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 every AI notetaker get banned from the meetings that matter most?

Otter, Fireflies and the rest join a call as a visible third-party bot, so client security, legal and IT block them on exactly the calls worth summarising, and the notes fall back to whoever is typing.

Category: LegalTech & Compliance · Trend: Voice · Opportunity score: 8.0 / 10

What is the “Why does every AI notetaker get banned from the meetings that matter most?” problem in 2026?

Who has this problem?

Sales, consulting and agency teams running client calls under NDA, plus the IT and compliance staff who decide what is allowed to join a meeting.

Recorded source context

Dataset source note: I've definitely seen clients straight-up ban any recording or transcription because of GDPR concerns or sensitive IP discussions, no exceptions.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/microsaas thread on companies banning notetaker bots, 9 September 2026, 41 comments; r/sales thread on undisclosed AI notetaking, 22 July 2026, 87 upvotes and 101 comments. (primary source).

Existing players in this space

Otter and Fireflies: Join the call as a separate participant everyone can see, which is the exact thing client IT policies block.
Microsoft Copilot: First-party inside the Microsoft stack so it draws less friction, but it does not help on a client's Zoom or Meet, and a no-recording rule still stops it.
Self-hosted Whisper and Ollama builds: Teams that need this badly enough build their own on local GPUs, which proves the demand and shows there is no product to buy.

What existing players are missing

A notetaker that never joins as a participant and never sends audio anywhere: capture on the device, transcribe and summarise locally, and hand the IT reviewer a one-page consent and retention record they can approve instead of a vendor security questionnaire.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

On-device speech to text (Whisper class models)
Local summarisation on a small language model
System audio capture with no meeting-bot participant
Consent capture and retention log for the compliance reviewer