Vibe coding has become one of those phrases that people either love or hate. Some use it to describe the future of software development. Others use it as shorthand for careless AI-generated code. So instead of asking whether vibe coding is “good” or “bad,” the better question is: where does it actually work, and where does it create hidden debt? Rather than relying on opinions or hype, this article looks at what recent studies and research actually say about vibe coding. We’ll explore where it can genuinely boost productivity, and where it falls short. So, let’s get started.
# 5 Things Vibe Coding Gets Right
// 1. Making Prototyping Easier
The best use case for vibe coding is not replacing an engineer. It is getting from idea to prototype faster.
For years, many good ideas died before the first usable version existed. Building even a simple app required setting up a frontend, backend, database, authentication, deployment, environment variables, and error handling. AI coding tools reduce that first barrier. You can describe a workflow and get something running quickly enough to test whether the idea is worth pursuing.
That is why vibe coding has taken off inside startup culture. TechCrunch reported that, according to Y Combinator managing partner Jared Friedman, a quarter of YC’s W25 batch had codebases that were 95% AI-generated. This does not mean the code was automatically good, but it does show that AI-generated software is already being used in serious startup workflows.
// 2. Making Programming More Conversational
Traditional coding forces users to translate intent into syntax. Vibe coding lets users begin with intent.
Instead of thinking:
Which framework should I use?
What files do I need?
How do I wire this API?
What should the component structure look like?
the user can start with:
Build me a dashboard where I can upload a CSV, filter customers by churn probability, and export the high-risk segment.
It makes coding more accessible to analysts, product managers, researchers, marketers, founders, and domain experts who understand the problem better than they understand the framework. This does not mean programming skill no longer matters. It means the first interface to software creation is changing from syntax to specification. The better your specification, the better the output.
// 3. Being Genuinely Useful for Boilerplate and Bounded Tasks
Some programming tasks are genuinely repetitive. Writing API wrappers, form validation, data transformation scripts, test scaffolds, simple UI components, SQL queries, and configuration files is not always where a developer’s highest-value thinking happens.
AI coding tools help here because these tasks have common patterns. A controlled GitHub Copilot experiment found that developers with access to Copilot completed a JavaScript HTTP server task 55.8% faster than those without it. This number should not be generalized to every software job. Mature production work is messier than a controlled task. But it does support a practical point: for bounded, well-specified coding tasks, AI can reduce friction. I think this is where vibe coding gets unfairly criticized. Not every generated line needs to be hand-crafted. Some code is just glue code. If it is tested, reviewed, and low-risk, AI generation is a reasonable productivity tool.
// 4. Improving the Feedback Loop
A useful vibe coding workflow is not:
Prompt once. Ship blindly.
It is:
Prompt. Run. Observe. Fix. Test. Repeat.
Slowly, the app becomes closer to what you wanted. This loop is powerful because it keeps the builder close to the output. Instead of spending hours reading documentation before seeing anything work, the user can react to a real interface, real errors, and real behavior. But the feedback loop only works when it is grounded in reality. So, the best vibe coders are not the people who accept everything. They are the people who keep forcing the generated system to prove itself.
// 5. Helping More People Participate in Software Creation
This is both exciting and uncomfortable.
Vibe coding lets non-engineers build things they previously could not build. A data analyst can make a Streamlit app. A teacher can build a quiz generator. A marketer can build an internal campaign tracker. A researcher can build an annotation tool. A founder can build a prototype before hiring engineers.
This is good. Software should not be locked behind unnecessary ceremony.
But the danger here is that vibe coding can help people build useful tools without automatically teaching them what production readiness means. It gives people reach before it gives them judgment. This is fine for small tools. It is risky for systems that handle money, health, private data, security, or business-critical workflows.
# 5 Things Vibe Coding Gets Wrong
// 1. Confusing “Working” with “Correct”
The most dangerous sentence in vibe coding is:
“It works.”
What usually works is the happy path. The form submits. The page loads. The chart appears. The login button redirects. The demo looks convincing.
But software quality lives in the unhappy paths. What happens when the file is malformed? When the API times out? When the user has the wrong permission? When the database field is null? When two users edit the same record? When an attacker sends unexpected input?
This is where vibe coding often fails. It can produce something that looks finished before it has been properly validated. A working demo is not the same as working software.
// 2. Creating Serious Security Problems
Security is one of the clearest weak spots.
A recent benchmark on agent-generated code for real-world software engineering tasks found that even when generated solutions were functionally correct, they were often insecure. In one reported result, SWE-Agent with Claude 4 Sonnet produced functionally correct solutions 61% of the time, but only 10.5% were secure.
This is the exact vibe coding risk: the app appears to do the requested thing, but it may do it insecurely. For anything involving authentication, payments, private data, permissions, file uploads, APIs, or production databases, vibe coding without security review is a liability.
// 3. Slowing Down Experts in Mature Codebases
The common assumption is that AI coding tools always make developers faster. The evidence is more complicated.
A METR randomized controlled trial studied experienced open-source developers working on their own mature repositories. The developers expected AI tools to reduce completion time, but the study found the opposite: when AI tools were allowed, developers took 19% longer.
This is an important reality check.
AI can help with isolated tasks, but mature codebases are not just collections of functions. They have history, conventions, implicit architecture, tests, review standards, hidden dependencies, and maintainability constraints. In that environment, AI can shift the burden from writing code to reviewing, correcting, and integrating code. Vibe coding works best when the system is small and the requirements are visible. It gets weaker when the codebase is large, old, domain-specific, and full of context the model does not fully understand.
// 4. Giving People Confidence Before Judgment
Vibe coding can make beginners productive earlier. That is a good thing. But it can also let people skip the learning that produces engineering judgment.
The issue is not that beginners use AI. The issue is when they repeatedly paste errors into an agent until the app works, without understanding what changed.
That creates dependency instead of skill.
Developers themselves are aware of this trust gap. Stack Overflow’s 2025 Developer Survey found that more developers distrust the accuracy of AI tools than trust them: 46% distrust the accuracy, compared with 33% who trust it. Only 3% highly trust the output.
That skepticism is healthy. The best developers using AI are not blindly accepting output. They are reviewing diffs, asking for explanations, writing tests, checking edge cases, and using AI as a collaborator rather than an authority. Vibe coding becomes dangerous when it replaces understanding instead of accelerating it.
// 5. Being Weak at Unstated Requirements
AI coding tools are good at satisfying what you asked for. They are not always good at protecting what you forgot to ask for.
For example, you may ask:
The model may build a login page. But did you specify password hashing, rate limiting, session expiry, email verification, cross-site request forgery (CSRF) protection, secure cookies, account lockout, audit logs, or OAuth callback handling?
Probably not.
This is where experienced developers still matter. They know the requirements that are missing from the prompt.
Vibe coding works best when the user can say:
Add validation for empty files.
Reject CSVs over 10MB.
Do not store API keys in the frontend.
Add unit tests for malformed input.
Use environment variables.
Add role-based access control.
Log failed imports.
The quality of the system depends on the quality of the constraints. If you do not know the constraints, the model will happily build something incomplete.
# Final Thoughts
Vibe coding gets one big thing right: software creation is becoming faster, more conversational, and more accessible.
It also gets one big thing wrong: a working app is not the same as a reliable system.
The future is not “AI replaces software engineers.” It is also not “AI coding is useless.” The real future is more uneven. People who can specify clearly, test carefully, review critically, and understand systems will build faster. People who skip those steps will ship fragile software faster than ever.
Vibe coding is not a replacement for engineering judgment.
It is a multiplier.
And like every multiplier, it multiplies both the good and the bad.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
