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't I see what data my AI agents are quietly shipping to third-party models?

Internal automations and agent flows pull private data, chain tool calls, and compose prompts on their own, and no gateway inspects what actually crosses to an external LLM.

Category: AI / Agents · Trend: AI Infra · Opportunity score: 7.2 / 10

What is the “Why can't I see what data my AI agents are quietly shipping to third-party models?” problem in 2026?

Internal automations and agent flows pull private data, chain tool calls, and compose prompts on their own, and no gateway inspects what actually crosses to an external LLM.

Who has this problem?

Security and data-governance leads at companies running n8n, LangChain, and homegrown agent workflows.

Evidence this problem is real

“Your agents move data you can't see. Your n8n flows pull internal data, chain tool calls, and compose prompts on their own. No gateway sees what crosses between steps.”

Sourced from Privent.ai (agent data-flow visibility); shadow-AI governance reporting, 2026. (link)

Existing players in this space

Network DLP / CASB: Blind to prompt bodies assembled inside agent runtimes
LLM observability (Langfuse, Helicone): Trace tokens and latency, not PII crossing the boundary
Browser DLP extensions: Cover human copy-paste, not server-side agent flows

What existing players are missing

An inline gateway that sees every prompt an agent or workflow composes before it leaves, redacts or blocks PII and secrets per policy, and produces a compliance-grade log of exactly what data reached which model.

How Real Problem AI scores this opportunity

Aggregate score: 7.2 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Inline LLM egress gateway
PII/secret detection on composed prompts
Policy-based redaction and blocking
Per-flow data-lineage log

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)