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 prove my AI image isn't trained on someone else's art?

Brands and creators paying for AI-generated visuals can't verify the model wasn't trained on copyrighted art. Lawsuits are stacking up. Indemnity is patchy.

Category: LegalTech & Compliance · Trend: GenMedia · Opportunity score: 8.0 / 10

What is the “Why can't I prove my AI image isn't trained on someone else's art?” problem in 2026?

Brands and creators paying for AI-generated visuals can't verify the model wasn't trained on copyrighted art. Lawsuits are stacking up. Indemnity is patchy.

Who has this problem?

Marketing teams, indie game devs, ad agencies using Midjourney, Sora, Veo, Flux.

Evidence this problem is real

“Legal won't sign off on the campaign because the AI vendor can't prove training-data provenance. We're back to stock photos.”

Sourced from Disney/Universal vs Midjourney filings, Getty vs Stability ongoing rulings, AdAge brand-safety panels (Q1-Q2 2026).

Existing players in this space

Adobe Firefly — Indemnified, lower quality ceiling
Getty AI — Licensed corpus, slow
C2PA content credentials — Adopted slowly, not a provenance proof

What existing players are missing

An independent provenance checker: hash an AI image, query against known training-data fingerprints, and return a defensibility score with citations. Plus an indemnity-grade attestation a brand legal team accepts.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Perceptual hashing (pHash, ImageBind)
Reverse-image at scale (Bing, TinEye API)
C2PA manifest reader
Legal-grade audit report generator