What is Real Problem AI?

Real Problem AI is a curated directory of 200+ 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 200+ 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 can an Indian techie not tell if her comp is fair this quarter?

Indian software engineers comparing offers across Bengaluru, NCR, Hyderabad and remote-US-payroll have no clean source for quarterly comp deltas; Levels.fyi is US-skewed and stale for India.

Category: HR Tech & Recruiting · Trend: AI Wrapper · Opportunity score: 7.9 / 10

What is the “Why can an Indian techie not tell if her comp is fair this quarter?” problem in 2026?

Indian software engineers comparing offers across Bengaluru, NCR, Hyderabad and remote-US-payroll have no clean source for quarterly comp deltas; Levels.fyi is US-skewed and stale for India.

Who has this problem?

Indian software engineers and product managers (3-12 years experience) negotiating offers or annual hikes.

Evidence this problem is real

“Last published Bengaluru SDE2 number is from 9 months ago. AI startups are paying 60% more now.”

Sourced from Levels.fyi India coverage + Blind / Grapevine threads, 2026 Q1-Q2

Existing players in this space

Levels.fyi — US-first; India coverage is sparse and stale.
Glassdoor / AmbitionBox — Self-reported; gamed and stale.
Grapevine — Anonymous chat; no structured comp data.

What existing players are missing

A quarterly-refreshed India comp index by city + role + function, sourced from offer letters scraped via a browser extension users install in exchange for free access.

How Real Problem AI scores this opportunity

Aggregate score: 7.9 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Browser extension
Anonymisation pipeline
Quarterly snapshot DB
Public site + API

Why this problem is archived

Capped at 100 per editorial policy; lower-score entries rotate to archive.