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 my LLM evals pass in dev and fail the moment real traffic hits?

Golden-set evals stay green. Production CSAT silently degrades. The gap is a known industry problem with no off-the-shelf fix.

Category: AI / Agents · Trend: LLMOps · Opportunity score: 7.5 / 10

What is the “Why do my LLM evals pass in dev and fail the moment real traffic hits?” problem in 2026?

Golden-set evals stay green. Production CSAT silently degrades. The gap is a known industry problem with no off-the-shelf fix.

Who has this problem?

AI platform teams running customer-facing LLM features at 1,000-100,000 calls per day.

Evidence this problem is real

“Our eval set is 200 cases the team curated. Production gets 50,000 unique conversations a day. The gap between the two is where all our customer escalations live.”

Sourced from Hamel Husain and Eugene Yan applied-LLM essays 2026, Latent Space podcast guests on production gaps.

Existing players in this space

Braintrust — Best for static golden sets
LangSmith — Trace-first
Ragas — RAG-specific

What existing players are missing

Production-traffic-derived evals: sample real conversations, cluster by intent, auto-promote representative cases into the golden set with a weekly refresh and rationale.

How Real Problem AI scores this opportunity

Aggregate score: 7.5 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Conversation clustering (embeddings)
LLM-as-judge promotion gate
Versioned golden-set storage
Dashboarded drift detection