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 does my rural clinic still use fax for every prior auth in 2026?

Insurance prior-authorisation workflows still depend on fax machines in many US small clinics. Each prior auth eats 30-90 minutes of nurse time, and patients wait.

Category: HealthTech · Trend: Agents · Opportunity score: 8.6 / 10

What is the “Why does my rural clinic still use fax for every prior auth in 2026?” problem in 2026?

Insurance prior-authorisation workflows still depend on fax machines in many US small clinics. Each prior auth eats 30-90 minutes of nurse time, and patients wait.

Who has this problem?

Rural and small-town US primary-care and specialty clinics with 1-5 providers.

Recorded source context

Dataset source note: I have one nurse whose entire job is faxing prior-auth forms back and forth with five insurance companies. She is not going to be replaced. She is going to be relieved.

This note may summarize the referenced material rather than quote it verbatim. Source label: AMA prior-auth survey 2026, KFF Health Policy 2025-2026 reports, r/medicine threads..

Existing players in this space

Cohere Health: Insurer-side, not provider-side
Olive AI: Pivoted away from prior-auth
Cofactor: Mid-market, missing the long tail

What existing players are missing

A drop-in, voice-enabled prior-auth agent for small clinics: takes the patient chart, drafts the request, navigates each payer's specific portal or fax queue, and reports back with a status the nurse can verify in 60 seconds. Per-auth pricing the small practice can afford.

How Real Problem AI scores this opportunity

Aggregate score: 8.6 / 10. Four-axis rubric:

Problem severity: 9 / 10
AI feasibility today: 9 / 10
Market signal: 9 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Browser agent for payer portals
Fax gateway (eFax API)
Chart-to-form extractor
Per-auth billing