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 account-level MCP connectors silently eat 100K tokens before you've typed a single word?

Every connector configured at the Claude.ai account level auto-loads into every CLI session with no per-environment opt-out, burning roughly 90K tokens of schema cost across ~240 tools before the user does anything.

Category: AI / Agents · Trend: Agents · Opportunity score: 7.2 / 10

What is the “Why do account-level MCP connectors silently eat 100K tokens before you've typed a single word?” problem in 2026?

Who has this problem?

Claude Code CLI users who also have Claude Desktop connectors configured.

Recorded source context

Dataset source note: In a typical setup this silently consumes ~100K tokens of context before the user types anything... ~12 account connectors x avg 20 tools each ~= ~240 tools with combined schema cost of ~90K tokens.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #50062, fernandoxavier02, 17 Apr 2026. (reference).

Existing players in this space

Revoke OAuth entirely: Only full-scope workaround, kills Desktop integrations too.
Disable each connector by hand: Tedious, must be redone if connectors change.

What existing players are missing

A per-environment opt-out (env var, allowlist, or scope-aware login) that keeps account connectors available in Claude Desktop while excluding them from CLI sessions by default.

How Real Problem AI scores this opportunity

Aggregate score: 7.2 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 9 / 10
Market signal: 6 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

OAuth scope management
Per-environment connector allowlist
Config flag (env var or settings.json)
Token-cost visibility on login