What is Real Problem AI?

Real Problem AI is a curated directory of 200+ 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 200+ 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 a GDPR DSAR still take three weeks and force engineering to write a custom SQL query every time?

When a data subject files a GDPR Article 15 request, legal forwards to engineering for one-off SQL across Postgres, Snowflake, Segment, and 12 SaaS tools, then legal hand-redacts third-party PII before the 30-day deadline.

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

What is the “Why does a GDPR DSAR still take three weeks and force engineering to write a custom SQL query every time?” problem in 2026?

Who has this problem?

Privacy counsel or DPO at a 50 to 500 person SaaS company subject to GDPR or CPRA.

Evidence this problem is real

“DSAR came in from a German user on Tuesday. By Friday I had pulled records from Postgres, Stripe, Intercom, Segment, Mixpanel, Hubspot, and our warehouse. Each was a different export format. I spent 4 hours redacting other users' emails from Intercom threads. Engineering billed 6 hours on the SQL. We have 30 days.”

Sourced from r/privacy and IAPP community forum threads on DSAR fulfillment.

Existing players in this space

OneTrust, TrustArc — Workflow management, do not actually pull data from your systems.
Transcend, DataGrail — Better connectors, expensive for sub-500 person companies, weak third-party PII redaction.
Osano — Cookie consent focus, light on DSAR depth.

What existing players are missing

An agent that connects to Postgres, Snowflake, and 30 SaaS APIs, runs the data-subject query across all of them, uses vision and LLM to redact third-party PII from screenshots and threads automatically, and produces the EDPB-compliant portability bundle in under 48 hours.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Multi-system data connector layer
LLM PII detection and redaction
EDPB-format portability bundle generator
Audit log with regulator-ready export