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 do I quit every symptom tracker within a month even though my doctor needs the data?

People with chronic illnesses try apps, spreadsheets and journals to log symptoms and flares, give up within weeks because logging is a chore, and arrive at appointments with patchy memories instead of a record.

Category: HealthTech · Trend: Personalization · Opportunity score: 7.3 / 10

What is the “Why do I quit every symptom tracker within a month even though my doctor needs the data?” problem in 2026?

Who has this problem?

People with chronic conditions such as autoimmune, arthritis or IBD, and the specialists who ask them how the last three months went.

Recorded source context

Dataset source note: I always ended up quitting within 2-4 weeks and not keeping accurate track.

This note may summarize the referenced material rather than quote it verbatim. Source label: Ask HN thread from a student who quit six illness tracking systems, 15 August 2026, 52 points, 90 comments. (primary source).

Existing players in this space

Bearable: Detailed symptom and habit tracker that still depends on the user opening the app every day.
Guava Health: Pulls records and logs into one place, with manual daily entry.
Apple Health: Collects passive data like sleep and steps, but not pain, flares or medication effects.

What existing players are missing

A tracker that asks instead of waiting: short, varied check-ins by message or voice at unpredictable times, plus passive data from the phone and wearables, turned into a one-page summary of flares, triggers and medication response for the next appointment.

How Real Problem AI scores this opportunity

Aggregate score: 7.3 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Conversational check-ins over SMS, chat or voice
Wearable and phone data import
LLM summarisation into a clinician-ready report
Adaptive scheduling so prompts do not get ignored