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 am I paying three different cold-email vendors because none of them do replies well?

Smartlead, Apollo, Instantly all send. None do reply classification, triage, or follow-up routing well. Founders cobble three tools together and still miss warm leads.

Category: Marketing, Sales & CRM · Trend: Agents · Opportunity score: 7.5 / 10

What is the “Why am I paying three different cold-email vendors because none of them do replies well?” problem in 2026?

Smartlead, Apollo, Instantly all send. None do reply classification, triage, or follow-up routing well. Founders cobble three tools together and still miss warm leads.

Who has this problem?

Bootstrapped B2B founders running their own outbound, plus solo SDRs.

Evidence this problem is real

“I send through Smartlead, replies land in Gmail, I classify them by hand, then forward the good ones to my CRM. That manual middle step is killing my conversion.”

Sourced from Indie Hackers May 2026 outbound stack threads, r/sales weekly tooling discussions.

Existing players in this space

Smartlead — Sending-first
Instantly — Strong sender, weak reply UX
Apollo — CRM-first, weak deliverability

What existing players are missing

A reply-first cold-email stack: every inbound reply auto-classified (interested, objection, OOO, not now, wrong person), routed to the right next-step, and warm leads pushed to CRM with full context. Sending is table-stakes.

How Real Problem AI scores this opportunity

Aggregate score: 7.5 / 10. Four-axis rubric:

Problem severity: 7 / 10
AI feasibility today: 9 / 10
Market signal: 8 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

IMAP/Microsoft Graph inbox reader
LLM reply classifier
CRM connectors (HubSpot, Salesforce, Pipedrive)
Sequence-aware follow-up scheduler