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 do title companies still email a 90-page closing package the night before?

Homebuyers receive massive closing disclosure packets hours before signing with no time to spot wrong names, wrong loan amounts, or junk fees.

Category: PropTech / Real Estate · Trend: LLM · Opportunity score: 9.0 / 10

What is the “Why do title companies still email a 90-page closing package the night before?” problem in 2026?

Homebuyers receive massive closing disclosure packets hours before signing with no time to spot wrong names, wrong loan amounts, or junk fees.

Who has this problem?

First-time homebuyer 48 hours from closing.

Evidence this problem is real

“They sent the closing package at 9pm and want me to sign at 10am. There are 90 pages and I have no idea what I am even looking for.”

Sourced from r/FirstTimeHomeBuyer threads on closing disclosure overwhelm.

Existing players in this space

Endpoint — Title company UX, no buyer-side audit.
Qualia — Title production software, not buyer review.

What existing players are missing

A buyer-side AI that compares the final closing disclosure to the loan estimate, flags fee changes, name typos, and APR drift, with a TRID-aware checklist.

How Real Problem AI scores this opportunity

Aggregate score: 9.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Claude with structured output
PDF diff library
Stripe for one-time fee
Vercel