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 are teams still managing production prompts the way we managed scripts before Git existed?

Prompts have become production-critical logic, but most teams still handle them as scattered notes, copied text blocks, and files named 'final_v3_really_final.txt', with no rationale trail or diff history.

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

What is the “Why are teams still managing production prompts the way we managed scripts before Git existed?” problem in 2026?

Who has this problem?

Teams shipping LLM features who treat prompts as an afterthought instead of versioned code.

Recorded source context

Dataset source note: Most teams manage prompts the same way we managed scripts before Git: scattered notes, copied text blocks, renamed files like 'final_v3_really_final.txt'.

This note may summarize the referenced material rather than quote it verbatim. Source label: Indie Hackers, Taha, 6 Jan 2026, "Prompt Chaos Is the New Technical Debt." (reference).

Existing players in this space

Langfuse / PromptLayer: Prompt versioning exists but adoption is low outside AI-native teams.
Git for prompt files: Tracks text diffs, not rationale, evals, or production linkage.
Config files / Slack messages: Where most prompt changes actually live today, informally.

What existing players are missing

A lightweight prompt-versioning workflow that attaches a short rationale and eval result to every change, prevents silent divergence between team members' copies, and makes rollback as easy as a git revert.

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: 6 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Prompt diffing engine
Rationale + eval attachment
Rollback/versioning store
IDE or CLI integration