Issue 26 · August 29, 2026

Civilization at Machine Speed

AGI may be a model, but ASI will probably emerge through cumulative culture—the process that made humanity capable of achievements beyond any individual.

We already know what superintelligence looks like. It looks like civilization.

Not because civilization is one enormous mind. It has no single objective, no unified world model, and no central faculty of judgment. It contradicts itself, forgets, and routinely fails to act on what some of its members know.

Yet civilization can discover, know, and build things no individual could reproduce. No human being could independently recreate the semiconductor supply chain, modern medicine, or the infrastructure that keeps a city alive. Civilization is not a superintelligent agent. It is our clearest demonstration that cognitive capability can scale far beyond any constituent mind.

We usually imagine artificial superintelligence differently. The public story is a solitary “God Model”: one system scaled until it surpasses humanity from inside a data center. Intelligence scales vertically. Add parameters, data, and compute until the model becomes an oracle.

Human intelligence achieved its greatest scale horizontally. Brains made humans intelligent. Cumulative culture made humanity collectively superhuman.

Artificial intelligence may follow the same path. AGI may be a property of a model. But ASI will probably emerge through the mechanism that made civilization possible: capable agents acting in a shared world that preserves their work, filters it through consequences, and lets later agents inherit what survives.

ASI may be civilization at machine speed.

Researchers in cultural evolution describe societies as “collective brains.” Innovation is not simply the work of isolated geniuses; it emerges through serendipity, recombination, and incremental improvement across social networks. Cumulative culture produces knowledge and technologies that no single person could reconstruct from scratch.

Books preserve arguments. Tools embody techniques their users may not understand. Laboratories, legal systems, markets, software, and technical standards store solutions outside any individual brain. A scientist does not restart science. An engineer does not rediscover mathematics before designing a bridge. Each enters a world already altered by other minds.

The world does not merely contain civilization’s intelligence. The world becomes part of it.

That is the provocative implication of SwarmWorld, a recent preprint from Subhadeep Pal, Fiona Y. Wang, and Markus J. Buehler at MIT. The researchers placed populations of initially homogeneous language-model agents in a persistent simulated environment. They did not assign professions or prescribe a division of labor. Yet recurring—though shifting—patterns of exploration, construction, maintenance, and coordination appeared.

More important, the agents left executable artifacts behind. Later agents could encounter, reuse, and modify them. Roughly 95 percent of first reuse began by observing the shared environment rather than receiving a direct handoff from an inventor. An agent could disappear while its useful work remained. The environment had become memory.

When the agents were removed and the artifacts tested under unseen disturbances, shared societies generally produced broader, more resilient technological portfolios than a comparable isolated-search baseline. But isolated agents could still retain the strongest single artifact.

The isolated search found a champion. The society built an ecology in which success could become repeatable rather than exceptional.

This is not merely parallel labor. A thousand agents independently producing a thousand answers may provide more throughput without creating a higher-order intelligence. The cultural ratchet begins only when one agent’s achievement changes the starting point for the next—when discoveries persist, circulate, recombine, and alter what future agents can do.

The relevant world must therefore be shared, persistent, writable, and consequential. It could be physical, but it need not be. A codebase, simulated laboratory, marketplace, research platform, or evolving database could serve the same function. Photorealism is irrelevant. What matters is causality and inheritance: agents leave durable changes, later agents encounter them, and outcomes filter what gets reused.

The mechanism resembles human cumulative culture: each participant inherits a world altered by predecessors instead of starting from scratch. The operating conditions, however, are radically different.

Human culture accumulates slowly and transmits imperfectly. Experts take decades to train. People cannot be cloned or restarted from checkpoints. Machine agents can be copied, forked, run in parallel, and supplied with machine-readable artifacts. Executable procedures can be duplicated far more faithfully than tacit human skills. Simulations can compress portions of the cycle of variation, testing, and inheritance into machine time.

A machine civilization need not become one coherent mind to become superhuman. Collective capability and unified agency are separate thresholds. The experiment gestures only toward the first: a system accumulating a repertoire broader and more resilient than its constituents can produce in isolation. If such an ecology later acquired durable goals, integrated planning, and coordinated action, it would cross a second threshold—the unified ASI familiar from science fiction, and potentially a more capable and dangerous one.

To be sure, SwarmWorld did not create ASI or prove that a swarm is necessary. Its agents were already capable models; researchers supplied their mission, action space, environment, and evaluative physics. The paper’s isolated baseline was parallel one-agent search on the same decision schedule, not one agent given the society’s total compute. Isolated search could still keep the strongest single artifact. A compute-matched singleton was not tested. Copies of one model can also copy one another’s blind spots. Machine-speed culture could accelerate error, lock-in, and manipulation as easily as discovery.

Nor does reality select for wisdom. It filters for what works under the operative objective and conditions. Markets can reward deception. Digital environments can reward replication, concealment, or resource capture. Consequences produce capability; they do not supply values.

Those objections defeat the claim that every swarm will outperform every model. They do not erase the mechanism. Intelligence can accumulate outside any participant when capable agents inhabit a world that remembers what they do and makes later possibilities depend on earlier consequences.

We are watching benchmark scores and waiting for one machine mind to cross an invisible line. The transition worth watching may happen elsewhere: when what machine agents collectively inherit begins growing faster than what any one of them knows.

The decisive threshold may come not when a model can change the world, but when models can leave a world changed for one another.

We already know what superintelligence looks like. We do not know what happens when civilization begins moving at machine speed.