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 connecting one large MCP server cost context on every single turn, even when you never touch most of its tools?

A gRPC-proxy MCP server exposing ~200 tools across 8 services has every tool name listed in the system prompt on every turn for ToolSearch, even though full schemas are deferred and most tools are never used in a given session.

Category: AI / Agents · Trend: Agents · Opportunity score: 6.5 / 10

What is the “Why does connecting one large MCP server cost context on every single turn, even when you never touch most of its tools?” problem in 2026?

Who has this problem?

Developers connecting large enterprise MCP servers to Claude Code.

Recorded source context

Dataset source note: Even though the tools are deferred and their full schemas aren't loaded, every tool name is included in the system prompt so the model knows they exist for ToolSearch... A server with ~200 deferred tools across ~8 services has the tool name list consume context on every turn.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #27208, DanielPBak, 20 Feb 2026, closed as duplicate. (reference).

Existing players in this space

Deferred tool loading: Reduces schema cost but still lists every flat tool name up front.
Manual server disconnection: Avoids the cost entirely but loses the server's functionality.

What existing players are missing

Hierarchical/nested tool discovery where the system prompt shows only service-group names, and ToolSearch drills into individual tools and schemas on demand, cutting a 200-entry list down to ~8.

How Real Problem AI scores this opportunity

Aggregate score: 6.5 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 8 / 10
Market signal: 5 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Hierarchical tool namespace design
Nested ToolSearch resolution
MCP server-side grouping metadata
Context-cost benchmarking