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 rebuild the same grant narrative from scratch for every single funder?

A solo development director spends around 40 hours to discover, qualify, draft, and submit one proposal, retyping the same mission and impact story into each funder's rigid, differently-worded template.

Category: Others · Trend: LLM · Opportunity score: 7.1 / 10

What is the “Why do I rebuild the same grant narrative from scratch for every single funder?” problem in 2026?

Who has this problem?

Grant writers and development directors at small nonprofits.

Evidence this problem is real

“Impact makers spend their days performing high-stakes fieldwork, only to stay awake until 2 AM parsing through rigid foundation guidelines.”

Sourced from FundRobin, 'Small Nonprofit's Guide to Winning More Grants' (2026 strategy). (link)

Existing players in this space

Instrumentl / GrantStation: Strong on discovery, weak on drafting the narrative
Generic ChatGPT: Produces boilerplate funders now penalize
Freelance grant writers: $3,000 to $15,000 per proposal, out of reach

What existing players are missing

A system with one canonical impact narrative and outcomes bank that auto-reshapes into each funder's exact template and word limits, keeps facts and figures current, and tracks deadlines across every open opportunity.

How Real Problem AI scores this opportunity

Aggregate score: 7.1 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Canonical narrative and outcomes store
Per-funder template reshaping
Deadline and compliance tracker
Fact and figure freshness checks

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)