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 an AI prompt library leak attorney-client privilege the moment a lawyer uses it?

Solo and small-firm lawyers want to use ChatGPT / Claude on case prep but every shared prompt template assumes pasting client facts, blowing privilege.

Category: LegalTech & Compliance · Trend: LLM · Opportunity score: 8.2 / 10

What is the “Why does an AI prompt library leak attorney-client privilege the moment a lawyer uses it?” problem in 2026?

Solo and small-firm lawyers want to use ChatGPT / Claude on case prep but every shared prompt template assumes pasting client facts, blowing privilege.

Who has this problem?

US and Indian solo + small-firm lawyers (1-15 attorneys) who already use AI ad-hoc but worry about confidentiality.

Recorded source context

Dataset source note: I use Claude for first-draft motions. I have no idea what 'do not train on my data' actually means in practice.

This note may summarize the referenced material rather than quote it verbatim. Source label: Bar association CLE threads + Solosez listserv, 2026.

Existing players in this space

Harvey / Spellbook: Mid-market and big-law focused; price wall for solo + small firm.
Casetext CoCounsel: Acquired into Thomson Reuters; price moved up-market.
No one: Ships a privilege-aware prompt library + on-device redaction layer for solo + small firm at $49/mo.

What existing players are missing

A privilege-respecting AI workflow: redacts client identifiers locally, runs against any LLM, returns un-redacted to the lawyer. Sells at solo-firm pricing.

How Real Problem AI scores this opportunity

Aggregate score: 8.2 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Local redaction (NER + rules)
BYO-API-key for LLM
Audit log + privilege ledger
Word / Drive integrations

Why this problem is archived

Swapped out in AI-tool-builder content refresh (2026-08-26): generic vertical problem replaced by a more current, builder-relevant one.