Vibe coding: where it works and where it breaks down
Vibe coding is not a fringe experiment any more. Roots Analysis estimates the global vibe coding market will grow from USD 2.96 billion in 2025 to USD 325 billion by 2040, a compound annual growth rate of 36.79%. That is a lot of money chasing a practice that did not have a name until Andrej Karpathy coined the term in 2025.
The definition matters here. Vibe coding is intent-driven: you describe what you want in plain language and a generative AI model writes the code, runs tests, and iterates. It is not the same as autocomplete or boilerplate generation, which have existed for years. The shift is that the developer’s main job moves from writing code to describing, evaluating, and iterating on what the AI produces.
Who Is Actually Using It
Adoption is spreading across four distinct groups, each using the practice differently. Developers are offloading repetitive implementation. Startups are compressing time to first prototype. Enterprises are chipping away at internal tooling backlogs. Non-technical users are building functional software for the first time.
️ Three Tool Categories
- AI pair programmers and IDE assistants: Embed inside code editors, read the full codebase, propose multi-file changes, and execute commands. Built for developers who want to stay in their familiar environment while handing off the repetitive parts.
- Full-stack app builders: Take a prompt and generate a working front-end, back-end, and sometimes a database without the user touching a code editor. Primarily used by founders and product managers iterating on prototypes.
- Agentic coding assistants: Break a high-level instruction into smaller steps, write and debug code against those steps, then review and improve the output. The furthest along the automation curve of the three categories.
Four Places It Genuinely Works
- Rapid prototyping and MVPs: Turning an idea into a clickable prototype in days rather than weeks, before anyone has committed to building something nobody wanted.
- Internal tools and automation: Dashboards, Slack bots, and small utilities that never make it onto an engineering sprint. One person can now build many of these without pulling senior resources.
- Debugging and legacy code exploration: AI assistants can explain what an unfamiliar function does, locate a bug’s root cause, and suggest fixes, cutting the time it takes to trace logic manually.
- UI and front-end iteration: The tight feedback loop of front-end work suits vibe coding well. Describe a change, see it rendered, keep iterating in conversation instead of wrestling with CSS.
The Guardrails That Matter
- Treat AI output as a first draft, not a finished pull request. Review it the way you would review a junior developer’s work.
- Keep version control discipline. You will need to roll something back eventually.
- Write tests for AI-generated features. The untraceable origin of the code makes tests more important, not less.
- Reserve vibe coding for prototypes, internal tools, and non-critical features until your team has a proven review process.
- Keep a human accountable for every piece of shipped code, regardless of what generated the first version.
The honest summary: vibe coding is strongest where speed matters more than durability, and weakest where correctness, security, and long-term maintenance matter. The job does not disappear. The expensive part shifts from writing to verifying.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
With over 14 years in tech, marketing, and AI, including my role as Head of SEO at CitizenShipper and co-founder of TaleBot at Intale AI, I’m dedicated to sharing genuine, useful product insights and tips.
At BizStack, I aim to cut through the digital noise to provide clear, actionable advice.
And more than all else, I’m a father to a (always) little girl and a husband.
Contact me at [email protected] for assistance.
