As the year draws to a close, it’s natural to look back and reflect on the road we’ve traveled. For me, one question has kept resurfacing over the past year: what does it really mean to prepare for the future?
In recent years, I’ve had many conversations with people from different generations. What’s striking is how much earlier—and how much more intensely—this sense of uncertainty is showing up. Many people have taken countless courses and earned every certification they could, yet still find themselves asking the same question: What does it actually mean to be ready?
These conversations have pushed me to rethink the learning paths we’ve long taken for granted. Traditionally, the sequence was clear: choose a major, spend years accumulating knowledge and skills, then enter the workforce and draw on what you’ve learned when real problems arise.
That model worked because industries evolved slowly and access to knowledge was expensive. If you didn’t prepare in advance, many doors simply remained closed.
But AI is fundamentally changing that assumption. Today, learning a new skill no longer requires years of upfront investment. As long as you have a sense of what you want to do, the relevant knowledge and tools can often be filled in later—with the help of AI. In that sense, knowledge itself is becoming inflated. Simply accumulating skills is no longer enough to create a lasting advantage.
Against this backdrop, the idea of “being fully prepared before you begin” feels increasingly outdated—and in some cases, inefficient. As we move from learn first, then apply to apply first, then learn, the real differentiator may no longer be how many skills you’ve mastered, but whether you’re clear about the problem you want to solve.
This reversal in learning order may feel counterintuitive, but it often leads to greater clarity. That doesn’t mean foundational knowledge is no longer important. Rather, it should function as a map—helping you identify good problems—rather than as the sole weapon you rely on.
Those who can identify meaningful problems early tend to see their learning efficiency grow exponentially. On the other hand, even vast amounts of knowledge can become scattered and unfocused if there’s no clear problem guiding it.
As we look ahead to 2026 and begin setting new learning goals, perhaps the better question to ask is this: What problem is worth solving in the coming year? When direction comes first, learning tends to follow naturally. And perhaps, this way of preparing for the future can make the year ahead feel more purposeful—and more exciting.
