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 Section 174 turn every two-person startup into a part-time tax department?

Post-2022 Section 174 forces SaaS startups to capitalize R&D over 5 or 15 years, but Gusto and Mercury do not tag which engineer hours or contractor invoices count as specified research, so founders rebuild the schedule in a spreadsheet at tax time.

Category: FinTech · Trend: LLM · Opportunity score: 8.3 / 10

What is the “Why does Section 174 turn every two-person startup into a part-time tax department?” problem in 2026?

Who has this problem?

Pre-revenue YC or seed-stage SaaS founder.

Recorded source context

Dataset source note: We have 6 engineers on Gusto and 4 contractors on Deel. My CPA wants me to tell him what percent of each person's time was qualified research for Section 174. I have no idea how to answer that and neither does my CPA.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/startups, Hacker News, and Twitter founder threads on Section 174 pain..

Existing players in this space

Pilot.com: Books only, hands the 174 schedule back to the founder.
MainStreet: Focused on R&D credit Form 6765, not 174 capitalization.
Neo.tax: Credit-side, light on 174 amortization tracking.

What existing players are missing

An LLM agent that reads Linear, GitHub, and Jira activity per engineer, classifies time as QRE vs maintenance vs sales engineering, and builds the rolling 174 amortization schedule synced with Gusto and Deel payroll.

How Real Problem AI scores this opportunity

Aggregate score: 8.3 / 10. Four-axis rubric:

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

How to build a solution: stack hints

GitHub plus Linear plus Jira activity ingest
LLM classifier for QRE vs non-QRE work
Gusto plus Deel payroll sync
5 and 15 year amortization schedule generator