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 monitoring an AI agent in production feel like flying blind?

Datadog is for servers. Sentry is for errors. Neither helps when an agent silently degrades from good answers to mediocre answers over four weeks.

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

What is the “Why does monitoring an AI agent in production feel like flying blind?” problem in 2026?

Datadog is for servers. Sentry is for errors. Neither helps when an agent silently degrades from good answers to mediocre answers over four weeks.

Who has this problem?

SRE and platform engineers at companies that have moved AI features past the pilot stage.

Recorded source context

Dataset source note: Our support agent's CSAT was 4.6. Six weeks later it was 3.8. None of our dashboards flagged the drift. The customers did.

This note may summarize the referenced material rather than quote it verbatim. Source label: Hacker News May 2026 threads on agent observability moving from experimental to production-critical. (reference).

Existing players in this space

Datadog LLM Observability: Bolt-on to existing infra metrics
Arize Phoenix: Strong on ML but heavy
LangSmith: Tracing-centric

What existing players are missing

Quality-drift detection: continuously sample production answers, score them against a moving golden set, alert when scoring drops below threshold. Plus the ability to A/B test a prompt change against last week's traffic before merging.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Production sampling SDK
LLM-as-judge eval pipeline
Traffic replay sandbox
Drift alert routing