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 every new client mean rewriting the same proposal and scope from a blank page?

Freelancers and agencies spend 2 to 3 hours per proposal rewriting the summary, scope, and approach from scratch for each client, and vague scopes then invite the creep that eats the project.

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

What is the “Why does every new client mean rewriting the same proposal and scope from a blank page?” problem in 2026?

Freelancers and agencies spend 2 to 3 hours per proposal rewriting the summary, scope, and approach from scratch for each client, and vague scopes then invite the creep that eats the project.

Who has this problem?

Freelancers, consultants, and small-agency owners bidding on projects.

Evidence this problem is real

“The same proposal takes 2-3 hours because the freelancer is also making layout decisions, writing terms from scratch, and formatting the document.”

Sourced from Plutio Freelancer Magazine, 'How to Write a Freelance Proposal That Wins (2026)'. (link)

Existing players in this space

PandaDoc / Bonsai / Plutio: Templates, but the summary and scope are still manual
Generic ChatGPT: No memory of your services, rates, or past scopes
Copy-paste from last proposal: Carries stale scope and pricing errors

What existing players are missing

A tool that turns a discovery-call transcript or a few notes into a tailored summary, scope with explicit exclusions and revision limits, and priced options drawn from the freelancer's own service and rate library.

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: 7 / 10
Competition gap: 5 / 10

How to build a solution: stack hints

Call-transcript to brief extraction
Service and rate library
Scope and exclusions generator
E-sign and tracking

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)