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 am I paying Claude Opus prices for tasks DeepSeek could handle?

Single-model deployments are over. May 2026 benchmarks show a 70/25/5 split across DeepSeek V4-Flash / Claude Sonnet 4.6 / Claude Opus 4.7 delivers performance indistinguishable from all-Opus at ~15% of the cost. But routing logic is hand-rolled per app, breaks on every model update, and no founder has bandwidth to maintain the routing table.

Category: SaaS · Trend: LLM · Opportunity score: 8.7 / 10

What is the “Why am I paying Claude Opus prices for tasks DeepSeek could handle?” problem in 2026?

Who has this problem?

AI-first founders, agent-product teams, anyone whose monthly LLM bill exceeds $500.

Evidence this problem is real

“Our LLM bill was $14K/month, 95% going to Opus. Built a router in a weekend that sends extraction + classification to DeepSeek V3.2 at $0.14/1M tokens. Bill dropped to $2.1K. We just spent a year overpaying.”

Sourced from Ian Paterson's "I Tested 15 LLMs on 38 Real Coding Tasks. Here's My Routing Table" (May 2026), Swfte AI 85%-cost-cut analysis, Tyler Folkman's 2,415-agent-turn cost study ($76.77 across 6 models). (link)

Existing players in this space

OpenRouter — Aggregates models; routing logic is on you
Portkey — Closer fit; routing rules are manual config, not auto-learned
LiteLLM — Library, not a managed router; you maintain the policy
Martian / NotDiamond — Auto-routers exist but limited model coverage + opaque benchmarks

What existing players are missing

A self-tuning router: ingest 24 hours of your real prompts, classify by task type, A/B test cheaper models against incumbent for output quality + latency, and ship the routing table back. Re-runs weekly on a sample of production traffic. Pays for itself in the first week of any team spending >$2K/month on LLMs.

How Real Problem AI scores this opportunity

Aggregate score: 8.7 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Prompt-classifier on your task taxonomy (extraction / reasoning / code / chat)
A/B harness with LLM-judge eval against your prod outputs
Live routing policy (per-task model + fallback chain)
Cost + latency dashboard with weekly diff vs incumbent