The Evolution of SourceCon and the State of Sourcing in 2020

Sourcing as a discipline has a traceable history, and much of what now runs automatically inside AI-powered recruiting platforms started as manual tricks shared at conferences by a small, tight-knit community. Tracing that history from a single 2007 gathering to today's talent intelligence tools shows how ideas first tested on stage became the backbone of modern candidate search.

• The five levels of search — keyword and title, conceptual, implicit, natural language, and indirect search — introduced by an early sourcing thought leader now underpins the matching logic of AI sourcing tools.
• Manual tactics such as pulling email addresses from GitHub profiles, writing JavaScript bookmarklets, scraping data, and aggregating feeds with tools like Yahoo Pipes were community workarounds years before people-aggregator platforms existed.
• A casually floated idea about combining an ATS, a CRM, LinkedIn data, and other sources into one searchable pool predates the unified talent data platforms now common in the industry.
• Sourcing conferences moved through distinct eras: locating data online, X-raying social networks after they overtook resume databases, automation via aggregators, the rise of AI and questions about what to automate, and now talent intelligence focused on identifying and engaging the right people at the right time.
• The attendees themselves shifted from hackers and early adopters, to mainstream professionals learning from them, to today's mix of newcomers and leaders who now send new hires to get trained.
• Certain arguments never resolve: what sourcing actually is, which parts of the workflow belong to sourcers versus recruiters, and whether sourcing is a junior role or a distinct discipline.

A walkthrough of a modern sourcing platform shows how these old manual habits now run as built-in features: prebuilt Boolean-style power filters that narrow millions of profiles down to a targeted candidate pool, an AI matching mode that learns preferences click by click, and layered data pulling GitHub coding activity, academic publications, and public profiles onto a LinkedIn-based candidate record. A diversity insights view breaks down candidate pools by demographic and location, letting recruiters bring hiring managers actual numbers instead of guesses when discussing where to focus a search.