Why Your Cursor-Built MVP Will Break at 1,000 Users (And How to Fix It Before It Does)

First, let's be specific about what failure looks like, because it's rarely a dramatic crash. It's usually:

• Response times that were 200ms at 10 concurrent users becoming 8 seconds at 200
• A database query that worked fine on 5,000 rows grinding to a halt on 500,000
• A background job that ran fine once a minute now running 800 instances simultaneously and exhausting your server's memory
• An authentication flow that was secure enough for beta testers becoming a liability when someone motivated decides to probe it
• A third-party API integration that worked fine until you hit the rate limit you didn't know existed

None of these are corner cases. They're the predictable, structural consequences of code that was written to demonstrate functionality, not sustain it.

1. Database Queries That Don't Scale

AI-generated database code is usually syntactically correct and logically sound. It does what you asked it to do. What it almost never does is consider query performance at volume.

The pattern looks like this: a query that works perfectly when your users table has 200 rows becomes catastrophically slow when it has 200,000. Not because the query is wrong — it returns the right results — but because there's no index on the column being filtered, or the query is doing a full table scan, or it's making N+1 queries in a loop (fetching a list of records, then making a separate database call for each one).

The N+1 problem deserves special mention because it's invisible in development. You load 10 posts and make 10 separate queries to fetch each post's author. That's 11 queries total, which takes maybe 50ms. At scale, you load 500 posts, make 500 author queries, and your page takes 4 seconds to render — if it renders at all.

The fix: Add indexes on every column you filter or sort by. Audit every query for N+1 patterns and replace them with joins or eager loading. Run EXPLAIN ANALYZE (in Postgres) or its equivalent on any query that touches large tables. Do this before you need to.

When you build with AI assistance, you write the happy path. The code does what you want it to do when users behave the way you expect them to. What it rarely accounts for is what happens when users (or automated processes pretending to be users) don't.

2. No Rate Limiting, No Throttling

Without rate limiting:

• A single user can send 10,000 requests in a minute and exhaust your server
• Someone can brute-force your login endpoint
• A webhook can fire repeatedly and create thousands of duplicate records
• A misconfigured client can poll your API every second and bring your infrastructure to its knees

This isn't a paranoid security scenario. It's just what happens when something gets popular enough to attract bad actors, or when a client-side bug causes a retry loop.

There's a common thread running through every failure mode above: they're all invisible at small scale.That's not a bug in AI-assisted development.

It's an inherent property of how the tools work. You describe what you want the software to do. The model writes code that does that thing. It can't optimise for conditions it hasn't been asked to consider.

This is also why the failure often feels sudden and mysterious. The codebase worked fine for months. Then, seemingly overnight, it doesn't. What actually happened is that the code always had a structural ceiling — you just hadn't hit it yet.