The first “breakout” session I attended was “The Future of Social Innovation on the Web”. Panel participants included: Premal Shah, Kiva.org; Jonathan Greenblatt, All for Good; Steve Newcomb, Virgence; and Ben Rattray, Change.org.

Some very interesting and critical discussions were raised ranging from for-profit v. non-profit, competition v. collaboration, and how to develop and foster social engagement through the Internet.

Where Web 2.0 was all about creating 2-way interaction over the Internet, Web 3.0 adds an “offline action” element. This is a great way of describing the “social innovation on the web” movement, and how online platforms can effect offline social change. The challenge faced is creating a vibrant and active user base. Premal Shah told us that 5% of Kiva users drive 50% of activity, and there is a huge amount of dormant capital in inactive user accounts.

The solution really lies in the “user experience”. User engagement tends to be more of a “consumptive decision” as opposed to an “investment decision”. There’s so much untapped potential out there that creating a system that users enjoy using lays at the heart of creating a system that can maximize its social impact. More often than not, this is best achieved by opening your code/API to developers to build their own platforms.

An interesting discussion took place over the friction between socially driven for-profit businesses and the non-profit sector. Premal Shah of Kiva (non-profit) mentioned flack that they get about “crowding out” the microfinance opportunities of in-country banks that could be generating profit and creating wealth. Jonathan spoke about Ethos water (for-profit) and how they are able to generate millions of dollars from selling bottled water to invest in sustainability projects worldwide, but receive flack from nonprofits who say that by selling bottled water they become part of the problem. The discussion concluded we need to get past the for- vs. non-profit discussion and just build high-impact solutions that work, placing less attention on labels and more attention on models.