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 my 15-cent-per-booking AI feature cost 90 cents by end of month?

Small-business AI features advertise sub-15 cent unit economics. The real per-customer cost balloons as agents retry, summarise, and re-summarise. Margins die quietly.

Category: AI / Agents · Trend: Agents · Opportunity score: 7.8 / 10

What is the “Why does my 15-cent-per-booking AI feature cost 90 cents by end of month?” problem in 2026?

Small-business AI features advertise sub-15 cent unit economics. The real per-customer cost balloons as agents retry, summarise, and re-summarise. Margins die quietly.

Who has this problem?

Indie SaaS founders shipping AI features to small-business customers (booking, scheduling, ops).

Recorded source context

Dataset source note: Pitched the board on $0.15 per booking. Actual is $0.91. Half my customers are now unprofitable. Took two months to figure out why.

This note may summarize the referenced material rather than quote it verbatim. Source label: May 2026 trending r/AI_Agents threads and Indie Hackers post-mortems on agent cost economics. (reference).

Existing players in this space

Helicone: Visibility, no auto-fix
OpenRouter: Routing, no margin protection

What existing players are missing

A unit-economics meter that pairs each AI call to a business event (booking, ticket, lead) and shows true blended cost per outcome. Plus an auto-router that drops to a cheaper model the moment per-outcome cost crosses a threshold the founder sets.

How Real Problem AI scores this opportunity

Aggregate score: 7.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Per-event cost attribution layer
Outcome-tagged telemetry SDK
Margin-protection model router
Cohort margin dashboard