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 our liveness check still pass a face that was never in the room?

Synthetic faces and virtual-camera injection walk through remote identity checks because most vendors rebadged liveness detection as deepfake protection and still benchmark against public datasets fraudsters solved years ago.

Category: FinTech · Trend: Vision · Opportunity score: 8.5 / 10

What is the “Why does our liveness check still pass a face that was never in the room?” problem in 2026?

Who has this problem?

Fraud and risk teams at banks, fintechs and marketplaces running remote identity verification at onboarding.

Recorded source context

Dataset source note: Most vendors still benchmark against public GAN datasets that fraudsters cracked years ago

This note may summarize the referenced material rather than quote it verbatim. Source label: r/FraudPrevention thread on deepfake detection in identity verification flows, 5 September 2026, 10 comments; Quora answers on how deepfakes complicate KYC identity verification. (primary source).

Existing players in this space

Passive liveness vendors: Built to catch a printed photo or a replayed video, not a live synthetic face driven in real time.
Virtual camera detection add-ons: Match known driver names, so anything unlisted passes straight through the check.
Document plus selfie matching: Compares two images, both of which can now be generated to match each other.

What existing players are missing

Detection that treats injection and synthesis as two separate attacks: prove the frames came from a real camera on a real device, test the face against current generative architectures rather than a dataset from 2022, and publish the evaluation set with its date so a buyer can see what the score actually covers.

How Real Problem AI scores this opportunity

Aggregate score: 8.5 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Device and camera attestation to prove capture provenance
Stream-injection detection below the application layer
Generative-artefact models retrained against current architectures
Dated, published evaluation set with a per-attack breakdown
Step-up challenge for high-risk actions