Unlike older algorithms that recommend content based on long-term follow history, TikTok's model prioritizes recent engagement. This 'TikTokification' across platforms means algorithms can now find an audience for off-niche content if it aligns with a viewer's immediate, short-term interests.

The feeling of deep societal division is an artifact of platform design. Algorithms amplify extreme voices because they generate engagement, creating a false impression of widespread polarization. In reality, without these amplified voices, most people's views on contentious topics are quite moderate.

Recommendation algorithms don't just predict what users like; they actively nudge users toward more extreme preferences. This makes behavior easier to predict and monetize, effectively creating an automated radicalization pipeline for the algorithm's own efficiency.

Conspiracy theories gain mainstream traction because social media platforms have a profit incentive to algorithmically elevate novel, engaging content. This amplification normalizes fringe ideas, making them seem self-evident and eroding institutional trust.

Algorithms optimize for engagement, and outrage is highly engaging. This creates a vicious cycle where users are fed increasingly polarizing content, which makes them angrier and more engaged, further solidifying their radical views and deepening societal divides.

A/B testing on platforms like YouTube reveals a clear trend: the more incendiary and negative the language in titles and headlines, the more clicks they generate. This profit incentive drives the proliferation of outrage-based content, with inflammatory headlines reportedly up 140%.

Since around 1% of online users produce almost all content, social media is not representative of humanity. It's an echo chamber for the loudest, most obsessive, and often most pathological individuals, distorting our perception of public opinion.

The online world, particularly platforms like the former Twitter, is not a true reflection of the real world. A small percentage of users, many of whom are bots, generate the vast majority of content. This creates a distorted and often overly negative perception of public sentiment that does not represent the majority view.

Generative AI models are trained on existing human-generated text, causing them to reflect and amplify mainstream thought. When prompted on contrarian topics, they will either omit them or frame them as fringe ideas. AI is a tool for understanding the consensus view, not for generating truly original, non-consensus insights.