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 is buying a house a 90-day blizzard of disclosure docs nobody reads?

Buyers get hundreds of pages of disclosures, HOA docs, inspection reports, and miss critical issues.

Category: PropTech / Real Estate · Trend: LLM · Opportunity score: 8.8 / 10

What is the “Why is buying a house a 90-day blizzard of disclosure docs nobody reads?” problem in 2026?

Buyers get hundreds of pages of disclosures, HOA docs, inspection reports, and miss critical issues.

Who has this problem?

First-time and repeat home buyers in the US.

Evidence this problem is real

“300-page HOA packet. Closing in 4 days. I read 12 pages.”

Sourced from r/FirstTimeHomeBuyer, r/RealEstate.

Existing players in this space

Disclosure AI
Endpoint
Side
Doss

What existing players are missing

Almost all are agent-side. No buyer-facing tool summarizes HOA, CC&Rs, inspection, prelim title in plain English with red-flag alerts and citations as the docs trickle in.

How Real Problem AI scores this opportunity

Aggregate score: 8.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Long-context LLM with real-estate legal training
Cross-doc anomaly detection (e.g., undisclosed easement)
Plain-English summary with citations
Slack/email alerts as docs arrive