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 Claude and GPT update quietly break my app overnight?

Model deprecations, prompt-format changes and tool-call schema tweaks ship without backwards-compatible aliases. Customers see the regression before the founder does.

Category: AI / Agents · Trend: LLMOps · Opportunity score: 7.8 / 10

What is the “Why does every Claude and GPT update quietly break my app overnight?” problem in 2026?

Model deprecations, prompt-format changes and tool-call schema tweaks ship without backwards-compatible aliases. Customers see the regression before the founder does.

Who has this problem?

Solo and small-team LLM-app developers shipping to paying customers.

Recorded source context

Dataset source note: Claude Sonnet 4.7 changed how it formats tool-call args. My customers noticed before my Sentry did. I lost a week.

This note may summarize the referenced material rather than quote it verbatim. Source label: Anthropic and OpenAI developer changelog threads 2026, r/ClaudeAI weekly breakage posts..

Existing players in this space

Statsig + feature flags: Helps roll out, not detect
PromptLayer: Logs only
Custom regression suites: Most teams skip

What existing players are missing

A model-update canary service: shadow every new provider release against your production traffic, score the diff, alert before the deprecation date if the regression is material.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Multi-provider shadow router
Output-diff scoring (LLM-as-judge + heuristics)
Deprecation calendar tracker
Slack/PagerDuty alerts