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 do my AI agents burn tokens silently without producing a single result?

Agent swarms loop, retry, and self-talk for hours before someone notices nothing has actually shipped. The bill arrives. The output is empty.

Category: AI / Agents · Trend: Agents · Opportunity score: 8.1 / 10

What is the “Why do my AI agents burn tokens silently without producing a single result?” problem in 2026?

Agent swarms loop, retry, and self-talk for hours before someone notices nothing has actually shipped. The bill arrives. The output is empty.

Who has this problem?

Engineering teams running multi-agent workflows in production (research bots, sales SDRs, ops automations).

Evidence this problem is real

“I had three agents running for six hours yesterday. Nothing shipped. Nothing was saved. The bill was $312. Nobody could tell me what they were doing.”

Sourced from Hacker News May 2026 thread on agent silent failures, GitHub gists cataloguing trending May 2026 r/AI_Agents discussions, Anthropic and OpenAI developer forum threads. (link)

Existing players in this space

LangSmith — Traces only, weak on silent-failure detection
AgentOps — Closer; setup-heavy
Helicone — Cost focus, not outcome verification

What existing players are missing

Outcome-verification middleware: every agent declares its expected output schema upfront, and the harness kills the run when nothing matching is produced within a deadline. Plus a forensic timeline so the team can see exactly when the agent went off the rails.

How Real Problem AI scores this opportunity

Aggregate score: 8.1 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 8 / 10
Market signal: 9 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

OpenTelemetry-based agent tracing
Outcome schema declaration SDK
Deadline-based kill switch
Forensic timeline UI