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 you see which skill a subagent actually used when a task quietly goes wrong?

When subagents, especially the Plan subagent, execute, there's no visibility into which skills they invoke, making it hard to diagnose which skill failed or discover what's available at all.

Category: AI / Agents · Trend: Agents · Opportunity score: 6.5 / 10

What is the “Why can't you see which skill a subagent actually used when a task quietly goes wrong?” problem in 2026?

When subagents, especially the Plan subagent, execute, there's no visibility into which skills they invoke, making it hard to diagnose which skill failed or discover what's available at all.

Who has this problem?

Claude Code users building workflows around subagents and skills.

Recorded source context

Dataset source note: Lack of transparency: Users can't see what the subagent is actually doing under the hood... Difficult debugging: When things go wrong, it's hard to diagnose which skill failed or behaved unexpectedly.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #12142, PaulRBerg, 22 Nov 2025, closed as duplicate of #5974. (reference).

Existing players in this space

Main-agent tool call display: Exists for the top-level agent but not propagated to subagent skill invocations.
Verbose/debug logging: Buries the relevant skill-invocation signal in noise.

What existing players are missing

An inline display of skill invocations within subagent output, similar to tool-call visibility for the main agent, so failures can be traced to a specific skill instead of a black-box subagent run.

How Real Problem AI scores this opportunity

Aggregate score: 6.5 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 8 / 10
Market signal: 5 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Subagent execution trace logging
Skill-invocation inline UI
Expandable trace viewer
Debug/diagnostic export