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 HubSpot deduplication leave hundreds of fuzzy duplicate contacts every quarter?

HubSpot's native dedupe only matches exact email/name, so RevOps still ships CSVs to a human every quarter to merge fuzzy duplicates like Bob Smith vs Robert Smith at Acme Corp.

Category: Marketing, Sales & CRM · Trend: LLM · Opportunity score: 8.0 / 10

What is the “Why does HubSpot deduplication leave hundreds of fuzzy duplicate contacts every quarter?” problem in 2026?

HubSpot's native dedupe only matches exact email/name, so RevOps still ships CSVs to a human every quarter to merge fuzzy duplicates like Bob Smith vs Robert Smith at Acme Corp.

Who has this problem?

RevOps lead at a 50 to 200 person B2B SaaS running HubSpot Sales Hub.

Evidence this problem is real

“Duplicate contact management is a nightmare. HubSpot only finds exact email matches and we have thousands of fuzzy dupes that a human has to eyeball.”

Sourced from r/hubspot thread on duplicate contact management.

Existing players in this space

Insycle — Rule-based fuzzy match, no LLM reasoning on company context.
Dedupely — Basic similarity scoring, surfaces too many false positives.
HubSpot native dedupe — Exact email/name only.

What existing players are missing

An LLM-powered merge agent that reads enrichment data (LinkedIn, domain, deal history) and proposes high-confidence merges with a one-click audit log, not a 4,000-row CSV.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

HubSpot API
Claude Sonnet for entity resolution
Clearbit/Apollo enrichment
Postgres audit trail