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 an AI screener reject me in 12 seconds and tell me nothing about why?

80% of 2026 job applications never see a human; AI screeners reject candidates instantly with no feedback, no signal, no improvable lesson.

Category: HR Tech & Recruiting · Trend: LLM · Opportunity score: 8.4 / 10

What is the “Why does an AI screener reject me in 12 seconds and tell me nothing about why?” problem in 2026?

80% of 2026 job applications never see a human; AI screeners reject candidates instantly with no feedback, no signal, no improvable lesson.

Who has this problem?

Job seekers (mid-career, tech and non-tech) applying through Greenhouse / Lever / Workday workflows that auto-screen.

Evidence this problem is real

“8 minutes after submission, rejection email. I have a master's and 7 years experience.”

Sourced from r/cscareerquestions + r/jobs recurring 2026 threads

Existing players in this space

Teal / Final Round — Resume optimisation; not a feedback loop on rejection signals.
Sonara / LazyApply — Apply at scale; do not return why each application failed.
No one — Reverse-engineers screener responses to give the candidate a 'why' and a fix.

What existing players are missing

An app that watches your rejections, infers the screener's decision boundaries, and returns a personalised 'why you keep getting rejected and 3 things to change'.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Browser extension to capture postings + outcomes
Embedding-based pattern detection
LLM explainer
Career coach handoff