“Vibe coding” — a term popularised in early 2025, referring to the practice of writing code primarily through AI interaction rather than manual typing — has become one of the most significant shifts in software development practice in years. Here is an honest assessment of what the practice actually achieves, where it works, and where experienced developers need to apply their own judgment.
What Vibe Coding Actually Means
The original usage (Andrej Karpathy’s February 2025 post): “I’m building a new paradigm of coding where you don’t code by hand anymore. There’s a new mode… vibe coding, where you fully give in to the vibes, embrace not fully understanding, and just ask the AI to fix things. […] It’s kind of like pair programming where the AI does most of the driving and you mostly direct things.” The key insight: for certain categories of tasks, particularly throwaway scripts, rapid prototyping, and building things where the exact code quality does not matter, AI can generate most of the code while the human directs at a higher level of abstraction. The distinction from traditional AI-assisted coding: vibe coding implies a more complete surrender of code control — you may not read or fully understand all the code that the AI generates. This is a meaningful difference from “GitHub Copilot completes my functions” or “Claude helps me debug this specific error.” What makes it possible in 2025: significantly improved code generation quality from models like Claude Sonnet, Gemini, and GPT-4o; the rise of integrated AI coding environments (Cursor, Windsurf, Aider, Claude Code) that provide full-file context rather than just line completion; voice interfaces that allow truly hands-off direction of coding tasks.
Where Vibe Coding Works Well
Rapid prototyping: building a working demo or prototype quickly, where the goal is demonstrating an idea rather than production quality. The code may be messy and the AI may take suboptimal approaches — but if the prototype works and the concept is validated, the code quality is secondary. Internal tools: dashboards, scripts, and tools that are used internally (not exposed to users or security contexts). The risk tolerance is higher when internal users are the only audience. Automations and scripts: one-shot scripts that process data, move files, parse logs. Here the AI can often generate a complete working script in minutes that would take an experienced developer 30–60 minutes to write carefully. Learning and exploration: trying out an unfamiliar library, framework, or API. The AI can generate working examples faster than reading documentation. The “it just needs to work” category: a wedding website, a family photo gallery, a simple landing page. No security concerns, no scale requirements, no long-term maintenance planned. The key success factor: the human must still be able to evaluate whether the output is correct. Vibe coding does not eliminate the need to understand what the code is supposed to do and test that it does it.
Where Vibe Coding Fails and the Experienced Developer’s Edge
Production systems with security requirements: AI-generated code has well-documented tendencies toward SQL injection vulnerabilities (concatenating user input into queries), XSS vulnerabilities (not escaping output), insecure direct object references, and other OWASP Top 10 issues. Without a code review by someone who knows what to look for, AI-generated code in security contexts is risky. Complex business logic: AI models generate code that looks syntactically correct but may not implement complex business rules correctly — particularly rules that span multiple systems or have subtle edge cases. The model does not know your domain. Architecture decisions: vibe coding tends toward immediate solutions rather than good architecture. A system built entirely by vibe coding accumulates technical debt rapidly — each feature added by AI may introduce patterns inconsistent with the rest of the codebase. Long-term maintenance: code that you did not write and do not understand is harder to debug and extend. Experienced developers can read AI-generated code and understand it; teams where the code is treated as a black box eventually get stuck. The experienced developer’s edge in 2025: the people who use AI tools most effectively are those with the deepest technical knowledge to evaluate and direct the output — not those with the least knowledge. Vibe coding lowers the floor (making some programming accessible to non-programmers) but raises the ceiling (experienced developers with AI tools can accomplish significantly more than they could without).
