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 onboarding a second engineer onto our AI codebase take a month?

AI-first startups have weird codebases: half human, half Cursor, half deprecated. New hires can't read it because Claude wrote it differently each session.

Category: AI / Agents · Trend: Agents · Opportunity score: 7.3 / 10

What is the “Why does onboarding a second engineer onto our AI codebase take a month?” problem in 2026?

AI-first startups have weird codebases: half human, half Cursor, half deprecated. New hires can't read it because Claude wrote it differently each session.

Who has this problem?

Seed-stage CTOs and tech leads hiring engineer #2 onto an AI-heavy codebase.

Evidence this problem is real

“New engineer asked why we have three different state-management patterns. The honest answer: Claude was in a mood that week.”

Sourced from YC Slack threads (Winter 2026 batch), r/cscareerquestions "my new job's codebase is AI slop" posts.

Existing players in this space

Sourcegraph Cody — Search-heavy, no onboarding flow
Greptile — PR review, not onboarding
Internal docs — Always stale

What existing players are missing

An onboarding agent that walks new hires through the codebase the way the senior dev would: "here's why this folder is weird", "here's the convention we settled on", with auto-generated runbooks and a quiz mode that surfaces gaps.

How Real Problem AI scores this opportunity

Aggregate score: 7.3 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Repo embedding + tree-sitter parsing
Git history archaeology agent
Interactive walkthrough UI
Convention-drift detector