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 fighting a trademark refusal cost a startup six hours of paralegal time per case?

When USPTO issues a Section 2(d) refusal, paralegals manually pull TSDR file wrappers on every cited mark, draft DuPont factor arguments, and assemble the response in a 50-page Word doc 30 days before the deadline.

Category: LegalTech & Compliance · Trend: LLM · Opportunity score: 8.3 / 10

What is the “Why does fighting a trademark refusal cost a startup six hours of paralegal time per case?” problem in 2026?

Who has this problem?

US trademark paralegal or solo IP attorney with 20 to 80 active applications.

Evidence this problem is real

“Client got a 2(d) refusal citing 4 prior marks. I have to pull TSDR file wrappers for each cited mark, read the examiner's reasoning, compare goods under the Coordinated Class Manual, and draft DuPont arguments. Six hours billed at $175 per hour, for a startup that thought trademark filing was one-and-done.”

Sourced from r/Trademark and r/Lawyertalk threads on Office Action responses.

Existing players in this space

Trademarkia, LegalZoom — File the application, hand off Office Actions to a real attorney with no software help.
Alt Legal — Docketing and deadlines, not Office Action substance.
CompuMark — Search tool, no response drafting.

What existing players are missing

An LLM that ingests the Office Action PDF, auto-pulls TSDR file wrappers for every cited mark, applies DuPont factors with side-by-side comparison, drafts the response with cited TTAB precedent, and flags whether amendment, consent agreement, or full argument is the highest-EV path.

How Real Problem AI scores this opportunity

Aggregate score: 8.3 / 10. Four-axis rubric:

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

How to build a solution: stack hints

TSDR scraping with case bundle assembly
Long-context LLM with TTAB and Federal Circuit case retrieval
DuPont factor structured comparison
Word doc export with USPTO formatting