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 is attribution for my SMB so wrong I'm flying blind on spend?

iOS/Cookieless killed pixel attribution; SMB marketers can't tell what's working between Meta, Google, podcast, and influencer spend.

Category: Marketing, Sales & CRM · Trend: LLM · Opportunity score: 7.5 / 10

What is the “Why is attribution for my SMB so wrong I'm flying blind on spend?” problem in 2026?

iOS/Cookieless killed pixel attribution; SMB marketers can't tell what's working between Meta, Google, podcast, and influencer spend.

Who has this problem?

DTC founders, SMB marketing leads, agency account managers.

Evidence this problem is real

“Meta says it drove 3x ROAS. Shopify says it drove 0.4x. Whom do I believe?”

Sourced from r/marketing, r/ecommerce, r/PPC.

Existing players in this space

Triple Whale
Northbeam
Rockerbox
Polar

What existing players are missing

Affordable post-purchase survey + MMM hybrid for sub-$5M brands with plain-English allocation recs, current tools start at $500/mo and need a data analyst to interpret.

How Real Problem AI scores this opportunity

Aggregate score: 7.5 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Post-purchase survey (Fairing-style)
Lightweight MMM (Robyn/Meridian)
LLM weekly allocation memo
Shopify/Stripe/Ads connectors

Why this problem is archived

Capped at 100 per editorial policy; lower-score entries rotate to archive.