90% of consumer AI ideas will die (use this 2 X 2 to validate)

The Tar Pit of Consumer AI

90% of consumer AI ideas look sexy but crash hard — most end up in the “tar pit” with zero real traction or revenue uplift.

For e.g. that new ‘productivity app’, that ‘event discovery platform’ and an all-time fav, ‘friend finder’ :)

In this video, I break down why so many consumer AI startups fail and share a realistic 2×2 framework that separates doomed ideas from winners.

• X-axis: Existing Behavior vs New Behavior

• Y-axis: ARPU * AI, i.e. Low ARPU uplift vs High ARPU uplift from AI (if AI does’t help you increase the TAM / revenue..then what’s the point).

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We cover real examples:

• Why short-news apps or generic AI tools built in hours almost always die (existing behavior + low ARPU)

• The massive opportunity in AI astrology (existing behavior + high ARPU via subscriptions)

• Why AI companions (new behavior + low initial ARPU) are VC catnip right now

• And the mysterious high-ARPU new-behavior quadrant — what could live there?

If you’re building in consumer AI, raising funds, or just trying to spot real opportunities instead of hype, this framework will help you avoid wasting time and money.

Do share your take on the framework.

Also, a related read 👇