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 do my LinkedIn comments take an hour and still sound like everyone else's?

Founders building in public on LinkedIn lose hours daily to commenting and DMs. AI tools generate the same generic responses everyone else uses.

Category: Marketing, Sales & CRM · Trend: Agents · Opportunity score: 7.3 / 10

What is the “Why do my LinkedIn comments take an hour and still sound like everyone else's?” problem in 2026?

Founders building in public on LinkedIn lose hours daily to commenting and DMs. AI tools generate the same generic responses everyone else uses.

Who has this problem?

Solo founders, indie hackers and creators using LinkedIn as primary distribution.

Evidence this problem is real

“I spent 90 minutes on LinkedIn this morning leaving comments. Every other AI-assisted comment sounded identical to mine. The signal is dying.”

Sourced from Indie Hackers and r/LinkedInLunatics threads 2026, creator economy reports.

Existing players in this space

AiLinkedMe — Voice-trained drafting; under-marketed
Taplio — Calendar + analytics
Engage AI — Generic suggestions

What existing players are missing

Voice-trained LinkedIn agents that learn from your last 200 posts, mirror your sentence-length distribution, refuse cliches, and never auto-send. Plus a measurable lift-vs-baseline metric so the founder sees the impact.

How Real Problem AI scores this opportunity

Aggregate score: 7.3 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 9 / 10
Market signal: 8 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Personal style embedding
Cliche detection layer
Manual-send gating
Engagement-lift attribution