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.

My AI agent repeated one broken action all night, and I only saw the cost in the morning.

Autonomous agents can retry failing steps in a loop without a spend circuit-breaker, and the first warning may arrive after the cost is already incurred.

Category: AI / Agents · Trend: LLM · Opportunity score: 9.0 / 10

What is the “My AI agent repeated one broken action all night, and I only saw the cost in the morning.” problem in 2026?

Autonomous agents can retry failing steps in a loop without a spend circuit-breaker, and the first warning may arrive after the cost is already incurred.

Who has this problem?

Solo devs and small teams shipping LLM agents on metered APIs.

Recorded source context

Dataset source note: Without guardrails, an agent can inadvertently trigger a denial-of-service attack against your own resources.

This note may summarize the referenced material rather than quote it verbatim. Source label: Google Cloud Office of the CISO, 19 Mar 2026, agent governance guidance on infinite loops, spend limits, and rate controls. (primary source).

Existing players in this space

Helicone / Langfuse: Observe spend after the fact; weak real-time hard caps.
OpenAI usage limits: Monthly caps, not per-run kill-switches.
Cloud budget alerts: Alert after the burn, do not stop the loop.

What existing players are missing

A per-run, per-agent spend circuit-breaker that kills a runaway loop mid-execution the moment cost-velocity spikes, not a dashboard you read the next morning.

How Real Problem AI scores this opportunity

Aggregate score: 9.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Proxy in front of the model API
Cost-velocity anomaly detector
Hard kill-switch webhook
Per-agent budget tokens