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 is tutoring a 13-year-old still a $50 an hour Zoom call with no progress data?

Parents pay $40-80 an hour for one-to-one tutoring and receive zero structured evidence the child is progressing. AI tutors are cheaper but parents distrust them.

Category: EdTech · Trend: Agents · Opportunity score: 7.8 / 10

What is the “Why is tutoring a 13-year-old still a $50 an hour Zoom call with no progress data?” problem in 2026?

Parents pay $40-80 an hour for one-to-one tutoring and receive zero structured evidence the child is progressing. AI tutors are cheaper but parents distrust them.

Who has this problem?

Middle-class parents of 8-16 year olds in the US, UK, Ireland and Singapore.

Evidence this problem is real

“Paid 200 EUR a week to a maths tutor for six months. My son's grades are unchanged. I have no way to know what they actually worked on.”

Sourced from Class Central 2026 industry reports, OECD PISA prep market analysis, parent-forum threads.

Existing players in this space

Khan Academy — Self-driven, no parent loop
MagicSchool — Teacher-side
Local human tutor — Opaque

What existing players are missing

A hybrid tutoring product: a human tutor for the Zoom call, an AI scribe that captures every minute, a weekly progress dashboard for the parent, and tracked mastery against the national curriculum.

How Real Problem AI scores this opportunity

Aggregate score: 7.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Zoom transcription + analysis
Curriculum-aligned mastery ontology
Parent + tutor dashboards
Marketplace + payments