What the history of artificial languages taught us, and why an artificial language for the AI era takes a different path
The Dream of 1887
In 1887, a Polish ophthalmologist named Ludwik Zamenhof published a common language for humanity.
Esperanto.
Zamenhof’s diagnosis was clear. Because every nation speaks a different language, misunderstandings arise. Misunderstandings breed conflict, and conflict breeds war. If all humans shared a single language, the world would move closer to peace.
Esperanto was beautifully designed. Its grammar is complete in 16 rules. No exceptions. Its roots are drawn from major European languages, making it easy to learn. Pronunciation matches spelling.
Zamenhof recognized the inefficiency of natural language and tried to solve it with a deliberately designed artificial language.
137 years later, Esperanto has roughly 2 million speakers. 0.025% of the world’s population. Esperanto failed.
Why?
Three Causes of Failure
Cause 1: You Have to Learn It
No matter how easy Esperanto is, learning a new language is a cost.
You have to master 16 rules. You have to memorize roots. You need practice thinking and writing in that language.
Say it takes a Korean speaker 100 hours to learn Esperanto. 100 hours is not trivial. To have the motivation to invest 100 hours, there must already be someone to communicate with in Esperanto.
But few people speak Esperanto. Because few speak it, there is no motivation to learn it. Because no one learns it, even fewer people speak it.
A textbook case of reverse network effects. The chicken-and-egg problem. Esperanto has been stuck in this trap for 137 years.
Cause 2: It Had to Replace Existing Languages
Esperanto’s goal was to supplement or replace existing natural languages.
This means changing the habits of all of humanity. With English already serving as the international lingua franca, introducing a new language requires every participant to switch simultaneously.
Even if one person learns Esperanto, it is pointless if the other person responds in English. The switch must happen simultaneously, and simultaneous switching is nearly impossible.
Cause 3: Communication Itself Was the Purpose
Esperanto’s reason for being was communication. People conversing with each other in Esperanto. Writing letters, reading books, holding debates.
But humans are already communicating. In their native languages, in English, through gestures. Imperfect, but it works.
“Better communication” is attractive, but the motivation to abandon “communication that already works” is insufficient.
An Artificial Language for the AI Era Can Avoid All Three
If an artificial language for AI existed, it would differ from Esperanto in almost every way.
Difference 1: No Need to Learn It
Esperanto is a language humans use directly. The primary user of this kind of language is AI.
This kind of language is an intermediate representation (IR) that operates inside AI. Users speak in natural language and receive answers in natural language. It operates invisibly in between.
Just as a programmer who knows nothing about LLVM IR can write C++, a user who knows nothing about this language can use AI.
The cost of learning is zero. The network effect problem does not arise. Not a single user needs to switch.
Difference 2: It Does Not Replace Existing Languages
Esperanto coveted the position of natural language. This kind of language has no interest in that position.
Humans continue speaking natural language. In Korean, in English, in Spanish. What it replaces is not natural language, but the role that natural language has been temporarily filling inside AI.
The medium of reasoning. The storage format for knowledge. The protocol for inter-system communication.
It removes natural language from this role and inserts a structured language. Nothing about human habits changes.
Difference 3: Communication Is Not the Purpose
Esperanto’s purpose was communication. This kind of language’s purpose is recording and verification.
Structuring AI’s reasoning and recording it. Making that record reusable. Making it possible for humans to verify that record.
Communication is natural language’s job. It already does it well. This kind of language handles what natural language cannot.
But Humans Can See This Language
There is one important distinction here.
Just because this language is an “invisible intermediate language” does not mean humans can never see it.
This kind of language should be open to humans through a visual editor.
When you want to know the basis for a judgment AI has made, you can open the reasoning graph directly.
You are not reading binary 16-bit words. The graph is visualized. Click a node and the entity’s information appears. Follow an edge and the reasoning path becomes visible. Sources, timestamps, and confidence levels are displayed visually.
This is not learning a language. This is reading a map.
Just as you do not need to study surveying to read Google Maps, you do not need to learn binary grammar to use the visual editor.
The Interface of Verification
The Visual Editor is the final piece that completes the white-box principle.
Even if reasoning is recorded transparently, transparency is meaningless if humans cannot access that record.
With a Visual Editor:
When AI answers “Yi Sun-sin defeated 133 ships with 12 ships,” the user can open the reasoning graph behind that answer.
Which entities were referenced? Yi Sun-sin, the Battle of Myeongnyang, the Joseon Navy. What sources did this information come from? The Annals of the Joseon Dynasty, Nanjung Ilgi, academic papers. What is the confidence level of the number 12? Where did 133 come from? Do Japanese records and Korean records disagree?
The user looks at this and judges: “Can I trust this answer?”
This is critical trust, not blind belief.
What Esperanto dreamed of was “a world where all humans communicate in the same language.” What this kind of language creates is “a world where humans can directly verify AI’s judgments.”
What Esperanto Taught Us
Esperanto’s failure was not because it was a bad language. Esperanto is an excellent language. Its design is elegant and logical.
What failed was the strategy.
Asking humans to learn a new language. Trying to replace the position of existing languages. Assuming all participants would switch simultaneously.
An artificial language for the AI era must precisely reflect these lessons.
| Esperanto | AI Internal Language | |
|---|---|---|
| Primary user | Humans | AI |
| Learning required | Yes | No |
| Replaces existing languages | Attempted | Does not |
| Human access | Learn and read | View through Visual Editor |
| Purpose | Communication | Recording and verification |
| Switching cost | All participants | AI systems only |
Esperanto tried to tear down the language barrier between humans. An artificial language for the AI era tears down the transparency barrier between humans and AI.
The purpose is different, the strategy is different, and that is why it avoids the trap Esperanto fell into.
Summary
Esperanto failed for three reasons.
- You had to learn it. It fell into the network effect trap.
- It tried to replace existing languages. Simultaneous switching was impossible.
- Communication was the purpose. There was no motivation to switch from communication that already worked.
An artificial language for the AI era can avoid all three traps.
- The primary user is AI, so humans do not need to learn it.
- It does not replace natural language. It only handles roles inside AI.
- The purpose is not communication but recording and verification.
And humans can see AI’s reasoning directly through a visual editor. Without learning the language. As if reading a map.
What Esperanto taught us: The success or failure of an artificial language depends not on the elegance of its design, but on its strategy.