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 my AI voice agent still talk over callers and miss when they go quiet?

Production voice agents (booking, support, reception) get turn-taking wrong: they interrupt, talk over, or sit in dead silence. The conversation feels broken and callers hang up.

Category: AI / Agents · Trend: Voice · Opportunity score: 7.6 / 10

What is the “Why does my AI voice agent still talk over callers and miss when they go quiet?” problem in 2026?

Production voice agents (booking, support, reception) get turn-taking wrong: they interrupt, talk over, or sit in dead silence. The conversation feels broken and callers hang up.

Who has this problem?

Teams shipping AI voice agents for SMB reception, booking and support.

Recorded source context

Dataset source note: Demo sounds great. In production it talks over an elderly caller, then goes silent for 4 seconds while they wait. Our hang-up rate is 30% and it is all turn-taking, not the words.

This note may summarize the referenced material rather than quote it verbatim. Source label: 2026 voice-agent developer threads on turn-taking and endpointing, Vapi / Retell / LiveKit community discussions..

Existing players in this space

Vapi / Retell: Orchestration, turn-taking still hard
LiveKit Agents: Infra-level, you tune endpointing yourself
Twilio + custom: Full DIY

What existing players are missing

A turn-taking layer that gets human rhythm right: semantic endpointing (knows the caller paused mid-thought vs finished), graceful barge-in, and back-channel cues. The hard 10% that separates a usable voice agent from a frustrating one.

How Real Problem AI scores this opportunity

Aggregate score: 7.6 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Semantic endpointing model
Barge-in + interruption handling
Prosody-aware silence detection
Drop-in layer over Vapi/Retell/LiveKit