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 onboarding an AI feature to a non-technical customer take an hour of Zoom?

Every AI feature shipped to SMB customers needs hand-holding: prompt setup, integration auth, sample data, expectations management. The founder is the bottleneck.

Category: SaaS · Trend: Agents · Opportunity score: 7.5 / 10

What is the “Why does onboarding an AI feature to a non-technical customer take an hour of Zoom?” problem in 2026?

Every AI feature shipped to SMB customers needs hand-holding: prompt setup, integration auth, sample data, expectations management. The founder is the bottleneck.

Who has this problem?

Solo and small-team founders shipping AI features into accounting, legal, dental, and trades verticals.

Recorded source context

Dataset source note: Every new customer means 60 minutes of me on Zoom showing them the prompts, the integration, the expected output. I have shipped to 14 customers. That is 14 hours a month gone.

This note may summarize the referenced material rather than quote it verbatim. Source label: Indie Hackers May 2026 founder pain threads, MicroConf community discussions..

Existing players in this space

Loom recordings: Asynchronous but not interactive
Intercom Fin: Generic, expensive
In-app tours: Don't cover AI-specific setup

What existing players are missing

An AI-onboarding agent that walks each new customer through the specific setup of the AI feature they bought: connects integrations, generates sample prompts, runs a verifying test, and only marks onboarding complete when the first useful result lands.

How Real Problem AI scores this opportunity

Aggregate score: 7.5 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Conversational onboarding agent
Integration auth wizard
Outcome-verifying test harness
Completion analytics