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 crypto cost basis still reset to zero every time you move ETH between MetaMask, Coinbase, and Ledger?

CoinTracker and Koinly mis-tag self-transfers across MetaMask, Coinbase, and Ledger as taxable sales when the user forgets to mark them, inflating gains by tens of thousands.

Category: FinTech · Trend: LLM · Opportunity score: 8.0 / 10

What is the “Why does crypto cost basis still reset to zero every time you move ETH between MetaMask, Coinbase, and Ledger?” problem in 2026?

CoinTracker and Koinly mis-tag self-transfers across MetaMask, Coinbase, and Ledger as taxable sales when the user forgets to mark them, inflating gains by tens of thousands.

Who has this problem?

DeFi-active retail crypto holder with 5+ wallets.

Evidence this problem is real

“Koinly thinks I sold 12 ETH because it cannot tell my Ledger withdrawal matches my Coinbase deposit. My gain is $80k that never happened. I have spent 3 weekends merging transactions and it is still wrong.”

Sourced from r/CryptoTax and Koinly support forum recurring threads on self-transfer mis-tagging.

Existing players in this space

CoinTracker — Self-transfer matching is heuristic, often wrong on bridges.
Koinly — Requires manual merging across chains and CEX.
TokenTax — Human-assisted, expensive, slow.

What existing players are missing

A long-context LLM agent that reasons across on-chain timestamps, amounts net of gas, and CEX deposit memos to confidently auto-link self-transfers including bridges and wrapped tokens, with a confidence score per match.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 10. Four-axis rubric:

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

How to build a solution: stack hints

On-chain indexer across EVM and Solana
Long-context LLM transfer matcher
CEX deposit memo parser
Form 8949 generator with audit trail