New Social Media Giant Giving $30,000 for Referrals

Today’s social media has spawned a race to the bottom. Motivated by a desire for clicks and likes at all costs, participants inject sensationalism and drama to gain attention. There’s precious little real and beneficial conversation, and there are constant issues with fake news, etc.

The root cause? The prevailing advertising revenue model. In a click-based revenue model, where content is driven by popularity, attention is monetized so gaining eyeballs by any means has been given value. This results in participants not being afforded the opportunity to engage in qualitative discussions, as they are constantly bombarded by demands on their attention. showcases a revolutionary model where quality and reputation matter, and content creators earn directly from their supporters. In principle, this is where social media needs to go. WeGather is structured so that only validated experts and enthusiasts lead discussions. Time limits on discussions add immediacy and further boost active and meaningful participation and interaction.

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Content creators can earn in multiple ways, ways which are similar to successful alternatives to the ad-revenue model that have emerged elsewhere. Creators may receive individual donations for their work, a revenue model similar to Patreon, or they can create exclusive high-quality content that subscribers pay to access, a model which has worked extremely well for Netflix.

Thus far, the internet has largely de-monitized expertise as it is structured with the intention to make all information and communication free. This aesthetic originally appeared to almost everyone to be good in principle, even noble, but it’s become evident it causes tremendous problems because it disincentivizes quality. In a recent Pew survey, 51% of respondents said they would pay for good content. High-quality knowledge is worth paying for and some consumers are realizing they need to financially support experts to receive the quality they want.

“One of the early visionaries of the internet, Jaron Lanier, stated he believes making information free via the ad-revenue model creates such perverse incentives that it may threaten the survival of our species,” said David Price, founder and CEO of “Whether or not this is hyperbole, the consequences of offering our knowledge and information for free disenfranchises the vast majority of people in our industrialized economies. Everyone has valuable knowledge and information. It’s our vision to create a market for this that all may benefit from.” provides easy-to-navigate access to trusted experts. Most people are aware of the challenges in trusting information found on Facebook. But try locating trustworthy experts on Twitter where there’s way too much clutter and confusion.

The site’s revenue model provides an environment more conducive to open and honest discussion. Most existing platforms are subject to the demands of advertisers. This has a chilling effect on free speech. Controversial opinions are unpopular and thus problematic for advertisers. By removing advertisers from the equation, creators, speakers and experts have more freedom to express themselves.

WeGather is unique in its attempt to put the expert creator’s content and their ability to earn from it in various ways, directly from their audience, all in one place. Creators can earn via:

  • Tips and donations for their free content;
  • Paid time-limited group discussions or polls;
  • Paid subscription communities;
  • Paid private chats and consultations.

Small regular donations from hundreds and thousands of participants can add up to excellent monthly earnings for content creators. experts earn significantly more from direct contributions than they would receiving a portion of ad revenue from an identically-sized group on YouTube. takes a five percent fee, the same percentage as Patreon. launched in July, 2018 and is making headway, especially with their latest marketing push to give $30,000 towards those who excel in referrals. See this link for all the details.

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