Also Read: An AI gadget mightier than the sword? We’re in a tech revolution with a vocabulary stuck in the dial-up days. We face a generational shift in technology without a stable vocabulary to navigate it. This language gap is not a side issue. It is a core challenge that requires a new discipline: a fierce scepticism of hype and a deep commitment to the details. The instinct to simplify is a trap. Once, a few minutes was enough to explain breakthrough apps like Google or Uber. Now, innovations in robotics or custom silicon resist such compression. Understanding OpenAI’s strategy or Nvidia’s product stack requires time, not sound-bites. We must treat superficial simplicity as a warning sign. Hot areas like AI ‘agents’ or ‘reasoning layers’ lack shared standards or benchmarks. Everyone wants to sell a ‘reasoning model,’ but no one agrees on what that means or how to measure it. Most corporate announcements are too polished to interrogate and their press releases are not proof of defensible innovation. Extraordinary claims need demos, user numbers and real-world metrics. When the answers are fuzzy, the claim is unproven. In today’s landscape, scepticism is not cynicism. It is discipline. This means we must get comfortable with complexity. Rather than glossing over acronyms, we must dig in. Modern tech is layered with convenient abstractions that make understanding easier, but often too easy. A robo-taxi marketed as ‘full self-driving’ or a model labelled ‘serverless’ demands that we look beneath the surface.