Instructed to answer in cablese — the telegraph operators' compressed dialect (drop the articles and filler, keep every fact) — a single one-sentence instruction, no examples and no codebook, elicits 40–49% fewer billed output tokens on the API's own meter, and models across four families still recover the information at full fidelity. For machine-to-machine traffic, that is half the output bill at any major API, today. The Victorian economics of the cable, reborn as token economics: the Telegraph Test benchmark.
The Telegraph era, where every word was metered
In 1866, sending a message across the new transatlantic cable cost $100 — for ten words . That’s real money today; it was serious money then. Telegraph companies charged per word, and an entire industry grew out of that price structure.
The Great Eastern laying the first successful Atlantic cable (oil painting, National Maritime Museum, public domain). The link that charged $10 a word — and taught a generation of correspondents to write in cablese.
Two compression strategies emerged, and they map onto two very different technologies:
Codebooks. Publishers sold massive commercial code dictionaries — Bentley’s ABC Telegraphic Code ran to a thousand pages mapping entire business phrases to single code words.
OTTER
…might mean
*steamship arrived, cargo intact, remit balance.*
Firms could cut a 40-word negotiation to a 6-word coded exchange. The codebook is a substitution technology: the message exists in full prose, and a dictionary renames chunks of it.
Cablese. The operators and correspondents evolved a written format like:
"ARRIVE TUESDAY BRING FUNDS STOP CONFIRM WIFE SAILS FRIDAY."
This disciplined style saved words without losing meaning, and it was emergent from the cost of the medium.
Over time, this format faded as new technologies like the telephone, fax, email, made the per-word cost premium collapse. Telegraphese kind of survives wherever metering in terms of cost or just the time it takes to tap out a message is still onerous: 160-character SMS begat a whole new abbreviation culture; early Twitter did it again.
Well guess what?
Tokens are metered words again
An LLM API bill is a telegraph bill. You pay per token.
So we ran both strategies from ye olden days against modern models:
The Codebook: take existing text, substitute code words from a fixed dictionary.
Result: about 10% savings. Most prose isn't dictionary-shaped, and the substitution can't remove words the author already wrote — it can only rename them.
Cablese
Instruct the model, “Write a complete record in telegraphese; drop articles and filler; abbreviate; keep every fact, number, and proper noun verbatim — in lowercase, not all caps.”
Why does this work across models?
The register is already in the weights — an artifact of training data. Telegraph cables, codebooks, and cablese’s cousins across the broader “telegram style” family (headlinese, teletype style, note-taking, SMS abbreviation) are all in the “all of human knowledge” corpora that most models share. Two observations back this. First, models produce fluent, conventionally-shaped cablese from a one-sentence instruction — no examples, no codebook. Second, the readers in the cross-family matrix never saw even that instruction: they were handed compressed records cold and read them at parity, across four model families. Shared zero-shot fluency like that is hard to explain unless the convention is latent in shared human text.
Haven’t we seen LLMs do this already?
Yes, BabelTele (arXiv, June 2026) demonstrated that LLMs can encode text in compact, non-standard forms — omnilingual word fragments, symbols, emoji — that other models recover with high fidelity (99.5% semantic fidelity at 27.9% of original length, by their metrics), including cross-model transfer, agent memory, and multi-agent communication. It proves the general phenomenon: human readability is not a requirement for model-to-model text.
Further, researchers running populations of LLM agents under token budgets have watched the same thing emerge spontaneously: put agents under compression pressure and they negotiate compact protocols instead of passing full English back and forth. GLOSSOGEN found it’s the budget pressure that produces the new communication system. Another paper, From Token Efficiency to Oversight Evasion, shows agent populations developing emergent languages under efficiency pressure.
Other work has agents inventing symbolic languages that cut tokens 3–6× at steady accuracy, and at the far end, frameworks that skip text entirely and pass raw embedding vectors between agents.
What’s already shipped
- Terse (terseai.org). A product selling “telegraph compression” for your prompts: rule-based stripping of articles and pronouns on the input side. The fidelity is asserted, not measured.
- Caveman (github.com/juliusbrussee/caveman). A Claude Code skill that makes the model answer in terse fragments. It has 100k+ stars on Github.
I think there’s more to do here, though.
So why does Cablese matter then?
If models can create something like a 6x compression on their own, why bother with a mere 2×? Here’s why:
These emergent protocols share a property profile: dense, efficient, portable between models (other LLMs can learn them in-context), but they are:
- unstable — they drift as negotiation continues
- illegible/unreadable to people
By contrast, Cablese is:
- a known standard, more deterministic, generalizeable, with no setup or initial negotiation
- readable, therefore auditable
In truth, the two techniques are different points on the compression/auditability frontier, and where your workload sits on that frontier should pick the point.