The AI Product Graveyard
I've advised 30+ AI startups in the last two years. Here's a rough categorization of what I've seen:
- 5% solved a real problem better than existing solutions
- 15% solved a real problem but not better than non-AI alternatives
- 80% were technically impressive demos that no one would pay for
The pattern is always the same: a talented engineer discovers AI can do something cool, builds a product around that capability, brings it to market, and discovers that the market doesn't care.
{
"type": "tree",
"title": "AI Product Lifecycle",
"color": "blue",
"steps": [
"Cool AI Capability",
"Build Product Around It",
"Launch",
{
"label": "Do Users Care?",
"branches": [
{ "condition": "80%: No", "color": "red", "steps": ["Pivot Frantically", "Run Out of Runway"] },
{ "condition": "15%: Meh", "color": "amber", "steps": ["Modest Traction", "Struggle to Scale"] },
{ "condition": "5%: Yes!", "color": "green", "steps": ["Product-Market Fit", "Growth"] }
]
}
]
}
The Inversion Principle
The best AI products I've seen all follow what I call the Inversion Principle: start with a painful, expensive human process and make it 10x faster or cheaper with AI.
Good: "Law firms spend $500/hour on document review. We do it in seconds for $0.50."
Bad: "What if your fridge could write poetry based on its contents?"
The difference isn't intelligence or technology. It's problem selection. The best AI founders I know spend 80% of their time talking to customers and 20% building. The worst spend 100% building and 0% talking to anyone outside their echo chamber.
The Uncomfortable Test
Before building any AI product, ask yourself one question: "Would this product be valuable if the AI were replaced by a team of competent humans doing the same thing?"
If yes — great, AI just makes it faster and cheaper. Build it.
If no — the value proposition was never real. You're building a tech demo, not a product.
