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Is vibe coding bad? The honest answer is that the question is too blunt. Vibe coding is a tool with a sharp edge: extraordinary for some jobs and genuinely risky for others. The loudest takes at both ends, "it will replace developers" and "it produces garbage", both miss what the evidence from 2025 and 2026 actually shows. This post lays out that evidence, where the bad reputation comes from, where the real productivity gains are, and a practical way to decide when vibe coding helps you and when it hurts you.
Andrej Karpathy, cofounder of OpenAI, coined the term in February 2025 to describe a specific style of building software: you describe what you want in plain language, accept every suggestion the AI makes, paste error messages back without comment, and judge the result by running it rather than reading the code. As MIT Technology Review points out, not all AI-assisted coding is vibe coding. If you review and edit what the model writes, you are doing AI-assisted development. Vibe coding specifically means surrendering the reading step and trusting the output. Source
That distinction matters because most arguments about whether vibe coding is bad are really arguments about the reading step. The code generation itself is now good enough that syntax errors are rare. What you give up when you vibe code is the human check on what the code actually does.
The security numbers are the strongest evidence for the "bad" side. Veracode's 2025 GenAI Code Security Report tested more than 100 large language models on 80 coding tasks with known potential for vulnerabilities, and found the models chose an insecure implementation in 45 percent of cases. They failed to defend against cross-site scripting in 86 percent of relevant tasks and log injection in 88 percent, and security performance stayed flat even as models improved at writing correct code. Bigger models did no better than smaller ones. Source
These are controlled benchmark tasks, not a sample of production code, so the rates do not translate directly into per-function odds in your application. What they do show is that when nobody states security requirements, which is exactly how vibe coding works, models routinely take the insecure path and nobody notices.
The people closest to the code are also skeptical. Stack Overflow's 2025 developer survey, with about 49,000 respondents, found 84 percent of developers use or plan to use AI tools, yet more actively distrust the accuracy of AI output (46 percent) than trust it (33 percent), and only 3 percent highly trust it. Positive sentiment toward AI tools fell from over 70 percent in 2023 and 2024 to 60 percent in 2025, even as usage rose. Source
A 2026 state-of-the-art review on arXiv that pulled together the first seventeen months of empirical evidence on vibe coding documented security failures in deployed applications, visible code-quality degradation in large-scale telemetry, and one operational surprise: code-review time climbed by as much as 441 percent in team-level measurements, because reviewing machine-written code you did not write is slow, hard work. Source
The "good" side has real numbers too, and the same arXiv review explains why they look contradictory. Peer-reviewed field experiments report developers completing 26 percent more tasks per week with AI assistance. Independent randomized trials measured a 19 percent slowdown on some tasks. The review argues both readings are true once you hold the measurement method, scope, and time horizon constant: gains are largest on new code written quickly by one person, and they shrink or even reverse on mature codebases where the AI lacks context and every change has to fit existing structure. Source
So the productivity question has the same shape as the security question. Vibe coding is fast at creating something from nothing. It gets slower and riskier as the something grows into a product other people depend on.
Vibe coding is genuinely good for work where speed of learning beats durability. Prototypes and demos that test whether an idea is worth building. Internal tools with a handful of users and no sensitive data. Landing pages and marketing sites. Scripts you run once. Learning projects where the goal is to understand a concept by seeing it work. For a non-technical founder, vibe coding can replace weeks of waiting for a first version you can put in front of customers, and that speed is a real business advantage even if every line gets rewritten later.
The shared trait of these cases is a short expected lifespan or a low blast radius. If the code fails, the cost is embarrassment or an afternoon, not customer data or revenue.
Vibe coding turns bad when the code has to be trustworthy and nobody reads it. Anything touching authentication, payments, personal data, or other companies' systems. Anything that will be maintained by a team, because unmaintained machine-written code becomes technical debt faster than hand-written code. Anything deployed to production where a silent vulnerability is a breach, not a bug. And anything built on a mature codebase, where the productivity evidence says the gains fade or reverse anyway. Source
The Stack Overflow data adds a social signal: the most experienced developers are the most cautious, with the highest "highly distrust" rates of any group. The people with the most context for judging AI output trust it least, and they are the ones accountable when it fails. Source
The teams getting good results treat vibe coding as a drafting stage, not a shipping stage. They vibe code the prototype, then apply a deliberate hardening pass before anything real depends on it. That pass is not exotic: pin dependencies, add authentication and authorization checks, set up row-level security on the database, review the AI-written code for the common failure patterns, and test the security checklist before launch. Our vibe coding security checklist covers the concrete items, and vibe coding to production walks through the full hardening path.
If you already have a vibe-coded product and you are not sure what state it is in, an outside review is the fastest way to find out. Our guide on how to audit vibe-coded apps before launch is the self-serve version. Codesprint Consulting also does this as a service for founders through our vibe coding for founders engagement: keep the speed you got from vibe coding, and we make the result production-grade.
Is vibe coding bad? No. Vibe coding without a reading step, applied to code that has to be trusted, is bad. Vibe coding as a fast draft followed by a real engineering pass is one of the most useful things to happen to software in years. The line between the two is not the tool. It is whether a qualified human reads and takes responsibility for the code before other people depend on it.
It depends on the goal. If the goal is a working demo, vibe coding is fine. If the goal is to become a developer, vibe coding without reading the output slows learning, because the reading and debugging steps are where the skill forms. The 2026 arXiv review also documents early evidence of skill atrophy among heavy users who stop engaging with code directly. Source
Unreviewed vibe coding is bad for production apps. In Veracode's benchmark, models picked insecure implementations 45 percent of the time when security requirements were not stated, so shipping vibe-coded authentication, payments, or data handling without review carries real breach risk. These are benchmark tasks rather than production sampling, but the failure pattern is consistent with documented incidents in deployed apps. Source
No. Those tools support both styles. Vibe coding is defined by the workflow: accepting all suggestions and judging by running rather than reading. Using the same tools while reviewing and editing the output is AI-assisted development, which carries much lower risk. Source
Stack Overflow's 2025 survey found 46 percent of developers actively distrust AI output accuracy versus 33 percent who trust it, with only 3 percent highly trusting it. Experienced developers distrust it most, because they are the ones who have debugged the plausible-looking mistakes. Source
You can build the first version of it, and many founders should. Plan on a hardening pass, or a rebuild of the critical parts, before you handle real customer data or money at scale. The productivity evidence shows vibe coding is strongest on new code and weakest on mature codebases, so the longer you wait to add engineering rigor, the more the speed advantage fades. Source
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