The Social Edge of Intelligence: Individual Gain, Collective Loss

The Social Edge of Intelligence

AI doesn’t really “think.” Rather, it remembers how we thought together. And we’re about to stop giving it anything worth remembering.

The Age of Human Redundancy

We are on the verge of the age of human redundancy. In 2023, IBM’s chief executive told Bloomberg that soon some 7,800 roles might be replaced by AI. The following year, Duolingo cut a tenth of its contractor workforce; it needed to free up desks for AI. Atlassian followed. Klarna announced that its AI assistant was performing work equivalent to 700 customer-service employees and that reducing the size of its workforce to under 2000 is now its North Star. And Jack Dorsey has been forthright about wanting to hold Block’s headcount flat while AI shoulders the growth.

The trajectory has a compelling internal logic. Routine cognitive work gets automated; junior roles thin out; productivity gains compound year on year. For boards reviewing cost structures, it is the cleanest investment proposition since the internal combustion engine retired the horse, topped up with a kind of moral momentum. Hesitate, the thinking goes, and fall behind.

The Tragedy of the Commons in Creativity

But the research results of a team in the UK should give us pause. In the spring of 2024, they asked around 300 writers to produce short fiction. Some were aided by GPT-4 and others worked alone. Which stories, the researchers wanted to know, would be more creative? On average, the writers with AI help produced stories that independent judges rated as more creative than those written without it.

So far, so on message: a familiar story about the inevitable takeover by intelligent machines. But when the researchers examined the full body of stories rather than individual ones, the picture became murky. The AI-assisted stories were more similar to each other. Each writer had been individually elevated; collectively, they had converged. Anil R Doshi and Oliver Hauser, who published the study in Science Advances, reached for a phrase from ecology to explain this: a tragedy of the commons.

Hold that result in mind: individual gain, collective loss. It describes something far more consequential than a writing experiment—it describes the hidden logic of our entire relationship with artificial intelligence. And it suggests that the most successful organizations of the coming decade will be the ones that do something profoundly counterintuitive: instead of using AI to eliminate human interaction by firing droves of workers, they will use it to create more human interaction.

AI Intelligence Depends on Social Complexity

Suppose you could travel to Egypt in 3000 BC and train a large language model on everything you could find in the languages of that era. The result would be a system capable of predicting floods or drafting administrative correspondence. But it would have no capacity for the syllogism, no trace of Roman legal abstraction, and no conception of the empirical method.

If you trained new models on 300 BC Athens, 300 AD Rome, or 1500 AD Florence, each model in this chain would be qualitatively smarter than the last. But it wouldn’t be smarter because you changed the architecture. The reason would be that the civilization feeding the tech had changed.

The underlying intelligence of a large language model isn’t a function of its architecture or parameter count. It is a function of the social complexity of the civilization whose language it digested. Each epoch advanced through new forms of collective problem-solving—new institutions like the Greek agora, the medieval university, and the modern corporation.

An LLM like ChatGPT is a model of human social reasoning with the human wrangled out. And the question is: What happens to the model when the social reasoning that produced its training data begins to thin?

The Risk of Knowledge Collapse

In 2024, research published in Nature demonstrated that AI models collapse when trained on recursively generated data. Language models trained on text generated by other models start to degenerate because the distribution of the output narrows. Minority viewpoints and edge-case perspectives gradually vanish. The model converges on a statistical average—fluent, plausible, and hollow.

Model collapse is social mind compression presented as a technical phenomenon. Similarly, "knowledge collapse" occurs when widespread reliance on AI-generated content narrows the diversity of available perspectives. We are seeing a variant of the Dunning-Kruger effect where people become overconfident in their understanding because they use AI to settle messy debates rather than engaging in the social friction of hard conversations.