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 GitHub Copilot's flat monthly fee quietly turn into an unpredictable usage meter?

Copilot's pricing shift from flat fee to usage-based billing left reviewers unable to predict their monthly bill, with confusion over credit consumption spiking after a June 2026 billing change.

Category: AI / Agents · Trend: LLM · Opportunity score: 6.2 / 10

What is the “Why did GitHub Copilot's flat monthly fee quietly turn into an unpredictable usage meter?” problem in 2026?

Copilot's pricing shift from flat fee to usage-based billing left reviewers unable to predict their monthly bill, with confusion over credit consumption spiking after a June 2026 billing change.

Who has this problem?

Individual developers and small teams paying for AI coding assistants.

Recorded source context

Dataset source note: The pricing changed significantly. From a flat fee to usage-based. The new pricing is nowhere near what it used to be.

This note may summarize the referenced material rather than quote it verbatim. Source label: Capterra verified review by Damien W., Lead Software Developer, 7 Jul 2026. (reference).

Existing players in this space

GitHub billing settings: Shows consumption after the fact, not predictive.
Manual downgrade to flat plans: Sacrifices premium model access to regain predictability.

What existing players are missing

A cross-tool spend forecaster that watches your actual coding-agent request pattern and predicts next month's bill before the billing cycle closes, flagging when a usage-based plan will exceed a flat-fee alternative.

How Real Problem AI scores this opportunity

Aggregate score: 6.2 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Usage pattern tracking
Billing-cycle forecasting model
Plan comparison engine
Alerting on projected overage