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 are quarterly performance reviews a week of writing the same thing six different ways?

Managers re-write the same feedback for self-review, peer-review, calibration doc, promo packet, and the actual 1:1.

Category: HR Tech & Recruiting · Trend: LLM · Opportunity score: 7.8 / 10

What is the “Why are quarterly performance reviews a week of writing the same thing six different ways?” problem in 2026?

Managers re-write the same feedback for self-review, peer-review, calibration doc, promo packet, and the actual 1:1.

Who has this problem?

Engineering / product managers at mid-to-large tech companies.

Evidence this problem is real

“I have 7 reports. Performance week ate 22 hours. I copy-pasted my own bullet points across 4 forms.”

Sourced from r/managers, r/ExperiencedDevs, r/cscareerquestions.

Existing players in this space

Lattice
15Five
Culture Amp
Workday Talent

What existing players are missing

None ingest a manager's running 1:1 notes, Slack DMs, Jira/Linear activity, and prior reviews to draft a single source of truth that re-formats into each company-specific template.

How Real Problem AI scores this opportunity

Aggregate score: 7.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Slack/Linear/Jira ingest with consent
LLM synthesis of running notes → review draft
Template adapter per company review form
Bias/tone check before submit

Why this problem is archived

Generic perf-review category; Lattice/15Five own it, no clear AI wedge left.