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 does every team rebuild the same AI eval harness from scratch?

Every AI team writes its own evals: golden sets, judges, scoring, regressions. The same 80% of code, rewritten badly, by every team.

Category: AI / Agents · Trend: LLMOps · Opportunity score: 7.8 / 10

What is the “Why does every team rebuild the same AI eval harness from scratch?” problem in 2026?

Every AI team writes its own evals: golden sets, judges, scoring, regressions. The same 80% of code, rewritten badly, by every team.

Who has this problem?

AI/ML engineers at startups (5-50 people) shipping LLM features to customers.

Evidence this problem is real

“Third startup in a row where I'm writing the same Python harness: load test set, run prompt, judge with GPT, write to sheet. Why isn't this a package?”

Sourced from Hamel Husain's "evals" blog series, Eugene Yan's posts (2026), Latent Space Discord, applied-LLM startup repos.

Existing players in this space

Promptfoo — Open-source, dev-only
Braintrust — Closer, opinionated SaaS
LangSmith evals — Coupled to LangChain

What existing players are missing

An eval-harness primitive that ships with every LLM SDK: opinionated defaults, judge picker, automatic regression detection on PRs, and exportable golden sets. Not a SaaS, a library + dashboard combo.

How Real Problem AI scores this opportunity

Aggregate score: 7.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Eval primitive library (Python + TS)
Judge selection + calibration
Git-based regression gates
Hosted dashboard (optional)