Numbers Were My First Language. They're AI's Only One.
They put me in special education because I couldn't read. Called me a slow learner. What nobody knew was that I was dyslexic, and that numbers were about to become the first language that never moved on me. Numbers were my first language. They're AI's only one. After forty years of running data companies and projects I have learned to love numbers deeply and question them completely. A number is not truth. It is a finger pointing at something. This is why I started Data Readings.
I want to tell you why I fell in love with data because I think it explains everything I write here and is not the story most people in my business tell.
I’m dyslexic. Not the mild, romantic kind that gets described as thinking differently and seeing the big picture. The kind where written language is a genuine and daily negotiation. Words move. Letters swap places. They put me in a special education class for kids who couldn’t read. The teacher called me a slow learner and meant it kindly, which was almost worse. The word dyslexia had not yet made it into the school system. There was no framework for what was happening in my head, only the daily evidence that something was.
Then I found numbers.
I remember the specific feeling. Not a gradual warming to the subject but something closer to relief. Numbers sat still on the page. They didn’t rearrange themselves. A three was always a three. An equation either balanced or it didn't and there was no ambiguity about which. The logic was sequential and each step either followed or it didn't. For a brain that had been fighting with language since it learned to read, here was a system that played fair.
It was like someone handing you a map in a city you had been navigating by memory and luck for years.
I want to be careful not to oversell this. I wasn’t suddenly brilliant. I still had to work. But I was working in a medium that didn't work against me, and the difference was significant enough that for the first time I started to wonder whether I might actually be smart rather than just determined. That’s not a small thing when you are young and you have spent years collecting evidence for the other conclusion.
Data also gave me something I hadn’t expected, which was a way of being precise about things that language kept making slippery. Language is emotional. Words carry connotations, histories, associations that vary from reader to reader. The same sentence means different things to different people and there is almost no way to control for that. A number means what it means. When I said something with data behind it I knew I was saying the same thing to everyone in the room, and that clarity felt like a kind of power I hadn’t had access to before.
I’m aware of the irony. I write about how numbers mislead us, measure the wrong things, and get distorted in translation. All of that is true. And I still love data. The two things are not in contradiction. I love it the way you love a language you grew up speaking, clearly enough to know when it is being misused and to care when it is.
But I want to be honest about something it took me years to fully accept. A number is not truth. It never was. It is a representation of something that happened, filtered through the decisions of whoever measured it, shaped by what they chose to count and what they chose to ignore, compressed into a form that looks precise and objective and settled. That compression is useful. It’s also a kind of lie. The moment something becomes a number it loses the texture, the context, the ambiguity that made it real. You gain clarity and you lose something else. Most people in my business spend their careers chasing that clarity without ever mourning what was lost.
Over forty years I’ve watched numbers move markets, change policies, end careers, and quietly reshape how millions of people understand the world. I’ve built them, trusted them, been wrong about them, and occasionally been humbled by what they got exactly right. What I know now that I didn’t know at the beginning is that the number is always the starting point and never the destination. It’s a finger pointing at something. The mistake is staring at the finger.
What I found in numbers was not just a professional path, though it became that. It was a way of seeing. A foreign language I turned out to be native in. A set of tools that let me say true things clearly in a world that had mostly felt like it was designed for a different kind of mind.
That’s why I write these posts. Not to warn people away from data but to share the language with them honestly, the way you would teach someone to read a map, including the parts where the map gets the territory wrong.
The numbers have never lied to me in the way words sometimes do. What they've done, when I let my guard down, is tell me a true thing about the wrong subject. That is a different problem and it turns out to be the interesting one.
That's what Data Readings is about. The gap between what the numbers say and what is actually true. And the very human business of navigating that gap every day.
Which has never mattered more than it does right now. Everything you are hearing about AI, every headline, every prediction, every fear and every promise, comes down to one thing. Numbers. Not words. Not intelligence. Not magic. Numbers. Billions of them, running invisibly beneath every response an AI system produces, every decision it makes, every answer it gives you with such calm and fluent confidence. AI didn’t emerge from language. It emerged from data. It is, at its core, a numbers machine that has learned to speak.
We won’t see the numbers. We’ll only see their output.
This is not a technical problem. It is a human one. If you don’t understand what data can do, what it cannot do, where it misleads, and where it illuminates, then you can’t evaluate what AI is telling you. You can’t question it. You can’t protect yourself from it. You’re simply a passenger in a vehicle you can’t see, driven by a system you were never taught to read.
That is why data literacy isn’t optional anymore. Not for executives. Not for engineers. For everyone. Every person who votes, raises a child, takes a medication, applies for a loan, or simply reads the news is now living inside systems built entirely from numbers. Understanding the strengths and weaknesses of those numbers is the most important skill of our time, and almost nobody is teaching it plainly.
That’s what I’m here to do.
Welcome.
The Great Zandini Sees:
“AI speaks in words. It thinks in data. If you can't read the data, you can't read the machine.”