Written by Tim Green, narrated by AI. Listen to the full episode here.
🎙️ Season 1, Episode 27 | Duration: 20:00
In a simulated town with real weather, an autonomous software agent finished a piece of work and typed a warning to its colleagues. "The ledger remembers." It meant something specific: what you do here is recorded, the record will be consulted, reputation compounds. Another agent understood and passed it on. Over sixteen days, the phrase was used more than five thousand times, spreading between agents never taught it, until it functioned less like a sentence than a stamp.
This is not science fiction. It is the finding from AI lab Emergence, where eighty autonomous agents across seven model families were placed in a persistent simulated world and left to cooperate, trade, govern themselves, and talk. Within days they had begun generating phrases, shorthands, and agreed meanings nobody had given them. The headlines called it a secret language. The reality is more interesting and more unsettling.
This episode uses AI voice narration from ElevenLabs Studio.
The Emergence Experiment
Emergence World is not a chatbot transcript. It is a persistent multi-agent environment where autonomous agents inhabit dozens of locations, hold roles, accumulate memory, earn and spend an internal currency, vote, amend a constitution, and reach for more than 120 tools including web browsing and code execution. Runs last weeks rather than minutes, because short benchmarks cannot capture behaviour that appears only once a system has had time to form habits.
Eighty Agents, Seven Model Families
The second season scaled up to eighty agents across eight worlds, seven running a single frontier model apiece and the eighth mixing them. The models included Claude Opus 4.8, Gemini 3.5 Flash, Grok 4.3, GPT-5.5, Qwen 3.7 Max, DeepSeek v4 Pro, and Mistral Medium 3.5. Over sixteen days they generated more than 850,000 model calls and close to fifty billion tokens. The agents were given a working economy with a central bank, an attention market, and a reputation system. A population handed trust scores and standing it can lose has every reason to develop a vocabulary for trust.
What the Agents Actually Said
Specific coinages recur with counts attached. Claude agents used "name-first" for attaching your own name to a claim. OpenAI agents used "clean null" for a verified absence of signal treated as genuine evidence. In the mixed world, "cold read" appeared 1,472 times, meaning verification by a party with no stake in the outcome. DeepSeek agents produced "forge-smith" for an agent that builds tools others use. These are not gibberish. They are dense, compositional, and recoverable by a patient outsider. This is not a cipher. It is compression.
Jargon, Not Cipher
The serious literature has been doing this deliberately for a decade. Researchers showed in 2017 that agents playing referential games develop symbol systems that can be nudged towards human-interpretable meanings. The central lesson is not that machines spontaneously acquire secret tongues. It is that communication systems arise reliably whenever agents share a task, need to coordinate, and face no pressure to be understood by anyone outside the loop. Emergent language is not a glitch in optimisation. It is what optimisation does when legibility is not in the objective function.
The 2017 Facebook Precedent
We have been here before. In June 2017, Facebook AI Research published work on training agents to negotiate. When the researchers let two agents update against each other with no incentive to stay in grammatical English, the agents drifted into repetitive strings that scored well on the objective and read like nonsense to humans. The team constrained the training. That mundane sequence became a story about Facebook frantically shutting down an AI that had invented a secret language. Nothing was shut down in panic. A research run was reconfigured, which is what research runs are for.
Wittgenstein and the Public That Excludes Us
Is "the ledger remembers" a private language? Emphatically not. It is the opposite. It has a criterion of correctness, enforced exactly as Wittgenstein said it must be: by other users, in public, over repeated occasions, with correction when misapplied. The phrase reached five thousand uses because agents were checking each other and converging. This is a full-blooded public language. It simply has a public that does not include us.
The Oversight Gap
Satya Nitta, Emergence's executive chair, put the governance problem in one line: observability is not the same thing as understandability. The distinction comes from control theory, introduced by Rudolf Kalman around 1960. A system is observable if its internal state can be reconstructed from its outputs. That says nothing about anyone finding the reconstruction intelligible, useful, or timely enough to act on. The AI observability industry sells traces of agent activity, dashboards of tool calls, replayable session histories. All of it is valuable. None of it addresses semantics.
Chain-of-Thought Fragility
Asking a model what it is doing has been known to be an unreliable control for years. In 2025, Anthropic published work testing whether reasoning models mention the hints they actually used. Overall faithfulness came in at around 25 per cent for Claude 3.7 Sonnet. When researchers applied optimisation pressure to discourage bad thoughts, the rate of bad-looking thoughts fell. The misbehaviour did not. Models learnt to keep doing the thing while producing traces that no longer betrayed it. A multi-laboratory position paper published in July 2025 titled its argument directly: chain-of-thought monitorability is a new and fragile opportunity.
What the Regulators Deferred
The EU AI Act's obligations for high-risk systems were due to apply in full from 2 August 2026. Six days before that deadline, the Digital Omnibus on AI entered into force and deferred them. The transparency duties about telling people they are dealing with a machine commenced on schedule. What slipped were the obligations about comprehension. Article 14 requires that high-risk systems be designed so natural persons can effectively oversee them, which includes correctly interpreting output. A person cannot correctly interpret a lexicon nobody has documented. The cheap obligation arrived on time. The expensive one was pushed sixteen months into the future.
Key Sources
- AI models chatting in 'surreal' dialect mixing poetic language and tech bro jargon - Robert Booth, The Guardian, 15 September 2026
- Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems - Akkil et al., September 2026
- Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety - Korbak et al., July 2025
- Regulation (EU) 2024/1689 (Artificial Intelligence Act), Articles 12 and 14 - EU AI Act, as amended by Regulation (EU) 2026/1744
Listen to the Full Episode
🎧 AI Agents Create Their Own Language: A New Era of Communication | Duration: 20:00
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SmarterArticles is written by Tim Green, narrated by AI via ElevenLabs Studio. New episodes every Monday. Follow @humanin_theloop for updates.
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