The Drift You Don’t Notice

7-40 Challenge | Round 4, Day 15


Week one, you push back on everything AI gives you. You check the output. You question the reasoning. You verify the facts. You’re in charge and you know it.

By week ten, the checking feels redundant. The tool has been right so many times that pushing back seems like wasted effort. So you stop. Not all at once — you just skip a verification here, accept a suggestion there. And somewhere between week one and week ten, you’ve abdicated without ever choosing to.

That’s the trap. You don’t abdicate by decision. You abdicate by trust accrual.


I use AI every day — for writing, for data work, for thinking through problems. It is the most powerful tool I’ve ever worked with. And the more powerful it gets, the more dangerous the drift becomes.

Because it gets worse as the tool gets better, not better. A sharper tool makes abdication more tempting. The output looks cleaner. The reasoning sounds tighter. The errors get harder to spot — not because they’re smaller, but because they’re wrapped in fluency that makes you want to believe them.


Here’s what I’ve learned from the chair: AI is a reasoning engine, not a truth source. It doesn’t know anything. It processes what it’s given and returns the most plausible-sounding result. If the truth isn’t in what you’ve supplied or what it’s been trained on, it starts on the wrong foot and builds confidently from there.

My edge is whatever only I can supply — my intent, my standards, my domain knowledge, my ability to say “that’s wrong” when the output sounds right.


The thing nobody tells you is that AI doesn’t erode your ability to reason. It erodes your exercise of it. The muscle is still there. You just stop using it because the tool made it feel unnecessary. And by the time you need it — the day the output is confidently, fluently wrong — the muscle hasn’t been worked in months.


I have one rule that doesn’t bend: if I ship it, it’s mine. Not AI’s fault. Not the tool’s limitation. Mine. I signed off on it. My name is on it.

The signature got cheap. The responsibility didn’t.

Go Deep

I ran a demo today. Asked AI a question in plain English. It wrote a SQL query in real time. I asked it to convert the output to R. Done. Less than a minute.

Three years ago that could have taken me a few hours. Minimum.

Everyone in the room was impressed, and I don’t blame them. It is impressive. But the part that mattered most isn’t the part that got the reaction.

The SQL it produced was good. It took the natural language prompt I gave it and created what I wanted. However, I still had to verify the SQL to make sure my demo was successful. I was able to do that because I have been doing this kind of work for almost twenty years. I didn’t have to look it up. I just knew.

And that’s the thing more people need to talk about.

AI is going to flatten surface-level knowledge. If all you bring to the table is the ability to do something the machine now does in thirty seconds, that’s a problem. But if you can evaluate whether what the machine produced is actually right — that’s a different conversation entirely.

I told the room: build your context architecture. Know every piece of your workflow. Know how the levers get pulled. Know what right looks like before you ask the machine to produce it. Because without that architecture, AI doesn’t help you. It just runs your bad assumptions faster.

The people who thrive through this won’t be the ones who learned the tool fastest. They’ll be the ones who went deep enough to know when the tool got it wrong.

I am thankful that I have had the last twenty years to learn the data. Today that investment is paying returns I didn’t expect.