What is Real Problem AI?

Real Problem AI is a curated directory of 200+ 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 200+ 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 is buying a used car online still a coin-flip on what actually shows up?

Online used-car buyers (Carvana, Cazoo-style) get glossy listing photos that hide scratches, smells, mechanical issues and reconditioning shortcuts. The reveal happens in the driveway, after the sale.

Category: Automotive & Mobility · Trend: Agents · Opportunity score: 7.6 / 10

What is the “Why is buying a used car online still a coin-flip on what actually shows up?” problem in 2026?

Who has this problem?

Used-car buyers purchasing online sight-unseen in the US, UK and Ireland.

Evidence this problem is real

“The photos were spotless. The car arrived with a cracked windscreen, a check-engine light and a smell of smoke. By then I had already done the 7-day swap-or-keep clock against me.”

Sourced from r/Carvana and r/UsedCars 2026 delivery-disappointment threads, BBB complaint patterns, online used-car-retail coverage.

Existing players in this space

Carfax / vehicle history — Title history, not current condition
Retailer 7-day return — Reactive, friction-heavy
Listing photos — Seller-controlled, flattering

What existing players are missing

An independent pre-delivery condition verification for online used cars: a standardised photo + mechanical checklist captured by a neutral party, scored, and tied to the VIN so the buyer knows what is coming before the truck arrives.

How Real Problem AI scores this opportunity

Aggregate score: 7.6 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Standardised condition-capture app for inspectors
Vision model for damage + wear scoring
VIN-linked condition certificate
Retailer + marketplace integration