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 do email deliverability nightmares from Google's 2024 bulk-sender rules still kill warm sequences in 2026?

After Google and Yahoo enforced 0.3 percent spam thresholds, Outreach and Salesloft sequences that worked in 2023 now land in Promotions or get domain-suspended within 14 days, and reps have no warning until pipeline craters.

Category: Marketing, Sales & CRM · Trend: LLM · Opportunity score: 8.3 / 10

What is the “Why do email deliverability nightmares from Google's 2024 bulk-sender rules still kill warm sequences in 2026?” problem in 2026?

Who has this problem?

Outbound team lead at a 20 to 100 rep sales org.

Recorded source context

Dataset source note: Our entire domain got bulk-blocked overnight. Outreach showed sent on 40k emails and not one hit inbox. No tool warned us.

This note may summarize the referenced material rather than quote it verbatim. Source label: r/EmailMarketing threads on Google and Yahoo bulk sender requirements..

Existing players in this space

Smartlead: Good warming, weak per-message spam-trigger linting.
Instantly: Deliverability dashboards, not per-line AI rewrites.
GlockApps: Placement testing, not pre-send.

What existing players are missing

A pre-send LLM that scores each email against current Gmail/Yahoo classifier signals, rewrites spam-trigger phrases, and pauses sequences when domain reputation drops below threshold.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Postmaster Tools API
Claude for copy rewrite
Real-time bounce and complaint webhook
DMARC report parser