You vibe-coded your MVP. Read this before you scale it.

You described a product, an AI tool generated it, and it works well enough that people are paying or signing up. Now you want to add the team, the features, the traffic. First, the uncomfortable framing: 2026 is being openly called "the year of technical debt," largely because of how much got vibe-coded into production in the last eighteen months (Salesforce Ben).

Vibe-coded codebases fail on a remarkably consistent schedule. The pattern teams report: warning signs around day 30 (duplicated logic, missing error handling, dependencies nobody can explain), measurable velocity loss around day 60 (features taking three to five times longer than estimated), and the full reckoning — the "do we rewrite this" conversation — between day 60 and 90 (Autonoma). The reason it compounds rather than accumulates linearly: each AI-made change is generated without the context of the ones before it, so by 6–12 months the codebase is effectively a black box even to the people who shipped it (GIANTY).

Security

Multiple independent studies find that close to half of AI-generated code carries at least one security issue — injection-prone queries, hardcoded credentials, missing authorization, debug endpoints left live. The model generates plausible code based on patterns it has seen, including the vulnerable ones.

Cost

Unoptimized schemas and inefficient queries that are invisible at demo scale can inflate cloud costs dramatically under real load — one engineering write-up put it as high as 400% (Ravoid).

Maintainability

The most expensive kind. Traditional tech debt is "I wrote this quickly and I know why." Vibe-coding debt is "it works, I don't fully know why, and when it breaks in production I can't reason about it."

You don't need an audit to get a first read. Answer these honestly:

• Does anyone on your team understand why the code works, not just that it does?
• Has a human reviewed the auth and data-access model — who can read and write what?
• Do you know your per-user infrastructure cost, and how it scales?
• Are there tests that would catch a regression, or do you find out from users?
• Could you onboard a senior engineer to this codebase in a week?

Three or more "no"s and you're not scaling a foundation — you're accelerating a countdown.

This is the actual decision, and the honest answer is usually stabilize first, then scale — but "stabilize" rarely means the dramatic full rewrite founders reach for. The reflexive rewrite has a well-documented failure mode: it takes longer than planned and quietly re-introduces the hidden business-logic bugs the old code had (Autonoma). We wrote a separate note on what a "replatform" request usually needs instead. The point here is narrower: scaling on an unstable base doesn't fix the instability, it multiplies it.

Speed got you here. It won't keep you here. The question isn't whether to pay the debt — it's whether you pay it on your schedule or your users'.