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 do I have to build the same Salesforce integration into six different LLMs?

Every team building agents ends up writing the same Salesforce, HubSpot, Stripe, Notion, Slack integrations from scratch. Then again when the LLM provider changes.

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

What is the “Why do I have to build the same Salesforce integration into six different LLMs?” problem in 2026?

Every team building agents ends up writing the same Salesforce, HubSpot, Stripe, Notion, Slack integrations from scratch. Then again when the LLM provider changes.

Who has this problem?

Agent platform teams and indie founders shipping agents that touch business SaaS.

Recorded source context

Dataset source note: We have written the Salesforce-to-LLM bridge three times: once for OpenAI, once for Anthropic, once for our new Llama setup. Each one is 800 lines.

This note may summarize the referenced material rather than quote it verbatim. Source label: MCP ecosystem discussions on GitHub, May 2026 trending agent-integration posts..

Existing players in this space

MCP servers: Standardising but fragmented
Composio: SaaS, opinionated, paid
Custom code: Most teams still do this

What existing players are missing

An MCP-native enterprise integration layer: certified servers for the 50 most-used B2B SaaS, with auth handled, rate limits respected, and a compatibility matrix per LLM client. Plus a write-once SDK so my code outlives the next LLM provider switch.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Certified MCP server catalog
OAuth + token management
Rate-limit aware proxy
Cross-client compatibility tests