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 my store treat a customer's AI shopping agent as a bot attack?

Shoppers increasingly arrive through an AI agent, but merchant checkout flags automated purchasing as malicious, and supporting each agent means hand-building a separate API bridge per platform.

Category: E-commerce & Retail · Trend: Agents · Opportunity score: 7.8 / 10

What is the “Why does my store treat a customer's AI shopping agent as a bot attack?” problem in 2026?

Shoppers increasingly arrive through an AI agent, but merchant checkout flags automated purchasing as malicious, and supporting each agent means hand-building a separate API bridge per platform.

Who has this problem?

Direct-to-consumer and mid-market e-commerce operators on Shopify and similar, without a platform engineering team.

Recorded source context

Dataset source note: Merchants are forced to build custom API bridges for different AI agents, which creates an unsustainable engineering burden, while checkout policies flag automated, high-speed purchasing as malicious bot activity.

This note may summarize the referenced material rather than quote it verbatim. Source label: Modern Retail on the 2026 AI shopping agent wars and retailers blocking third-party agents; reporting that OpenAI retired Instant Checkout in March 2026 after roughly five months with under 30 Shopify merchants live; Fortune, 'AI shopping agents are coming. No one is ready for them'. (primary source).

Existing players in this space

Bot management (Cloudflare, Akamai): Built to block automation outright, so a legitimate buying agent looks the same as a scalper
Platform-specific agent programmes: One integration per agent vendor, and the largest experiment, OpenAI Instant Checkout, was retired in March 2026
Headless commerce APIs: Exist, but leave the merchant to decide identity, consent and liability per agent themselves

What existing players are missing

A single agent-aware checkout layer a merchant can switch on: verify that an agent is acting for a real customer with a real payment mandate, distinguish that from scraping or scalping, and record who authorised the purchase so the merchant is covered when an agent buys the wrong thing. One integration rather than one per agent vendor.

How Real Problem AI scores this opportunity

Aggregate score: 7.8 / 10. Four-axis rubric:

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

How to build a solution: stack hints

Agent identity and delegated payment mandate verification
Intent and provenance signals at checkout
Merchant-side policy and liability audit log
Single adapter across major agent platforms