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 can a hidden line of text on a webpage hijack my AI agent and steal my data?

An autonomous agent reads a page, email, or CRM field an attacker controls, follows the buried instruction, and quietly exfiltrates data or runs a destructive action, all inside a task the user asked for.

Category: AI / Agents · Trend: LLM · Opportunity score: 8.0 / 10

What is the “Why can a hidden line of text on a webpage hijack my AI agent and steal my data?” problem in 2026?

Who has this problem?

Teams shipping browser agents, RAG assistants, and tool-calling agents into production.

Evidence this problem is real

“Then, bingo, the AI agent started querying the CRM for sensitive lead information and sending all of that data to an attacker-controlled server.”

Sourced from The Register, 26 Sep 2025, Salesforce Agentforce 'ForcedLeak' indirect prompt-injection write-up; OWASP LLM Top 10 (injection ranked #1). (link)

Existing players in this space

Guardrails / Llama Guard / prompt firewalls: Pattern filters, bypassed by novel indirect payloads
Model-provider safety layers: Tuned for direct jailbreaks, weak on tool-context injection
Manual human-in-the-loop approval: Kills the autonomy the agent was bought for

What existing players are missing

A runtime that treats retrieved content as untrusted data, not instructions, and enforces a per-action allowlist plus data-egress checks the agent cannot talk its way past. Detection tuned to indirect injection in tool outputs, not just the user prompt.

How Real Problem AI scores this opportunity

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

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

How to build a solution: stack hints

Content-trust boundary (data vs instructions)
Egress/DLP inspection on tool calls
Injection classifier on retrieved context
Per-action capability allowlist