You will be able to name the skills in your work most likely to fade if AI does them every time.
Think of a phone number you knew by heart fifteen years ago. A friend's, your parents' home line, your first office. Many people can still recite numbers they memorised as children but can't remember their own partner's mobile today. Nothing went wrong with their memory. They stopped needing it, so they stopped practising, and the skill quietly went.
The same thing can happen with skills that matter far more than phone numbers, once AI starts doing them for you every day. This lesson is about which skills are at risk, why that matters even if the AI is good, and what you can do about it.
Skills need practice to stay sharp. That's not a new idea, and it's not specific to AI. People who stop driving for years feel nervous behind the wheel. People who used to write long reports by hand find a blank page harder after years of filling in templates.
AI makes this happen faster and across more skills at once, because it can now do so many tasks that used to require practice. The skills most often mentioned include writing from a blank page, mental arithmetic and rough estimation, reading a long document carefully, summarising an argument in your own words, structuring a presentation, and working through a problem step by step before reaching for an answer.
Siew Ling, the accountant from lesson 7.1, noticed it in herself. After months of asking an assistant to draft variance commentary, she sat down to write one by hand during a system outage and found it surprisingly slow. She knew what she wanted to say. The words just didn't come as easily as they used to.
This might sound like nostalgia. If the AI does the task well, why does it matter whether you still can?
Because the skill you've stopped practising is often the same skill you need to check the AI's work. Lesson 7.1, Why we trust confident machines too much, showed that automation bias is strongest when you couldn't easily do the task yourself. Skill decay pushes more and more tasks into that category.
If Siew Ling's ability to read a variance table and write about it fades, so does her ability to spot when the assistant's commentary gets it wrong. If your mental arithmetic weakens, you're less likely to notice when a spreadsheet formula or an AI calculation is off by a factor of ten. If you stop reading long documents closely, you lose the sense that tells you a summary has left out something important.
In other words, the less you practise, the less able you are to catch errors, and the more you depend on the tool being right.
For experienced people, the worry is a skill fading. For people early in their careers, there's a different risk: the skill may never form in the first place.
Consider Ravi, a graduate who joined Siew Ling's team this year. He uses an assistant to draft almost everything from his first week. His output looks polished. But he hasn't yet written fifty variance commentaries by hand, struggled with them, and had them marked up by a senior colleague. That struggle is how most professionals develop judgement. Without it, Ravi may be able to produce good-looking work without fully understanding why it's good, or noticing when it isn't.
This isn't an argument against beginners using AI. It's an argument for beginners, and the people who manage them, being deliberate about which skills are built by hand first.
The fix is simple to describe: keep practising the skills you care about, on purpose, even when AI could do them.
A few ways to do that without giving up the benefits of AI:
Draft first, then ask AI to critique. You do the hard thinking, and the assistant improves it. Estimate before you calculate. Guess the answer roughly in your head, then check it against the tool's result. Read the original before the summary, at least for documents that matter. Pick one task a week to do entirely by hand, the way an athlete keeps doing basic drills.
Siew Ling now writes the first paragraph of each month's commentary herself and asks the assistant to draft the rest. Ravi's manager asked him to write his first three commentaries by hand before using AI for them. Neither change costs much time, and both keep the underlying skill alive.
Look at the tasks you now hand to AI most often. Behind each one is a skill you used to practise, whether writing, calculating, reading, structuring or deciding, and it's worth knowing which of those you still use.
List five tasks you now hand to AI and mark which underlying skill each one uses and whether you still practise it.
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