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 an IEP team rewrite the same goal-progress paragraphs every nine weeks instead of pulling from data they already have?

Special ed teachers spend 6 to 10 hours every quarter writing IEP progress reports by hand, copying scores from Aimsweb, IXL, and paper running records into a Word template for every one of their 14 students.

Category: EdTech · Trend: Multimodal · Opportunity score: 8.3 / 10

What is the “Why does an IEP team rewrite the same goal-progress paragraphs every nine weeks instead of pulling from data they already have?” problem in 2026?

Who has this problem?

K-12 special education teacher with caseload of 12 to 20 IEP students.

Recorded source context

Dataset source note: Every 9 weeks I write IEP progress reports for 17 kids. Each has 3 to 5 goals. That is 60+ paragraphs. The data is in Aimsweb, IXL, and my paper conferring notebook. I literally retype 'Student A read 62 wpm on Sept 15, 71 wpm on Oct 20, 78 wpm on Nov 15'. Sixty times. By hand. Due Friday.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/specialed and Teachers Pay Teachers forum threads on IEP progress monitoring..

Existing players in this space

IEPWriter, Frontline IEP: Stores the goal, does not pull live probe data from Aimsweb or IXL.
Aimsweb Plus: Has data but exports only as a generic chart, not parent-friendly narrative.
Goalbook: Helps draft goals, not progress narratives.

What existing players are missing

A tool that ingests Aimsweb, IXL, DIBELS, and a photo of the teacher's running-record notebook, matches each data stream to the right IEP goal, and writes a parent-readable progress paragraph in the teacher's voice from 3 prior reports.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Vision OCR for handwritten running-record notebooks
API connectors for Aimsweb, IXL, DIBELS
Style-transfer LLM trained on teacher's prior reports
FERPA-compliant district SSO