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 does every LLM session start from zero for knowledge workers?

Lawyers, analysts, founders and doctors paste the same context into ChatGPT or Claude 20+ times a day because no portable cross-provider memory layer exists. Single-provider memory locks you in.

Category: Others · Trend: Agent · Opportunity score: 8.3 / 10

What is the “Why does every LLM session start from zero for knowledge workers?” problem in 2026?

Lawyers, analysts, founders and doctors paste the same context into ChatGPT or Claude 20+ times a day because no portable cross-provider memory layer exists. Single-provider memory locks you in.

Who has this problem?

Knowledge workers who consult an LLM 20+ times daily and re-explain themselves every session.

Evidence this problem is real

“LLMs are like a coworker with anterograde amnesia. They do not consolidate or build long-running expertise once training is over; all they have is short-term memory (the context window).”

Sourced from Andrej Karpathy at YC AI Startup School (link)

Existing players in this space

OpenAI Memory / Claude Projects — Single-provider; cannot move with you to the next model.
Mem.ai — Notes app, not a structured memory layer for LLM consumption.
Rewind.ai — Keystroke-level capture; not structured for LLM grounding.

What existing players are missing

A portable, cross-provider personal memory layer that any LLM can read at session start (MCP-shaped, browser extension or local daemon).

How Real Problem AI scores this opportunity

Aggregate score: 8.3 / 10. Four-axis rubric:

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

How to build a solution: stack hints

MCP server
Local SQLite + embeddings
Browser extension
Provider adapters for ChatGPT/Claude/Gemini