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.

Our multi-country expense reports haven't matched the ledger in 3 years.

Finance teams pull expense reports from a handful of country systems, dump them into one spreadsheet, and lose days a month reclassifying because the same expense maps to a different account in every entity.

Category: FinTech · Trend: Agents · Opportunity score: 8.0 / 10

What is the “Our multi-country expense reports haven't matched the ledger in 3 years.” problem in 2026?

Who has this problem?

Finance and controllership teams at companies operating across multiple countries or legal entities.

Evidence this problem is real

“Every month we pulled reports from 6 different systems, dumped everything into Excel, and spent 2 to 3 days reclassifying line items because a taxi receipt in Munich hits a different account than one in Singapore.”

Sourced from r/Accounting, 2026: a controller describing 3 years of expense reports that never reconciled to the GL. (link)

Existing players in this space

SAP Concur / Expensify: Capture and route expenses, weak on cross-entity account mapping
NetSuite / consolidation tools: Consolidate at the GL level, not per-line reclassification
Manual Excel reclassification: The 2 to 3 days a month this problem is about

What existing players are missing

An agent that learns each entity's chart-of-accounts mapping, auto-classifies every expense line to the right account per country, and flags only the genuinely ambiguous ones, so the monthly close is not a manual reclassification marathon.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Per-entity chart-of-accounts model
Line-item classifier trained on historical mappings
Multi-system expense ingestion
Exception queue for ambiguous lines