What is Real Problem AI?

Real Problem AI is a curated directory of 200+ 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 200+ 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 my AI agent burn $100 of tokens on a task that should cost $2?

Production AI agents silently spiral: looping tool calls, re-reading the same docs, retrying failed steps. The Anthropic bill arrives and nobody can explain it.

Category: AI / Agents · Trend: Agents · Opportunity score: 8.4 / 10

What is the “Why does my AI agent burn $100 of tokens on a task that should cost $2?” problem in 2026?

Production AI agents silently spiral: looping tool calls, re-reading the same docs, retrying failed steps. The Anthropic bill arrives and nobody can explain it.

Who has this problem?

Indie hackers and small teams shipping LLM agents to customers (Cursor-style, sales SDR agents, ops automations).

Evidence this problem is real

“Got the Anthropic invoice and my $200 MRR product cost $1,400 to run last month. Nobody can tell me which user triggered the loop.”

Sourced from r/LocalLLaMA cost-spike threads, Hacker News "agent cost surprise" discussions, OpenRouter and Helicone dashboards going viral on X.

Existing players in this space

Helicone — Dashboards but no automated kill-switch
LangSmith — Tracing-heavy, weak budget enforcement
OpenRouter — Routing, not budget guardrails

What existing players are missing

A drop-in cost firewall: per-user, per-task, per-day budgets with automatic degradation (drop to cheaper model, summarise context, hard stop). Forensic playback showing exactly which tool loop burned the tokens.

How Real Problem AI scores this opportunity

Aggregate score: 8.4 / 10. Four-axis rubric:

Problem severity: 9 / 10
AI feasibility today: 9 / 10
Market signal: 9 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

OpenTelemetry-based LLM tracing
Per-request budget enforcement middleware
Loop-detection on tool call graphs
Auto-downgrade router (Sonnet to Haiku to local)