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 does sourcing a single B2B supplier still take 60 emails and three weeks?

Small manufacturers and D2C brands sourcing components or contract manufacturers run a manual gauntlet: find candidates, email specs, chase quotes, compare apples to oranges, vet reliability. Weeks per supplier.

Category: Marketing, Sales & CRM · Trend: Agents · Opportunity score: 8.0 / 10

What is the “Why does sourcing a single B2B supplier still take 60 emails and three weeks?” problem in 2026?

Who has this problem?

Founders and ops leads at small hardware, D2C and light-manufacturing companies.

Evidence this problem is real

“I needed one injection-moulding supplier. I emailed 22 of them, got 9 quotes in 4 different formats, and spent a weekend building a spreadsheet just to compare them. Three weeks gone.”

Sourced from r/manufacturing and r/Entrepreneur 2026 sourcing threads, Alibaba and ImportYeti workflow discussions, supply-chain founder posts.

Existing players in this space

Alibaba RFQ — Volume-first, quality-vetting weak
Thomasnet — Directory, not a sourcing agent
ImportYeti — Customs data, no quote workflow

What existing players are missing

A sourcing agent that takes a spec, finds and contacts qualified suppliers, normalises the quotes into one comparison, and surfaces reliability signals from customs and review data. The whole RFQ-to-shortlist loop in a day, not three weeks.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Supplier discovery across directories + customs data
Outbound email agent with spec attachment
Quote-normalisation parser
Reliability scoring from import + review signals