Social startups focused on real world connection always fail – AI fixes that

Thousands of "meetup alternative" and "travel planner" and "meet people who share your interests" startups have failed due to the cold start problem. Unlike geo-mapping with Open Street Map, there's no open event dataset for the real world. Generative AI now makes this possible by parsing unstructured event data from various sources and formats.

Events are scattered across platforms, and manual curation is impractical. Companies like Songkick and IRL.com have burned through billions attempting to solve event discovery, facing consistent challenges:

1. Cold Start Problem: New platforms can't attract organizers and attendees without critical mass 2. Data Silos: Proprietary datasets prevent comprehensive coverage 3. Curation Overhead: Manual curation doesn't scale 4. Network Effects Favor Incumbents: Users go where events already exist

ActivityPub has failed to penetrate because organizers post where their audience is. Event organizers want an open dataset where any social app can tap into based on relevance filters.

Why Previous Attempts Failed

The critical limitation was always the extraction bottleneck:

Technical Barriers: - Unstructured Data: Most event information exists in formats traditional software can't parse - Format Diversity: Dates written in various formats or embedded in images - Visual Information: Details in posters and images that OCR couldn't extract - Context Dependency: Understanding phrases like "doors at 7, show at 8" requires contextual reasoning

Compounding Problems: - Temporal Complexity: Events have complex lifecycles requiring real-time updates - Verification Challenges: Event details change frequently - Commercial Conflicts: Event data enables revenue, creating incentives against sharing - Quality Control: Platforms must handle spam and rapidly-changing details at scale

- The paradigm shift: LLMs eliminate the extraction bottleneck, making comprehensive event discovery economically viable.

The AI-First Opportunity

LLMs and generative AI enable:

- Automated Data Extraction: AI can process any format and extract structured event data with human-level accuracy - Contextual Understanding: LLMs comprehend contextual references and can match locations to maps - Quality Assessment: AI can evaluate legitimacy and consistency of event information - Multilingual Adaptability: Modern LLMs handle international formats without custom programming - Cost Effectiveness: Processing costs fractions of a penny per event

Core Architecture

A federated network of AI-powered nodes that:

1. Discovers events through AI scouts monitoring web sources 2. Curates data through automated extraction with human verification 3. Shares information between nodes through token-based exchanges 4. Maintains quality through distributed reputation systems

This could reshape how people find real-world activities, reduce screen addiction, and solve many downstream negative effects of our current social media paradigm.