I AmThe Traffic...Who'da Guessed? - Intelligent People Assume Nothing

Ten thousand, five hundred and fifty-two.

That was the number sitting at the top of my traffic report this morning. One address. One week. Ten and a half thousand requests to my little website.

I run a small operation. I write four post a day. I am a retired man in Kentucky with a laptop and some opinions about how machines ought to be governed.

Ten thousand hits from a single source is not a normal Tuesday around here.

So I looked at it. And for about four seconds, I felt pretty good.

Then I looked at the second line.

Twenty-nine.

The next address down had twenty-nine requests. Then twenty-seven. Then twenty-seven again. Twenty-four. Twenty-three.

One address had ten thousand. Everybody else had two dozen.

That is not a reader. That is not even a hundred readers. A number that far out of line with everything around it is not good news. It is a signal that something in the measurement is wrong.

So I traced it.

The address belonged to an internet provider that runs through the middle of the country. And the pages it was hitting hardest were not articles. They were the admin screens. The back end. The part of the website only one person ever sees.

It was me.

That was my desk. That was my laptop, logged in, writing posts about how nobody checks their numbers.

Three quarters of a week’s traffic to my website was me, walking around inside my own house, and the counter at the door was clicking every time.

Now I want to be careful here, because the easy version of this story is that the dashboard lied to me. It did not.

The dashboard did exactly what it was built to do. It counted requests. Every request. It was told to measure traffic, and it measured traffic, and I was traffic.

Nobody ever told it to separate the man who owns the building from the people coming to visit. So it didn’t. It handed me one honest number that meant something completely different from what I assumed it meant.

The machine was right. The reading was wrong. Those are two different failures and only one of them was the machine’s.

I spent a working life around measurement.

In mining you weigh things. You measure grade. You count loads. And the first thing anybody teaches you is that a scale is only as good as what you agreed to put on it.

If you weigh the truck with the load, you get one number. If you weigh the truck empty first and subtract, you get a different one. Both are honest weights. Only one tells you what you hauled.

Tare weight. That is the word for it. What the container weighs before anything goes in.

Every industry that survived any length of time figured out tare weight, usually the hard way, usually after somebody got badly cheated.

And here we are in the age of artificial intelligence, and I want you to ask yourself a question.

What is the tare weight on the numbers these companies are showing you?

Because they are showing you numbers. A lot of them.

Safety scores. Alignment benchmarks. Refusal rates. Percentage of harmful requests blocked. Evaluation results with decimal points on them.

Every one of those numbers came out of a measurement somebody built.

And in most cases, the somebody who built the measurement is the same somebody being measured.

I wrote a couple of weeks back about a chief financial officer at a very large chip company. Critics said the company’s financing arrangements looked circular. She named the criticism herself, right out loud, and then answered it in four words.

We see it differently.

I keep coming back to that line, and this morning I understand why.

That is a company reading its own dashboard and reporting the top number.

Not lying. I have no reason to think anybody lied. The instrument probably says exactly what they claim it says.

But nobody outside checked what was inside the count.

That is the whole thing. That is the gap.

Here is the part that ought to make you uncomfortable, and it made me uncomfortable, which is why I am writing it down instead of quietly closing the tab.

I caught mine by accident.

I was not auditing anything. I was looking at a screen hoping to see a good number, and the good number was so absurdly good that it broke the spell. If that address had shown four hundred requests instead of ten thousand, I would have believed it. I would have believed it all week.

A wrong number that looks plausible does not get caught. Ever. It just sits there being wrong and being believed.

So when somebody tells me their AI system blocks ninety-four percent of harmful requests, my question is not whether they are honest.

My question is who defined harmful. My question is who picked the test cases. My question is whether the system was graded by the people who built it or by somebody with no reason to want a good score.

My question is what is the tare weight.

And the answer, most of the time, is that nobody has published one. There is no empty-truck number. There is just a full-truck number with a decimal point on it and a press release around it.

My mistake this morning cost me nothing. Four seconds of feeling good, and then the honest correction, and then this post.

The same mistake, made at scale, by a company deciding whether a system is safe enough to turn loose on the public, does not cost nothing.

It costs somebody. Later. In a way nobody traces back to a chart.

I am not asking anyone to distrust every number they see. That is just a different kind of lazy.

I am asking for one thing.

Before you believe a number, find out who is inside it.

Somebody has to ask that question out loud, every time, or the number is just a mirror with a decimal point.

Here is your challenge.

Pick the last number somebody showed you about a system you rely on. A safety rate. An accuracy score. A reliability figure.

Now answer two questions.

Who built the measurement.

And who is counted in the total that shouldn’t be.