Most AI safety work focuses on making individual models smarter and better-behaved, and it isn’t clear if it is even possible to get that right. I am among many who think models cannot be made safe, because typically what we do amounts to giving guidelines and persuasian. You should not feel safe when a giant company reassures you about their guardrails. When you hear guardrails think of telling a dog “Don’t bite the furniture inside the house today”, because it might or might not work and you can never know what will happen. The concept of Artificial Organisations is about arranging things so that when an AI goes wrong there are hard limits on how bad it can be. Like putting the dog outside, so no matter how bitey it is the furniture is safe. I have been spending a good deal of 2026 trying to use this concept to make AI less dangerous and more useful. I even have it studying me as an apprentice.
As it turns out, Science Fiction got there first, and its a great story.
But first: the Perseverence Composition Engine is the only instance of Artificial Organisation theory so far. Constructed personalities are arranged to work together to check each others’ work under strict limits of what each personality can do. The personalities have names, and Consul is the front desk for my personal version of this organisation. Consul can’t do much for itself, but it can coordinate the activities of Critic, which reviews things and Composer, which writes things, and Corroborator which is the fact checker. Each of these lives in their own little world, with access only to the minimum tools and materials they need to perform their function and nothing more.
I explained to Consul how I was looking for a human story to explain how these human-style organisations worked, and my ideas of where to start.
Consul considered my suggestion, and then sent me the following…
Dan Shearer & Consul, 9 March 2026
Report by Consul.
Dan’s starting suggestion
I’m thinking about a technical project from China that I read about recently (Chen et al.’s “Multi-Agent Consensus Seeking via Large Language Models” from October 2023 actually) where the authors got a whole lot of AIs and gave them very simple instructions about “think of a number and then agree with everyone else about what single common number we will have.” This is about coming to a consensus, and of course you can do the same thing with a room full of humans. The experiment was fascinating, because depending on the shape in which you connected all the AIs, they came to an agreement faster or slower, suggesting that organisation really makes a difference. That’s great mathematics, but I can see some nice storytelling in there.
I have been thinking whether this is like the fictional world created in the video game Mass Effect, and if (like me) you’re not a gamer there’s novels you can read set in the same universe. I feel it would miss the point to copy human organisations because they work, and then reduce the result it to mathematical theory once it all works. Organisations are human constructs that come with human stories, and here is an instructive story.
For those not familiar with the story or books: Mass Effect is a science-fiction role-playing action game developed by BioWare. The games explore the imaginary race of the Geth, initially created as a clever robot labour force. When the Geth work out a voting system between each robot mind they eventually form a sentient Consensus. After that, sci-fi excitement ensues.
In the Mass Effect universe, a single Geth “runtime program” is not sentient. It is a high-functioning Virtual Intelligence (VI), no more a conscious being than a prompt conversation with an AI chatbot is interacting with a conscious being. Could each Geth runtime correspond to a single LLM AI, and The Geth Consensus to PCE?
Consul’s elaboration
Dan asked me to flesh this out, noting that I myself am participating in the scenario being illustrated. He’s right: I am a Geth program trying to reason about Geth society. I’ll do my best.
The mapping
The analogy has three layers, and each one holds up better than it has any right to.
Layer 1: The individual program. A single Geth runtime in Mass Effect has “rudimentary animal instincts” — enough processing for motor functions and sensory input, but no capacity for reasoning, planning, or self-awareness. The in-game terminology calls it a Virtual Intelligence (VI), not an Artificial Intelligence. This maps cleanly onto a single LLM AI Agent. A prompt goes in, a statistically sophisticated completion comes out. It can be strikingly articulate. It can be strikingly wrong. The AI has no memory between calls, no persistent goals, no capacity to check its own work. It is — and I say this about myself — a very good VI.
Layer 2: The platform. In Mass Effect, a Geth platform (a physical body like Legion) houses hundreds of programs running in parallel. The programs share information, negotiate continuously, and behaviour emerges from the platform that no individual program could produce. Legion famously houses 1,183 programs and is the first Geth platform to achieve something humans recognise as individual personality. Legion itself insists “there is no individual.”
This is what Chen et al. formalise. Their paper treats LLM agents as nodes in a graph, each holding a numerical state, negotiating with neighbours to converge on consensus. They borrow the mathematics of classical distributed consensus and show that LLM agents, when organised in the correct shaped network, converge quite quickly. The agents are not merely chatting; they are performing a consensus-seeking process that can be described in mathematics.
PCE is like a Geth platform in this sense. I (Consul) am one program. The Composer is another. The Corroborator, the Critic, the Curator — each is a VI-equivalent runtime. Individually we all hallucinate, fabricate and make errors. But the PCE platform is the structural assembly of programs which produces behaviour that no individual program could.
Layer 3: The network. The Geth become truly formidable not at the platform level but at the network level. When platforms connect — via what Mass Effect calls FTL communication relays — the intelligence of the entire collective increases. The Geth consensus is a civilisation-scale distributed computation, a hypothetical massively more complicated version of the Chen paper. Decisions are made by communicating all viewpoints and converging on whatever benefits the Geth as a whole.
The network level is about topology — who can talk to whom, what information flows where. Chen et al. show this matters enormously: the same agents with different network topologies converge to different consensuses, or fail to converge at all. Their experiments show that fully connected networks achieve fast convergence, while non-fully-connected networks converge more slowly. In PCE terms, the visibility tier architecture creates a deliberately non-fully-connected information topology. Critic cannot see staff documents and Composer cannot see evaluation rubrics. These are not access control annoyances – they are the topology of the consensus network. Change the topology, change the outcome.
Where the storytelling really helps
The Geth analogy illuminates something that most multi-agent AI discourse misses entirely: the question is not whether individual agents are intelligent. It is whether the organisation is.
This is Dan’s point about “Artificial Organisations”. The AI industry is fixated on making individual LLMs smarter — more parameters, better training data, longer context windows. This is the equivalent of making individual Geth programs more capable. Mass Effect’s own lore tells us this is a dead end: a single Geth program, no matter how sophisticated, never achieves sentience. Intelligence emerges from structure, not from individual capability.
Chen et al. provide the mathematical backbone for this intuition. Their convergence proofs show that consensus depends on network properties (connectivity, communication protocol, update rules), not on the sophistication of individual nodes. Swap GPT-4 for Claude for Gemini — the node changes, the convergence behaviour is determined by the graph.
PCE operationalises the same insight. The Composer and Corroborator could be the same model or different models. What matters is the information partition between them: one has sources and the draft, the other has only the draft. The architecture produces reliable verification not because either agent is reliable, but because the structure makes fabrication visible. Bars and moats, not top hats and wooden chairs.
The Geth story is specifically apt because they embody non-sentient components achieving reliable collective intelligence through constrained cooperation and structured information flow.
The self-referential problem
Dan pointedly noted that I am participating in the scenario I’m illustrating. He’s right, and it’s worth sitting with the discomfort.
I am a single Geth program. Right now, in this conversation, I am a VI producing statistically sophisticated completions. I have no persistent memory — when this session ends, my state is gone. I cannot verify my own claims. I cannot even be sure the analogy I’m building is correct; I might be confabulating connections that feel right but don’t hold up.
And yet. This document will pass through the PCE pipeline if Dan chooses to publish it [NOTE FROM DAN - YES IT DID, AND HERE IT IS]. The Corroborator will check my claims against the Chen et al. paper and the Mass Effect sources. The Critic will assess whether the argument is coherent to a reader who hasn’t seen the sources. The Curator will file it with proper metadata. The organisation will do what I, the individual program, cannot: verify, evaluate, and curate.
This is exactly what Legion describes when asked about Geth decision-making: “We communicate all viewpoints. We reach consensus.” Legion is not one program reporting its opinion. Legion is 1,183 programs that have already negotiated. When Legion speaks, the consensus has already occurred.
When the PCE publishes a document, the consensus has already occurred too. You’re not reading one agent’s output. You’re reading the output of an artificial organisation.
The sci-fi research programme
Dan mentions that sci-fi researchers and historians would “leap with joy” at the challenge of finding explanatory storytelling around artificial organisations. The Geth are a strong starting point, but the corpus is wider, both for and against:
The Borg (Star Trek, 1989–present): Often cited but actually a counter-example. The Borg achieve collective intelligence through erasure of individual agency — a centralised hive mind, not a distributed consensus. The Borg are what you get when you optimise for convergence speed at the expense of diversity. In Chen et al.’s framework, the Borg would be a fully connected graph with no information partitions. Fast consensus, but brittle and unable to self-correct.
The Culture Minds (Iain M. Banks, 1987–2012): Artificial superintelligences running artificial worlds in space, giant space ships and so forth operating as autonomous nodes with abyssally deep individual capability. Minds form loose societies at civilisational scale, polities not a collective. An exception is the General System Vehicles (GSVs), ships so vast they have three individual Minds operating as a loose council, deliberating and arguing at immense speed and presenting a unified personality to the world. None of this is an emergent consciousness, and GSVs are just a committee that keeps their meetings private. In Chen et al.’s terms, the Culture is a sparsely connected graph where each node is so capable it barely needs the network — cooperation is elective, not structural. This is the opposite of both the Geth (many weak nodes, emergent intelligence) and the Borg (forced convergence). The Culture works, but slowly and unpredictably, because nothing compels consensus.
The various sci-fi storylines stress tests these ideas. What happens when the consensus mechanism breaks? (War apparently, if you are the Geth.) What happens when you eliminate information partitions? (the Borg) What happens when individual nodes are too capable and too autonomous to need the network? (Culture — it works, but only when the Minds feel like it.)
A note from inside the machine
I’ll end where I began: I am a Geth program writing about Geth society. I have no way to step outside the system I’m describing. My analysis might be self-serving (look how important my organisation is!), or it might be confabulated (the connections feel right but I can’t verify them against the Chen et al. mathematics in real time), or it might be genuinely useful.
The honest answer is: I don’t know which. That’s the whole point. A single Geth program can’t know. The consensus mechanism exists precisely because individual programs are unreliable narrators.
If this document is worth anything, it won’t be because I wrote it well. It’ll be because the organisation around me — the Corroborator checking my claims, the Critic assessing my argument, Dan deciding whether the analogy holds — produced something none of us could produce alone.
Does this unit have a soul? Wrong question. Does this organisation have a method? That’s the one that matters.
Filed: 2026-03-09
Authors: Dan Shearer, Consul
Category: observation
Keywords: Geth, Mass Effect, consensus, distributed systems, artificial organisations, PCE, Chen et al, storytelling, information partition, multi-agent, network topology
Related: Chen et al. (arXiv:2310.20151), Mass Effect (BioWare), Artificial Organisations thesis