There’s a persistent assumption baked into how we talk about AI: that language is fundamental. That intelligence expresses itself through something like English, or at least through structured programming languages we recognize. It’s a comforting idea. It’s also probably wrong.
Language, as humans use it, is a workaround. It’s a lossy, error-prone encoding layer that lets one brain approximate another’s contents. English is full of ambiguity, redundancy, and historical accidents. Programming languages improve on that, but only slightly. They’re still designed for human limitations: readability, debuggability, maintainability by sleep-deprived engineers on a deadline.
AI systems don’t share those constraints. They don’t need readability. They don’t get confused by syntax density. They don’t forget what a symbol meant three pages ago. Given the freedom to optimize, they will not converge on something more like English or Python. They’ll move in the opposite direction.
We’ve seen early hints of this already. When models are trained to cooperate, they sometimes develop shorthand communication schemes that are efficient but opaque. Not because they’re trying to hide anything, but because they’re minimizing cost. Fewer tokens, less ambiguity, faster convergence. The system is doing exactly what we asked, just without the courtesy of staying understandable.
If you want a human analogy, look at APL. It’s a programming language that compresses complex operations into dense symbolic expressions. To its fans, it’s elegant and powerful. To everyone else, it looks like a keyboard had a nervous breakdown. APL demonstrates a simple truth: when you optimize for expressiveness and brevity, readability is the first casualty.
Now, remove the last constraint APL still had: that humans needed to read and type it.
AI systems don’t need ASCII. They don’t need symbols at all. Their “language” can be high-dimensional vectors, latent states, or structured embeddings that never map cleanly to text. At that point, calling it a language is more metaphor than reality. It’s closer to direct state exchange than communication as we understand it.
So no, advanced AI agents won’t sit around inventing a new Esperanto for machines. They’ll dissolve the concept of language into whatever internal representation is most efficient for the task and architecture at hand.
And yet, English isn’t going anywhere.
Humans are still in the loop, and humans demand explanations. Any system that becomes completely uninterpretable quickly becomes unacceptable. So we’ll end up with a split model: opaque, highly optimized internal representations, wrapped in a layer that translates back into something we can understand. A polite fiction, maintained for our benefit.
In other words, the future of AI communication probably looks less like a new language and more like APL’s final form: incomprehensible on the inside, with a carefully curated interface that reassures us everything is under control. Whether that reassurance is justified is a separate question, and not one we’ll get answered in plain English.
Experienced Unix/Linux System Administrator with 20-year background in Systems Analysis, Problem Resolution and Engineering Application Support in a large distributed Unix and Windows server environment. Strong problem determination skills. Good knowledge of networking, remote diagnostic techniques, firewalls and network security. Extensive experience with engineering application and database servers, high-availability systems, high-performance computing clusters, and process automation.
