Recently, there has been a lot of discussion in the industry about the recommendation mechanisms of large social platforms. Ultimately, the algorithm logic of these platforms is quite straightforward—optimize user dwell time, as long as the data looks good.

The problem arises. To make this metric look better, content creators on these platforms are forced to make compromises. What can keep people engaged? Superficial, emotional, and quickly reaction-provoking content. That's why we see an abundance of motivational articles and clickbait headlines, while in-depth discussions are becoming increasingly rare.

The impact of this phenomenon goes far beyond the surface. When such data streams become training material for AI models, what the models actually learn is this "optimized," algorithm-filtered, dimension-reduced content. In other words, an algorithm-driven content ecosystem is shaping the thinking patterns of the next generation of AI in a reverse manner.

In the long run, this feedback loop could lock the entire ecosystem into a ceiling—AI will increasingly resemble the data it is trained on, operating within a gradually narrowing framework of thought.