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 replying to Google reviews across my 12 locations eat half my week?

A multi-location operator loses hours tab-switching between Business Profiles, reviews go unanswered past the window that customers and the algorithm reward, and profile data drifts out of sync.

Category: Marketing, Sales & CRM · Trend: Agents · Opportunity score: 6.4 / 10

What is the “Why does replying to Google reviews across my 12 locations eat half my week?” problem in 2026?

A multi-location operator loses hours tab-switching between Business Profiles, reviews go unanswered past the window that customers and the algorithm reward, and profile data drifts out of sync.

Who has this problem?

Local multi-location businesses and the marketers managing their profiles.

Evidence this problem is real

“The average local business owner spends 4 hours a week on Google reviews and still feels behind.”

Sourced from Wiremo, 'Google Reviews for Business: 7 Tasks That Win in 2026'. (link)

Existing players in this space

Birdeye / Podium: Full suites, priced per location, overkill for many
GLocal / GBP autopilot tools: Scheduling focus, generic replies
Manual replies: Too many tabs, slow, inconsistent by location

What existing players are missing

An agent that drafts on-brand, location-aware replies to every review within the 24-hour window, flags the ones a human must handle, keeps all profile fields in sync, and reports review velocity per site.

How Real Problem AI scores this opportunity

Aggregate score: 6.4 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 8 / 10
Market signal: 7 / 10
Competition gap: 4 / 10

How to build a solution: stack hints

GBP API multi-location connector
Brand-and-location-aware reply drafting
Escalation flags for sensitive reviews
Velocity and response-rate dashboard

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)