Invested 470,000 TWD, the moment when the AI startup dream shatters after 18 months

When AI becomes the new “Gold Rush,” most entrepreneurs are just repeating the same mistakes.

After ChatGPT exploded in 2023, there are everywhere on the internet voices like: “I developed an AI tool that makes over ten thousand a month” “Our AI startup raised 2 million” In this wave, a former software consultant turned entrepreneur also fell into the trap, investing 470,000 TWD and 18 months to develop an AI copywriting tool, only to end up with: 12 paying users and total revenue of just 340 TWD.

How Failed Products Are Born

The initial idea was “smart”: build an AI copywriting tool for small and micro businesses. The logic seemed perfect: small businesses lack copywriting skills, don’t want to hire a copy team, AI writes pretty well, and a subscription model can generate continuous income.

The validation phase was “solid”—asked a bunch of friends “Would you pay?” and got a lot of “Yes.” But here’s the first fatal mistake: People say they’re willing, but actually paying is two different things.

From month 1 to month 18, this project followed a typical startup death path:

Stage 1 (Months 1-3): Claiming to build an MVP, but ended up piling on custom AI training, 47 templates, user authentication, payment system, admin backend, data analysis… investing 12,000 TWD.

Stage 2 (Months 4-8): Beta users start requesting features, each feature adds more work, each takes 2-3 times longer than expected. Costs balloon to 28,000 TWD.

Stage 3 (Months 9-12): Code debt explodes, features conflict, spent 4 months refactoring and bug fixing. Total investment reaches 39,000 TWD.

Stage 4 (Months 13-18): Started marketing—Product Hunt rank 47, Facebook posts, 500 cold emails, Reddit ads, Google ads, LinkedIn promotion. Spent another 8,000 TWD, only 73 sign-ups, 12 paid.

Final tally: spent 470,000 TWD, earned 340 TWD.

Why was it so disastrous?

A careful analysis shows the problem isn’t technical, but mindset:

Issue 1: Solving the wrong problem

Small and micro businesses don’t need “better copy,” they need “more customers.” The difference is huge. After talking to actual users, it turns out—they’re too busy, don’t trust AI’s brand tone, and would rather pay 50 bucks for a neighbor kid to help.

Issue 2: Facing ChatGPT’s indiscriminate competition

Why would customers pay 29 bucks for your tool when ChatGPT Plus costs 20 bucks and is much more powerful? The only selling point was “easier to use than ChatGPT,” but even 10% easier isn’t worth paying 45% more.

Issue 3: Ignoring sales and marketing

In 14 months of development, only 4 months were spent on marketing. The reality should be the opposite: 4 months to develop, 14 months to market. This reflects a common developer misconception—“If we build a good product, customers will come”—which is the most naive idea.

Issue 4: Customer acquisition cost vs. customer lifetime value

Customer acquisition cost is 650 TWD (8000 TWD marketing ÷ 12 customers), with each customer contributing an average of 28 TWD (most churn after a month). This clearly shows the business model itself is flawed.

Issue 5: Building for oneself, not for customers

Designing products based on assumptions about small businesses without actual research. Customers don’t care how beautiful the UI is; they just want to know “Will this help me make money?”

The true stratification of the AI startup ecosystem

Over these 18 months, talking to many “AI entrepreneurs,” a clear stratification emerged:

Top 5%: True success stories

• Have deep industry expertise before entering
• Solve specific real-world problems
• Target B2B with corporate budgets
• Examples: radiology AI diagnosis, legal document review, financial compliance tools

Next 15%: Lifestyle entrepreneurs

• Simply package OpenAI API
• Serve highly niche vertical markets
• Monthly income 5,000–20,000
• Examples: dentist AI email replies, job description generators

Next 30%: Strugglers

• Have flashy tech demos
• Cannot find paying users
• Burning through savings or investor funds
• The original entrepreneur is in this layer

Bottom 50%: Dreamers

• Dream of beating Google
• Fundraise with PPT
• Fail within two years

The real scalable model for AI business

After talking with successful entrepreneurs in the first two layers, some patterns emerged:

Pattern 1: Choose “boring” industries

The hottest AI companies attract all attention and funding, but plumbers also need software—this space has much less competition.

Pattern 2: Charge enterprise prices

If you can save a company 40 hours a week, charge based on that value. Don’t be fooled by consumer apps priced at 29 bucks/month.

Pattern 3: Focus on compliance and risk reduction

Companies are willing to spend big to avoid lawsuits or fines. This “risk mitigation” AI value is 10 times that of “productivity-enhancing” AI.

Pattern 4: Become an industry expert first

Before building AI for a specific industry, spend 2-3 years deeply understanding it. AI itself isn’t hard; the hard part is truly understanding where the problem lies.

Current new attempts

This entrepreneur didn’t give up, just shifted approach:

• Pick an industry with connections (website development firms)
• Identify specific, expensive pain points (annual costs over 100,000)
• Build the simplest solution (sometimes without AI)
• Price reasonably (monthly 500–2000, not 29)
• Secure 10 paying clients before developing fancy features

New product: a project management tool for web development agencies, integrated with existing tech stacks, automatically generating client reports. No AI hype, just solidly solving a tough problem.

Four harsh truths about the AI gold rush

Truth 1: Most AI startups are just high-end consulting firms

If your business model is “AI makes work faster,” you’re essentially selling labor arbitrage—just consulting with some flair.

Truth 2: Big companies will eat your territory

Competitive advantage? “We fine-tuned GPT”? That’s no advantage, at best a 6-month window. Once the giants move, you’re out of business.

Truth 3: Customers only care about results

They don’t care what tech you use. “AI-driven” isn’t a selling point; “saving 10 hours a week” is.

Truth 4: Technical barriers are lower than ever

Building AI products is easier than ever, meaning everyone is doing it. You need a real business advantage, not just a technical one.

Things that should have been understood earlier

• Start from market, not tech: Find people facing tough problems first, then figure out how to solve them.
• B2B is always better than B2C: Companies have money and understand ROI; consumers just want free stuff.
• Market segmentation to the extreme: “AI for small micro businesses” isn’t a niche; “AI for orthodontists scheduling” is.
• Validate with real money: Don’t ask “Would you pay?” ask “Are you paying now?”
• Budget needs to be tripled: Everything in AI takes longer because the technology is still evolving.

Final words

This 470,000 TWD tuition isn’t a waste; 18 months of lessons taught me more about business than five years of consulting. But you could have learned these lessons for much less.

The opportunity in AI does exist, but not as the Twitter influencers hype it—just building a ChatGPT wrapper isn’t enough. You need to deeply understand a specific industry and use AI to solve their most urgent problems.

Most people jump into the AI wave because the technology is exciting. But exciting tech doesn’t make a business; understanding customer problems does.

The core problem in entrepreneurship today is this—everyone is looking for quick riches, a universal key, a secret weapon.

There are no secret weapons. Only dull, boring work: deeply understanding your customers, then solving their problems.