What is Real Problem AI?

Real Problem AI is a curated directory of 100 real problems worth building an AI startup around. Each problem is scored honestly on severity, AI feasibility, market signal, and competition gap. We're for builders looking for what to build, not for ethics debates about AI.

What is the difference between Real Problem AI and AI ethics or AI risks indexes?

Real Problem AI catalogues problems AI can solve — productive, commercial opportunities for builders. AI ethics indexes catalogue problems with AI itself (bias, environmental cost, copyright, consciousness). Different intent, different audience: founders and indie hackers vs researchers and policy folks.

What should I build with AI in 2026?

Look for problems with high severity (it costs people hours, sleep, or revenue every week), high AI feasibility (LLMs, vision or voice are great at this today), strong market signal (Reddit threads + willingness to pay), and a clear competition gap (incumbents miss the same obvious thing). Real Problem AI scores 100 such problems for you on those four axes.

Where do Real Problem AI's startup ideas come from?

From three sources, scanned each cycle: 1) Reddit threads where people describe a real friction in their own words, 2) podcasts and talks of founders young builders trust (Nikhil Kamath, Aman Gupta, Ankur Warikoo, Raj Shamani, Andrej Karpathy, Greg Isenberg), and 3) a continuous 1,000+ source scan covering forums, app store reviews, regulator filings, and field interviews.

Is Real Problem AI free?

Yes. Browsing every problem, opening every score breakdown, raising your hand on a co-founder match, and the founder vault are all free. There is no paywall.

How is each problem scored?

Four axes, each 1-10, weighted: Problem Severity (30%), AI Feasibility (25%), Market Signal (25%), Competition Gap (20%). Weighted average is the Opportunity Score on each card. Only problems clearing 7.0+ make the live list.

Why does the TRAI template rule break every Indian D2C brand's WhatsApp broadcast?

Indian DTC brands lost 20-40% of their WhatsApp campaign reach in 2026 after TRAI tightened template approvals; existing tools have not caught up.

Category: E-commerce & Retail · Trend: AI Wrapper · Opportunity score: 8.4 / 10

What is the “Why does the TRAI template rule break every Indian D2C brand's WhatsApp broadcast?” problem in 2026?

Indian DTC brands lost 20-40% of their WhatsApp campaign reach in 2026 after TRAI tightened template approvals; existing tools have not caught up.

Who has this problem?

Indian DTC and SMB brands sending 10k+ WhatsApp marketing messages a month via Wati / Interakt / Gupshup.

Evidence this problem is real

“Half my templates are stuck in pending. The other half got rejected for words I have used for two years.”

Sourced from Indian D2C operator threads + TRAI 2026 template guidelines

Existing players in this space

Wati / Interakt — Submit templates manually; no compliance pre-check.
Gupshup — Larger volume but same approval guesswork.
No one — Pre-checks templates against the latest TRAI guidance + suggests rewrites.

What existing players are missing

An AI lint for WhatsApp templates: trained on TRAI 2026 rules + every approved/rejected template the customer has, suggests rewrites with predicted approval probability.

How Real Problem AI scores this opportunity

Aggregate score: 8.4 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 8 / 10
Market signal: 9 / 10
Competition gap: 8 / 10

How to build a solution: stack hints

WhatsApp Business API integration
Template corpus + classifier
LLM rewriter
Wati/Interakt/Gupshup connectors