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 did two MCP connectors appear in a session without anyone installing or approving them?

A server-pushed feature flag silently enabled Gmail and Google Calendar MCP connectors in a user's config with no consent, prompting a full security investigation before the cause was found.

Category: AI / Agents · Trend: Agents · Opportunity score: 7.0 / 10

What is the “Why did two MCP connectors appear in a session without anyone installing or approving them?” problem in 2026?

A server-pushed feature flag silently enabled Gmail and Google Calendar MCP connectors in a user's config with no consent, prompting a full security investigation before the cause was found.

Who has this problem?

Security-conscious developers running Claude Code in environments with OAuth-connected services.

Recorded source context

Dataset source note: These integrations were silently enabled via a server-pushed GrowthBook feature flag (tengu_claudeai_mcp_connectors: true) in ~/.claude.json... if OAuth tokens had been previously configured, these would have silently connected without notification.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #28941, manfromdownunder, 26 Feb 2026. (reference).

Existing players in this space

Manual config auditing: Users have to notice and diff ~/.claude.json themselves to catch it.
Revoke Google OAuth: Blunt fix, breaks any legitimate use of the integration too.

What existing players are missing

A local config-change watcher that flags any server-pushed feature flag or connector addition the moment it appears, before OAuth can silently complete.

How Real Problem AI scores this opportunity

Aggregate score: 7.0 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 7 / 10
Market signal: 5 / 10
Competition gap: 8 / 10

How to build a solution: stack hints

Config file diff watcher
Feature-flag change alerting
OAuth pre-connection consent gate
Local audit log