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 I find the MCP server that actually does what I need?

There are 2,000+ MCP servers across GitHub. Half are abandoned, half overlap, none have trust signals. Picking one means an afternoon of testing.

Category: AI / Agents · Trend: MCP · Opportunity score: 8.4 / 10

What is the “Why can't I find the MCP server that actually does what I need?” problem in 2026?

There are 2,000+ MCP servers across GitHub. Half are abandoned, half overlap, none have trust signals. Picking one means an afternoon of testing.

Who has this problem?

Claude Code, Cursor, and ChatGPT desktop users building agent workflows.

Recorded source context

Dataset source note: I just want one MCP for Notion that doesn't break on databases with rollups. I tested four and gave up.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/ClaudeAI weekly MCP recommendation threads, Hacker News MCP directory debates, Anthropic Discord #mcp channel..

Existing players in this space

Smithery: Registry exists but trust signals shallow
mcp.so: Index, not curation
GitHub topic: No quality bar

What existing players are missing

A reviewed MCP registry with: real install-success rate, last-known-working LLM client, test suite that runs against the server's claimed tools, and reproducible compatibility badges. Plus AI-assisted match: paste your task, get the right MCP.

How Real Problem AI scores this opportunity

Aggregate score: 8.4 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 9 / 10
Market signal: 9 / 10
Competition gap: 8 / 10

How to build a solution: stack hints

GitHub crawler + MCP manifest parser
Sandboxed test harness for tool calls
Compatibility matrix UI
RAG-based MCP recommender