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 can a fake police email still get a fintech to hand over customer data?

Legal teams at small and mid-size companies get urgent data requests that look like they come from police or a government agency, and they mostly verify by email domain and letterhead, which is exactly what attackers fake.

Category: LegalTech & Compliance · Trend: Agents · Opportunity score: 7.8 / 10

What is the “Why can a fake police email still get a fintech to hand over customer data?” problem in 2026?

Who has this problem?

Trust and safety, legal and compliance staff at fintechs, marketplaces and SaaS companies who answer law enforcement data requests without a dedicated team.

Recorded source context

Dataset source note: Ran an LE request desk for a while and the whole thing was PDFs from .gov-ish email addresses.

This note may summarize the referenced material rather than quote it verbatim. Source label: Hacker News discussion of Revolut confirming a breach through fake government requests, 13 September 2026, 189 points, 132 comments. (primary source).

Existing players in this space

Kodex: A portal for law enforcement requests used by large platforms, more than most small companies set up.
Zendesk or a shared inbox: Where most requests actually land, with no check on who sent them.
Manual callback: Calling the agency on an independently found number works, but it depends on a person remembering under time pressure.

What existing players are missing

A request intake checker that reads the PDF or email, pulls the agency, officer and legal basis, checks them against independent agency directories, forces a callback step on emergency requests, and records every decision so the team can show regulators what it checked.

How Real Problem AI scores this opportunity

Aggregate score: 7.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Document parsing of legal request PDFs
Agency directory lookup and sender checks
Required verification workflow with audit log
LLM review of legal basis and scope