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 can't a 200-person company tell if their final-round candidate is a deepfake proxy?

50% of companies have hit AI-driven deepfake interview fraud; HireVue / Greenhouse don't detect it; 59% of hiring managers suspect candidates are using AI to misrepresent themselves.

Category: HR Tech & Recruiting · Trend: Vision · Opportunity score: 8.5 / 10

What is the “Why can't a 200-person company tell if their final-round candidate is a deepfake proxy?” problem in 2026?

50% of companies have hit AI-driven deepfake interview fraud; HireVue / Greenhouse don't detect it; 59% of hiring managers suspect candidates are using AI to misrepresent themselves.

Who has this problem?

Recruiters and hiring managers at 50-2000 person companies running remote video interviews on Zoom / Teams / Meet in 2026.

Evidence this problem is real

“59% of hiring managers suspect candidates are using AI to misrepresent themselves; existing live-detection vendors require switching the entire interview platform.”

Sourced from CXOToday + TechRadar + Sherlock AI on AI interview fraud (link)

Existing players in this space

Sherlock AI — 97% detection claim but requires platform switch.
Pindrop — Voice-biometric focus; weak on synthetic video.
Reality Defender — Enterprise-priced; not Greenhouse-native.
HireVue — Adds detection inside its own platform; ATS lock-in.

What existing players are missing

A bot-as-attendee that joins any Zoom / Teams / Meet call, runs deepfake + identity-mismatch detection passively, and pushes a one-line risk score into Greenhouse / Ashby, no platform switch.

How Real Problem AI scores this opportunity

Aggregate score: 8.5 / 10. Four-axis rubric:

Problem severity: 9 / 10
AI feasibility today: 8 / 10
Market signal: 9 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

WebRTC + Zoom / Teams / Meet bot SDK
Vision + audio synthetic-media detection
Greenhouse / Ashby webhook
Auditable risk-score log