, but not without trade-offs.

That doesn’t mean vibe coding has no place. It’s a powerful tool for prototyping, exploring ideas, or lowering the barrier to entry. However, when it becomes the default approach for solving complex problems or is used as a shortcut to bypass foundational engineering practices, and creeps into production workflows, this is when we start seeing real consequences. Bugs. Outages. And yes, security vulnerabilities.

Code generated without deep understanding often lacks the guardrails, validations, and context-aware logic that experienced developers instinctively build in. If no one truly understands what the code is doing, then no one is truly in control of what it might allow.

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How Tanium strengthens the foundations of vibe coding

Tanium is helping close the resource gaps created by developer shortages, tightening budgets, and the growing demand for faster, more secure operations by empowering more people, not just seasoned coders, to build secure, scalable IT automation. But that’s just one part of our mission.

At its core, Tanium’s low- to no-code automation is about unlocking agility and control across IT and security teams. With Tanium Automate, a key component of Tanium Autonomous Endpoint Management (AEM), users can create dynamic, multi-step playbooks that respond to real-time data, without needing to write complex scripts. Each playbook is built from clearly defined steps that can be reviewed, adjusted, and sequenced with full transparency. This gives teams the confidence to automate without losing visibility or control.

[Read also: Announcing general availability of Tanium Autonomous Endpoint Management (AEM)]

It’s also about democratizing automation. Whether you’re a security analyst, IT operator, or systems engineer, Tanium provides you with the tools to act on insights without waiting for development cycles. This not only accelerates response times but also frees up teams to focus on strategic initiatives and automating the rest.

And because these automations are built on Tanium’s real-time endpoint visibility, they’re not just fast—they’re context-aware. That means making smarter decisions, reducing false positives, and achieving more resilient execution across your environment.

By enabling secure, scalable, and intelligent automation—and giving users the ability to see and understand every step—Tanium allows teams to move from insight to action in minutes, not days. It’s not just about filling gaps; it’s about unlocking agility and empowering more people to solve problems at speed, scale, and with confidence.

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