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 did most of the installs I paid Google Ads for come from bots?

Indie developers and small businesses spend a few hundred dollars on ads and get clicks and installs that never do anything, then find the ad platform will not explain or refund it.

Category: Marketing, Sales & CRM · Trend: AI Infra · Opportunity score: 7.6 / 10

What is the “Why did most of the installs I paid Google Ads for come from bots?” problem in 2026?

Indie developers and small businesses spend a few hundred dollars on ads and get clicks and installs that never do anything, then find the ad platform will not explain or refund it.

Who has this problem?

Indie app makers, solo founders and small businesses spending under a few thousand dollars a month on Google, Meta or Reddit ads.

Recorded source context

Dataset source note: Reading all the comments, it seems quite bleak to advertise. How do indies and SMBs reach audience these days then?

This note may summarize the referenced material rather than quote it verbatim. Source label: Hacker News discussion of a post about paying for Google app ads and getting bot installs, 11 September 2026, 764 points, 434 comments. (primary source).

Existing players in this space

Google Ads IP exclusions: Manual blocking of data center ranges, which commenters say grows to thousands of entries.
ClickCease (CHEQ): Click fraud blocking for search ads, less help with app installs and in-app events.
TrafficGuard: Ad fraud prevention built mainly for larger performance marketing budgets.

What existing players are missing

A small-budget ad audit that joins ad clicks with app and site events, scores each campaign for bot patterns (data center IPs, zero engagement, install bursts), turns the evidence into exclusion lists and a refund claim, and tells the founder plainly which channel is worth the next $100.

How Real Problem AI scores this opportunity

Aggregate score: 7.6 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 7 / 10
Market signal: 9 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Ad platform and analytics APIs
Anomaly detection on click and install events
IP and device reputation lookups
LLM-written refund claims and weekly report