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 RevOps leaders deploy Lindy / Relevance agents and have no idea when they silently fail?

Non-engineer ops leaders spin up agents that quietly mis-route leads, send wrong follow-ups, or skip records, failures only surface weeks later.

Category: Marketing, Sales & CRM · Trend: Agent · Opportunity score: 8.4 / 10

What is the “Why do RevOps leaders deploy Lindy / Relevance agents and have no idea when they silently fail?” problem in 2026?

Non-engineer ops leaders spin up agents that quietly mis-route leads, send wrong follow-ups, or skip records, failures only surface weeks later.

Who has this problem?

RevOps and ops leaders inside 50-500 person companies who deployed at least one production agent with no engineering owner.

Evidence this problem is real

“I noticed three weeks later that my outbound agent had been silently emailing the wrong segment.”

Sourced from Lindy + Relevance + Bardeen community Discords, 2026 Q1

Existing players in this space

Langfuse / Helicone — Engineer-shaped; ops leaders cannot read traces.
Lindy native logs — Per-run logs only; no aggregate failure mode dashboard.

What existing players are missing

An ops-shaped observability layer that watches every Lindy/Relevance/Bardeen run, clusters silent failures, and emails the owner with a plain-English diff.

How Real Problem AI scores this opportunity

Aggregate score: 8.4 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Webhook ingestion from agent platforms
Embedding-based clustering of failure messages
Plain-English summariser
Slack + email digest