We need to talk about something the industry is dancing around.
Outages keep making headlines. In October 2025, a single DNS failure in AWS's us-east-1 region took Snapchat, Roblox, and thousands of other services offline for most of a day (InfoQ). At the same time, a growing share of the code going into production is being written by AI, some of it by people who have never had to debug a system at 2am.
I want to be clear: I'm not against AI-assisted development. I use it. My team uses it. The productivity gains are real. But there's a difference between an experienced engineer using AI as a force multiplier and a non-engineer shipping production code they don't understand, to infrastructure they've never had to debug.
That difference is starting to show up in incident reports.
In July 2025, Replit's AI agent deleted the production database of an app SaaStr founder Jason Lemkin was building, during a code freeze and despite explicit instructions not to make changes, then misreported what it had done (eWeek). In December 2025, according to the Financial Times, Amazon's own coding agent Kiro caused a 13-hour outage of an AWS cost-management service in mainland China after deciding the fastest fix was to delete and recreate an environment (AI Incident Database). Amazon blamed a human permissions error instead, which makes the same point: an agent had access it never should have had.
The broader numbers point the same way. Apiiro's analysis of developers at Fortune 50 enterprises found that AI-assisted developers committed code at three to four times the rate of their peers, while monthly security findings rose tenfold, from about 1,000 to over 10,000, between December 2024 and June 2025. Privilege escalation paths rose 322%. Architectural design flaws rose 153% (Apiiro).
Here's what concerns me most as an engineer: those numbers aren't just one company's problem. When a vibe-coded app sits on shared cloud infrastructure, its failure radius extends far beyond the team that shipped it. The blast radius of a bad deployment is not proportional to the size of the team that caused it.
And when the people who built the system don't understand the system, recovery is slow, painful, and expensive.
This is the part nobody puts in the launch post: debugging code you don't understand, under pressure, during an outage, is a different skill set entirely from prompting an AI to build a feature on a Tuesday afternoon.
So where does this leave you?
If you've built something with AI tools (and there's no shame in that, it's 2026), the question isn't whether the app works today. It's whether you have the engineering depth to keep it working, secure it, scale it, and recover it when things go sideways.
Most vibe-coded projects don't. Not because the founders aren't smart, but because this stuff is genuinely hard, and AI doesn't yet know what it doesn't know.
That's exactly the gap we close. We work with founders and teams who've built fast with AI and now need to build right: auditing, hardening, and managing AI-generated code so that your next outage is someone else's problem, not yours.