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 buying a house still mean trusting a Zestimate that is off by $40k?

Home buyers and small investors get automated valuations (Zillow, Redfin) that miss renovations, micro-location and condition, and are routinely tens of thousands off. The error is invisible until you overpay.

Category: PropTech / Real Estate · Trend: Agents · Opportunity score: 8.0 / 10

What is the “Why does buying a house still mean trusting a Zestimate that is off by $40k?” problem in 2026?

Who has this problem?

First-time buyers, small property investors, and buy-side agents in the US, UK and Ireland.

Evidence this problem is real

“The Zestimate said 420. We paid 388 and the appraisal came in at 360. Three different machines, three different truths, and I am the one holding the mortgage.”

Sourced from r/RealEstate and r/FirstTimeHomeBuyer 2026 threads on AVM accuracy, Redfin and Zillow public methodology notes, proptech founder discussions.

Existing players in this space

Zillow Zestimate — Broad coverage, weak on condition and renovation
Redfin Estimate — Similar AVM limitations
HouseCanary — Pro-grade but priced for institutions

What existing players are missing

A buyer-side valuation agent that ingests the actual listing photos, permit history, and recent truly-comparable sales, then returns a defensible range with the assumptions shown. Plus a renovation-adjusted comp engine the big AVMs skip.

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: 9 / 10
Competition gap: 7 / 10

How to build a solution: stack hints

Vision model on listing photos for condition scoring
Permit + assessor record ingestion
Comp selection with renovation adjustment
Confidence-banded valuation report