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 can't I see a used EV battery health report before I buy?

A 4-year-old Model 3 might have 92% pack health or 71%. The difference is $8K of resale value. Carfax and Carvana hide it. OEM dealers refuse to share. Buyers eat the risk.

Category: Automotive & Mobility · Trend: AI · Opportunity score: 7.9 / 10

What is the “Why can't I see a used EV battery health report before I buy?” problem in 2026?

A 4-year-old Model 3 might have 92% pack health or 71%. The difference is $8K of resale value. Carfax and Carvana hide it. OEM dealers refuse to share. Buyers eat the risk.

Who has this problem?

Used EV buyers on Carvana, CarMax, Facebook Marketplace; small dealers running EV-heavy lots.

Evidence this problem is real

“I bought a used Bolt. Two months later the BMS started throwing range estimates that didn't match my charge curve. The dealer said normal. A $30 OBD reader would have told me at the lot.”

Sourced from Recurrent Auto market reports (2024 to 2026), r/electricvehicles used-buyer threads, AAA EV depreciation studies. (link)

Existing players in this space

Recurrent Auto — Best-in-class report, partnered with some marketplaces, not on the listing pages buyers actually use
OEM battery reports (Tesla, Ford) — Locked to the original owner, won't show pre-sale
OBD scanners (Leaf Spy, ABRP) — Per-model, technical, not buyer-friendly

What existing players are missing

A pocket app plus a $50 OBD dongle the buyer brings to the test drive. 10 minutes, multi-OEM coverage, returns a one-page PDF the seller has to either accept or counter. Builds a public score history, like Carfax for batteries.

How Real Problem AI scores this opportunity

Aggregate score: 7.9 / 10. Four-axis rubric:

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

How to build a solution: stack hints

OEM-specific OBD reader profiles (Tesla, Ford, GM, Hyundai, Kia, Nissan)
State-of-health estimator using charge-curve telemetry, not just BMS self-report
Buyer-facing PDF and resale-value impact estimate
Public history layered on top of VIN