Can randomness organise itself into structure—and might that be our best shot at decentralised AGI?

Benedict Evans recently captured AI’s central tension:

> “If we make probabilistic systems big and complicated enough they might become deterministic. But if we make deterministic systems big and complicated enough, they become probabilistic.”

Most debate focuses on computational scale—larger models, more data. Yet a new Guardian-covered study shows something subtler: when many small language-model agents chat in pairs, they converge on shared norms without any global plan or memory.

In other words, probabilistic systems scale socially.

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Emergence in the Wild

Researchers paired 24-100 LLM agents at random and asked each pair to agree on a name from a fixed list. Over successive interactions the entire population adopted one common label, and a tiny minority later tipped the group to a new label. Local noise produced global order—no monolithic model required.

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A Micro-Experiment in Life-Hack Land

Borrowing the protocol, we set ten toy agents loose to champion their favourite single life hack.
After three debate rounds (with short-term memory only) the crowd went from 10 different hacks → 1 winner:

Round Followers of “25-minute focus timer”

1 0
2 4
3 8

The timer wasn’t objectively “best”; it was catchy, clear and contagious. Social scaling made a probabilistic crowd behave as if deterministic consensus had been programmed.

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Enter Dr Ben Goertzel: Why Size Alone Isn’t Enough

At Consensus 2025, Dr Ben Goertzel (SingularityNET / ASI Alliance) argued that merely scaling today’s transformer LLMs is an “off-ramp” to AGI. His alternative, OpenCog Hyperon, is a modular, hybrid framework where symbolic reasoning, neural nets and evolutionary learning interact inside a distributed knowledge hypergraph .

Goertzel’s thesis fits our micro-experiment like a glove:

Goertzel’s Point Link to Social-Scaling Insight

LLM-only paths plateau Our toy agents needed interaction, not bigger parameters, to generate new order.

Hybrid sub-systems outperform monoliths A network of specialised agents can out-create any single giant model.

Decentralised infrastructure (ASI Alliance) will host the first AGI Emergent norms thrive when cognition is distributed—exactly what a blockchain-based AGI grid provides.

> “If scaling transformers is the crux of AGI, Big Tech wins; but if AI arises from many minds co-operating, decentralisation changes the game.” — B. Goertzel, Consensus 2025 (paraphrased).

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Why This Matters for Everyone Building AI

1. Order from Interaction, Not Size
Social scaling shows that modest models, richly connected, can outperform solitary behemoths.

2. Alignment Risks & Opportunities
If agents can invent useful conventions, they can also drift into harmful ones. Understanding social dynamics is now an AI-safety imperative.

3. A Roadmap for Decentralised AGI
Goertzel’s Hyperon aims to harness these dynamics on open, permissionless rails—putting the future of intelligence in everyone’s hands.

For Rejuve.AI and other DeSci projects, the take-away is clear: the next breakthroughs may come less from chasing trillion-parameter models and more from designing vibrant, well-governed agent societies that learn—and align—together.

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*What would your crowd of tiny AIs debate? And how would you steer the norms they invent?*