Founders are shipping faster than ever. A weekend, a few prompts, and a product is live. No developer hired, no budget spent, no waiting. For the first few weeks, it works. Users sign up, the demo is clean, and the founder feels unstoppable. Then something breaks. A user can't log in, a security researcher flags an exposed API key, or traffic spikes and the server just stops responding. This is the reality of many AI-built apps that quietly fall apart once they hit the real world.
I'm Asaduzzaman "Asad" Pavel, a senior software engineer and consultant. Since 2011, I've been building production systems across fintech, streaming, and SaaS. I'm seeing more and more founders run into this wall: they built something that looks like a product but is actually a liability.
The problem with AI-built apps
The issue isn't the AI; it's what it doesn't tell you. These tools generate code that runs, not code that's maintainable. I've seen codebases where adding a single button took weeks because the AI generated a 2,000-line file that tangled authentication logic with database queries. It works in a local demo, but it stays invisible as a problem until you try to grow.
Security holes don't show up in demos
The most common mistake I find is hardcoded credentials. API keys end up in source files, get pushed to GitHub, and are compromised in minutes. I assumed AI tools would at least handle basic safety, but they usually just follow the path of least resistance.
Then there's input handling. AI-generated forms often lack sanitization, meaning a malicious user can hand a stranger your entire database via a contact form. Your first 100 users won't trigger this. Your 101st might.
It holds until you actually start scaling
A system handling 20 users is easy. Scaling to 2,000 is a different engineering problem. Most AI tools don't architect for load. They answer the immediate question. This means no database indexing, no caching, and no redundancy. The moment your Product Hunt launch drives real traffic is usually the moment the product fails most visibly. I think we're in a phase where "shipping fast" is being confused with "shipping correctly," and the gap between the two is where businesses die.
...And honestly, the moment anyone else needs to work on it, that undocumented chaos has a price. AI code tends to work while making zero structural sense. I've worked with teams that spent days just mapping what a codebase was doing before they could safely change a single line. That time is billable, and that delay is real.
What to do before it becomes a crisis
If your product is already live, it's not too late, but the technical debt is already compounding.
- Get a technical review. One day of an experienced engineer looking at your architecture is worth more than weeks of emergency fixes later.
- Treat AI output as a draft. It needs review and restructuring before it handles real user data.
- Understand your legal exposure. If you're collecting emails or payment details, you're responsible for them. AI commonly stores data in ways that would make a GDPR auditor have a heart attack.
If any part of this made you think about your own product, that instinct is worth following. The founders who get ahead of this are the ones who ask uncomfortable questions before a breach or an outage forces the issue.
I'm Asaduzzaman "Asad" Pavel, a senior software engineer with over 13 years of experience. If you want a straight read on where your product stands, you can find me at iampavel.dev.
Top comments (1)
The "it works in a local demo but breaks in production" problem is the defining issue of AI-generated code right now. The tools optimize for making something that runs, not something that scales, is secure, or is maintainable. That distinction does not matter at 20 users. It matters a lot at 2,000.
The hardcoded credentials issue is embarrassing because it is so preventable. Environment variables are not a complicated concept but AI tools consistently take the path of least resistance and inline secrets directly in source files. One push to a public GitHub repo and your API keys are compromised before you even notice. This should be the first thing any founder checks after generating code with AI.
The 2,000 line tangled file problem is real too. AI tools do not refactor. They add. Every prompt generates more code on top of what already exists. After a few weeks you have massive files where authentication, database queries, and UI logic are all mixed together. Adding a button takes weeks because touching anything risks breaking everything else.
The advice about treating AI output as a draft is the right framing. Use AI to get the first version out fast but then have a real engineer review the architecture before you go to production. The cost of a one-day technical review is nothing compared to the cost of an emergency rewrite after your Product Hunt launch crashes because nothing was indexed or cached.
The founders who use AI tools successfully are the ones who already know how to code. They use AI to move faster, not to replace understanding. The ones who get burned are the ones who cannot tell the difference between code that works and code that is production-ready.