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 my SOC 2 prep still feel like a six-month spreadsheet marathon?

Vanta and Drata automated evidence collection. They did not automate writing the policies, training the team, mapping controls to product reality, or talking to the auditor. Founders still lose a quarter to it.

Category: LegalTech & Compliance · Trend: LLM · Opportunity score: 8.1 / 10

What is the “Why does my SOC 2 prep still feel like a six-month spreadsheet marathon?” problem in 2026?

Who has this problem?

Seed and Series A founders chasing their first SOC 2 Type 2 to close enterprise contracts.

Evidence this problem is real

“Vanta told me I was 87% compliant in week one. That last 13% took 14 weeks and a $30K external consultant who wrote my policies in Word.”

Sourced from Founder communities (On Deck, South Park Commons, YC Bookface) repeated SOC 2 threads, Vanta and Drata user reviews on G2.

Existing players in this space

Vanta — Evidence collection, policy templates, but you still hire a vCISO
Drata — Similar shape; weakest on the human side
Secureframe — Strong onboarding, still bills out a manager
Boutique vCISO firms — $25K to $80K; the actual mode of getting it done today

What existing players are missing

An AI vCISO that reads your AWS, GCP, Linear, Notion, Slack, writes policies grounded in your actual stack, runs the auditor Q&A, drafts the management responses, and trains your team in 5-minute videos generated weekly. Fixed price under the consultant alternative.

How Real Problem AI scores this opportunity

Aggregate score: 8.1 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 9 / 10
Market signal: 9 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Read-only integrations with cloud, IdP, ticketing, code review
Policy generator that cites your real controls, not a template
Auditor-question router with retrieval over your evidence
Personalised security training generated per team member