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                    "Posts\n\t\t\n\t\tTalks\n\t\t\n\t\tPhotos\n\t\t\n\t\tAbout\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\n\n\n\n\t\n\t\t\n\t\t\t\n\t\t\tPosted on Sep 7, 2026\n\t\t\t\t\u00b7 5 min read\n\t\t\t\t\n\t\t\t\n\t\t\n\n\t\t\n\n\t\t\n\n\t\t\n\t\t\t\nWhoever speaks badly, thinks badly and lives badly. We must find the right words: words are important! \u2014Nanni Moretti\n\nA reader reached out to me the other day to talk about the future of tech writing as a profession. They asked me a pretty good question: What skills would you prioritize in this age of constant change and uncertainty? Is it AI? Maybe coding? Well, not really.\nMy suggestion is to go back to the humanities and learn a language or two (not the ones you use to code; the other kind). When AI can take care of retrieving facts and putting words together in reassuringly trite ways, your edge as a carbon-based lifeform lies in your ability to go deep, have stances, and know how to convey and defend them. You can do that if you train in the dojo of Plato and Chomsky.\nLet me offer an alternative to learning a trade like plumbing or carpentry. Cast the monkey wrench aside for a moment. Instead, start looking at the intellectual foundations of human culture and consider studying philosophy (of any kind), literature, linguistics (the computational flavor is OK), math, history, or anthropology. If you already hold a degree in that area, it\u2019s time to dust it off. And if going back to university is not your thing (I know it\u2019s not mine), self-study is the way to go. If you\u2019re an engineer, adding a bundle of Classics to your skill tree will do you good. Not that degrees guarantee you\u2019ll become a thinker, mind you, but some of it might stick on you still.\nAs counterintuitive as it might sound in this age of reckless tech and unstoppable progress, the only way forward is down to the roots of human thought and theory. A growing share of knowledge work is not far from becoming a polite exchange of statements between AI agents, mediated by that patina of human ritual that we call professionalism. Less charitable takes represent the humans in the loop as \u201cmeat proxies.\u201d The way to break these self-imposed, lazy shackles is what it has always been: taking a long detour, paving new paths, and coming up with your own thoughts, however wrong they may feel to you.\nHow to wire things together still matters, of course, but for most developers it has rarely been enough. Many developers are not great merely because of their knowledge of Java boilerplate and Bash tips and tricks, as many tech writers are not great due to their command of Markdown or DITA: professionals are great because they know how to learn fast, how to think hard, and how to picture whole systems in their minds, including their hairy human factors. You can still decide to become highly proficient at purely technical skills, but this is no longer the only direction for growth in tech, and possibly not the most urgent to cover.\nDon\u2019t fall into the trap of thinking this is the stuff of academia and ivory towers on leafy campuses. Philosophy of mind, philosophy of science, computational linguistics, cognitive science\u2026 They all helped supply the conceptual foundations of the AI systems we\u2019re now building. Logic and ontology reign supreme in software, from database design to information architecture. UX design draws heavily on the arts and anthropological accounts of tools and their use. Projects such as NOPE turn clinical psychology into standards for safer AI behavior. Linguistics and literary theory can help us find the source of beautiful docs.\nWhat does this mean in practice? A lot of different and exciting things. It could mean, for instance, learning to read project proposals closely enough to detect bullshit, constructing an argument that survives disagreement, separating evidence from inference, or recognizing when the wheel is being reinvented. None of these are exclusive to academia. You need these skills to question AI output, design information architectures that make sense to humans, and explain products whose hardest problems are more concerned with ideas than with technical challenges. They are the skills that will help you address real user needs.\nI mentioned languages. Another thing that helps a lot when learning how to think is learning the languages in which thoughts have been created, or not yet created. After learning some basic Persian a few years ago, I\u2019m now gearing up for the somewhat foolish task of learning Mandarin Chinese, not because I expect to be able to work or write in it (that\u2019s wishful thinking), but because the mere act of attempting to think in a radically different language is a majestic Trojan horse carrying culture, history, and different worldviews. The more you expose yourself to different languages and cultural substrates, the easier you\u2019ll navigate problems.\nSo yeah, my advice is to begin your own Grand Tour. Become the most flexible, underscore-shaped mind you can cultivate, the best-prepared proto-diplomat and translator of cultures, the family philosopher (because you no longer need someone to fix your printer). Become a humanist in tech, like the ones AI labs are hiring, someone ready to explore product depths from novel angles.\nI used to say that the most useful thing I learned was how to find your way in a library. What I\u2019m telling you here is the upgraded version of that advice: learn how to read the map of the world, and then be ready to navigate it using the tools at your disposal. The rest are implementation details.\n\nWe have to go back \u2014Several characters from Lost"
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