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.

Someone opened a function to fix one bug and it was 300 lines nobody wrote by hand.

AI-generated code ships fast, then the team inherits sprawling functions no human can safely change.

Category: AI / Agents · Trend: LLM · Opportunity score: 9.0 / 10

What is the “Someone opened a function to fix one bug and it was 300 lines nobody wrote by hand.” problem in 2026?

AI-generated code ships fast, then the team inherits sprawling functions no human can safely change.

Who has this problem?

Eng leads and maintainers inheriting vibe-coded codebases.

Recorded source context

Dataset source note: The fix touched a 300-line function the model generated and no one on the team could explain it.

This note may summarize the referenced material rather than quote it verbatim. Source label: Vibe-coding technical-debt reporting, May 2026. (primary source).

Existing players in this space

SonarQube / linters: Style and complexity flags, no AI-provenance view.
Copilot / Cursor: Generate the debt, do not map it.
Code review: Humans skim AI diffs they did not write.

What existing players are missing

A maintainability map that flags AI-generated hotspots by blast radius and proposes safe, tested refactors before the next change lands on top.

How Real Problem AI scores this opportunity

Aggregate score: 9.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

AST + git-blame provenance
LLM refactor proposer with tests
Complexity + blast-radius scoring
CI gate