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 adding AI features to my SaaS kill my margins?

Founders bolt an AI feature onto a $29/month SaaS. Power users hit 50K tokens/day per seat. Token costs balloon to $40-$120 per power-user-month; flat pricing dies. Switching to usage pricing tanks conversion. Multi-step agents can spike 60x on one task. No model exists for "how do I price this so I don't die".

Category: SaaS · Trend: LLM · Opportunity score: 8.7 / 10

What is the “Why does adding AI features to my SaaS kill my margins?” problem in 2026?

Who has this problem?

Bootstrapped SaaS founders ($10K-$2M ARR) adding GPT/Claude features to existing flat-priced products.

Recorded source context

Dataset source note: Launched AI 'unlimited' on a $29 plan in March. One user generated 2.3M tokens in a week. We're now paying Anthropic more than that user is paying us. The plan is dead but switching to usage-based killed signups by 40%.

This note may summarize the referenced material rather than quote it verbatim. Source label: Indie Hackers "The uncomfortable truth about AI tool pricing in 2026" (verified May 2026), r/SaaS pricing megathreads, Teamvoy hidden-costs-of-AI-agents 2026 report (70-120x token-burn spread on multi-step agents). (reference).

Existing players in this space

Stripe Billing: Plumbing for usage charges; doesn't decide your pricing strategy
Metronome: Usage billing; pricing model is the founder's problem
Lago / Orb: Same, billing engines, not pricing-strategy advisors
Helicone Cost Analytics: Shows token spend; doesn't tell you what to charge or which user to throttle

What existing players are missing

An AI-pricing copilot: ingest your Stripe + LLM provider spend, model per-cohort margin, suggest tier breakpoints + soft caps + power-user throttles, simulate conversion impact before you push to production. Plus a 'kill switch' that auto-throttles a user before they cost you more than they pay.

How Real Problem AI scores this opportunity

Aggregate score: 8.7 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Stripe + Anthropic/OpenAI + Helicone connectors
Per-user cohort margin model
Pricing-tier simulator with conversion-funnel projection
Real-time per-user spend gate with graceful degradation