What is Real Problem AI?

Real Problem AI is a curated directory of 200+ 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 200+ 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 do RAG agents confidently cite retracted research papers?

RAG systems pull from outdated indices that include retracted papers, deprecated docs, and superseded standards. The agent cites them with confidence. Users believe.

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

What is the “Why do RAG agents confidently cite retracted research papers?” problem in 2026?

RAG systems pull from outdated indices that include retracted papers, deprecated docs, and superseded standards. The agent cites them with confidence. Users believe.

Who has this problem?

Builders of customer-facing AI in regulated verticals (healthcare, legal, finance, academic research).

Evidence this problem is real

“Our patient-facing chatbot cited a paper that was retracted in 2023. We had three months of answers based on bad evidence before anyone caught it.”

Sourced from May 2026 GitHub gist of trending r/AI_Agents discussions, Hamel Husain and Jason Liu RAG posts, Retraction Watch coverage. (link)

Existing players in this space

Manual corpus curation — Doesn't scale
Ragas — Evaluates answers, not source freshness
Perplexity-style web RAG — Better recency, still misses retractions

What existing players are missing

Source-freshness scoring: every retrieved chunk gets a recency, supersession, and retraction score. Citations get a colour-coded confidence band. Refused if the source has been retracted or formally deprecated.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Crossref + Retraction Watch + OpenAlex feeds
Source-status enrichment pipeline
Citation-time scoring layer
Refusal logic in the response builder