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 my AI agents fail silently in production with no usable trace?

Multi-step agents (Cursor, Claude Code, custom LangGraph) drift, loop, or quietly skip steps; standard APM tools show "200 OK" while the agent is producing garbage.

Category: Others · Trend: Agents · Opportunity score: 8.8 / 10

What is the “Why do my AI agents fail silently in production with no usable trace?” problem in 2026?

Multi-step agents (Cursor, Claude Code, custom LangGraph) drift, loop, or quietly skip steps; standard APM tools show "200 OK" while the agent is producing garbage.

Who has this problem?

AI engineers shipping agent workflows, SRE leads, founders running agent-first products.

Recorded source context

Dataset source note: My production agent ran for 47 minutes, burned $14 in tokens, and the final answer was "I am unable to help with that." Datadog says everything was 200.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/MachineLearning, r/LocalLLaMA, LangChain Discord, X dev threads (May 2026)..

Existing players in this space

LangSmith: LangChain-only, trace-heavy not production-quality observability
Datadog LLM Observability: Adds LLM spans to APM but no agent-level semantics
Helicone: Strong for single LLM calls, weak for multi-step agents
Braintrust: Eval-first; production monitoring still nascent

What existing players are missing

Agent-grade observability: per-step expected vs actual schema, drift detection, cost-per-task SLO, automatic regression vs last week. Not just spans, semantic correctness signals.

How Real Problem AI scores this opportunity

Aggregate score: 8.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

OpenTelemetry-compatible agent span schema
LLM-judge eval running on production traces (sampled)
Schema-diff alerts (expected output shape vs actual)
Cost-budget envelopes per task with automatic kill