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Language models and agents are changing what work looks like. Not all at once, not everywhere, not for everyone, but really, and the change is still unfolding.
The pattern so far: tasks that used to need specialised skill became easy to approximate with a well-aimed prompt. First drafts, code skeletons, research summaries, translation, reshaping data. For people who know how to work with these tools, all of that got faster.
But notice the careful word, approximate. This is a shift in what one person can do, not a replacement of the person. The model can draft, but cannot decide what is worth drafting or whether the draft is any good. It can surface patterns, but cannot tell you which ones matter. It can help you go faster, but cannot tell you where to go. The judgement stayed with you.
And the jobs most touched are not the ones people guessed. A lot of knowledge work that felt brainy but was actually routine, transcribing, summarising, first-drafting, basic lookup, turns out to be the easiest to hand off. What resists is the work that leans on judgement, relationships, being physically present, or genuine originality.
So the durable skill is not coding, and it is certainly not being able to derive a transformer from scratch. It is fluency: understanding what these systems really are, what they can do, where they fail, and how to tell a solid answer from a confident-sounding wrong one.
Which is worth saying plainly, because it is about you. That fluency is exactly what this course has been building, slab by slab, from the transistor onward. You did not learn to use a particular app that will look dated in a year. You learned how the machine underneath actually works. That is the thing that lasts, and you have earned it.