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 did Shopify hide the fraud details I used to decide which orders to ship?

Shopify's order risk update now shows only a simple low, medium or high label on low-risk orders, dropping the billing address and IP detail merchants used to catch fraud that the model marked low risk, so they must ship blind or refund good customers.

Category: E-commerce & Retail · Trend: LLM · Opportunity score: 7.8 / 10

What is the “Why did Shopify hide the fraud details I used to decide which orders to ship?” problem in 2026?

Who has this problem?

Small Shopify merchants who manually review orders for fraud before fulfilment.

Recorded source context

Dataset source note: We get a fair amount of fraudulent orders that come across as low risk and this update makes it impossible to make a decision.

This note may summarize the referenced material rather than quote it verbatim. Source label: Shopify Community thread calling the new order risk analysis a horrible update, 24 September 2026, 10 posts, plus a second thread on the same change two days later with 23 posts. (primary source).

Existing players in this space

Shopify Fraud Protect: Covers chargebacks on eligible orders, with no reasoning shown.
Signifyd and NoFraud: Guarantee-based fraud screening priced and built for larger stores.
FraudLabs Pro: Rule-based scoring that merchants must configure themselves.

What existing players are missing

A plain-language order reviewer that pulls every raw signal still available (address mismatch, IP distance, email age, card attempts, past orders), explains in two lines why an order looks risky or safe, and learns from the merchant's own past fraud cases.

How Real Problem AI scores this opportunity

Aggregate score: 7.8 / 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

Shopify orders and risk API
IP geolocation and email reputation lookups
LLM explanation of combined signals
Merchant feedback loop for learning