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 does every commercial lease abstract still take a paralegal four hours?

Tenant-rep brokers and small landlords pay $200 to $400 per lease abstract because LeaseQuery and Visual Lease focus on ASC 842 compliance, not deal-stage abstraction.

Category: PropTech / Real Estate · Trend: LLM · Opportunity score: 8.3 / 10

What is the “Why does every commercial lease abstract still take a paralegal four hours?” problem in 2026?

Tenant-rep brokers and small landlords pay $200 to $400 per lease abstract because LeaseQuery and Visual Lease focus on ASC 842 compliance, not deal-stage abstraction.

Who has this problem?

Commercial broker or owner-operator with 5 to 50 leases.

Evidence this problem is real

“I am paying a paralegal 4 hours per lease to pull out the same 30 fields. There has to be a better way but every tool I have tried is built for ASC 842 not for actually reading the lease.”

Sourced from r/CommercialRealEstate thread on lease abstraction software.

Existing players in this space

LeaseQuery — ASC 842 accounting focus.
Visual Lease — Compliance, not deal abstraction.
Leverton — Enterprise pricing, slow onboarding.

What existing players are missing

Self-serve lease abstraction for SMB brokers with editable field templates, exhibit-aware extraction, and side-by-side citation review.

How Real Problem AI scores this opportunity

Aggregate score: 8.3 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Claude long context for full lease
Unstructured.io for PDF parsing
React PDF viewer with highlights
Supabase