A founder shows us an app they built in six weeks with Lovable or Cursor. It logs users in, it takes payments, it looks sharp on a laptop. Then comes the question we hear more and more often: if AI can do all this, why would anyone still need software engineers?
It’s a fair question, and we get why people ask it. Vibe coding and software engineering can run on the same AI models, sometimes inside the same tools. They just aim at different results. And when the code misbehaves at 2 a.m. with real users inside it, only one of them comes with someone on the hook.
If the term is new to you, start with our guide on what vibe coding is. Here, we look at vibe coding vs software engineering: where the line sits, and why founders should care about it even more than developers do.
Vibe Coding vs Software Engineering: The Short Answer
Vibe coding means building software by describing what you want to an AI and accepting what it generates, judged mostly by whether it looks and behaves right on screen. Software engineering is the discipline of building software that keeps working: code other people can read, change, secure, test, deploy and support for years.
Simon Willison, one of the most respected voices on AI-assisted development, draws the line in a way we agree with. For him, vibe coding is “building software with an LLM without reviewing the code it writes.” Once someone reads that code and answers for how it behaves, you’ve crossed into engineering, with AI still in the loop.
Two Different Definitions Of “Done”
Most of the confusion starts here.
In vibe coding, done means the demo works. You tap the button, the right screen appears, the data shows up. You see the result right away, and honestly, that’s what makes it so addictive.
In software engineering, done is harder to see. It means the feature still holds up on an older Android phone with one bar of signal, that nobody can pull up someone else’s records by tweaking a request, and that a bad release can be rolled back before most users notice. It also means a developer who’s never touched the project can find a feature and change it without knocking something else over.
None of that shows in a demo, which is how two apps can look identical on screen and have very little in common underneath. As our co-founder Liam Coughlin puts it:
“Many AI-generated apps look production-ready long before they actually are.” Liam Coughlin, co-founder
Vibe Coding vs Software Engineering, Side By Side
| Vibe coding | Software engineering | |
| Main goal | Get something working fast | Keep it working as it grows |
| Who reads the code | Often nobody | Every change gets reviewed |
| How success is judged | It looks right on screen | It behaves right under real conditions |
| Working loop | Prompt, run, look | Spec, build, test, review, release |
| Environments | Usually one, and it’s live | Dev, QA and production kept apart |
| When something breaks | Another prompt | A diagnosis, then a targeted fix |
| Security | Whatever the model thought of | Designed in, then verified |
| Best fit | Prototypes, demos, internal tools | Products with users, data and payments |
The Same AI, Doing Two Different Jobs
Something people tend to forget: our engineers use AI every day, like most serious teams we know. The difference is in what we let it do.
In vibe coding, the AI writes and the human watches the result. On an engineering team, a developer still owns every line, and the AI works more like a very fast assistant whose output gets reviewed before anything ships.
Take the audits we run as part of our vibe coded app rescue work. We sometimes use an AI with good context to scan a large codebase and flag patterns worth a closer look. What we don’t do is hand those findings to another AI and ask it to fix everything. We’ve seen what happens when people try. The two models start working against each other, a fix in one place causes problems somewhere else, and the code ends up in worse shape than before. That’s the moment a person who understands the whole system has to step in.
Think of a bare “make me an app” prompt as asking an AI to write you a beautiful text in Italian without giving it any Italian. No dictionary, no examples. It’ll write something, it’ll sound sure of itself, and it’ll be riddled with mistakes nobody catches. Give it a dictionary and a few good examples and you get a very different text. In software, that dictionary is what engineering brings: the architecture and the platform rules the output has to respect.
What Engineering Adds That Prompts Can’t
A Structure That Survives Change
AI-generated code tends to be organized around the last request rather than around the product. Each session tacks something on or shuffles something around. After a few hundred prompts, the same logic can live in four places and behave four slightly different ways.
Engineering gives each concept one home, so the fiftieth change doesn’t carry more risk than the first. That’s also why engineers spend time on refactoring, which means reorganizing code without adding a single feature. It never shows up in a demo, but you feel the difference the first time a bug needs fixing in a hurry.
A Way To Ship Without Breaking Things
The vibe coding world mostly skips what engineers call DevOps: versioning, separate environments, and a way to ship a release and pull it back when it goes wrong. In most vibe coded apps we open, that layer simply isn’t there.
The result is founders fixing bugs directly in the version their users are using, with no clear record of which fix went into which release. Imagine the Instagram team patching the app live while you scroll. Part of an engineer’s job is making sure that never happens.
Healthy Distrust Of The Output
AI writes with the same confidence whether its code is excellent or dangerously wrong. Engineers learn early not to trust code, their own included, until it’s been tested.
That habit is what catches an API key sitting in plain text in the app, or an endpoint that returns another user’s data when you change one number in a request. These are the security issues we find in AI generated apps all the time, and they rarely show up by clicking through the interface.
Where Vibe Coding Wins
To be clear, we like vibe coding. For a lot of work, it’s the right call. Validating an idea before paying for a team, building an investor demo, testing a user flow, spinning up an internal tool for five colleagues: vibe coding gets you there faster, for a fraction of what a development team would cost.
There’s also a benefit we didn’t expect. A founder who has vibe coded their own app shows up with a working version of what they want, and that tells a development team more than pages of requirements ever would.
In that sense, the prototype becomes the spec.
Where Software Engineering Stops Being Optional
In our experience, the switch becomes necessary as soon as one of these shows up:
- real users who depend on the app working
- personal data, and especially sensitive data
- money moving through the product
- a listing on the App Store or Google Play
- a second person who needs to work on the code
You can keep using AI past that point. Someone just has to own the code.
And this isn’t about how smart the founder is. Most of the people we work with understand databases and can challenge a technical decision. Even they miss a lot, because some of this only comes from years spent shipping mobile apps.
The Real Risk Is Switching Too Late
What usually hurts founders is staying in vibe coding mode after the product has outgrown it.
Every extra prompt on a codebase nobody fully understands adds to a bill that engineering will eventually have to pay. That’s why the final stretch of an AI generated app so often takes far more effort than it looks, and why the hidden costs of AI development tend to land right before launch.
The practical move is to decide early where the handoff happens. Vibe code the prototype freely. Then, before real users, data or payments arrive, bring in someone to read what was built and decide what stays. Sometimes most of it does. Sometimes parts of it shouldn’t, and knowing when to fix and when to rebuild is itself an engineering call.
Where This Leaves Founders
Online, the vibe coding vs software engineering debate often gets framed as a fight between AI builders and developers protecting their turf. We don’t see it that way.
More and more products start as vibe coded prototypes, and that’s fine. Engineering is what gets them through contact with real users. For a founder, the question worth asking is simpler: when does this code need an owner? For a demo, maybe never. For an app that stores people’s data and takes their money, the answer is before launch.
That handoff is the work we do every day. If your app was vibe coded and you’re wondering whether it’s ready for the next phase, our team helps founders finish and launch vibe coded apps without throwing away what they built.
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Frequently Asked Questions
Is vibe coding a form of software engineering?
Not really. Vibe coding produces software, but it skips the parts that define engineering: reviewing the code, testing it against real conditions, and taking responsibility for how it behaves over time. Once those parts come back in, the work becomes AI-assisted engineering.
Can a software engineer vibe code?
Yes, and many do, usually for quick experiments or throwaway prototypes. The difference is that an engineer knows when they’re doing it and switches back to reviewing and testing before the code goes anywhere that matters.
Do software engineers use AI coding tools?
Most do. They use them to write boilerplate faster, explore unfamiliar code or flag patterns during an audit. What they don’t do is ship AI output they haven’t read and understood.
When should a founder move from vibe coding to software engineering?
Before the app handles real users, personal data or payments, and before you submit it to the App Store or Google Play. Waiting until after launch usually means fixing problems under pressure, with users already affected.
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