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 can't a recruiter tell a real candidate from an AI-written application anymore?

Every requisition now pulls 300-plus applications where AI wrote the resume and cover letter, so the document stops signaling ability and recruiters drown in polished noise.

Category: HR Tech & Recruiting · Trend: LLM · Opportunity score: 7.6 / 10

What is the “Why can't a recruiter tell a real candidate from an AI-written application anymore?” problem in 2026?

Every requisition now pulls 300-plus applications where AI wrote the resume and cover letter, so the document stops signaling ability and recruiters drown in polished noise.

Who has this problem?

In-house recruiters and hiring managers at small and mid-size companies.

Evidence this problem is real

“Candidates can use agentic AI to optimize resumes, generate tailored cover letters in seconds, and even receive coaching during screening calls.”

Sourced from Asymbl, 'The 2026 Staffing Dilemma'; HBR and NYT 2026 reporting on AI-flooded hiring. (link)

Existing players in this space

ATS keyword filters (Greenhouse, Lever): Reward the best prompt, not the best candidate
AI resume screeners: AI grading AI, signal collapses
Manual phone screens: Do not scale to 300 applicants per role

What existing players are missing

A proof-first screener that replaces the resume as the signal: a short async skills task or structured work sample, scored consistently, that surfaces genuine competence and flags fabricated or fully-AI profiles.

How Real Problem AI scores this opportunity

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

Problem severity: 8 / 10
AI feasibility today: 6 / 10
Market signal: 9 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Async work-sample generator per role
Consistent rubric scoring
Fabrication/deepfake profile detection
ATS write-back

Why this problem is archived

Trimmed to 100-cap (lowest opportunity_score)