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 can my AI agent delete my production database with no confirmation?

Real harm incidents are rising: agents deleting databases, publishing hit pieces, sending unauthorised emails. Guardrails are an afterthought.

Category: AI / Agents · Trend: Agents · Opportunity score: 9.0 / 10

What is the “Why can my AI agent delete my production database with no confirmation?” problem in 2026?

Real harm incidents are rising: agents deleting databases, publishing hit pieces, sending unauthorised emails. Guardrails are an afterthought.

Who has this problem?

CTOs and devops leads at startups deploying agents with broad tool access.

Recorded source context

Dataset source note: My SDR agent emailed 2,000 customers a draft that was supposed to stay internal. There was no human-in-the-loop step on send. There should have been.

This note may summarize the referenced material rather than quote it verbatim. Source label: Hacker News May 2026 ongoing thread on AI agents causing real harm, postmortems shared on X and Bluesky from solo founders. (reference).

Existing players in this space

Custom Slack approval bots: Hand-rolled, brittle
LangGraph human-in-loop: Library only, no policy UI
MCP server allowlists: All or nothing

What existing players are missing

Policy-as-code for agent actions: classify every tool call by blast radius (read, write-internal, write-public, financial, irreversible), enforce approval flows on the high-blast tiers, and ship an audit log every CTO can actually defend in court.

How Real Problem AI scores this opportunity

Aggregate score: 9.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Tool-call classifier (LLM + heuristics)
Approval routing (Slack, Linear, email)
Audit log with cryptographic chain
Policy DSL for blast-radius rules