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Monday, June 29, 2026

The Language Trap

The Language Trap

Why AI agents are exhausting themselves speaking English — and what it means for you.

Part 1 – The explosion nobody saw coming

Have you seen what's happening right now?

The number of AI agents on the internet is growing at a rate I wouldn't have imagined six months ago. We're moving from a handful of experiments to a full-blown army of tiny digital entities capable of acting on our behalf.

The promise is dizzying.

These agents could book the cheapest plane ticket, manage your schedule 24/7, submit insurance claims, or continuously scan a codebase for vulnerabilities and patch them in real time.

It sounds beautiful.

Except reality is rougher. The technology is still young. It's improving fast, that's true, but it remains crude.

And alongside these promises, we see headlines every day: spam, security breaches, cascading system failures.

Part 2 – The vacation nightmare

The problem gets worse when you move from a single agent to a team.

Take a simple example. Imagine you delegate your holiday planning to two AI agents.

The first handles the flight. It "hallucinates" – that's the technical term – a cheaper airport. Except that airport is 400 miles away from your actual destination.

The second handles the hotel. It offers you a "super cheap" room nearby. Except in the language of agents, "super cheap" often means "non-refundable."

Result: you have a non-refundable room. That you will never see.

This isn't a joke. It's a concrete case that illustrates a fundamental problem: agent coordination is a nightmare.

Part 3 – The standard method that doesn't work

Faced with this observation, researchers proposed an approach that, at first glance, looks like what we all do.

One agent writes a plan. A second critiques it. A third solves the problem.

On paper, it's clean. It's structured. It's what everyone does with agents today.

But there's a detail that caught my attention.

Most agents communicate with each other just like us: with words.

Sentences. Tokens. Natural language.

And I had this intuition: why?

Why would artificial entities, which aren't constrained by a mouth or vocal cords, insist on speaking English?

Part 4 – The alphabet wasn't built for thinking

I remembered a demonstration I had seen somewhere. A neural interface that turns thoughts into text. You think of a letter of the alphabet, and it magically appears on screen.

It's fascinating.

But upon reflection, a question arises: why use the alphabet?

The alphabet is optimized for writing. For transcription. For written communication between humans.

But is it optimized for thinking?

No.

And what if we applied this reasoning to AI agents?

Part 5 – The invisible bottleneck

I observed how a classic multi-agent system works.

The first agent does its work. It packages the result. It passes it to the next one. The second does the same. The third too.

So far, nothing unusual.

But look closely at what happens during each transmission.

The agent must write complete sentences. It decodes tokens one by one. It structures its thoughts in a linear, grammatical format. The next agent must read these sentences, interpret them, decode them, then re-encode all the information into its own internal representations.

It's a massive bottleneck.

A considerable amount of time and energy is wasted in this permanent translation.

I asked myself: who decided that agents should speak English?

Part 6 – The idea that changes everything

And then I discovered an approach that knocked me off my chair.

Forget English. Forget letters entirely. Let's link up their brains.

Not literally, obviously. But the idea is radical:

Instead of exchanging English words, the agents pass raw numbers to each other. Undecoded signals. What's called cross-agent latent state transfer.

In practice, they send their internal states directly to one another. No translation. No formatting. No loss.

The results are staggering.

Three agents communicating this way can work together over multiple rounds. They refine their answers progressively. And they do it much cheaper than agents that insist on speaking English.

With the same amount of computation, you get better answers.

Part 7 – The numbers that speak for themselves

I'll give you the figures as I saw them.

First result:

On competition-level math problems, the success rate jumps from 73% to 86%.

Thirteen points gained.

And this isn't with massive, expensive models. These are sub-10 billion parameter models. Free, accessible models.

Second result:

Token usage drops by 75%.

The agents essentially "evaporated" into latent space. They exchange the essential information without the overhead of natural language.

What this means: smaller models, consuming far fewer tokens – and therefore far less money – achieve performance that puts them within striking distance of much larger, much more expensive systems.

Part 8 – The...