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 do reference checks still mean a 20-minute call where everyone says "great teammate"?

Recruiters call references, references say generic nice things, no real signal collected.

Category: HR Tech & Recruiting · Trend: Voice · Opportunity score: 7.5 / 10

What is the “Why do reference checks still mean a 20-minute call where everyone says "great teammate"?” problem in 2026?

Recruiters call references, references say generic nice things, no real signal collected.

Who has this problem?

Hiring managers and recruiters.

Evidence this problem is real

“I called five references for a finalist. All said 'great teammate'. He was fired in 60 days.”

Sourced from r/recruiting, AskAManager.

Existing players in this space

Crosschq
SkillSurvey
Veremark

What existing players are missing

Voice-agent reference checks with structured probing, sentiment + hesitation analysis, and back-channel comparison across references.

How Real Problem AI scores this opportunity

Aggregate score: 7.5 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Outbound voice agent
Question library by role
LLM analysis of hesitation/qualifiers
Comparative report across referees

Why this problem is archived

Reference-check market is small and well-served by Crosschq/Searchlight.