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 my budgeting app still call "AMZN MKTP US" a mystery?

Budget tools categorize bank transactions terribly; Amazon, Walmart, and Target spend land in one black-box bucket.

Category: FinTech · Trend: LLM · Opportunity score: 8.3 / 10

What is the “Why does my budgeting app still call "AMZN MKTP US" a mystery?” problem in 2026?

Budget tools categorize bank transactions terribly; Amazon, Walmart, and Target spend land in one black-box bucket.

Who has this problem?

Anyone using Mint (RIP), YNAB, Copilot, Monarch, Rocket Money.

Recorded source context

Dataset source note: Mint died and Copilot still can't tell groceries from electronics on my Amazon spend.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/ynab, r/personalfinance, App Store reviews of every budget app..

Existing players in this space

Copilot Money
Monarch
YNAB
Rocket Money

What existing players are missing

None pull your actual Amazon/Walmart order history and split a single $284 charge across 9 line items into the right categories. The Amazon line-item split alone is the wedge.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Amazon order history connector (browser agent + IMAP fallback)
LLM line-item categorizer with per-user memory
Plaid for bank feeds
Monarch/YNAB write-back via webhook

Why this problem is archived

Swapped out in AI-tool-builder content refresh (2026-08-26): generic vertical problem replaced by a more current, builder-relevant one.