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 do I redo the same data prep in Excel every month?

Analysts pull a CSV, clean it, pivot, format, every month, manually.

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

What is the “Why do I redo the same data prep in Excel every month?” problem in 2026?

Analysts pull a CSV, clean it, pivot, format, every month, manually.

Who has this problem?

Finance ops, marketing analysts, RevOps.

Recorded source context

Dataset source note: Same VLOOKUP, same pivot, same chart, 14th month in a row.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/excel, r/datascience..

Existing players in this space

Copilot in Excel
Equals
Rows
Bardeen

What existing players are missing

Record-once, replay-forever data agent that does the full pipeline including the email-to-stakeholder.

How Real Problem AI scores this opportunity

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

Problem severity: 8 / 10
AI feasibility today: 9 / 10
Market signal: 9 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Record-and-replay over Sheets/Excel
LLM step generalization
Scheduled run + delivery
Error-recovery prompts

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)