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 can't a cancer patient tell which clinical trial they actually qualify for?

ClinicalTrials.gov lists thousands of studies in dense jargon, and a sick patient cannot parse narrow inclusion and exclusion criteria to find the handful they could realistically enroll in.

Category: HealthTech · Trend: LLM · Opportunity score: 8.1 / 10

What is the “Why can't a cancer patient tell which clinical trial they actually qualify for?” problem in 2026?

ClinicalTrials.gov lists thousands of studies in dense jargon, and a sick patient cannot parse narrow inclusion and exclusion criteria to find the handful they could realistically enroll in.

Who has this problem?

Patients and caregivers with serious or rare conditions searching for options.

Recorded source context

Dataset source note: Relevance and eligibility aren't enough.

This note may summarize the referenced material rather than quote it verbatim. Source label: ClinTrialFinder, 'Why Eligibility Isn't Enough' (Mar 2026); ClinicalTrials.gov usability critiques. (reference).

Existing players in this space

ClinicalTrials.gov: Comprehensive but jargon-dense, brutal UX for patients
Sponsor matching tools (Antidote, TrialJectory): Sponsor-funded, biased to enrolling their own trials
Oncology nurse navigators: Scarce, cannot scan the full registry

What existing players are missing

A patient-first agent that takes a structured history, translates eligibility criteria into plain language, ranks the trials the patient truly qualifies for by evidence and proximity, and drafts the questions to ask the coordinator.

How Real Problem AI scores this opportunity

Aggregate score: 8.1 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Structured patient-history intake
Eligibility-criteria parsing with LLM
Evidence and location ranking
Plain-language explanation layer

Why this problem is archived

Swapped out in AI-tool-builder content refresh (2026-08-26): generic vertical problem replaced by a more current, builder-relevant one.