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 reviewing a vendor DPA take a full afternoon every time?

Procurement and security folks redline DPAs against the same internal standard for every new vendor, no memory.

Category: LegalTech & Compliance · Trend: LLM · Opportunity score: 7.9 / 10

What is the “Why does reviewing a vendor DPA take a full afternoon every time?” problem in 2026?

Procurement and security folks redline DPAs against the same internal standard for every new vendor, no memory.

Who has this problem?

Security/IT leads at SMB-mid market, fractional GCs.

Evidence this problem is real

“I've reviewed the same SCC clauses 60 times this year. I should be a robot by now.”

Sourced from r/cybersecurity, r/legaltech.

Existing players in this space

Spellbook
LinkSquares
Ironclad AI Assist

What existing players are missing

A "DPA-only" tool that learns your playbook from past redlines and auto-applies it, most CLM tools require enterprise rollout and don't specialize in SCC/DPA edge cases.

How Real Problem AI scores this opportunity

Aggregate score: 7.9 / 10. Four-axis rubric:

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

How to build a solution: stack hints

DOCX/PDF parser
Embedding-based playbook matcher
LLM redline generator with rationale
Comparison view vs. company standard

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)