Somewhere between “AI can write code now” and “AI can write your entire product,” a lot of businesses skipped a step. That step is common sense — the unglamorous, unautomatable judgment that decides whether AI-generated code is actually safe to ship, or just fast to produce. Vibe coding, the now-widely-used term for building software by describing what you want in plain English and accepting what the AI produces, has become genuinely useful. It’s also become a genuine liability when common sense isn’t applied alongside it, and understanding exactly where that line sits is one of the more valuable things a growing business can learn right now.
Where the Term Actually Comes From
The phrase “vibe coding” was coined by AI researcher Andrej Karpathy in a February 2025 social media post, where he described a style of programming built almost entirely on trusting an AI coding agent — accepting its changes without reading the diffs, pasting error messages back in without comment, and letting the codebase grow past his own detailed comprehension of it. It was, by his own framing, a description of a specific and fairly extreme mode of working — not necessarily a recommendation for every kind of project.
That distinction matters more than it usually gets credit for. As developer and researcher Simon Willison has pointed out, there’s a meaningful difference between vibe coding in the strict sense — building software with an AI without reviewing the code it produces — and the much broader category of AI-assisted programming, where a developer uses AI heavily but still reviews, tests, and understands what gets shipped. A lot of the public conversation conflates the two, and that conflation is where a lot of avoidable risk creeps in.
Notably, even Karpathy himself has since moved toward a more disciplined framing. In a follow-up post roughly a year later, he proposed the term “agentic engineering” as a more accurate description of mature AI-assisted development — work where a developer orchestrates AI agents but retains real oversight, expertise, and engineering judgment over the result, rather than fully disengaging from the code. The shift in his own language is a useful signal: the industry’s own most prominent advocate for AI-driven coding has been walking the definition back toward exactly the kind of judgment this article is about.
What “Common Sense” Actually Means Here
It’s worth being specific, because “use common sense” is the kind of advice that sounds true and says nothing. In the context of AI-generated code, common sense breaks down into a handful of concrete habits:
Understanding what the code actually does before it ships, even if you didn’t write it line by line. This doesn’t mean manually reviewing every character of AI output — it means understanding the shape of what was built, where the sensitive logic lives, and what would happen if a given piece of it failed.
Recognizing which parts of a system are low-stakes and which aren’t, and calibrating review effort accordingly. A prototype landing page and a payment processing flow do not deserve the same level of scrutiny, and treating them identically either wastes time on the former or under-protects the latter.
Testing behavior, not just checking that it runs. AI-generated code frequently compiles, runs, and even produces a plausible-looking result while quietly mishandling an edge case, a permission boundary, or a data validation rule that never came up in the prompt that generated it.
Knowing when to slow down. The entire appeal of vibe coding is speed. Common sense is the discipline to recognize the specific moments — authentication logic, payment handling, data deletion, anything touching customer information — where slowing down is the right tradeoff, even when everything else about the workflow is optimized for velocity.
Why This Gap Is Showing Up in Real Systems
The productivity gains from AI-assisted coding are real and well documented, and that’s exactly why the common-sense gap matters so much right now — adoption has outpaced the review habits that used to be built into slower, more manual development processes. When writing code took longer, there was more natural time built into the process for a developer to think through what they were building. AI coding tools compress that time dramatically, which is a genuine benefit, but it also compresses the moments where judgment used to naturally get applied.
This shows up in a few recognizable patterns. Code that handles the common case correctly but mishandles the edge case that wasn’t described in the prompt, because the AI generated exactly what was asked for and nothing more. Security-sensitive logic — authentication checks, input sanitization, permission boundaries — that looks correct at a glance but was generated without the specific threat model a human reviewer would have brought to it. And architectural decisions that make sense for a single feature in isolation but create real problems once several AI-generated pieces are stitched together into a larger system, because no single generation pass had visibility into the whole picture.
None of this means AI-generated code is inherently worse than human-written code — plenty of human-written code has exactly the same categories of bugs. It means AI-generated code removes some of the natural friction that used to force a human to think through these questions, and that friction needs to be deliberately reintroduced rather than assumed to still be there.
A Practical Framework for Applying Common Sense to Vibe-Coded Work
Rather than treating this as an abstract caution, it helps to have an actual framework for deciding how much scrutiny a piece of AI-generated code needs.
Classify before you generate. Before asking an AI tool to build something, take thirty seconds to classify it: is this disposable (a prototype, an internal script, a one-off analysis) or durable (something that will run in production, touch real user data, or become load-bearing infrastructure)? Disposable work can genuinely be vibe-coded with minimal review. Durable work needs a review process attached to it from the start, not bolted on after the fact.
Review the interfaces, not just the internals. Even for code you don’t want to review line by line, review what it exposes — what inputs it accepts, what permissions it requires, what it’s allowed to touch. This is a much smaller review surface than reading every line, and it catches the highest-consequence mistakes.
Write tests for the behavior you actually care about, not just the happy path. If a piece of AI-generated code handles customer refunds, the test that matters isn’t “does it process a valid refund correctly” — it’s “what happens when the refund amount exceeds the order total,” or “what happens when this gets called twice for the same order.” These are exactly the cases a prompt rarely specifies and an AI tool rarely generates for unprompted.
Treat AI-generated code the same way you’d treat a contractor’s first draft. A capable contractor can genuinely speed up a project, but a competent business still reviews the contract, inspects the work, and doesn’t hand over the keys unchecked. AI-generated code deserves the same basic posture — trust that scales with track record, not trust as a default setting.
Build the review step into the workflow, not into your memory. The businesses that get burned by AI-generated code aren’t usually the ones who lack good judgment in the abstract — they’re the ones whose actual workflow has no forcing function that makes the judgment happen. A review checklist, a required test suite, or a second set of eyes on anything touching production data turns common sense from an aspiration into a process.
Where the Real Tradeoff Lives
It’s worth being honest that this isn’t a call to abandon vibe coding or to slow everything down to a pre-AI pace. The speed gains are genuinely valuable, and for a large share of what businesses build — prototypes, internal tools, first drafts, low-stakes automation — a lighter-touch, faster process is the correct tradeoff, not a corner being cut. The mistake isn’t using AI to move fast. The mistake is applying the same low-scrutiny process uniformly, regardless of what’s actually at stake in a given piece of code.
This is, in a sense, exactly what Karpathy’s own shift toward “agentic engineering” was gesturing at: the goal isn’t fewer AI agents doing the work, it’s better-calibrated human oversight sitting on top of them. Speed and rigor aren’t actually in tension nearly as often as they seem to be — the tension mostly comes from treating every task as if it needs the same amount of either.
The Cost of Skipping This Step
It’s worth walking through what actually happens when common sense gets skipped, because the failure mode is rarely dramatic in the moment — it’s usually quiet, which is exactly what makes it dangerous. A vibe-coded internal tool works fine for months. Then someone extends it to handle a slightly different case, and it silently does the wrong thing because the original logic was never built to handle that case — it just happened to work for the inputs it was tested against at the time. Nobody notices until the wrong thing has already happened several times.
This pattern shows up with particular consequence around a handful of predictable areas: input validation that only handles the shapes of data the original prompt anticipated, authentication and authorization logic that looks correct but has a gap a deliberate security review would have caught, and data handling code that works correctly for a single record but breaks quietly at scale or under concurrent access. None of these are exotic failure modes. They’re the same categories of mistakes that have existed in software for decades — AI-assisted development hasn’t invented new kinds of bugs, it’s just changed how much friction exists before a bug like this reaches production.
The financial and reputational cost of these failures also tends to scale with exactly the dimension common sense is meant to address: how much was actually at stake in the part of the system that failed. A bug in a disposable internal script costs an afternoon. A bug in a payment flow, a permissions system, or anything touching customer data can cost meaningfully more — in direct remediation cost, in trust, and in the time spent retroactively building the review process that should have existed from the start.
Common Sense Scales With the Team, Not Just the Code
One pattern worth naming explicitly: common sense isn’t purely an individual habit, it’s something that needs to scale across a team the same way any other engineering discipline does. A single experienced developer vibe coding a personal project can hold the relevant judgment in their head. A team of five people, several of whom are newer to engineering and leaning more heavily on AI tools to move fast, cannot rely on that same implicit judgment being consistently applied — not because anyone lacks the underlying skill, but because judgment that only lives in individual heads doesn’t transfer reliably under deadline pressure.
This is exactly why the framework above emphasizes building review steps into the actual workflow rather than trusting that everyone involved will independently apply the right level of scrutiny at the right moment. A shared checklist for what counts as “durable” versus “disposable” work, a lightweight required review step for anything touching production data, and a habit of writing tests for the edge cases a prompt didn’t specify — these turn common sense from a personal trait into a team-level system property, which is the only version of it that reliably survives contact with a real deadline.
When we take on AI integration work, this framework isn’t theoretical — it’s baked into how we scope and deliver projects. We classify what’s actually being built early, we design test coverage around the failure modes that matter for that specific system rather than generic coverage targets, and we build review checkpoints around anything that touches production data, payments, or customer information, regardless of how much of the underlying code was AI-generated.
This is also a common gap we see when businesses come to us after a vibe-coded prototype has grown into something people actually depend on — the code works, but nobody can confidently say what happens in its edge cases, because it was never built with that question in mind. Retrofitting that confidence after the fact is possible, but it’s meaningfully more expensive than building it in from the start.
If your team has been vibe coding its way toward something real and you’re wondering whether it’s ready for more weight, book a discovery call and we’ll give you an honest read on where the gaps actually are. If you need dedicated engineering capacity to bring real review discipline to an AI-accelerated codebase, our hire talent model puts experienced engineers directly into your workflow.
If you’re an agency whose clients are asking for AI-assisted development and you want to offer it with real engineering rigor behind it rather than reselling unreviewed output, our white-label development services let you deliver that under your own brand. For anything else, get in touch and we’ll point you toward the right next step.
The Bottom Line
Vibe coding isn’t going away, and it shouldn’t — it’s a genuinely useful mode for a large share of what gets built today. But the term itself, in its original and most literal sense, describes an absence of the very judgment that determines whether software is trustworthy. Common sense is what fills that gap: not a rejection of AI-assisted development, but the deliberate, structured discipline of knowing when to trust the vibes and when to slow down and actually check. The businesses getting the most durable value out of AI coding tools right now aren’t the ones using them the least carefully — they’re the ones who’ve figured out exactly where careful needs to live.