Why AI-Generated MVPs Fail at 1,000 Users (And How to Fix It)
Direct Technical Summary
AI-generated MVPs fail at scale because AI tools write naive SQL queries without database indexes, omit connection poolers, and evaluate security on the client side instead of the database layer. When concurrent users grow past 1,000, table scans cause database CPU to spike to 100%, leading to timeout crashes and connection pool exhaustion.
1. Unindexed Database Queries & Connection Exhaustion
Most AI code generators output simple SQL queries or ORM calls without defining proper database indexes or connection pooling parameters.The Problem
SELECT * FROM orders WHERE user_id = '...' perform sequential scans across millions of rows.PostgreSQL error: too many clients already.The Solution
1. Connection Pooling: Implement PgBouncer or Supabase Transaction Mode pooling. 2. Indexing: Add composite indexes on high-frequency lookup fields:
sql
snippet
CREATE INDEX CONCURRENTLY idx_orders_user_status ON orders(user_id, status);
2. Client-Side Authentication Leaks
AI tools often attempt to keep state simple by evaluating authorization logic directly in React component state rather than at the API gateway or database row level.The Problem
user.isAdmin in local state, which can be bypassed in browser Developer Tools.