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 are companies losing millions to deepfake candidates in remote interviews?

Remote technical interviews are being gamed by candidates using real-time face-swap and voice-clone tools; one person passes interviews on behalf of three, then the hire shows up unable to write a loop.

Category: HR Tech · Trend: Vision · Opportunity score: 8.8 / 10

What is the “Why are companies losing millions to deepfake candidates in remote interviews?” problem in 2026?

Who has this problem?

Talent leaders, technical recruiters, engineering managers running remote-only orgs, IT-staffing CTOs.

Recorded source context

Dataset source note: We extended an offer, the person who joined on day one was not the person who interviewed. Same name, different face, different skills. Lost $180K.

This note may summarize the referenced material rather than quote it verbatim. Source label: Reddit r/recruiting, KPMG/Deloitte 2025 fraud reports, LinkedIn discussions May 2026..

Existing players in this space

HackerRank ID verification: Static ID check at start, no continuous proof-of-self
CodeSignal video proctoring: Records but doesn't actively detect face-swap
Sumsub Liveness: KYC-focused; not interview-context aware

What existing players are missing

Continuous liveness + face-consistency + voice-consistency throughout the interview, with cryptographic attestation. Plus a real-time 'this is a deepfake feed' classifier with adversarial training against 2026-era models.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Real-time face-swap classifier (vision model fine-tuned on deepfake datasets)
Continuous voice-print verification
Behavioural-biometric signals (typing cadence, mouse motion)
Cryptographic recording attestation for audit