I've used Cursor and Claude throughout the development of all 7 of my SaaS products. AI coding tools are genuinely useful in 2026. But there are specific places where they produce subtly broken output - and if you don't know where those are, you'll ship bugs you can't easily debug.
What AI Tools Get Right
UI scaffolding is where AI tools shine. Give them a component description and they produce clean, accessible JSX quickly. CRUD operations, form validation, basic API routes - all solid starting points. I'd estimate AI tools produce a useful first draft for roughly 70% of the boilerplate in a typical SaaS project.
Where They Still Break
Complex webhook logic. AI will produce a webhook handler that looks correct but misses idempotency, signature verification edge cases, or race conditions. Payment edge cases - proration, failed payment retries, subscription pausing - AI gets these wrong consistently. Custom business logic that requires understanding your specific domain.
The Architecture Problem
AI tools can't make architecture decisions for you. They don't know your scale requirements, your team's capabilities, or your business constraints. Ask them to design your database schema and they'll give you something technically valid but potentially wrong for your use case. Architecture requires judgment - and judgment requires experience.
The Right Mental Model
Think of AI tools as a fast, knowledgeable assistant who knows every syntax and pattern but has never shipped a product to production. They can produce the code. You need to know whether the code is correct, secure, and appropriate for your context.
// AI tools will produce this correctly:
const user = await supabase.from("users").select("*").eq("id", userId);
// What requires developer judgment:
// What if the webhook fires twice?
// What if the user was deleted between the webhook and this query?
// What if this runs during a database migration?I use AI tools to ship faster. The engineering judgment is still mine. Book a call if you want both.
