Vibe coding is fast. But who owns the technical debt?

Vibe coding makes speed visible. Technical debt stays hidden until later. You can describe an app, watch it appear, and demo it the same afternoon — but the question that decides whether that speed was a gift or a trap is one nobody asks on day one: who is responsible for the code underneath?

This is not an argument against AI-assisted building. Used well, it is one of the most powerful things to happen to early-stage product work in years. It is an argument for knowing which use it is — because the same tool that builds a brilliant prototype can quietly build a product you cannot maintain.

What vibe coding means

Vibe coding is building software mostly by describing what you want in plain language and letting AI tools generate or heavily assist the code. Instead of writing every line, a person steers — prompting, accepting, adjusting — and the AI fills in the implementation. The barrier to a working result drops dramatically, which is exactly why it has caught on.

Why vibe coding became popular

• Faster prototypes — an idea becomes a clickable thing in hours, not weeks
• A lower barrier for non-technical builders — founders can produce working software themselves
• Cheaper experiments — testing ten ideas costs what one used to
• Stronger AI coding tools — models and agents are dramatically more capable than a year ago

JPMorgan's own guide for startups frames vibe coding as a real lever for founders to validate ideas quickly — while being clear that it suits experimentation more than production-critical systems.

Where vibe coding works well

The sweet spot is work where speed matters and the cost of a mistake is low:

• Prototypes — proving an idea is worth building properly
• Internal tools — small utilities for a known, forgiving audience
• Landing page experiments — testing messaging and demand
• Simple CRUD apps — straightforward create-read-update-delete flows
• Throwaway MVPs — built to learn, expected to be replaced

For these, technical debt barely matters — you are going to throw the code away or rebuild it once the idea is validated. Speed is the whole point.

Where vibe coding becomes risky

The danger starts when the same speed-first approach is used for things people depend on:

• Payments — money handling has to be exactly right
• Authentication — who can log in, and as whom
• Permissions — who can see and do what
• Customer data — privacy, storage, and compliance
• Integrations — code that must behave against real external systems
• Long-term product architecture — choices that are expensive to undo later
• Production reliability — staying up under real load and edge cases

Vibe coding is excellent at making something work once. Production is about making something keep working — for everyone, under pressure, a year from now.

The founder's problem: speed is visible, debt is hidden

Here is the trap that catches non-technical founders. A vibe-coded demo can look completely finished — it clicks, it flows, it impresses an investor. But the demo shows behaviour, not structure. Underneath, the code may be inconsistent, untested, duplicated, or built in a way that makes the next change slow and risky.

That gap between "looks done" and "is maintainable" is technical debt. It does not show up in the demo. It shows up three months later, when a small new feature takes two weeks, or a change to one screen breaks three others. By then it is expensive to fix — and nobody decided to take it on; it just accumulated.

What every AI-generated PR should be checked for

You do not catch hidden debt in the demo. You catch it in the pull request — the actual change to the code. For each AI-generated PR, the questions are:

• Does it match the task? Or did the AI build something adjacent to what was asked?
• Does it touch risky code? Payments, auth, permissions, data?
• Are tests included? Especially for anything users rely on?
• Is the architecture consistent? Or a new pattern bolted onto the side?
• Does it create future maintenance risk? Duplication, shortcuts, or fragility?

Why pull requests are better evidence than demos

A demo is a performance. A pull request is the record. The demo answers "does it appear to work?"; the PR answers "what was actually built, and how?" For a founder trying to judge whether fast delivery is also safe delivery, that distinction is everything.

This is the same reason PRs matter for billing and delivery generally — they are the artifact that does not lie about scope. (We make the broader case in Pull requests are the new invoice evidence, and on the review burden AI adds in The verification tax.)

How InsightPM helps founders understand AI-assisted delivery

InsightPM lets a non-technical founder read that evidence without reading code. It connects Jira and GitHub, analyzes each pull request, and produces a plain-English report: what changed, how complex and risky it was, whether it touched sensitive areas, and whether the logged effort lines up with the scope.

It will not tell you the code is elegant — that is an engineer's call. But it will tell you when a "quick" AI-assisted change quietly touched payments, shipped without tests, or ballooned far beyond its task. That is exactly where vibe-coded debt hides. See why InsightPM exists, or how this connects to AI coding agents opening their own pull requests and to reading pull requests as a non-technical founder.

FAQ

Sources & further reading

• J.P. Morgan — Vibe coding: a guide for startups and founders
• Stack Overflow Developer Survey 2025 — AI
• DORA — Balancing the tensions of AI in software delivery
• Hacker News — discussion on AI code quality and debt (developer signal, not data)
• Hacker News — discussion on vibe coding (developer signal, not data)