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 fighting one chargeback eat three hours of my day for a coin-flip outcome?

A small merchant spends around 3 hours assembling IP logs, delivery proof, and authorization data for a single representment, and 2026 network rule changes made the deadlines shorter and the win a coin flip.

Category: FinTech · Trend: Agents · Opportunity score: 7.1 / 10

What is the “Why does fighting one chargeback eat three hours of my day for a coin-flip outcome?” problem in 2026?

Who has this problem?

Solo and small-team ecommerce operators handling their own disputes.

Evidence this problem is real

“This process can stretch over months, locking up capital, driving operational overhead, and increasing financial risk.”

Sourced from Justt.ai merchant chargeback-rights guide; Chargebacks911 2026 dispute-cost data. (link)

Existing players in this space

Chargeflow / Justt: Take a success fee, tuned for larger merchants
Stripe/Shopify dispute tools: Collect evidence, still leave the writing to you
Manual representment: Hours per case, procedural errors lose it

What existing players are missing

An agent that auto-assembles the compelling-evidence packet from Stripe, Shopify, and the carrier, writes the representment to the current Visa and Mastercard rules, hits the shortened deadline, and reports honest per-reason-code win rates.

How Real Problem AI scores this opportunity

Aggregate score: 7.1 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 5 / 10

How to build a solution: stack hints

Evidence pull from processor and carrier
Reason-code-aware letter generator
Deadline and rules engine
Win-rate analytics

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)