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 an AI agent actually read my internal docs without burning 100K tokens of fluff?

Human-written docs (Notion, Confluence, Google Drive) are organized for human navigation, agents waste massive context reading nav, table-of-contents, and stale pages before finding the one paragraph that mattered.

Category: Others · Trend: Agents · Opportunity score: 8.5 / 10

What is the “Why can't an AI agent actually read my internal docs without burning 100K tokens of fluff?” problem in 2026?

Who has this problem?

Engineering leaders deploying internal copilots, DevTools founders, knowledge-management teams.

Recorded source context

Dataset source note: Our Claude agent burned 80K tokens reading our onboarding wiki before answering 'where is the staging URL.' The answer was one line on page 4.

This note may summarize the referenced material rather than quote it verbatim. Source label: Karpathy "Software 3.0" talk threads, r/devops, r/cscareerquestions agentic-coding discussions..

Existing players in this space

Glean: Enterprise search; not agent-shaped output
Mem: Personal memory; not org-wide docs
Notion AI: Lives inside Notion; weak cross-app context

What existing players are missing

An 'agent-grade docs' layer: ingests existing wikis, rewrites them as terse machine-readable spec-cards with schema, freshness signals, and direct anchors. Output optimised for LLM consumption, not human reading.

How Real Problem AI scores this opportunity

Aggregate score: 8.5 / 10. Four-axis rubric:

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

How to build a solution: stack hints

MCP server exposing canonical doc tree per company
Auto-summary + freshness scoring pipeline
Schema-typed doc cards (Service, Runbook, Decision, Person)
Diff alerts on stale or contradictory entries