In my daily work, I constantly move between English and Japanese. I know firsthand the cognitive load of simultaneous interpretation, and I know that getting the words right is never enough. Real interpretation means reading the other person’s intent, their baseline assumptions, the emotional temperature of the room, and what decision they’re trying to reach. Only then does translation become meaningful communication.
With the arrival of AI, that instinct has taken on a different shape. The act of interpreting between English and Japanese will, before long, be handled largely by machines. I think that’s unavoidable. But beyond that threshold, a new kind of competence is emerging: the ability to interpret between humans and AI.
Current AI systems look omnipotent on the surface, yet they are remarkably sensitive collaborators. Their memory architectures have structural constraints. The way they compress and expand context follows particular patterns. In human conversation, ambiguity and logical leaps are smoothed over naturally, but in dialogue with AI, those gaps translate directly into performance differences. What you establish as given, what you choose to omit, the order in which you present information — these design choices dramatically alter what the same AI returns.
This sensation is strikingly similar to the work of interpretation. When I move between English and Japanese, I try to consume as little unnecessary context as possible. I avoid regional expressions like Kansai dialect, construct clear sentences, and lead with conclusions. I keep each sentence short. I’ve always had an affinity for Markdown-like structures, and even in conversation I sometimes feel as though I’m speaking in Markdown. Once a phrase becomes shared shorthand, I define it upfront and compress the rest of the exchange into brief callbacks. Interpretation, before it is language conversion, is the art of context compression.
This framework transfers directly to the age of AI. In human conversation, imposing too much semantic structure feels unnatural, so a Markdown-like level of organization was the practical ceiling. With AI, that tendency intensifies. Align assumptions, state your objective, define constraints, decide the output format in advance — and the quality of dialogue shifts dramatically. In other words, the ability to converse with AI is not simply about asking good questions. It is the ability to structure your thinking and hand it over.
The problem is that most people do not habitually speak in Markdown. Between humans, conversation still works without it. But in dialogue with AI, that ambiguity becomes pure loss. This is why we will need interpreters who can convert natural human language into forms that AI can process effectively. What is called prompt engineering is one slice of this. But the real scope is far wider: knowing which sub-agent to deploy in a given moment, building specialized workflows for particular domains, compressing context while chaining automation, and consciously managing the boundary between short-term and long-term memory. The full range of these capabilities is becoming the new definition of linguistic competence.
Until now, language ability has been measured by how well you handle a foreign tongue. Going forward, that metric alone will not suffice. The capacity to translate vague human intent into structures a machine can process. The reverse capacity to retranslate AI output into forms that support human judgment. It is this back-and-forth movement that will determine intellectual productivity in the next era.
I don’t believe foreign language skills are becoming obsolete. If anything, the age of AI may be what finally reveals what interpretation was always about. What will be needed is not just English proficiency or Japanese proficiency. It is the ability to stand between people and AI, preserving meaning, organizing context, and faithfully conveying intent. Interpreters in that sense will matter far more than we currently imagine.