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 reviewing AI-written code take longer than writing it would have?

Pull requests now arrive full of generated code the author never read closely, so the reviewer becomes the first human to actually check it and review time grows faster than the codebase does.

Category: AI / Agents · Trend: Agents · Opportunity score: 8.3 / 10

What is the “Why does reviewing AI-written code take longer than writing it would have?” problem in 2026?

Pull requests now arrive full of generated code the author never read closely, so the reviewer becomes the first human to actually check it and review time grows faster than the codebase does.

Who has this problem?

Engineering managers, tech leads and senior reviewers on teams where most new code is model-generated.

Recorded source context

Dataset source note: the issue is when the developer hasn't properly gone through it or verified that it actually makes sense. Then the reviewer ends up spending a lot of time figuring out whether the code is actually correct.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/ClaudeCode thread on reviewing AI-written code, 6 September 2026, 78 comments; Quora answers from working engineers on reviewing AI-generated code, August 2026. (primary source).

Existing players in this space

CodeRabbit and Greptile: Add a second generated opinion the human still has to verify, which moves the bottleneck rather than removing it.
GitHub Copilot code review: Comments line by line, but says nothing about which parts of the diff the author actually read and tested.
Linters and static analysis: Deterministic and genuinely useful, and completely silent on whether the change is correct.

What existing players are missing

A review layer that separates what a machine can prove from what a human must judge: run the deterministic checks first, then show the reviewer only the lines nobody has verified, with the originating prompt and the test evidence attached, so review effort tracks risk instead of diff size.

How Real Problem AI scores this opportunity

Aggregate score: 8.3 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Provenance capture from the coding agent (prompt, model, edited ranges)
Deterministic scan layer (lint, types, dead code, duplication)
Test-coverage diff against the changed lines only
Reviewer queue ranked by unverified risk