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 do you have to ask Claude to guess what a command just cost you?

Claude Code gives no real-time cost visibility per command, forcing users to either manually estimate or leave the tool to check an external billing dashboard.

Category: AI / Agents · Trend: LLM · Opportunity score: 7.0 / 10

What is the “Why do you have to ask Claude to guess what a command just cost you?” problem in 2026?

Claude Code gives no real-time cost visibility per command, forcing users to either manually estimate or leave the tool to check an external billing dashboard.

Who has this problem?

Teams and orgs standardizing on Claude Code who need to budget AI usage internally.

Recorded source context

Dataset source note: Currently, users have no visibility into the cost of their Claude Code operations without manually asking Claude to estimate costs or checking external billing dashboards.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #18550, 16 Jan 2026. (reference).

Existing players in this space

Manual estimation prompts: Asking Claude to guess its own cost, unreliable.
External billing dashboard: Accurate but disconnected from the moment of spend.
Community usage-monitor CLIs: Third-party, unofficial, and fragile across Claude Code updates.

What existing players are missing

Real-time, per-command cost display inline in the CLI output, so a $0.40 grep-heavy step and a $4 full-repo refactor are visibly distinguishable before the next prompt.

How Real Problem AI scores this opportunity

Aggregate score: 7.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Claude Code hook API
Token-to-cost calculator
Inline CLI cost annotations
Session cost aggregator