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't I tell which of my students actually learned this week?

Teachers run weekly assessments, grade them, return them, and still cannot say with confidence which kids understood which concept. The data is there. The integration is not.

Category: EdTech · Trend: Agents · Opportunity score: 7.5 / 10

What is the “Why can't I tell which of my students actually learned this week?” problem in 2026?

Teachers run weekly assessments, grade them, return them, and still cannot say with confidence which kids understood which concept. The data is there. The integration is not.

Who has this problem?

K-12 classroom teachers in the US, UK, Australia and Ireland with 25-35 students per class.

Evidence this problem is real

“I have eight different score sheets across Kahoot, Quizizz, Google Forms, and paper. I know who got 80% on Monday and 60% on Thursday. I cannot tell you why.”

Sourced from r/Teachers May 2026 weekly tooling threads, OECD Education Working Papers 2025-2026.

Existing players in this space

PowerSchool — Gradebook only
Khan Academy — Self-study, not classroom view
Magicschool — Lesson plans, not assessment integration

What existing players are missing

A learning-pulse view: ingest scores from Kahoot, Quizizz, Forms, paper, map each to a concept, surface per-student concept-mastery, and recommend the next 10-minute warm-up that fills the gap. Free for individual teachers, priced for districts.

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: 8 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Score ingestion connectors
Concept mapping ontology
Per-student mastery model
Warm-up recommendation engine