Vibe coding: what it means for product teams and software buyers

In February 2025, Andrej Karpathy — one of the founding researchers at OpenAI — posted a description of how he'd been building software. He called it "vibe coding": describing what he wanted in natural language, accepting AI suggestions, and often not reading every line of the generated code.

The term stuck because it described what a lot of developers were already doing — and what many more were about to start.

This post isn't about whether vibe coding is good or bad. It's about what it means if you're buying software, managing a product team, or trying to understand what changed in software development in 2025.

Key Takeaways

• Vibe coding generates code from natural language prompts. Developers accept AI suggestions — sometimes without reading every line. This is now common practice, not an experiment.

• For prototypes and low-stakes features, vibe coding reduces development time by 40–70%. For production systems handling payments, health data, or access control, unreviewed AI-generated code is a real risk.

• Your development team uses AI coding tools. The question is whether their review process catches what the AI misses.

• As a software buyer, don't ask if your agency uses AI tools. Ask about the review process for AI-generated code.

What vibe coding actually is

Traditional software development: the developer thinks through the problem, designs the solution, writes the code line by line, and understands what every piece does.

Vibe coding: the developer describes the goal in natural language ("add a route that returns the last 20 orders for a given customer ID"), the AI tool (Cursor, GitHub Copilot, Claude) generates the implementation, and the developer accepts it — reviewing it to varying degrees, sometimes fully, sometimes partially, sometimes barely at all.

The tools have gotten good enough that the generated code works most of the time. The developer ships it. They move on.

This is a meaningful change in how software gets built. It's faster — sometimes dramatically faster. It also changes what the developer knows about the code they're shipping.

Where it's genuinely useful

Vibe coding has made some categories of work substantially faster:

Prototyping and MVP development. When you need to test whether an idea works before investing in a proper implementation, speed matters more than code quality. AI-generated code for a prototype that will be thrown away or rewritten is low-risk. Karpathy's original post was specifically about throwaway prototypes.

Internal tools. A dashboard for your ops team. An admin panel for content management. A script that transforms data formats. These are low-stakes, often used by small internal audiences, and don't require the same robustness as customer-facing production software. Vibe coding is appropriate here.

Boilerplate and standard patterns. API route definitions, database model scaffolding, test fixtures, form validation logic — code that follows established patterns where the AI is generating something it's seen thousands of times. The risk of a mistake is low and the review cost is low.

Greenfield features with low complexity. Adding a new page to an existing app. Building a simple notification system. Creating a report export function. When the feature is self-contained, reversible, and not handling sensitive data, the speed gain from vibe coding is worth the reduced code ownership.

Where it introduces real risk

Security-sensitive code

AI tools generate plausible code. They don't have a reliable model of what's security-critical in your specific application. They'll generate SQL queries that look correct but are vulnerable to injection. They'll implement authentication flows that miss edge cases. They'll write API endpoints that don't validate authorization properly.

The AI doesn't know that this particular endpoint handles payment data. It doesn't know your threat model. It generates code that works in the test case the developer described — not code that's secure against the ways a real attacker would approach it.

An analysis of AI-generated code across popular repositories in 2025 found security vulnerabilities in roughly 40% of AI-generated functions handling user input or external data (Veracode, 2025). Not critical vulnerabilities in all cases — but a rate that requires deliberate review, not casual acceptance.

Edge cases at scale

AI tools are trained on typical cases. They generate code that handles the common path well. The 2%, 5%, or 15% of inputs that are unusual — malformed, unexpected, at the edge of a boundary condition — are where AI-generated code fails more often.

For a prototype with 10 users, that failure rate is acceptable. For a production system with 10,000 transactions/day, it's a reliability problem.

Technical debt accumulation

Vibe coding often produces working code that isn't well-structured. It solves the immediate problem without considering how it fits into the larger codebase. Functions duplicate logic that already exists elsewhere. Abstractions are missing. The code works but it's hard to change.

This compounds over time. A codebase built primarily through vibe coding often becomes harder to modify after 6–12 months — not because the individual pieces are wrong, but because the structure has no coherent architecture. The AI generated each piece to solve the immediate problem, not to fit into a system.

When the developer doesn't understand the code

This is the most fundamental risk. If a developer ships code they don't understand, they can't:

• Debug it when it fails in unexpected ways

• Modify it safely when requirements change

• Identify which parts are making which tradeoffs

• Know whether a security review is needed

Vibe coding creates a class of code that's in the codebase but not fully owned by any developer. That's fine for a prototype. In a production system that a business depends on, it's a liability.

What changed, and what it means for software buyers

A year ago, most software agencies were using AI coding tools to speed up implementation while maintaining full engineering review of the output. That's still the responsible approach.

What's shifted is that the speed advantage has become competitive pressure. Agencies that review AI-generated code more carefully ship slower than agencies that don't. In a competitive market, some of that pressure pushes toward less review.

The right question for a software buyer isn't "do you use AI coding tools?" — assuming a quality agency doesn't is naive. The right questions are:

What does your code review process cover for AI-generated code? A good answer describes who reviews it, what they're checking, and what the acceptance criteria are before it merges.

What's your testing requirement before AI-generated code ships to production? A minimum should be: the feature is tested against real inputs, edge cases are documented and tested, and security-sensitive paths have explicit review.

How do you manage technical debt from AI-generated code? Good engineering teams have a regular refactor cycle. If an agency can't describe theirs, the codebase will become hard to maintain faster than traditionally-built code.

Who is responsible for security review? The answer should name a person or a process — not "the AI takes care of it" or "the developer checks it."

The right frame for product teams

Vibe coding is a productivity tool that makes good engineers faster and covers up the gaps in weaker engineering teams — until it doesn't.

The teams using it well are the ones that have:

• Clear rules about what requires full review (security, payments, auth, data handling)

• Automated testing that catches the edge cases AI tools routinely miss

• Architecture guidelines the AI-generated code has to conform to, not just work around

• Senior engineers who understand enough about the generated code to own it

The teams using it poorly are the ones where the speed gain is the only metric. Those codebases become difficult to maintain, debug, and extend — often within a year.

For product teams evaluating agencies or internal development practices: vibe coding is mainstream now. Your job is to ask the right questions about the review process that surrounds it, not to decide whether to allow it.

For a broader look at when AI tools are the right choice versus custom development, see AI tools vs custom AI development and build vs buy AI.

Frequently asked questions

What is vibe coding?

Vibe coding is a development approach where engineers describe their intent in natural language and AI tools generate the implementation — often without the developer reading each line of the output. The term was coined by AI researcher Andrej Karpathy in February 2025. It speeds up development significantly for prototypes and new features but introduces risks for production code that require review processes to manage.

Is vibe coding safe for production software?

It depends on what the code does and what review process surrounds it. For UI components, internal tools, and low-stakes features reviewed by a senior engineer, it's safe to ship. For authentication, payment processing, data encryption, or access control, AI-generated code requires careful security review before it reaches production. The AI tools don't know what's sensitive — that judgment requires a human.

How much faster is vibe coding than traditional development?

Practitioner reports and early studies suggest 40–70% faster for feature development on greenfield code. The speed advantage is real. The risk is that speed comes partly from skipping the mental work of understanding the code — which is also the work that catches security issues and edge cases.

What should a software buyer ask about AI coding tools?

Don't ask if your development team uses AI coding tools — assume they do. Ask instead about their review process for AI-generated code: what does code review cover? Who catches security issues? What testing is required before AI-generated code merges? How do they manage technical debt from AI-generated code over time?

What is the difference between vibe coding and traditional software development?

Traditional development has the developer design the solution, write the code, understand every line, and take responsibility for it. Vibe coding shifts the writing step to the AI — the developer describes intent, reviews (sometimes partially) the output, and moves on. The key difference is whether the developer understands the code they're shipping. In vibe coding, that understanding is partial, which creates risks for complex or security-sensitive systems.