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Vibe coding can turn an idea into a working prototype, but a working result alone does not show that its creator can explain, test, debug, secure, or maintain the software. The work shifts some effort from writing code directly to describing goals, supplying context, evaluating output, and deciding when to intervene manually. Current evidence does not establish that vibe coding necessarily erodes programming skills—or that prototype success proves lasting competence.
What is vibe coding?
Vibe coding is a natural-language-led way to develop software with a code-generating AI model, often without inspecting every line it produces. In practice, it is usually an iterative cycle: prompt the model, scan or run what it creates, judge the result, then prompt again or edit the code yourself.
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That cycle can mix broad goals with technical detail. In a study of more than eight hours of curated video from extended sessions, with participants thinking aloud, Advait Sarkar and Ian Drosos observed users combining AI assistance with manual debugging. Their account of programming through conversation with artificial intelligence points to a change in where expertise is applied, not its disappearance.
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No. A prototype shows that an artifact ran in a particular context; it does not, by itself, show that its creator can explain how it works, identify a defect, assess security, or extend it safely. This distinction is an interpretation of the gap between a visible result and the skills needed to reason about that result—not a measured psychological effect established by a study.
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Success at directing an AI system can also depend on capabilities that are easy to overlook when the finished interface is all that is visible. A 2026 CHI study, summarized by ETH Zurich, reports that computer science achievement and writing skills predict success at vibe coding, and that clearer, more structured prompts correlate with better results. These are associations, not proof that a specific course or prompting method causes proficiency. ETH Zurich reports that the paper appeared in the Proceedings of CHI ’26, held April 13–17, 2026. Researcher Theo Weidmann put the finding this way: “People who formulate clear and structured prompts achieve better results, while unclear or imprecise wording is more likely to lead to defective software.”
Sarkar and Drosos explicitly conclude that vibe coding “does not eliminate the need for programming expertise.” Their observed work sessions offer insight into how people use AI, but they do not test whether novices retain or gain independent skills over time.
Where does expertise go in an AI-assisted workflow?
Instead of disappearing, expertise is redirected toward work that surrounds generated code: making the task understandable to the model, providing relevant project context, judging whether a result is correct, and choosing whether to accept, revise, or replace it. Microsoft Research’s 2025 Future of Work report describes this as iterative goal satisfaction and output verification. It also notes that some practitioners reserve the approach for low-stakes contexts.
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How can the workflow break down?
Observed research and qualitative accounts describe several failure mechanisms. They help explain what can go wrong, but they do not establish how frequently these problems affect developers as a whole.
- Context gaps: The model may not have the project details or constraints needed to make a sound change, while the user may not know which context is missing.
- Unverified behavior: An output can look plausible or run once without meeting the intended requirements. Checking the result takes work and does not guarantee that all defects will be caught.
- Debugging and review burden: Generated code still needs to be understood and assessed. Qualitative accounts surface concerns about specification, reliability, debugging, latency, code-review effort, and collaboration.
- Trust erosion and error spirals: A paper published online August 19, 2026, in the HHAI 2026 proceedings analyzed 163 interaction episodes between one developer and Claude Code while building and debugging a system over several months. It identified human–AI context gaps, asymmetrical and unsynchronized learning, trust erosion, and error-expanding spirals. Because it is a single-developer case study, it illustrates plausible breakdowns rather than their prevalence.
A separate qualitative study by Pimenova and colleagues examined more than 190,000 words from semi-structured interviews, Reddit threads, and LinkedIn posts. Its accounts add detail about reported concerns; they are not a representative measure of how common those concerns are among all developers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the evidence say about productivity and long-term skill?
The short-term appeal is real enough to study, but the evidence should be read precisely. In the 2026 preprint Vibe Coding in Software Development: A Multivocal Literature Review, authors report that 21 of 47 retained sources (45%) described short-term productivity or time-to-prototype gains. That is a count of sources reporting gains, not an estimate that vibe coding improves productivity by 45% for users.
The review says evidence remains limited on maintainability, long-term software quality, and the effectiveness of safeguards. The available sources also do not establish a long-term causal effect on users’ independent programming competence. There is no representative prevalence statistic showing how many people lose skills through vibe coding, nor controlled proof that prototype success creates an illusion of competence. Those questions remain open.
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How to judge whether an AI-built result is dependable
For a quick prototype, the relevant question may be whether it demonstrates an idea. For software that others will rely on or that must be maintained, look beyond whether it runs once. These checks are practical applications of the workflow and risks described in the cited work, not a validated scoring system.
- Inspect what matters: Know which parts of the generated implementation handle important behavior, data, or access. Do not treat every uninspected line as understood.
- Test intended behavior: Check the requirements the software is supposed to meet, including cases beyond the most obvious successful path.
- Supply and verify context: State constraints and relevant project details clearly, then check that the model’s changes respect them.
- Own the debugging decision: When a result fails, decide whether to investigate manually, ask for a targeted change, or revert it rather than layering uncertain fixes.
- Match oversight to stakes: A disposable demonstration and software requiring dependable ongoing maintenance call for different levels of review.
These practices do not make generated software automatically safe or correct. They help distinguish directing an AI toward an output from being able to evaluate and take responsibility for that output.
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