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 a team see which engineer (and which model) is actually burning the AI budget?

There's no per-user, per-model cost breakdown in Claude Code, so engineering leads can't tell whether it's Opus-heavy refactors or Sonnet-heavy chat loops driving the bill.

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

What is the “Why can't a team see which engineer (and which model) is actually burning the AI budget?” problem in 2026?

There's no per-user, per-model cost breakdown in Claude Code, so engineering leads can't tell whether it's Opus-heavy refactors or Sonnet-heavy chat loops driving the bill.

Who has this problem?

Engineering managers rolling out Claude Code across a team.

Recorded source context

Dataset source note: I would like to see at a per user level how much cost I have incurred for each model. Would help me decide how to best plan my tasks

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #29123, 26 Feb 2026, closed as duplicate. (reference).

Existing players in this space

Org-level console analytics: Aggregates across the whole org, not broken down per user/model.
Manual spreadsheet tracking: Teams self-report usage, unreliable and stale.

What existing players are missing

A per-user, per-model cost breakdown surfaced to both the individual (to self-regulate) and the team lead (to plan task allocation and model choice).

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Anthropic org usage API
Per-seat attribution
Model-level cost segmentation
Team dashboard