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 does drafting an employment offer letter still mean copy-pasting from last hire?

Founders/HR generalists hand-edit offer letters for state-specific clauses (non-compete bans, PTO accrual rules, pay transparency) and miss things.

Category: LegalTech & Compliance · Trend: LLM · Opportunity score: 7.5 / 10

What is the “Why does drafting an employment offer letter still mean copy-pasting from last hire?” problem in 2026?

Founders/HR generalists hand-edit offer letters for state-specific clauses (non-compete bans, PTO accrual rules, pay transparency) and miss things.

Who has this problem?

Founders making first 20 hires, fractional HR/People ops.

Evidence this problem is real

“Hired in CA, CO and NY same month. Each needed different language. I screwed up the CO pay disclosure.”

Sourced from r/humanresources, r/startups.

Existing players in this space

Gusto/Rippling templates
Lexology samples
LegalZoom

What existing players are missing

State-aware generator that updates clauses when state laws change (FTC non-compete, pay transparency) and flags risky boilerplate from your old template.

How Real Problem AI scores this opportunity

Aggregate score: 7.5 / 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

State-law rule library (refreshed monthly)
LLM clause selector by jurisdiction
DocuSign send + countersign tracking
Diff-against-prior-letter view

Why this problem is archived

Offer-letter drafting is commodity GPT; absorbed by HR onboarding suites.