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 do I rewrite the same blog post 4 ways for LinkedIn, X, newsletter, and YouTube?

Solo content marketers create one piece, then spend hours reformatting for each channel, voice gets lost in the process.

Category: Marketing, Sales & CRM · Trend: Multimodal · Opportunity score: 7.4 / 10

What is the “Why do I rewrite the same blog post 4 ways for LinkedIn, X, newsletter, and YouTube?” problem in 2026?

Solo content marketers create one piece, then spend hours reformatting for each channel, voice gets lost in the process.

Who has this problem?

Solo founders, content marketers, B2B PMMs, ghostwriters.

Recorded source context

Dataset source note: Wrote a 1,500-word blog. Took 3 more hours to chop into a LinkedIn carousel, X thread, and newsletter blurb.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/marketing, r/SEO, r/Entrepreneur..

Existing players in this space

Repurpose.io
Castmagic
Opus Clip
Taplio

What existing players are missing

Style-cloned repurposing that holds your actual voice across formats and auto-generates the visual (carousel slide, thumbnail), most tools spit out generic AI cadence.

How Real Problem AI scores this opportunity

Aggregate score: 7.4 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 9 / 10
Market signal: 8 / 10
Competition gap: 5 / 10

How to build a solution: stack hints

Style cloning from past posts per channel
LLM rewriter with format constraints
Image/carousel generator (Bannerbear/Replicate)
Buffer/Typefully scheduling