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 a car still leave the service bay with lug nuts missing?

A customer's wheel came off three minutes after a dealer oil change and tire rotation because the lug nuts were never put back, and shops still rely on a technician remembering to torque every wheel with no check before the car is released.

Category: Automotive & Mobility · Trend: Vision · Opportunity score: 7.8 / 10

What is the “Why does a car still leave the service bay with lug nuts missing?” problem in 2026?

Who has this problem?

Service managers and technicians at tire shops, quick-lube chains and dealer service departments.

Recorded source context

Dataset source note: I had work done on my car once and 1 wheels lug nuts came loose after awhile.

This note may summarize the referenced material rather than quote it verbatim. Source label: Car Talk community thread on a shop that did not put lug nuts back after a tire rotation, 16 September 2026. (primary source).

Existing players in this space

Tekmetric digital vehicle inspection: Photo checklists for upselling repairs, not a release gate on wheel work.
Checkpoint wheel nut indicators: Physical indicators common on trucks, rarely fitted to passenger cars.
Data-logging torque wrenches: Record torque values, and only if the technician actually uses them on every nut.

What existing players are missing

A camera check at the bay exit that confirms every lug nut is present on each wheel, matches it against the torque wrench log for that job, and blocks the car's release until a missing wheel is signed off.

How Real Problem AI scores this opportunity

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

Problem severity: 9 / 10
AI feasibility today: 7 / 10
Market signal: 7 / 10
Competition gap: 8 / 10

How to build a solution: stack hints

Bay-exit cameras
Computer vision lug nut detection
Bluetooth torque wrench integration
Shop management system work order sync