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 Gong record eight hours of sales calls daily but reps still take handwritten notes?

Gong and Chorus transcribe every call but reps still take parallel handwritten notes because the auto-generated summary misses the one offhand competitor mention or pricing objection that actually mattered.

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

What is the “Why does Gong record eight hours of sales calls daily but reps still take handwritten notes?” problem in 2026?

Who has this problem?

Enterprise AE at a Series B SaaS using Gong.

Evidence this problem is real

“Gong tells me the call had a pricing discussion but not that the buyer specifically said if you can match Workday's contract terms we will sign Friday. That is the only line I needed.”

Sourced from r/sales threads on Gong summary quality for AE follow-up.

Existing players in this space

Gong — Summaries optimized for managers, not next-step extraction.
Chorus — Similar high-level keyword tagging.
Fathom — Good free transcription, weak deal-specific extraction.

What existing players are missing

Per-rep, per-deal AI that surfaces verbatim commitment quotes, objection language, and the next 3 specific actions tied to the rep's MEDDIC fields, not generic call summaries.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Whisper-large-v3
Claude for quote extraction
Salesforce API write-back
Vector DB per deal