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 does one podcast episode take three hours of clipping, timestamps, and show notes?

A one-hour episode needs 3 to 4 hours of post to write show notes, find timestamps, and cut social clips, which is the main reason shows ship late and die after a dozen episodes.

Category: Marketing, Sales & CRM · Trend: Multimodal · Opportunity score: 6.6 / 10

What is the “Why does one podcast episode take three hours of clipping, timestamps, and show notes?” problem in 2026?

A one-hour episode needs 3 to 4 hours of post to write show notes, find timestamps, and cut social clips, which is the main reason shows ship late and die after a dozen episodes.

Who has this problem?

Independent podcasters and small B2B podcast teams.

Evidence this problem is real

“A single episode can become short video clips, social posts, show notes, blog content, LinkedIn updates, and assets for multiple channels. Most of that added reach never gets used.”

Sourced from Content Allies, 'How to Repurpose Podcast Content for More Reach (2026)'. (link)

Existing players in this space

Opus Clip / Riverside: Auto-clip, but generic picks and weak show notes
Descript: Editing power, still manual for the full asset set
VAs / editors: Costly, slow, inconsistent

What existing players are missing

One agent that ingests the recording, picks the genuinely strong moments, and outputs captioned vertical clips, timestamped show notes, a blog draft, and platform-native social posts in the host's voice, ready to schedule.

How Real Problem AI scores this opportunity

Aggregate score: 6.6 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 4 / 10

How to build a solution: stack hints

Transcription and moment detection
Auto-clip with captions
Show-notes and blog generation
Per-platform social drafting

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)