The Moment It Hit Me
I'd been heads-down for three months building a real estate investment simulator. It was a proper SaaS — loan calculators, renovation cost modeling, rental income projections, cash flow scenarios for old Japanese houses (kominka). I had Stripe integration, a Pro plan at ¥2,980/month, the works.
Then one evening, I watched someone type "simulate the rental yield for a 6-room property in Kamakura, purchase price ¥25M, renovation ¥8M, rent ¥65,000/room" into Claude — and get back a detailed cash flow breakdown in about 10 seconds.
Three months of my work. One prompt.
What I Built and Why
I'm an SRE engineer by day, and I'd gotten into real estate investing on the side — specifically old Japanese houses (kominka) that you can convert into rental apartments. The math is complex: you're juggling purchase price, renovation costs per room, loan terms, vacancy rates, property tax, management fees, and a dozen other variables.
I kept building the same spreadsheet over and over for each property I evaluated. So I thought: why not turn this into a product? Other investors must have the same pain.
I spent three months building it. Feature after feature — multiple property comparison, scenario modeling, loan amortization charts, break-even analysis. I even built dark mode. (Every indie hacker's favorite procrastination feature.)
Here's where I made my first mistake: I kept adding features without talking to users. The UI got complex. Really complex. And I had no idea which features actually mattered because I'd never validated with anyone except myself. When you're the developer AND the only user, everything feels essential.
Then Stripe rejected my payment integration. That stung, but looking back, it was the universe trying to tell me something.
What Happened Next
Around the same time, AI models got seriously good at financial analysis. Claude, ChatGPT — they could all handle multi-variable real estate calculations conversationally. You describe a property, ask your questions, and get answers. No UI to learn, no subscription to pay for.
The "SaaS is Dead" narrative started picking up steam in indie hacker circles. And while I think that take is overblown for most categories, for calculation-heavy tools with no network effects or proprietary data? It hit close to home.
My simulator was essentially a structured UI for math that a language model could do on the fly. The only "advantage" was a nice interface — but even that was debatable, since my UI had gotten too complex for its own good.
The Question I Should Have Asked
Before writing a single line of code, I should have asked:
"Can AI do this well enough that a dedicated service adds no unique value?"
I never even considered it. In 2025, when I started building, AI-as-calculator wasn't as obvious. But the trajectory was clear if I'd been paying attention. And more importantly, there's a broader version of this question that every indie hacker should ask:
"What makes this worth being a product instead of a prompt, a script, or a spreadsheet?"
If the answer is "a nicer UI" — that's not enough anymore.
The AI Replacement Test
Here's what I do now before building anything. It takes about an hour.
Step 1: Try to Replace It with AI (15 minutes)
Open Claude, ChatGPT, or whatever model you prefer. Describe your product's core use case as a prompt. Be specific.
If the AI produces 80%+ of the value your product would deliver — stop. Your product needs a fundamentally different value proposition, or it shouldn't exist as a product.
For my simulator, the AI nailed the math. It couldn't save scenarios across sessions or generate comparison charts, but honestly? Most users would be fine with copy-pasting into a spreadsheet.
Step 2: Identify Your "Moat Against AI" (15 minutes)
Ask yourself what your product does that AI can't replicate:
- Proprietary data — Do you have data the model doesn't? (e.g., real-time pricing, user-generated datasets)
- Network effects — Does it get better with more users? (e.g., marketplace, community)
- Workflow integration — Does it plug into a system where copy-pasting AI output would be painful? (e.g., CI/CD, CRM)
- Compliance/trust — Does the domain require auditability, consistency, or certification that AI can't guarantee? (e.g., medical, legal, financial reporting)
- Collaboration — Do multiple people need to work on it together in real-time?
If you can't check at least one of these — you're building a nice wrapper around something AI gives away for free.
Step 3: Ask 5 People (30 minutes)
Not "would you use this?" — that question is useless. Everyone says yes.
Instead, ask:
- "How do you handle [this problem] today?"
- "Have you tried asking ChatGPT/Claude to do this?"
- "What was missing from the AI's answer?"
If they haven't tried AI for this yet, suggest they try it right then. Watch their reaction. If they say "oh wow, this is good enough" — you have your answer.
Step 4: Write Down Your Hypothesis Before Building
Write one sentence:
"People will pay for [my product] instead of using AI because [specific reason]."
If you can't fill in that blank convincingly — don't build it yet. Validate the "[specific reason]" part first.
What I Do Now
That experience — three months of building something that AI made redundant — changed how I approach every new project. I now validate hypotheses before writing code. I decompose ideas into testable assumptions and kill the ones that don't hold up.
I actually built a tool to manage this process for myself: KaizenLab. But honestly, even a notebook works. The tool doesn't matter. The discipline does.
The Uncomfortable Truth
The hardest part of this story isn't that AI replaced my product. It's that I could have figured this out in an afternoon if I'd been willing to question my own idea.
I didn't want to test the hypothesis because I was afraid the answer would be "don't build it." And I was right to be afraid — that was the answer. But finding that out in an afternoon is infinitely better than finding it out after three months.
If you're an indie hacker reading this: before your next npx create-next-app, spend one hour on the AI Replacement Test. It might save you three months.
Or it might confirm that your idea is genuinely defensible — and then you'll build with way more confidence.
Either way, you win.
Have you had a project disrupted by AI? Or found a way to build something AI can't easily replace? I'd love to hear your story in the comments.
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