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 AI-native founders lose 3-7 days per launch wiring the non-code stack?

AI agents generate working code in minutes, but every non-code surface (Vercel project settings, DNS, auth providers, Stripe webhooks, secret stores, log drains) is a human-shaped dashboard with no agent-callable contract.

Category: Others · Trend: Agents · Opportunity score: 8.6 / 10

What is the “Why do AI-native founders lose 3-7 days per launch wiring the non-code stack?” problem in 2026?

Who has this problem?

Solo and 2-person AI-native founders shipping agent-built v1s on Vercel + Supabase + Stripe stacks.

Recorded source context

Dataset source note: The trouble was deploying it on Vercel, wiring services, settings, DNS, auth, payments, secrets, and production configuration.

This note may summarize the referenced material rather than quote it verbatim. Source label: Andrej Karpathy, Sequoia Ascent 2026 summary (reference).

Existing players in this space

Vercel / Netlify: Assume a human in the dashboard; no agent-callable deploy contract beyond CLI.
v0 / Bolt / Lovable: Generate code but punt at deployment + auth + payments wiring.
Clerk / Auth0: Auth only; agent has to glue them into webhooks/redirects manually.
Pulumi / Terraform: IaC for engineers, not opinionated for AI-built SaaS.

What existing players are missing

A single agent-facing control plane that takes 'I want auth + payments + custom domain live' and returns a tested URL. Sells per-deployment ($20-50) or per-app SaaS ($49-149/mo).

How Real Problem AI scores this opportunity

Aggregate score: 8.6 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Vercel / Cloudflare Workers API
Auth (Clerk / Supabase) + Stripe orchestration
Secrets + DNS + log-drain glue
Agent-tested deploy verifier