Social media platforms love to call themselves the digital town square. The metaphor is seductive: in a real square, a child and a CEO, a poet and a scientist, can all raise their voices and be heard.
But scale changes everything. A physical square might hold a few hundred people. Every voice can, in principle, reach every ear. A digital square holds millions — sometimes billions. In that environment, voices cannot simply travel through the air. They must be filtered.
To their credit, today’s platforms are not dumb filters. They do, in principle, amplify multiple sides of arguments. That is no small thing. It is a massive improvement over censorship, and we should not take it for granted.
Algorithms are also not blind. They approximate meaning through proxies — who you follow, who engages quickly, what trends within your network. This can feel like a town square, and in many ways it outperforms legacy broadcast, where gatekeepers were the deciders.
But proxies are not principles. At scale, they break down.
The problem isn’t that algorithms ignore meaning; it’s that they rely on imperfect proxies for it. These proxies work well for already-visible voices. A celebrity’s every post is amplified because millions are predisposed to find it meaningful. They fail for new or invisible voices, no matter how profound their contribution.
At scale, fame becomes a stand‑in for meaning.
Consider a household name and an invisible newcomer. Elon Musk can post something trivial — “Dropped my phone in the toilet lol” — and it will be amplified to tens of millions worldwide. And honestly, many of us do want to hear anything Elon has to say. That signal is real.
But imagine if Albert Einstein joined X today. His first post: “E=mc².” As a new, invisible account, the algorithm would likely throttle it. The idea might die at zero impressions.
The difference isn’t the intrinsic value of the content. It’s the weight of prior visibility. Meaning becomes unevenly distributed: the already-visible are treated as inherently meaningful. The invisible struggle to break through, even when their contributions could reshape how we see the world.
That’s not a town square. That’s an attention economy dressed up as one.
None of this is to say that laughter lacks value. To millions of people, a lighthearted joke from Elon may be more meaningful in the moment than an abstract scientific equation. And that is valid. Meaning is complex, contextual, and time‑dependent. What makes people laugh today can coexist with what reshapes physics tomorrow.
The real problem is imbalance. Established voices are surfaced by default. Invisible voices must fight uphill, even when their contributions could matter profoundly. A genuine town square would balance both kinds of meaning — immediate and enduring, humorous and profound.
In Weapons of Mass Distraction, I argued that the attention economy is not neutral. It is engineered to maximize time‑on‑site for advertisers. The casualty isn’t just your time. It’s your agency.
When algorithms optimize for attention, they don’t ask: What matters most? They ask: What keeps you here? Over time, autopilot becomes the default. Reflection disappears. Agency fades.
Applied to the town square metaphor, this means the crowd hears what will hold their gaze — not necessarily what will add meaning.
So what’s the alternative? It isn’t to abandon algorithms — at the scale of billions, filtering is unavoidable. Nor is it to silence popular voices — people do want to hear Elon.
The alternative is to shift the foundation: from attention to meaning.
Think of it as a burn multiple for life: attention is the burn — the cost you pay. Meaning is the return — the benefit you get back. A platform that traps your attention but leaves you emptier burns value. A platform that multiplies the value of your attention creates meaning.
A true digital town square would not confuse fame with meaning. It would still amplify established voices, because popularity is a real signal. But it would also surface new contributions based on their substance, not just their source. It would ensure that the invisible can become visible when they add value.
That’s the promise of a meaning economy: a system where the next Einstein isn’t muted simply because he hasn’t yet built a follower count.
We should give today’s platforms credit: they amplify multiple sides of debates, and that openness is far better than censorship. But amplification is not equality. And proxies for meaning are not meaning itself.
At the scale of billions, fairness demands a new filter — one that doesn’t just reward what captures attention, but what contributes meaning. The next step is clear: move from an attention economy to a meaning economy.
Only then will we have earned the right to call these platforms a true town square.