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 are a million AI services publicly exposed with no auth?

A May 2026 scan found ~1M AI services (vector DBs, MCP servers, agent inboxes, internal copilots) reachable on the public internet with default creds, no auth, or hard-coded keys. Hackers shipped the first AI-developed zero-day 2FA bypass the same month. AI-first SaaS teams have shipped agents faster than their security posture.

Category: Others · Trend: Agents · Opportunity score: 8.9 / 10

What is the “Why are a million AI services publicly exposed with no auth?” problem in 2026?

Who has this problem?

Solo / small-team AI founders shipping LLM features, vector DBs, MCP servers and internal copilots without a security review pipeline.

Recorded source context

Dataset source note: We pointed Shodan at default Chroma / Qdrant / Pinecone ports and ran a no-auth fingerprint. Six hours, 1.04M services, no creds needed to read or write. Half had customer data in them.

This note may summarize the referenced material rather than quote it verbatim. Source label: The Hacker News "We Scanned 1 Million Exposed AI Services" (May 2026), Penligent.ai "AI Agents Hacking in 2026", r/cybersecurity AI-attack threads. (reference).

Existing players in this space

Snyk / Semgrep: Code-side SAST; misses runtime AI infra exposure
Wiz / Orca: Cloud security; expensive, enterprise-priced, no AI-specific posture
AWS Inspector / GuardDuty: AWS-only; doesn't classify AI surfaces
Prompt-injection scanners (Lakera, PromptArmor): Prompt-layer; ignore infra-exposure layer

What existing players are missing

An AI-posture scanner shaped for indie / small-team founders: point it at a domain, get back every exposed vector DB, MCP endpoint, embedding API, agent inbox, with a severity ranking and one-click remediation snippets. Priced like a developer tool ($29-$79/mo) not enterprise SaaS.

How Real Problem AI scores this opportunity

Aggregate score: 8.9 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Shodan-style fingerprinter for AI infra ports (vector DBs, MCP servers, LangServe, Ollama, vLLM)
Per-service config-default checks (Chroma anonymous mode, Pinecone open indexes)
Severity classifier with remediation templates
Continuous monitoring with diff alerts