The Algorithm has Decided Too Much

Why Social Media’s Next Phase Will Be Defined by Incentives, Not Value

For years, social media companies have promised that better algorithms would fix the internet’s growing chaos. Smarter ranking systems, they said, would surface quality, reduce noise, and help people find what mattered to them.

Instead, feeds have grown louder and harder to trust.

Creators describe a constant pressure to adapt to narrow, algorithm-friendly formats. 

Users report exhaustion and disengagement, doom-scrolling through content that feels interchangeable. With the rise of AI, the volume of material competing for attention has effectively become infinite.

A recent article by Oak Hill Gazette argues that this moment represents more than a moderation challenge or a temporary imbalance.

It marks a structural failure in how online value is measured.

Algorithms did not malfunction. They did exactly what they were designed to do. They optimized for attention.

Algorithms were introduced to solve a practical problem: scale. 

Faced with more content than any human could sort through, platforms turned to engagement signals – clicks, likes, views, and shares – as a substitute for judgment.

At first, the system felt efficient. Feeds appeared personalized. Discovery seemed effortless.

Over time, however, engagement became the objective rather than the signal. What could be measured instantly was rewarded. What required patience, originality, or context was not. 

Emotional extremes traveled faster than thoughtful ideas. Repetition outperformed substance.

Was this an accident? Or was it an outcome of incentives?

Generative AI did not create this dynamic, but it exposed its limits.

When content production is cheap, instant, and unlimited, the weakness of engagement-based ranking becomes impossible to ignore. If everything can be produced at near-zero cost, attention alone cannot determine value. In an environment of abundance, amplification loses meaning.

 Machines remain excellent at prediction and personalization. What they cannot do is judge significance. They do not understand our context, feel our relevance, or incur a cost for being wrong.

Judgment, by contrast, is shaped by consequence. 

Most online environments have removed all consequences; likes are free, Comments are performative, and attention is easily manipulated. And without stakes, even choice becomes noise.

This is why calls for “curation” often fall short. Audience involvement matters – but only when decisions carry weight. Without incentive, judgment does not scale.

Some platform designers and economists are beginning to focus less on feeds and more on incentives.

 Their argument is simple: when people must make a deliberate choice, especially one that carries a small financial cost, then behavior changes. Therefore, the audience’s attention becomes deliberate, and content competes on perceived value rather than raw visibility.

Even modest financial signals can reveal more about what matters than millions of passive interactions. Payment, unlike engagement, offers a sense of choice.

This shift also has large implications for creators.

In advertising-driven systems, creators are rewarded for volume, frequency,and algorithmic compatibility. Originality becomes a threat to the algorithm. Depth becomes inferior and burnout becomes commonplace.

In a transaction-based system where creators earn directly from audiences, the incentives change. Success depends less on pleasing machines and more on resonating with each creator’s fanbase. Creators do not need a massive reach. They can sustain themselves by serving smaller, more emotionally invested audiences.

This does not eliminate algorithms. AI can assist discovery, reduce friction, and scale operations – but it does not decide what matters. We, the audience, will choose with our own free will.

Meaning emerges from choice.

The future of social media will be shaped by whether platforms continue to optimize for attention or begin to optimize for value.

One model extracts engagement.
The other rewards judgment.

In a world flooded with content, that distinction may matter more than any algorithm ever could.