I studied software engineering at a time when AI was a niche subject taught in one of our eight semesters. At the time, it didn’t have much “practical utility”. There weren’t many AI companies (if there were any at all), interviewers were obsessed with data structures, algorithms, Big O notation, and students were focused on cracking the social hierarchy of technology companies – Tier 1 was Facebook, Google, or any other company that either paid a lot or offered relocation to a different country, Tier 2 was any company with a recognised brand-name even if the pay wasn’t great, etc. That was the state of software engineering, and for the average-to-above average student, the state of technology itself. There was work happening in cloud computing, mobile, social media of course, but nothing as all-encompassing as the current AI craze.

I had surprised myself and everyone else by outperforming in the AI subject in that one semester. For some reason it had made instinctive, natural sense to me, possibly because it sounded less like computer science and more like brains in general. Reading the whole material (sadly, it was all theoretical plus some pseudocode) had felt like reading a really great book. A book about brains and thinking, even if artificial.

AI’s current ubiquity, thus, comes as no surprise to me. Perhaps the architects of AI capitalism recognised this crucial insight: AI’s immediate relatability and resonance with the broader public. This relatability stems from our deepest cognitive patterns – confirmation biases, personal beliefs, ingrained assumptions – the very foundations that AI systems often mirror and amplify.

When a technology achieves such profound and widespread relatability, it also creates an extraordinary grace period. Entrepreneurs can launch businesses built on deeply flawed, possibly even harmful solutions, confident that public fascination will sustain them. This window persists until one of two outcomes: either the public awakens to the technology’s inherent dangers and limitations, or its creators manage to use the grace period to successfully resolve those fundamental problems. The race between disillusionment and genuine progress will determine AI’s ultimate trajectory.

I have come to realise that most people just want to hear a good story. And outside of its technological and mathematical complexities, AI offers not just great stories but great storytelling opportunities. The question now isn’t whether AI will continue to dominate our collective imagination – it’s whether the stories we tell about AI will shape the technology itself. In a field where the boundary between fact and fiction, science and art grows thinner each day, the storytellers may well become the architects of our AI future. And that possibility is both intriguing and terrifying.