Back when I was eight I had my first run-in with a computer. You know, the kind of archaic machines - think Commodore 64 and ZX Spectrum days - that loaded software from cassette tapes while you made a sandwich. That early exposure didn’t just date me; it shaped how I interacted with technology forever. By the time Google showed up and turned the internet into humanity’s greatest knowledge buffet, I was ready to gorge.
My friends eventually dubbed me “Google Bora” partly as a joke, but mostly because I developed a borderline obsessive skill for crafting super-precise search queries. You needed an obscure driver from some forgotten tech forum circa 1998? Give me 30 seconds. That skill wasn’t trivial - it was foundational, sharpening my understanding of how humans talk to machines, ask for information, and more importantly, how machines respond.
Today, everyone’s drooling over AI. But let’s cut through the hype - Large Language Models like ChatGPT and Gemini aren’t some magical oracles. They’re a new, radically intuitive way for humans to query and interface with the world’s collective knowledge. They’re not thinking; they’re predicting, pattern-matching machines that turn unstructured data into dialogue. Think of them as the evolution - a new modality - of how we’ve always interacted with computers.
Go back far enough and you’ll find that we started with punch cards - literal holes punched into cards that instructed massive refrigerator-sized computers what to do. Fast forward a bit, and the early web gave us search engines like Lycos and AltaVista. They were primitive, but revolutionary. Then Google came along, doing for data retrieval what the assembly line did for cars: efficiency at scale.
But search engines, for all their power, still forced humans to adapt to the machine’s syntax - learning to speak in query language based on precise keywords and boolean logic. Today, LLMs flip the script. Now, we can query human knowledge naturally, conversationally. We don’t just retrieve; we explore, critique, and discover in real-time dialogue with data.
Designers, pay attention: this is your moment. If punch cards demanded engineers and Google searches called for obsessive query-builders like I was back in the day, then the age of conversational interfaces calls for design thinkers who deeply understand human psychology, language, and interaction. Your job is more than just aesthetics or usability - it’s guiding meaningful, fluid conversations between people and data.
Because here’s the thing: humans don’t need magic; they need clarity. They want to talk to computers in their own terms and get answers that resonate. The role of human-computer interaction, which used to be a niche discipline, is now central to how we build everything.
We’re no longer just designing interfaces. We’re responsible for guiding and shaping how humanity asks questions, finds answers, and ultimately, understands the world. So the real question isn’t how magical LLMs are - it’s whether we’re ready to design for the most human way of knowing we’ve ever created.
