As we approach the last quarter of 2023, it is quite apparent that almost everyone has tried out, or are actively using Social Media Platforms. Content Generation, Content Distribution and Content Recommendation has vastly changed over the years, without the average user even noticing it.

Furthermore, with the emergence of Artificial Intelligence (AI), we are starting to embark into a New Era with the addition of AI Generated Content (AIGC), which is expected to be a catalyst in the acceleration, and in turn the amount of content being generated reaching levels not seen before.

Understanding and agreeing with this point, we can safely assume that the acceleration in terms of the amount of content generated will definitely increase over a period of time. Therefore do refer to the diagram below.

Social Media has changed the way in which we view content, where the distribution side and recommendation side is typically focused on maximum outreach.

In addition, as we reach an era where time is precious and everyone is seeking maximum efficiency, due to people having shorter attention spans, we have to redefine the whole distribution/recommendation system in Social Networks.

The AI Era will change the way in which we view content where we focus less on the outreach and more on the quality and relevancy of the content being recommended to the “right” audience.

Content consumers will always focus more on the quality and relevancy of the content, as well as the ease of discovering it.

Therefore the amount of content generation and the attention span will be seen to have an* inverse relationship as time goes by.*

Therefore, understanding this need for better quality of content distribution/recommendation, let us break down several key aspects.

Breaking down Content Distribution vs Content Recommendation

In terms of Distribution;
With new technology emerging, content generation will soon see a parabolic growth, and in which there will be a need for higher quality content distribution and recommendation models to handle this accelerating effect.

Tako Protocol, with our Application Takoyaki, would take the popular yet relatively unsustainable X-To-Earn Model to the next level, as we develop a new system to introduce a new era of personalized value matching.

In terms of Recommendation & Value Discovery;
To deal with the vast amount of content and bots/spams, there must be some kind of mechanism to deal with it.

a) We can assume that content creators with “good content” would be more willing to place a certain amount of “stake” in order to get their content seen and in turn recommended to audiences.

b) We can assume that influencers with “reputation at stake” would only recommend content that they deem worthy to be curated and recommended by them. They would also deem this content to be of good quality to be shown to their audience.

Therefore with this idea in mind, this “stake” could possibly be used as a price in which the content value is generated, putting a quantitative aspect in terms of content value discovery, as part of the new paradigm.