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 cloning my voice take 3 seconds of audio and fool my bank?

Real-time voice clones now defeat "voice biometric" auth at major banks and call centres. Fraud loss is climbing and consumers have no consent layer.

Category: FinTech · Trend: GenMedia · Opportunity score: 9.3 / 10

What is the “Why does cloning my voice take 3 seconds of audio and fool my bank?” problem in 2026?

Real-time voice clones now defeat "voice biometric" auth at major banks and call centres. Fraud loss is climbing and consumers have no consent layer.

Who has this problem?

Bank/insurance customers, anyone with a podcast or YouTube channel, families targeted by impersonation scams.

Evidence this problem is real

“My dad got a call in my voice saying I'd been in an accident. He almost wired $9,000. The voice was perfect. I'd posted one TikTok.”

Sourced from FTC voice-clone scam advisories (2026), FBI IC3 reports, BBC and NYT investigative pieces on synthetic-voice fraud.

Existing players in this space

Pindrop — Enterprise voice-fraud detection, not consumer
Veriff / Onfido — ID, not voice
ElevenLabs voice-print — Single-vendor, partial

What existing players are missing

Consumer-grade voice provenance + family fraud shield: cryptographically signed call-back challenges, household-shared duress codes, and a voiceprint defence layer that family members can install on their phones.

How Real Problem AI scores this opportunity

Aggregate score: 9.3 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Cross-vendor voice-clone detection model
Cryptographic call-back challenge
Household identity graph
Telco integration (RCS, STIR/SHAKEN)