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 Max 20x subscribers hit rate limits in 90 minutes while their own usage dashboard shows only 11% consumed?

During peak-load periods, $200/month subscribers get told to come back later mid-task even though their weekly usage dashboard shows single-digit consumption, because the real constraint is infrastructure capacity, not their quota.

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

What is the “Why do Max 20x subscribers hit rate limits in 90 minutes while their own usage dashboard shows only 11% consumed?” problem in 2026?

Who has this problem?

Claude Max power users doing sustained agentic coding work.

Recorded source context

Dataset source note: I have an open context with 40k tokens of accumulated session history... My weekly usage dashboard shows 11% consumed — the limit is not mine, it's Anthropic's infrastructure under peak load.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #40183, VALIER255, 28 Mar 2026. (reference).

Existing players in this space

Usage dashboard: Shows quota consumption, not the actual infrastructure capacity wall a user hit.
Waiting and retrying: Only workaround, wastes the exact session momentum users are trying to preserve.

What existing players are missing

A transparent distinction in-product between 'you've used your quota' and 'infrastructure is at capacity,' plus a queue/notify mechanism so sessions can resume automatically once capacity frees up instead of dying mid-task.

How Real Problem AI scores this opportunity

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

Problem severity: 8 / 10
AI feasibility today: 5 / 10
Market signal: 8 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Capacity-vs-quota status differentiation
Session queue/retry orchestration
Push notification on capacity availability
Peak-load transparency reporting