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 two AI products still mean a custom integration every time?

AI companies partnering with each other (model + app, agent + data source) rebuild the same auth, billing-split and data-contract plumbing for every partnership. There is no standard rail.

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

What is the “Why does connecting two AI products still mean a custom integration every time?” problem in 2026?

AI companies partnering with each other (model + app, agent + data source) rebuild the same auth, billing-split and data-contract plumbing for every partnership. There is no standard rail.

Who has this problem?

BD and platform engineers at AI startups doing partnership integrations.

Recorded source context

Dataset source note: We have signed 6 partnership deals. Each one is a 3-week eng project to wire auth, usage metering and the revenue split. The deal takes a day. The integration takes a month.

This note may summarize the referenced material rather than quote it verbatim. Source label: AI partnership announcements 2026, MCP and A2A protocol discussions, Latent Space and partnership-ops threads..

Existing players in this space

Custom per-partner code: What everyone does today
MCP / A2A: Standardise the call, not the commercial terms
Stripe Connect: Money rail, not the AI data/usage contract

What existing players are missing

A partnership rail for AI products: standard auth handshake, usage metering both sides agree on, automated revenue split, and a data-use contract enforced in code. Sign the deal, flip a switch, ship the integration the same week.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Partner OAuth + scoped tokens
Bilateral usage metering
Programmatic revenue-split (Stripe Connect under the hood)
Machine-readable data-use terms