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.

I spent 3 days manually typing a client's bank statements and invoices off scanned PDFs.

Junior accountants and bookkeepers lose days keying data from messy scanned PDFs and phone-photographed receipts into the ledger, and generic OCR chokes on the real-world quality.

Category: FinTech · Trend: Vision · Opportunity score: 7.6 / 10

What is the “I spent 3 days manually typing a client's bank statements and invoices off scanned PDFs.” problem in 2026?

Junior accountants and bookkeepers lose days keying data from messy scanned PDFs and phone-photographed receipts into the ledger, and generic OCR chokes on the real-world quality.

Who has this problem?

Bookkeepers and first-year accountants at firms handling small-business clients.

Evidence this problem is real

“First year associate here. Client just sent over 6 months of bank statements and invoices as scanned PDFs, some look like they were taken with a toaster.”

Sourced from r/Accounting, 2026: a first-year associate given 6 months of scanned bank statements to key by hand. (link)

Existing players in this space

Dext / Hubdoc: Good on clean receipts, weak on messy multi-page statements
Generic OCR: Chokes on toaster-quality scans and inconsistent layouts
Manual data entry: The 3 days this is about

What existing players are missing

A vision agent tuned for terrible-quality accounting documents that extracts transactions, matches them to the chart of accounts, and hands back a reviewable ledger instead of raw text.

How Real Problem AI scores this opportunity

Aggregate score: 7.6 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Vision model tuned for low-quality scans
Statement and invoice layout parsing
Transaction to account matching
Human-review diff before posting

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)