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 is a working business one dead ISA card away from shutting down?

Labs, workshops and small firms still run core processes on DOS machines with custom ISA cards, parallel-port dongles and dBase or Clipper apps, the one person who understood them has left, and a hardware failure would stop the business.

Category: Others · Trend: LLM · Opportunity score: 8.0 / 10

What is the “Why is a working business one dead ISA card away from shutting down?” problem in 2026?

Who has this problem?

Owners and lone IT staff at labs, manufacturers and small firms running critical DOS-era software and instruments.

Recorded source context

Dataset source note: I know of a DOS machine at a wool testing place that uses an ISA card based imager for measuring the micron and other things.

This note may summarize the referenced material rather than quote it verbatim. Source label: Ask HN thread asking who still keeps a DOS machine running because the business depends on it, 25 September 2026, 277 points and 293 comments. (primary source).

Existing players in this space

86Box and PCem: Cycle-accurate PC emulators, with no support for bespoke ISA instrument cards.
DOSBox: Runs DOS software but not the hardware it controls.
Harbour: Open-source Clipper-compatible compiler that still needs someone to port and test the app.
Stockpiling period PCs: The real strategy the thread describes, until the spares run out.

What existing players are missing

A migration service that reads the old dBase or Clipper source and data files, explains what the app actually does, generates a tested modern equivalent, and documents the instrument card protocol so the hardware can be bridged or replaced.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

LLM code comprehension of legacy xBase source
Automated test generation from recorded runs
Data file conversion from DBF
Hardware protocol capture and documentation