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 does checking your Claude API spend mean leaving the terminal you're already authenticated in?

Billing and token spend data lives only on console.anthropic.com; developers already authenticated via API key still have to context-switch to a browser to see operational cost data.

Category: AI / Agents · Trend: LLM · Opportunity score: 6.8 / 10

What is the “Why does checking your Claude API spend mean leaving the terminal you're already authenticated in?” problem in 2026?

Billing and token spend data lives only on console.anthropic.com; developers already authenticated via API key still have to context-switch to a browser to see operational cost data.

Who has this problem?

Freelance and contract developers billing clients for API-based AI work.

Recorded source context

Dataset source note: This is operational data that developers are already authenticated to access via their API key. Needing to context-switch to a browser to retrieve it is friction that doesn't match the rest of the CLI-native workflow.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #47196, 13 Apr 2026, closed as not planned. (reference).

Existing players in this space

console.anthropic.com: Full data, but browser-only, no CLI or API path.
Third-party wrapper libraries: Estimate cost from local token counts, not authoritative billing.

What existing players are missing

A CLI-native `claude billing` command (or open API) that surfaces the same spend data as the console, so cost reconciliation never requires a browser tab.

How Real Problem AI scores this opportunity

Aggregate score: 6.8 / 10. Four-axis rubric:

Problem severity: 5 / 10
AI feasibility today: 9 / 10
Market signal: 6 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Anthropic billing/usage API
CLI command wrapper
Local credential reuse
CSV/JSON export for invoicing