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.