AI Coding: Your MVP Ships Faster, Your Security Review Gets Expensive
When a vibe-coded product sells to Wix for $80 million six months after launch, it is tempting to conclude that AI coding tools are simply a net win. The reality is more nuanced and more interesting: they are a net win on a specific kind of work and a net cost on another specific kind of work, and the teams that understand that distinction are the ones who ship faster without accumulating hidden debt.
The Feeling Problem
A recent METR study put experienced developers in front of real issues in their own large codebases and measured actual time with and without AI assistance. Before they started, participants estimated AI tools made them about 24% faster. After the work was done, they estimated 20% faster. The actual measurement showed they were 19% slower with AI assistance.
That gap between perception and reality is the most important finding in AI coding research. AI assistance makes the typing phase faster and the reviewing phase slower, and humans are remarkably bad at noticing the review cost because it feels like normal work. The 15 minutes you saved on scaffolding looks like a win. The 25 minutes you spent debugging the "almost right" output it produced does not feel like a loss — it feels like your job.
Where the Speed-Up Is Real
The research consensus points clearly to one category: new code in unfamiliar territory. GitHub's controlled study found developers built a web server from scratch 55% faster with Copilot. Field experiments across multiple companies found 26% more tasks completed, with junior developers gaining 27-39% more output on short-tenure tasks. McKinsey's lab work shows documentation and greenfield code arriving in roughly half the time.
That is the MVP profile. A blank project, a stack you are learning, boilerplate that mostly copies itself, or a feature you can define in a short prompt. On that work, the tools do exactly what the marketing says. The key is recognizing that is not all of software development.
Where the Slowdown Creeps In
METR's slowdown happened precisely where you would expect: experienced developers maintaining codebases they had written themselves for years. The model produced plausible-sounding code for a system it did not understand, the developer spent time evaluating whether it was correct, and that evaluation cost more than just writing the function would have.
At scale, this is where teams get into trouble. A startup that leans hard on AI coding to ship its MVP hits product-market fit, starts growing, and discovers three months later that the "code that works" includes row-level security checks that are commented out, an admin panel accessible to any authenticated user, and API keys that ended up in the client-side bundle. The AI wrote fast. The AI also introduced a security review that nobody scheduled.
The Faros AI research, which measured over 10,000 developers across real teams, found that AI assistance actually slowed teams down in 20-40% of scenarios — particularly in codebases over 100,000 lines where the context window cannot hold the whole picture. That is the brownfield problem, and it is where most established teams live most of the time.
The Security Bill Nobody Mentions
Every week brings another story: a startup's AI-generated code exposed user data, or an AI-assisted deployment left a database port open, or a prompt injection found its way into a production system. These are not exotic edge cases. They are the predictable output of pointing a tool optimized for plausible code at security-sensitive work without a security expert reviewing the result.
The pattern is consistent. AI coding tools are trained on publicly available code, which includes a lot of code with known vulnerabilities, misconfigured permissions, and hardcoded secrets. When you ask one of these tools to build you a user authentication system or a payment integration, you are often getting a plausible version of what that looks like — which may or may not be a secure version.
For startups moving fast, this is the critical risk. You are not just building an MVP; you are building a reputation and a compliance surface. A data breach in your first year is not a technical problem. It is a company-ending problem.
The Practical Framework
The research points to a clear operational model:
Use AI aggressively for greenfield work. New projects, prototypes, scaffolding, unfamiliar stacks, and well-scoped features are where the speed-up is real and large. This is most of what gets an MVP live, and this is where these tools earn their subscription cost.
Use AI selectively for brownfield work. In a codebase you know well, or on anything touching authentication, payments, or user data, treat AI output as a first draft that needs a security review. The time you budget for that review is the real cost of the tool on that work. Do not let the "feel faster" signal talk you into skipping it.
Ship small, with tests. The instability in AI output shows up most in large, complex changes. Small incremental changes with real test coverage catch the subtle errors that pass the review and cause incidents in production. This is good practice in general, but it becomes critical when AI is in the loop.
Harden before users touch it. Turn row-level security checks on. Remove secrets from client-side code. Do not point an AI agent at a production database. These are not exotic security measures — they are the baseline for any system that handles real user data. AI coding does not change that baseline; it just makes it easier to miss.
The Bottom Line
AI coding tools are genuinely useful. They also introduce costs that are real, predictable, and almost never mentioned in the marketing materials. The teams shipping fastest are not the ones using AI for everything — they are the ones using it strategically, where the speed-up is real, while protecting the parts of their system where correctness matters more than velocity.
If you are building an MVP on Vibe Hosting, use AI tools to move fast on the parts that can change. Use them carefully on the parts that have to be right. And if you are not sure which is which, that is probably your next question.