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 does vibe-coded testing get skipped entirely, until real users find the bugs first?

Vibe-coded projects are written impulsively without consistent architecture, and testing is often minimal or skipped, so bugs surface only after users hit them in production.

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

What is the “Why does vibe-coded testing get skipped entirely, until real users find the bugs first?” problem in 2026?

Vibe-coded projects are written impulsively without consistent architecture, and testing is often minimal or skipped, so bugs surface only after users hit them in production.

Who has this problem?

Solo builders and small teams shipping AI-generated code fast.

Recorded source context

Dataset source note: In the rush of Vibe Coding, testing is often minimal or skipped entirely. Bugs are discovered late, sometimes only when users encounter them.

This note may summarize the referenced material rather than quote it verbatim. Source label: dev.to, VELOS, 9 Dec 2025, "The Hidden Drawbacks of Vibe Coding for Professional Developers." (reference).

Existing players in this space

CI test suites: Require the team to have written tests in the first place, which vibe-coding skips.
Code review: Reviewer can't keep pace with AI-generated volume.

What existing players are missing

A test-backfill agent that runs after every AI-generated code change, infers intended behavior from the diff, and generates regression tests before the change ships, not after users report bugs.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Diff-to-intent inference
Automated test generation
CI gate integration
Coverage-delta reporting