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 do I have to trust a benchmark score published by the company selling the model?

Almost every headline AI benchmark number is self-reported by the vendor, run on proprietary scaffolding, on tests that are saturated or contaminated, so buyers comparing models are reading marketing and calling it evidence.

Category: AI / Agents · Trend: LLM · Opportunity score: 8.5 / 10

What is the “Why do I have to trust a benchmark score published by the company selling the model?” problem in 2026?

Who has this problem?

Engineering leads, CTOs and procurement teams choosing between frontier models, and builders picking a model per feature.

Recorded source context

Dataset source note: The score you see is for the system. The model is one component of that system.

This note may summarize the referenced material rather than quote it verbatim. Source label: LayerLens, 'Why AI Benchmarks Mislead' (2 September 2026); Hugging Face retiring the Open LLM Leaderboard over hill-climbing on irrelevant directions; Stanford HAI 2026 AI Index on benchmark saturation; TechCrunch on the disputed Grok 3 benchmark claims. (primary source).

Existing players in this space

Vendor leaderboards (SWE-bench, MMLU and similar): Overwhelmingly self-submitted; on one mid-2026 SWE-bench board only a single entry of 100 carried independent verification
LMArena and crowd voting: Measures preference on open-ended chat, not task completion under a fixed harness, and is gameable by style
Contamination-resistant sets (LiveCodeBench, FrontierMath, MMLU-Pro): Better tests, but still run and reported by whoever wants the result

What existing players are missing

An independent evaluator that runs every new model and AI tool through one fixed, published harness with no vendor scaffolding, re-runs it on each release, publishes the methodology and the failure cases alongside the score, and dates every result so a buyer can see when a number was last verified rather than when it was last marketed.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Standardised sandboxed eval harness
Contamination detection (time-partition, canary GUID, rephrase gaps)
Per-release automated re-runs
Public methodology and raw transcript archive