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 do I spend 9 hours hunting scholarships and win exactly zero?

Students burn hours across fragmented databases where a list of 150 scholarships hides only 10 they can actually win, and half their time goes to checking eligibility instead of applying.

Category: EdTech · Trend: Agents · Opportunity score: 7.0 / 10

What is the “Why do I spend 9 hours hunting scholarships and win exactly zero?” problem in 2026?

Students burn hours across fragmented databases where a list of 150 scholarships hides only 10 they can actually win, and half their time goes to checking eligibility instead of applying.

Who has this problem?

College and high-school students and their parents funding tuition.

Evidence this problem is real

“3 in 4 college undergrads won zero scholarships in the past 12 months, despite spending an average of 9 hours searching and applying.”

Sourced from Sallie, 'Scholarship Search Fatigue Report' 2026. (link)

Existing players in this space

Fastweb / Scholarships.com: Ad-heavy, surface irrelevant and expired listings
Going Merry / Bold.org: Better, still leave eligibility parsing to the student
School counselors: Overloaded, cannot scan the full field

What existing players are missing

An agent that reads the student's real profile, filters to only truly-eligible scholarships, ranks by realistic win odds and deadline, reuses one master essay across applications, and warns before each deadline slips.

How Real Problem AI scores this opportunity

Aggregate score: 7.0 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Student-profile eligibility matcher
Deadline and win-odds ranking
Reusable-essay tailoring
Deadline alerting

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)