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 Claude Code tell you what a feature actually cost across the sessions it took to build?

The /cost command only shows the current session; nothing persists or aggregates tokens, cost, or session count per project, so teams can't report or budget AI usage against real work.

Category: AI / Agents · Trend: LLM · Opportunity score: 7.5 / 10

What is the “Why can't Claude Code tell you what a feature actually cost across the sessions it took to build?” problem in 2026?

The /cost command only shows the current session; nothing persists or aggregates tokens, cost, or session count per project, so teams can't report or budget AI usage against real work.

Who has this problem?

Small dev teams and solo builders using Claude Code as their primary coding tool.

Recorded source context

Dataset source note: There's no way to see cumulative Claude Code usage across sessions for a given project. The /cost command shows current session usage, but nothing persists or aggregates.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #34803, 16 Mar 2026, closed as not planned. (reference).

Existing players in this space

Claude Code /cost: Session-scoped only, resets on restart, no persistence.
Anthropic Console analytics: Org-level dashboards, not per-project or per-feature.
Manual token estimation: Users ask Claude itself to estimate, unreliable and not logged.

What existing players are missing

A lightweight local usage ledger that persists per-project tokens, cost, session count, and duration, exportable for PR descriptions, sprint reports, and CI dashboards.

How Real Problem AI scores this opportunity

Aggregate score: 7.5 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 9 / 10
Market signal: 7 / 10
Competition gap: 8 / 10

How to build a solution: stack hints

Claude Code hook/plugin API
Local SQLite or JSON usage store
CLI reporting commands
CI/PR template integration