This essay argues that ASI will not emerge through parameter scaling alone, but through the emergent properties of multi-agent systems. Drawing on Minsky's Society of Mind, Langton's Ant, and recent empirical evidence from OpenAI and ICLR 2026, I propose that intelligence at the highest levels is collective.

The Shifting Baseline

Claims that "AGI is already here" have become ubiquitous. They are not entirely wrong.

In 2023, Google DeepMind published a framework classifying AGI progress into six levels. Current systems — GPT-4, Claude, Gemini — were classified as Level 1: Emerging AGI, defined as "equal to or somewhat better than an unskilled human." We are now three years later. The baseline has shifted. The question is no longer whether AGI is "here." The question is: what emerges from it?

This essay proposes a specific answer: ASI will not arise through continued scaling of monolithic models. It will emerge from the combinatorial dynamics of multi-agent systems. The mechanism is not bigger models. It is topology scaling — the expansion of agent interactions across domains, hierarchies, and time.

Levels of AGI: Three Years Later

A 2026 study by METR measured AI capability using a new metric: the duration of tasks AI models can complete with 50% success rate. The study evaluated 12 frontier models on 170 software engineering tasks, with human baselines from 800+ skilled professionals.

Capability has been doubling every seven months since 2019, accelerating since 2023. Extrapolation predicts AI systems will reach a one-month task horizon between 2028 and 2031. The first emergence — the arrival of Level 1 — was only the beginning.

Society of Mind

"What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle." — Marvin Minsky, Society of Mind (1986)

Minsky's central claim: intelligence does not arise from a single, unified process. It emerges from the interaction of many simple, unintelligent agents. An agent that recognizes vertical lines. An agent that detects motion. None intelligent on their own. Together, they constitute thought. This is not metaphor. It is architecture.

Langton's Ant

An ant moves on a grid: on white, turn right and flip to black; on black, turn left and flip to white. Repeat. For thousands of steps, chaos. Then, abruptly, a "highway" — a diagonal, infinitely repeating pattern. Nobody designed it. It emerged from simple rules applied iteratively.

This is emergence: new, higher-level properties arising in complex systems that cannot be predicted from individual components. As Aristotle observed: "The whole is more than the sum of its parts."

Evidence from Multi-Agent Systems

In 2019, Baker et al. at OpenAI placed agents in a game of hide-and-seek with one reward signal: visibility. No instruction to use objects. What followed were six distinct phases of emergent strategy — shelter-building, ramp use, box surfing — each a response to the previous one. None were programmed. They emerged from multi-agent competition over hundreds of millions of episodes.

In 2026, Riedl (ICLR 2026) closed the gap between observation and measurement. Three questions: Do multi-agent LLM systems exhibit emergence? Does it improve performance? Can we steer it? Testing groups of GPT-4.1, Llama, Gemini, and Qwen3 on a coordination task, all conditions showed significant emergence capacity.

But here's the catch: neither synergy nor redundancy alone predicted success. Only together — redundancy creating alignment, synergy extracting novel information — did performance improve significantly. Prompt design shifted systems from loose aggregates to integrated collectives. The implication: emergence isn't just observable. It's measurable, steerable, and functionally relevant.

Two paradigms — RL and LLMs — converge: multi-agent interaction produces capabilities not reducible to individual agents. Three caveats: both studies use game environments, the emergence observed is coordination rather than superintelligence, and emergence is easier to recognize post hoc than to predict.

Scaling as Topology

The dominant narrative of AI progress is scaling: more parameters, more data, more compute. Not wrong, but incomplete. The first emergence was parameter scaling. The second will not come from making models bigger. It will come from making them many.

A multi-agent system scales through topology — the structure of interactions between agents. The system becomes more capable through orchestration: how agents divide labor, share context, and coordinate action.

Hypothesis: ASI arises not from the scaling of a single agent, but from the scaling of many agents in interaction. This is grounded in three precedents: Minsky's Society of Mind, Langton's Ant, and empirical evidence from Baker and Riedl that emergence from interaction is a recurring, measurable pattern in complex systems.

Conclusion

Nobody planned the highway in Langton's Ant. Nobody planned the internet. Nobody planned the global economy. Emergent systems — order arising from interaction, not design.

ASI will be no different. It will not arrive as a single, finished artifact. It will emerge from the scaling of multi-agent systems, from the collective dynamics of many models interacting, competing, and cooperating.

The question is not whether this will happen. It is whether we will understand it while there is still time to shape it.