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't a ten-person company answer a customer's AI Act questionnaire?

A small vendor that added AI features gets a twelve-question compliance questionnaire from its first EU customer and has no risk classification, no AI inventory and no literacy evidence to point at, so the deal stalls on paperwork nobody on the team can produce.

Category: LegalTech & Compliance · Trend: LLM · Opportunity score: 8.0 / 10

What is the “Why can't a ten-person company answer a customer's AI Act questionnaire?” problem in 2026?

Who has this problem?

Founders and operators at small software companies selling into the EU, plus the compliance staff answering for AI tools the team already uses.

Recorded source context

Dataset source note: Twelve questions, most of which I couldn't answer confidently.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/AI_Governance thread on the AI Act literacy obligation for smaller companies, 20 August 2026, 21 comments; r/Entrepreneurs thread on a first EU AI Act customer questionnaire, 6 March 2026. (primary source).

Existing players in this space

Vanta and Drata: Strong on SOC 2 and ISO 27001, and their AI Act modules still assume a human has already done the classification work.
Law-firm readiness reviews: Accurate, priced for enterprises, and out of date the moment a vendor in the stack ships a new AI feature.
An internal spreadsheet: What most small teams actually keep, and no enterprise buyer accepts it as evidence.

What existing players are missing

A living AI register that detects which tools in the stack shipped AI features this quarter, classifies each use by risk tier, generates the transparency notice and the literacy completion record, then answers a customer questionnaire from that stored evidence instead of from memory.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 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

SaaS discovery to inventory AI features across the stack
Risk-tier classification against the Act's categories
Generated transparency notice and disclosure copy
Literacy training log with per-employee completion evidence
Questionnaire answering grounded in the stored evidence