“I Can Give This to AI and Have My SaaS Built in Two Weeks”
That’s exactly why most AI-built SaaS apps fail. They’re built from prompts, not problems.
Alex Finn built an app using Cursor that’s doing 300K ARR. Launch day? 100K in 15 minutes. Everyone sees the success and thinks “I can do that too.”
Here’s what they miss: it took him three months. Three months of daily iteration, user feedback, and systematic building.
He didn’t prompt AI to “build an app.” He thought like a developer, breaking every step down, testing, refining. That’s why it worked.
The Illusion: AI Builds Products
Many founders think AI tools replace developer thinking. They don’t. They just reveal who can think systematically and who can’t.
You give AI a prompt like “Build an app that tracks my tweets and gives me content advice” and it generates something that looks like a product. Until users try it. Authentication breaks. Data doesn’t sync. AI responses are generic. They try it once, never come back.
AI doesn’t make you a builder. It magnifies your thinking (good or bad).
When Alex found Cursor in August 2024, he had a prototype in five minutes. He realized he could build enterprise software himself. But that prototype wasn’t the product. It was the starting point for three months of actual work.
If you don’t know what you’re building or why, AI just helps you build the wrong thing faster.
The Pattern: Why They Fail
Here’s what I see repeatedly: Founder gets excited about AI coding. Spends two days with Cursor. Ships something that technically works. Launches. Gets a few signups. Then nothing.
They built features, not solutions.
Alex wasn’t building “an AI content app.” He was solving his own problem: spending hours every night putting tweets in spreadsheets, analyzing patterns, figuring out what worked. That process was painful enough he’d been doing it manually for months.
That’s the difference. He started with a problem he understood deeply. Most founders start with a prompt.
Alex spent December beta testing with 150 people. Met with each one. Walked them through the product. Watched where they got confused. Saw what they actually used. That feedback loop turned a prototype into something people paid for.
How He Actually Built It
Alex’s approach was simple: break everything into the smallest possible steps.
Instead of “build a brain dump feature,” he’d prompt: “Build an input where users can enter an essay.” Then “Build a button that will repurpose the content.” Then “Command the AI model to turn that essay into a tweet.”
Feels slower. It’s faster. You don’t spend days debugging complex features AI built wrong. You catch issues immediately because each step is small and testable.
He even used ChatGPT as a product manager. Describe a feature, ask it to break it into micro-steps, feed those steps to Cursor one at a time.
That’s developer thinking. Not knowing syntax. It’s systematic problem decomposition.
What This Means for You
You don’t need to code. But you need to think clearly:
Break problems into steps. Not “build a dashboard.” Instead: “create user auth,” then “fetch data,” then “display in table,” then “add filters.”
Test brutally early. Alex had a prototype day one but didn’t launch until January. Months of feedback, finding bugs, watching users struggle with “obvious” features.
Focus ruthlessly. Alex built one core flow: import tweets, analyze patterns, get coaching. Everything else waited.
Iterate in loops. Build small, show users, see what breaks, fix it, add one thing, repeat.
When Alex launched January 24th, hundreds of people were ready to buy. Not because he built fast. Because he’d shared his journey for three months, shown progress, built community around the problem.
Launch day, everything broke. He took a 45-minute walk, came back to bug reports. But he could fix them. He understood every piece because he’d built it step by step.
The Real Advantage (It’s Not AI)
Alex’s stack is simple: Windsurf, ChatGPT, Vercel (20/month), Supabase (20/month), AI APIs. Total costs: 5,300/month. Revenue: 25K/month. That’s 80% margins.
Technology didn’t make him successful. Process did.
But here’s what actually matters: distribution.
Alex has 300K+ followers. He spent three years creating content before launching a product. Analyzed the X algorithm. Wrote viral threads. Built audience around content insights.
Anyone can clone his app with AI. But if they have 10 followers and Alex has 300,000, he wins. Every time.
When anyone can build anything, product isn’t the moat. Distribution is.
Final Take
AI is a multiplier. Clear process? It builds fast. Confused process? It fails faster.
Alex succeeded because he:
- Solved his own painful problem (not someone else’s imagined one)
- Broke everything into testable micro-steps (not big vague prompts)
- Got feedback from 150 beta users (not his own assumptions)
- Built distribution first (3 years of content, 300K followers)
The tool was just the tool. The thinking made the difference.
Stop asking “What can I build with AI?” Start asking “What problem am I solving, and for who?”
Then break it into the smallest steps. Test each one. Iterate based on what you learn.
That’s how you build products that work. Not fast. Right.
Next up: “Decision Framework to Choose Your SaaS Tech Stack” Subscribe to get it in your inbox next week.