English | EN 
During my high school years, there was an unspoken distinction between jobs that involved interaction with technology and those that were human-centered, focusing on engagement with people. The former attracted those interested in inanimate objects, while the latter appealed to those interested in human interaction.
These two job categories—let’s call them tech-centric and human-centric, required different language skills. Tech-centric roles relied on mathematics to convey relationships and complex ideas, such as those found in physics and chemistry, underlying the physical world. In contrast, human-centric jobs utilized lengthy essays and textual analysis to discuss complex phenomena, typically concerning interpersonal relationships, politics, economics, and social customs.
Your proficiency in one of these languages largely determined your future career path.
I recall individuals in my high school declaring, “I’m not good at maths,” when, they may have been struggling with the language of mathematics. Conversely, others would state, “I only understand numbers; I don’t grasp the point of lengthy essays.”
Upon reflection, isn’t it peculiar that language skills could dictate one’s lifelong vocation?
Perhaps ambiguity plays a role. Mathematical language is unambiguous by design, leaving no room for interpretation. Many people gravitate towards numbers and programming for their precision and consistency; digital infrastructure relies on this predictability.
Conversely, human-to-human interactions abound with ambiguity, embedded in the nuances of our language. When we say we’re going for a walk, our preconceived notions fill in the gaps—what shoes to wear, where to walk, how to place one foot in front of the other, and countless other ambiguities inherent in a simple statement.
Presently, this job dichotomy is evolving. It’s not due to a change in the nature of the jobs themselves, but rather a shift in our language abilities brought about by Large Language Models (LLMs).LLMs can translate between languages, interpret human-machine interactions, and bridge the gap between them. They can write code when asked in human language and explain code in a human-readable format.
Although the inner workings of LLMs remain mysterious, their value is undeniable. Trained on human-to-human interaction data, they excel at understanding written communication. Therefore, the better one can articulate thoughts in writing, the more effectively people can engage with LLMs and derive meaningful insights.
This evolution blurs the distinction between tech-centric and human-centric jobs. We’re no longer confined by our language skills; LLMs empower us to understand both realms. To leverage this capability, honing writing skills is essential. So, even if you doubted its importance during high school, mastering the art of writing might prove to be your most valuable asset—the only one you’ll ever need.
