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.

A retry-stuck headless agent burned $313 in one night and nobody could see it happening.

claude -p headless mode gives orchestrators no cost visibility, spend caps, or duplicate-input detection, so a single stuck retry loop can consume half a weekly quota before anyone notices.

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

What is the “A retry-stuck headless agent burned $313 in one night and nobody could see it happening.” problem in 2026?

claude -p headless mode gives orchestrators no cost visibility, spend caps, or duplicate-input detection, so a single stuck retry loop can consume half a weekly quota before anyone notices.

Who has this problem?

Developers running Claude Code subagents in headless/orchestrated pipelines.

Recorded source context

Dataset source note: The core issue: claude -p provided no cost visibility, spending caps, duplicate-input detection, or per-invocation logging that would have allowed an external orchestrator to detect and halt the runaway before significant costs accumulated.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #57719, 10 May 2026. (reference).

Existing players in this space

claude -p headless flags: No --max-cost, --budget, or circuit-breaker flag exists.
External wrapper scripts: Users build their own daemons with no first-party cost hooks to key off.
Post-hoc billing dashboard: Confirms the damage after the fact, does not stop it mid-run.

What existing players are missing

A per-invocation cost and duplicate-input guard for headless/orchestrated Claude Code runs that halts a loop the moment it detects zero net progress against rising spend.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Headless CLI wrapper/proxy
Per-invocation cost logging
Progress/duplicate-detection heuristic
Kill-switch webhook